DETAILED STUDY OF STRUCTURAL HEALTH MONITORING SESSION : 2007-2011
PROJECT ADVISOR: PROF. DR. MOHAMMAD ILYAS
GROUP MEMBERS
UBAID ULLAH ZIA KHAN HAMZA PARVEZ KHAN OSAMA FAROOQ CHOUDHARY MUHAMMAD ARSLAN
2007-CIVIL-094 2007-CIVIL-098 2007-CIVIL-118 2007-CIVIL-123
DEPARTMENT OF CIVIL ENGINEERING UNIVERSITY OF ENGINEERING & TECHNOLOGY, LAHORE
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Dedicated to Our beloved Parents and worthy teachers who encouraged us to produce the best of us.
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ABSTRACT The objectives of this thesis is to have better understanding of the Structural Health Monitoring(SHM), which is new era technology or field which correlate Civil Engineering with Electronics Engineering. Structural health monitoring is promising field for upcoming generation. It opens the doors to enter into new galaxy of inter departmental research programs. Structural health monitoring is constantly growing and innovative field. It is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued ‗fitness for purpose‘ of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. In this study we have devised some capacitance-based sensor; by the help of it we can actually identify the minor crack in structures before it can subject to a failure (brittle or ductile).This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‗health‘.
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ACKNOWLEDGEMENT Foremost, we are grateful to ALLAH Almighty WHO made it possible for us to complete this project. Then we would like to express our sincere gratitude to our advisor Prof. Dr. Mohammad Ilyas, Chairman Civil Engineering Department, for the continuous support of work and research, for his patience, motivation, enthusiasm, stimulating discussion sessions and immense knowledge. His guidance helped us in all the time of research and writing of this report. We could not have imagined having a better advisor and mentor for our
final
year
project
of
B.Sc.
Civil
Engineering.
Besides our advisor, we are also indebted to many other staff members of the university including: Dr. Aun Bashir (Deptt. of Civil Engg.), Dr. Rashid Hameed (Deptt. of Civil Engg), Engr. Vaiza Shairaz (Deptt. of Civil Engg), Engr. Muhammad Ali Amir (Deptt. of Electrical Engg.), Engr. Muhammad Usman, Engr. Zafir Asghar, Engr. Muhammad Ahmad and Engr. Rameez Tariq for their ample support and help. They guided us to resolve the problems that we encountered.
We also owe our deepest gratitude to Engr. Ali Ahmad (Deptt. of Civil Engg) for his precious time, help, encouragement and insightful comments. He has made his support available in a number of ways which made our experimentation possible.
We also thank Dr. Asif Hameed, the lab supervisor and the staff of the Test Floor Lab, Civil Engineering Department, U.E.T. Lahore, for their cooperation.
Last but not the least; we would like to thank our parents supporting us spiritually throughout our lives.
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Study of Methods of Structural Health Monitoring (Session 2007 – Civil Engineering) The documentation is to be submitted to the Department of Civil Engineering, University of Engineering and Technology, Lahore for the fulfillment of the requirement for the Bachelor‘s degree in Civil Engineering. Approved on: _____________________________
Internal Examiner
External Examiner
Signature: ______________________________
Signature: ______________________________
Prof. Dr. Mohammad Ilyas
Name: _________________________________
Chairman Civil Engineering Department,
Designation: ___________________________
UET Lahore
Chairman
Dean
Signature: ______________________________
Signature: ______________________________
Prof. Dr. Mohammad Ilyas
Prof. Dr. A.S. Shakir
Chairman Civil Engineering Department,
Dean Faculty of Civil Engineering,
UET Lahore
UET Lahore
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TABLE OF CONTENTS ABSTRACT ................................................................................................................................................... іv ACKNOWLEDGEMENT ............................................................................................................................. v TABLE OF COMTENTS ..............................................................................................................................1 1
A GENERAL INTRODUCTION TO STRUCTURAL HEALTH MONITORING ................................2 1.1
Introduction .................................................................................................................................................2
1.2
Brief historical overview .............................................................................................................................3
1.3
The statistical pattern recognition paradigm ................................................................................................6
1.4
Challenges for SHM ..................................................................................................................................11
2
INTRODUCTION TO SHM IN THE DOMAIN OF CIVIL ENGINEERING ......................................13 2.1
Fundamental objectives of civil infrastructure monitoring ........................................................................14
2.2
20th Century & Development of SHM in Civil Engineering .....................................................................15
2.3
Limitations & Problems In The Application Of SHM To Civil Engineering Structures ...........................24
3
LITERATURE REVIEW ............................................................................................................................27 3.1
Methods of Structural Health Monitoring .................................................................................................27
3.2
Statistical Pattern Recognition Paradigm Approach ..................................................................................27
3.3
The Fundamental Axioms Of SHM ...........................................................................................................37
3.4
SHM Components .....................................................................................................................................37
3.5
Structural Health Monitoring for bridges ..................................................................................................44
4
STRUCTURAL HEALTH MONITORING APPLICATIONS ...............................................................45 4.1
Technologies For Structural Testing And Structural Health Monitoring ..................................................45
4.2
Applications ...............................................................................................................................................53
5
EXPERIMENTATION ................................................................................................................................73 5.1
Crack Detection In Armor Plates Using Ultrasonic Techniques ...............................................................73
5.2
Methodology..............................................................................................................................................74
5.3
Detection Of Transverse Cracks In A Composite Beam Using Lamb Wave And Vibration Techniques .75
5.4
Capacitance Sensor For Civil Structures ...................................................................................................78
5.5
Experimentation ........................................................................................................................................83
5.6
Conclusions ...............................................................................................................................................87
5.7
Recommendations .....................................................................................................................................87
6
CONCLUDING REMARKS .......................................................................................................................88 6.1
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Concluding Remarks .................................................................................................................................88 BIBLIOGRAPHY.........................................................................................................................................91 1
1.
A GENERAL INTRODUCTION TO STRUCTURAL HEALTH MONITORING
1.1 INTRODUCTION In the most general terms, damage can be defined as changes introduced into a system that adversely affects its current or future performance. Implicit in this definition is the concept that damage is not meaningful without a comparison between two different states of the system, one of which is assumed to represent the initial, and often undamaged, state. This theme issue is focused on the study of damage identification in structural and mechanical systems. Therefore, the definition of damage will be limited to changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the current or future performance of these systems. In terms of length-scales, all damage begins at the material level. Although not necessarily a universally accepted terminology, such damage is referred to as a defect or flaw and is present to some degree in all materials. Under appropriate loading scenarios, the defects or flaws grow and coalesce at various rates to cause component and then system-level damage. The term damage does not necessarily imply a total loss of system functionality, but rather that the system is no longer operating in its optimal manner. As the damage grows, it will reach a point where it affects the system operation to a point that is no longer acceptable to the user. This point is referred to as failure. In terms of time-scales, damage can accumulate incrementally over long periods of time such as that associated with fatigue or corrosion damage accumulation. On relatively shorter time-scales, damage can also result from scheduled discrete events such as aircraft landings and from unscheduled discrete events such as enemy fire on a military vehicle or natural phenomena hazards such as earthquakes. The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). This process involves the observation of a structure or mechanical system over time using periodically spaced measurements, the extraction of damage-sensitive features from these measurements and
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the statistical analysis of these features to determine the current state of system health. For longterm SHM, the output of this process is periodically updated information regarding the ability of the structure to continue to perform its intended function in light of the inevitable aging and damage accumulation resulting from the operational environments. Under an extreme event, such as an earthquake or unanticipated blast loading, SHM is used for rapid condition screening. This screening is intended to provide, in near real-time, reliable information about system performance during such extreme events and the subsequent integrity of the system. Damage identification is carried out in conjunction with five closely related disciplines that include SHM, condition monitoring, non-destructive evaluation statistical process control and damage prognosis. Typically, SHM is associated with online–global damage identification in structural systems such as aircraft and buildings. CM is analogous to SHM, but addresses damage identification in rotating and reciprocating machinery, such as those used in manufacturing and power generation. NDE is usually carried out off-line in a local manner after the damage has been located. There are exceptions to this rule, as NDE is also used as a monitoring tool for in situ structures such as pressure vessels and rails. NDE is therefore primarily used for damage characterization and as a severity check when there is a priori knowledge of the damage location. SPC is process-based rather than structure-based and uses a variety of sensors to monitor changes in a process, one cause of which can result from structural damage. Once damage has been detected, DP is used to predict the remaining useful life of a system.
1.2 BRIEF HISTORICAL OVERVIEW Recently, the development of quantifiable SHM approaches has been closely coupled with the evolution, miniaturization and cost reductions of digital computing hardware. In conjunction with these developments, SHM has received considerable attention in the technical literature and a brief summary of the developments in this technology over the last 30 years is presented below. To date, the most successful application of SHM technology has been for CM of rotating machinery. The rotating machinery application has taken an almost exclusive non-model based
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approach to damage identification. The identification process is based on pattern recognition applied to displacement, velocity or acceleration time histories (or spectra) generally measured at a single point on the housing or shafts of the machinery during normal operating conditions and start up or shut down transients. Often this pattern recognition is performed only in a qualitative manner based on a visual comparison of the spectra obtained from the system at different times. Databases have been developed that allow specific types of damage to be identified from particular features of the vibration signature. For rotating machinery systems, the approximate damage location is generally known making a single-channel fast Fourier transform analyzer sufficient for most periodic monitoring activities. Typical damage that can be identified includes loose or damaged bearings, misaligned shafts and chipped gear teeth. Today, commercial software integrated with measurement hardware is marketed to help the user systematically apply this technology to the operating equipment. The success of CM is due in part to: (i)
Minimal operational and environmental variability associated with this type of monitoring.
(ii)
well-defined damage types that occur at known locations
(iii)
large databases that include data from damaged systems
(iv)
well-established correlation between damage and features extracted from the measured data
(v)
clear and quantifiable economic benefits that this technology can provide.
These factors have allowed this application of SHM to have made the transition from a research topic to industry practice several decades ago resulting in comprehensive condition management systems such as the US Navy's Integrated Condition Assessment System. During the 1970s and 1980s, the oil industry made considerable efforts to develop vibration-based damage identification methods for offshore platforms. This damage identification problem is fundamentally different from that of rotating machinery because the damage location is unknown and because the majority of the structure is not readily accessible for measurement. To circumvent these difficulties, a common methodology adopted by this industry was to simulate candidate damage scenarios with numerical models, examine the changes in resonant frequencies that were produced by these simulated changes, and correlate 4
these changes with those measured on a platform. A number of very practical problems were encountered including measurement difficulties caused by platform machine noise, instrumentation difficulties in hostile environments, changing mass caused by marine growth, varying fluid storage levels, temporal variability of foundation conditions and the inability of wave motion to excite higher vibration modes. These issues prevented adaptation of this technology and efforts at further developing this technology for offshore platforms were largely abandoned in the early 1980s. The aerospace community began to study the use of vibration-based damage identification during the late 1970s and early 1980s in conjunction with the development of the space shuttle. This work has continued with current applications being investigated for the National Aeronautics and Space Administration's space station and future reusable launch vehicle designs. The shuttle modal inspection system (SMIS) was developed to identify fatigue damage in components such as control surfaces, fuselage panels and lifting surfaces. These areas were covered with a thermal protection system making them inaccessible and, hence, impractical for conventional local non-destructive examination methods. The SMIS has been successful in locating damaged components that are covered by the thermal protection system. All orbiter vehicles have been periodically subjected to SMIS testing since 1987. Space station applications have primarily driven the development of experimental/analytical methods aimed at identifying damage to truss elements caused by space debris impact. These approaches are based on correlating analytical models of the undamaged structure with measured modal properties from both the undamaged and damaged structures. Changes in stiffness indices as assessed from the two model updates are used to locate and quantify the damage. Since the mid-1990s, studies of damage identification for composite materials have been motivated by the development of a composite fuel tank for a reusable launch vehicle. The failure mechanisms, such as delaminating caused by debris impacts, and corresponding material response for composite fuel tanks are significantly different to those associated with metallic structures. Moreover, the composite fuel tank problem presents challenges because the sensing systems must not provide a spark source. This challenge has led to the development of SHM based on fiber optic sensing systems. The civil engineering community has studied vibration-based damage assessment of bridge structures and buildings since the early 1980s. Modal properties and quantities derived 5
from these properties, such as mode shape curvature and dynamic flexibility matrix indices, have been the primary features used to identify damage in bridge structures. Environmental and operating condition variability presents significant challenges to the bridge monitoring application. The physical size of the structure also presents many practical challenges for vibration-based damage assessment. Regulatory requirements in Asian countries, which mandate that the companies that construct the bridges periodically certify their structural health, are driving current research and commercial development of bridge SHM systems. In summary, the review of the technical literature presented by Doebling et al. (1996) and Sohn et al. (2003) shows an increasing number of research studies related to damage identification. These studies identify many technical challenges to the adaptation of SHM that are common to all applications of this technology. These challenges include the development of methods to optimally define the number and location of the sensors; identification of the features sensitive to small damage levels; the ability to discriminate changes in these features caused by damage from those caused by changing environmental and/or test conditions; the development of statistical methods to discriminate features from undamaged and damaged structures; and performance of comparative studies of different damage identification methods applied to common datasets. These topics are currently the focus of various research efforts by many industries including defense, civil infrastructure, automotive and semiconductor manufacturing where multi-disciplinary approaches are being used to advance the current capabilities of SHM and CM.
1.3 THE STATISTICAL PATTERN RECOGNITION PARADIGM There are many ways by which one can organize a discussion of SHM. We have chosen that defines the SHM process in terms of a four-step statistical pattern recognition paradigm. This following four-step process includes: i.
operational evaluation,
ii.
data acquisition, normalization and cleansing,
iii.
feature selection and information condensation, and
iv.
statistical model development for feature discrimination.
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OPERATIONAL EVALUATION Operational evaluation attempts to answer four questions regarding the implementation of a damage identification capability. i.
What are the life-safety and/or economic justification for performing SHM?
ii.
How is damage defined for the system being investigated and, for multiple damage possibilities, which cases are of the most concern?
iii.
What are the conditions, both operational and environmental, under which the system to be monitored functions?
iv.
What are the limitations on acquiring data in the operational environment? Operational evaluation begins to set the limitations on what will be monitored and how the monitoring will be accomplished. This evaluation starts to tailor the damage identification process to features that are unique to the system being monitored and tries to take advantage of unique features of the damage that is to be detected.
DATA ACQUISITION, NORMALIZATION AND CLEANSING The data acquisition portion of the SHM process involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware. Again, this process will be application specific. Economic considerations will play a major role in making these decisions. As data can be measured under varying conditions, the ability to normalize the data becomes very important to the damage identification process. As it applies to SHM, data normalization is the process of separating changes in sensor reading caused by damage from those caused by varying operational and environmental conditions. One of the most common procedures is to normalize the measured responses by the measured inputs. When environmental or operational variability is an issue, the need can arise to normalize the data in some temporal fashion to facilitate the comparison of data measured at similar times of an environmental or operational cycle. Sources of variability in the data acquisition process and with the system being monitored need to be identified and minimized to the extent possible. In general, not all sources 7
of variability can be eliminated. Therefore, it is necessary to make the appropriate measurements such that these sources can be statistically quantified. Variability can arise from changing environmental and test conditions, changes in the data reduction process and unit-to-unit inconsistencies. Data cleansing is the process of selectively choosing data to pass on to or reject from the feature selection process. The data cleansing process is usually based on the knowledge gained by individuals directly involved with the data acquisition. As an example, an inspection of the test set-up may reveal that a sensor was loosely mounted and, hence, based on the judgment of the individuals performing the measurement, this set of data or the data from that particular sensor may be selectively deleted from the feature selection process. Signal processing techniques such as filtering and re-sampling can also be thought of as data cleansing procedures. Finally, it should be noted that the data acquisition, normalization and cleansing portion of the SHM process should not be static. Insight gained from the feature selection process and the statistical model development process will provide information regarding changes that can improve the data acquisition process.
FEATURE EXTRACTION AND INFORMATION CONDENSATION The area of the SHM process that receives the most attention in the technical literature is the identification of data features that allows one to distinguish between the undamaged and damaged structure. Inherent in this feature selection process is the condensation of the data. The best features for damage identification are, again, application specific. One of the most common feature extraction methods is based on correlating measured system response quantities, such as vibration amplitude or frequency, with the first-hand observations of the degrading system. Another method of developing features for damage identification is to apply engineered flaws, similar to ones expected in actual operating conditions, to systems and develop an initial understanding of the parameters that are sensitive to the expected damage. The flawed system can also be used to validate that the diagnostic measurements are sensitive enough to distinguish between features identified from the undamaged and damaged system. The use of analytical tools such as experimentally validated 8
finite element models can be a great asset in this process. In many cases, the analytical tools are used to perform numerical experiments where the flaws are introduced through computer simulation. Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features. This process may involve induced-damage testing, fatigue testing, corrosion growth or temperature cycling to accumulate certain types of damage in an accelerated fashion. Insight into the appropriate features can be gained from several types of analytical and experimental studies as described above and is usually the result of information obtained from some combination of these studies. The operational implementation and diagnostic measurement technologies needed to perform SHM produce more data than traditional uses of structural dynamics information. A condensation of the data is advantageous and necessary when comparisons of many feature sets obtained over the lifetime of the structure are envisioned. Also, because data will be acquired from a structure over an extended period of time and in an operational environment, robust data reduction techniques must be developed to retain feature sensitivity to the structural changes of interest in the presence of environmental and operational variability. To further aid in the extraction and recording of quality data needed to perform SHM, the statistical significance of the features should be characterized and used in the condensation process.
STATISTICAL MODEL DEVELOPMENT The portion of the SHM process that has received the least attention in the technical literature is the development of statistical models for discrimination between features from the undamaged and damaged structures. Statistical model development is concerned with the implementation of the algorithms that operate on the extracted features to quantify the damage state of the structure. The algorithms used in statistical model development usually fall into three categories. When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification referred to as supervised learning. Group classification and regression analysis are categories of the supervised learning algorithms. Unsupervised learning refers to algorithms that are applied to data not containing examples from the damaged structure. Outlier or novelty detection is the 9
primary class of algorithms applied in unsupervised learning applications. All of the algorithms analyses statistical distributions of the measured or derived features to enhance the damage identification process. The damage state of a system can be described as a five-step process along the lines of the process discussed in Rytter (1993) to answer the following questions. i.
Existence. Is there damage in the system?
ii.
Location. Where is the damage in the system?
iii.
Type. What kind of damage is present?
iv.
Extent. How severe is the damage?
v.
Prognosis. How much useful life remains? Answers to these questions in the order presented represent increasing knowledge of the damage state. When applied in an unsupervised learning mode, statistical models are typically used to answer questions regarding the existence and location of damage. When applied in a supervised learning mode and coupled with analytical models, the statistical procedures can be used to better determine the type of damage, the extent of damage and remaining useful life of the structure. The statistical models are also used to minimize false indications of damage. False indications of damage fall into two categories: (i)
false-positive damage indication (indication of damage when none is present)
(ii)
false-negative damage indication (no indication of damage when damage is present).
Errors of the first type are undesirable, as they will cause unnecessary downtime and consequent loss of revenue as well as loss of confidence in the monitoring system. More importantly, there are clear safety issues if misclassifications of the second type occur. Many pattern recognition algorithms allow one to weigh one type of error above the other; this weighting may be one of the factors decided at the operational evaluation stage.
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1.4 CHALLENGES FOR SHM The basic premise of SHM feature selection is that damage will significantly alter the stiffness, mass or energy dissipation properties of a system, which, in turn, alter the measured dynamic response of that system. Although the basis for feature selection appears intuitive, its actual application poses many significant technical challenges. The most fundamental challenge is the fact that damage is typically a local phenomenon and may not significantly influence the lowerfrequency global response of structures that is normally measured during system operation. Stated another way, this fundamental challenge is similar to that in many engineering fields where the ability to capture the system response on widely varying length- and time-scales, as is needed to model turbulence or to develop phenomenological models of energy dissipation, has proven difficult. Another fundamental challenge is that in many situations feature selection and damage identification must be performed in an unsupervised learning mode. That is, data from damaged systems are not available. Damage can accumulate over widely varying time-scales, which poses significant challenges for the SHM sensing system. This challenge is supplemented by many practical issues associated with making accurate and repeatable measurements over long periods of time at a limited number of locations on complex structures often operating in adverse environments. Finally, a significant challenge for SHM is to develop the capability to define the required sensing system properties before field deployment and, if possible, to demonstrate that the sensor system itself will not be damaged when deployed in the field. If the possibility of sensor damage exists, it will be necessary to monitor the sensors themselves. This monitoring can be accomplished either by developing appropriate self-validating sensors or by using the sensors to report on each other's condition. Sensor networks should also be ‗fail-safe‘. If a sensor fails, the damage identification algorithms must be able to adapt to the new network. This adaptive capability implies that a certain amount of redundancy must be built into the sensor network. In addition to the challenges described above, there are other non-technical issues that must be addressed before SHM technology can make the transition from a research topic to actual
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practice. These issues include convincing structural system owners that the SHM technology provides an economic benefit over their current maintenance approaches and convincing regulatory agencies that this technology provides a significant life-safety benefit. All these challenges lead to the current state of SHM technology, where outside of condition monitoring for rotating machinery applications SHM remains a research topic that is still making the transition to field demonstrations and subsequent field deployment. There are lots of ongoing and new structural monitoring activities, but these systems have been put in place without a predefined damage to be detected and without the corresponding data interrogation procedure. As such, these monitoring activities do not represent a fully integrated hardware/software SHM system with pre-defined damage identification goals.
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2.
INTRODUCTION TO STRUCTURAL HEALTH MONITORING IN THE DOMAIN OF CIVIL ENGINEERING
Civil infrastructure provides the means for a society to function and includes buildings, pedestrian and vehicular bridges, tunnels, factories, conventional and nuclear power plants, offshore petroleum installations, heritage structures, port facilities and geotechnical structures, such as foundations and excavations. Depending on importance, ownership, use, risk and hazard, such structures have inspection, monitoring and maintenance programmes that may even be mandated by law. The effectiveness of maintenance and inspection programmes is only as good as their timely ability to reveal problematic performance, hence the move to supplement limited and intermittent inspection procedures by continuous, online, real-time and automated systems. Major drivers in this area have been the oil industry, operators of large dams and highways agencies, whose installations have received the greatest attention and research effort. Residential and commercial structures have received relatively little attention due to potential obligations and consequences of owners knowing about poor structural health. In these cases, structural health monitoring (SHM) can only be implemented after efforts have been made to educate owners or to coerce them via building control protocols (legislation) or insurance premiums. A significant challenge in developing an SHM strategy for civil infrastructure is that except for certain types of public and private housing, every structure is unique. This means that there is no baseline derived from type-testing or the expensive qualification procedures applicable for aerospace structures. Hence, a unique feature of SHM for civil infrastructure is that a major part of the system has to be geared towards a long-term evaluation of what is ‗normal‘ structural performance or ‗health‘, the two terms being synonymous.
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2.1 FUNDAMENTAL OBJECTIVES OF CIVIL INFRASTRUCTURE MONITORING Various Researchers identified the following cases where the following structural monitoring may be required: 1. modifications to an existing structure, 2. monitoring of structures affected by external works, 3. monitoring during demolition, 4. structures subject to long-term movement or degradation of materials, 5. feedback loop to improve future design based on experience, 6. fatigue assessment, 7. novel systems of construction, 8. assessment of post-earthquake structural integrity, 9. decline in construction and growth in maintenance needs, and 10. the move towards performance-based design philosophy. Historically, the monitoring of structures has involved many ingredients of the modern SHM paradigm, such as data collection and processing followed by diagnosis. At the simplest level, recurrent visual observation and assessment of structural condition (cracking, spalling and deformations) could be viewed as SHM, yet the aim of present-day research is to develop effective and reliable means of acquiring, managing, integrating and interpreting structural performance data for maximum useful information at a minimum cost while either removing or supplementing the qualitative, subjective and unreliable human element. Historical developments in SHM have generally focused on subsets of the SHM paradigm, but in recent years, a few research teams have begun to focus on, or at least recognize, the need for a holistic approach to optimization of SHM. At its core, SHM is a continuous system identification of a physical or parametric model of the structure using time-dependent data. The signals used in SHM are derived not only from vibrations, but also from slowly changing quasi-static effects, such as diurnal thermal cycles.
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Once a baseline system model is identified, SHM procedures are aimed at identifying occurrences when output signals do not correspond to predictions based on the established form. One of the many sub-disciplines within SHM is ‗condition assessment‘ (CA), a one-off but thorough identification of the structural system. SHM should be capable of carrying out a minimal level of CA in real time, but it is more probable that a follow-up investigation (CA) would be triggered by the SHM system and supported by the evidence it provides. In the short term, developments of SHM for civil infrastructure may not be expected to have an inherent capability for damage location and quantification and, while many research teams are progressing in this area, it is still not a reality to recover reliable component-level structural information in real time by system identification. Despite the body of research dedicated to vibration-based damage detection, success is limited to simulations, laboratory studies and wellcontrolled experiments, such as the Z24 bridge, and its effectiveness still remains to be proven for operational civil structures. Hence, short-term aims are less optimistic and focus on automatic provision of reliable and timely indication of a progressive or novel structural fault along with limited diagnostic information.
2.2 20TH CENTURY & DEVELOPMENT OF SHM IN CIVIL ENGINEERING Significant developments in SHM have originated from major construction projects, such as large dams, long-span cable-supported bridges and offshore gas/oil production installations. The term SHM is a recent ‗standard‘ that has evolved from activities formerly known as structural monitoring, structural integrity monitoring or just monitoring. It is impossible to identify the first form of SHM, but discounting simple periodic visual observation, formal structural monitoring and interpretation using recording instruments began in the latter half of the last century and accelerated with the use of electronic data storage and computer data acquisition. So much recent attention in the civil SHM community has been focused on bridges that it has overshadowed the formal application of SHM technology to other infrastructures such as dams that has been a legal
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requirement in the various parts of the world such as UK, USA, Canada, Australia etc. at least for several decades.
DAMS Dams are historically the first class structure for the mandated application of SHM, and there is much to learn from this experience that can be applied to other structures. In the dam engineering community, SHM is equivalent to surveillance. Many of the elements of the modern SHM paradigm are in place, for example: 1. a range of instrumentation optimized to provide safety-critical response data, supplemented by visual inspections, 2. automated data collection, and 3. intelligent interpretation of data against established behavior patterns and identification of anomalies. Considering the example of UK which is a significantly developed country in the field of SHM, the item (iii) is typically the role of the supervising engineer, while research in artificial intelligence (AI) applications for this role was spearheaded by the ISMES, the research arm of the Italian electricity utility ENEL. Every major dam in the ENEL inventory of over 260 structures is equipped with transducers activated by central processor at regular intervals to measure static ‗structural effects‘, such as 1. relative or absolute displacements: horizontal crest displacements are the most important for concrete dams; 2. strains (for concrete dams) with temperature correction; 3. uplift pressures quantifying loads, which, for example, contributed to the failure of Malpasset Dam in 1959; and 4. seepage rates. Transducers are also activated to record ‗external influences‘ to which the dam responds with structural effects, for example: 16
1. water level; 2. structural temperature; and 3. meteorological conditions. The variations of structural effects are evaluated for acceptability in the light of the environmental variations. This is the judgment that requires deep knowledge of the dam and its structural behavior. Traditionally, this is the role of a supervising engineer, but this has been taken over by developments of the monitoring system. Salvaneschi et al. (1996) reported that the commercial implementation of the information system under the name of MIDAS had, since 1985, been managing data of 200 dams in 10 different countries. Recognizing the limitations of such a system, ENEL foresaw the need to integrate the formalized tools, such as MIDAS, with the non-formal information from historical observations and engineering judgment. MISTRAL is a real-time system that considers groups of effects with or without relation to influences. In the former case, physics-based or statistical models are used for comparison and identifying anomalies, whereas in the latter case, it is still possible to identify anomalous behavior. DAMSAFE aimed at assisting engineers with dam safety management procedures. It works off-line and functions more like an ‗expert system‘ incorporating past experiences into its knowledge base. This line of research has now largely disappeared from public view, but is being developed in other countries for bridges and dams. Dynamic response monitoring plays a part in dam SHM for two reasons. First, earthquakes are a serious threat to the safety of dams and every opportunity is taken to improve understanding of seismic dam performance specifically and generically through calibration of models and simulations. Second, estimates of dynamic characteristics obtained from ambient monitoring deliberate forced vibration provide a means to track the structural characteristics as indicators of structural health.
BRIDGES Bridge monitoring programmes have historically been implemented for the purpose of understanding and eventually calibrating models of the load–structure–response chain. One of the earliest documented systematic bridge monitoring exercises, by Carder (1937), was 17
conducted on the Golden Gate and Bay Bridges in San Francisco in an elaborate programme of measuring periods of the various components during their construction to learn about the dynamic behavior and possible consequences of an earthquake. University of Washington (1954) describe monitoring of the first Tacoma Narrows Bridge over its short life before it collapsed due to wind-induced instability, again focusing on vibration measurements, but with an obviously warranted concern for the health of the structure. The Tacoma Narrows experience has far reaching importance since almost all of the long-span suspension bridge monitoring exercises to date have been related to concerns about windinduced response and possible instability, for example at Humber (Brownjohn et al. 1994) and Deer Isle (Kumarasena et al. 1990). Despite great progress in understanding bluff-body aeroelasticity, there continue to be surprises in long-span bridge aerodynamics (Larsen et al. 2000), and the strategic importance and capital investment justify the expense of the most elaborate SHM systems applied to civil infrastructure. In the last decade, permanent bridge monitoring programmes have evolved into SHM systems, which have been implemented in major bridge projects in Japan, Hong Kong and, latterly, North America. Long-span bridge monitoring systems also provide ideal opportunities to implement and study SHM systems; for example, the wind and SHM system (Wong 2003) implemented on the Lantau fixed crossing has stimulated SHM research in Hong Kong, not only concerning the performance of the bridges themselves, but also of SHM methodologies. Being the important lifeline structures, modern long-span suspension bridges typically have elaborate inspection and maintenance programmes, so that significant damage and deterioration of the superstructure is likely to be picked up visually, whereas an SHM system would require a high density of sensors to detect it. It is probably that only global changes such as foundation settlement, bearing failure or major defects, such as loss of main cable tension or rupture of deck element, are detectable by global SHM procedures with a minimum of optimally located sensors. Less glamorous but possibly ultimately more beneficial developments of SHM would be for optimal monitoring approaches for conventional short-span bridges. There is a history of research in full-scale testing for short-span highway bridge assessment (Salane et al. 1981;
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Bakht & Jaeger 1990) and obvious opportunities to extend such approaches to automated monitoring exercises. For smaller bridges, global response is more sensitive to defects, visual inspection is less frequent and SHM systems can and do (Alampalli & Fu 1994) make a real contribution. European research has been focused on the shorter-span bridges, where the BRIMOS system (Geier & Wenzel 2002) has been used to track dynamic characteristics. Studies in Australia have focused on the typical very short-span highway and railway bridges; in one case leading to a commercial product the ‗Bridge Health Monitor‘ or HMX (Heywood et al. 2000), which is programmed to record selected waveforms of vehicle-induced response while logging statistics of strains due to such events. Direct motivation for monitoring is also found within bridge management programmes (Yanev 2003) and bridge upgrading projects, where some form of validation is required, which is even made the responsibility of the contractor to ensure the effectiveness of the upgrade. Examples include the Severn and Wye Bridge Refurbishment (Flint & Smith 1992), Tamar Bridge (List 2004) in the UK and Pioneer Bridge, Singapore (Brownjohn et al. 2003a,b). In a related sense, the role of monitoring within build, own, operate and transfer contracts can provide investors with confidence in their assessment and subsequent owners' evidence of good condition at the end of the concession.
OFF-SHORE INSTALLATIONS In the 1970s, the energy crisis and discovery of large oil reserves in the North Sea led to rapid developments in offshore infrastructure, specifically the fixed steel and concrete production installations operating in water depths of 150 m or more and subjected to extreme environmental loads. With mandatory requirements for inspection the expense and danger of diver inspection came a flurry of interest in vibration-based diagnostic systems. For example, environmental and platform performance (E- and P-) data were collected, under the responsibility of the platform operator according to the requirements of the Norwegian Petroleum Directorate: ‗in order to assess the safety of the load carrying structure of the platforms and their foundation‘. The studies resulted in identification of dynamic characteristics and load–response mechanisms for a number of installations on the Norwegian continental shelf.
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At about the same time, a range of system identification techniques for ambient response were developed that were precursors of the modern discipline of ‗operational modal analyses. Most of the studies concluded that while detection was possible, it was either under controlled conditions or where severe and usually obvious structural damage had occurred. The study by Structural Monitoring Limited went on to state that ‗techniques will not be developed further unless the offshore industry develops an integrated philosophy covering …inspection, maintenance and monitoring and works with contractors to produce an effective tool‘. If such effective tools now exist, it is probable that they will be proprietary, not in the public domain. A particular problem for offshore installations is that the structure is a non-stationary system with continual changes to mass properties through structural modifications, loading or unloading of stores, fluid movements in processing plant and drilling operations. An apocryphal tale tells of a damage detection system being fooled into identifying a failed structural member because a new structural module was added without the system knowing about it.
BUILDINGS & TOWERS Historically, developments in monitoring of buildings were motivated by the need to understand building performance during earthquakes and storms. Originally, understanding of lowamplitude dynamic response was obtained from vibration testing (Hudson 1977; Jeary & Ellis 1981), but it has always been preferable to know the building response during a typical but not ultimate large amplitude loading event, and this has required long-term monitoring. In California, mandatory structural monitoring is managed by the California Strong Motion Instrumentation Programme (CSMIP) (California Geological Survey 2003), which uses levies on building owners to fund installation and operation of strong motion instruments on buildings and other structures of their choosing. While such data can provide some feedback on structural health, the aim is to provide information on ground motions and for improvement in structure design based on performance subject to these ground motions. The need to identify full-scale structural performance has always been central to earthquake engineering research.
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Hence, the majority of monitoring exercises on buildings and towers has been aimed at improving understanding of loading and response mechanisms, not only for earthquakes, but also for wind loads, for example studies on the Bank of Commerce Building in Toronto (Dalgleish & Rainer 1978) and Hume Point, London (Littler & Ellis 1990). In Singapore, studies on high-rise apartment blocks have been aimed at assessing wind loads on even taller blocks yet to be built where occupant comfort is a concern. Significant motivation for SHM of buildings has also resulted from recent major earthquakes, such as in Kobe and Northridge, where timely information about the condition of structures would be invaluable in assessing safety and need for repair. This is one application where SHM provides the trigger for and then assists CA, and has provided an ideal opportunity for an integrated approach to SHM involving discrete autonomous novel sensors, embedded systems, communications, data management and mining, etc. Evolved out of two decades of experience on full-scale structural testing and monitoring, the approach to SHM advocated by Jeary and co-authors is a simple and pure approach to the classical SHM paradigm. The success of the approach means that it has been commercialized and details are not in the public domain. In essence, the system uses high specification accelerometers and data acquisition equipment and developments of random decrement techniques to track fundamental mode damping along with building tilt. Both these parameters provide indication of structural distress and have been used to diagnose problems in tall buildings in Hong Kong. This rare application of damping for SHM is notable compared with the abundance of less successful techniques based on tracking natural frequencies.
NUCLEAR INSTALLATIONS For Explanation of SHM application to nuclear structures which are vital for the existence of a country we consider UK‘s nuclear power stations where SHM was applied. Smith (1996) provides an overview of the inspection and monitoring regime for a sample of the UK's civil nuclear reactors. For the safety-critical structural components of nuclear reactors, instrumentation for measuring structural response is used to validate and calibrate designs during performance testing and also contributes to the condition monitoring during normal operation.
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In the UK, each civil nuclear power station uses a pair of reactors and all but one of these reactors is either Magnox or an advanced gas-cooled reactor (AGR), which is a development of Magnox. For all AGRs, plus the most recently built pair of Magnox reactors, the critical structural component is the prestressed concrete pressure vessel (PCPV), which contains the reactor core and primary coolant. The PCPV is typically a very thick cylindrical vessel with massive steel reinforcement, including redundant helically wound non-grouted post-tensioning cables maintaining the vessel in a constant state of compression. As described by installation operators (Smith 1996; Smith & McCluskey 1997), licences to operate nuclear reactors are granted by the Nuclear Installations Inspectorate (NII) under the provisions of the Nuclear Installations Act (1965). One of the conditions of granting or renewing such a license is that the licensee shall have proven the performance of the PCPV during testing as well as during any previous operation. It is up to the licensee to demonstrate, within a ‗safety case‘ presented to NII, that the reactor is fit for purpose and capable of meeting its nuclear safety role. Reactors have ‗statutory outages‘ or programmed shutdowns at 3-year intervals for thorough inspections. As a condition for restarting the reactor, a report must be submitted to NII by a responsible appointed examiner (AE), who has a similar role to the supervising engineer for dams and is nominally independent from the licensee and NII. The AE reports on results of inspections and tests during the outage, together with structural and other performance data from monitoring during pre-outage operation. This report forms part of the licensee's application to the NII for consent to restart the reactor. Obviously, the critical and constantly monitored performance parameter is temperature, while the structural performance data, from vibrating wire strain gauges (SGs), play a lesser role. The primary role of the strain data is by post-processing to assess in-service structural performance and to calibrate and update analytical simulations of the temperature-dependent structure performance. Hence, it is fair to say, in the UK at least, that online monitoring of structural response so far does not play a major role in tracking the health of the PCPV. The potential is there, as the strain data are automatically logged and show clear indications of changes in other operational parameters. The discovery of a 15 cm cavity in the pressure vessel head of the Besse-Davis nuclear reactor (Cullen 2002) may motivate such applications.
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TUNNELS & EXCAVATIONS Tunnel monitoring is aimed at ensuring whether tunnel deformation is within limits in terms of stability and effects on or from adjacent structures. Hence, while stresses and strains may be measured, the emphasis is on deflections. Monitoring of heritage and other structures during nearby tunneling or mining is a major concern; examples include the monitoring of Mansion House in London during construction of extension to an underground railway and monitoring of listed nineteenth century mining facilities in Australia close to explosive blasting in nearby opencast mining operation. These ground surface monitoring exercises are temporary, but feature all the technology of permanent monitoring systems. In Singapore, tunnel deflection monitoring systems (Tan & Chua 2003) are also required during construction activities taking place at the surface that may affect tunnel alignment and integrity. Surface and tunnel deflection monitoring employ wireless remote monitoring technology for data transmission, with Internet access or text messaging to operators indicating threshold crossings of deflection parameters. Geotechnical constructions stand to derive significant benefits from true SHM systems while offering the simplest situations to study. A recent fatal collapse of tunneling excavations in Singapore in April 2004 highlights this point (Loh 2004). Post-accident examination of recordings from instrumentation revealed that some movements in the excavation wall had been detected two months before the accident and had exceeded trigger levels. Such relatively slow and monotonic movements and any acceleration in the movement could most probably have been identified easily and reliably by limited online processing with resulting automated alarm. This incident has accelerated the applications of wireless automated monitoring in Singapore. The SHM technology has also been applied to landslide monitoring, for example Reid & LaHusen (1998) used telemetry to monitor the Clevelend Corral landslide for 5 years and Civera et al. (2003) developed MEMS inclinometers, communicating by wireless to provide warning of ground movements around quarry excavations.
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2.3 LIMITATIONS & PROBLEMS IN THE APPLICATION OF SHM TO CIVIL ENGINEERING STRUCTURES Directions in SHM research and development towards the goal of autonomous systems for assisting structure managers maintaining their structures safely and efficiently will depend on dealing with key problem areas in SHM.
SYSTEM RELIABILITY As SHM systems are intended to assist infrastructure operators/owners in managing their facilities, an exhaustive cost–benefit analysis will be required to show that benefits outweigh costs. Apart from the initial outlay in planning, purchase and installation, operation and maintenance costs of the system should be low. INAPPROPRIATE INSTRUMENTATION & SENSOR OVERLOAD In the growing number of instances where a tender specification for structural works includes a requirement for instrumentation and monitoring, there may be no incentive for careful selection of sensors and locations with a mind to obtaining useful information about the performance of the structure. There is a tendency to over-instrument, as installation of post-construction will usually be more troublesome. For a given budget, this will affect the quality of the instruments and their survival rate, so an understanding of expected performance and critical locations in the structure will help plan for fewer but more reliable sensors. Inappropriate instrumentation often results in where the end-user is separated from the instrumentation contractor by layers of main contractor and consultants; a specialist consultant should oversee the design, implementation and early operation of the instrumentation system.
DATA STORAGE & DATA OVERLOAD A corollary of over-instrumentation is data overload, more data than can be assimilated without an elaborate database management system. Limited research has been done on optimal placement of sensors, and careful planning using scenarios of detectable degradation or damage, together with examination of similar operational SHM systems, will lead to more efficient sensor deployment. 24
COMMUNICATIONS Wireless or land links are a necessity for remote unmanned installations; dial-up modems or permanently connected leased lines may be used. These may not be 100% reliable so allowance had to be made for their fallibility. Robust low-cost wireless systems play an increasing communications role but due to present limited data capacity, data compression or preprocessing will be necessary unless data are slowly sampled static signals. The most widely used public network is the 9.6 kilobit per second (kbps) GSM data communication system, but as the 30 kbps GPRS network increases in popularity, the cost of real-time monitoring using wireless technologies for SHM will reduce. The 700 kbps 3G networks will provide the possibility of simple, cost effective, real-time wireless dynamic response monitoring from PDA, laptops and cell phones. Zigbee is used in some commercial wireless SHM solutions.
ENVIRONMENT FACTORS & NOISE VBDD using accelerometer signals may work in controlled conditions, but, as many exercises on full-scale structures have demonstrated, changes in modal properties due to environmental conditions will greatly reduce the probability of successful damage detection and location. Partial mitigation of such effects can be achieved through physics-based or statistical models of the environmental effects, but a level of noise will still remain, even with slowly varying static signals. Successful SHM procedures will incorporate the means to compensate for or filter out the environmental and noise effects or at least establish confidence levels for anomaly detection against noise. DATA M INING & INFORMATION PRESENTATION One of the most significant issues with SHM is converting data to information, an issue addressed in detail elsewhere in this set of papers. Not to be overlooked is the charting or presentation of information to operators who are very unlikely to be familiar with the sophisticated underlying numerical procedures.
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FUNDING Informed research communities and their funding agencies have begun to realize that as infrastructure development slows and existing stock ages, there is a growing need to support research programmes in SHM. The fruits of such research are slow to grow and funding applications have to compete with the more fashionable and immediately productive research areas. For this reason, present mono-disciplinary and single-institution paradigms for SHM research with narrow focus may have lower chances of survival. Approaches to SHM developing in the twenty-first century involve the whole spectrum of the problem from sensors to data mining via advanced sensor technology, embedded systems, communications, IT, expert systems and AI. They involve collaborations across disciplines and institutions, as well as across national boundaries, and are ideal cases for industrial support. Serious research (that is likely to end up being applied) into SHM for legally mandated applications (dams, offshore and nuclear installations) is generally conducted by owners– operators and the bulk of SHM research reported by academia relates to bridges, buildings and geotechnical installations, where infrastructure owners lack capacity for in-house research and stand to benefit, with universities, from collaboration. Without financial or legal pressure on private owners, SHM studies on tall buildings will be limited to cases where there is positive publicity or a specific problem. Owners would simply prefer that any defects do not become public knowledge, as knowledge leads to liability. LACK OF COLLABORATION Clearly, different levels of progress have been made for different classes of structure, and there is much to be learnt from studying procedures not only in other disciplines (such as condition monitoring of rotating machinery), but also within the various branches of civil-structural engineering. The experience of dam monitoring is transferable to other structures, particularly bridges. However, once a solution has been found for a problem in one part of the industry, only those with academic background would wish to make technology available elsewhere. Hence, as research converges on a solution, so it becomes invisible making the ideal even less attainable without large resources for a single group.
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3.
LITERATURE REVIEW
3.1 METHODS OF STRUCTURAL HEALTH MONITORING ORIGIN Qualitative and non-continuous methods have long been used to evaluate structures for their capacity to serve their intended purpose. Since the beginning of the 19th century, railroad wheeltappers have used the sound of a hammer striking the train wheel to evaluate if damage was present. In rotating machinery, vibration monitoring has been used for decades as a performance evaluation technique. In the last ten to fifteen years, SHM technologies have emerged creating an exciting new field within various branches of engineering. These technologies are currently becoming increasingly common.
3.2 STATISTICAL PATTERN RECOGNITION PARADIGM APPROACH The SHM problem can be addressed in the context of a statistical pattern appreciation model. This model can be broken down into four parts: (1) Operational Evaluation (2) Data Acquisition and Cleansing (3) Feature Extraction and Data Compression (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from real world structures, it quickly becomes apparent that the ability to cleanse, compress, normalize and fuse data to account for operational and environmental variability is a key implementation issue when addressing Parts 2-4 of this paradigm. These processes can be implemented through hardware or software and, in general, some combination of these two approaches will be used.
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OPERATIONAL EVALUATION Operational evaluation attempts to answer four questions regarding the implementation of a damage identification capability: i) What are the life-safety and/or economic justification for performing the SHM? ii) How is damage defined for the system being investigated and, for multiple damage possibilities, which cases are of the most concern? iii) What are the conditions, both operational and environmental, under which the system to be monitored functions? iv) What are the limitations on acquiring data in the operational environment? ECONOMIC AND/OR LIFE -SAFETY ISSUES Economic and life-safety issues are the primary driving force behind the development of structural health-monitoring technology. Garibaldi and Gorman (1999) state the need for accurate numerical models for bridge applications in which some modal frequencies are closely spaced. The authors note that the closely spaced frequencies can get obscured during the modal analysis of measurement data from limited measurements on the bridge-span sides and can lead to poor analysis results. Furthermore, because of the inaccessibility of the bridges under deck or the cost of closing the bridge to instrument measurements on the roadway, it is difficult to take dynamic measurements under the bridge or in the middle of a lane traversing the bridge. Sikorsky and Stubbs (1997) discuss the goals of bridge management systems including SHM that the American Association of State Highway and Transportation Officials (AASHTO) intend to implement. In particular, the authors advocate a quality-management initiative directed toward the construction stage of bridge structures coupled with modal-based nondestructive damage evaluation (NDE) throughout the lifespan of the bridges. These goals are to help engineers and decision makers determine when and where to spend funds so that structural safety is enhanced, the existing infrastructure is preserved, and the needs of commerce and the motoring public are satisfied.
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DEFINITION OF DAMAGE Doebling and Farrar (1997), damage has been intentionally introduced into a structure in an effort to simulate damage without having to wait for such a damage to occur. In other cases, the authors postulate a damage-sensitive feature and then develop an experiment to demonstrate the effectiveness of this feature. In these cases, there is no need to formally define damage. Most laboratory investigations fall into this category. When a SHM system is deployed on an in situ structure, it is imperative that the investigators first clearly define and quantify the damage that they are looking for; then, that they can increase the likelihood that the damage will be detected with sufficient warning, and to make optimal use of their sensing resources. Rytter and Kirkegaard (1997), define damage simply as reduction in a bending stiffness, EI. Here, I am the moment of inertia of the beam‘s cross section, and E is the elastic modulus. They analyze the time history data from a four-story structure subjected to earthquake loading in the laboratory. The stiffness reduction of beams and columns are investigated in this study. ENVIRONMENTAL AND /OR OPERATIONAL CONSTRAINTS Using an analytical model of a cantilever beam, Cawley (1997) compares the effect of crack formation on the resonant frequency to that of the beam‘s length. In this study, the crack is introduced at the fixed end of the cantilever beam, and the length of the beam is varied. This study is part of a general discussion on the limitations of using modal properties for damage diagnosis. His results demonstrate that the resonance-frequency change caused by a crack, which is 2% cut through the depth of the beam, is 40 times smaller than caused by a 2% increase in the beam‘s length. The implication here is that changes in the structure‘s surroundings or boundary conditions such as thermal expansion can produce more significant changes in resonant frequencies than damage. Then, the authors propose to use the Lamb waves instead of vibration measurements to discern damage from a change in the boundary condition. In Lamb wave testing, the measurements are limited to the short time period between the generation of the wave and the onset of the first reflection signal at the boundaries. For an example of a beam, a wave is generated at one end of the beam, and the first echoes from the other end of the beam are monitored. Therefore, the sensitivity to the boundary conditions is reduced by excluding multiple reflections signals from the boundaries. Any additional reflection signals arriving before the 29
boundary reflection would indicate the presence of damage. This Lamb wave testing is applied to detect delamination in a composite plate. Pirner and Fischer (1997) separate mechanically induced loadings from thermally induced ones in a TV tower in Prague, Czechoslovakia. The researchers note that it is impossible to directly distinguish these loadings using time histories alone. They note, however, that temperature changes produce major and slowly changing stresses, while wind loads produce faster changes. The authors are then able to infer the temperature-induced loadings from a curve relating the number of stress cycles to the stress magnitude. DATA MANAGEMENT Seeing a deficiency in modern management methods in the bridge and highway infrastructure, Aktan et al. (2000) set out to present and discuss the issues prerequisite to creating a meaningful and successful health-monitoring system. His proposal entailed the implementation of an integrated-asset management that would facilitate a cost-effective optimization of operational performance and life-cycle preservation with an encompassing data network. The data components would include event-based, intermittent and on-line monitoring, and would combine temporal data collection and spatial-position-based data. A management system would diagnose the useful life only in collaboration with sensing damaged and undamaged states of systems. The author adds that inventories of existing structure and infrastructure safety-related information are limited in detail. For example, critical welds may be monitored and determined to be damaged, but with accurate information regarding the bridge safety values, the bearing load of a bridge is found to be within safety values. As such, the authors suggest that the infrastructures be reevaluated with appropriate system identification allowing for effective health monitoring Operational evaluation begins to set the limitations on what will be monitored and how the monitoring will be accomplished. This evaluation starts to tailor the damage identification process to features that are unique to the system being monitored and tries to take advantage of unique features of the damage that is to be detected.
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DATA ACQUISITION AND SIGNAL PROCESSING The data acquisition portion of the SHM process involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware. Again, this process will be application specific. Economic considerations will play a major role in making these decisions. The intervals at which data should be collected are another consideration that must be addressed. Because data can be measured under varying conditions, the ability to normalize the data becomes very important to the damage identification process. For instance, the measured fundamental frequency of the Alamosa Canyon Bridge in New Mexico varied approximately 5% during a 24-hour test period. Because the bridge is approximately aligned in the north and south direction, there was a large temperature gradient between the west and east sides of the bridge deck throughout the day. EXCITATION METHODS The excitation methods fall into the two general categories of ambient and forced excitation methods. During ambient excitation, the input to a system is not generally measured. In contrast, excitation forces are usually applied in a controlled manner and measured when the forced excitation method is employed. In this review, local excitations such as an excitation using a piezoelectric actuator is addressed in an independent section separated from the conventional forced excitation. FORCED EXCITATION In the forced-excitation testing of structures, a wide variety of forcing techniques is used, including actuators, shakers, step relaxation, and various methods of measured impact (see Figure). For most of the forced-vibration tests, the input-forcing function is well characterized and system-identification techniques for determining the modal characteristics (resonant frequencies, mode shapes, and modal-damping ratios) of the structures subjected to measured inputs are well established. One advantage of the forced-vibration test is that the input force is typically strong enough to dominate other noise disturbance, resulting in a strong signal to noise ratio.
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FIGURE 3.1: MECHANICAL ECCENTRIC MASS SHAKER: GENERATES SINUSOIDAL ACTION, BUT DIFFICULT TO APPLY IN VERTICAL DIRECTION
FIGURE 3.2: IMPACT EXCITTION WITH AN IMPACT
Rytter and Kirkegaard (1997) perform a vibration test of a full-scale, four-story reinforced concrete building at the European Laboratory for Structural Assessment (ELSA). This building is subjected to an earthquake generated by a pseudo-odynamic testing method. Readers interested in the details of the experiment are referred to Negro et al. (1994). The experimental data are used to validate vibration inspection techniques based on a multilayer-perceptron neural network and a radial-based function network. The relative changes in the modal parameters are used as inputs of the networks to detect the bending stiffness change at the output layer. AMBIENT EXCITATION Ambient excitation is defined as the excitation experienced by a structure under its normal operating conditions. All structures are consistently subject to ambient excitation from various sources. The input force is generally not recorded or cannot be measured during dynamic tests that use ambient excitation. Because the input is not measured, it is not known if this excitation source provides input at the frequencies of interest, how stationary the input is, or how uniform the input is over a particular frequency range. Even when measured input excitation (forced excitation) is used, ambient vibration sources are often still present, producing undesirable and often unavoidable extraneous inputs to the structure. For the development of online, real-time SHM, the use of ambient excitation, however, provides an 32
attractive means of exciting the structure. This type of excitation is a particularly attractive alternative to forced vibration tests during dynamic testing of bridge structures because bridges are consistently subject to ambient excitation from sources such as traffic, wind, wave motion, pedestrians, and seismic excitation. Except for seismic excitation, the input force is generally not recorded or cannot be measured during dynamic tests that use ambient excitation. The use of ambient vibration often provides a means of evaluating the response of the structure to the actual vibration environment of interest. Pedestrian loading is generated by people walking across the bridge. This excitation method seems limited to footbridges and small-scale bridges and is unappreciable to typical road bridges or large-scale bridges. This type of excitation is often employed to study the psychological perception of pedestrians and passengers in stopped vehicles to bridge vibrations. It is suggested that human sensitivity to vibrations is most closely related to acceleration rather than deflection. Brownjohn (1997) reported ambient vibration tests of a footbridge in Singapore using a person walking and jumping on the bridge as the excitation source. The analysis of the bridge‘s response to the pedestrian inputs showed that the ―bouncy‖ response was caused almost entirely by two modes near 2 Hz (symmetric and asymmetric vertical modes) that coincided with the typical frequency of normal pedestrian footfall. LOCAL EXCITATION Local excitation methods are a subset of the forced excitation techniques. However, because the local excitation tends to excite a specific region of a structure rather than the whole system and this excitation needs special types of actuators, the local excitation is addressed in a separate section. An impedance-based method is introduced by Park, Cudney, and Inman (1999a). In this SHM technique, a piezoelectric transducer (PZT) is used for both actuation and sensing of a structure‘s response. The electrical impedance (or the transfer function) of the PZT is directly related to the mechanical impedance of the bonded structure, and consequently is related to structural properties such as stiffness, mass, and damping. Then, damage metric is defined as the sum of the squared differences of the impedance between the current and undamaged states of the structure over the frequency content of interest. When this damage metric becomes larger than a 33
predetermined damage threshold value, the current state of the system is categorized into a damaged state. The authors note advantages of an impedance-based technique for SHM over the conventional techniques based on modal parameter extraction. First, the technique is not based on any numerical model. The nature of high-frequency excitation, which is typically above30 kHz, makes this technique very sensitive to local changes within the structure. Finally, this technique can be implemented for continuous online SHM without requiring costly, scheduled base inspections. DATA NORMALIZING Data normalization is the process of separating changes in sensor reading caused by damage from those caused by varying operational and environmental conditions. One of the most common procedures is to regulate the measured responses by the measured inputs. When environmental or operational variability is an issue, the need can arise to control the data in some temporal fashion to facilitate the comparison of data measured at similar times of an environmental or operational cycle. Sources of variability in the data acquisition process and with the system being monitored need to be identified and minimized to the extent possible. In general, not all sources of variability can be eliminated. Therefore, it is necessary to make the appropriate measurements such that these sources can be statistically quantified. Variability can arise from changing environmental and test conditions, changes in the data reduction process, and unit-to-unit discrepancies.
FIGURE 3.3: THE MEASURED FUNDAMENTAL FREQUENCY OF THIS ALAMOSA CANYON BRIDGE IN NEW MEXICO VARIED APPROXIMATELY 5% DURING A 24-HOUR TEST PERIOD. THIS VARIATION EMPHASIZES THE IMPORTANCE OF DATA NORMALIZATION
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DATA C LEANSING Data cleansing is the process of selectively choosing data to pass on to or reject from the feature selection process. The data cleansing process is usually based on knowledge gained by individuals directly involved with the data acquisition. As an example, an inspection of the test setup may reveal that a sensor was loosely mounted and, hence, based on the judgment of the individuals performing the measurement, this set of data or the data from that particular sensor may be selectively deleted from the feature selection process. Signal processing techniques such as filtering and re-sampling can also be thought of as data cleansing procedures. Finally, the data acquisition, normalization, and cleansing portion of SHM process should not be static. Insight gained from the feature selection process and the statistical model development process will provide information regarding changes that can improve the data acquisition process.
FEATURE EXTRACTION AND DATA COMPRESSION The area of the SHM process that receives the most attention in the technical literature is the identification of data features that allows one to distinguish between the undamaged and damaged structure. Inherent in this feature selection process is the condensation of the data. The best features for damage identification are, again, application specific. One of the most common feature extraction methods is based on correlating measured system response quantities, such a vibration amplitude or frequency, with the first-hand observations of the degrading system. Another method of developing features for damage identification is to apply engineered flaws, similar to ones expected in actual operating conditions, to systems and develop an initial understanding of the parameters that are sensitive to the expected damage. The flawed system can also be used to validate that the diagnostic measurements are sensitive enough to distinguish between features identified from the undamaged and damaged system. The use of analytical tools such as experimentally-validated finite element models can be a great asset in this process. In many cases the analytical tools are used to perform numerical experiments where the flaws are introduced through computer simulation. Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features. This 35
process may involve induced-damage testing, fatigue testing, corrosion growth, or temperature cycling to accumulate certain types of damage in an accelerated fashion. Insight into the appropriate features can be gained from several types of analytical and experimental studies as described above and is usually the result of information obtained from some combination of these studies. The operational implementation and diagnostic measurement technologies needed to perform SHM produce more data than traditional uses of structural dynamics information. A condensation of the data is advantageous and necessary when comparisons of many feature sets obtained over the lifetime of the structure are envisioned. Also, because data will be acquired from a structure over an extended period of time and in an operational environment, robust data reduction techniques must be developed to retain feature sensitivity to the structural changes of interest in the presence of environmental and operational variability. To further aid in the extraction and recording of quality data needed to perform SHM, the statistical significance of the features should be characterized and used in the condensation process.
STATISTICAL MODEL DEVELOPMENT The portion of the SHM process that has received the least attention in the technical literature is the development of statistical models for discrimination between features from the undamaged and damaged structures. Statistical model development is concerned with the implementation of the algorithms that operate on the extracted features to quantify the damage state of the structure. The algorithms used in statistical model development usually fall into three categories. When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification referred to as supervised learning. Group classification and regression analysis are categories of supervised learning algorithms. Unsupervised learning refers to algorithms that are applied to data not containing examples from the damaged structure. Outlier or novelty detection is the primary class of algorithms applied in unsupervised learning applications. All of the algorithms analyze statistical distributions of the measured or derived features to enhance the damage identification process.
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3.3 THE FUNDAMENTAL AXIOMS OF SHM Based on the extensive literature that has developed on SHM over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or general principles, have emerged. The axioms are listed as follows: Axiom I: All materials have inherent flaws or defects; Axiom II: The assessment of damage requires a comparison between two system states; Axiom III: Identifying the existence and location of damage can be done in an unsupervised learning mode, but identifying the type of damage present and the damage severity can generally only be done in a supervised learning mode; Axiom IVa: Sensors cannot measure damage. Feature extraction through signal processing and statistical classification is necessary to convert sensor data into damage information; Axiom IVb: Without intelligent feature extraction, the more sensitive a measurement is to damage, the more sensitive it is to changing operational and environmental conditions; Axiom V: The length- and time-scales associated with damage initiation and evolution dictates the required properties of the SHM sensing system; Axiom VI: There is a trade-off between the sensitivity to damage of an algorithm and its noise rejection capability; Axiom VII: The size of damage that can be detected from changes in system dynamics is inversely proportional to the frequency range of excitation.
3.4 SHM COMPONENTS SHM System's elements include:
Sensors
Data acquisition systems
An example of this technology is embedding sensors in structures like bridges and aircraft. These sensors provide real time monitoring of various structural changes like stress and strain. In the 37
case of civil engineering structures, the data provided by the sensors is usually transmitted to a remote data acquisition centers. With the aid of modern technology, real time control of structures (Active Structural Control) based on the information of sensors is possible.
INSTRUMENTS USED IN STRUCTURAL HEALTH MONITORING SENSOR A sensor is a device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. In order to fulfill the purpose of structural health monitoring the following primary criteria for selecting the right type of sensors are considered: advance, sensitivity, precision, proper operating environmental conditions, low cost, high reliability, high durability, high stability, easy installation, replace ability, rehabilitation & expansibility. Now we will discuss about the type of sensors related to it ANEMOMETER
FIGURE 3.4: ANEMOMETER
An anemometer is a device for measuring wind speed, it can work properly under bad weather such as windy and rainy, and temperature variation range in winter & summer. There are several types of anemoscope, such as ultrasonic, turbine and so on. Ultrasonic anemoscope can measure speed, direction and angle, so the performance is absolutely better than other types of anemoscopes.
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WEIGHBRIDGES
FIGURE 3.5: WEIGHBRIDGES
The weight and speed of vehicles are measured by weighbridges or weight-in-motion. The weighbridges are embedded underground in pavement, and thus the replacement cost is inexpensive. The short life due to bad durability is the main defect of this instrument. The weighbridge is made in steel-concrete composite structure, which has the same life with bridge itself. The weighbridge provides a good candidate for measuring traffic loads. TEMPERATURE SENSORS
FIGURE 3.6: TEMPERATURE SENSORS
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The temperature sensor measurement range is determined according to the highest & lowest temperatures of historical records. For concrete structure, temperature sensors are usually embedded into concrete, the replacement and rehabilitation are difficult and haven burden-inlabor. Thermocouples are typical type of sensors to record temperature. There are many kinds of thermocouples at present, such as contact-type, infrared-type and so on. STRAIN SENSORS
FIGURE 3.7: STRAIN SENSORS
Precision, sensitivity, stability and durability are primary criteria to be considered for selecting strain sensors. Dynamic and static properties of sensors are also criteria for some cases. The precision and sensitivity demand on strain sensors can be determined based on strain level induced by dead loads and live loads. Generally, the materials of strain sensor itself have high durability. However, if the strain sensor is mounted onto the specimen by inserter adhesive between strain sensor and specimen, the creep of adhesive will affect precision and durability of strain sensors. There is a wide variety of strain sensors, such as strain gauge, measuring vibrating wire strain gauges, optical fiber strain sensors. Strain gauge has been widely used in measurement of strain in the past decades. However, the adhesive which is used to mount strain gauge onto specimen, has disadvantage impact on precision and durability. This factor limits the application of health monitoring system in civil infrastructure. Measuring vibrating wire strain gauges can mount specimen via weld, which extend the life of gauges. However, this type of sensors cannot 40
measure dynamic strain, only appreciation for static state response. Various optical fiber strain sensors are well developed in recent years. ACCELEROMETERS
FIGURE 3.8: ACCELEROMETER
High precision, a wide range of frequency, suitable measurement range, operating environment condition, high stability and decay of signal with increasing distance are the factors to be considered for selecting accelerometers. Acceleration is more readily to measure. There are many types of accelerometers at present. For bridge, the natural modal frequency is much low, and thus the accelerometers with DC frequency is the first candidate to choose. DISPLACEMENT TRANSDUCERS
41 FIGURE 3.9: DISPLACEMENT TRANSDUCERS
High precision, a wide range of frequency, measurement range high stability and easy installation are the primary criteria to be considered for selecting displacement transducer. It is still very difficult to measure absolute displacement or deflection of bridge so far. There is wide variety of displacement transducer. Contact and un-contact are the main two categories. For uncontact CCD is the typical sensor to measure absolute displacement or deflection. GPS is the ideal sensor to measure displacement or deflection due to the advantages of high precision, convenient installation, automatization operation and so on. There are other sensors available for health monitoring system, such as magneto-elastic sensor for measuring tension force of cable, acoustic emission technology for detection of breakage, crack and so on. It is no doubt that higher level of health monitoring system is, the higher performance of sensors is.
DATA ACQUISITION SYSTEM Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be operated by a computer. Data acquisition systems (abbreviated with the acronym DAS or DAQ) typically convert analog waveforms into digital values for processing. The components of data acquisition systems include:
Sensors that convert physical parameters to electrical signals.
Signal conditioning circuitry to convert sensor signals into a form that can be converted to digital values.
Analog-to-digital converters, which convert conditioned sensor signals to digital values.
Data acquisition applications are controlled by software programs developed using various general purpose programming languages such as BASIC, C, Fortran, Java, Lisp, Pascal. Specialized software tools used for building large-scale data acquisition systems include EPICS. Graphical programming environments include ladder logic, Visual C++, Visual Basic, MATLAB and Lab VIEW.
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FIGURE 3.10: DATA ACQUISITION SYSTEM
Data acquisition begins with the physical phenomenon or physical property to be measured. Examples of this include temperature, light intensity, fluid flow, and force. Regardless of the type of physical property to be measured, the physical state that is to be measured must first be transformed into a unified form that can be sampled by a data acquisition system. The task of performing such transformations falls on devices called sensors. A sensor, which is a type of transducer, is a device that converts a physical property into a corresponding electrical signal (e.g. a voltage or current) or, in many cases, into a corresponding electrical characteristic (e.g. resistance or capacitance) that can easily be converted to electrical signal. The ability of a data acquisition system to measure differing properties depends on having sensors that are suited to detect the various properties to be measured. There are specific sensors for many different applications. DAQ systems also employ various signal conditioning techniques to adequately modify various different electrical signals into voltage that can then be digitized using an Analog-to-digital converter (ADC). Signals may be digital (also called logic signals sometimes) or analog depending on the transducer used. Signal conditioning may be necessary if the signal from the transducer is not suitable for the DAQ hardware being used. The signal may need to be amplified, filtered or demodulated. Various other examples of signal conditioning might be bridge completion, providing current or 43
voltage excitation to the sensor, isolation, linearization. For transmission purposes, single ended analog signals, which are more susceptible to noise can be converted to differential signals. Once digitized, the signal can be encoded to reduce and correct transmission errors.
3.5 STRUCTURAL HEALTH MONITORING FOR BRIDGES Health monitoring of large bridges shall be performed by simultaneous measurement of loads on the bridge and effects of these loads. It typically includes monitoring of:
Wind and weather
Traffic
Prestressing and stay cables
Deck
Pylons
Ground
Provided with this knowledge, the engineer can:
Estimate the loads and their effects
Estimate the state of fatigue
Forecast the probable evolution of the bridge
Integrated SHM system has the following advantages over existing methods to inspect and assess the structural integrity of buildings:
Real-time assessment of structural stability and impending collapse
Stand-off mode, allowing safer operation with less risk to emergency response personnel
Map of structural damage and temperature, to identify and locate hot spots
Reduced cost and time spent on structural inspections
Improved structural reliability
Minimized catastrophic structural failure
Convenience and automation of inspection
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4.
STRUCTURAL HEALTH MONITORING APPLICATIONS
The safety, maintainability, and livability of the civil infrastructure depend on structural testing and structural health monitoring (SHM) applications. Structural health monitoring refers to continuous monitoring of structures in the field under natural operating conditions. Structural health monitoring combines various physical sensing and measurement techniques with continuous, remote processing to capture real-time data, log it to a historical record, and continuously analyze it because of the size and complexity of the structures being monitored, you must be well-versed in multiple disciplines ranging from sensing techniques to multisystem synchronization to structural dynamics to data management and analysis and more.
4.1 TECHNOLOGIES FOR STRUCTURAL TESTING AND STRUCTURAL HEALTH MONITORING Multimodal sensor systems Robust, precision signal conditioning Distributed measurement systems Software technologies
MULTIMODAL SENSOR SYSTEMS Sensor technology is one of the most active areas of structural testing and monitoring research and technological advancement. SHM systems integrate a variety of sensors, and sensor technology options continue to expand. Most SHM systems today use sensors such as strain gages, vibration or accelerometer sensors, and displacement sensors to track the stresses or movement of a structure. Additionally, the systems usually include sensors for environmental or weather monitoring. A number of emerging sensor technologies uses nondestructive testing (NDT) approaches, such as acoustic emissions, to directly detect defects in the structure. Sensors based on fiber-optic technology are also seeing increased usage as that technology continues to evolve and mature. Some structural engineers are even incorporating video images into structural monitoring systems.
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FIGURE 4.1: SENSOR TECHNOLOGIES FOR STRUCTURAL MONITORING
ROBUST, PRECISION SIGNAL CONDITIONING The most common measurements in structural monitoring and testing are strain and vibration. Strain measurements are usually made with resistive foil strain gages arranged in full-, half-, or quarter-bridge configurations. Piezoelectric accelerometers with a built-in charge amplifier, commonly referred to as IEPE accelerometers, are typically used for dynamic vibration acquisition. Servo, or force balance, accelerometers are often used in seismic recording applications. Other sensors regularly incorporated in structural monitoring systems include linear-voltage differential transformers (LVDTs) and string potentiometers for displacement, tilt and crack sensors, thermocouples and resistance temperature detectors (RTDs) for temperature measurements, and other environmental sensors for humidity and wind speed and direction. To achieve best-in-class quality for measurements, we need to consider the several types of conditioning required for sensor measurements as well as the several types of analog components used in the instrumentation including analog-to-digital converters (ADCs).
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FIGURE 4.2: ADC
SIGNAL CONDITIONING FOR SENSOR MEASUREMENTS An ADC takes an analog signal and turns it into a binary number. Therefore, each binary number from the ADC represents a certain voltage level. The ADC returns the highest possible level without going over the actual voltage level of the analog signal. Resolution refers to the number of binary levels the ADC can use to represent a signal. To determine the number of binary levels available based on the resolution, simply take 2Resolution. Therefore, the higher the resolution, the more levels you have to represent your signals. Figure below shows a digital representation of signals by 12-, 16-, and 24-bit ADCs. we can now use the 24-bit technology, which allows for extremely accurate measurements, for static as well as dynamic applications.
FIGURE 4.3: 16-BIT VERSUS 24-BIT RESOLUTION
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Some companies like National Instruments provide modules which in addition to strain and vibration, can be used for virtually any sensor required for structural testing and monitoring, including displacement sensors, thermocouples, and RTDs.
FIGURE 4.4: MODULES PROVIDE DIRECT CONNECTIVITY TO STRUCTURAL MONITORING AND TESTING SENSORS
DISTRIBUTED MEASUREMENT SYSTEMS Continuous monitoring of real-time structural performance data is emerging as a critical strategy in the long-term maintenance of bridges, buildings, stadiums, and other large structures. These applications require rugged, intelligent data acquisition systems that can operate reliably in remote, unattended locations without sacrificing the measurement performance or versatility to deliver reliable, accurate sensor data.
FIGURE 4.5: EMBEDDED DATA ACQUISITION AND CONTROL SOLUTIONS FOR LONG-TERM STRUCTURAL MONITORING
EMBEDDED INTELLIGENCE AND DATA STORAGE Continuous, long-term monitoring applications require a system that can reliably operate standalone for long periods of time. This requires a real-time embedded system that can acquire sensor data, log the data locally, and periodically transmit the data to a host system. The ability of the system to operate stand-alone and unattended protects valuable sensor data from network interruptions or PC system failures.
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REMOTE COMMUNICATIONS AND CONNECTIVITY Some of the monitored structures such as bridges typically do not include communications or network infrastructure, the monitoring system usually requires remote communications capability. The most popular approaches to remote communications today include Wi-Fi (if a host PC is located nearby) or cellular data (such as CDMA, GSM/GPRS, EDGE, and so on). Other options are proprietary long-range radios and satellite communications. SYNCHRONIZED DISTRIBUTED M EASUREMENTS Monitoring the health of structures can involve large numbers of sensors distributed over a wide area. A distributed measurement system that uses multiple networked data acquisition devices, each connected to a cluster of sensors, can dramatically reduce the amount of sensor cabling and greatly simplify installation. However, because most health monitoring systems require a reliable, system-wide time reference, distributed systems must be able to accurately and reliably time-synchronize sensor measurements across the entire structure. While most communications networks do not provide such synchronization capabilities, more advanced systems can use GPS or new deterministic networking technologies for system-wide synchronization.
WI-FI DATA ACQUISITION For more temporary or shorter-term structural testing applications, such as live load testing and structural diagnostics, quick setup and teardown are important. By eliminating the need to run cabling between the installed instrumentation and the data collection PC, wireless data acquisition systems can save time and money in a variety of structural testing applications. The testing and monitoring of structures operating in the field presents unique challenges for traditional data acquisition and data-logging devices. Whether used as a portable structural testing system for structural diagnostic studies or installed permanently for monitoring long-term performance and health. LONG-TERM MONITORING VS PORTABLE TESTING Structural monitoring and application testing in the field can be classified as one of two general types continuous monitoring portable testing 49
LONG-TERM CONTINUOUS MONITORING systems collect data from a structure over a long period of time – months, years, or permanently. Continuous monitoring systems can provide ongoing information regarding the behavior and health of a structure, fatigue performance, or general operational information over an extended period of time. Because the system operates continuously at an unattended and often remote location, it is critical that the system be optimized for high reliability and remote operation. PORTABLE TESTING Systems are installed temporarily on a structure to assess the current condition of a structure or its components. For example, you may install a strain system on a bridge for a day of load rating measurements, or you may use a vibration system to capture the magnitude and frequency of vibration modes for diagnostic studies. Quick and easy setup and connectivity with a laptop PC are important attributes for portable testing systems. SENSORS AND M EASUREMENTS FOR S TRUCTURAL APPLICATIONS The most common measurements in structural monitoring and testing are strain and vibration. Strain measurements are usually made with resistive foil strain gages that are set up in full-, half, or quarter-bridge configurations. Piezoelectric accelerometers with a built-in charge amplifier, commonly referred to as IEPE accelerometers, are often used for dynamic vibration acquisition. Servo, or force balance, accelerometers are usually incorporated in seismic recording applications. Other sensors often incorporated in structural monitoring systems include linear-voltage differential transformers (LVDTs) and string potentiometers for displacement, tilt, and crack sensors; thermocouples and resistance temperature detectors (RTDs) for temperature measurements; and other environmental sensors for humidity and wind speed and direction. WIRELESS DAQ FOR PORTABLE S TRUCTURAL TESTING For more temporary or shorter-term structural testing applications, such as live load testing and structural diagnostics, quick setup and teardown are important. By eliminating the need to run cabling between the installed instrumentation and the data collection PC, wireless data acquisition systems can save time and money in a variety of structural testing applications. 50
FIGURE 4.6: WI-FI DATA ACQUISITION SYSTEM
EMBEDDED SYSTEMS FOR LONG-TERM CONTINUOUS MONITORING Continuous monitoring of real-time structural performance data is emerging as a critical strategy in the long-term maintenance of bridges, buildings, stadiums, and other large structures. These applications require rugged, intelligent data acquisition systems that can operate reliably in remote, unattended locations without sacrificing measurement performance or versatility to deliver reliable, accurate sensor data. EMBEDDED INTELLIGENCE AND DATA S TORAGE Continuous long-term monitoring applications must incorporate a system that can reliably operate stand-alone for long periods of time. This requires a real-time embedded system that can acquire sensor data, log the data locally, and periodically transmit the data to a host system. The ability of the system to operate stand-alone and unattended protects valuable sensor data from network interruptions or PC system failures. RUGGEDNESS AND RELIABILITY IN HARSH ENVIRONMENTS Many data acquisition products were designed for the controlled laboratory environment – not the harsh environmental conditions encountered in the field on bridges, dams, and so on. Using laboratory-grade equipment in the field often requires expensive and bulky environmentally controlled enclosures.
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REMOTE COMMUNICATIONS AND CONNECTIVITY Monitored structures such as bridges typically do not include communications or network infrastructure, the monitoring system often requires remote communications capability. The most popular approaches to remote communications today include Wi-Fi (if a host PC is to be located nearby) or cellular data (such as CDMA, GSM/GPRS, EDGE, and so on). Other options include proprietary long-range radios and satellite communications. SYNCHRONIZED DISTRIBUTED M EASUREMENTS Monitoring the health of structures can involve large numbers of sensors distributed over a wide area. A distributed measurement system that uses multiple networked data acquisition devices, each connected to a cluster of sensors, can dramatically reduce the amount of sensor cabling and greatly simplify installation. However, because most health monitoring systems require a reliable system-wide time reference, distributed systems must be able to accurately and reliably time synchronize sensor measurements across the entire structure. Most communications networks do not provide such synchronization capabilities, but more advanced systems can use GPS or new deterministic networking technologies for system-wide synchronization.
SOFTWARE Software is a critical component of an SHM system. Whether performing a portable test on a structure or deploying a long-term monitoring system, consider your software application needs for data acquisition, data logging, real-time processing and control, communications, data visualization and reporting, and post-data processing and management.
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FIGURE 4.7: EASY-TO-USE EXPERIENCE AND DATA-LOGGING
4.2 APPLICATIONS ALAMOSA CANYON BRIDGE The Alamosa Canyon Bridge in southern New Mexico has been designated as a bridge test facility by the New Mexico State Highway and Transportation Department. Numerous modal tests have been performed on this structure for the purposes of damage detection. With only limited abilities to introduce damage into this structure, recent tests have focused on quantifying the statistical variations in modal properties that result from changing environmental conditions. It is imperative that these changes be quantified and that changes resulting from damage are shown to be either greater than or different from those resulting from the test-to-test variations. Recent tests have been performed with the intent of comparing different statistical analysis procedures.
53RD AVENUE EXTENSION BRIDGE, BETTENDORF, IOWA The accelerated deterioration of steel and concrete bridges has caused significant problems in the management and maintenance of the highway infrastructure. To date there have been relatively few in-service vehicular bridges constructed and monitored. While the potential exists for durable bridges to be built from FRP, their performance can only be evaluated through a series of monitoring programs. This proposal to the City of Bettendorf is to allow the transfer of the 53
monitoring/evaluation portion of the IBRC Program funding to Iowa State University to perform these tasks. This bridge is a three-span, prestressed concrete girder bridge constructed using funding from the Federal Highway Administration's Innovative Bridge Research and Construction program. The innovative portion of this bridge is the use of fiber reinforced polymer (FRP) transverse deck panels which are used in place of a conventional reinforced concrete deck. The panels are made of nominal eight-inch thick Duraspan Deck Tubes compositely connected to the prestressed concrete girder superstructure.
FIGURE 4.8: 53RD AVENUE EXTENSION BRIDGE, BETTENDORF, IOWA
Peak strain sensors were installed on this bridge Friday, September 26, 2003. Since then the sensors have been monitoring and recording the peak flexural strain induced in the 10 most heavily loaded girders. The sensors were mounted to the bottom flange at midspan as shown in the sketch below. To see a graph of peak strain versus time, click on each sensor (large black circle) in the sketch below.
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FIGURE 4.9: SKETCH OF SENSOR PLACEMENT ON 53RD AVENUE BRIDGE IN BETTENDORF, IOWA
HUMBER BRIDGE, UK Completed in 1981 is a suspension bridge which from 1984 to 1998 held the world record for largest span, at 1410 m.
FIGURE 4. 10: HUMBER BRIDGE, UK
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Facts
Location : Upon Hull , England
Main Span : 1410 m, Total Length 2220 m
Height of Towers : 152 m
Number of sensors installed : 58 sensors
Design Life Span : 120 years
Instrumentation designed by : Bristol University and Stretto di Messina Spa
Purpose To establish the performance of long span suspension bridges subject to dynamic loads
FIGURE 4.11: TEMPORARY SHMS INSTALLED AT THE HUMBER BRIDGE, UK.
Benefits of using SHMS technologies in the project The monitoring exercise provided data to establish relationships between loading effects and responses.
FARØ BRIDGES, DENMARK Bridge complex is one of the longest in Europe. It consists of two bridges, a northern bridge from Zealand to the small island of Farø and a southern bridge from Farø to Falster.
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FIGURE 4.12: FARØ BRIDGES, DENMARK
Facts
Name and Location: Farø Bridges – Farø, Denmark
Spans: main span 290 m, total length 3322 m, span lengths 120 m - 290 m - 120 m
Structure category: Main bridge, Cable stayed bridge, one centre cable plane
Number of sensors installed: 32 sensors
Purpose The installed monitoring system if one of the first based on small data acquisition computers based on open platform PC‘s. Benefits of using SHMS technologies in the project Besides helping in the process of design verification, the benefits of this SHMS are very small. To-day only wind measurements are used for giving side wind warnings to the users and temperature for slippery road condition warnings in winter time. 57
SUNSHINE SKYWAY BRIDGE, USA This bridge was equipped with a bridge protection system, designed by Parsons Brinkerhoff. This protection system was developed to withstand an impact from an 87,000-ton tanker travelling at 10 knots.
FIGURE 4.13: SUNSHINE SKYWAY BRIDGE, USA
Facts
Name and Location: Sunshine Skyway Bridge, Tampa Bay, St. Petersburg, Florida, USA
Structure category: Main bridge, Cable stayed bridge, one centre cable plane
Spans: main span 380 m, total length 7000 m, span lengths 170 m - 380 m - 170 m
Number of sensors installed: 534 sensors
Instrumentation design by: CTL Group, Leica and General Positioning LLC.
Purpose The objective of the structural monitoring program was to monitor construction-related loading, measure time-dependent inelastic structural response, and verify design assumptions. Benefits of using SHMS technologies in the project The SHMS has apparently only been used for design verification.
SKARNSUNDET BRIDGE, NORWAY
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The Skarnsundet Bridge is a cable stayed bridge across a strait in the inner part of the Trondheims-fjord, Norway.
FIGURE 4.14: SKARNSUNDET BRIDGE, NORWAY
Facts
Name and Location: Skarnsundet Bridge, Tronheimsfjord, Norway
Structure category: Main bridge, Cable stayed bridge, one centre cable plane
Spans: main span 530 m, total length 1010 m
presently holds the world record span for cable stayed bridges with concrete deck
Number of sensors installed: +50 sensors
Instrumentation design by: NGI & Noptel OY.
Purpose The monitoring program was designed to monitor critical construction operations, as well as to acquire data needed for design verification studies and long-term performance assessment. Subsequent to completion of the bridge the monitoring program was extended to include performance measurements during the first two winter storm seasons. Benefits of using SHM technologies in the project The monitoring program lasted two years and the instrumentation functioned well. The laser devices were used mainly in the first phase of the monitoring program and both were reported to have functioned satisfactory. 59
CONFEDERATION BRIDGE, CANADA The bridge crosses Northumberland Strait, connecting the Canadian provinces of Prince Edward Island (PEI) and New Brunswick. Heavy storms with winds in excess of 30 ms -1 and the presence of ice in the strait for four months each winter, along with other harsh environmental conditions at the bridge site posed many challenges for the design and construction of the bridge.
FIGURE 4.15: CONFEDERATION BRIDGE, CANADA
Facts
Name and Location: Confederation Bridge – PEI / New Brunswick, Canada
Structure category: Long span girder
Spans: 45 spans, 43 spans are 250 m long and 2 spans 165 m long
Number of sensors installed: 113 sensors
Instrumentation design by: Public Works and Government Services, Canada
Purpose The purpose of the instrumentation of Confederation Bridge is to gather data on a continuous basis that will tell engineers about the long-term properties of the materials in 60
response to the harsh environmental conditions at the bridge site. Analysis of the data shall make reliable and timely diagnoses on the conditions of the bridge structure possible. Benefits of using SHM technologies in the project Using SHM technologies in the Confederation Bridge project provides information about the health of the bridge due to dynamic loads, ice forces, short- and long-term deformations, thermal effects, and corrosion.
TSING-MA BRIDGE, HONG KONG Tsing Ma Bridge of Hong Kong is the world's sixth largest suspension bridge. It has two decks and carries both road and rail traffic. The upper deck carries a dual three-lane carriageway and there are two tracks of railway and a two-lane emergency roadway in the lower deck for maintenance and the diversion of traffic during high winds.
FIGURE 4.16: TSING-MA BRIDGE, HONG KONG
Facts
Name and Location: Tsing Ma Bridge, Hong Kong, China
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Structure category: Suspension bridge with girder carrying road traffic on top of girder and rail traffic inside the girder.
Spans: main span 1377 m, total length 2032 m, span lengths 280 m - 1410 m - 530 m
Number of sensors installed: 350 sensors, 900 for the common monitoring system
Instrumentation design by: Fugro.
Purpose The Wind and Structural Health Monitoring System (WASHMS) is a sophisticated bridge monitoring system, costing US$1.3 million, used by the Hong Kong Highways Department to ensure road user comfort and safety of the Tsing Ma, Ting Kau, and Kap Shui Mun bridges that run between Hong Kong and the Hong Kong Airport. The sensory system consists of approximately 900 sensors and their relevant interfacing units. With more than 350 sensors on the Tsing Ma Bridge, 350 on Ting Kau and 200 on Kap Shui Mun, the structural behavior of the bridges is measured 24 hours a day, seven days a week. The sensors include accelerometers, strain gauges, displacement transducers, level sensing stations, anemometers, temperature sensors and dynamic weight-in-motion sensors and GPS. They measure everything from tarmac temperature and strains in structural members to wind speed and the deflection and rotation of the cables and any movement of the bridge decks and towers. These sensors are the early warning system for the bridges, providing the essential information that helps the Highways Department to accurately monitor the general health conditions of the bridges. The Tsing Ma Control Area (TCMA) WASHMS is properly the most advanced and well equipped bridge structural monitoring system in operation. Benefits of using SHMS technologies in the project •
The ability to collect information of real loading effects and bridge responses.
•
The ability to provide data useful in validating and updating damage-oriented structural modeling and in identifying damage-sensitive features 62
•
The opportunity to provide data in verifying the feasibility and reliability of damage detection methods
•
The ability to help in maintenance and rehabilitation planning, and to predict the deterioration when combined with the analytical model.
ERMANNINSUO RAILWAY EMBANKMENT , FINLAND Ermanninsuo is located near Humppila, Finland, where the Turku-Toijala railway section is located. At the location, the railway has been built on boggy land. When the railway section was straightened about ten years ago, about 400-metre concrete slab on piles was constructed to support the railway.
FIGURE 4.17: ERMANNINSUO RAILWAY EMBANKMENT, FINLAND
Facts •
Name and Location: Ermanninsuo, Humppila, Finland
•
The speed limit under normal conditions is 170 km/h.
•
Number of sensors installed: 28 sensors
•
Instrumentation design by: Futurtec OY
Purpose 63
To design and provide a structural monitoring solution to analyze the stability of the new design and ensure safety by means of real-time alarming facility from the remote site. Benefits of using SHM technologies in the project Both the design and the construction process have been modified during the project. Information from the SHMS solution and analyses provided has played a key role to identify change requirements early and assist in redesign to create the improved solution.
NAINI BRIDGE, INDIA The 4 lane highway bridge crosses the Yamuna River near the intersection to the Ganges River and links the cities Naini and Allahabad in the state of Uttar Pradesh.
FIGURE 4.18: NAINI BRIDGE, INDIA
Facts •
Name and Location: Naini Bridge, Allahabad, UP, India
•
Structure category: Main bridge, Cable stayed bridge, double cable plane
•
Spans: main span 260 m, total length 1510 m, main span lengths 185 m - 260 m - 185 m
•
Number of sensors installed: 534 sensors
•
Instrumentation design by: COWI / Devcon Infrastructures Private Ltd.
Purpose Naini Bridge is an example of a fully integrated structural monitoring system based on a low cost installation approach and without the capability of automatically providing the basis for structural health evaluation.
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The system has been designed according to the principals of having the purpose of monitoring carefully selected points of the bridge in order to provide the basis for design verification, user safety, maintenance planning and trouble shooting. Benefits of using SHM technologies in the project The SHMS was already used in the construction phase for geometrical monitoring and measurement of cable forces as the balanced cantilever was moved forward during the girder construction.
MESSINA BRIDGE, ITALY The planned Messina Strait Bridge will connect the coasts of Sicilia and Calabria in southern Italy. It is planned to carry a four lane highway with emergency lanes and a dual railway line.
FIGURE 4. 19: MESSINA BRIDGE, ITALY
Facts •
Name and Location: Messina Bridge, Strait of Messina, Italy
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•
Structure category: Main bridge, suspension bridge, double main cables and three girders, two for road and one for rail traffic.
•
Spans: main span 3300 m, total length 3666 m, span lengths 333 m - 3300 m - 333 m
•
Number of sensors planned to be installed: 2400 sensors
•
Design life: 200 years.
•
Instrumentation tender design by: COWI A/S for Impregilo Spa.
Purpose The purpose of the structural monitoring and the data acquisition is to supply information on all relevant events related to operation and status of the bridge structure to the operator and assist him to take the necessary corrective actions, either through manual commands or automatic responses, if allowed in advance by the operator. The monitoring and control activity is necessary: •
To check the physical-environment, structural and traffic conditions of the Bridge.
•
To identify, verify and notify anomalous events and situations, such as trespassing of attention and/or criticality thresholds in the monitored area.
•
To constitute the infrastructure's history, through data collection and elaboration.
•
To constitute the data base necessary for the infrastructure's management and maintenance.
•
To visualize the status of the systems on displays in the control room.
•
To assist the operator in his management of the bridge and the traffic on the bridge.
The SHMS will consist of three independent sub-monitoring systems, namely the •
Meteorological Monitoring System
•
Seismic Monitoring System
•
Structural Monitoring System (including geotechnical monitoring)
GOLDEN GATE BRIDGE A Wireless Sensor Network (WSN) for Structural Health Monitoring (SHM) is designed, implemented, deployed and tested on the 4200ft long main span and the south tower of the
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Golden Gate Bridge (GGB). Ambient structural vibrations are reliably measured at a low cost and without interfering with the operation of the bridge.
FIGURE 4. 20: GOLDEN GATE BRIDGE
Facts •
Name and Location: Golden Gate Bridge, San Francisco Bay
•
Structure category: suspension bridge
•
Spans: mid span 1280 m, total length 1970 m
Details of Structural Health Monitoring System Installed
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Hardware
FIGURE 4.21: DETAILS OF TWO ACCELEROMETERS (ADXL 202E AND SD 1221L) ARE IN THE TABLE BELOW. A THERMOMETER IS USED FOR TEMPERATURE CALIBRATION
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Software
Deployment
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Results
Time and Frequency Plots of Transverse (Horizontal) Sensor Located at Quarter span,365m North of the South Tower. The data matches the fundamental frequency of the bridge in past studies.
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Transverse (Horizontal) Sensor, Mid-Span
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SOME OTHER EXAMPLES :
James Joyce Bridge - Dublin, Ireland
Bronx Whitestone Bridge - New York, NY
Brooklyn Bridge - New York, NY
Verrazano Narrows Bridge - New York, NY
Williamsburg Bridge - New York, NY
Throgs Neck Bridge - New York, NY
Medway Bridge - Kent, UK
Menai Bridge - North Wales
I-83 Ramp - Harrisburg, PA
Birmingham Bridge- Pittsburgh, PA
Sawmill Run Bridge - Pittsburgh, PA
Neville Island Bridge - Pittsburgh, PA
SR 33 Bridge - Easton, PA
Girard Point Bridge - Philadelphia, PA
Church Street Bridge - Melbourne, Australia
David Trumpy Bridge - Queensland, Australia
15 Mile Creek Bridge - The Dalles, OR
I-5 Mckenzie Bridge - Eugene, OR
Willamette River Bridge, Near Willamette, OR
I-64 over Kanawha River - Charleston, WV
AMTRAK Susquehanna River Bridge - Perryville, MD
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5.
CRACK IDENTIFICATION
Cracks are always the key problems in the CIVIL ENGINEERING which can't be eliminated. The cracks generation in the structures is obvious reality which can't be neglected. Cracks may develop in the structure at random location with unknown magnitude. In civil engineering random cracks of minor widths at unknown locations are more devastating than the cracks of large widths at known location, because at known locations we are in the position to provide remedial measures against those cracks. Various remedial measures are available to not only fill the cracks but to check the propagation of cracks, but all those rescue operations are useless until or unless we are not in the stats to identify the location of the cracks. As long as the probabilities of generation of cracks are available, the importance of identification of cracks can't be neglected. Crack identification can be done by using various methods and instruments based on:
Ultrasonic technique Lamb wave and vibration techniques Frequency analysis using FINITE ELEMENTS METHOD Strain gauges Capacitance based sensors
Followings are the research experimentations on damage identifications:
5.1 CRACK DETECTION IN ARMOR PLATES USING ULTRASONIC TECHNIQUES This method has been devised by Thomas j. Meitzler and Gregory Smith, two professors at University of Potsdam, Germany. This is non destructive testing technique in which they used piezoelectric lead zirconate titanate transducers (PZT) to characterize the vibrational modes of ceramic Vehicle Body Armor Support System(VBASS) for the purpose of crack identification. In this method, they actually compared the amplitudes of the vibrational modes of undamaged plates with the vibrational modes of damaged plates. They found that there is clear difference in the transmission of signal between damaged and undamaged plates.
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FIGURE 5.1: SCHEMATIC DIAGRAM OF THE CIRCUIT
5.2
METHODOLOGY
To maximize the contact between the PZT and the plat, a small area of canvas is removed along with a thin layer of the adhesive used to keep the canvas attached to the plate. PZT transducers are then bounded to the two ends of the ceramic plates using standard consumer grade epoxy. An alternating voltage is applied to the driving PZT, causing it to vibrate. This movement excites the mechanical wave in the plate which forces the receiving transducer to vibrate and generate voltages via the piezoelectric effect. These signals are observed using an oscilloscope. The basic method of this technology is to use the signal generator to sweep through a frequency range of a few hundred kHz to characterize the response of an undamaged plate and then to use that data as a baseline to determine the condition of other plates that are suspected of being damaged.
FIGURE 5.2: GRAPHICAL VALUES OF THE EXPERIMENT
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5.3 DETECTION OF TRANSVERSE CRACKS IN A COMPOSITE BEAM USING LAMB WAVE AND VIBRATION TECHNIQUES This experimentation research was made by Ramadas C, M Joshi and C.V Krisnamurthy, professors of Indian Institutes of Technology, Madras. In this experiment they used composite beam, composites are prone to damages like transverse cracking, fiber breakage, delimitations, matrix cracking and fiber-matrix de-bonding when subjected to service conditions. Transverse cracks can occur when a single ply in a multi-ply laminate fails. This leads to additional loads on the other plies and also increased stress concentrations at the edges of the cracks. This leads to further growth of the transverse crack size leading to reduce the stiffness and stability of the structures. The aim of the work is to find out the location and depth of a transverse crack in composite beam combining various features of Lamb wave and vibration based damage detection techniques that were simulated using analytical models. In this work, we selected a cross-ply composite. The thickness of each ply is 0.5mm. The length, depth and width are 300 mm, 3 mm and 12 mm respectively as shown in Fig.
FIGURE 5.3: BEAM CROSSSECTION
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The crack can be anywhere in the beam from x = 30 to 300 mm, we call this domain of damage. It is assumed that the depth of the crack is equal to number of plies failed multiplied by thickness of each ply. Five different depths of cracks of 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm and 2.5 mm deep, which correspond to one, two, three, four and five plies failure respectively, have been considered for damage detection. It is assumed that the crack extends throughout the width of the beam. This is because, in general, the beams in structural applications are loaded whole width. This induces uniform stress along the width. So, the lamina can fail completely along the width of the beam. For complete characterization of vertical crack in the beam, we need to find out the location of crack from reference position, x = 0, and the depth of the crack. Two features of Lamb wave, TOF and amplitude ratio of the reflected wave from the crack can be used for characterization of the crack. If the depth of the crack increases the amplitude of the reflected wave also increases. So, amplitude ratio feature can be used for predicting the depth of the crack. The arrival time of the reflected wave group is linearly proportional to the location of the crack. So, TOF feature can be used for predicting the location of the crack. If the crack is located away from the boundary, the reflected wave is purely from the crack. In such cases, the above two features can be used for predicting accurately, the location and depth of the crack. If the crack tip location is close to the boundary, the reflected waves from the crack and boundary interfere. The amplitude of this interference wave will be higher than the reflected wave from the crack. In such cases, amplitude based damage detection gives a wrong interpretation about depth of the crack. When TOF is used for predicting the location of the damage, we look for the time of arrival of the reflected wave group from the crack. Here also, if the damage is close to the boundary, it is difficult to find out the arrival time of the reflected wave group because of interference between the boundary reflection and the reflection from the crack. So, when the crack location is approaching the boundary, the accuracy of prediction using amplitude ratio and TOF will come down. In such cases, we have to use some other technique for prediction. When there is damage, the stiffness of the structure will change, in general it reduces. Since the natural frequencies of a structure depend on stiffness, the natural frequencies will also change when there is damage. In the present problem, we proposed to use first two natural frequencies along with damage feature of Lamb wave technique for damage detection in the beam. In 76
complex structures, the changes in some of the natural frequencies may be less without and with damage. In such cases, we may have to look for some high frequencies and/or strain energy which will change with respect to damage. The sensitiveness of a particular feature for a particular type of damage can be better understood by performing numerical simulations. The prior knowledge or understanding of the structure‘s dynamic behavior helps in selection of certain features for damage detection. We made an attempt to use TOF, amplitude ratio and first and second natural frequencies in ANN environment for damage detection in a beam. The input to the neural network is TOF, amplitude ratio, first and second natural frequencies. The output is location and depth of the damage. The domain of damage is from x = 30mm to 300mm. This domain has been subdivided into three zones, zone-1, zone-2 and zone-3. In each zone three locations have been chosen. In each location, the depth of the crack varies from 0.5mm to 2.5mm in steps of 0.5mm. The step size, 0.5mm, has been chosen to simulate the failure of each ply.
The transmitter is located at the free end of the beam and receiver or observation point at x = 20mm. The mode of excitation is primary anti-symmetric Lamb mode, Ao. The excitation frequency and number of cycles are 200 kHz and five respectively. The numerical simulations have been carried out using finite element (FE) method. The element selected is an eight node element with two degrees of freedom, translations in x and z directions, at each node. The size of the element chosen is 0.5 mm. 77
DEMERITS OF SIGNAL BASED SENSORS In the past, Lamb (guided) waves have shown the greater potential for damage detection. Lamb waves have characteristics of multimode propagation and dispersion. When a Lamb mode interacts with a typical damage reflection, transmission and mode conversion takes place Because of complexity of the received signals that sometimes may also overlap on each other it is difficult to interpret the signals and characterize the damages using Lamb waves. When Lamb waves are used for damage detection applications, there are no guidelines available on what feature(s) of Lamb waves should be used. Time of Flight (TOF), amplitude, mode conversion, etc., are some of the features, which may be used for damage detection using Lamb waves. Sometimes damage detection using all of the above ultrasonic features may also become challenging due to difficulties in extracting these features from complex overlapped signals. For an effective NDE and SHM, various techniques of damage detection like guided waves, strain and vibration based techniques may be combined.
5.4 CAPACITANCE SENSOR FOR CIVIL STRUCTURES INTRODUCTION In order to eliminate the problems associated with signal based devices such as Lamb waves have characteristics of multimode propagation and dispersion, When a Lamb mode interacts with a typical damage reflection, transmission and mode conversion takes place Because of complexity of the received signals that sometimes may also overlap on each other it is difficult to interpret the signals and characterize the damages using Lamb waves. Due to this prescribed problem we have developed some other technique, which identify the propagation of crack in the structure so that we will be in a state to provide remedial measures for that minor cracking and avoid the conversion of that minor cracks into a large cracking mechanism. With the help of this sensing technique we can make the Structural health monitoring more effective and accurate. Moreover, this senescing technique is meant for laboratory studies, it's not for practical application as the probability of generation of cracks in large structures comprise on large area for that purpose we required large size capacitors for that, so as the area of capacitor increases its sensitivity decreases. 78
METHODOLOGY The basic principal of this sensor depends upon measuring the capacitance of the capacitor. This sensor consists of a capacitor, a capacitor is basically consists of an insulator which is sandwiched between two electrodes, as shown below:
The capacitance of the capacitor can be measured as follows:
FIGURE 5.4: SCHMETIC DIAGRAM OF SENSOR
𝐶 = ᶓr ᶓo 𝐴/𝑑 Where C= capacitance of the capacitor ᶓr= relative permeability of the medium between the electrodes ᶓ0 = the permeability of the vacuum A= area of the electrode d= the distance between the electrodes.
This capacitor is glued with the structural portion which is liable to cracking, as the crack in the structure generates the area of that portion increases due to which the corresponding area of electrode of the capacitor which is attached with that structure also increases, leading to the change in capacitance of the capacitor as per above mentioned formula.
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PROPOSED SENSOR The basic principle of the proposed sensor is that, the deformation on the structure leading to change the capacitance of the capacitor which we can measure by using capacitance meter.
FIGURE 5.5: PROPOSED SENSOR
CAPACITOR The capacitor consists of two 10x10cm copper sheets, and acrylic sheet is sandwiched between these two copper sheets. The basic reason to employ these materials in making capacitor is that as both of these materials, i.e., copper and acrylic are cheaper, readily available and technical plus point is both of them are flexible which is the core requirement of our experimentation, as we are required to transform the cracking and bending in the beam to the capacitor.
FIGURE 5.6: COPPER AND ACRYLIC CAPACITOR
The thickness of the capacitor is 12mm.
ADHESIVE MATERIAL The material to be used to stick the capacitor with the beam surface is to be stiffness enough to transmit the original deflection from the beam surface to the capacitor. If it fails to transmit it and the capacitor will be chipped off the beam surface during 80
loading the beam then the SHM as a whole will fail. In order to incorporate all this hazards we have to employed that sticking stuff which is capable to withstand the loadings. For this, we used MAGIC DEPOXY 4 minutes raisins. This depoxy has strength of 40,000psi and its stiff enough to full fill our purpose.
FIGURE 5.7: MAGIC DEPOXY GLUE
In order to have better bond between the concrete beam and the copper capacitor. You have to stick it before 24 hours of experiment time. If there is shortage of time at least provide 90mints to make the bond dry and keep the sticking surface under the electric bulb.
DATA ACQUISITION SYSTEM The Stress Analysis Data System 5000 is a powerful hardware / software approach for Data Acquisition, Reduction and Presentation for Strain Gauges and related Sensors for Stress Analysis. Strain smart is a ready-to-use, Windows-based software system for acquiring, reducing, presenting and storing measurement data from strain gauges, strain-gauge-based transducers, thermocouples, temperature sensors, LVDT's, potentiometers, piezoelectric sensors and other commonly used transducers. And, it is designed to function seamlessly with System 5000.
FIGURE 5.8: DATA ACQUISITION SYSTEM
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The analog signals coming from the capacitance meter is feed to the SMART STRAIN SYSTEM 5000 DAS. This will convert the signals from the meter into electrical digital format and provide the capacitance of the system and plot the desired graph between time on abscissa and capacitance in micro farad on ordinate.
CAPACITANCE METER Capacitance Meter is a device which can measure the capacitance of the capacitor. In its core, there is an INTEGRATED CIRCUIT, ACAM PSϕ21, which along with, measuring the capacitance of the capacitor, is capable to incorporate the temperature effects. WIRES
LEDs
TUNER.
RESET BUTTON
This electronic device it's self gives the capacitance in digital format up to 3 decimal points. Five LEDs are there to give capacitance in micro farad. There is also and tuner available, whose function is to tune the capacitor for calibration purposes. Five electric cables are also provided to attach this electronic device with the DAS. The least count of capacitance meter is .002 micro farad. The calibration factor for the STRAIN SMART DAS is 1µF=1.1 mV.
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5.5 EXPERIMENTATION An
experiment
is
conducted
in
the
UNIVERSITY
OF
ENGINEERING AND TECHNOLOGY, LAHORE, in order to identify the propagation of cracks in the concrete beam by employing the Capacitance based capacitor. ASSUMPTIONS: The loading rate is quick enough that temperature remains constant throughout the experimentation. The epoxy resin is stiff enough to transfer all the deformation from beam to the capacitor. Electrode of the capacitor deformed at the same rate as the beam deformed. End conditions of the beam are hinged. Maximum bending moment is generated at the mid span. Cracking beyond the mid span is neglected. Change in capacitance due to temperature is neglected.
PROCEDURE A concrete beam of 100cm length (1m) is divided into three zones ( Z1,Z2,Z3). Starting from left at x=0-0.5 m. Z1 is from 0-0.15 m, Z2 is from 0.15-0.35 m and Z3 is from 0.35-0.50 m. As the beam is subjected to two point loading so the maximum bending moment is present in the Zone2 of the beam and Zone 2 is more liable to generate cracking. Due to this reason we paste our capacitor in zone 2, as shown below:
FIGURE 5 9: ZONE DIVISION
Beam is loaded at points C and D, with the help of Strain Controlled machine. A constant load is applied for a set period of time, and then released in order to simulate the load/ no load conditions, where the no load condition are the approx 50N plateau. The value of constant load is increased every step by 50N increments using a constant load rate of 50N/min. the objectives of the periodic loadings tests is look at the capability of the sensor to detect cracks, and the periodic
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load ensures that the changes in measurements are not a result of change in curvature from an applied load.
FIGURE 5.10: LOADING ARRANGEMENT
The capacitor is which is 10x10cm, placed at the bottom mid span of the Zone 2 in order to identify flexural cracks. This capacitor is attached with the capacitor meter which measures the capacitance of the capacitor. The capacitance meter is comprised of ACAM PS021 integrated circuit which measures the capacitance of the capacitor and then generates the signal which is amplified within the capacitance meter. The capacitance meter is connected with the DATA ACQUISITION SYSTEM which converts those analog signals coming from the capacitance meter into the amplified digital signals and sends them to the computer. On computer, self made software receives those values from the DATA ACQUISITION SYSTEM and start plotting simultaneous graphs between the capacitance of the capacitor on ordinate and time interval on abscissa. As the cracks starts propagates the capacitance changes at slow rate but when the width of the crack is large there is drastic increase in the capacitance, and the graph on the computer screen will shows a vertical jump, which is the indication of the crack development. On the other hand the computer will generates a system sound on the development of the crack.
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SCHEMATIC DIAGRAM OF THE EXPERIMENT
FIGURE 5.11(A): LOADING SETUP
FIGURE 5.11(B): LAYOUT OF EXPERIMENT
OBSERVATION & CALCULATIONS As per procedure described above, the experiment is conducted according to the script. Upon loading the concrete beam on Strain Controlled Machine the graph of the capacitance and time is plotted simultaneously, which is as follows:
FIGURE 5. 12: MICRO CRACK IDENTIFICATION CURVE
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The graph tells that the capacitance of the system is kept on horizontal line with minor fluctuations which are due to the bending of the beam under loading. The first vertical line on graph formed at time interval 185sec and at the capacitance of the system 9.1µF and went on increasing up to 10µF after that the line again becomes almost horizontal. This vertical jump is basically the initiation of the micro crack and at time interval 201sec the micro crack was developed as there is drastic increase in the capacitance from 10-13.5µF. This vertical jump on the graph is the indication of the micro crack development and hence, it‘s the purpose of our experimentation development and hence, it‘s purpose of our experimentation.
FIGURE 5.13: JUST AFTER 201SEC
Just after 201sec, in the fraction of seconds the visible crack appeared on the surface of the beam and again in fraction of seconds the beam as a whole cracked into two pieces, as shown below:
FIGURE 5.14: JUST AFTER THE VISIBLE CRACK
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5.6 CONCLUSIONS The results obtained in the form of graphical representation confirms that the capacitance based sensor effectively detect the micro cracks in the structure. This sensing device is new era technology, which is relatively cheaper but accurate. By employing this technique we can effectively forecast the development of the cracks in the structure and will be in the state to provide remedies well before collapse of the structure. Such technology plays vital role in the rehabilitation of the structures and makes structural health monitoring easier. Another important feature of this sensor is, as this technology help us to know the development of crack in anticipation, this will certainly minimize the role of F.O.S, which will ultimately make the structure much cheaper by using the ultimate strengths of the materials.
5.7 RECOMMENDATIONS This electronic technique can be made more effective by using R.C.C beam as it provides more time to develop crack and hence the time of experiment can be enhanced by which we can carefully examine the development of cracks on the computer as well as on surface of beam. This technique can be made effective by incorporate the temperature effect, this can be done by using two capacitors of same materials and one can be used as dummy capacitor and in that we will measure the relative capacitance between the dummy capacitor and one which is stick with the beam. This experimentation can be made more worthy, by measuring the loading and plot that loading on ordinate along with the capacitance. This can be achieve by having one computer which can record loading as well as capacitance at the same time. Due to having technology limitations we can't achieve this thing in our experimentations.
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6.
CONCLUDING REMARKS
6.1 CONCLUDING REMARKS Structural Health Monitoring can have a very wide definition and be implemented in many different ways for varying motives, but all approaches have common component classes at the following different levels: 1. sensors, 2. data storage, 3. data transmission, 4. database management leading to feature extraction, 5. data mining for feature extraction, 6. load/effect model development from study of data, 7. learning from past experience (heuristics), and 8. decision-making based on identified features in combination with identified models. Not only is there a limited volume of literature published relating to civil SHM, but also a body of published work that discusses developments of SHM. Consensus appears to be reached on a number of issues, which are as follows. 1. Consideration for SHM needs to be incorporated at the design stage, so that structures whose performance is uncertain or critical can be monitored in-service. 2. Measures of structure performance (such as strain) and priority for monitoring should be stated to aid choice and optimization of sensors. 3. The design should allow for access to sensors via personnel, cables or radio signals. It is universally agreed that gigabytes of data are useless until distilled into manageable amounts of information. Either signals are acquired selectively or conversion of data to information should begin with pre-processing in the data logger. Hence, for example, time-series of acceleration are converted to mean and variance values, discrete Fourier transforms to determine peak frequencies or auto-regressive model coefficients. This is a form of ‗embedded system‘ and
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with time, sensor, logger and pre-processor will merge with the communication device. Such systems are already being built. For performance measurements on full-scale structures in real-life conditions, the greatest practical problems have often been with cabling and difficulty in establishing reliable communication for data transmission and control. In recent years, development of wireless local area networks and public radio systems has mitigated these problems, lowering set-up and maintenance costs. Management of data/information depends on application and operator, but this is probably the key area for future research, as various tools of IT are applied to merge and manipulate information to support decision-making by infrastructure operators. The most productive future SHM research will probably go with the database management, system identification and AI procedures representing the ‗back box‘ interface with the operators, an area in which dam engineers already have a head start. Lessons learnt from SHM experiences are beginning to find their way into guidelines and codes of conduct (ISIS practice; Maguire 1999; Mufti 2001; BSI 2004; Rucker et al. 2006). Also, a number of national and international SHM interest groups and networks have formed. Some, like SAMCO (www.samco.org), ISIS (www.isiscanada.com) and ITR (healthmonitoring.ucsd.edu) have evolved from funded initiatives; others like ISHMII (www.ishmii.org) have developed from the interests and meetings of key international experts. ISHMII has general aims that include provision of a focal point for sharing SHM knowledge and experience, promoting international collaboration, fostering harmonization of design and application standards and showing the benefits of SHM in civil infrastructure management. The development of robust SHM technology has many elements that make it a potential ‗grand challenge‘ for the engineering community. First, almost every industry wants to detect damage in its structural and mechanical infrastructure at the earliest possible time. Industries' desire to perform such monitoring based on the tremendous economy and life-safety benefits that this technology has the potential to offer.
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Significant future developments of this technology will, in all likelihood, come by way of multidisciplinary research efforts encompassing fields such as structural dynamics, signal processing, motion and environmental sensing hardware, computational hardware, data telemetry, smart materials and statistical pattern recognition, as well as other fields yet to be defined. These topics are the focus of significant discipline-specific research efforts, and to date not all technologies from these fields that are relevant to the SHM problem have been explored by the SHM research community. Furthermore, there are few efforts that try to advance and integrate these technologies with the specific focus of developing SHM solutions. Without such a focus in mind, these technologies may not evolve in a manner that is not necessarily optimal for solving the SHM problem. Finally, the problem of global SHM is significantly complex and diverse that it will not be solved in the immediate future. Like so many other technology fields, advancements in SHM will most likely come in small increments requiring diligent, focused and coordinated research efforts over long periods of time.
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7.
BIBLIOGRAPHY
Doebling, S. W., Farrar, C. R., Prime, M. B. & Shevitz D. W. 1996 Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Los Alamos National Laboratory report LA-13070-MS. Farrar C.R, Doebling S.W, Nix D.A, 2001 Vibration-based structural damage identification. Phil. Trans. R. Soc. A. 359, 131–149 Rytter, A. 1993 Vibration based inspection of civil engineering structures. Ph.D. Dissertation, Department of Building Technology and Structural Engineering, Aalborg University, Denmark. Worden K, Farrar C.R. 2007 An introduction to Structure Health monitoring Phil. Trans. R. Soc. Aktan, A. E., Chase, S., Inman, D. & Pines, D. 2001 Monitoring and managing the health of infrastructure systems. In Proc. SPIE 4337, xi–xxi. Ashkenazi, V. & Roberts, G. W. 1997 Experimental monitoring of the Humber Bridge using GPS. Proc. Inst. Civil Eng. 120, 177–182. Barr. I. G., Waldron, P. & Evans, H. R. 1987 Instrumentation of glued segmental box girder bridges. In Proc. IABSE colloquium, monitoring of large structures and assessment of their safety, Bergamo. Brederode, P., De Winter, P., Van Staalduinen, P. & Segers, W. 1986 Dynamic offshore structure test (DOST) project—a new approach to quality assessment of offshore structures. In Proc. Inspection, repair and maintenance IRM/AODC86, Aberdeen. Brownjohn, J. M. W. & Pan, T. C. 2001 Response of a tall building to long distance earthquakes. Earthquake Eng. Struct. Dyn. 30, 709–729. (doi:10.1002/eqe.32) Brownjohn, J. M. W., Moyo, P., Rizos, C. & Tjin, S. C. 2003a Practical issues in using novel sensors in SHM of civil infrastructure: problems and solutions in implementation of GPS and fibre optics. In Proc. 4th Int. workshop on structural health monitoring, Stanford University. pp. 499–506. Destech Publications Inc., USA.
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