GIS based PAVEMENT MAINTENANCE & MANAGEMENT SYSTEM (GPMMS)
A Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of
Master of Technology In Civil Engineering (Traffic and Transportation Planning) NIJU.A Roll No. CEO4M024
Department of Civil Engineering National Institute Of Technology Calicut Calicut, Kerala 673 601 May 2006
Certificate
This is to certify that the thesis entitled “GIS based PAVEMENT MAINTENANCE & MANAGEMENT SYSTEM (GPMMS)” is a record of the
bona fide work done by Mr. NIJU.A (Roll No.
CE04M024)
under my
supervision and guidance. This thesis is submitted to the National Institute of Technology Calicut in partial fulfillment of the requirements for the award of the degree of Master of Technology in Civil Engineering (Traffic & Transportation Planning) during 2004-06.
Sri M.V.L.R. Anjaneyulu Programme Coordinator
Dr. S. Chandrakaran
Department of Civil Engineering N.I.T. Calicut
Department of Civil Engineering National Institute of Technology Calicut, Kerala-673601
NITC, Calicut Date :
Professor and Guide
Dr. V. Mustafa Professor & Head Department of Civil Engineering National Institute of Technology Calicut, Kerala-673601
ACKNOWLEDGMENTS I express my profound sense of gratitude to Dr. S. CHANDRAKARAN, Professor, Department of Civil Engineering, for his systematic guidance, valuable advice and constant encouragement throughout this project work. I express my sincere gratitude to Dr. B.N NAGARAJ, Professor (Retd.), Department of Civil Engineering, National Institute of Technology, Calicut, for his valuable suggestions for the improvement of this work. I am thankful to Dr. V. MUSTAFA, Professor & Head, Department of Civil Engineering and Dr. N GANESAN, former Head of the Department of Civil Engineering, National Institute of Technology, Calicut for providing all the facilities in the department. I wish to convey my sincere thanks to Mr. JAYASURIAN. M, MCA student, NITC, for all his supports and backups render to me throughout, then Mr. SIJU, Lab Assistant, Transportation Engineering laboratory and all members of transportation family for their wholehearted co-operation. Finally I would like to extend my deepest gratitude to all my friends who gave valuable suggestions and encouragement especially Ms. KEERTHI.M.G, MTech, Traffic and Transportation, which were very helpful to me throughout this project work.
NIJU.A
i
ABSTRACT The road networks are capacity constraint and structurally deficient due to lack of timely maintenance, rehabilitation and upgradation. This has adversely affected the traffic movement, resulting into higher operating costs and delays. Maintenance and upgradation of such a large network is a challenging task because of the logistics and constraints of resources. There is a need to manage the network more efficiently in a scientific manner; the most important aspect lacking is the application of information system. Therefore there is a need to establish a centralized facility where information on road and road transportation can be utilized for the development of effective and efficient maintenance and rehabilitation measures and for planning upgradation strategies. Aim of this work is to build a GIS based system that provides information for
use
preventive
in
implementing
maintenance
cost-effective
programs
and
reconstruction, results
in
rehabilitation,
pavement
design
and to
accommodate current & forecasted traffic and pavement deteriorations, in a safe, durable, and a cost-effective manner. A well-designed geographic information system (GIS) provides a platform on which all aspects of the PMMS process can be built. The resulting system, GPMMS, represents a significant enhancement of all aspects of the PMMS process. A variety of spatially integrated data are important to pavement management decision making. GIS technology is shown to be the most logical way of relating these diverse, but relevant, data. The GIS based pavement management system would eventually lead to the development of the frame work for GIS based Pavement Maintenance & Management System (GPMMS). Here I had reviewed the role of GIS (GeoMedia environment) for pavement management system. Looking at the PMMS process in its entirety leads to the enumeration of a set of functions to be embedded in the GIS platform that is required for effective GPMMS. These functions include thematic mapping, a flexible data base editor,
ii
Linear Referencing System, dynamic segmentation, statistics, charting, network generation, and integration with external programs. The most important pavement management tools in GeoMedia are Linear Referencing System and Dynamic Segmentation. Dynamic segmentation is the overlay and display of attributes describing a linear referenced road network. Dynamic segmentation can accommodate multiple attribute tables, describing a road network, without requiring duplication of network geometry or data. Only a single, graphic representation of the highway network is required. The locations of attribute records along the road network are identified using a linear referencing method. A comprehensive plug-in software, GeoMedia Pavement Maintenance and Management Assistant (GPMMA) for GeoMedia has also been developed, which provides no bounds for PMMS analysis in GeoMedia. Important features in GPMMA are Deterioration prediction, Economic analysis, BBD overlay design, Maintenance Prioritization, Overlay Cost Calculator, Maintenance scheduler etc An exemplar GPMMS analysis was carried out on the whole of Calicut district. A well scaled georeferred Calicut district road map was developed in GeoMedia, GIS environment. Almost all the available data, including bridge inventory details, culvert inventory details, and condition survey details had been incorporated using dynamic segmentation for the analysis. Altogether a concise and succinct approach for pavement maintenance and management have been developed using GeoMedia in hand with GPMMA.
iii
CONTENTS TITLE
PAGE NO
CERTIFICATE ACKNOWLEDGEMENT
i
ABSTRACT
ii
CONTENTS
iv
LIST OF FIGURES
viii
LIST OF TABLES
xi
CHAPTER 1
INTRODUCTION
1-6
1.0
GENERAL
1
1.1
PAVEMENT MAINTENANCE & MANAGEMENT SYSTEM
1
1.2
FEATURES OF PMMS
2
1.3
PMMS INPUTS
2
1.4
ANALYTICAL TOOLS AND OUTPUTS
2
1.5
STRUCTURE OF PMMS
3
1.6
NEED FOR THE STUDY
4
1.7
OBJECTIVES OF THE STUDY
4
1.8
PROBLEMS, CHALLENGES AND THREATS
5
1.9
SCOPE OF THE STUDY
5
1.10 ORGANIZATION OF THE DISSERTATION WORK
5
1.11 CONCLUSIONS
6
CHAPTER 2
LITERATURE REVIEW
7-24
2.0
GENERAL
7
2.1
STATE - OF - THE – ART
7
2.2
GLOBAL PMMS SCENARIO
8
2.2.1
United Stases of America
8
2.2.2
Canada
10
2.2.3
Australia
11
2.2.4
United Kingdom
12
2.2.5
France
13
2.2.6
Germany
13
2.2.7
Denmark
14
iv
2.2.8
New Zealand
14
2.2.9
Sweden
15
2.2.10 Austria
16
2.2.11 Indian Scenario
16
2.2.12 Studies at universities and Research Institutions
17
2.3
GIS TECHNOLOGY AND BENEFITS
20
2.4
INFORMATION TECHNOLOGY FOR PMMS
21
2.5
CONCLUSION
24
CHAPTER 3
A BRIEF REVIEW OF GeoMedia
25-34
3.0
GENERAL
25
3.1
GEOMEDIA PROFESSIONAL
25
3.2
3.3
3.4
3.1.1
GeoWorkspace
25
3.1.2
Co-ordinate system
26
3.1.3
Warehouse
27
3.1.4
Windows
27
3.1.5
Legend
27
3.1.6
Feature and feature class
29
FUNCTIONS OF GEOMEDIA
31
3.2.1
Digitization
31
3.2.2
Queries
31
GEOMEDIA TRANSPORTATION MANAGER 5.2
32
3.3.1
Linear referencing
32
3.3.2
Dynamic segmentation
33
3.3.3
Routing network
33
3.3.4
Routing analysis
33
CONCLUSIONS
34
CHAPTER 4 PAVEMENT MAINTENANCE & MANAGEMENT ASSISTANT
35-55
4.0
GENERAL
35
4.1
AN OVERVIEW OF GPMMA
35
4.2
BEHIND GPMMA
35
4.2.1
GPMMA & External Component Installation
35
4.2.2
GPMMA Command Installation
37
4.2.3
External Software Installation
38
v
4.3
GPMMA OVERVIEW & IMPORTANT FEATURES 4.3.1
4.3.2
4.3.3
PMMA Deterioration Prediction
4.4
40
4.3.1.1
Prediction of Yearly Change in CSA
40
4.3.1.2
Prediction of Yearly Change in Deflection
41
4.3.1.3
Prediction of Yearly Change in Unevenness
41
4.3.1.4
Prediction of Yearly Change in PSR
41
PMMA Economic Analysis
42
4.3.2.1
Construction Cost
43
4.3.2.2
Vehicle Operation Cost
43
PMMA BBD Overlay Design 4.3.3.1
4.3.4
39
Conversion of Curves into Mathematical Forms
PMMA Maintenance Prioritisation
46 47 50
4.3.4.1
Index Ranking Method
50
4.3.4.2
Percentile Ranking Method
51
4.3.4.3
Weightages Given For Various Parameters
52
4.3.5
PMMA Overlay Cost Calculator
53
4.3.6
PMMA Maintenance Scheduler
53
4.3.7
Hooks to External Softwares
54
CONCLUSIONS
55
CHAPTER 5
GPMMS INPUTS
56-67
5.0
GENERAL
56
5.1
GPMMS INPUTS
56
5.2
GPMMS DATABASE
56
5.3
5.2.1
Inventory Data
57
5.2.2
Construction Data
58
5.2.3
Traffic Data
59
5.2.4
Condition Data
60
5.2.4.1
Physical Distress Data
60
5.2.4.2
Roughness Data
61
5.2.4.3
Structural Capacity Data
62
5.2.4.4
Friction Data
62
UPDATING GPMMS DATABASE 5.3.1
62
Linear Referencing
62
vi
5.3.2
Dynamic Segmentation
64
5.4
DATA ENTRY
65
5.5
GEOREFERENCED DIGITISED BASE MAP
66
5.6
PMMS ANALYSIS TOOLS (GPMMA)
67
5.7
CONCLUSIONS
67
CHAPTER 6
GPMMS OUTPUTS & RESULTS
68-88
6.0
GENERAL
68
6.1
GPMMS OUTPUTS
68
6.1.1
Thematic Maps
68
6.1.2
Deterioration Prediction
73
6.1.3
Economic Analysis
74
6.1.3.1
6.2
Net Present Value Method (NPV)
75
6.1.4
Maintenance Scheduling
79
6.1.5
Maintenance Prioritization
80
6.1.6
BBD Overlay Design
83
6.1.7
Overlay Cost Calculation
84
6.1.8
Other Outputs
85
CONCLUSIONS
88
CHAPTER 7 CONCLUSIONS, LIMITATIONS & SCOPE OF FUTURE WORK 89-90
7.0
GENERAL
89
7.1
SUMMARY AND CONCLUSIONS
89
7.2
LIMITATIONS AND SCOPE OF FURTHER WORK
90
7.3
CONCLUSIONS
90
REFERENCES
91
vii
LIST OF FIGURES FIGURE NO.
TITLE
PAGE NO.
1.1
Conceptual representation of PMMS
3
2.1
Structure of the PMS implemented in the Oklahoma State
10
2.2
Framework of Canadian Pavement Management System
11
2.3
Principal Components of HAPMS
12
2.4
Structure of VISAGE and GIRR
13
2.5
Three Levels of PMS Along With Three Types of Databases
15
2.6
Inputs – Analysis – Output - Chart
21
3.1
A Legend
28
3.2
Style Keys representing feature classes in Legend
29
3.3
Style Keys representing errors in Legend
29
3.4
Illustrating Linear Referencing
33
4.1
GPMMA & External component installation setup files
36
4.2
GPMMA & External component installation setup-1
36
4.3
GPMMA & External component installation setup-2
37
4.4
GPMMA Command Installation setup-1
37
4.5
GPMM Command Installation setup-2
38
4.6
GPMMA Command Installation setup-3
38
4.7
External Software Installation linking form
38
4.8
PMMA command installed in menubar
39
4.9
PMMA command windows
39
4.10
GUI for PMMA Deterioration predictor
42
4.11
GUI for PMMA Economic analysis 1
45
4.12
GUI for PMMA Economic analysis 2
45
4.13
GUI for PMMA Economic analysis 3
46
4.14
GUI for PMMA BBD overlay design
49
4.15
Access Database for PMMA BBD overlay design
49
4.16
Index Ranking Method process
50
viii
4.17
GUI for Priority Ranking
52
4.18
GUI for PMMA Overlay Cost Calculator
53
4.19
Graph showing best time for Maintenance scheduling
54
4.20
GUI for PMMA Maintenance scheduler
54
4.21
Snap Shots of External Softwares windows
55
5.1
Microsoft Access Database showing Inventory data
58
5.2
Microsoft Excel Database showing Traffic data
59
5.3
Microsoft Access Database showing Physical distress data
60
5.4
Microsoft Access Database showing Roughness data
61
5.5
Linear Referencing Command Toolbar & Work Flow
63
5.6
Linear Referenced Access Warehouse table & road network
63
5.7
Basic Concepts of Dynamic Segmentation
64
5.8
Dynamic Segmentation Command Toolbar & Work Flow
65
5.9
Microsoft InfoPath form Query View and Data Entry View
66
5.10
Georeferenced Raster Images & Digitized Map
67
6.1
Thematic Map showing the lengthwise distribution of MDR
69
6.2
Thematic Map showing the bridge inventory details
70
6.3
Thematic Map showing the bridge attribute details
70
6.4
Thematic Map showing the ODR condition details
71
6.5
Thematic Map showing the SH condition details
71
6.6
Thematic Map showing the Culvert inventory details
72
6.7
Thematic Map showing the Culvert attribute details
72
6.8
Thematic Map showing the IRQP Phase of NH
73
6.9
Deterioration Prediction - output
74
6.10
Economic Analysis – Deterioration Prediction
76
6.11
Economic Analysis – Overlay Cost Calculation
77
6.12
Economic Analysis – NPV & Strategy Selection
78
6.13
Present Serviceability Index Vs Age of Pavement
79
6.14
Maintenance scheduling- output
80
6.15
Maintenance prioritisation of MDR’s
81
6.16
Maintenance prioritisation of SH’s
81
ix
6.17
BufferZones & Closest path around Koduvally
82
6.18
BufferZones & Closest path around Koduvally
83
6.19
BBD Overlay design-output
84
6.20
Overlay cost calculation -output
85
6.21
Customised Window for chart output
85
6.22
Bar Chart showing Raveling details along the stretch
86
6.23
Bar Chart showing Crack details along the stretch
86
6.24
Bar Chart showing Potholes details along the stretch
87
6.25
Bar Chart showing Patchwork details along the stretch
87
x
LIST OF TABLES TABLE NO.
TITLE
PAGE NO.
4.1
Equations for vehicle operating cost
44
4.2
Percentage of vehicles and growth rate
44
4.3
Equations for moisture correction factor
48
4.4
Equations for moisture correction factor
48
5.1
Data collected from 12th Mile to Kattankal of MDR-2
57
xi
Chapter 1
INTRODUCTION 1.0 GENERAL A
comprehensive
fully
integrated
Pavement Maintenance & Management
Systems (PMMS) is the key to better reconstruction, restoration and maintenance decision-making
of
pavements.
It
weaves
together
information
on
all
pavement
inventories, condition and performance databases, and alternative investment options. An operating PMMS provides the road authorities the ability to better plan and manages highway, street, and road pavements. The Pavement Maintenance & Management Systems is a set of tools or methods that can assist decision makers in finding cost effective strategies for providing, evaluating, and maintaining pavements in a serviceable condition. It provides the information necessary to make these decisions. The PMMS consists of two basic components: A comprehensive database, which contains current and historical information on pavement condition, pavement structure, and traffic. The second component is a set of tools that allows us to determine existing and future pavement conditions, predict financial needs, and identify and prioritize pavement projects. 1.1 PAVEMENT MAINTENANCE & MANAGEMENT SYSTEM According to the World Bank Report, “The developing countries have lost precious infrastructure worth billions of dollars through the deterioration of roads. The cost of restoring these roads is going to be three to five times greater than the bill would have been for timely and effectively maintenance. Vehicle operating cost rapidly outpaces the cost of road repair as the condition of road passes from good to fair to poor”. With several thousand vehicles per day moving on the highways, even a small saving in vehicle operation cost can justify very large investments on pavements. Pavement Maintenance & Management Systems are useful tools in quantifying the overall maintenance needs of pavements and presenting the alternative maintenance strategies under budget constraints. The most important aspect of development of a PMMS is to collect, manage and analyse the pavement condition data in a considerably detailed format. Since geographical information systems (GIS), with their spatial analysis capabilities, match the geographical nature of the road networks, they are considered to 1
be the most appropriate tools to enhance pavement management operations, with features such as graphical display of pavement condition.
The Pavement Maintenance &
Management Systems is comprised of: ?? Storage, analysis and reporting software ?? Collection of stored pavement data ?? Maintenance and treatment costs ?? Data and formulas on pavement deterioration ?? Algorithms that calculate future needs and budget scenarios 1.2 FEATURES OF PMMS A PMMS is any tool or process that helps a road agency to manage pavement in other words, any tool or process that helps an agency to maintain a network of safe and serviceable pavements in a cost-effective manner. When most agencies refer to the term “pavement management system,” they usually mean a computerized system where pavement condition information is stored, analyzed, and displayed. 1.3 PMMS INPUTS At the heart of the pavement management system is the database, which is the storehouse for all pavement-related information collected. This database possesses several features, including: a large capacity, user friendly access, flexibility for future expansion, security features, and compatibility with other databases that store related information (such as bridge, congestion, and traffic crash data). Every piece of information in the database is referenced to the particular section of pavement (i.e., the particular intersection or segment of road) which it describes. The information collected and stored in the database can be divided into five categories: ?? Inventory data, ?? Pavement history, ?? Construction data, ?? Traffic data, ?? Condition data (physical distress, roughness, structural capacity, friction), 1.4 ANALYTICAL TOOLS AND OUTPUTS While the database is the “heart” of a pavement management system, data are not useful unless they are presented in a meaningful way. It is the role of analysis 2
procedures to transform the raw collected data into products such as charts, graphs, and reports that are helpful to decision-makers. A pavement management system can transform a spreadsheet containing pavement condition data into a map. A map can be quickly and easily used to examine the health of pavement in ways that are not readily apparent from columns of numbers. Analytic procedures are grouped into four categories: ?? Simple queries, ?? Pavement condition score calculations, ?? Remaining service life calculations, and ?? Strategy selection procedures. 1.5 STRUCTURE OF PMMS Pavement Management in its broadest sense, encompasses all the activities involved in planning, design, construction, maintenance and rehabilitation of the of the pavement portion of public works program. The integration of both Attribute data and spatial data is made possible through GIS. The analysis of data is carried out through PMMA & GeoMedia analysis tools. This can be conceptually represented in fig 1.1
Fig 1.1 Conceptual representation of PMMS
3
1.6 NEED FOR THE STUDY In India, due to the large scale industrialization and commercial activities, there has been an unprecedented traffic growth during the last four decades. The high volume of vehicular traffic and increasingly heavy axle loads witnessed on Indian highways have brought the existing arterial road network to such a crippling stage that heavy investments are needed for restoring it to a desired serviceability level. This is a particular difficult situation, because pavements often are deteriorating faster than they are being corrected. Effective management of pavements is essential in these challenging times. Therefore, there is a need to link together explicitly the activities of planning, design, construction and maintenance of pavements. The Road User Cost Study in India has established that due to improper maintenance and poor surface condition of road pavements, there is a considerable economic loss to the country due to increase in vehicle operation costs. If the road pavements are maintained to the desired level at an appropriate time, it is possible to save the losses in road user cost. In view of the budgetary constraints and the need for judicious spending of available resources, the maintenance planning and budgeting are required to be done based on scientific methods. The whole life cycle cost analysis based on the road user cost relationships enables the decision makers to examine financial and economic implications of various options for formulating appropriate strategies for cost effective use of resources. 1.7 OBJECTIVES OF THE STUDY ?? Collection of relevant data for analysis. ?? To develop a digitised road map of Kozhikode district in GIS environment. ?? To assess the overall pavement condition based on functional and structural evaluation data. ?? Design of flexible pavement overlays. ?? To find the rate of progression of structural and functional deterioration. ?? To develop a plug-in software program for GeoMedia using Microsoft Visual Basic 6.0 environment, ?? To assess the impact of different maintenance strategies on the performance of pavement during the design period. 4
1.8 PROBLEMS, CHALLENGES AND THREATS ?? Lack of structural information regarding the thickness of the overlays, the maintenance method used, the type of bitumen used, or the construction quality. ?? Lack of knowledge about the exact age of pavement. ?? Lack of fixed evaluation segments for the condition and other surveys. ?? Increased rate of deterioration. (pavements deteriorate fast) ?? Overloading of vehicles. (no commitment with the legal loading) ?? Rapid traffic growth. (high increase of vehicle ownership ) ?? Poor maintenance. (improper materials, wrong implementation, etc) ?? Improper design and implementation. ?? Limited resources (geometry, funds, equipment, materials, etc) ?? Insufficient information for decision-making. ?? Inefficient current traditional management system. 1.9 SCOPE OF THE STUDY This work is an essential requirement for project planning and budget allocation. This work will help to reduce the effort needed than in conventional methods, to collect and analyse the data periodically by reducing the repeated works. The present work consists of the analysis and design of pavement data of Kozhikode District. The important aims of PMMS are: ?? An essential requirement for project planning and budget allocation. ?? Flexible pavement deterioration models include Deflection, Unevenness, and Present Serviceability Rating models representing structural condition and also functional condition model. ?? The performance and life of the Overlay has been assured on the basis of acceptable limits for deflection, UI and maintenance cost. 1.10 ORGANIZATION OF THE DISSERTATION WORK The contents of the study are organized and presented in a chapter wise manner as follows: Chapter
1: General
introduction,
need,
challenges. 5
objectives,
scope
of
the
study,
Chapter
2: Literature Review, general. Introduction, some definition, its basic
components are explained. Also planning activities are described in detail. Chapter
3: Discusses the capability of the software, GeoMedia Professional
used for this work. Chapter
4: In this chapter, About the Plug-in Software PMMA and the
methodology used were described. Chapter
5: The inputs for the GPMMS, data entry methods and Dynamic
segmentation methods were discussed. Chapter
6: The inputs for the GPMMS, Data entry methods and Dynamic
segmentation methods were discussed. Chapter
7: Concludes the thesis by pointing at the limitations of the study and
scope for the further study. 1.11 CONCLUSIONS This chapter gives a brief review about the basics of Pavement Maintenance & Management Systems. The objectives, scope, layout of the thesis were also explained.
6
Chapter 2
LITERATURE REVIEW 2.0 GENERAL The purpose of this chapter is to review the available literature on Pavement management system, maintenance system and pavement management softwares and to discuss about the international PMS scenarios. An extensive literature survey was carried out to keep abreast with the latest development in the Pavement Management Systems. The work done both at the International level and domestic level is reviewed. Research work being carried out in various academic research institutions is also considered. 2.1 STATE - OF - THE – ART Most highway agencies of the developed countries are now engaged in the development, implementation, and operation of pavement management systems. As early as 1980, five states in USA viz. Arizona, California, Idaho, Utah, and Washington were reported to be in various stages of development of systematic procedures for managing pavement networks on a project-by-project basis. AASHTO has had a significant role in furthering the development and use of Pavement Management Systems through the years. Federal Highway Administration (FHWA), the Transportation Research Board (TRB), and the National Cooperative Highway Research Programme (NCHRP) have contributed to major Technical studies. Notable among them is the NCHRP- Project 1-35 A, FY 1997 which was the basis for Guide for Pavement Management. The “Guidelines for Pavement Management Systems,” published by AASHTO in July 1990, contains information needed for establishing a framework for a pavement management system. However, this document didn’t address the day-to-day issues encountered by pavement engineers or the issues associated with new and emerging technologies. Upon further research, a revised final report has been distributed to the members of the AASHTO Joint Task Force on Pavements and the AASHTO Highway Subcommittee on Design and has been approved for publication as AASHTO Guide for Pavement Management.
7
Universities and Research Institutes have developed research based Pavement Management System. Notable contributions are from Texas Transportation Research Institute
(USA),
Louisiana
Transportation
Research
Centre
(UK),
University
of
Birmingham (UK) and Transport Research Laboratory (TRL, UK). Apart from these studies there had been a number of studies by Private Consulting Agencies as part of World Bank funded Highway Development projects in developing countries. 2.2 GLOBAL PMMS SCENARIO In the early seventies, the phrase “Pavement Management System” began to be used by researchers to describe the entire range of activities involved in managing and maintaining pavements. At the same time, initial operational systems were also developed. Since then, the following factors have provided great impetus for growing interest in PMS development ?? Increasing budgetary constraints in relation to maintenance needs. ?? Recognition of direct effect of pavement condition on road user costs ?? Awareness of social and environmental values affected by road transport and road surface characteristics. ?? Advances in the development of pavement technology. ?? Increased
capacity
in
pavement
condition
monitoring
through
advanced
measuring equipment. ?? Advanced in computerization and information systems ?? General growth in awareness of management methods. Presently, highway authorities in developed countries are using systematic and objective method to determine pavement condition and programming maintenance in response to observed conditions, as budget permits. In many of the developing countries, PMS is in various phase of working process with diversified approaches as per the respective needs and problems of each country. 2.2.1 United Stases of America The concept of PMS took root in USA during the recent era of austere budgets. The first PMS model was developed by the Washington State Department of Highways in the mid seventies. This model consisted of development of performance prediction 8
model and a cost model based on a databank of information collected in Washington over a period of 6 to 8 years. Since then, various state departments of transportation have developed their own PMS methodologies suitable to their own needs and requirements. The Arizona Department of Transportation has reported savings to the tune of $2000 million over a period of five years as a consequence of successful implementation of PMS program to optimize pavement rehabilitation expenditure. The use of PMS at California Department of Transportation has resulted in improved communications between political and technical decision makers as regarding priority programming of pavement rehabilitation projects. The Pavement Management System (called PMS-III) developed and implemented for the Ohio Department of Transportation (ODOT), is a network level system that can prescribe optimal maintenance and rehabilitation actions and the required budget for each year for a 6 year planning period. On the basis of present network condition and deterministic
prediction
model,
PMS-III
forecasts
future
network
condition
and
rehabilitation needs and as associated budget. The optimal maintenance policies recommended by this pavement management system are based on maximizing the preservation of pavement investments for a given annual budget or on minimizing the cost of maintaining the network condition for a given performance level. The State of Iowa has developed an Iowa Pavement Management Program (IPMP) to support both project level and network level PMS conducted by local and regional governments and the Iowa Department of Transportation. The PMS of Oklahoma Department of Transportation is characterized by the integration of a pavement performance - modeling tool with a new pavement network optimization model for identifying and selecting cost effective projects for maintenance and rehabilitation. The unique feature of this system is the integration of a pavement performance modeling tool and a global optimization model for pavement network analysis. Pavement performance models can be updated whenever new data is available. All models can be evaluated and alternative models can be developed using the interactive modeling too. This system can produce satisfactory results for pavement engineers to perform pavement maintenance and rehabilitation planning up to 20 years. Flow chart shows the structure of the Pavement Management System implemented in the Oklahoma State.
9
Database
Master Table
Sufficiency
Build Performance Model
Structural History Performance Models
Analyze Models
Network Optimization Setup Distress Deducted Values Material Categories Treatments Performance Indices Grouping Variables Pavement Groups
Treatments Assigned to each pavement group for each year
Choose Best Scenario
Multiple Years Prioritization Treatments Assigned to each pavement section for each year. Generate / Modify Projects Sections are aggregated in to Projects
Fig 2.1 Structure of the PMS implemented in the Oklahoma State 2.2.2 Canada The Primary method of structural evaluation in Canada are deflection base e.g. Benkelman beam, Dynaflect and Falling Weight Deflectometer, with the latter becoming the primary device of choice and the former now seeing only very limited use. The indices commonly used are Riding Comfort Index (RCI), Structural Adequacy Index (SAl), Surface Distress Index (SDI), a composite measure Pavement Quality Index (PQI) and Pavement Condition Index (PCI), Performance prediction models commonly are for RCI, PQI, or PCI v/s pavements age, and are mainly developed through regression, Markov and Bayesian techniques. 10
Fig 2.2 Framework of Canadian Pavement Management System 2.2.3 Australia For a population of just over 20 million, Australia covers a very large landmass with an extensive network of roads, providing one of World’s road lengths per head of population. The Australian Road Research Board (ARRB) has been actively engaged in developing and promoting the adoption of PMS by road authorities to improve the efficiency of decision making and ensuring that maximum value is obtained from the funds allocated to road improvement. There are over 12 different PMS software packages available, which can cater for range of roads from National Highways to low volume rural roads.
11
2.2.4 United Kingdom The United Kingdom Pavement Management System (UKPMS) has been designed to assist highway authorities in structural maintenance of pavements, It does so by improving both the systematic collection of information and the decision making process required to optimize resources and to generate a works program and the corresponding
budget.
UKPMS
uses
innovative
technology
to
improve
treatment
selection and by optimizing the allocation of funds for various rehabilitation schemes. A new generation pavement management system called HAPMS, for the National Roads of England has been developed by the Highways Agency, an executive body of the Department of Environment, Transport and the Regions (DETR) of the Central Government. The network of roads managed by the Highways Agency comprises only 4 per cent of the total road network of England, but carries 25 per cent of the total traffic. The applications working on this data are concerned with the presentation to all users of data in convenient format, including map backgrounds, the preparation and allocation of budgets
and
the
determination
of
priorities
for
investment
maintenance. Fig. 2. 3 show the principal components of HAPMS.
Fig 2.3 Principal Components of HAPMS 12
in
major
pavement
2.2.5 France The French Directorate of Road in association with LCPC (Central Public Works Laboratory) and the SETRA (Roads and Motorways Engineering Department) has developed a road management system, based on two complimentary suits of tools the VISAGE road database with its own facilities for the graphic representation of data and the GIRR package with its various data analysis modules. This system was implemented in 1992 by the state departments on the National Roads Network and extended more widely in 1996 to regional network also. The VISAGE AND GIRR software package as shown in Figure 2.4 use a rational approach for the management of pavement maintenance. It is an approach designed around four main stages; after an inventory on the nature of the road network, and an assessment of its condition, a maintenance policy must be defined and then applied through works programming. Finally, network follow up is carried out on a regular basis to measure the effects of the policy put in place and, when applicable, to adopt the appropriate corrective actions.
Fig 2.4 Structure of VISAGE and GIRR 2.2.6 Germany In Germany, there are 11000 km of motorways and 45000 km of federal roadways reported. To ensure that this road network is always functional for economy and society, a system was introduced in 1992, whereby the condition of the federal trunk roads is rated every three years. A road preservation management program called UMASTER has been developed with the objective of computations of the cost to the agencies responsible for financing road construction and preservation. But the road preservation program 13
includes only those types of conditions, which are relevant to medium and long range planning, and not those which require immediate attending e.g. filling of potholes. Funding needs are computed based on periodically performed surveys of roadway conditions and related predictions of the behavior of the future course of development of the condition of highway. Methods have also been developed for forecasting the behavior of flexible pavements
taking
into
account
their
conditions
of
use
and
climate
influences,
Quantitative forecasting model for the aspect of road conditions described as rut depth, water retention and network of cracks are in the process of development. On the basis of the condition rating and evaluation process ( called ZEB in German), a standard National Pavement Management System has been developed and applied by road construction authorities in 8 different states since January 1999. The Federal Ministry of Transport is now contemplating to extent this pavement management process to all 16 states. Tools now exist for the data required for a PMS, which can create a graphical representation of the data on plan and maps so that it is possible to get a quick overview of the road network condition. 2.2.7 Denmark The Technical University of Denmark in cooperation with Dynatest Engineering developed the Dynatest Pavement Maintenance and Rehabilitation Management System (DPMS) during eighties. This system is capable of predicting future pavement condition for number of years on project as well as network level. It contains an optimization procedure to determine that combination of M&R measures that will ensure the optimal use of the budget. The system is not only based upon objectively measured functional and structural pavement characteristics, but it also ensures that the knowledge and experience of the local engineer is incorporated. The system ensures compatibility in all the steps of data flow, right from the collection of data out on the road to the final consequence analysis. 2.2.8 New Zealand It was only in late 1998 that the Government of New Zealand decided to implement a National PMS. The initial objective was to have a completed preliminary system is place and integrated with the existing National Road Asset Management Program (RAMM). This consisted of a basis inventory and pavement condition database 14
along with an algorithm for selecting maintenance treatments. The new PMS has been build on the existing road management inventory system and the existing funding framework. The software package dTIMS (Deighton Total Infrastructure Management System) design total Infrastructure funding framework. The software package dTIMS along with a hybrid set of predictive models from HDM-II and HDM-4 has been adopted for the development of PMS would be used by over 70 different road - controlling authorities (city, district and state level) responsible for a network of more than 100,000 km of sealed and unsealed roads. Consultants engaged in management of the road networks would also use it. 2.2.9 Sweden The Swedish PMS has been developed and implemented at several levels i.e, strategic level, programming level and Project level. The main objective of PMS at strategic level is to produce objective information as decision support in fund raising, in allocation of available budgets to the regions. The objective of PMS at programming level is to serve as a tool to identify candidate projects and the objective of the PMS at project level is to assist in planning and design of individual projects. Figure 2.5 shows the three levels of PMS along with three types of databases.
Fig 2.5 Three Levels of PMS Along With Three Types of Databases
15
2.2.10 Austria Increased road deterioration in Austria combined with the demand for a fair distribution of the available budgets urged the Australian Federal Road Administration to take necessary steps for the implementation of a nationwide PMS. A number of extensive studies by different investigators and improvements in administration ultimately paved the way for the start of very first application in practice of the Austrian Pavement Management System VIAPMS-AUSTRIA
in
late 1999. The Institute for Road
Construction and Maintenance and Vienna University of Technology provided the road specific data and the practical use of VIAPMS analytical software. This PMS is the only available tool in Austria that underlined the necessity of allocating budget resources for pavement maintenance. 2.2.11 Indian Scenario The absence of coordinated research has impaired the progress of developing an implementable PMS for India. Nevertheless there had been notable contributions from different research institutes and organizations like Central Road Research Institute (CRRI) (Updating Road User Cost Data URUCS 1991, Pavement Performance Study (PPS-EPS 1993), RITES (HDM Calibration Studies -1994), Bangalore University (Transition Probability Matrices for Optimal maintenance decisions-1995), & Indian Institute of Technology Kharagpur (Analytical Pavement Design (999). These studies laid the basis for the pavement data analysis and development of Pavement deterioration model for Indian roads. Apart from research institutes some private consultants have also tried to develop Pavement Management options in connection with some of the externally aided projects. The PMS for National Highways funded by Asian Development Bank had been completed in 1995. Initially it was installed on a pilot basis in the states of Karnataka and Uttar Pradesh and at the National level in the ministry of Surface Transport. Another World Bank study was the 4 States PMS towards instituting a network level PMS in the States of Bihar, Maharashtra, Rajasthan and Uttar Pradesh. The study included a section of State Highways, Major District Roads and Other District Roads which are directly managed by States. The ARAN (Automated Road Analyzer) was used for the collection of pavement data. For the National Highways the PMS adopted is called NETTER-PMS and for the four states the PMS adopted is dTIMS (Deighton Total Infrastructure 16
Management System). The NETTER VOC model uses the Brazilian relationships from the World Bank HDM III model. The road deterioration and maintenance models for dTIMS had been established using HDM-III equations with adaptation for local conditions. 2.2.12 Studies at universities and Research Institutions A brief review of the studies reported by various researchers is attempted in the following pages. Dr. S S Jam, Dr. A K Gupta & Sanjeev Rastogi (1992) have made an attempt to analyze the data of nine test sections of overlaid flexible pavements located in the States of Uttar Pradesh and Himachal Pradesh. The data has been analyzed for pre-monsoon, post-monsoon and winter season for the years between 1980 to 1990. The performance and life of the overlays has been assessed on the basis of acceptable limits for deflection, rut depth, cracks and cracking pattern and maintenance cost. Models are also incorporated for the choice of type and thickness of materials for overlays on different sub grade soils economically without sacrificing the safety of road structure. Models developed in these studies are capable of predicting life of an overlay for given values of pavement thickness, overlay thickness, traffic intensity and acceptable limits for deflection, rut depth and cracking. The general model includes wide variation of climate, terrain, rainfall, temperature and thickness of overlay can be chosen economically. Prof. (Dr.) S S Jam, Prof. (Dr.) A K Gupta, Prof. (Dr.) S K Khanna and Dayanand (1992) have studied the performance of twelve test sections of overlaid flexible pavement located in the states of Himachal Pradesh and Uttar Pradesh. The data has been analyzed for pre-monsoon and post monsoon seasons for the year 1994 for assessing the needs of corrective and strengthening measures and the pavement outputs with time has been presented using data from 1980 to 1993 from studies conducted at University of Roorkee. The influence parameter considered includes deflection, roughness, rutting, cracking and pot holes and the availability of resources for the choice of the type and thickness of materials for overlay and these parameters were recorded for the year 1993 and 1994 whose comparison showed the deterioration of flexible pavements with passage of time. The investment need for corrective measures were obtained and the investment strategy has been developed for maintenance and rehabilitation of flexible pavements. The conditions of the flexible pavements can be asserted by functional and structural 17
evaluations. The maintenance and rehabilitation need comes if any of the influencing parameters reaches its lowest acceptable limits, the overlays needs to be designed based on the cumulative number of standard axles rather than total number of commercial vehicles. Based on the requirements of corrective and strengthening measures, the investment estimation is done. Hence the study helps in taking decisions for maintenance and rehabilitation of flexible pavements rationally. Maj. C R Ramesh, B P Nityananda, Y S Madvesh and Dr. C E G Justo (1994) conducted a study done jointly by Karnataka PWD and Department of Civil Engineering, Bangalore University on a stretch of cracked Cement Concrete pavement on Bangalore Mysore State Highway to compute the relative performance of bituminous overlay with and without geo fabric at the interface. Installation of geo fabric at the interface between cracked CC pavement and bituminous overlay retards the propagation of reflection cracking and also there is a reduction in formation of new cracks on the overlay surface and a lower rate of increase in unevenness index. Geo fabric is a geo synthetic material like non-woven polypropylene fabric. A field study for ten years period using geo textile as stress-relieving interlayer has been reported to have given excellent results. In terms of service life treatment is likely to be economical. It is also desirable to vary the thickness of bituminous overlay with or without geo synthetic and to continue observation until failure of these overlays, for arriving at equivalency factors and also for comparison of cost on more realistic terms. Cost analyses of various alternatives are also done. S. Chakrabarti, Ms Rawat and B Mondal (1995) have done the calibration of HDM-II and adaptation aspects of HDM Road Deterioration and Maintenance Effect (RDME) relationship for Indian conditions. The methodology for calibrations using pavement performance data, notably, the Pavement Performance Study (PPS) has been described. The deterioration factors have been derived for the pavement types and traffic loading levels appropriate for the country. The study claims that DDM RDME is robust, yet flexible enough to predict the deterioration for road in the country with the deterioration pavements factors very close to default values. V K Sood and B M Sharma (1996) have reported the status of road network deficiencies
in
the
present
maintenance practices in our country. A pavement
Performance Study was conducted with a view to develop data for total transportation 18
cost model for Indian conditions, to be achieved through development of pavement performance data to attempt development of layer equivalence and strength coefficients as feasible. Data was collected on the construction and maintenance inputs of different pavements based on studies carried out on nine pavement sections for a period of about 10 years. A brief description on various models such as cracking models, cracking progression models, ravelling models, pot holes models and roughness progression models have been included. Validations of models have been done based on limited fieldwork. Turki I Al-Suleiman and Azm. S Al-Homoud (1996) have reported the work of evaluating the effects of pavement characteristics on pavement condition of the street network in Irbid City in Northern Jordan using the concept of Pavement Condition Index (PCI). It was found that 35.48 percent of the inspected pavement sections in Irbid City were in poor condition while 6.45 percent of the inspected pavement sections were in excellent condition. Alligator cracking, rutting, depression and swell distresses were found to be the most frequent distress types that caused the pavement deterioration. Pavement age, traffic level and pavement thickness were found to be highly significant and affect the pavement condition to great extent. Some of the asphalt mix properties such as air voids, bulk specific gravity and asphalt contents were found to have small effect on pavement condition. Pavement section of low air voids in the asphalt mix suffered from distortion and cracking due to the small resistance to compaction under traffic. Statistical models were developed to describe the relationship between PCI and pavement characteristics. Mr. S C Sharma and R K Pandy (l997) made a study on the existing pavements completed in recent years presenting and extensive indigenous research back up and basic relationships to develop a total transportation cost model for Indian conditions. Indian research results have been used to develop this model and therefore, its predictions and results are considered truly reflective of Indian conditions. The model makes it possible to apply a rational approach in road maintenance decisions for obtaining best results from available funds including benefits of periodic maintenance, cost effectiveness of maintenance strategies etc.
19
Maj. Ramesh, Dr. A Veeraraghavan, R Sridhar, and Chandrasekhar S Pichika (1999)’ have collected an extensive field data develop the performance models and in the determinations of the life cycle cost. A computer program is developed to calculate the cost, first stage strengthening cost, user delay cost and salvage value. The program has the capability to compute the life cycle cost for any design period and for any number of sections by varying the threshold Present Serviceability Index Value which is on a scale of 1 to 10. The budget scenario can also be varied and the effect of budget level on pavement performance can be studied. 2.3 GIS TECHNOLOGY AND BENEFITS A GIS is a computerized data base management system for accumulating, storage, retrieval, analysis and display of spatial (i.e. locationally defined) data. A GIS contains two broad classifications of information, geocoded spatial data and attribute data. Geocoded spatial data define objects that have an orientation and relationship in two or three-dimensional space. Attributes associated with a street segment might include its width, number of lanes, construction history, and pavement condition and traffic volumes. An accident record could contain fields for vehicle type, weather conditions, contributing circumstances and injuries. This attribute data is associated with a topologic object (point, line or polygon) that has a position somewhere on the surface of the earth. A well-designed GIS permits the integration of these data. The sophisticated database in a GIS has the ability to associate and manipulate diverse sets of spatially referenced data that have been geocoded to a common referencing system. The software can transform state plane coordinates and mile point data to latitude-longitude data and vice versa. A GIS can expand the decision making on repair strategies and project scheduling by incorporating such diverse data as accident histories, economic needs hazardous materials shipment and vehicle volumes. A GIS can perform geographic queries in a straightforward, intuitive fashion rather than being limited to textual queries; A GPMMS can be used to build projects through spatial selection, can compute traffic impacts of various PMS plans and can incorporate the results of life cycle forecasts into measurements of future mobility. The network-level PMS has been integrated with GIS for the selected highway network. The input data and results obtained under life-cycle cost analysis of the highway
20
network have been used to demonstrate the use of GIS in enhancing the pavement management system. The commercially available GIS software GeoMedia has been used for this purpose. A spatial map of the study area has been created, which is comprised of various GIS themes such as national highways, pavement sections, section nodes and districts. The input data and the results of the life-cycle cost analysis of highway network have been imported into GIS as attributes of the pavement sections. GIS has been used to enhance pavement management information with its typical features, such as graphical display of highway network and current and future pavement condition of the selected pavement sections. GIS also provides an excellent spatial query and analysis capability to select the candidate pavement sections in need of immediate maintenance.
Pavement Performance Forecasting, Optimization & Simulation INPUT Historical traffic condition Historical Pavement distress condition
Where, when, what to treat
Given funding
Pavement Performance Forecasting, Optimization and Simulation models
Forecasting time frame (5 or 10 year later)
Treatment Determination Deterioration models
OUTPUT 1. Avg. statewide composite future performance index 2. Funding balance among congressional districts 3. Workload balance among working districts 4. Future pavement performance visualization and spatial analysis
Optimization models Simulation models GIS mapping, visualization and spatial analysis
Fig 2.6 Inputs – Analysis – Output - Chart 2.4 INFORMATION TECHNOLOGY FOR PMMS
21
For performing GIS based Pavement Maintenance & Management Analysis, there are so many softwares used world wide. Among them the leading PMS software’s in practice are: RoadSoft RoadSoft, available from the Local Technical Assistance Program (LTAP) at Michigan Technological University, is a GIS-based roadway management system. The software package was developed for local road agencies within Michigan and engineers and managers to analyze roadway information within their jurisdictions. The software uses the Michigan Accident Location Index (MALI) as a physical reference base. RoadSoft has a road surface inventory module for rating pavement condition using the PASER (Pavement Surface Evaluation and Rating) system. PASER is a simple method of rating asphalt, concrete, and gravel roads developed by the University of Wisconsin’s Transportation Information Center. Manuals filled with pictures detail a oneto-10 evaluation system in which "10" means excellent while "one" indicates a failed road. This system is used to obtain consistent ratings based on the types of wear evident on the roadway surface, such as cracks and deformations. Based on the types of defects, general characteristics of the roadway, and age of the pavement, PASER makes recommendations for the types of fixes that would be appropriate for the road. There are rating manuals available for concrete, asphalt, and gravel roads. MicroPAVER MicroPAVER is a pavement management system developed by the U.S. Army Construction
Engineering
Research
Laboratory
and
distributed
by
the
Technical
Assistance Center at the University of Illinois at Urbana-Champaign. The software contains a full-featured PMS, including manuals for evaluating pavement conditions. MicroPAVER is a decision-support tool, allowing agencies to systematically determine maintenance and repair needs and priorities. The system also enables users to compare budget scenarios and their effect on pavement networks, and data can be linked to a GIS. Inspection data from the road network is input into the system’s database. By taking samples of a section of the entire network, MicroPAVER can calculate the Pavement Condition Index (PCI). Information from the PCI is used to accurately predict the overall health of the pavement network. Using pavement life cycle models in the
22
software, this system can quickly determine which pavements need attention the most, and also calculate the critical condition pavements. (Critical condition pavements are those that are close to the point where they deteriorate rapidly.)
HDM - 4 The internationally recognised Highway Development and Management System (HDM-4) have been used to develop this PMS. Since the size of the highway network is not very large and the analysis period is of medium duration, the ‘programme analysis’ application of HDM-4 has been used for the network-level pavement management analysis. The results obtained through the network-level pavement management analysis have
been presented through various applications of GIS.4 This helps in easy
identification of the candidate pavement sections, due for maintenance during the analysis period, and the associated details of timing, type and cost of maintenance activities can also be readily determined. GeoPave GeoPave can extract and display information that a Pavement Manager requires. It has Dynamic GIS link to your MTC-PMS system. Easy to use menu selections. Also it has Plug-in extension to ArcView / ArcMap. The main features of GeoPave are, it can maintain a good Pavement History, it can analyse Maintenance & Rehabilitation Workplans. It can also directly account for Funding Scenarios. Stantec PMS Stantec developed a Pavement Management Application (PMA) within their Infrastructure Management System (IMA) software. The Infrastructure Management Application is a tool for the management and graphical display of asset information, structural condition, and other available data for municipal utilities and right-of-way assets, either individually or as a group. IMA is a network planning tool for municipal assets. Physical
characteristics,
structural
condition
assessment,
and
rehabilitation
program development for each asset can be objectively compared with the entire network for planning and rehabilitation and maintenance programs. Information can be analyzed 23
graphically, or through the use of a GIS. A rehabilitation module analyzes road condition to prioritize rehabilitation activities and budget programming. The system can also recommend maintenance activities, such as crack sealing and pothole patching.
Hansen PMS Hansen develops asset management software for both the public and private sector.
Hansen
developed
a
pavement
management
system
to
complement
the
infrastructure management software developed to assist agencies maintain assets under their control. The pavement management system is completely customizable by the user. Working with consultants from the company, the management system is tailored to meet the needs of the user. Hansen works with the agency to determine which inputs are needed to perform analysis, fits the deterioration curves to inputs, and calculates outputs. CarteGraph PavementView PavementView
pavement
management
software,
developed
by
CarteGraph
Systems, is part of their more general infrastructure management products. Based on concepts introduced by the Federal Highway Administration and the U.S. Army Corps of Engineers, this system integrates data collection, inspection records, asset history, along with performance modeling to accurately assess current and future pavement condition. PavementView is able to graphically display pavement performance using maps, graphs, or charts. This system allows users to develop queries and reports using all database fields. Users can generate standard or custom reports to assess inventory condition and help manage scheduled and completed 2.5 CONCLUSION A brief review of the available literature on Pavement management system, Pavement maintenance system and pavement management softwares were done. The international PMS scenario was also discussed.
24
Chapter 3
A BRIEF REVIEW OF GeoMedia 3.0 GENERAL This chapter gives a brief description about various functions and tools available in GeoMedia 5.2 as well as GeoMedia Transportation Manager 5.2. 3.1 GEOMEDIA PROFESSIONAL GeoMedia Professional is a fully functional desktop GIS solution. Based on Jupiter technology from Intergraph Corporation, this product is an enterprise GIS for the Windows 2000, Windows NT and Windows XP operating systems. Using GeoMedia Professional we can make live connections to geospatial data in multiple data warehouses simultaneously; analyse data relationships; turn information into precise, finished maps for distribution and presentation; and put geospatial data into the hands of users. GeoMedia’s extensive object model is accessible for customization through industrystandard programming languages such as Microsoft Visual Basic, Microsoft Visual C++, PowerBuilder, and Delphi. Some of the important terminologies associated with GeoMedia are given below. 3.1.1 GeoWorkspace A GeoWorkspace is the container for all our work. Within its confines are the warehouse connections to our data, map windows, data windows, toolbars, coordinatesystem information, and queries we have built. The first thing we have to do is to open an existing GeoWorkspace or create a new one. Once we are in a GeoWorkspace, we can change its coordinate system, establish warehouse connections, run queries, display data, and perform spatial analyses. The settings and connections we define in a GeoWorkspace are saved in a .gws file, although the actual data remains stored in the warehouse. Every GeoWorkspace is built on a template, and we can create our own templates or use an existing one. The software is delivered with a default GeoWorkspace template, normal.gwt, which contains an empty map window, an empty legend, and a predefined
25
coordinate system. Opening an existing GeoWorkspace may take a long time. The amount of time varies with the number of feature classes being loaded into displays, the amount of data per feature class, and the processing time of any queries. To improve performance, we can delay the loading of data by selecting the ‘Do not load data when opening GeoWorkspace’ check box on the General tab of the Options dialog box. 3.1.2 Co-Ordinate System A coordinate system provides the mathematical basis for relating the features in our study area to their real-world positions. The software supports the following types of coordinate systems: ?? ?A geographic coordinate system (the default) references a spheroid, expressing coordinates as longitude, latitude, where longitude is the angular distance from a prime meridian, and latitude is the angular distance from the equator. ?? ?A projected coordinate system references a projection plane that has a wellknown relationship to a spheroid, expressing coordinates as X,Y, where X normally points east on the plane of the map, and Y points north at the point chosen for the origin of the map. The X coordinate called easting, and the Y coordinate is called northing. ?? A geocentric coordinate system references an earth-centred Cartesian system, expressing coordinates as defining the position of a specific point with respect to the centre of the earth. These coordinates are Cartesian (X, Y, Z) where the X axis of the geocentric system passes through the intersection of the prime meridian and the equator, the Y axis passes through the intersection of the equator with 90 degrees East, and the Z axis corresponds with the earth’s polar axis. The X and Y-axes are positive pointing outwards, while the Z axis is positive towards the North Pole. Each feature class stored in a warehouse can have its own unique coordinate system. If we change the co-ordinate system after displaying data, the data is transformed to the new co-ordinate system, and the display is updated. Changing the co-ordinate system in the GeoWorkspace does not affect the data in the warehouse, only data in the map window. Finally, co-ordinate systems are heavily data dependant; therefore, one 26
should not define them arbitrarily. To be displayed accurately in a GeoWorkspace, all data must specify a co-ordinate system. To accommodate data with no specified coordinate system, we must first define a co-ordinate system file (.csf) outside of the software. 3.1.3 Warehouse Warehouse is considered as the source of both graphic and non-graphic information. We can display feature geometries and attribute data in a GeoWorkspace through connections to warehouses where the data are stored. Each warehouse connection uses a data server to convert the data into a format that the software can display. All warehouse types are read-only, except for Access, Oracle, and SQL Server. This protects the integrity of our source data. So, if we want only to display data in the software from one or more warehouses, we can simply create one or more warehouse connections and then use map window and data window to display the data. 3.1.4 Windows The GeoMedia GeoWorkspace can contain one or more windows—map window, data window, and a layout window. These windows provide us with different ways of visualizing our data. The map window shows graphic display or features. The data window shows the same features in attribute form, that is, non-graphic data associated with the geographic objects. Thus, if a feature is displayed in multiple map and data window, it highlights in all when selected. The window allows you to design and to plot a map layout. Map graphics in the layout window can be optionally linked to reflect changes made in the map window, or they can be a static snapshot reflecting the characteristics of the map window at the time of placement. Each map window contains the following marginalia items: a legend, a north arrow, and a scale bar. 3.1.5 Legend The legend contains the following parts: ?? ?A title bar, which we can turn on or off. The title bar must be turned on before we dismiss the legend.
27
?? ?Legend entries, which we use to control the display of the objects in the active map window. Legend entries can have titles, subtitles, and headings. The legend contains a separate entry for each map object. When a feature class or query has multiple geometry or text attributes, a separate entry is added to the legend for each of these attributes. An example of a legend is shown in Fig 3.1.
Fig 3.1 A Legend Each entry contains a title and a style key. If statistics for a legend are turned on, the entry displays the count of map objects in parentheses next to the title. Style keys for feature classes and queries are dynamic and represent the geometry type of the feature class (point, line, area, or compound). Style keys for thematic displays, images, and text are static and represent the object type. The various feature classes and their style keys appearing in the legend are shown in Fig 3.2 and Fig 3.3. Style keys include the following:
28
Fig 3.2 Style Keys representing feature classes in Legend Style keys can also indicate the state of the following legend entries:
Fig 3.3 Style Keys representing errors in Legend We can add the following types of map objects as entries to the legend: ?? ?Feature classes ?? ?Queries ?? ?Thematic displays ?? ?Raster images 3.1.6 Feature and Feature Class A feature is represented in a map window by geometry and is further defined by non-graphic attributes in the database. The values of these non-graphic attributes can be viewed as cells in the data window view on the non-spatial data of the feature. In a read/write warehouse, we can create a new feature class, delete a feature class, and edit a 29
feature class definition. We can edit a feature class in the following ways: by adding attributes, by removing attributes or ?by changing attributes In a read/write warehouse, we can also manage feature data in the following ways: by changing attribute values??by adding or deleting features. Geometry refers to the graphic representation of a feature in the map window. The following geometry types represent features: ??
A point feature is represented by one or more points on a map that represent the location of a feature. A point can also represent features that cannot be mapped at the defined map scale. Elevation control points, buildings, and manholes are all examples of point features. A linear feature is represented by one or more lines and/or arcs. Rivers, railroad tracks, utility lines, and roads are examples of linear features. An area feature is represented by closed boundaries. Counties, land parcels and water bodies are examples of area features. A compound feature may have point, linear, and/or area geometry within the feature class or even within a single feature. A text feature is represented by text that appears at a point location on a map. You can place text in an existing text feature class or create a new one to contain it. Text can have an orientation, that is, it can be rotated. An image feature is a raster image.
A feature class is the classification in which each instance of feature is assigned. The software allows creating feature classes in three ways: from scratch, by copying some of the information from an existing feature class into a new feature class in the same warehouse, and by attaching an external data source.
30
3.2 FUNCTIONS OF GEOMEDIA The
main
functions
of
GeoMedia
include
digitisation,
building
queries,
performing joins, buffer analysis, overlays, creating special filters etc., 3.2.1 Digitization For digitizing first of all the images are to be registered. A raster image, such as a scanned map sheet, an aerial photograph or a satellite image can be inserted into a read/write warehouse. The image is not moved from its original location, but the path to the image is saved in the warehouse. To edit or change the image, the source file must be edited.
Inserting multiple images with the same file name into a single warehouse must
be avoided, even if the images are stored in different directories.
The file type and
information contained in the file determine whether the file can be inserted interactively or automatically. ?? Interactive placement requires a fence to be drawn in the map window to define the size and location of the image. ?? Automatic placement inserts geo-registered images directly into map window and preserves image geometry. There are two types of automatic placement, Georeferenced and by header. GeoMedia Professional 5.2 also provides tools that maintain data integrity by reviewing
geometry
information,
validating
geometry
and
validating
connectivity.
GeoMedia provides tools to correct data by trimming and extending geometry to intersections, inserting intersections, fixing connectivity and fixing geometry. 3.2.2 Queries A query is a request for information. When we display a query, we are requesting to see features that meet specific criteria. With GeoMedia Professional, we can build a query by making selections on a dialog box without needing to know SQL. Queries present current information in the warehouse. This means that each time we display a query; we get the current information in the warehouse. Queries are stored in the GeoWorkspace so that, if a warehouse changes, all queries are updated each time they are displayed. If a spatial filter is applied to the warehouse connection at the time the query is defined, the query is limited to the geographic area defined by that spatial filter.
31
?? Attribute-filter query allows limiting the search to individual features whose attributes contain values that meet the conditions specified by an operator.
An
operator is a symbol or expression, such as = (equals) or > (is greater than), that represents the relationship between two values. ?? Spatial query allows limiting the search to individual features whose geometry has a spatial relationship to features from another feature class or query. ?? Combined attribute and spatial query requests features with certain attribute values that meet specified spatial conditions, such as overlapping or being contained by another feature class or query. 3.3 GEOMEDIA TRANSPORTATION MANAGER 5.2 ?? Used for linear data analysis as well as routing analysis. ?? Easiest way to merge linear referencing and geospatial technology. ?? Increases the value of our data by turning it into business critical, decision support information. There are two major divisions in GeoMedia Transportation Manager; ??Linear Referencing System (LRS) ??Dynamic Segmentation ??Network Routing 3.3.1 Linear Referencing Linear Referencing is simply the tracking and analysis of data that is associated with locations along a linear network. For example, tracking the condition of signage, condition of pavements, location and severity of accident occurrences etc. As an example for Linear Referencing, the preceding diagram shows a portion of road on the left and its geospatial representation on the right. The road has kilometer posts that indicate cumulative linear measures along the road. It also has a road name, Highway 6 in this example. A section of fencing along the road is also shown in both the left and right views. Based on the kilometer posts, it can be determined in the field that this stretch of fence runs along Highway 6 from kilometer measure 2.0 to 2.6. These LRS Linear Features are the backbone of the LRS and are used in automating the mapping of linearly referenced data, such as this stretch of fencing, onto the map view.
32
Fig 3.4 Illustrating Linear Referencing 3.3.2 Dynamic segmentation Dynamic segmentation is the overlay and display of attributes describing a linear referenced network. Dynamic segmentation can accommodate multiple attribute tables, describing a highway network, without requiring duplication of network geometry or data. Only a single, graphic representation of the highway network is required. The locations of attribute records along the network are identified using the linear referencing method. In the present work Linear Referencing and Dynamic segmentation analysis of GeoMedia Transportation Manager is used to its full extent. 3.3.3 Routing Network A routing network is a system of connected linear features that can be used to support the simulated transportation of goods, services, or communications between locations on the network. A network can be thought of as an abstract model that is derived from a set of linear features and their relationships. Each network model is primarily composed of a set of geographic features called Edges and a set of implied features called Nodes. Each Edge in a network represents one component of the transportation system that is being modeled. 3.3.4 Routing Analysis The major Manager are:
routing
analysis
tools
33
provided
with GeoMedia
Transportation
Best Path – This command gives the ability to find the best path for a vehicle that needs to make one or more stops. We select whether Paths will be optimized so as to minimize distance traveled or to have the command minimize other “user costs,” such as time, money, or even safety. It also has an option to optimize ht e order of stops to further optimize the path. The timesaving for maintenance, delivery, or other vehicles can result insignificant cost savings, or in the case of emergency vehicles, even saved lives. Find Closest Stops – This command finds the closest n destination Stops to a set of origin Stops. This is particularly useful for finding, for example, the two closest hospitals to each of a set of sports facilities. Path optimization may be by distance or by user cost. 3.4 CONCLUSIONS This chapter gives a brief review about the software GeoMedia Professional and GeoMedia Transportation Manager used for the work. Important terminologies associated with GeoMedia are also briefly discussed. Besides this a brief description about the functions of GeoMedia is also given.
34
Chapter 4
PAVEMENT MAINTENANCE & MANAGEMENT ASSISTANT 4.0 GENERAL This chapter describes in detail about the plug-in software, GeoMedia Pavement Maintenance and Management Assistant (GPMMA) for GeoMedia. 4.1 AN OVERVIEW OF GPMMA For performing PMMS analysis in GeoMedia, it is essential to customize it based on our needs. A comprehensive plug-in software, GeoMedia Pavement Maintenance and Management Assistant (GPMMA) for GeoMedia was developed, which provides no bounds for PMMS analysis in GeoMedia. Important features in GPMMA are Deterioration prediction, Cost analysis, BBD overlay design, Prioritization, Overlay Cost Calculator, Maintenance schedule etc 4.2 BEHIND GPMMA The GPMMA software is so developed that it can be used with any GeoMedia application. GPMMA is developed in Visual Basic platform with direct link to Microsoft Access database. So it can also be used separately for PMMS analysis, by directly linking with Access database and the UGI developed. GPMMA
also links GeoMedia with other useful external softwares like,
RomdasRMS, MicroPAVER 5.2, STIP, SW-1, Real Cost LCCA etc. For GPMMA to work properly three sets of installation procedure is needed. First is the GPMMA & External component installation, second is GPMMA Command Installation and External software installation. 4.2.1 GPMMA & External Component Installation This is the primary step for installing GPMMA. With out this preliminary installation GPMMA can’t give hooks to external build softwares. The installation process is as shown in fallowing figures:
35
Fig 4.1 GPMMA & External component installation setup files
Fig 4.2 GPMMA & External component installation setup-1
36
Fig 4.3 GPMMA & External component installation setup-2 4.2.2 GPMMA Command Installation To bring GPMMA as a plug-in command to GeoMedia, the command .dll files had to be properly registered into the system registry. The GPMMA command installation will take care of this. The procedure for command installation is as shown below:
Fig 4.4 GPMMA Command Installation setup-1 37
Fig 4.5 GPMM Command Installation setup-2
Fig 4.6 GPMMA Command Installation setup-3 4.2.3 External Software Installation GPMMA won’t directly install the external softwares in to the system. External softwares can be installed any time later from GPMMA GUI. The procedure for command installation is as shown below:
Fig 4.7 External Software Installation linking form 38
4.3 GPMMA OVERVIEW & IMPORTANT FEATURES For performing PMMS analysis in GeoMedia, it is essential to customize it based on our needs. Important features in GPMMA are Deterioration prediction, Economic analysis, BBD overlay design, Prioritisation, Overlay Cost Calculator, Maintenance scheduler etc
Fig 4.8 PMMA command installed in menubar
Fig 4.9 PMMA command windows
39
4.3.1 PMMA Deterioration Prediction The Deterioration Predictor is an analysis tool to predict the pavement deterioration for any selected design life. Working from the data collected, the tool analyses current road conditions, age, and pavement type. These data are factored with aging of pavement and fix strategies to estimate the future condition of the road network. The base for Indian pavement deterioration prediction models are mainly the contributions from different research institutes and organizations like Central Road Research Institute (CRRI) (Updating Road User Cost Data URUCS 1991, Pavement Performance Bangalore
Study University
(PPS-EPS
1993),
(Transition
RITES
Probability
(HDM
Calibration
Matrices
for
Studies -1994),
Optimal
maintenance
decisions-1995), & Indian Institute of Technology Kharagpur (Analytical Pavement Design (999). These studies laid the basis for the pavement data analysis and development of Pavement deterioration model for Indian roads. The following Models are found to be well suited for Indian conditions and are in practice.
4.3.1.1 Prediction of Yearly Change in Cumulative Standard Axles (CSA)
365 x A[(1+r) n -1] x vdf x tdf CSA= r x 10 6 Where CSA
=
Cumulative number of standard axles to be catered for in the design in millions.
A
=
initial traffic in the year of completion of construction in terms of the number of
vehicles commercial vehicles per day duly modified to
account for lane distribution. r
=
growth rate of vehicles
n
=
design life in years
vdf
=
vehicle damage factor
tdf
=
transverse distribution factor
40
4.3.1.2 Prediction of Yearly Change in Deflection
d = do (1+0.125 CSA / log10H) Where: do
=
initial deflection value immediately after construction
CSA
=
Cumulative number of standard axles in millions.
d
=
yearly change in deflection value.
H
=
equivalent granular thickness in mm.
4.3.1.3 Prediction of Yearly Change in Unevenness UI = Ulo (1+ 0.11 CSA / log10 H) Where: UI
=
Unevenness after a given number of traffic load repetitions in mm/km
UI0
=
initial unevenness soon after the construction of overlay in mm/km
CSA
=
cumulative standard axles in millions
H
=
Granular overlay thickness in mm
4.3.1.4 Prediction of Yearly Change in Present Serviceability Rating (PSR) The following model developed as a part of PDM research project at Bangalore University is used for predicting the yearly change in serviceability over the design life. PSR = -1.9326 loge (UI) + 14.3765 Where: PSR
=
Present Serviceability Rating
UI
=
Unevenness Index in cm/km
41
Fig 4.10 GUI for PMMA Deterioration predictor
4.3.2 PMMA Economic Analysis The Life cycle cost of a pavement is the total highway transportation cost during the design life of the pavement. The total cost of highway transportation is made up of the highway cost and the cost of operating motor vehicles over the highways. The highway cost is that cost borne by the people through their highway department and the vehicle operation cost is the cost which is borne directly by the owners of the motor vehicles. In order to get the total cost of highway transportation, it is therefore, necessary to consider the two sources of basic costs. 42
4.3.2.1 Construction Cost Construction cost depends upon the type and thickness of the overlay. In the present work, the construction cost for Bituminous Concrete, Bituminous Macadam, Mixed Seal Concrete, Asphalt concrete was calculated per kilometer length based on the standard schedule of rates of Kerala P.W.D. and based on the rate of last ten tenders for works. Three different strategies was considered like, ?? BM & AC + MSC with Profile Correction (Once in 5yr) ?? BBD Overlay (BM&BC in 2:1) (Designed Overlay) ?? Periodic Repair 25BM+25BC (Once in 2yr) 4.3.2.2 Vehicle Operation Cost The vehicle operation cost differs between different classes of vehicles under the same roadway conditions. Hence, the total vehicle operation cost is the total operating cost of all vehicles plying on the road. The vehicle operation cost for the same class of vehicle depends upon the width of the road, unevenness of the road and the gradient of the road. Kadiyali et.al has given different equations for different classes of vehicles for calculating the vehicle operating cost per kilometer excluding the tax. For determining the vehicle operating costs in future years, there are six different methods of economic analysis. According to Winfrey the Net Present Value method is reliable as a measure of the comparisons of alternatives. The concept of the Net Present Value is that the decrease in value of the property in any given year, and therefore its depreciation for the year, is equal to the decrease for that year in the present value of its portable future returns. The vehicle operation cost for the design life of the pavement is calculated using the Net Present Value method in the present study. Net present value (Present worth) method is based on the discounted cash flow (DCF) technique. In NPV method benefits are treated as positive and costs as negative and the net present value are found. Any project with a positive Net Present Value is acceptable. In comparing more than one project, a project with the highest net present value should be accepted.
43
VEHICLE TYPE
VECHICLE OPERATION COST EQUATION
New technology Cars
LogeVOCNC=.2 175-. 1 149w+0.000098U1+.01075Rf
Old technology Cars
LogeVOCOC 0.3589-.0916w+0.000009834U1+0.0l042Rf
Heavy Commercial vehicles 2-axle
LogeVOCHCV = 1 .5004-.8 188 W+0.00003732UI+0.00 1 55Rf
Buses
LogeVOCB=1 .4041 -.08462W+0.00003939U1+0.0 11 33Rf
Heavy commercial vehicles (Multi-axle)
LogeVOCOCV= 1 .9989-.07709W+0.00003 856U1+0.0 1 OO3Rf
Light Commercial Vehicles
LogeVOCLCV 1.5541 -0.07768W+0.00002833U1+0.00 1 55Rf
Two-Wheelers
LogeVOCTW=- 1.1531-0.0591 6W+0.OOQ 1 076U1+0.008786Rf Table 4.1 Equations for vehicle operating cost
VEHICLE TYPE New tech .cars Old tech cars
% TOTAL 0.080
GROWTH RATE 0.075
0.070
0.075
Heavy commercial vehicle
0.2452
0.075
Buses
0.1362
0.075
Heavy commercial vehicle Light commercial vehicles
0.0039 0.2097
0.075 0.075
Two wheelers
0.2545
0.075
Table 4.2 Percentage of vehicles and growth rate Inputs for the Economic analysis will be taken directly from Microsoft Access Database which intern is connected to GeoMedia as its Warehouse. Other varying inputs are being given through the Graphical User Interface (GUI) developed. Snap shot of the GUI are given below.
44
Fig 4.11 GUI for PMMA Economic analysis 1
Fig 4.12 GUI for PMMA Economic analysis 2
45
Fig 4.13 GUI for PMMA Economic analysis 3 4.3.3 PMMA BBD Overlay Design The rebound deflection tests were carried out on permanent deflection observation points at equal intervals along the outer wheel path at 90 cm from the edge of the twolane pavement. The tests were conducted using a Benkelman beam and a loaded truck having rear axle weight of 8170 kg fitted with two pairs of dual wheels with inflation pressure of 5.6 kg/cm2. About 20 measurements per pavement stretch were made during the field studies. The deflection tests were carried out as per Canadian Good Roads Association (CGRA) procedure as mentioned in the guidelines of the IRC: 81-1997. The pavement temperature and the subgrade moisture data were collected during each cycle. 46
The temperature observations were made on the bituminous layers by digging a small pit of 40 mm depth and filling it with glycerol. After the glycerol attained the pavement temperature, the temperature measurements were made. When the pavement temperature is different than the standard of 350 C needs to be corrected. The correction will be positive if pavement temperature is less then 350 C and negative if pavement is higher then 350 C. Correction for temperature is required for pavement having a substantial thickness of bituminous construction. Separate sub programs where written for finding moisture correction, temperature correction, vehicle damaging factor (VDF), Transverse Distribution Factor (TDF) etc. Most of the inputs where taken directly from Microsoft Access Database. Remaining varying data are input through the GUI. Equivalent Single Axles (ESA). VDF =
TDF =
Number of Vehicles Total no. of commercial vehicles traveling along 90 cm wheel path in both directions. Total no. of commercial vehicles in both the directions during the period of survey.
4.3.3.1 Conversion of Curves into Mathematical Forms IRC recommends the moisture correction factors for different conditions of soil. These were converted into mathematical equations for different conditions of plasticity index (P1), type of subgrade soil, annual rainfall and field moisture content. For development of mathematical equations, a curve was divided into a number of segments depending upon its shape and variation in the slope. The best fit equations were obtained by using the GRAPHER package. The converted equations are shown in Table 5.3. To calculate the thickness of Bituminous Macadam overlay(mm), design curves of IRC 81-1997 were converted into mathematical equations for different values of characteristic deflection (mm) and the Cumulative number of standard axles. The results of overlay thickness are shown in Table 5.4.
47
Fig. No. of IRC: 81-1997
Moisture Contents (%) (x)
Equation for moisture correction factor (y)
For Sandy/Gravely soil subgrade for low rainfall areas 2
4 to 11
y = .0001x4 - 0.0051x3 + 0.0922x2 - 0.7039x + 2.9628
For Sandy/Gravely subgrade for low rainfall areas 3
4 to 13
y = -.0001x4 + 0.0036x3 - 0.0425x2 + 0.1448x + 1.2743
For clayey subgrade with low plasticity (P1 < 15) and low rainfall areas 4
4 to 22
y = .000006x4 - 0.0005x3 + 0.0171x2 - 0.2446x + 2.4393
For clayey subgrade with low plasticity (P1 < 15) and low rainfall areas 5
4 to 22
y = .00002x4 - 0.0012x3 + 0.0303x2 - 0.3546x + 2.8708
For clayey subgrade with low plasticity (P1 > 15) and low rainfall areas 6
4 to 20
y = .00002x4 - 0.0013x3 + 0.0333x2 - 0.4092x + 3.0917
For clayey subgrade with high plasticity (P1 > 15) and high rainfall areas 7
4 to 20
y = .00004x4 - 0.0026x3 + 0.0596x2 - 0.6317x + 3.8078
Table 4.3 Equations for moisture correction factor CSA
Characteristic
Equation for Overlay thickness (y)
deflection (x) 0.1
3 to 6
y = 4.1667x3 - 70x2 + 415.83x - 730
0.5
2 to 6
y = -2.5x4 + 45x3 - 302.5x2 + 930x - 970
1.0
1.68 to 6
y = -2.1093x4 + 37.187x3 - 245.09x2 + 742.82x - 717.56
2
1.4 to 6
y = -1.6162x4 + 28.811x3 - 191.18x2 + 583.9x - 514.85
5
1.15 to 6
y = -1.6824x4 + 29.356x3 - 189.19x2 + 556.93x - 431.6
10
1 to 6
20
.8 to 6
y = 0.4583x5 - 10.208x4 + 88.958x3 - 379.79x2 + 820.58x - 520 y = -0.6306x6 + 13.867x5 - 122.43x4 + 554.52x3 - 1362x2 + 1765.7x - 779.01
100
.55 to 6
y = -0.6834x6 + 14.393x5 - 120.43x4 + 510.44x3 - 1159x2 + 1387.4x - 487.06
Table 4.4 Equations for moisture correction factor 48
Fig 4.14 GUI for PMMA BBD overlay design
Fig 4.15 Access Database for PMMA BBD overlay design
49
The output of the design will directly get saved in corresponding column of the Access database table. From there it is directly called to GeoMedia for its graphical representation in map. 4.3.4 PMMA Maintenance Prioritisation Timely maintenance or rehabilitation of all pavement stretch may not be possible due to limited funds, material shortage, environmental restriction and so-forth. So the questions such as what, where and when to maintain and rehabilitate have been common, but the solution used have not always been the right one. The need for prioritising the stretches is of principal importance. The various techniques for prioritising projects for maintenance and rehabilitation works are listed below: ?? Univariate Time Series of Serviceability Index Model ?? Dynamic Decision Model ?? Index Ranking Method ?? Percentile Ranking Method ?? Successive Subsetting method In this work prioritising techniques like Index Ranking Method and Percentile Ranking Method are considered. 4.3.4.1 Index Ranking Method The index uses ranking the proportion of distance that a given segment factor value lies between the best and worst factor values. The total distance between the best and the worst factor values in the needs list is called the “range.” A better value is the one that would place a segment lower in the priority list than the segment currently under consideration. The process is illustrated in the Fig 5.13 Most needy segment, Fw X Segment under consideration
R Range
Least needy segment, Fb Fig 4.16 Index Ranking Method process 50
For every segment, an index value is evaluated by using the formula.
? X ? Ij= ? ?× 1 0 0 ? R ? Then composite index for each segment (Ic) is calculated as: n
?
Ij × Wj
j=1
IC =
n
?
W
j
j=1
Where: Fw
= Worst value of factor for segments in needs list
Fb
= Best value of factor for segment in needs list
X
= Difference between Fw and the factor value
R
= Difference between Fb and Fw the range of values
Ii
= Segment index value, based on its value for factor j
Ic
= Composite factor index of the segment under consideration
Wj
= Weight for j factor
th
4.3.4.2 Percentile Ranking Method The segments percentile ranking represents that proportion of the other segments in the needs list that fail to be as deserving of road funds as measured by the value of the factor under consideration. For a single factor Percentile rank is given by,
Where: P
= Percentile rank of the segment
B
= Number of segments with better values
W
= Number of segments with worse values
As in the index method, a better value is one that would place a segment lower in the priority list than the segment currently under consideration. The percentile ranking is
51
done separately for each factor, and then combined into a weighted sum of the weights W to produce the composite percentile Pc.
4.3.4.3 Weightages Given For Various Parameters In this work, parameters average daily traffic, deflection was given a weightage of 1.0 as these are the variable mostly leading to pavement structural failure. Variables Unevenness Index is given a weightage of 0.75 as it represents functional condition of the pavement. Pavement serviceability rating as it is not a measured quantity but is a assumed value, weightage given for it is 0.5 only. If the pavement is not structurally satisfactory, naturally it deteriorates at a faster rate then the pavement, which fails functionally. Hence, the factors contributing to structural behavior of pavement are given more weightage than factors representing functional behavior of the road.
Fig 4.17 GUI for Priority Ranking
52
4.3.5 PMMA Overlay Cost Calculator For economical planning under restricted funds and materials, Overlay cost calculator will be of great help to serve the need. We can calculate the cost of overlay with varying materials and varying overlay thickness. Construction cost depends upon the type and thickness of the overlay. In the present work, the construction cost for Bituminous Concrete, Bituminous Macadam, Mixed Seal Concrete, Asphalt concrete was calculated per kilometer length based on the standard schedule of rates of Kerala P.W.D. and based on the rate of last ten tenders for works.
Fig 4.18 GUI for PMMA Overlay Cost Calculator 4.3.6 PMMA Maintenance Scheduler The questions such as what, where and when to maintain and rehabilitate have been very general in pavement management system. The PMMA Maintenance scheduler will give you a fair answer for the question “when? & what?”.
53
Fig 4.19 Graph showing best time for Maintenance scheduling
Fig 4.20 GUI for PMMA Maintenance scheduler 4.3.7 Hooks to External Softwares GPMMA links GeoMedia with other useful external softwares like, RomdasRMS, MicroPAVER 5.2, STIP, MnPAVE, SW-1, Real Cost LCCA etc. 54
Fig 4.21 Snap Shots of External Softwares windows 4.4 CONCLUSIONS This chapter gives a brief review about the plug-in software, GeoMedia Pavement Maintenance and Management Assistant (GPMMA) for GeoMedia. Important functions of GPMMA are also briefly discussed. Besides this a brief description about the methodology fallowed in each function is also briefly described.
55
Chapter 5
GPMMS INPUTS 5.0 GENERAL This chapter deals with the description of inputs and the techniques used to feed the inputs for a standard GeoMedia based Pavement Maintenance and Management System. 5.1 GPMMS INPUTS The main inputs of GeoMedia based Pavement Maintenance and Management System are:
?? Well Prepared Database ?? Georeferenced Digitized Base Map ?? PMMS Analysis Tools (GPMMA) 5.2 GPMMS DATABASE The heart of a pavement management system is its database, which is the storehouse for all pavement-related information collected. A program is only as good as the data stored in it. A PMS must have usable, accurate, and timely data to produce credible outputs. The effectiveness of data analysis will increase if the data entered is accurate. This database possesses several features, including: a large capacity, user friendly access, flexibility for future expansion, security features, and compatibility with other databases that store related information. Every piece of information in the database is referenced to the particular section of pavement which it describes. Table 5.1 shows the data collected from 12th Mile to Kattankal of Kunnamangalam-Augustianmuzhi road. The information collected and stored in the database can be divided into five categories:
?? ?? ?? ?? ??
Inventory data, Construction data, Traffic data, Condition data (physical distress, roughness, structural capacity, friction), Treatment data.
56
Sl. No
Chainage
1 2
6/330 - 7/240 6/330 - 7/240
Cracking % Pothole Area %
0.0133 Nil
3 4 5
6/330 - 7/240 6/330 - 7/240 6/330 - 7/240
Ravelling % Patch works % Edge break
Nil Nil Nil
6 7 8 9
6/330 - 7/240 6/330 - 7/240 6/330 - 7/240
Roughness in UI Characteristic deflection (mm) Skid resistance value Shoulder i. Paved/concreted
6/330 - 7/240 6/330 - 7/240 10 6/330 - 7/240
Item
ii. Unpaved Predominant land use i. Left ii. Right Terrain Pavement Type Drainage
6/330 - 7/240 6/330 - 7/240 6/330 - 7/240
Camber Level of adjacent land i. Left ii. Right
16
6/330 - 7/240
Traffic
17
6/330 - 7/240
Year of construction/repair/surfacing/strengt hening
14 15
18
300 m one side remaining Institution
6/330 - 7/240 6/330 - 7/240 6/330 - 7/240 6/330 - 7/240
11 12 13
Value (Max)
Institution Plain BT Nil 2.5% Plain Plain
2005-06’ (widening and Overlaying)
6/330 - 7/240
Inventory i. Carriage way
7.5 m
6/330 - 7/240
ii. Right of way
15 m
Table 5.1 Data collected from 12th Mile to Kattankal of KunnamangalamAugustianmuzhi road (MDR-2) 5.2.1 Inventory Data Inventory data is a collection of the physical characteristics of the pavement, and usually do not change between maintenance activities. The most basic information about 57
the road is included to reference the pavement, such as the road name or route number, location (or referencing system), number of lanes, and pavement type. Other inventory data may include:
?? ?? ?? ?? ??
Type of pavement (asphalt, concrete, composite), Width of road, Number of lanes, Thickness of pavement layers, and Drainage conditions.
Fig 5.1 Microsoft Access Database showing Inventory data 5.2.2 Construction Data Related to inventory data, construction data contain information about the history of the pavement. This information is important because roads can only be rehabilitated a
58
limited number of times before a full-scale reconstruction of the road is necessary. The type of construction data collected includes:
?? ?? ?? ?? ??
Year built, Design service life, Date and type of rehabilitation and maintenance projects, Materials used in construction activities, and Cost of maintenance activities.
This set of data is lacking in the present work. If we don’t have a proper knowledge about construction details of a pavement, it will be difficult to calculate its remaining life as well as we can’t predict the deterioration correctly. 5.2.3 Traffic Data The lifespan of a road is dependent on the amount of traffic that uses it. Traffic count data are useful for calculating the remaining service life of a pavement. Estimating traffic type is also important. Heavy loads, such as those generated by trucks, break down pavement quicker than passenger cars. Knowing traffic volumes and type will be useful for planning future pavement rehabilitation.
Fig 5.2 Microsoft Excel Database showing Traffic data 59
5.2.4 Condition Data Condition data refers to information about the past and present surface condition of a section of pavement. Accurate historical pavement condition information is absolutely essential for operation of the pavement management system, because all system recommendations are ultimately based on past and present condition data. Sophisticated PMMS databases contain four different types of condition data: physical distress data, roughness data, structural capacity data, and friction data. 5.2.4.1 Physical Distress Data Physical distress is a measure of road surface deterioration caused by traffic, environment, and aging. Distress can be measured by type, severity, and extent of breakdown of pavement. The type of distress can be broken into three categories: fracture (cracking), distortion (rutting), or surface wear (raveling). This information is the most important information in the entire database.
Fig 5.3 Microsoft Access Database showing Physical distress data 60
5.2.4.2 Roughness Data Roughness is a measure of ride quality on a particular pavement section. Studies have consistently shown roughness can be directly related to both user satisfaction and user costs. Therefore, road roughness measurements are important pieces of information in a PMMS. Unevenness index is the cumulative measure of the vertical undulations of the pavement surface per unit horizontal length of the road. ,
UI =
B
x R x 25.4
W Where: UI
= Unevenness Index in mm/km
B
= Bump Integrator reading
W
= Number of wheel revolutions
R
= Number of revolutions per km (460)
Fig 5.4 Microsoft Access Database showing Roughness data
61
5.2.4.3 Structural Capacity Data Structural capacity is the ability of a pavement to support traffic with little or no structural damage. The structural capacity of a pavement is most commonly estimated through the use of non-destructive testing equipment. This equipment measures the deflection, curvature, and/or joint efficiency of the pavement under a specified loading. Structural capacity data is normally used to evaluate past pavement designs or to select a maintenance, rehabilitation, or reconstruction treatment for a specific project, not to evaluate the performance of the entire highway network. 5.2.4.4 Friction Data Friction data is collected using equipment that measures the skid resistance of the pavement. Friction data are usually examined to determine if lack of friction is a probable cause of traffic crashes at high-crash locations, or to identify potentially unsafe locations that have yet to experience a large number of traffic crashes. In this respect, these data may belong more to the area of safety management than pavement management (although the two systems are certainly related). 5.3 UPDATING GPMMS DATABASE The PMMS databases are usually updated by integrating the new set of database and existing database by using Linear Referencing and Dynamic Segmentation. Linear Referencing and Dynamic Segmentation are the process of merging new set of database and existing database by using some primary key, say ID, Road Name or something. 5.3.1 Linear Referencing Linear Referencing is one of the most important tools in GeoMedia for PMMS. It helps to consider the actual road length of the Road network, rather than the airline distance by which the whole network will get automated. It allows the real time tracking of both linear and point features along the road. Linear Referencing is simply the tracking and analysis of data that is associated with locations along a linear network. For example, tracking the location of and condition of pavement, the location and severity of accident occurrences etc.
62
Fig 5.5 Linear Referencing Command Toolbar & Work Flow
Fig 5.6 Linear Referenced Access Warehouse table & road network
63
5.3.2 Dynamic Segmentation Dynamic Segmentation is the process of generating geometry for events based on a LRS. Event features contain sufficient LRS information such that they can be dynamically segmented to produce geometry based on an LRS feature class. The flexibility of dynamic segmentation, with respect to data collection, management, and integration, was the primary reason it was selected for the GPMMS database.
Fig 5.7 Basic Concepts of Dynamic Segmentation
64
Fig 5.8 Dynamic Segmentation Command Toolbar & Work Flow 5.4 DATA ENTRY Pavement Management system needs a huge amount of data to meet its indented aim properly.
In this work Microsoft Access had been used as the Database as well as the
read/write warehouse for GeoMedia. For entering the relevant data in to the Access database a user-friendly GUI had been developed using Microsoft InfoPath. The main motive behind making such a data entry form is to reduce the work in data entry by auto filling the repeating data; also it help in querying and editing of present data at any time. PMMS related data will be collected by various groups and organizations, so there is a need to standardise and centralise these data. One of the main Challenges in present work was to clean the whole data collected by various organization and to bring it in to a standard environment. Microsoft InfoPath form can be used over internet to collect the relevant data by publishing the form and the supporting Access database in to a subscribed webpage.
At
any time later we can retrieve the data collected in the forms in web by various private groups and organizations. These data can be directly used for Dynamic Segmentation.
65
Fig 5.9 Microsoft InfoPath form Query View and Data Entry View 5.5 GEOREFERENCED DIGITISED BASE MAP The Scaled road map of Calicut district was scanned and brought to GeoMedia in image feature classes as raster images. Image feature classes are distinguished from one another based on the coordinate system of the feature class. You can only insert images into an existing feature class when the coordinate systems of the image and the feature class are in agreement. All the images in an image feature class can be represented by a either a single legend entry or multiple legend entries. You can add images to existing feature classes as needed, without the images being displayed, thereby managing system resources more efficiently. A minimum of four or more known coordinate points from the scanned map where selected and was placed in the corresponding coordinate in the GeoMedia map window using point feature. Now we have a set of known coordinate points both in scanned map and in GeoMedia map window. Now the scanned map was brought to GeoMedia using the option Insert > Interactive Image . Using the image registration option the corresponding points in scanned map and GeoMedia map window was linked to get a Georeferenced map of Calicut District.
66
S TUDY AREA CALICUT DISTRICT
Latitude: Between
110 04I 14II to 11 0 47I 51II
Longitude: Between
750 31I 17II to 76 0 08I 45II
Fig 5.11 Georeferenced Raster Images & Digitized Map 5.6 PMMS ANALYSIS TOOLS (GPMMA) For performing PMMS analysis in GeoMedia, it is essential to customize it based on our needs. A comprehensive plug-in software, GeoMedia Pavement Maintenance and Management Assistant (GPMMA) for GeoMedia was developed, which provides no bounds for PMMS analysis in GeoMedia. Important features in GPMMA are Deterioration prediction, Cost analysis, BBD overlay design, Prioritization, Overlay Cost Calculator, Maintenance schedule etc. About GPMMA has already been briefly described in chapter.5. For More details about the functions and working of GPMMA see its help. 5.7 CONCLUSIONS This chapter gives a brief review about the inputs of GPMMS. Important functions of GeoMedia like Dynamic Segmentation and Linear Referencing were also briefly discussed. Besides this a brief description about the input features are also briefly described.
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Chapter 6
GPMMS OUTPUTS & RESULTS 6.0 GENERAL This chapter deals with the description of Work done and preferred Outputs & Results of a standard GeoMedia based Pavement Maintenance and Management System. <
6.1 GPMMS OUTPUTS GPMMS
provides
variable
outputs
that
provide
information
for
use
in
implementing cost-effective reconstruction, rehabilitation, and preventive maintenance programs and results in pavement design to accommodate current and forecasted traffic in a safe, durable, and a cost-effective manner. This can help in reducing the time allocated to the maintenance activity and facilitating the decision making process. While the database is the “heart” of a pavement management system, data are not useful unless they are presented in a meaningful way. It is the role of analysis procedures to transform the raw collected data into products such as charts, graphs, and reports that are helpful to decision-makers. A pavement management system can transform a spreadsheet containing pavement condition data into a map. A map can be quickly and easily used to examine the health of pavement in ways that are not readily apparent from columns of numbers. Analytic procedures are grouped into four categories:
?? ?? ?? ?? ?? ?? ?? ??
Thematic Maps Deterioration prediction Economic analysis Maintenance scheduling Maintenance prioritization Overlay design Overlay cost calculation Other outputs
6.1.1 Thematic Maps Thematic Maps (also called choropleth map) are the symbolised representation of the geographic features according to nongraphic attribute data through the use of colors and other user-defined display properties. We can create a thematic display from a feature class in 68
any open warehouse connection, or from a query in the active GeoWorkspace. In a cartographic context, thematic mapping is the mapping that is based on the classification of data of a common theme. Figure 6.1 to 6.8 are some examples of various outputs as Thematic Maps.
Almost all structural and functional attributes can be shown in thematic map form.
The condition data, Unevenness data inventory data are the most suited to represent through thematic mapping. Thematic map will run on all features, feature classes, dynamic segmented events, and quires etc. Thematic map can be drawn for a range of values or for unique values. Range calculation and clustering techniques are also available with thematic map option.
Fig 6.1 Thematic Map showing the lengthwise distribution of MDR 69
Fig 6.2 Thematic Map showing the bridge inventory details
Fig 6.3 Thematic Map showing the bridge attribute details 70
Fig 6.4 Thematic Map showing the ODR condition details
Fig 6.5 Thematic Map showing the SH condition details 71
Fig 6.6 Thematic Map showing the Culvert inventory details
Fig 6.7 Thematic Map showing the Culvert attribute details 72
Fig 6.8 Thematic Map showing the IRQP Phase of NH
6.1.2 Deterioration Prediction This is an important output of PMMA analysis. These results will give an overall idea about the future of the pavement. These predicted values are used as inputs for various other PMMA analyses. A well equipped database is required to provide the input for Deterioration prediction analysis. The base for Indian pavement deterioration prediction models are mainly the contributions from different research institutes and organizations like Central Road Research Institute (CRRI) (Updating Road User Cost Data URUCS 1991, Pavement Performance Study (PPS-EPS 1993), RITES (HDM Calibration Studies -1994), Bangalore University (Transition Probability Matrices for Optimal maintenance decisions-1995), & Indian Institute of Technology Kharagpur (Analytical Pavement Design (999). In the following example (Fig 6.9) the road stretch from Arayadathupalam to Eranhipalam of Calicut Mini-Bypass is taken for illustration. Apart from the input given through the GUI, the other required data have been taken directly from corresponding Microsoft Access Database. The methodology adopted for the calculation where as given by
73
IRC for predicting CSA, Deflection and Unevenness. The PSR value was predicted using the model developed as a part of PDM research project at Bangalore University.
Fig 6.9 Deterioration Prediction - output 6.1.3 Economic Analysis Economic analysis has found a ready application in problem concern with the evaluation of alternative transportation plans. International lending institutions, such as the World Bank, attach the greatest important to assuring themselves that a particular project for which a loan its required is not only economically and technically sound, but that its order of priority in relation to other possible projects has been carefully determined in the light of the overall development of the country concerned. The basic principle behind any method of 74
economic analysis is to measure the cost of the project, determine the benefits that are likely to accrue and compare the two. The objectives of the Economic Analysis are: 1. Whether the plan under consideration is worth investment at all. 2. To rank scheme competing for scarce resources in order of priority. 3. To compare mutually exclusive schemes and select the most economic. 4. To assist in phasing the programme over time period. After having determined the costs and benefits of a scheme, a method has to be evolved for relating these two so as to arrive at an assessment of the soundness of a scheme in economic terms. The important methods are Rate of Return Method Benefit cost ratio method, First year rate of return method. Discounting Cash Flow Method Net present value method (NPV), Internal rate of return method According to Winfrey (1994) the NPV method is reliable as a measure of the comparisons of alternatives. In the present work NPV is selected as the evaluation method. 6.1.3.1 Net Present Value Method (NPV) Net present value (Present worth) method is based on the discounted cash flow (DCF) technique. In this method, the stream of costs/benefits associated with the project over an extended period of time is calculated and is discounted at a selected discount rate to give the present rate. Benefits are treated as positive and costs as negative and the net present value are found. Any project with a positive Net Present Value is acceptable. In comparing more than one project, a project with the highest net present value should be accepted. The net present value is algebraically expressed as:
NPV ?
N
?
t?0
Where,
Bt - C t
?1 ? i ?
t
NPV
=
Net present value in the base year.
Bt
=
Value of benefits which occur in the year t
Ct i N
= = =
Costs which occur in the year t Discount rate per annum. The number of years for which the return is to be calculated. 75
.
In the following example (Fig 6.10) the road stretch from Chorode to Vadakara of NH-17 (Vadakara Sub-division) is taken for illustration. Apart from the input given through the GUI, the other required data have been taken directly from corresponding Microsoft Access Database. The evaluation was done for a design life of 10 years, vehicle growth rate was taken as 7.5% and a discount rate of 4.5%. The methodology adopted for the deterioration prediction calculation where as given by IRC for predicting CSA, Deflection and Unevenness. The PSR value was predicted using the model developed as a part of PDM research project at Bangalore University.
Fig 6.10 Economic Analysis – Deterioration Prediction 76
Construction cost depends upon the type and thickness of the overlay. In the present work (Fig 6.11), the construction cost for Bituminous Concrete (BC), Bituminous Macadam (BM), Mixed Seal Concrete (MSC), Asphalt concrete (AC), was calculated per kilometer length based on the standard schedule of rates of Kerala P.W.D. and based on the rate of last ten tenders for works. Three different strategies was considered like, 1. BM & AC + MSC with Profile Correction Course (Once in 5years) 2. BBD Design Overlay (BM & BC in 2:1 ratio) (From Overlay Design) 3. Periodic Repair 25mm BM + 25mm BC (Once in every 2years)
Fig 6.11 Economic Analysis – Overlay Cost Calculation
77
The vehicle operation cost differs between different classes of vehicles under the same roadway conditions. Hence, the total vehicle operation cost is the total operating cost of all vehicles plying on the road. The vehicle operation cost for the same class of vehicle depends upon the width of the road, unevenness of the road and the gradient of the road. For determining the vehicle operating costs in future years, there are six different methods of economic analysis. Among them the Net Present Value method is the reliable, as a measure of the comparisons of alternatives. See Fig 6.12 for details.
Fig 6.12 Economic Analysis – NPV & Strategy Selection
78
6.1.4 Maintenance Scheduling At the present day, the development of systematic procedure for scheduling maintenance and rehabilitation activities is one of the major concerns of highway agencies today. For every road link, there is a particular period in its life span, when it is most effective to undertake particular type of maintenance measure, beyond which the deterioration of the pavement increases rapidly.
Routine Maintenance
100 90 80 PCI
30% of life
Preventative Maintenance 75% of life Rehabilitation
60% of life Rehabilitation or Deferred Action
A B
70 60
C
50
D
40 30
E
20 10
F
Reconstruction
0 Age of Pavement
Fig 6.13 Present Serviceability Index Vs Age of Pavement The questions such as what, where and when to maintain and rehabilitate have been very general in pavement management system. The PMMA Maintenance scheduler will give you a fair answer for the question “when? & what?”. The strategy where developed by using optimization techniques to maximize the benefit with in a given set of constrains. In Fig 6.14 the road stretch from Arayadathupalam to Eranhipalam of Calicut Mini-Bypass is taken for illustration. Apart from the input given through the GUI, the other required data have been taken directly from database.
79
Fig 6.14 Maintenance scheduling - output 6.1.5 Maintenance Prioritization Timely maintenance or rehabilitation for every good link may not be possible because of limited fund, material shortages, environmental restrictions and so forth. Hence the need for prioritising the stretches is of principal importance. In this work prioritising techniques like Index Ranking Method and Percentile Ranking Method are considered. The prioritization was done for each road type separately. Based on the fund available per year, the road stretches to be considered for Maintenance in each year will also be calculated based on the total kilometerage of the considered stretches and the rate for maintenance/kilometer length. In Fig 6.15 prioritisation of MDR’s are taken for illustration.
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Fig 6.15 Maintenance prioritisation of MDR’s
Fig 6.16 Maintenance prioritisation of SH’s 81
The vehicle operation cost is directly proportional to the distance traveled and the travel time. So it is necessary to make sure that the vehicles are able to use the non circuitous routes.
Normally most of the vehicles will be using those path which are in good surface
conditions and having better serviceability (Say, NH’s, SH’s), even though the route may be more circuitous. Till now there is not much effective methods used to include this fact in a PMMS analysis. In this work an attempt had been made to incorporate this, utilizing the functions in GeoMedia like buffer-zones, closest path and so on. Twenty-eight major centers in Calicut where identified, based on their population density and expert opinions. A buffer-zone of around 10 to 12 km (based on their importance) where made around each major center. All other major centers coming in the buffer zone were found out and the closest routes from the major center considered to all other major centers inside the bufferzone were also found out, see Fig 6.17. Likewise it was repeated for all the 28centers. The roads coming in these closest paths were given priority for widening activities, Preventive maintenance and other routine maintenance works.
Fig 6.17 BufferZones & Closest path around Koduvally 82
Based on the rankings values, a thematic map was made to show the priority rank for maintenance of the stretch. For an available fund per maintenance works, the total length of road that can be maintained with that fund was calculated, and the road network was made in to different cluster priority groups.
Stretches for maintenance where hence find out using the
priority. In Fig 6.18 a four level cluster prioritisation of MDR’s in Calicut.
Fig 6.18 BufferZones & Closest path around Koduvally 6.1.6 BBD Overlay Design Benkelman
Beam
Deflection
analysis
is
used
to
evaluate
the
strengthening
requirement of existing flexible road pavements. Performance of flexible pavements is closely related to the elastic deflection of pavement under the wheel loads. The deformation or elastic deflection under a given load depends upon subgrade soil type, its moisture content and compaction, the thickness and quality of the pavement courses, drainage conditions, pavement surface temperature etc. In Fig 6.19 the road stretch from Arayadathupalam to Eranhipalam of Calicut MiniBypass is taken for illustration. Apart from the input given through the GUI, the other required data have been taken directly from corresponding Microsoft Access Database. The methodology adopted for the calculation where as given by IRC: 81-1997.
83
Fig 6.19 BBD Overlay design-output 6.1.7 Overlay Cost Calculation It is necessary to know the overlay cost of a selected stretch, for various combinations of over lay materials. This GUI provides the user a flexible and friendly overlay cost calculation method. In Fig 6.20 the road stretch from Kunnamangalam to Augustianmuzhi (MDR No 2) is taken for illustration. Apart from the input given through the GUI, the other required data have been taken directly from corresponding Microsoft Access Database. The result from Overlay cost calculator is not written to any access table it is only to try and see the rate for various combination as needed. The rates entered are the prevailing PWD rates. For more reliable calculation we had to update the rates periodically.
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Fig 6.20 Overlay cost calculation -output 6.1.8 Other Outputs The important other types of outputs are Charts and Reports. There are already so many customised commands for GeoMedia available in the internet. Among them is one for making chart from the data window entries which can be either an attribute or a dissected value using GeoMedia analysis tools. Fig 6.21 shows the window for this command.
Fig 6.21 Customised Window for chart output 85
6250 6300 6350 6400 6450 6500 6550 6600 6650 6700 6750 6800 6850 6900 6950 7000 7050 7100 7150 7200 7250 7300 7350 7400 7450 7500 7550 7600 7650 7700 7750 7800 7850 7900 7950 8000 8050 8100 8150 8200 8250 8300 8350 8400 8450 8500 8550 8600 8650 8700 8750 8800
Area of Cracks (%)
6250 6300 6350 6400 6450 6500 6550 6600 6650 6700 6750 6800 6850 6900 6950 7000 7050 7100 7150 7200 7250 7300 7350 7400 7450 7500 7550 7600 7650 7700 7750 7800 7850 7900 7950 8000 8050 8100 8150 8200 8250 8300 8350 8400 8450 8500 8550 8600 8650 8700 8750 8800
Area of Ravelling (%)
Ravelling
(Arayedathupalam-Eranjipalam)
4
3.5
3
2.5
2
1.5
1
0.5
0
Chainage (m)
Fig 6.22 Bar Chart showing Raveling details along the stretch (Arayedathupalam-Eranjipalam)
Cracks
10.00
9.00
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Chainage (m)
Fig 6.23 Bar Chart showing Crack details along the stretch
86
0.00
6250 6300 6350 6400 6450 6500 6550 6600 6650 6700 6750 6800 6850 6900 6950 7000 7050 7100 7150 7200 7250 7300 7350 7400 7450 7500 7550 7600 7650 7700 7750 7800 7850 7900 7950 8000 8050 8100 8150 8200 8250 8300 8350 8400 8450 8500 8550 8600 8650 8700 8750 8800
0
6250 6300 6350 6400 6450 6500 6550 6600 6650 6700 6750 6800 6850 6900 6950 7000 7050 7100 7150 7200 7250 7300 7350 7400 7450 7500 7550 7600 7650 7700 7750 7800 7850 7900 7950 8000 8050 8100 8150 8200 8250 8300 8350 8400 8450 8500 8550 8600 8650 8700 8750 8800
Area of Patchwork (%)
Area of Potholes (%)
Potholes
(Arayedathupalam-Eranjipalam)
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
Fig 6.24 Bar Chart showing Potholes details along the stretch Chainage (m)
Patchwork
(Arayedathupalam-Eranjipalam)
30.00
25.00
20.00
15.00
10.00
5.00
Chainage (m)
Fig 6.25 Bar Chart showing Patchwork details along the stretch
87
Figures 6.22 to 6.25 shows the various out puts from the attribute of the road stretch from Arayadathupalam to Eranhipalam of Calicut Mini-Bypass. Likewise we can get outputs in various forms of graphs 2D’s or 3D’s. All the PMMA results can be directly given for printing and can be made in the form of a report, which will be very useful for the decision makers to come across a conclusion. 6.2 CONCLUSIONS This chapter gives a brief review about the outputs and other results of GPMMS. Important functions of GeoMedia like Thematic Mapping and PMMA Analysis were also briefly discussed. Besides this a brief description about the output features are also included.
88
Chapter 7
CONCLUSIONS, LIMITATIONS & SCOPE OF FUTURE WORK 7.0 GENERAL This chapter contains the important features like summary, conclusions, limitations & scope of future work. 7.1 SUMMARY AND CONCLUSIONS A Pavement Maintenance & Management System for a the whole of Kozhikode District was developed using Intergraph’s GeoMedia Professional, GeoMedia Transportation Manager 5.2 version and GeoMedia Pavement Maintenance & Management Assistant. ?? Road Network as well as Water bodies and Rail of whole Kozhikode District were digitised. ?? Prepared map can be used as the base map for all present and future analysis like LRS and Dynamic Segmentation. ?? Database was created with all the available details of the road network like Inventory data, Construction data, Traffic data, Condition data, etc. ?? A plug-in software, GPMMA which contains user-friendly menus has been developed to present PMMS results to justify the decisions made. ?? This work shows that a PMMS which is based on the direct integration between PMMA and GeoMedia Professional can be used to facilitate the decision making process for managing pavements. ?? The Management System developed is capable of handling a large network, and hence it can even be used at the network level. ?? A centralized Calicut district PMMS data collection should be initiated involving all the Sub-Divisions in Calicut. ?? The results of the present study can be used for the selection of type and thickness of material for overlay on different subgrade soil economically without sacrificing the safety of the road structure. ?? The VOC per year can be calculated, the program is capable of prediction of appropriate period for strengthening and life cycle of .different alternatives and choosing the best alternative for which the total transportation cost is the least. 89
?? The Net Present values (NPV) of benefits for the three strategies considered were found out and the most economic one for various design life period was selected. ?? The best time for Preventive maintenance as well as for Regular maintenance was also found out by optimizing the NPV of the corresponding benefits. ?? Thematic maps, Charts and Reports for various useful attributes were developed for further decision making. 7.2 LIMITATIONS AND SCOPE OF FURTHER WORK The present study is an essential requirement for project planning and budget allocation. Comprehensive data is not available for all stretches; even this data is not available with the Public Works Departments. The data available is also spread over a number of organisations. As a scope of future work Data Entry forms can be published over internet and can be get filled from those various organisations from there office itself. These data can be retrieved at a later time. In this study an attempt is made to give some of the methods for setting priority of the projects for maintenance and rehabilitation. These methods can be still be improved by combining subjective aspects with objective information more effectively by adopting goal programming and Monrovian decisions process analytical hierarchy techniques. The plug-in software developed for PMMS analysis is in the first phase. More functions as well as modification for the present work can be made based on the needs. The data collected was old for most of the stretches and only one time data per stretch was available for analysis. 7.3 CONCLUSIONS This chapter gives a brief review about the Summary and other conclusions of GPMMS. The limitation and the scope of future work were also included in this chapter.
90
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Pavements”, IRC
Journal Vol.53-i, Page 93-143. 3. Ali G. A. and Al-Qatabi Kh., "Efforts in Road Maintenance Management in Oman: Development and Sensitivity Analysis", in Proceedings International Conference on New Horizons in Roads and Road Transport, Vol. 1, ICORT-95, December 1995. 4. Dr. S S Jam, Dr. A K Gupta, Dr. S K Khanna & Dayanand (October 1996), Development of Maintenance and Rehabilitation Investment Strategy for Flexible Pavements”, IRC Journal Vol 57-2, Page 367-418 5. Sood V. K., “Highway Maintenance Management System – An Overview”, in Proceedings International Conference on New Horizons in Roads and Road Transport, Vol. 1, ICORT-95, December 11-14, 1995. 6. Maj. CR Ramesh, B P Nityananda, Y.S.Madvesh & Dr. C E G Justo (October 1994), “Test Crack studies on Bituminous overlay over cracked concrete Pavement using Geofabric”, IRC journal Vol 55-2, Page 2 19-254 7. S.
Chakarbarti,
Standards Model
MSRaKwat
&B.
Mondal
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Design
and
Maintenance
(HDM) calibration and adaptation of Indian conditions’ IRC
Journal, Volume 56-1, April 1995, Pages 75-101 8. K.P. George, “MDOT Pavement Management System: Prediction Models and Feedback System” The University of Mississippi, October 2000 9. V K Sood & B M Sharma (November 1996), “Development of Pavement Deterioration Models for Indian Conditions”, IRC Journal Vol 57-3, Page 481-528 10. Omar G. Smadi, Zachary N. Hans, Tom Maze “Iowa Pavement Management Program Database: Integration and Delivery” Center for Transportation Research and Education, ISU Research Park, 2625 N. Loop Drive, Suite 2100, Ames, Iowa 50010
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11. Turki I Al. Suleiman (Obedat) & Azm. S Al-Homoud (September 1996), “Development of Models of Pavement Characteristics on Pavement Condition”, IRC Journal, Vol 57-1, Page 201-219 12. S C Sharma & R K Pandev (December 1997), “Road Development & Maintenance Investment Decision Model based on Indian Research”, IRC Journal Vol 58-3, Page385-425 13. B Balabhaskara Reddy & Dr. A Veeraragavan (November 1998), “Methodology for Sample size Detennination in Pavement performance Data Collection”, Indian Highways, Vol 26 (11), Page 15-27 14. Maj. CR Ramesh, Dr. A Veeraragavan, R Sridhar & CL S Pichika (October-1999), “Life Cycle Analysis of Pavements for Planning Maintenance Budget”, IRC Journal Vol 60-2, Page 323-354 15. Dr. B. Bhaskara Reddy, K Nagaraju & Dr. A Veeraraghavan (1999) ‘Practical applications on flexible pavement deterioration models’ Highway Research Bulletin No:61, December 1999, pages 21-68. 16. S.SJain, Dr. M Panda and Dr. Sanjiv Aggarwal, Application of HDM-4 for network level pavement management system for Indian National Highway network. CE & CR, December 2004, Page no.46-55. 17. I R Arya, C. Kamaraj &Agia Ram. Maintenance Cost Study on flexible pavements in India. Highway Research Bulletin, No.61, December 1999, Page 101-1 10
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