Intelligent Building Research-A Review

Automation in Construction 14 (2005) 143–159 www.elsevier.com/locate/autcon

Review article

Intelligent building research: a review J.K.W. Wong a,*, H. Li a , S.W. Wang b a

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong Department of Building Services Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

b

Abstract

Within the last two decades, substantial amount of literature on intelligent building has been generated. However, there is a lack of systematic review of existing research efforts and achievements. A comprehensive review on existing research provides great benefits to identify where more efforts are needed and therefore the future research directions. For this purpose, this paper reviews the literature related to the subject area of intelligent building. Our review indicates that previous research efforts have dealtt main deal mainly ly with three rese research arch aspe aspects cts inclu including ding adva advanced nced and inno innovativ vativee intel intelligen ligentt tech technolo nologies gies rese research arch,, perfo performan rmance ce evaluatio eval uation n meth methodol odologie ogiess and inve investme stment nt eval evaluatio uation n anal analysis. ysis. It is also iden identified tified that amon among g the three research aspects, aspects, relatively less literature has been found addressing the issues of investment evaluation of intelligent buildings. Based on a comprehensive literature review, the paper also summarizes a few future research directions, which are useful to researchers working in this important area. D 2004 Published by Elsevier B.V. Keywords: Intelligent building; Definition; Performance evaluation; Investment evaluation; Net present value; Life cycle costing analysis; Cost benefit analysis; Analytical hierarchy process; Fuzzy set theory

1. Introduction

The word ‘intelligent’ was first used to describe buildings in the United States at the beginning of the 1980s. The concept of ‘intelligent building’ was stimulated by the development of information technology [47,56] and increasingly sophisticated demand for ‘comfort living environment and requirement for increased occupant control of their local environments’ [94].. Research on intelligent building has been con[94] ducted ubiquitously and research results have been published in many academic journals. Much research * Corresp Correspond onding ing aut author hor.. Tel. el.:: +852 +852-27 -276666-511 5111; 1; fax fax:: +85 +85222764-3374. 0926-5805/$ - see front matter D 2004 Published by Elsevier B.V. doi:10.1016/j.autcon.2004.06.001

work has been focusi focusing ng on the discussion of intelligent building technology development and performance evaluation methodologies. However, little literature has been devoted to addressing investment evaluation techniques of intelligent buildings. Also, there exists insufficient information and support for investment decision-making at the conceptual stage of intelligent building development. The growing investment on intellig inte lligent ent buil building dingss and the gre greater ater dem demand and for dem demononstratingitsprofitabilityofintelligentbuildinghaveledto the inve investig stigati ation on for meth methods ods and tec techniq hniques ues that can be of assistance in evaluating intelligent building investments, preferably preferably at the conceptual stage. The purpose of this paper is to provide a succinct and sys system temati aticc rev review iew of the exi existi sting ng res resear earch ch in

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J.K.W. Wong et al. / Automation in Construction 14 (2005) 143–159

intelligent building in order to identify and suggest future research directions. This paper begins with the discussion of the definition of intelligent buildings. Then, the paper summarizes current research areas in intelligent building into three sections. The first section provides an overview of research in intelligent building. The second section presents methodologies for investment evaluation for investment evaluation of intelligent building projects. The third section presents future research directions.

2. Definitions of intelligent building

There have been a myriad of academic and technical literature discussing the definition of intelligent buildings. According to the research conducted by Wigginton and Harris [94], there exist over 30 separate definitions of intelligence in relation to building. Early definitions of intelligent building focused almost entirely centered on technology as pect and did not suggest user interaction at all [47,72,94]. Cardin (1983, cited in Ref. [94]) defined intelligent building as ‘one which has fully automated building service control systems’. The Intelligent Building Institution in Washington (1988, cited in Refs. [29,56]) defined intelligent building as ‘one which integrates various systems to effectively manage resources in a coordinated mode to maximize: technical performance, investment and operating cost savings, flexibility’. The purely technological definition of intelligent building has been criticized by many researchers. For example, DEGW in mid-1980s found that buildings which were unable to cope with changes in the organizations that occupy them, or in the information technology that they use, would become prematurely obsolete or require substantial refurbishment or demolition. Authors such as Robathan [76], Loveday et al. [59], Preiser and Schramm [74] and Wigginton and Harris [94] suggested that intelligent buildings must respond to user requirements. According to Clements-Croome [29], there has been growing awareness that the services systems and work process management of a building have close relationships with the well-being of human. The building environment affects the wellbeing and comfort of human in the workplace, and in turn it influences

human’s productivity, morale and satisfaction. Some authors [9,18] suggested the intelligent building accentuates a ‘multidisciplinary effort to integrate and optimize the building structures, systems, services and management in order to create a productive, cost effective and environmentally approved environment for the building occupants’. Most recently, a number of authors have extended the definition of intelligent building and have added ‘learning ability’ and ‘performance adjustment from its occu pancy and the environment’ in the definition [94,98]. They proposed intelligent building is not only able to react and change accordingly to individual, organizational and environmental requirement, but is also capable of learning and adjusting performance from its occupancy and the environment. On the other hand, it appears that different intelligent building professional bodies also have different understanding of intelligent building. So et al. [86] pointed out both the intelligent building institutes in the United States and the United Kingdom have inconsistent interpretation of building intelligence. The Intelligent Building Institute of the United States defines an intelligent building as ‘one which provides a productive and cost-effective environment through optimization of its four basic elements including structures, systems, services and management and the interrelationships between them’ [94]. In contrast, the UK-based European Intelligent Building Group defines an intelligent building as ‘one that creates an environment which maximizes the effectiveness of the building’s occupants, while at the same time enabling efficient management of resources with minimum life-time costs of hardware and facilities’ [94]. The difference indicates the UK definition is more focused on users’ requirements, while the US definition is more concentrated on technologies. In addition, So et al. [86] argued that ‘intelligent buildings are not intelligent by themselves, but they can furnish the occupants with more intelligence and enable them to work more efficiently’. Moreover, most existing definitions of intelligent buildings are ‘either too vague to be useful guidance for detailed design which either places an unbalanced focus on technologies only or do not fit that culture of Asia’. The need of a precise intelligent building definition is critical as ‘without a correct definition, new building will not be optimally designed to meet the next


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century’ [86]. In response to this, So et al. [86] suggested a two-level strategy to formulate an appro priate intelligent building definition. The first level comprises nine ‘Quality Environment Modules (QEM)’ (M1– M9) and the second level includes three areas of key elements which are functional requirements, functional spaces and technologies. Chow [27] proposed the inclusion of additional modules (M10) as supplement to the existing nine modules in order to deal with the health issues for buildings. The revised ‘QEM’ (M1–M10) includes:

search efforts have dealt mainly with three research streams, including advanced/innovative technologies, performance evaluation methodologies and investment evaluation analysis. Fig. 1 shows the framework for intelligent building research and the connections between the various research streams included. These three research streams are further described in subsequent sections.

M1: environmental friendliness—health and energy conservation; M2: space utilization and flexibility; M3: cost effectiveness—operation and maintenance with emphasis on effectiveness; M4: human comfort; M5: working efficiency; M6: safety and security measures—fire, earthquake, disaster and structural damages, etc. M7: culture; M8: image of high technology; M9: construction process and structure; and M10: health and sanitation.

A plethora of research efforts have been placed on intelligent building technologies. Previous research efforts in this stream have been focused on the advanced development of system integration [41,87,93], network protocol [8,21,22,28,37,82,83] and building subsystem services, which include HVAC system [15,61,68,84,87,90,94], lighting system [48,70,87], fire protection system [48,87,89,90], lift system [48,64,80,87], security system [87,89,90] and communication system [38,87]. Technologically, intelligent building performs and arranges differently from a conventional one. First, intelligent building technologies are characterized by a hierarchical presentation of system’s integration [9,23,24,31,41,43,47,82,87]. Building systems and structure integration, as pointed out by Bradshaw and Miller [20], are to provide the ‘qualities that create a productive and efficient environment such as functionality, security and safety; thermal, acoustical, air-quality and visual comfort; and building integrity’. According to Carlini [23,24] and Arkin and Paciuk [9], many intelligent buildings comprise three levels of system integration which include:

n

n n

n n n

n n n n

Each of 10 key modules mentioned above will be assigned a number of key elements in an appropriate order of priority. So et al. [86] redefined intelligent building as one which ‘designed and constructed based on an appropriate selection of ‘Quality Environmental Modules’ to meet the user’s requirements by mapping with appropriate building facilities to achieve long term building values’. So and Wong [85] suggested that the new definition has two folds, which enable the consideration of technologies, and the needs of users. Also, this new definition gives designers a clear direction and sufficient details to enable a high quality intelligent building design consistent with intelligent building definition, and to provide a fair platform for users and the general public to evaluate the performance of an intelligent building.

3. Previous intelligent building research

An overview of literature related to intelligent building research works indicates that previous re-

3.1. Research in advanced and innovative technologies

n

n

The top level which is dealt with the provision of various features of normal and emergency building operation as well as the communication management; The middle level which is performed by the building automation system (BAS), energy management system (EMS), communication management system (CMS) and office automation (OA) system, which control, supervise and coordinate the intelligent building subsystems. BAS would perform the function of energy management

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Fig. 1. Taxonomy of research in intelligent building.

n

system and groups all relevant subsystems in some occasions [43,60]; and The bottom level which contains subsystems including heating, ventilation and air-condition-

ing (HVAC) systems, lighting system, fire protection system, vertical transportation system, security system and communication system.


Moreover, intelligent building allows interaction and integration among building subsystem services [48,70,84,87]. System integration is the process of ‘connecting systems, devices and programs together in a common architecture so as to share and exchange data’. Arkin and Paciuk [9] suggested the key to the effective operation of intelligent building was not related to the sophistication of the building services systems, rather it was the integration among the various systems, between the system and the building structure. Examples of major intelligent building systems interaction include [48,87,90,93]: n

n

n

n

n

Fire alarm system would be integrated with other building systems, such as HVAC, lighting and security through BAS. HVAC systems can be used to prevent the smoke from spreading by opening exhaust dampers and closing outdoor air intake dampers of the fire floor if there is a fire on one floor of building; Vertical transportation system is interacted with fire alarm or the security systems in order to define the number of elevators required, the mode of operation and in some instances the accessible floor levels; Fire alarm program would be interfaced with security to release specific locked doors under alarm conditions; Security system is interfaced with the lighting and HVAC subsystems to define activation of necessary lighting paths and the specific room occupy mode; and Facility management is integrated with BAS.

Ivanovich [53] reviewed current research in intelligent building technologies. Contemporary research efforts have been attempting to develop software with the use of automated diagnostic tools introducing neural networks, fuzzy logic, as well as other software-intensive, artificial-intelligence-based technologies designed to detect problems, such as building services systems/components, sensors [92] and control devices that are hard detected by human-beings. The IEA BSC research program Annex 25 [51] and Annex 34 [33], involving over 10 universities and research institutions from different, conducted extensive research on the methodology, strategy and application of fault detection and diagnosis in HVAC

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systems. Many researchers also paid efforts in developing intelligent control method to be used in modern building management system for improving and optimizing the energy and environmental performance of buildings [91]. In addition, the application of wireless technologies with buildings or networking building systems is also a popular research area [37] which has currently attracted attentions of many researchers and industry practitioners. Typical intelligent building technologies are summarized in Table 1. 3.2. Research in performance evaluation methodologies

Apart from intelligent technologies research and development, there have been substantial amount of research devoting to evaluating intelligent building. Serafeimidis [81] considered the evaluation process as a feedback mechanism aimed to facilitate learning, while [100] considered the process of evaluation as ‘a series of activities incorporating understanding, measurement and assessment. It is either a conscious or tacit process which aims to establish the value of or the contribution made by a particular situation. . . and can relate the determination of the worth of an object’. Building performance evaluation is a crucial procedure which offers feedback function on the performance of building materials and com ponents for future improvement and reference ([73]: cited in Ref. [74]). Different authorities have tried to develop evaluation models to assess the performance of intelligent building [9,47,73,74,85,98]. Early performance evaluation models were developed by Manning in 1965 and Markus et al. in 1972 [74]. Preiser and Schramm later (in 1997) improved their evaluation models and pro posed an ‘integrative building performance evaluation framework’ to evaluate and review the stance in all six major phrases of building delivery and life cycle including planning, programming, design, construction, occupancy and recycling. Many similar studies have also been conducted attempting to measure the level of intelligence that a building exhibited and to set up criteria for selection of the best intelligent building [47]. Preiser [75] developed the ‘post-occupancy evaluation process model (POE)’ in order to determine the intelligence level of intelligent buildings. The POE process model is generally executed in three stages.

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Table 1 Intelligent building technologies and systems (adapted from Refs. [21,37,38,48,80,87,89]) Intelligent building Software/program systems

Hardware/device

BAS

n

Network control units, operator workstations, network expansion units, application specific controllers and sensor system, etc.

n

n

Air handling unit (AHU) controller; distributed controller; fully air-conditioned variable air-volume system (VAV) controller, centralized chiller plant; heating/ cooling elements located across the occupancy zones of the floor; and other devices such as pressure, temperature, flow sensors, etc.

n

n

n

HVAC system

Lighting system

Standard Protocol (i.e. BACnet, LonWorks, etc.) Direct Digital Control (DDC)

Software program such as Duty Cycle Program, Unoccupied Period Program, Chillers Optimum Start-stop Program, Unoccupied Night Purge Program, Enthalpy Program, Load Reset Program, Zero-energy Band Program and Heating/cooling Plant Efficiency Program; and n Other specific programs for HVAC operation n Occupied – unoccupied lighting control program (time-based lighting control program); and other specific programs for lighting control n

n

Vertical transportation system

n

Specific programs for lift operation and monitoring

n

Fire protection system

n

Specific programs for fire protection and detection

n

Security system

n

Specific programs for security protection, detection and safety system

n

Private automatic branch exchanges (PABX), integrated service digital network (ISDN), local area network (LAN) and Internet system, and other software program enabling remote building control and monitoring

n

Communication system

n

n

Charge-coupled device (CCD) cameras, intelligent lighting controller (ILC)/lighting management system controller, motion detectors, light sensor, and other device such as touch switch, etc. Lift sensors and passenger detectors, neural network-based controller, and other devices such as CCD camera, etc.

Intelligent fire controller (IFC), fully addressable automatic fire alarm and detector (sensor) system Intelligent Access Controller (IAC); CCTV surveillance, e-Card access, motion detectors, intruder alarm system and special presence detection sensors Traditional telephone systems; aerials, transmission cables, amplifiers, mixers, splitters, repeat amplifiers, attenuators and final TV outlets; and dish antennas for satellite communication

Recent development

n

n

Use of Web-enabled devices for the building automation system which allows remote building control and monitoring by interaction of the central BAS workstation with the remote dial-up system via modem. Computer vision system (allows counting of number of residents within an air-conditioned space and informs the control system of the distribution of the residents) Internet-based HVAC system allows authorized users keep close contact with the BAS wherever the user is

Internet-based lighting system

Advanced drives and artificial intelligence based supervisory control n Computer vision technologies have been used in intelligent building in counting the number of passengers and to aid lift control n Sophisticated fire alarm systems which include stand-alone intelligent fire alarms and intelligent initiating circuit sensors n Internet-based security system n

n

Use of Web-enabled devices which allows remote building control and monitoring


First, to develop compatible data collection instructions in the conceptual phase; second, to apply and pilot testing of evaluation instruments in field studies on intelligent office building; third, to carry out comparative analysis of data collected and development of recommendations and guidelines for the utilization of the data-gathering instruments worldwide. Preiser and Schramm [74] applied the POE process model to evaluate intelligent building in the cross-cultural context and suggested that the POE model could ‘enhance building performance evaluation in intelligent buildings especially in a long-term, continuing basis’ because the evaluation system allows the ‘tracking of performance of new high-tech systems and their effects on building occupants as well as the effectiveness of these systems in general’. Without a rating system, it is difficult to classify and justify the level of intelligence of intelligent buildings. Therefore, there have been many studies trying to develop rating systems for the intelligent building. One of the essential performance rating systems was the ‘building rating method’ developed by DEGW in 1995 based on the ‘building IQ rating method’ and the ‘building quality assessment’ (developed by Intelligent Buildings Europe Work). The method employs five categories of factors which are combined to produce overall assessments of the suitability of intelligence provided by the subject building. On the other hand, Arkin and Paciuk [9] developed a ‘‘Magnitude of Systems’ Integration’’ Index (MSIR) to examine the level of systems’ integration of intelligent buildings according to the extent of integration among their systems, and between systems and the building’s structure. This assessment methodology can be used for evaluation and comparison of single aspect of building’s intelligence and to create a unified index for evaluation of system’s integration in intelligent buildings. This model has been adapted by other researchers in the intelligent building performance evaluation such as Yang and Peng, 2001 [98]. More recently, the Asian Institute of Intelligent Buildings [85] constructed a quantitative assessment method, namely the intelligent building index (IBI). The individual assessment index for this methodology was originated from the nine ‘Quality Environment Modules’ (M1– M9), each index possesses a score which is a real number (within the range of 1–100) calculated by a conver-

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sion formula. A building can be ranked from A to E to indicate the overall intelligent performance. A summary of the hierarchical development of intelligent building assessment methods is illustrated in Table 2. However, some of the performance evaluation models have been criticized for fraught with problems of fairness and partially su bjective assessment. According to So and Wong [85], the shortcomings identified are in the following areas: n

n

n

n

n

Inconsistence between final assessment index and human thinking; Slightly different assessment in terms of the weight or priorities of elements for each individual intelligent building project; Important elements do not receive sufficient emphasis and less important elements are ignored; Current assessment method do not contain a learning curve and unable to evolve from time to time; and Binary approach of each rule or question is not a good practice. The mere provision of a particular facility, or system, in a building is not a conclusion of its intelligence.

In response to insufficiencies of existing performance evaluation models, researchers have currently attempted to construct a set of objective evaluation model in order to reflect the performance and justified price of intelligent building. For example, So and Wong [85] suggested the criteria for an efficient performance evaluation model which states as follows: n

n

n

n

Encourage well-balanced performances, emphasize important elements but, at the same time, discourage total ignorance of minor subjects; Consistent with human preferences while random judgments must be minimized; Practically extension of the utility theory, measuring all levels of building performances, weighting as attributes and combining them systematically, to form the overall intelligent building index; and Have learning ability and able to be upgraded and modified from time to time.

Further research is needed to develop performance evaluation models that can meet the above criteria.

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Table 2 Intelligent building performance assessment methods (adapted from Refs. [19,27,85]) Year

Research agency

Details of assessment methods

1983 1985

DEGW DEGW

1988

Camegie Mellon University

1991

Kuala Lumpur City Hall

1992

Intelligent Building Research Group

1992

Intelligent Building in Europe Project

1992– 1994

Holland, New Zealand and Canada

1995

DEGW

1997

Arkin and Paciuk

Orbit 1: multi-client study (building use studies) Orbit 2: degree of matching between the building, the organizations occupying it and IT (using nine key organizations issues and eight key IT issues) Measures of quality, satisfaction and efficiency (using six performance criteria and five system integration criteria) Guidelines specifying features of office buildings based on location, design, systems and services (six-star, five-star and four-star). Building IQ rating method: considering needs (10 for individual user, 15 for organizational, 6 for local environmental and 5 for global environmental). Project was not completed Intelligent building rating: key questions based on building shell characteristics, services and applications (not published) Development of three evaluation methodologies to evaluate the quality of buildings and the suitability for different tenant types: real estate norm, building quality assessment, and serviceability tools and methods Building rating method: involving five sections (A –E) including namely (A) building site/location (7 items), (B) building shell issues (14 items), (C) building skin issues (3 items), (D) organizational and work process issues (11 items), and (E) building services and technology (12 items) where the result is an overall score by combination of all items Magnitude of systems’ integration: to determine the level of systems’ integration in intelligent buildings. ‘‘MSI’’ was used to evaluate as objective index that quantifies and summarizes the various aspects of integration (Eq. (1)). A simple cumulative index is obtained by summing all the ratings ( Ri) attributed to the integration features of various systems in the building, and then dividing the sum by the number of available systems; NS

X

Ri

MSI ¼

1998

Harrison et al.

2002

Preiser and Schramm

2002

So and Wong (AIIB)

i¼t

NS

ð1Þ

Building rating method (results matrix): based on the building rating method constructed by DEGW (1995) and demonstrated its use in evaluations through the two plots of the categories (A–B/C, D–E). The categories are each dimensioned as percent and the four quadrants of each plot are considered to indicate the building’s performance Post-occupancy evaluation process model (POE): three phases of process model include: n First phase: planning POE involves liaison with client, performance criteria and planning the data collection process; n Second phase: conducting POE involves methods and instruments—initiating data collection, monitoring data collection and analyzing data; n Third phase: applying POE involves reporting findings, recommending actions and reviewing outcomes Intelligent building index (IBI): quantitative assessment methods for IB which was originated from the nine ‘Quality Environment Modules’ (M1–M9)


3.3. Research in investment evaluation analysis

Another stream of research has been focusing on evaluating economic and financial aspects of intelligent building, which is referred as the investment feasibility evaluation of intelligent building projects. Investment to intelligent building has increased dramatically in the Asia Pacific region in recent years [62]. A number of authors [26,94] suggested that the growing interest in investment has been credited to potential benefits that an intelligent building delivered to the investors. These benefits include ‘reducing operating and occupancy costs; providing a flexible, convenient and comfortable environment for occu pants; offering advanced technological facilities tog e th e r w i th r e du c e d m a in t en a nc e c o st s ; a n d improving operational effectiveness, efficiency and marketability’. However, many investors have the mentality of ‘high-risk and low-return’ towards investment in intelligent building. This mentality may be explained by following reasons [57,88,98]: n

n

n

Investors are unaware of the total cost in relation to the built asset that their business required; Investors are failed to observe the connections between initial capital cost, operating and maintenance cost as well as the equipping of the building with automation and communication; Investors are lack of information and support for investment decision-making at the conception stage of intelligent building development.

There is a growing demand for tools to support intelligent building investment decision-making. For example, Choi [101] pointed out that the financial viability of intelligent building is the major concern of the developers. Wong et al. [95] argued that many investors would consider cost and benefit when they decide whether it is worthwhile to invest in a new technology. Also, Mawson [63] remarked the necessity of justifying the intelligent building investment on the basis of cost and benefit related to the users or investors’ business priorities in order to illustrate the profitability of intelligent building investment. Despite all these, however, there is still a lack of generally accepted tool for supporting intelligent building investment decision making. Many prevailing investment evaluation techniques were extended

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conceptually and functionally from the tradit ional investment decision making techniques [47,95]. The apparent insufficiency of traditional investment evaluation t echniques has been identified by many authors [49,99] as these techniques have ‘failed to reflect the dynamic and constantly hanging reality of businesses’. These techniques have also failed to provide a comprehensive picture of developers’ returns on investment. It is for this reason that the development of new investment evaluation model has become the focus of intelligent building research.

4. Investment considerations and evaluation techniques for intelligent building

Traditionally, investors use various types of methods to assess the financial feasibility of a proposed project. Some property investors review historic performance and request assessment of future performance of property portfolios in formulating the investment strategy decision [3]. Others apply evaluation techniques to review t he project feasibility and adherence to their goals [4]. Many of the investment evaluation techniques aims to compare project benefits against costs in an attempt to ‘determine acceptability, and to set a ranking order among competing projects’ [5]. Therefore, before the evaluation of investment project, the project costs and benefits need to be identified and classified. Without such identification works, it is difficult to assess and judge the financial viability of the project. Many authors have attempted to identify and classify the cost components of intelligent building. Flax [39] emphasized the importance of technologies, data systems, and telecommunication in the total building and fit-out capital costs of intelligent building. Myers [70] identified six types of costs including in an intelligent building project: equipment costs, installation costs, commissioning costs, spares costs, special software costs and staff/training costs. In the evaluation of project costs of intelligent building, Hetherington [48] suggested the assessment should be made on ‘a project by project basis taking into account the overall project size, the number of intended work stations and the nature of each intelligent building system to be implemented’. Wong et al. (2001) [95] attempted to analyze and examine the

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project costs of both intelligent building and conventional building. The findings suggested the total project costs of intelligent building were generically higher than that of conventional building by 8%, and the expenditures on building services were higher than conventional building project by 5%. Simply because, the intelligent building involved more application of advanced technological materials and components in building services systems than the conventional building. Apart from project costs, many authors have tried to evaluate the benefits generated by the intelligent building (e.g., [8,10,22,31,32,39,47,48,58,70,88] . Flax [39] pointed out intelligent building can minimize the cost on all ongoing expenses (i.e., power, airconditioning, environmental controls) and reduce relocation cost of individuals and services, or large group revisions. Suttell [88] suggested intelligent building can improve the productivity of building operations, and reduce energy consumption of the facility which can be quantified in dollar terms. In the area of investment evaluation, a plethora of evaluation techniques have been developed to assist investors to examine and evaluate the economic desirability of projects. Remer and Nieto [78,79] identified 25 different techniques for project investment evaluation. Among these techniques, net present value (NPV), internal rate of return (IRR) and pay back period (PB) are often used to appraise capital investment in building projects [20,54,67]. These

techniques are based on ‘time-cost-of-money’ princi ples and are used in slightly varied procedures to estimate the expected investment monetary returns [67]. However, research related to investment evaluation of intelligent building projects are very limited. Only a few authors or research groups (e.g., Refs. [14,54,95,98]) developed techniques and models to assist the process of intelligent building investment evaluation. Our overview of empirical studies for the intelligent building investment evaluation revealed that the three most commonly mentioned approaches are the NPV method, life cycle costing analysis (LCCA) and cost benefit analysis (CBA). A summary of the empirical studies and research efforts reviewed are illustrated in Table 3. 4.1. Net present value method

One of the most commonly used investment evaluation techniques in the construction industry today is the NPV method. The NPV is a traditional technique designed to ‘net the present value of the investment from the present value of the benefit of the project’ [4]. It examines cash flows of a project over a given time period and resolves them to one equivalent present date cash flow by using various economic evaluation factors [78]. The basic rule of net present value method is to accept the project with a positive net present value and reject if the value is negative.

Table 3 Empirical studies for IB investment evaluation summary Year

Authors/researchers

Approach/methodologies

Evaluation tools

2001

Wong et al.

NPV method

2001 (proceeding)

ABSIC Group

Net present value approach systematic assessment of financial viability of IB by comparing two alternatives: conventional and IB building Cost benefit analysis approach ‘‘BIDS’’, a multi-media decision support tool based on the CBA framework

2001 (proceeding)

Yang and Peng

2003

Lohner (cited in Ref. [54])

Life cycle costing approach accessing the design alternatives which considering all the significant costs of ownership Life cycle costing approach comparing the life cycle cost of intelligent buildings with different levels of integration approach (non-integrated building, partial integration, and full integration)

Software-based evaluation model, evaluation tool was not specified in the Report However, according to Kingston [55], NPV is also a basic tool of CBA approach NPV/discounting method

NPV method


When two or more projects are evaluated, the one with higher present value is generally selected. The NPV can be computed using the following formula [4]; where NCFi represents the net cash flow from the project at period i, k represents the capital cost and T is the project life span.

X" T

NPV ¼

f ¼1

NCFi

ð1 þ k Þ f

# X" T

À

j ¼1

I i

ð1 þ k Þ f

#

Wong et al. [95] applied the NPV technique to analyze the financial viability of two project alternatives: conventional or intelligent building. The optimal determination of building life cycles was integrated into the analysis in order to provide a comprehensive financial viability results for intelligent building. The study concluded that the intelligent building was more favorable option which had a higher property value at the end of life cycle period. Despite its simplicity, Wong et al. [95] noted that the reliability of the NPV technique can be affected by the unavailability of relevant cost and benefit data. 4.2. Life cycle costing analysis

The LCCA is another approach employed for the evaluation of intelligent building investment. Generally, LCCA serves two major functions. First, it applies in evaluation of alternatives in various aspects [34]. It is used to examine the building performance with different initial investment costs, different operating and maintenance and repair costs, and possibly different lives [42]. Second, it can be used as an asset management system throughout product’s life cycle [36]. LCCA approach is widely applied in various aspects of construction and building projects (e.g., Refs. [2,11,13,17,40,44,77,96,97]). For example, Abraham and Dickinson [2] and Bogensta¨tter [17] applied the LCCA to the prediction, optimization and quantification of the construction cost during disposal and design stages, respectively. Aye et al. [13] employed the LCCA to evaluate project investment options and make selection between competing alternatives, while Gluch and Baumann [44] used the LCCA to determine the environmental decision-making in building investment. LCCA has been employed in a number of empirical studies, as an investment evaluation technique of

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intelligent building. Yang and Peng [98] used the LCCA to assess various design alternatives. Their approach starts with the selection of design alternatives, and it then determines the capital cost and costin-use for each alternative. The cash flow for each option is converted to a common time basis for rational comparison using the NPV technique. The solution which outperforms in functions and qualit y is then recommended. In addition, Keel, 2003 [54] applied the LCCA to compare and evaluate the total investment life cycle costs of intelligent building at different levels of integration (non-integrated building, partial integration and full integration). The NPV method was used to calculate the life cycle cost of each model of integration. Results suggested that the fully integrated intelligent building had the lowest life cycle cost compared with non-integrated and partially integrated intelligent buildings.

4.3. Cost benefit analysis

The purpose of CBA is to give management ‘a reasonable picture of the costs, benefits and risks associated with a given project so that it can be compared to other investment opportunities’ [30]. CBA has been traditionally applied to fields including policies, programs, projects, regulations, demonstrations and other government int erventions [16,50]. Many capital investment projects [69] such as budget planning, dams and airports construct ion, and safety and environmental programs planning [52] have adopted the CBA approach to compare the costs and benefits. Similar to LCCA, the CBA technique relies on the NVP method as the basis for analysis [16,30,35,55,69]. In the use of the CBA technique, attention should be paid to the following aspects. First, project costs and benefits at each stage of the life cycle are discounted into the present values. Second, some studies suggest that information such as development and operating costs, and tangible benefits by time period must be obtained before performing the CBA [30]. Third, analysis results can be highly affected by the discount factors employed in the CBA. Kingston [55] and Islas et al. [52] suggested that wrong selection of discount rate would produce a great variation in benefit/cost ratio. An unworthy project may be recommended if the chosen rate is

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too low as the distant benefits are underweighted relative to near-term costs. There are only several applications of CBA in evaluat ing intelligent building investment. ABSIC Group [14] employed CBA to evaluate the investment in advanced and innovative building system. A software program, named as the ‘Building Investment Decision Support (BIDS)’ system, based on the CBA framework was developed and incorporated within a multi-media decision support tool. The analytical approach was built on a three dimensional matrix: design options, cost benefit factors, and scenarios. In general, although the ABSIC’s model was very practical and suggestive, it is difficult to interpret the nature of the methodologies based on the fact that it is a program-based analytical model. Limitations of existing techniques for evaluating investment projects on int elligent building have been recognized [4,5,49,67,99]. Many researchers pointed out that traditional evaluation methods are unable to accommodate the task of evaluating int elligent building projects. Specifically, Ho and Liu [49] critiqued that the NPV method fails to ‘respond and capture management’s flexibility to adapt or revise later decision when, as uncertainty is revolved, future events turn out differently from what management expected at the beginning’. Akalu [4] also noted that the NPV method would lead to different decision results in mutually exclusive projects. The ‘equal class of risk’ assumption in the NPV calculation for both cash inflows and outflows of projects is not practical in the real world. Moreover, Abdel-Kader and Dugdale [1] pointed out the use of ‘arbitrarily’ high hurdle discount rates in the NPV calculation for new technology investment would affect the accuracy of evaluation. Many of these criticisms indicated that traditional NPV based evaluation techniques are not capable of evaluating investment relating to advanced technologies or systems. Akin to the NPV method, there are drawbacks in the LCCA. One drawback is that the result is subject to the availability and reliability of input data due to the complexity of building process and numerous components in a building [12,44]. The accuracy of the result is highly dependent on the assumptions and estimates made whilst collecting data, as there is always an element of uncertainty associated with the estimates and assumptions.

Macedo et al. (1978) [102] summarized some major uncertainties of the LCCA: n

n

n

n

n

Differences between actual and expected performances of a system could affect future operation and maintenance costs; Changes in operational assumptions arising from modifications in user activities; Future technological advances that could provide lower cost alternatives; Changes on the price level of a major resource such as energy or manpower; and Errors in estimating relationship.

Furthermore, the LCCA fails to handle irreversible decisions and the results are biased towards the decision maker’s personal values [44]. The CBA approach has also revealed several limitations in its capacity t o evaluate building investment projects [30,46,69]. First, the standard CBA approach considers only tangible benefits and is unable to measure intangible benefits in financial terms. Second, CBA fails to include a method for coping with uncertainty dur i ng the evaluation of investment opportunities [69]. 4.4. Analytical hierarchy process

These identified limitations suggest the need to develop new methods to evaluate intelligent building investment projects. Recently, the analytical hierarchy process (AHP), an evaluation approach which com bines the basics of qualitative and quantitative research, is suggested to remedy the existing problems [25]. The AHP is a decision analyzing and structuring method developed by Saaty in 1970s [6,7,45,65,66]. AHP comprises a comprehensive framework which is designed to ‘cope with the intuitive, the rational and the irrational when the users make multi-objective, multi-criterion and multi-actor decisions with and without certainty for any number of alternatives’ [65]. AHP can be used to model a decision making framework, which assumes a uni-directional hierarchical relationship among decision levels. AHP has been applied in investment evaluation. For example, Al-Harbi [7], Al Khalil [6] and Nassar et al. [71] extended the AHP to evaluating and analyzing building and construction projects. Al-Harbi [7] applied

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AHP to the building contractor prequalification decision-making so that the client can determine the contractor’s competence or ability to participate in bidding. Al Khalil [6] employed AHP in the evaluation and selection of an appropriat e pr oject delivery method. Furthermore, Nassar et al. [71] applied AHP as a decision-making technique to select the appro priate building materials and components based on a set of user-specified criteria and their relative importance weights.

However, the usefulness of AHP in investment evaluation has also been questioned by Chan et al. [25]. They argued that AHP is basically concerned with the analytical factors evaluation. AHP (as well as the traditional evaluation methods) is based on the concept of accurate measurement and crisp evaluation (i.e., the measuring values must be exact and numerical) where exact assessment data such as investment cost, gross income, expenses, depreciation, salvage value, are difficult to obtain in real world. Abdel-

Table 4 Comparison of investment evaluation approaches Investment evaluation approaches Current approaches Life cycle costing (LCC)

Purpose/reasons Select the best design alternatives based on the life cycle costs

Recommended approach CBA

Select the best design alternatives based on comparison of cost, tangible benefit and associated risk of design alternatives

AHP

Comprehensive framework designed to cope with the intuitive, the rational and the irrational when we make multi-objective, multi-criterion and multi-actor decisions with and without certainty for any number of alternatives Basic tool NPV NPV Comparison matrices Authors [93]; Lohner, 2003 [14] Not applied in IB yet Advantages n Enable investment n Able to consider life n Considers tangible and options to be more cycle cost and intangible, quantitatively effectively evaluated tangible benefits measurable and qualitative factors n Consider the impact n Provide a reasonable n Hierarchical of all costs rather than picture of the costs, representation of a system only initial capital costs benefits, and risks n Provides more associated with a given information on the structure n Facilitate choice between competing system development and function of a system in alternatives project so that can the lower level compare it to other n Provides an overview of the actors and their purposes investment opportunities in the upper levels Disadvantages/ n Fails to handle n Considers only n Not consider any limitations uncertainty and tangible benefits assessment regarding irreversible decisions ‘linguistic terms’ n Poor availability and n No consideration of n Does not reflect the reliability of data uncertainty qualitative and subjective nature of many factors n Relies on estimated variables n Exact assessment data is difficult to obtain in real world n Biased results Conceptual confusion n

Fuzzy multi-criteria decision-making method (combining AHP and fuzzy set theory) An integration of risks financial and non-financial factors in investment evaluation. Based on AHP with fuzzy set theory

AHP and fuzzy set theory Not applied in IB yet n Able to deal quantitatively with imprecision or uncertainty n

To tackle the ambiguities involved in the process and to assure a more convincing and effective decision-making

n

Lacks empirical evidence for their applicability and wide acceptance in construction industries

156


Table 5 Literature review summary Research area

The research subject

Credible publications

Definition

Defining IB

[9,18,23,26,27,29,47, 56,59,72,77,85,86,94] [8,21,28,32,37,41,70, 82,83,87,93]

Advanced and System integration, innovative building automation technologies system and communication network HVAC system

Performance evaluation

Investment evaluation

[15,33,48,51,61,68, 70,84,87,90,91,92,94] Lighting system [48,70,87] Fire protection system [48,87,89,90] Lift system [48,64,80,87] Security system [48,87,89,90] Communication system [38,87] Evaluation and Intelligent Buildings construction of Europe Work (cited performance models and in Refs. [9,19,47,74, indexes for IB 75,98]), the Asian Institute of Intelligent Buildings (cited in Ref. [85]) Construction of investment [14,95,98]; Lohner evaluation techniques (2003, cited in and methodologies for IB Ref. [54])

Kader and Dugdale [1] also critiqued that the use of precise value in AHP does not ‘reflect the qualitative and subjective nature of many factors’. In view of the inadequacies of the traditional evaluation techniques and AHP, Chan et al. [25] proposed a ‘fuzzy multi-criteria decision-making method’, a systematic approach by combing fuzzy set theory with AHP, for the purpose of technology selection. This method overcomes deficiencies of traditional evaluation models. Similarly, Abdel-Kader and Dugdale [1] suggested the same methodology to model and evaluate investment in advanced manufacturing technology, in order to ‘tackle the ambiguities involved in the process and to assure a more convincing and effective decision-making’ [25]. A summary of methods/techniques used for investment evaluation is presented in Table 4 below.

5. Conclusions

This paper summarizes existing research in the area of intelligent building. Specifically, the paper indicates that previous research efforts have dealt mainly

with advanced and innovative intelligent technologies; performance evaluation methodologies; and, investment evaluation analysis (Table 5 listed main credible publications in these research aspects). The paper also revealed that relatively less attention has been paid to addressing investment evaluation of intelligent building. Simultaneously, the growing amount of investment in intelligent buildings in recent years has led to a great demand for better methods and techniques for evaluating investments in order to maintain investment profitability. The paper further proposes a ‘fuzzy multi-criteria decision-making method’ (FMCDCM), which com bining the use of fuzzy set theory with AHP, to overcome the inefficiencies of traditional evaluation techniques. FMCDCM has been applied in problems related to technology selection and advanced manufacturing investment evaluation. By using the FMCDCM, it is expected that ambiguities involved in the evaluation process can be minimized.

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