Intelligent Building Research-A Review

www.elsevier.com/locate/autcon

Automation in Construction 14 (2005) 143–159

Review article

Intelligent building research: a review

J.K.W. Wonga,*, H. Lia, S.W. Wangb

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

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

dealt mainly with three research aspects including advanced and innovative intelligent technologies research, performance

evaluation methodologies and investment evaluation analysis. It is also identified that among the three research 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 stimu-

lated by the development of information technology

[47,56] and increasingly sophisticated demand for

‘comfort living environment and requirement for in-

creased occupant control of their local environments’

[94]. Research on intelligent building has been con-

ducted ubiquitously and research results have been

published in many academic journals. Much research

0926-5805/$ - see front matter D 2004 Published by Elsevier B.V.

doi:10.1016/j.autcon.2004.06.001

* Corresponding author. Tel.: +852-2766-5111; fax: +852-

2764-3374.

work has been focusing 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

intelligent buildings and the greater demand for demon-

strating its profitabilityof intelligentbuildinghave led to

the investigation formethods and techniques that can be

of assistance in evaluating intelligent building invest-

ments, preferably at the conceptual stage.

The purpose of this paper is to provide a succinct

and systematic review of the existing research in

J.K.W. Wong et al. / Automation in Construction 14 (2005) 143–159144

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 sec-

tion 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 tech-

nical 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 man-

age 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 de-

molition. 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 pro-

cess management of a building have close relation-

ships 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, serv-

ices and management in order to create a productive,

cost effective and environmentally approved envi-

ronment 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 occupancy 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 intel-

ligent 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

J.K.W. Wong et al. / Automation in Construction 14 (2005) 143–159 145

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 mod-

ules (M10) as supplement to the existing nine mod-

ules in order to deal with the health issues for

buildings. The revised ‘QEM’ (M1–M10) includes:

n M1: environmental friendliness—health and ener-

gy conservation;

n M2: space utilization and flexibility;

n M3: cost effectiveness—operation and mainte-

nance with emphasis on effectiveness;

n M4: human comfort;

n M5: working efficiency;

n M6: safety and security measures—fire, earth-

quake, disaster and structural damages, etc.

n M7: culture;

n M8: image of high technology;

n M9: construction process and structure; and

n M10: health and sanitation.

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 Envi-

ronmental 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-

search efforts have dealt mainly with three research

streams, including advanced/innovative technologies,

performance evaluation methodologies and invest-

ment 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 subse-

quent sections.

3.1. Research in advanced and innovative

technologies

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 sys-

tem [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 integra-

tion [9,23,24,31,41,43,47,82,87]. Building systems

and structure integration, as pointed out by Brad-

shaw 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 build-

ing integrity’. According to Carlini [23,24] and

Arkin and Paciuk [9], many intelligent buildings

comprise three levels of system integration which

include:

n The top level which is dealt with the provision of

various features of normal and emergency build-

ing operation as well as the communication

management;

n The middle level which is performed by the

building automation system (BAS), energy man-

agement system (EMS), communication manage-

ment system (CMS) and office automation (OA)

system, which control, supervise and coordinate

the intelligent building subsystems. BAS would

perform the function of energy management

Fig. 1. Taxonomy of research in intelligent building.


system and groups all relevant subsystems in

some occasions [43,60]; and

n The bottom level which contains subsystems

including heating, ventilation and air-condition-

ing (HVAC) systems, lighting system, fire

protection system, vertical transportation sys-

tem, 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 sys-

tems interaction include [48,87,90,93]:

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;

n 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;

n Fire alarm program would be interfaced with

security to release specific locked doors under

alarm conditions;

n Security system is interfaced with the lighting and

HVAC subsystems to define activation of neces-

sary lighting paths and the specific room occupy

mode; and

n Facility management is integrated with BAS.

Ivanovich [53] reviewed current research in intel-

ligent 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 soft-

ware-intensive, artificial-intelligence-based technolo-

gies designed to detect problems, such as building

services systems/components, sensors [92] and con-

trol 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 exten-

sive research on the methodology, strategy and appli-

cation of fault detection and diagnosis in HVAC

systems. Many researchers also paid efforts in devel-

oping intelligent control method to be used in modern

building management system for improving and opti-

mizing 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 proce-

dure which offers feedback function on the perfor-

mance of building materials and components for

future improvement and reference ([73]: cited in Ref.

[74]). Different authorities have tried to develop eval-

uation models to assess the performance of intelligent

building [9,47,73,74,85,98]. Early performance evalu-

ation 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, construc-

tion, 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 eval-

uation process model (POE)’ in order to determine the

intelligence level of intelligent buildings. The POE

process model is generally executed in three stages.

Table 1

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

Intelligent building

systems

Software/program Hardware/device Recent development

BAS n Standard Protocol (i.e. BACnet,

LonWorks, etc.)

n Direct Digital Control (DDC)

n Network control units, operator

workstations, network expansion

units, application specific

controllers and sensor system, etc.

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.

HVAC system n 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 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 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)

n Internet-based HVAC system allows

authorized users keep close contact

with the BAS wherever the user is

n Other specific programs for

HVAC operation

Lighting system n Occupied–unoccupied lighting

control program (time-based

lighting control program); and other

specific programs for lighting control

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.

n Internet-based lighting system

Vertical

transportation

system

n Specific programs for lift

operation and monitoring

n Lift sensors and passenger

detectors, neural network-based

controller, and other devices such

as CCD camera, etc.

n 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

Fire protection

system

n Specific programs for fire

protection and detection

n Intelligent fire controller (IFC),

fully addressable automatic fire

alarm and detector (sensor)

system

n Sophisticated fire alarm systems

which include stand-alone

intelligent fire alarms and

intelligent initiating circuit sensors

Security system n Specific programs for security

protection, detection and safety

system

n Intelligent Access Controller

(IAC);

n CCTV surveillance, e-Card access,

motion detectors, intruder alarm

system and special presence

detection sensors

n Internet-based security system

Communication

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 Traditional telephone systems;

aerials, transmission cables,

amplifiers, mixers, splitters, repeat

amplifiers, attenuators and final

TV outlets; and dish antennas for

satellite communication

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 compar-

ative 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 con-

text and suggested that the POE model could ‘enhance

building performance evaluation in intelligent build-

ings especially in a long-term, continuing basis’ be-

cause 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’ (de-

veloped 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 be-

tween systems and the building’s structure. This

assessment methodology can be used for evaluation

and comparison of single aspect of building’s intel-

ligence 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 intelli-

gent 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-

sion formula. A building can be ranked from A to E

to indicate the overall intelligent performance. A

summary of the hierarchical development of intelli-

gent 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 subjective assessment.

According to So and Wong [85], the shortcomings

identified are in the following areas:

n Inconsistence between final assessment index and

human thinking;

n Slightly different assessment in terms of the weight

or priorities of elements for each individual

intelligent building project;

n Important elements do not receive sufficient

emphasis and less important elements are ignored;

n Current assessment method do not contain a

learning curve and unable to evolve from time to

time; and

n 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 perfor-

mance 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 andWong

[85] suggested the criteria for an efficient performance

evaluation model which states as follows:

n Encourage well-balanced performances, emphasize

important elements but, at the same time, discour-

age total ignorance of minor subjects;

n Consistent with human preferences while random

judgments must be minimized;

n Practically extension of the utility theory, measur-

ing all levels of building performances, weighting

as attributes and combining them systematically, to

form the overall intelligent building index; and

n 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.

Table 2

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

Year Research agency Details of assessment methods

1983 DEGW Orbit 1: multi-client study (building use studies)

1985 DEGW Orbit 2: degree of matching between the building, the organizations

occupying it and IT (using nine key organizations issues and eight key

IT issues)

1988 Camegie Mellon University Measures of quality, satisfaction and efficiency (using six performance

criteria and five system integration criteria)

1991 Kuala Lumpur City Hall Guidelines specifying features of office buildings based on location, design,

systems and services (six-star, five-star and four-star).

1992 Intelligent Building Research Group 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

1992 Intelligent Building in Europe Project Intelligent building rating: key questions based on building shell

characteristics, services and applications (not published)

1992–1994 Holland, New Zealand and Canada 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

1995 DEGW 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

1997 Arkin and Paciuk 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;

MSI ¼

XNSi¼t

Ri

NSð1Þ

1998 Harrison et al. 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

2002 Preiser and Schramm 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

2002 So and Wong (AIIB) 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 intelli-

gent 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 to-

gether with reduced maintenance costs; and

improving operational effectiveness, efficiency and

marketability’. However, many investors have the

mentality of ‘high-risk and low-return’ towards in-

vestment in intelligent building. This mentality may

be explained by following reasons [57,88,98]:

n Investors are unaware of the total cost in relation

to the built asset that their business required;

n Investors are failed to observe the connections

between initial capital cost, operating and mainte-

nance cost as well as the equipping of the building

with automation and communication;

n 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 neces-

sity 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. De-

spite all these, however, there is still a lack of

generally accepted tool for supporting intelligent

building investment decision making. Many prevail-

ing investment evaluation techniques were extended

conceptually and functionally from the traditional

investment decision making techniques [47,95]. The

apparent insufficiency of traditional investment eval-

uation techniques has been identified by many authors

[49,99] as these techniques have ‘failed to reflect the

dynamic and constantly hanging reality of busi-

nesses’. 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 meth-

ods to assess the financial feasibility of a proposed

project. Some property investors review historic per-

formance and request assessment of future perfor-

mance of property portfolios in formulating the

investment strategy decision [3]. Others apply evalu-

ation techniques to review the project feasibility and

adherence to their goals [4]. Many of the investment

evaluation techniques aims to compare project bene-

fits against costs in an attempt to ‘determine accept-

ability, 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 identifi-

cation 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 build-

ing. 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 intelli-

gent building system to be implemented’. Wong et al.

(2001) [95] attempted to analyze and examine the


project costs of both intelligent building and conven-

tional 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 build-

ing. 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 mini-

mize the cost on all ongoing expenses (i.e., power, air-

conditioning, environmental controls) and reduce re-

location 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 invest-

ment 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

Table 3

Empirical studies for IB investment evaluation summary

Year Authors/researchers Approach/methodolog

2001 Wong et al. Net present value app

assessment of financia

by comparing two alte

conventional and IB b

2001 (proceeding) ABSIC Group Cost benefit analysis

a multi-media decisio

based on the CBA fra

2001 (proceeding) Yang and Peng Life cycle costing app

the design alternatives

all the significant cost

2003 Lohner (cited in Ref. [54]) Life cycle costing app

the life cycle cost of i

with different levels o

approach (non-integra

partial integration, and

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 evalu-

ation 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 eval-

uation 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.

ies Evaluation tools

roach systematic

l viability of IB

rnatives:

uilding

NPV method

approach ‘‘BIDS’’,

n support tool

mework

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

roach accessing

which considering

s of ownership

NPV/discounting method

roach comparing

ntelligent buildings

f integration

ted building,

full integration)

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.

NPV ¼XTf¼1

NCFi

ð1þ kÞf

" #�XTj¼1

Ii

ð1þ kÞf

" #

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. Gener-

ally, 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 oper-

ating 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 Bogenstatter [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 alter-

natives, while Gluch and Baumann [44] used the

LCCA to determine the environmental decision-mak-

ing in building investment.

LCCA has been employed in a number of empir-

ical studies, as an investment evaluation technique of

intelligent building. Yang and Peng [98] used the

LCCA to assess various design alternatives. Their

approach starts with the selection of design alterna-

tives, and it then determines the capital cost and cost-

in-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 quality 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 build-

ing, 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, demonstra-

tions and other government interventions [16,50].

Many capital investment projects [69] such as budget

planning, dams and airports construction, and safety

and environmental programs planning [52] have adop-

ted 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


too low as the distant benefits are underweighted

relative to near-term costs.

There are only several applications of CBA in

evaluating intelligent building investment. ABSIC

Group [14] employed CBA to evaluate the investment

in advanced and innovative building system. A soft-

ware 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 prac-

tical 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 intelligent 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 intelligent build-

ing 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 Dug-

dale [1] pointed out the use of ‘arbitrarily’ high hurdle

discount rates in the NPV calculation for new tech-

nology investment would affect the accuracy of eval-

uation. 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 Differences between actual and expected perform-

ances of a system could affect future operation and

maintenance costs;

n Changes in operational assumptions arising from

modifications in user activities;

n Future technological advances that could provide

lower cost alternatives;

n Changes on the price level of a major resource such

as energy or manpower; and

n 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 to evaluate building invest-

ment 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 during 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 re-

search, 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 hierar-

chical 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 build-

ing and construction projects. Al-Harbi [7] applied


AHP to the building contractor prequalification deci-

sion-making so that the client can determine the

contractor’s competence or ability to participate in

bidding. Al Khalil [6] employed AHP in the evalua-

tion and selection of an appropriate project 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 impor-

tance weights.

Table 4

Comparison of investment evaluation approaches

Investment evaluation approaches

Current approaches

Life cycle costing (LCC) CBA A

Purpose/reasons Select the best design

alternatives based on the

life cycle costs

Select the best design

alternatives based on

comparison of cost,

tangible benefit and

associated risk of design

alternatives

C

d

i

t

m

m

m

a

a

Basic tool NPV NPV C

Authors [93]; Lohner, 2003 [14] N

Advantages n Enable investment

options to be more

effectively evaluated

n Able to consider life

cycle cost and

tangible benefits

n

n Consider the impact

of all costs rather than

only initial capital costs

n Facilitate choice

between competing

alternatives

n Provide a reasonable

picture of the costs,

benefits, and risks

associated with a given

system development

project so that can

compare it to other

investment opportunities

n

n

n

Disadvantages/

limitations

n Fails to handle

uncertainty and

irreversible decisions

n Poor availability and

reliability of data

n Relies on estimated

variables

n Biased results

n Considers only

tangible benefits

n No consideration of

uncertainty

n

n

n

n Conceptual confusion

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 numer-

ical) where exact assessment data such as investment

cost, gross income, expenses, depreciation, salvage

value, are difficult to obtain in real world. Abdel-

Recommended approach

HP Fuzzy multi-criteria

decision-making method

(combining AHP and fuzzy

set theory)

omprehensive framework

esigned to cope with the

ntuitive, the rational and

he irrational when we make

ulti-objective,

ulti-criterion and

ulti-actor decisions with

nd without certainty for

ny number of alternatives

An integration of risks

financial and non-financial

factors in investment

evaluation. Based on AHP

with fuzzy set theory

omparison matrices AHP and fuzzy set theory

ot applied in IB yet Not applied in IB yet

Considers tangible and

intangible, quantitatively

measurable and qualitative

factors

n Able to deal quantitatively

with imprecision or

uncertainty

Hierarchical

representation of a system

Provides more

information on the structure

and function of a system in

the lower level

Provides an overview of

the actors and their purposes

in the upper levels

n To tackle the ambiguities

involved in the process and to

assure a more convincing

and effective decision-making

Not consider any

assessment regarding

‘linguistic terms’

Does not reflect the

qualitative and subjective

nature of many factors

Exact assessment data is

difficult to obtain in real world

n Lacks empirical evidence for

their applicability and wide

acceptance in construction

industries

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]

Advanced and

innovative

technologies

System integration,

building automation

system and

communication network

[8,21,28,32,37,41,70,

82,83,87,93]

HVAC system [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]

Performance

evaluation

Evaluation and

construction of

performance models and

indexes for IB

Intelligent Buildings

Europe Work (cited

in Refs. [9,19,47,74,

75,98]), the Asian

Institute of

Intelligent Buildings

(cited in Ref. [85])

Investment

evaluation

Construction of investment

evaluation techniques

and methodologies for IB

[14,95,98]; Lohner

(2003, cited in

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 manufac-

turing technology, in order to ‘tackle the ambiguities

involved in the process and to assure a more convinc-

ing and effective decision-making’ [25].

A summary of methods/techniques used for invest-

ment evaluation is presented in Table 4 below.

5. Conclusions

This paper summarizes existing research in the area

of intelligent building. Specifically, the paper indi-

cates that previous research efforts have dealt mainly

with advanced and innovative intelligent technolo-

gies; 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 manu-

facturing investment evaluation. By using the

FMCDCM, it is expected that ambiguities involved

in the evaluation process can be minimized.

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Intelligent Building Research-A Review