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International Journal of Civil Engineering and Built Environment
Vol.1, No.1, 2014; ISSN 2289-6317
Published by YSI Publisher
A Review on Expert System and its Applications
in Civil Engineering
Muhammad Akram1*, Ismail Abdul Rahman1, Irfana Memon2
Faculty of Civil and Environmental Engineering, University Tun Hussein Onn Malaysia, Malaysia
Quaid-e-Awam University of Engineering, Sciences and Technology, Nawabshah, Sindh, Pakistan
*Corresponding Author:[email protected]
knowledge and inference techniques at the level of a
human expert [1]. Expert systems are developed from the
study of artificial intelligence (AI), which is a field of
computer science and aims of to transfer human brain
power into the machines [2]. An expert system is used to
extract the information of a human expert within a
specific domain and makes this knowledge available to
less experienced users through a computer coded program
[3].
Abstract
Aim of Study
This study provides in depth review of expert system and
potential benefits achieved with its application in the field of the
Civil Engineering.
Need of Study
Currently, construction projects are facing various chronic
problems. One of the reason contributing to these problems is
delay in decision making. This leads to need of mechanisms
which can enable practitioners in making prompt decision.
Hence, expert systems are investigated for seeking opportunities
and studying applicability in construction projects to use as a
tool for aiding in decision making process.
Expert system also known as knowledge based expert
system is a computer program that information and
experience in a specific area for decision making. The
computer program system contains a database which
stores a collection of information and rules which
describes all the data about the problem domain. Expert
system provides high quality experience, domain specific
information; apply heuristics, forward or backward
reasoning, uncertainty and explanation capability. For
information representation techniques, forward and
backward chaining rules are used. Expert system is
developed to imitate the human experts decision making
ability in a particular domain such as construction
management or any other field of knowledge where there
is a shortage of comprehension engineer or experts and
can also give advices and explanations [4]. The
knowledge base elicited from the expert by a qualified
information engineer using various methods can include
systematic interviews. Normally, the expert data field is
"fuzzy" in nature and contains a great deal of procedural
facts; so the knowledge engineer must be an expert in the
process of information elicitation. Expert system
represents a way of capturing, coding and reusing of
information. Fundamentally, an expert system comprises
of some representation of expertise, or a problem to be
solved, and some mechanisms to apply the expertise to a
problem in the form of rules[5].
Research Approach
This study is carried out through reviewing previous studies
conducting in addressing the importance and issues of the expert
system. It also includes various research works demonstrating
the applications of an expert system in different research areas of
civil engineering.
Research Findings
This study highlighted that expert system is very useful
approach for benefiting the practitioner in making quick
decision. It will be helpful in avoiding waiting times. It can be
successfully applied in the areas of civil engineering specifically
construction management.
Limitations
This paper presents a review on expert system with its
application. It has focused on four established methods which
are Rule based system (RBS), Case based system (CBS), Fuzzy
expert system (FS), Neural network (NN).
Importance and Contribution
This review paper contributes in the field of Civil
Engineering in highlighting the usage and advantages of an
expert system.
II.
CHARACTERISTICS OF EXPERT SYSTEM
The most significant element in an expert system is the
knowledge. The power of an expert system exists in the
particular, high-quality knowledge about the task in a
specific domain. In expert system knowledge is estranged
from its processing for example. The knowledge base and
the inference engine are split up. An established program
is a combination of knowledge and the control structure to
process this knowledge [5]. Expert system consists of a
database to apply the data to any particular situation
Keywords: Expert system, Expert system applications, Type of
Expert system, Expert Systems in Civil Engineering
I.
INTRODUCTION
Expert System is an efficient computer program which
provides the solution of problems based on task specific
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which is described to the program based on accumulated
experience and set of rules. Expert system can be built by
using part of development software known as a ‘tool’ or a
‘shell’. A shell is a complete development environment
for building and maintaining knowledge-based
applications [6]. Complicated expert systems can be
enhanced or upgraded with additions to the database or set
of rules.
Vol. 1 No. 1, 2014
discussed because it is less rigorous, more experiential
and judgmental [6].
B. Inference Engine
An inference engine implements the reasoning process
of artificial intelligence; which is an analogy to human
reasoning [7]. Its role is to work with the available data
from the system and the user to derive a solution to the
problem. The purpose of an inference engine is to extract
information and from the data base for the provision of
answers, predictions and suggestions just like a human
expert. Backward chaining and forward chaining are the
two types of inference engines [8]. In backward chaining,
the system first establishes a desired solution and works
backwards to find facts that support the solution.
Backward chaining is goal-driven; thus it is used when the
solution is known. In forward chaining, the system first
collects data which is used when the solution is known.
Forward chaining is data-driven; therefore it is used when
the absolute solution is not known [9].
Following are some of the characteristics of an expert
system
a. Expert System has vast quantities of domain specific
knowledge to the minute details and reduces the search
area for a solution by applying heuristics rules.
b. Expert system provides the high-quality performance
which solves difficult programs in a domain is almost
equivalent or better than human experts.
c. Explanation capability is a unique feature of an expert
system which enables the expert system to review and
explain its decisions.
d. Problem solving has been carried out by the expert
system using symbolic reasoning. Different types of
knowledge including facts, concepts and rules, are
represented by symbols.
e. Expert system can advice, modified, update, expand
and deals with doubtful and unrelated data.
C. Knowledge Acquisition
The knowledge acquisition facility is responsible for
providing the knowledge to the database in an expert
system [10]. This facility operates an editor for entering
the knowledge directly to the expert system. Editing of
knowledge can be carried out in two ways: either by the
knowledge engineer or expert system itself to generate and
modify the file of rules [11].
III. ARCHITECTURE OF EXPERT SYSTEM
Expert system consists of following components
which are: knowledge base, an inference engine,
knowledge acquisition, explanation facility and a
graphical user interface. The structure of an expert system
is shown below in figure 1.
D. Explanation Facility
The explanation facility provides a particular solution
by showing a path to the user in order to reach a certain
conclusion [12].
E. User Interface
The user interface manages the dialog between the
user and the system. It provides facilities such as menus,
graphical interface etc. Thus; it is an intermediary that
allows communication between the user and the system.
The function of the user interface is to ease the usage of
an expert system by developers, users and administrators
[13].
F. Knowledge Engineering
Figure 1.
Knowledge Engineering is the process that builds an
expert system. Human resources such as the domain
expert, user and knowledge engineer and system
maintenance personnel, are involved in developing an
expert system. Domain expert has special knowledge,
judgment, experience and methods to give advice and
solve problems. It provides knowledge about task
performance. Knowledge engineer is involved in the
development of the Knowledge base, Inference Engine
and User Interface. The expert and knowledge engineer
should expect user’s need while designing an expert
system.
Architecture of Expert System
A. Knowledge Base
A knowledge base is considered as the heart of an
expert system; it consists of facts and rules which
provides all the knowledge and information about the
problem domain. Knowledge base is warehouse of the
domain specific knowledge captured from the human
expert through knowledge acquisition. The knowledge
base of an expert system contains both factual and
heuristic knowledge and represents that knowledge in the
form of production rules, frames logic etc. Factual
knowledge is a widely shared knowledge obtained from
text books and journals. Heuristic knowledge is rarely
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Vol. 1 No. 1, 2014
are not able to understand the underline causes and effects
in the system. Moreover; the typical expert system is not
capable of generalizing their knowledge to reason the new
situation. Therefore, lack of fundamental knowledge is a
practical limitation of various expert systems. The
gathering of human expert knowledge can be time
consuming and the output depends mostly on the
knowledge engineering.
IV. ADVANTAGES OF EXPERT SYSTEM
According [14]. Many other traditional forms of
software, an expert system offers advantages over human
experts such as:
Accessibility: The knowledge of numerous human
experts can be combined to give a more knowledgeable
system as compared to a single person’s knowledge.
Expert systems are always available for use when human
experts are not readily available.
VII. TYPES OF EXPERT SYSTEM
Consistency: Expert systems are less likely to contain
inaccuracies provided the expert system has good
knowledge representation. Inaccuracies or errors can be
easily prevented.
The different types of expert system are described
bellow as:
Time constraints: The number of copies of an expert
system can be made whereas, the training process of new
human expert is time consuming and expensive.
The rule-based expert system has domain information
encoded in the form of rules from an expert [17] and
presents that information in the form of rules, such as IF–
THEN. In a rule-based expert system, a knowledge base is
usually stored in terms of if-then rules which can be used
to reach conclusions. Applications of rule-based systems
on expert system are included: state transition analysis,
psychiatric treatment, production planning, advisory
system, teaching, electronic power planning, automobile
process planning, etc [16].
A. Rule Based Expert System (RBES):
Stability: It can assist a human expert in problem
solving and is more likely to consider all possibilities.
Efficiency: An expert system is capable of reviewing
all the transactions as compared to a human expert who
can only capable reviewing a sample.
V. LIMITATIONS OF HUMAN SYSTEM
B. Case Based Reasoning (CBR):
• Human experts have more limitations over expert
systems.
Case-Based Reasoning (CBR) is not new to the
engineering community. It is Artificial Intelligence (AI)
technique to support the capability of reasoning and to
learn in advanced decision support systems [18]. The
basic idea of CBR is to solve the new problem by
adapting the solutions used to solve previous problems.
The following four steps summarize the process as given
bellow. Figure.2 illustrates the process of how case base
reasoning deals with uncertainties [19].
• Human responses react slowly in recalling
information stored in memory.
• Human gets tired from physical or mental
workload.
• Humans are not capable of understanding huge
amounts of data rapidly.
• Human experts are unable to maintain large
amounts of data in memory.
Retrieve the most similar case(s), with respect to the
current input situation, contained in the case repository,
which is known as the case base;
Despite; the abovementioned limitations, human expert
also have an advantage over expert systems. Human
experts have common sense but expert systems do not
have common sense up till now. Human experts can
respond creatively and efficiently to unusual situations as
compared to an expert system. Human experts can
automatically adapt the changing the environments
whereas as, expert systems are required to be
unambiguously updated. Expert systems fail to recognize
the problems outside the area of their expertise and when
no answer exists. For this reason, any output or advice
from an expert system must be concluded and tested by a
human expert [15].
Reuse them, or more precisely their solutions, in order
to solve the new problem;
Revise the proposed new solution (if it is considered
necessary);
Retain the current case for possible future problem
solving.
VI. LIMITATIONS OF EXPERT SYSTEMS
Although; the abovementioned advantages, expert
systems have certain limitations that make worse their
effectiveness in applying human like decision making
methods. Expert systems are knowledge dependent,
therefore; they are only as good as the knowledge stored
into them. For that reason, the expertise of an expert
system is limited to a specific knowledge domain that the
system contains [16]. Unlike humans, the expert systems
Figure 2.
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Case Based Reasoning Cycle Process
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Some of the CBR implemented applications are
manufacturing process design, knowledge management,
power system restoration training, ultrasonic inspection,
etc.
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VIII. APPLICATION OF EXPERT SYSTEM IN
CONSTRUCTION INDUSTRY
There exist numerous expert systems applications in
the construction industry. Following are some applications
of an expert system in the construction industry.
C. Fuzzy Expert System
A. COMIX
The world of information is surrounded by uncertainty
and imprecision. The human reasoning process can handle
inexact, uncertain, and unclear concepts in an appropriate
manner. Usually, the human thinking, reasoning, and
perception process cannot be expressed precisely. These
types of experiences can rarely be expressed or measured
using statistical or probability theory. Fuzzy logic provides
a framework to model uncertainty, the human way of
thinking, reasoning, and the perception process. Fuzzy
systems were first introduced by Zedah (1965) [20].
COMIX is a rule and frame based expert system which
provides suggestions on the design of normal weight
concrete mixes. The designed system was used by concrete
engineers, design engineers, and consultants. COMIX was
developed at Central Laboratories in New Zealand. The
mix design uses the New Zealand concrete code
“Specification for Concrete Construction'. The system
refers the type of structure to the consistency and the
placement method. The system calculates the amount of
cement by suggesting water/cement ratio from a specified
strength. Finally the volume of coarse aggregate and sand
is calculated. The masses of the components of concrete
mix is being displayed by the system [28].
A fuzzy expert system is simply an expert system that
uses a collection of fuzzy membership functions and rules,
instead of boolean logic, to reason about data [21]. The
rules in a fuzzy expert system are usually of a form similar
to the following:
B. BETVAL
If A is low and B is high then X=medium
The recommendation on the selection of ready-mix
concrete at the job site is provided by rule based expert
system known as BETVAL. The main function of the
system is to help the construction site staff for selecting
the type of fresh concrete ordered from the ready mix
concrete plant. Technical Research Center of Finland
(VTT) developed the BETVAL expert systems using
Insight2+ and an IBM PC/XT or AT computer. BETVAL
demonstration system is primarily used as a learning tool,
and it enhances the database for BETVAL [29].
Where A and B are input variables, X is an output
variable.
Here low, high, and medium are fuzzy sets defined on
A, B, and X respectively. The antecedent (the rule’s
premise) describes to what degree the rule applies, while
the rule’s consequent assigns a membership function to
each of one or more output variables.
Fuzzy expert systems implement some applications
such as waste water treatment, online scheduling,
performance indexing, computer security, gesture
recognition, and medical diagnosis [11, 16, 22].
BETVAL system is based on following areas of
knowledge:
 The appropriate concreting techniques (e.g.,
curing, heating and heat treatment) and compressive
strength class.
 Concrete consistency value based on the type of
structure and the production equipment.
 Suggestions for selecting the maximum size
aggregate.
D. Nueral Network
Artificial neural network is a form of artificial
intelligence, which attempts to simulate the biological
structure of human brain and nervous system by means of
their architecture [23]. Artificial neural network learn “by
example” in which an actual measured data set of input
variables and the corresponding outputs are presented to
determine the rules that govern the relationship between
the variables. This model is used to implement software
simulations for the massively parallel processes that
involve processing elements interconnected in network
architecture. The artificial neuron receives inputs that are
analogous to the electrochemical impulses that the
dendrites of biological neurons receive from other neurons.
The output of the artificial neuron corresponds to signals
sent out from a biological neuron over its axon. These
artificial signals can be changed similarly to the physical
changes occurring at neural synapses [24].
C. BIDEX
BIDEX (Bidding Expert) is a rule based expert system
which gives suggestions on the design of bid decision.
Construction contractors use this expert system for
making bid decision [30-31]. It is developed using
EXSYS (an expert system shell). The decision making of
BIDEX undergoes two stages. First stage is based on the
decision whether or not to bid, and the second stage is
based on the selection of markup factors. Such as owner,
type of job, size of the job, location of job and strength of
the firm are important for the bid / no bid decision,
although the degree of difficulty, degree of hazard, risk in
investment, , uncertainty in the estimate and reliability of
sub-contractors are important for the present mark up
decision.
Some applications applied by neural network are
speech synthesis, diagnostic problems, medicine, business
and finance, robotic control, signal processing, computer
vision, mitigation process control and biomedical
application [25-27].
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Initially, the system was developed for the Federal
Highway Administration and the Illinois Department of
Transportation provides continued support for the
development of the system. The knowledgeable and
experienced pavement engineers provide information to
the system for the identification of the type and general
causes of deterioration exist in the pavement. Three
pavement types are considered by the EXPEAR system:
Jointed reinforced concrete pavement (JRCP), Jointed
plain concrete pavement (JPCP), and Continuously
reinforced concrete pavement (CRCP). EXPEAR is an
operational system [33].
D. AMADEUS (Diagnostic and Repair)
AMADEUS is a rule-based expert system that is
designed for assisting building inspectors during
emergency post earthquake damage assessment.
AMADEUS is a prototype system; It was tested following
an earthquake that occurred in Barrea, Italy. Expert
system is expected to be developed further as additional
knowledge becomes available. The system records field
inspection data and makes recommendations concerning
the safety of buildings that have been subjected to
earthquake damage. It provides a detailed survey and
evaluation of the seismic damage to masonry structures.
The system gives recommendations regarding usability,
severity of damage and habitability of structures based on
qualitative measures of safety of a building under
inspection. The expert system attempts to achieve its goal
and sub-goals involving the following categories:
Geotechnical situation of and around the building, State of
the structural system, Hazards due to non-structural
elements and Danger inducted on the building by its
nonstructural components. The system recommendations
are classified as high, uncertain, or low risks. The system
is inter-active and uses information provided by the end
user to give recommendations. The system improves the
questionnaire-type form method and guides the user in
reasoning about the situation by focusing on main factors
under given conditions, and ignores irrelevant details. End
user may inquire why and how questions and input values
may be changed during the course of an interactive
session. Also, the inspector’s confidence in this response
is obtained by using uncertainty factors. AMADEUS is
primarily a rule-based system. Knowledge is represented
in parameters (input Information), rules, and frames. The
system was developed using ‘PcPIus’, a Lisp-based expert
system development tool, which runs on a personal
computer [32].
G. Pavement Expert
Inspectors and Engineers make field observations by
assessing the conditions on concrete pavements by using a
rule based Pavement Expert system. The proposed system
support the decision making to automate the process of
observation and pavement rating. This system was
developed at United Kingdom (UK).
The system is established on the manual Pavement
Condition Rating (PCR) index for pavement which
considers the incidence, severity, and the extent of the
range of distress for each road section. The knowledgebased contained in this system was extracted from the
documents for the PCR, as well as some experts in this
field. It was developed using expert system shell SAVOIR
and written in PASCAL. The highway staff of United
Kingdom used this system as an operational prototype
system [33].
H. Advisory System for Managers (Planning and
Management)
The construction site managers and foreman utilize
this rule based advisory expert system in daily routine
tasks and prior planning. The system is a developmental
prototype system that has been implemented for small
jobs. The goal this system is to systematize the process of
decision making. The system advises on such tasks as
supervising incoming and outgoing information, costs,
technical problems of the site and future problems. The
system uses programs to perform calculations for data
intensive tasks, and expert systems to obtain an expert’s
experience. The topics covered thus far by the advisory
system which includes that; crane disposition,
construction crew schedules and concrete plant
dimensioning. The system will include a cost model, a
resource model and an administrator model and this
system runs on a minicomputer [29].
E. Concrete Mix Designer
Concrete Mix Designer is a rule-based expert system
that is developed to provide knowledge on the testing mix
proportion of concrete. Concrete Mix Designer represents
information in the form of IF-THEN rules, which are
combined together as ‘frames’. Every frame represents a
component of the concrete, such as the amount of coarse
aggregate, and includes an expert system goal. This
system was developed at the University of Miami
(Department of Civil, and Architectural Engineering) to
serve a tool for engineering student and practicing
engineers. The questions and answering capability was
provided by Computer programs are written in Visual.
The conclusions expert system can be expanded inclement
and are easy to understand. The knowledge based expert
system provide modularity by interfacing with the
computer program [28].
IX.
CONCLUSION
Expert System is an intelligent computer program
which solves the complex problems equivalent to the level
of a human expert by using task specific information and
inference techniques. This paper summarizes a thoroughly
literature review on the methodologies and application of
expert systems based on various previously published
research articles. It has been concluded that expert system
methodologies are tending to build up expertise
orientation and applications development is a problem-
F. EXPEAR
Expert System for Pavement Evaluation and
Rehabilitation is a knowledge based system, which is
designed as a tool to assist the highway engineers.
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[14] K. Brenkel and K. Makhubele, "A Knowledge-Based System for
Medical Advice Provision," 2012.
oriented domain. It is recommended that different
methodologies such as production planning, advisory
system, mitigation process control, biomedical application
and human behavior can implement as expert systems.
Finally, it has been observed that the capability to change,
modify and to attain a new understanding is the backbone
of an expert system which can be implemented in a
number of applications in future work.
[15] Y. Huang, et al., "Development of an expert system for tackling the
public's perception to climate-change impacts on petroleum
industry," Expert Systems with Applications, vol. 29, pp. 817-829,
2005.
[16] S.-H. Liao, "Expert system methodologies and applications—a
decade review from 1995 to 2004," Expert Systems with
Applications, vol. 28, pp. 93-103, 2005.
[17] D.-L. Xu, et al., "Inference and learning methodology of belief-rulebased expert system for pipeline leak detection," Expert Systems
with Applications, vol. 32, pp. 103-113, 2007.
ACKNOWLEDGMENT
This project is funded by Ministry of Higher Education
under Fundamental Research Grant Scheme (FRGS). The
authors are thankful to Universiti Tun Hussein Onn
Malaysia and Ministry of Higher for financial support and
providing the necessary infrastructure to carry out research
work.
[18] R. Lopez De Mantaras, et al., "Retrieval, reuse, revision and
retention in case-based reasoning," The Knowledge Engineering
Review, vol. 20, pp. 215-240, 2005.
[19] A. Aamodt and M. Nygård, "Different roles and mutual
dependencies of data, information, and knowledge—an AI
perspective on their integration," Data & Knowledge Engineering,
vol. 16, pp. 191-222, 1995.
REFERENCES
[1] N. Bolloju, et
[20] L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, pp. 338353, 1965.
al., "A knowledge-based system for improving the
consistency between object models and use case narratives," Expert
Systems with Applications, vol. 39, pp. 9398-9410, 2012.
[2]
K. Tripathi, "A Review on Knowledge-based Expert System:
Concept and Architecture," Artificial Intelligence Technique 2011.
[3]
E. Dogantekin, et al., "An intelligent diagnosis system for diabetes
on linear discriminant analysis and adaptive network based fuzzy
inference system: LDA-ANFIS," Digital Signal Processing, vol. 20,
pp. 1248-1255, 2010.
[4]
W. Shiue, et al., "A frame knowledge system for managing financial
decision knowledge," Expert Systems with Applications, vol. 35,
pp. 1068-1079, 2008.
[5]
M. G. Omran, et al., "Self-adaptive differential evolution," in
Computational intelligence and security, ed: Springer, 2005, pp.
192-199.
[6]
[21] Z. Xu, et al., "System regression test planning with a fuzzy expert
system," Information Sciences, 2010.
[22] E. Carrasco, et al., "Diagnosis of acidification states in an anaerobic
wastewater treatment plant using a fuzzy-based expert system,"
Control Engineering Practice, vol. 12, pp. 59-64, 2004.
[23] R. Gupta, et al., "Prediction of concrete strength using neural-expert
system," Journal of materials in civil engineering, vol. 18, pp. 462466, 2006.
[24] B. Pradhan, et al., "A GIS-based back-propagation neural network
model and its cross-application and validation for landslide
susceptibility analyses," Computers, Environment and Urban
Systems, vol. 34, pp. 216-235, 2010.
[25] H. Takagi, "Introduction to fuzzy systems, neural networks, and
genetic algorithms," in Intelligent Hybrid Systems, ed: Springer,
1997, pp. 3-33.
W. Shen, et al., "Systems integration and collaboration in
architecture, engineering, construction, and facilities management:
A review," Advanced Engineering Informatics, vol. 24, pp. 196207, 2010.
[7]
B. N. Prasad, et al., "An expert system for diagnosis and therapy in
lung transplantation," Computers in biology and medicine, vol. 26,
pp. 477-488, 1996.
[8]
S. L. Kendal and M. Creen, An introduction to knowledge
engineering: Springer, 2007.
[9]
A. R. Khan, et al., "Knowledge-Based system‟ s modeling for
software process model selection," International Journal, 2011.
Vol. 1 No. 1, 2014
[26] L. O. Chua and L. Yang, "Cellular neural networks: Applications,"
Circuits and Systems, IEEE Transactions on, vol. 35, pp. 12731290, 1988.
[27] L. Fu, "A neural-network model for learning domain rules based on
its activation function characteristics," Neural Networks, IEEE
Transactions on, vol. 9, pp. 787-795, 1998.
[28] M. Abdullahi, et al., "A review on expert systems for concrete mix
design," ICCBT. A (21), pp. 231-238, 2008.
[29] L. J. Kaetzel and J. R. Clifton, "Expert/knowledge based systems for
materials in the construction industry: State-of-the-art report,"
Materials and Structures, vol. 28, pp. 160-174, 1995.
[10] L. Ooshaksaraie, et al., "RP< sup> 3</sup> CA: An expert system
applied in stormwater management plan for construction sites in
Malaysia," Expert Systems with Applications, vol. 39, pp. 36923701, 2012.
[30] I. Minkarah and I. Ahmad, "Expert systems as construction
management tools," Journal of Management in Engineering, vol. 5,
pp. 155-163, 1989.
[11] F. N. Raza, "Artificial intelligence techniques in software
engineering (AITSE)," in International MultiConference of
Engineers and Computer Scientists (IMECS 2009), 2009.
[31] E. A. Saoud, "Expert Systems for Management Training in the
Construction Industry," 1996.
[32] A. Berrais and A. Watson, "Expert systems for seismic engineering:
the state-of-the-art," Engineering Structures, vol. 15, pp. 146-154,
1993.
[12] R. M. Reffat and E. L. Harkness, "Expert system for environmental
quality evaluation," Journal of performance of constructed facilities,
vol. 15, pp. 109-114, 2001.
[33] N. Ismail, et al., "An overview of expert systems in pavement
management," European Journal of Scientific Research, vol. 30, pp.
99-111, 2009.
[13] M. Daniel, et al., "CADIAG-2 and MYCIN-like systems," Artificial
Intelligence in Medicine, vol. 9, pp. 241-259, 1997.
29