Studying the Reasons for Delay and Cost Escalation in Construction

Proceedings of the 2014 Industrial and Systems Engineering Research Conference
Y. Guan and H. Liao, eds.
Studying the Reasons for Delay and Cost Escalation in Construction
Projects: The Case of Iran
Abstract ID:
Hamed Samarghandi
Edwards School of Business, University of Saskatchewan
Saskatoon, SK, Canada, S7N 5A7
[email protected]
Seyed Mohammad Moosavi Tabatabaei
Tabuck Construction Co. Ltd., Tehran, Iran
Pouria Taabayan
Idea Group, Tehran, Iran
Email: [email protected]
Ahmad MirHashemi
ICT Organization of The City of Tehran Municipality, Tehran, Iran
Abstract
Construction projects act as a major driving force for a country’s economic development. However, in Iran, numerous
construction activities face delays that are uncalled for. Undesirable delays impose excessive costs on top of the initial
expenditure estimates and become a primary reason for loss of trust between owner and contractor. Identifying the
delay causing factors and determining their role and contribution to delays is extremely important in reducing delays
and eliminating redundant expenditures. In order to identify delay factors, an open questionnaire is designed and
surveys are conducted with consultants and engineers, contractors, owners, and experienced construction
professionals. In order to determine the importance of the myriad of delay factors revealed through these interviews,
a closed questionnaire is designed and filled out by 200 owners and contractors. A statistical model is designed to
effectively analyze the results obtained by the closed questionnaires. Finally, regression models are developed to
determine the amount of delay and excessive costs to be expected, in case the delay factors are not remedied. Such
regression models prove to be very effective in revising the contracts and initial estimates.
Keywords:
Construction projects, Delay, Statistical Analysis, Regression, Iran
1. Introduction
Construction is among the most flourishing business sectors in Iran. Construction projects absorb immense
investments and play an important role as a major driving force in the growth of several other sectors in the economy,
including but not limited to mining and natural resources extraction, transportation and logistics, insurance,
consultation and management, and even education and training.
Although project management and planning techniques have progressed significantly during the past decade, and use
of these techniques is very popular among Iranian companies, especially construction contractors, delays in
construction projects in Iran are very common. These delays further contribute to the inaccuracy of initial time and
cost estimates. Consequently, profitable projects often turn into costly and money-losing contracts. This is undesirable
for both the owner and the contractor, and reduces the trust that should exist between contractors and owners for future
projects. For instance, direct costs (not including lost opportunity costs) of delays in provincially funded construction
projects in Iran in the year 2000 alone is evaluated as 575 million USD [1]. In construction terminology, delay is
defined as the extension of some part of a project beyond the original plan due to unanticipated circumstances [2].
Companies would be able to avoid or minimize these delays if major contributing factors were identified and planned
for in a timely manner. In this research, these factors are identified, and their importance and contribution to the
lateness of a typical project is measured. Accordingly, by applying the Pareto principle [3], contractors could focus
on major delay factors in order to reduce costs and eliminate or minimize delays. To perform this analysis, a statistical
model based on multinomial distribution is developed. This model categorizes several delay factors under four main
headings: 1) owner defects, 2) contractor defects, 3) consultant defects, and 4) law, regulation, and other general
defects.
In order to provide more insight into the estimated probabilities, confidence intervals are also provided for each point
estimator. Moreover, regression analyses are performed to provide contractors and owners with approximation
methods that enable them to re-evaluate the initial time and cost estimates according to present delay factors. Statistical
tests are conducted to verify robustness of the developed models. The developed model can be easily adapted for
similar studies in other countries or environments; moreover, the results of this paper are very helpful for project
managers and regulatory bodies if they want to minimize or eliminate delays in construction projects.
The rest of the paper is organized as follows. Section 2 provides a literature review and highlights the contribution of
this study. The developed statistical model is explained in Section 3. Methodology of the research as well as the
identified delay factors are described in Section 4. This section also calculates point and interval estimates for each
identified delay factor. Discussions and regression analyses are included in section 5. Conclusions and directions for
future research are summarized in Section 6.
2. Literature Review
The importance of construction projects, frequency of delayed projects, and direct and indirect costs associated with
such delays have inspired many researchers. The literature is rich with studies that have identified different delay
factors and the risks associated with them under different assumptions. Table 1 lists the research that has identified
the reasons for delay of construction projects in developing countries in Asia, the Middle East, and Africa. A review
of the literature reveals that:
1. Although similarities exist between different studies, each study looks at the construction delay issue
according to the influential parameters and factors of the environment where the research is conducted.
2. Development of robust statistical models and testing different research hypotheses arising from the research
is almost non-existent.
3. There are almost no studies to determine the reasons for delay in construction projects in Iran.
In terms of contribution, this paper constructs a statistical model in order to measure the amount of contribution of
each delay factor; point and interval estimations of each factor are provided. Regression analysis is performed to
provide a guideline for both owners and contractors to revise their initial time and budget proposal.
Citation
[4]
[5]
[6]
[7]
[8]
Table 1 – Studies on the reasons for delay in construction projects
Country
Major Causes of Delay
 Financial difficulties
Jordan
 Change orders
 Owner interference
 Inadequate contractor experience
Jordan
 Financing and payments
 Labor productivity
 Slow decision making
 Change orders
 Weather and site conditions
Jordan
 Late deliveries
 Economic conditions
Libya and UK
 Several delay factors for each country are identified
 Insufficient coordination
Libya
 Ineffective communication
Citation
Country
[9]
Saudi Arabia
[10]
Saudi Arabia
[11]
Hong Kong
[12]
India
[13]
India
[14]
UAE
[15]
UAE
[16]
Turkey






















Major Causes of Delay
Financial difficulties
Delay in obtaining permits
Slow preparation and approval of shop drawings
Late contractor payments
Change orders
Human resources
Poor workmanship
Project scope
Complexity
Environmental factors
Management attributes
Client’s interference
Inefficient construction planning
Several factors, categorized as excusable and nonexcusable
Several factors including change order, ineffective
communication, etc.
Slow preparation
Lack of early planning
Ineffective decision making
Human resources
Poor management
Low productivity
Several factors including ineffective communication,
conflicts between contractor and owner, etc.
3. Statistical Model
Multinomial probability distribution is an extension to binomial distribution. Multinomial distribution models the
probability of success in n independent Bernoulli experiments. In this paper multinomial distribution is selected to
estimate the probability of occurrence of each delay factor. In other words, occurrence of a specific delay factor in a
late construction project is considered a success, and the probability of this success is calculated. According to
multinomial distribution, if the probability of occurrence of
f ( x1 ,
, xk ; n, p1 ,
 n!
p1 x1

 x1 ! xk !
0

X i ,1  i  k
k
is
pi
(
p
i 1
i
 1 ), then:
, pk )  Pr( X 1  x1 ,X 2  x2 ,..., X k  xk ) 
k
pk xk
when  xi  n
(1)
i 1
otherwise
Section 4 of this paper describes a questionnaire which is utilized for sampling and determining
pi ,1  i  k . Section
4 introduces 36 delay factors, extracted from the literature review and several interviews with industry experts. A
questionnaire is designed with 36 polar or yes-no questions. A respondent would select yes for a specific question if
that particular delay factor was present in his/her recently delayed project. Suppose that this questionnaire is filled by
n respondents. Therefore, (2) provides an unbiased estimator for parameter pi :
n
pˆ i 
In (2),
x
i 1
n
i
; n is the number of respondents
(2)
xi  1 if a specific respondent selects yes for i th delay factors, and it is zero otherwise. Moreover, for n  30
n
Z
 x  n. p
i 1
i
i
~ N (0,1)
n. pi .(1  pi )
(3)
As a result:
Pr(  z  Z  z )  1  
2
(4)
2
Z in (4) by its value obtained in (3), and solving the resulting equation for pi , a
Therefore, by replacing
100(1   )% confidence interval for pi can be developed:
pˆ i  z .
2
pˆ i (1  pˆ i )
pˆ (1  pˆ i )
 pi  pˆ i  z . i
n
n
2
(5)
In this paper, the above multinomial distribution function is utilized for the delay factors under each of the major
categories. As a result, four different multinomial distributions are introduced in section 4. Formally, assume that:
kj
Pˆj   pˆ i ; j  1,2,3,4
(6)
i 1
Note that the values of
k 36
 pˆ i  1 and
i 1
larger
kj
 pˆ
i
i 1
pˆ i
are not normalized to form a multinomial function for category
j ; in other words,
 1 . Therefore, the value of Pˆj ; j  1,2,3,4 is directly dependent on the value of k j (a
k j means a larger Pˆ j ). In order to remove the effect of the value of k j in the value of Pˆ j , these values should
36
:
kj
be multiplied by
36
Pˆj ,k j  Pˆj . ; j  1,2,3,4
kj
(7)
4
In order to have a multinomial distribution between major categories, one should make certain that
 Pˆ
j 1
Therefore, values of
Pˆj ,k j ( N ) 
Pˆj ,k j
4
 Pˆj,k j
j ,k j
 1.
Pˆ j ,k j should be normalized. Therefore:
; j  1,2,3,4
(8)
j 1
Once
Pˆj ,k j ; j  1,2,3,4 and pˆ i ;1  i  k j ; j  1,2,3,4 are normalized based on the above equations, they are
ˆi ) 
unbiased point estimators of the parameters of the probability distributions to which they belong ( E ( p
pi ; i
,
E ( Pˆj ,k j )  Pj ,k j ; j ). In order to normalize pˆ i ;1  i  jk so that they form a multinomial probability distribution
for major category
pˆ i ,k j ( N ) 
pˆ i
;1  i  n; j  1,2,3,4
kj
 pˆ
w1
j that consists of k j delay factors, an equation similar to (8) is formulated:
w
The next section lists the identified delay factors and their corresponding probabilities.
(9)
4. Delay Factors and Statistical Analyses
Identifying the delay factors in construction projects is the first step toward developing a reliable statistical model. In
order to accurately identify such factors, several interviews were conducted with owners, contractors, consultants,
industry experts, and regulatory bodies. Results of these interviews were carefully discussed and compared with
similar studies available in the literature. Accordingly, several delay factors in construction projects were identified
and categorized under four main classes: 1) owner defects, 2) contractor defects, 3) consultant defects, and 4) law,
regulation, and other general defects.
Based on the identified delay factors, a questionnaire was designed. An accompanying fishbone diagram was used to
visualize the delay factors and their root causes for interviewees. According to the described probability model,
respondents were asked if they had experienced lateness in one of their recent construction projects. In case of a
positive answer, the respondents were asked to determine which delay factors contributed to this lateness. Respondents
were also asked to report the initial proposed price and timeline as well as the final price and actual timeline. Results
of these questionnaires were further used in data analysis and model development.
It is worthwhile to mention that in Iran, the approval and execution of construction projects, especially government
funded construction projects, are governed by complicated regulations; owners, contractors, and consultants have to
follow procedures that are enacted to ensure successful completion of the projects. Major steps that the involved
parties should follow include: owner performs feasibility studies; owner selects the consultant; consultant prepares
the initial plans; consultant prepares the executive plans; owner selects a contractor (this is done through public call
for bids with its own set regulations for government projects); contractor signs the contract, consigns trust and security
funds, and takes over the construction site; contractor starts the construction and submits progress reports on a regular
basis; contractor submits official documents to consultant and owner to report the completion of the project; consultant
and owner investigate and validate completion of the project; at this point trust funds are released and final payments
are made to the consultant and contractors. However, many involved parties believe that these regulations are outdated
and should be considered as a contributing delay factor. Table 2 verifies authenticity of such claims. To learn more
about the laws and regulations in the Iranian construction environment, please refer to [17].
The questionnaire was filled out by 200 respondents active in the Iranian construction industry. Out of the 200
collected questionnaires, 86 were identified as suitable for further investigation. Table 2 summarizes the delay factors
and their corresponding point and interval estimates.
Based on the probabilities assigned to the delay factors, it is possible to calculate probabilities for the four major
categories of Table 2. First, one should calculate the values of
k1
12
i 1
i 1
Pˆj ; j  1,2,3,4 based on (6). Therefore:
Pˆ1  Owner defects   pˆ i   pˆ i  0.52  0.44  ...  0.69  7.804
Pˆ2  Contractor defects  3.291
(10)
Pˆ3  Consultant defects  5.979
Pˆ4  Other defects  4.577
The next step is to remove the effect of the value of
k j ; j  1,2,3,4 by (7):
36
36
Pˆ1,k  Pˆ1 .  7.804 
 23.412
k1
12
1
36
Pˆ2,k  3.291 
 14.81
8
36
Pˆ3,k  5.979 
 21.524
10
36
Pˆ4,k  4.577 
 27.462
6
2
3
4
(11)
Table 2 – Defects, Delay Factors, and Corresponding Estimates
Number
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
1.10
1.11
1.12
2
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
4
4.1
4.2
4.3
4.4
4.5
4.6
Delay Factors
Owner Defects
Lack of attention to the feasibility studies and improper location planning
Lack of knowledge about different defined execution models
Delay in obtaining permits
Inefficient budgeting schedule
Incomplete drawings and plans
Ineffective change order communication
Delay in transferring construction site
Improper selection of contractors based on quantitative and qualitative factors
Ineffective site management
Too many change orders
Lack of attention to inflation
Lack of knowledge about regulations
Contractor Defects
Inaccurate budgeting and resource planning
Using low quality material and inadequate equipment
Human resources issues such as hiring inexperienced technical staff
Ineffective project planning
Adherence to outdated construction methods
Inaccurate pricing and bidding
Lack of knowledge about regulations
Weak cash flow
Consultant Defects
Lack of accuracy in reviewing feasibility studies
Mistakes in technical documents
Inaccuracies in technical drawings such as electrical or mechanical drawings
Tardiness in preparing change orders
Inaccurate first drafts that cause confusion
Ineffective project planning
Delay in updating project status
Having too many unforeseen items in cost lists
Assigning inexperienced personnel to supervisory duties
Lack of executive experience
Law, Regulation, and Other General Defects
Outdated standard mandatory terms in contracts
Outdated standard mandatory items in cost lists
Financial difficulties stemming from governmental budgeting
Lack of attention of government authorities to inflation
Outdated bidding procedures
Extreme weather and environmental conditions
Finally, these values should be normalized based on (8):
95% Confidence
Interval
Lower Upper
Limit
Limit
Point
Estimate
0.014
0.0079
0.013
0.047
0.028
0.023
0.023
0.029
0.014
0.021
0.051
0.029
0.12
0.1067
0.118
0.182
0.149
0.139
0.14
0.15
0.121
0.136
0.188
0.15
0.067
0.057
0.066
0.115
0.089
0.081
0.081
0.089
0.069
0.078
0.119
0.089
0.129
0.012
0.024
0.004
0.068
0.079
0.051
0.093
0.302
0.115
0.139
0.095
0.215
0.232
0.189
0.253
0.217
0.064
0.081
0.049
0.141
0.155
0.12
0.173
0.027
0.053
0.028
0.035
0.065
0.017
0.041
0.04
0.025
0.037
0.146
0.192
0.147
0.16
0.211
0.127
0.172
0.17
0.142
0.165
0.087
0.123
0.088
0.097
0.138
0.072
0.106
0.105
0.083
0.101
0.101
0.105
0.103
0.093
0.068
0.057
0.265
0.271
0.268
0.253
0.216
0.2
0.183
0.188
0.185
0.173
0.142
0.129
Pˆ1,k ( N ) 
1
Pˆ1,k
 Pˆj ,k
j 1
Pˆ2,k
2(N
)

1
4
23.412
23.412  14.81  21.524  27.462
 0.27
j
 0.17
(12)
Pˆ3,k ( N )  0.25
3
Pˆ4,k
4(N
)
 0.31
5. Discussion and Results
According to (12), the contribution of owner defects to delays is 27%, the contribution of contractor defects to delays
is 17%, and so on. Table 2 makes it possible to perform a Pareto analysis on the delay factors. For instance, 1.4, 1.5,
1.8, 1.11, and 1.12 contribute to more than 50% of the owner defects. Hence, by resolving budgeting issues, having
complete and accurate drawings, properly selecting contractors by both qualitative and quantitative factors,
considering inflation in bidding wars, and familiarizing the owners with regulations, it is possible to reduce owners’
defects by more than 50%.
In the case of the contractor defects, more than 60% of the contribution comes from inaccurate budgeting and resource
planning, resisting modern construction methods, inaccuracy in bidding, and weak cash flow. In the case of
consultants, more than 57% of delay stemmed from inaccurate technical documents, inaccurate first drafts, delays in
updating project status, inaccurate cost lists, and lack of executive vision. Similarly, outdated standard contract terms
and cost lists, along with government budgeting flaws, contribute to more than 55% of delayed projects.
5.1 Selecting Contractors
Selecting contractors has been traditionally based solely on the prices offered by the bidders. However, today, when
it comes to selecting a contractor, many owners do not consider the price as the single selection criterion; instead they
pay attention to a combination of several parameters such as price, reputation of the bidders, history of previous
projects, major construction quality indicators, prepared drawings, suggested construction methods, etc. As a result,
nowadays selecting a contractor is not a straightforward procedure performed by sorting the bids based on the offered
price; moreover, there is rarely a bidder that can dominate the rest of the bidders in all of the relevant criteria.
Therefore, owners sometimes fail to select the best contractor as the final winner of the bid.
Moreover, government corporations have to adhere to a set of regulations that obliges them to select the contractor
that offers the lowest price. In other words, regulations require government corporations to disregard all the important
criteria mentioned above and select a contractor only by the offered price. This issue contributes to more than 8% of
the delayed projects and is identified in item 1.8 in Table 2.
5.2 Lack of Attention to Inflation
Lack of attention to inflation is among the most important delay factors. In Table 2, this factor is indicated as 1.11 for
owners with probability of 0.119; 2.6 for contractors with probability of 0.155; and 4.4 for law, regulation, and other
general defects with probability of 0.173. Government authorities have enacted certain rules to compensate owners
and contractors when higher than normal inflation results in a spike in construction costs and reduces the forecast
profits. However, these rules do not fully compensate the contractor for elevated costs and cause dissatisfaction (item
4.4).
On the other hand, bidders do not pay attention to inflation rate and construction costs throughout the life cycle of the
project when they estimate the project costs (item 2.6), which results in inaccurate bidding, and frustration and delay
during the lifespan of the project. Owners also do not pay full attention to the reported inflation rates in the bids since
a lower inflation rate in the bid translates into a less expensive project. Therefore, owners disregard the true inflation
rates during the bidding procedure, which results in disputes and costly legal actions between owners and contractors
during the project life cycle (item 1.11).
5.3 Outdated Standard Mandatory Items in Cost Lists
Government authorities publish a standard list of construction items and materials on an annual basis. According to
regulations, this list must be used by owners and contractors as a basis for estimating project costs. However, the
published lists do not always consist of the new construction materials and innovative items that are introduced to the
market. This results in inaccurate cost estimates and disagreements between owners and contractors when selecting
construction materials. This issue is indicated under item 4.2 in Table 2 (outdated standard mandatory items in cost
lists), and contributes to more than 18% of the delays in construction projects. Item 4.2 further contributes to item 3.8
under consultant defects with more than 10% probability (too many unforeseen items in cost lists), and item 2.6 under
contractor defects with 15.5% probability (inaccurate pricing and bidding).
5.4 Projects Owned by Government
Construction projects are defined by government for a variety of reasons. Once government defines all the construction
projects it intends to launch during a certain fiscal year, a budget approval request is sent to the parliament. Time span
and budgets for these construction projects are decided on mainly because of political considerations and without
enough attention to the accompanying feasibility studies. Once the project is enacted by parliament and a budget is
assigned to it, the government calls for tenders; at this point, consultants and contractors scrutinize the timelines and
the assigned budgets. If they conclude that the assigned budget and enacted timelines are not realistic, the government
sends revision requests to the parliament. This inefficient procedure is responsible for more than 18% of the delays
under law, regulation, and other general defects, and is presented as 4.3, financial difficulties stemming from
governmental budgeting. The above discussion reveals that high interaction exists between different delay factors.
Ineffective regulations result in improper supervisory and executive procedures that further contribute to delays and
disputes. Governmental regulatory bodies are recommended to find prompt and effective resolutions to these
problems.
Valuable data with regard to the initial cost estimates and proposed timelines as well as final costs and actual timelines
for several construction projects were gathered by the closed questionnaire. Section 5.5 conducts a number of
regressions using these figures.
Final Length
5.5 Regression Analysis
Results of the regression analyses makes it possible for owners, consultants, and contractors to revise their initial
proposals in terms of cost and length, if a causal relationship between initial and final proposals exists. This analysis
is performed on the reported initial and final length and cost, obtained from the questionnaires. Figure 1 depicts the
relationship between the initial and final project length. Figure 2 illustrates the relation between the initial and final
project cost. Both of these figures verify that there is a high degree of linear relationship between these variables. In
both figures, the horizontal axis belongs to initial estimates and the vertical axis belongs to actuals.
70
60
50
40
30
20
10
0
0
5
10
15
20
Initial Length
25
30
35
Figure 1 – Initial Vs. Final Length of Projects
For the case of length of projects, the regression line is:
y  5.804  1.076 x
(13)
Where x is the number of initial months in the first proposal and y is the actual length of project in months. In other
words, for any proposed length, one should expect that the actual timeline would take 5.8 months more than the initial
proposal. Table 3 provides the results of the goodness of the regression test at a 95% confidence level. The reported
p  value is 0.000 for the regression coefficient and 0.001 for regression constant. Thus, one can conclude that the
regression line is significant. The last two columns of this table present a 95% confidence interval for the coefficient
and intercept values. Similarly, a regression line can be generated for project costs:
y  113,762.9  1,470 x
(14)
7,000,000.00
Final Cost
6,000,000.00
5,000,000.00
4,000,000.00
3,000,000.00
2,000,000.00
1,000,000.00
0.00
0.00
1,000,000.00 2,000,000.00 3,000,000.00 4,000,000.00 5,000,000.00 6,000,000.00
Initial Cost
Figure 2 – Initial Vs. Final Cost of Projects
x is the initial cost in thousands of USD and y is the actual cost of the project in USD. Table 4 presents the
results of the goodness of the regression test at the 95% confidence level. Once again, the resulting p  values
Here,
conclude a significant regression line in the selected confidence level.
Table 3 – Results Goodness of Regression Test for Length of Projects
Standardized
Unstandardized Coefficients
95% Confidence Interval for B
Coefficients
t
Sig.
Std.
Upper
B
Beta
Lower Bound
Error
Bound
Constant
5.804
1.719
3.377
0.001
2.377
9.231
0.747
VAR1
1.076
0.114
9.477
0
0.85
1.302
Results of the reported regression analyses are extremely important for owners, contractors, and consultants if they
wish to reduce project tardiness and propose a more accurate cost structure for a construction project.
Constant
VAR1
Table 4 – Results Goodness of Regression Test for Project Costs
Standardized
Unstandardized Coefficients
95% Confidence Interval for B
Coefficients
t
Sig.
Std.
Upper
B
Beta
Lower Bound
Error
Bound
113,762.90
25.902
3.074
0.003
39,964.29
187,561.43
0.974
1,470
0.028
36.133
0.000
1,388.57
1,550.00
6. Conclusions
This paper studied the reasons for delay in construction projects and selects Iran, as a developing country
with several ongoing construction projects, for the case study. Since construction projects are usually capital intensive
and act as a driving force for the whole economy of a country, reducing delays and the costs arising from such delays
is very important. Although one can list a large number of studies about the reasons of delay for different countries in
the Middle East and North Africa, there are almost no similar studies for Iran. In this paper, an open questionnaire
was used along with an extensive literature review to identify the reasons for delays in construction projects. Several
interviews with owners, active contractors, consultants, and other experts were conducted accordingly. Afterward, a
closed questionnaire was developed and filled out by 200 respondents; out of the 200 questionnaires, 86 had the
appropriate characteristics and were chosen for further analysis.
A multinomial probability model was developed to estimate the amount of contribution of each delay factor in a
delayed construction project. Point and interval estimates were then reported. The delay factors and their interactions
with each other were further discussed. Pareto analysis of the reported factors provided a deep insight into the best
places to start if one wishes to reduce delays. Moreover, regression analysis was performed to provide more insight
for owners, contractors, and consultants about the differences between initial and final estimates of a typical
construction project in terms of both length and cost. Regression analysis provides a baseline for project managers
and cost estimators, should they aim to reduce inaccuracies in terms of project length and cost.
Finally, it can be noted that a significant amount of delay stems from regulations, outdated standard contract terms,
and lack of planning by government authorities. Therefore, a future research direction can be focusing on and resolving
any of these delay factors. Moreover, developing an expert system with learning abilities that can update and correct
the results of this study and other similar studies is very promising. The described expert system is very valuable for
regulatory bodies and government authorities, if they wish to reduce delays and the accompanying costs.
7. References
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