Natural Risks. Inclement. Weather. 0.0297 0.0132. Wars and Civil. Disorders ..... Hard", Technical Report, KSL-87-27, Medical Computer Science Group,.
Estimating Residential Projects Cost Contingencies Using a Belief Network By Ahmed M. G. Khalafallah1, Mahmoud A. Taha2, and Moheeb El-Said3
ABSTRACT: The aim of this research is to develop a system for estimating cost contingencies during a tender preparation. The proposed system is composed of a causal belief network and a risk-contingency model. A survey was conducted to evaluate the significance of twenty two factors affecting residential projects' cost overruns. The results were employed to define the conditional probability distributions of the belief network nodes. The main role of the belief network is to assess the level of risk and uncertainty associated with a project. Based on this anticipated level and a risk-contingency model developed based on experts' opinions, an estimated suitable contingency percentage can be predicted. Finally, a user friendly interface was developed to facilitate the use of the system using Visual Basic programming language. KEYWORDS: Belief Networks- risk - uncertainty- contingency- risk analysis 1. INTRODUCTION Dealing with risks and uncertainties is usually a problem for contractors and owners. This problem might end up with substantial financial losses for both parties. The sources of risks and uncertainties in a project are several. And not only is the size of a project the main factor that causes risk, but there are also other factors such as cash flow, underestimation of direct costs, and quality problems. In approaching the problem of risk analysis and dealing with uncertainties, several methods were developed. Among these methods, providing incentives (contingencies) – if any of the participants is expected to take a greater risk – is one of the most important. Contingencies may be added to budget items (cost contingencies) or schedule activities (duration contingencies). Many contractors as well as owners rely on this approach to protect them against certain types of risk. The traditional methods used for risk analysis and contingency estimation are usually neglected by cost estimators because of a number of shortcomings associated with them. Generally, these models are characterized by their 1
Graduate student, Structural Engineering Department, Cairo University. Assistant Professor, Structural Engineering Department, Cairo University. 3 Professor of Construction Engineering and Management, Structural Engineering Department, Cairo University. 2
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complexity and high mathematical treatment and thus difficulty for application. As a result, usually most of the contractors neglect these methods and based on their intuition they set a percentage for cost contingencies. In Egypt, contingency is usually estimated either as a fixed percent of the project directs costs or using simple superficial analysis of the tender documents. Usually the contingency is set by the estimator and the top management according to the company policy. Bayesian Belief Networks (BBNs) applications for decisionmaking under uncertainty are widespread. The uncertainty inherent in risk analysis makes it an ideal application area for BBNs. This study aims to develop a fast and reliable system using Bayesian Belief Networks - which demonstrated high abilities for dealing with uncertainties and incomplete data - to assist in the estimation of cost contingencies. The scope of the study is focused on residential buildings projects with direct cost less than 25 million L.E. 2. PREVIOUS WORK: Several research works have been developed to address the problems of risk analysis and contingency estimation. The main approaches to risk and contingency estimation can either be deterministic, probabilistic (Hassanein and Cherlopalle, 1999). Other approaches depended on the fuzzy sets theory or neural networks. • Spooner (1974) suggested the use of probabilistic estimation using subjective three-value estimates for the primary quantities of each account. The standard deviation of the total estimate produced is considered a measure of contingency in the conventional estimate. • Ibbs and Crandall (1982) developed a risk decision mathematical model based on utility theory. The researchers suggested the use of a multiattribute objective function and the utility theory to develop a risk decision mathematical model. • Paek et al. (1993) developed a risk-pricing fuzzy set model for quantifying risk-associated consequences under uncertainty and incorporating the quantified consequences into the bidding price decision process. • Hassanein and Cherlopalle (1999) developed a model to estimate the total construction costs using the fuzzy sets theory. The authors concluded that the accuracy of this simple model is comparable to the previous more sophisticated models. • Mak and Picken (2000) used a risk analysis methodology (estimation under risk analysis (ERA)) to determine construction project contingencies. ERA estimates the contingency of a project by identifying and costing risk events associated with a project. This method is derived 2
from the MERA (Multiple Estimating using Risk Analysis) technique developed by the Public Services Agency of the UK. • Hastak and Shaked (2000) developed a model for international construction risk assessment. The model uses 73 risk indicators to assess the risk involved in an international construction operation. • Chen et al. (2000) developed a neural network approach to risk assessment and contingency allocation. • Bent (2001) proposed using a scoring system for 25 contingency factors and using a contingency chart to allocate the corresponding contingency percentage. The previous work on belief networks in the construction area can be summarized in the following points: • McCabe et al. (1998) developed a method to automatically improve the performance of construction operations by integrating computer simulation and belief networks. • Raimondi and McCabe (1999) developed a belief network model for diagnosing specific concrete pavement problems and assigning an applicable repair suggestion. • Njardardottir et al. (1999) developed a concrete bridge deck deterioration diagnostic tool using belief networks. • Pershad (2000) developed a Bayesian Belief Network to assess corporate credit risk. • Eyers and McCabe (2001) developed a belief network for the analysis of direct cost risk in building construction. 3. SYSTEM DEVELOPMENT The new proposed system attempts to overcome the drawbacks of the previous models by avoiding complexity, high mathematical treatment and thus difficulty of application. The proposed system also aims to be less time-consuming. This system is composed of a BBN and a risk-contingency model. The role of the belief network is to identify the level of risk associated with a project and the role of the risk-contingency model is to allocate the corresponding contingency percentage. Following are the steps of achieving the preset objectives: 3.1. Data Collection and Preparation: The first step was to identify the factors affecting the level of risk and uncertainty of a project and consequently affecting the contractor's estimation of contingency. These factors were identified through a literature review and a number of unstructured interviews with experts in the domain of residential 3
buildings construction. The criterion behind the identification of these factors was to select the factors with relevance to the construction of residential buildings in Egypt and which are considered to be a contractor's responsibility. Following are the identified factors grouped into five groups to facilitate the definition of the conditional probability tables of the belief network. 3.1.1. Construction risks: Different site conditions Quality problems Equipment failure Poor productivity
Poor site safety and security Labor strikes Defective work
3.1.2. Design risks: Insufficient detailing Design errors
Design changes
3.1.3. Financial risks: Inadequate cash flow Inflation, foreign currency fluctuation and exchange rate changes Underestimation of direct costs 3.1.4. Political and regulatory risks: Changes in laws and regulations Wars and civil disorders
Defaults by subcontractors and suppliers
Problems with licenses and permits
3.1.5. Natural risks: Collapse and slide of the Floods land. Losses due to fires Earthquakes Inclement weather A survey through a number of interviews was performed over some of the construction firms working in Egypt to collect information about the identified factors. The interviewee was to evaluate the factors by assessing two attributes for each factor; (α) the probability level of the risk occurrence and (β) the level of loss should the risk materialize. α Was assigned three values high, medium, or low probability of occurrence. β Was assigned a range from 0 to 5 where the very severe level of loss is represented as 5. The rating attributes, α andβ, were assigned numerical conversions to be used in the analysis. 3.2. Data Analysis Risk significance, denoted by RS, can be described as a function of the two attributes α and β [21] as follows: 4
RS= ƒ (α,β) (1) In order to assess the significance of the identified factors, a significance score for each factor can be calculated, as illustrated in Eq. (2), by multiplying the probability of occurrence (α) by the degree of impact (β) [21]. (2) S i j = αi j β i j Where Sij is the significance score for risk i as acknowledged by respondent j αij is the probability of occurrence for risk i, as acknowledged by respondent j βij is the level of contractor's potential loss (degree of impact) for risk i, as acknowledged by respondent j Accordingly, a relative significance index score (RSIS) can be calculated for each factor through the following model: N
RSIS = ( ∑ Sij ) / N
(3)
i
j=1
Where RSISi is the relative significance index scores for risk i. N is the number of the respondents According to Eq. (3), the calculated RSISs and their standard deviations are shown in Table 1. The results show some variability in the opinions of the interviewees. However, this variability decreases in the calculated average of the β values which are used to define the conditional probability tables of the BBN. The 22 identified risk factors were ranked according to their RSISs. This order represents the order of significance by which estimators consider these risk factors when analyzing risk and assigning contingency. The top ten significant factors, identified through the RSIS analysis, are shown in Figure 1. Where, ACF: Inadequate cash flow, INSD: Insufficient detailing, UEC: Underestimation of direct costs, DE: Design errors, QP: Quality problems, DW: Defective work, DSC: Different site conditions, CLPS: Collapse and slide of the land, DC: Design changes and PSS: Poor site safety and security
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Table 1 The Relative Significance Index Scores (RSIS) for the different risk factors
Quality Problems Equipment Failure Poor Productivity Poor Site Safety & Security Labor Strikes
0.1954
0.1040
0.2343
0.0986
0.0663 0.1320
0.0452 0.0953
0.1480 0.0177
0.0758 0.0152
0.2126
0.0997
0.1206
0.0664
0.0491
0.0288
0.1257
0.0657
0.3154
0.1280
0.2931
0.1778
0.1006
0.0503
0.1240
0.0754
Financial Risks
Political Risks
Defective Work Changes in Laws and Regulations Wars and Civil Disorders Problems With Licenses Inadequate Cash Flow Underestimation of Direct Costs Inflation, Exchange Rate & Foreign Currency Fluctuations Defaults By Subcontractors and Suppliers
6
RSIS Design Risks
Construction Risks
Different Site Conditions
Std. Dev.
Natural Risks
RSIS
Insufficient Detailing Design Errors Design Changes Earthquakes Fire Floods Collapse and Land Slide Inclement Weather
Std. Dev.
0.3114 0.1051 0.2486 0.0887 0.1811 0.0643 0.0371 0.0275 0.0326 0.0270 0.0223 0.0204 0.1840 0.0898 0.0297 0.0132
RSIS
0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
ACF
INSD
UEC
DE
QP
DW
DSC
CLPS
DC
PSS
Risk Factor
Figure 1 - The top 10 significant risk factors and their RSISs
The analysis also shows that the most significant risk group is the "construction risks" group followed by the "Financial Risks", "Design Risks", "Natural Risks" and finally "Political Risk" groups. Figure 2 shows the relative significance of the different risk groups: Financial Risks 26%
Design Risks 23%
Natural Risks 10% Political Risks 9%
Construction Risks 32%
Figure 2 - Relative significance among the risk groups 3.3. Developing the BBN using MSBN: The benefits of belief networks in dealing with uncertainties are employed here. A causal belief network is introduced to assess and predict the level of risk and uncertainty associated with a project based on the factors identified. The software used to develop and construct the belief network is Microsoft® Belief Networks (MSBN™) version 1.001. 7
To build a belief network, there are some steps that should be carried out. First, the variables of the network should be identified. Second, the relationships between these variables are to be set. Third, the states of the variables are defined. Finally, the probabilities and joint probabilities should be defined for parent and child nodes. Following are the steps of constructing the BBN: 3.3.1 Variables Definition: The input variables of the belief network are 21 risk factors of the identified risk factors. And because the number of probabilities to be assessed for a variable is exponential in the size of the variable's parental set, these factors will not be connected directly to the target node. Instead, the approach of divorcing parents by introducing intermediate variables (Olesen, K.G. 1989) will be adopted in this model. This will be achieved by categorizing the factors into five groups (the risk groups). Also, "Labor Strikes" factor is ignored from the "construction risks" category because both α and β for this factor are assessed to be very low. The benefit of ignoring this factor is the reduction of the conditional probabilities to be assessed at the category node of this factor (Construction risk occurs) from 256 to 128 probabilities. The five category nodes are connected to the target node which represents a final variable of the BBN. 3.3.2 Variables Relationships: In a belief network model, variables are connected with arcs to represent the relationships between them. In this model, the relationship defined between variables is the conventional "cause and effect" relationship. In this sense, the parent nodes are considered the causes of their child node. For example, in this model, the input variables are considered the causes of their categorizing node and similarly the categorizing nodes are considered the causes of the target node. The relationships between the risk factors are ignored because they aren't many. 3.3.3 Variables States: Because MSBN uses discrete variables only, each variable of the belief network will be assigned two states; true and false. This also helps to reduce the costs of the construction and maintenance of the BBN. Figure 3 presents the constructed singly connected causal belief network.
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Table 2 A Sample of the conditional
P( Project is Risky = False)
P( Project is Risky = True)
Natural Risks
Political risks
Financial Risks
Design Risks
Construction Risks
3.3.4 Defining Probabilities: probability table for the target node The last task in the construction of the belief network is to set the probabilities that represent its quantitative part. This task is often the most bothering task in the whole process. In most of belief networks applications, probabilistic information is available from various sources like statistical data, literature and human experts. However, in applications like risk 1.000 0.000 management, historical statistical T T T T T 0.679 0.321 data are usually unavailable and if F T T T T 0.874 0.126 available they are incomplete. Thus, T F T T T 0.553 0.447 the only source of data for such F F T T T 0.738 0.262 applications would, probably, be the T T F T T experts of the domain. The average values of the expected levels of loss, obtained from the survey, are adopted to represent the knowledge of the domain experts. These values are employed to define the conditional probability tables for child nodes assuming that their parental nodes are uncorrelated. Table 2 presents a sample of the conditional probability table for the target node. The certainty (Probability of the node's "True" state) of the target node, called "Project is Risky", will be considered as the indicator of the project's level of risk and uncertainty. The estimator is sets the certainty (likelihood of occurrence) of the risk factors (the root notes of the belief network) according to his own perception or according to any historical data he might have in his hand. According to the input values, a value for the likelihood of occurrence of the target node is generated to represent the level of risk and uncertainty in a project. By increasing/decreasing the certainty of an input variable (prior probability), the evidence in the target node increases/decreases accordingly. 3.4. Developing the Risk-Contingency Model: This step involves the development of a model to estimate a contingency percentage (with respect to the tender's total direct costs) based on the level of risk and uncertainty predicted by the belief network. Through the survey, the interviewee was asked to give his opinion about the suitable contingency
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percentage to be set for different levels of risk and uncertainty. The results showed some variation in the opinion especially at high levels of risk and
Inclement Weather
Changes in Laws and Regulations
Wars and Civil Disorders Problems with Licenses and Permits
Collapse and Land Slide
Political Risk Occurs
Fire
Natural Risk Occurs
Underestimation of Direct Costs Inadequate Cash Flow
Earthquakes
Floods
Project is Risky Different Site Conditions
Construction Risk Occurs
Quality Problems
Equipment Failure
Defective Work
Poor Productivity
Poor Site Safety
Financial Risk Occurs
Defaults by Subcontractors and Suppliers
Design Risk Occurs
Design Changes
Inflation, Exchange Rate & Foreign Currency Fluctuations
Insufficient Detailing Design Errors
uncertainty. This variation might be attributed to three reasons: (1) Experts recognize the same risk level differently; (2) Experts behave differently; some are more conservative than the others; and (3) The competition factor. The majority of experts also stated that they wouldn't be willing to enter a bidding process if the contingency percentage is more than 5% of the total direct costs of a project. To deal with the variation in experts' opinions, it is worthy to consider the majority of these opinions rather than ignoring the extremities. Here we will consider the variation in the opinion is attributed mainly to the second reason. As a result, three curves - the less conservative, the modest and the more conservative - would be plotted rather than one as illustrated in Figure 5-6. Here we propose a PERT-like approach to calculate the expected contingency percentage using the values obtained from the three curves. The "Modest" curve is plotted by curve fitting the points representing the mode values. The averages of the highest and the lowest thirds of data collected were used to plot the "More Conservative" and the "Less Conservative" curves respectively.
10
45
Estimated Cost Contingency %
40
Less Conservative
35
y = 22.306x3 - 8.1349x2 + 27.185x + 0.062
Modest
30 25
More Conservative
20 15
y = -18.029x4 + 41.222x3 - 22.409x2 + 17.135x - 0.036
10
y = 3.4294x2 + 2.1615x + 0.0179
5 0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Project Risk Level
Figure 4 - The Risk-Contingency model 3.5. Testing and Validation: After constructing the system, the next step is to test it and determine its percentage error of estimation. Three completed projects were used in testing and validating the results obtained from the system. The estimator who prepared the tenders of the three projects was asked to give and estimate the likelihood of occurrence values for the belief network's attributes at the time the bid is prepared and without accounting for the real unusual circumstances that materialized unexpectedly. The percentage errors in estimating the cost contingency in the three projects were calculated and their absolute values were 19.2%, 12.95% and 20.4%. Thus, the average error of the system is 17.5%. For subjective problems like the problem in hand, these errors seem suitable. However, because projects 1 and 3 witnessed unusual materialization of risk events, it is unfair to compare the expected estimate directly (without adjustment) to the real cost overruns. The adjustment can be done by setting the states of the risk factors that materialized severely to true in the constructed belief network. Following this approach, the estimates were adjusted and the errors in estimating the contingency for the three projects were 11.5%, 12.95% and 15.5%. Thus, the average error in estimation of the system is 13.3%. It is argued about the worth of modifying the system's inputs (probabilities and states of the input risk factors) in order to reflect real time circumstances after an estimate has already been made and reflected in a contract, yet this modification is still important because it gives an indication about how much the contractor's 11
profit may be damaged by providing the estimate of the new anticipated cost overruns. Accordingly, it highlights how strong should new measures be taken to control and reduce any further anticipated damage. 3.6. Sensitivity Analysis: The next step in the process of the system development is to investigate the effects of inaccuracies of the system's parameters (input attributes) on its output (expected contingency percentage). The top ten significant risk factors presented were chosen to undergo a sensitivity analysis test. One of the projects used in testing the system was also randomly chosen for this test. The input attributes are varied in a range from -20 to +20 percent of the base estimate input and a new estimated contingency percentage according to each change was obtained from the system. For each factor, the sensitivity factor is calculated by dividing the percentage change in the expected contingency by the percentage change in the input variable. Thus, the sensitivity factors for the top ten significant risk factors can be summarized as shown in table 3. The values shown in Table 3 obviously demonstrate that the system is highly insensitive to the input attributes' values. This result agrees with the opinion that probabilistic networks can be highly insensitive to inaccuracies in the numbers in their quantitative part (Henrion 1996), (Pradhan, 1996) and (Druzdzel, 2000). This result is considered an asset for the developed belief network because the variation in input among different estimators for the same project will not effectively change the expected value of contingency. Table 3 Input attributes sensitivity factors
-20% 0.09833 0.03704 0.07616 0.01408 0.05399
Inadequate Cash Flow Insufficient Detailing Underestimation of Direct Costs Design Errors Quality Problems
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Percentage Change -10% 10% 0.09755 0.09859 0.03652 0.03704 0.07616 0.07616 0.01408 0.01408 0.05373 0.05425
20% 0.09807 0.03678 0.07590 0.01408 0.05399
0.08868 0.08659 0.11085 0.05008 0.03312
Defective Work Different Site Conditions Land Collapse or Slide Design Changes Poor Site Safety
0.08868 0.08712 0.11059 0.05008 0.03339
0.08868 0.08816 0.08607 0.08633 0.11007 0.11033 0.04956 0.04956 0.03286 0.03286
3.7. System Integration: The final step of the system development process is to construct a prototype to integrate the belief network and the risk-contingency model together forming the system of estimation. The system is intended to be user friendly and easy to use to overcome the deficiencies in previous systems. Another objective is to make the system a stand-alone. Microsoft® Visual Basic™ Version 6.0 programming language is used to integrate the belief network – developed using MSBN™ – with the contingency model through a user friendly interface. MSBN authoring tool facilitates the integration process by presenting its functions embedded in a DLL called MSBN32.DLL and authorizing the distribution of the DLL with the software for non commercial purposes. The calls for the functions follow the Windows standard “Pascal” calling convention as is recommended for all DLLs in the Windows environment. As the user starts the program by clicking the "Start" button, the belief network loads from a file called riskbelief.dsc into the computer's memory. The first screen that appears is the "Natural Risks" screen as shown in Figure 4. If the user's intention is to estimate a new project, he/she assigns a probability of occurrence for each factor. For this purpose the option button should be set to the "Not observed" position. If a risk factor will be mitigated using methods other than contingency, the option button should be set to "Observed-False" or assigning a zero probability of occurrence for that risk factor. The option "Observed-Will Occur" is to be used if the risk factor is highly anticipated, expected to have a severe effect or already have materialized with unexpected high effect. The probabilities entered should be represented within the range from 0 to 1 and they should correspond to the probability of a true state at each node. These probabilities can be captured from data about previous completed projects or from the estimator's own experience and perception.
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Figure 5 - "Natural Risks" screen Pressing the "Next" button on the "Natural Risks" screen, the probabilities are loaded into the belief network using the function "EcsUiSetProbsDisc" of the MSBN32.dll and the "Construction Risks" screen appears as shown in Figure 5. The parameters of the "EcsUiSetProbsDisc" are the handle (model index), the node number, the count of entries in the probability set and the probabilities vector.
Figure 6 - "Construction Risks" screen 14
The same procedure is repeated again, entering the "Political Risks", "Financial Risks", and "Design Risks" through different screens. Finally the user reaches the last screen called "Cost Contingency Percentages" shown in Figure 6.
Figure 7- The results screen "Cost Contingency Percentages" When the user presses the "Estimate Cost Contingency Percentages" button the probability of the target node's true state is retrieved from the belief network using the function EcsUiBelOfNode embedded in the MSBN32.dll. This value is used to predict the different contingency probabilities from the contingency model and the expected contingency percentage using the PERT technique. 3.8. Limitations: In order to use the developed system efficiently, it is advised to go by the following recommendations and limitations: Any risk factor other than those addressed by the system is to be considered outside its scope and should be mitigated separately. The competition factor, the effect of bidding time frame and owner's poor reputation are not taken into consideration by the system. These factors, among other factors, might affect how an estimator behaves conservatively. The belief network's knowledge is considered static and based on the average expected levels of loss obtained from a survey of 35 interviews with 15
construction experts. However, this knowledge can be modified by editing the belief network's file (riskbelief.dsc) using MSBN. This will require some knowledge of MSBN. It is advised that contingency, in general, should be used with risks which are assessed to be of a low likelihood of occurrence. It is advised to investigate all avenues of responding to and mitigating risks before addressing the contingency approach. It is not advised to use the system for projects of high risk level natures. The system could produce considerable error in estimation due to the fact that there is a large variability in experts' opinions for high risk level projects as illustrated in Fig. 4. 4. Summary and concluding remarks: This research aimed to develop a system based on a BBN to be used by cost estimators as a guide for estimating cost contingencies during a tender preparation. The proposed system is entitled CCEUBN (Cost Contingency Estimation Using a Belief Network) and is composed of a BBN and a riskcontingency model. The research scope focuses on lump sum residential buildings projects with direct costs less than 25 million L.E. Through the literature review and unstructured interviews, twenty two factors are identified to be the most significant factors affecting projects' cost overruns. These factors are the BBN attributes. Thirty five effective interviews were conducted and their results were employed to develop a relative significance index score for these risk factors. The risk factors are then ranked from the most significant to the less significant. The BBN was constructed using Microsoft Belief Networks software. The interviews data analysis was employed to define the conditional probability distributions of the BBN nodes. The definition of these conditional probabilities was facilitated by categorizing the risk factors into five groups. The main role of the BBN is to anticipate the level of risk and uncertainty associated with a project. Based on experts' opinions, a riskcontingency model, consisting of three curves, was developed. A PERT like technique was employed to estimate a suitable expected cost contingency allowance for the anticipated level of risk and uncertainty. The developed system was tested using historical completed projects and its average error of estimating the cost contingencies was calculated to be 13.3%. A sensitivity analysis was conducted on the system and showed that the system is highly insensitive to the inputs' inaccuracies. Finally, a user friendly interface was developed to facilitate the use of the system using Visual Basic programming language. The encouraging results of the system invite research to develop more powerful models, using larger and more representative data sets to improve the estimation. 16
. 6. References 1. Al-Bahar, J. F., and Crandall, K. C., "Systematic Risk Management Approach for Construction Projects", Journal of Construction Engineering and Management, Vol. 116, No. 3, September 1990, pp. 533547. 2. Carr, R. I., "Paying the Price of Construction Risk", Journal of the Construction Division, Vol. 103, No. 1, March 1977, pp. 153-161. 3. Charniak, E., "Bayesian Networks Without Tears", AI Magazine, 12(4), 1991, pp. 50-91. 4. Cooper, G.F., "Probabilistic Inference Using Belief Networks is NPHard", Technical Report, KSL-87-27, Medical Computer Science Group, Stanford University, 1987. 5. Druzdzel, M. J., and Van Der Gaag, L.C., "Building Probabilistic Networks: Where Do the Numbers Come from?” IEEE Transactions on Knowledge and Data Engineering, 12(4), 2000, pp.481-486. 6. Hassanein, A. A. and Cherlopalle V., "Fuzzy Sets Theory and Range Estimating", 1999 AACE International Transactions, Risk.04, AACE International, Morgantown, WV, 1999. 7. Hastak, M., and Shaked, A., "ICRAM-1: Model for International Construction Risk Assessment", Journal of Management in Engineering, Vol. 16, No. 1, January/February 2000, pp. 59-69. 8. Henrion, M., Breese, J. S., and Horvitz, E. J., "Decision Analysis and Expert Systems", AI Magazine, Vol. 12, No.4, 1991, pp. 64-91. 9. Henrion, M., Pradhan, M., del Favero, B., Huang, K., Provan, G., and O'Rorke, P., "Why is Diagnosis Using Belief Networks Insensitive to Imprecision in Probabilities?" Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, 1996, pp. 307314. 10. Hovel, D., "Belief Network Applications Programming Interface", Decision Theory Group, Microsoft Research, Revision 0.8, December 1995. 11. Ibbs, C. W., and Crandall, K. C., "Construction Risk: Multi-attribute Approach", Journal of Construction Engineering and Management, Vol. 108, No. 2, June 1982, pp. 187-200. 12. Mak, S., and Picken, D., "Using Risk Analysis to Determine Construction Project Contingencies", Journal of Construction Engineering and Management, Vol. 126, No. 2, March/April 2000, pp. 130-136. 13. Marzouk, M. M. M., "Predicting Markup Using Neural Networks", M.Sc. Degree, Cairo University, 1997. 17
14. McCabe, B., "Belief Networks for Engineering Applications", International Journal of Technology Management, 2001, 21 :( 3/4), pp. 257-270. 15. McCabe, B., AbouRizk, S. M., and Goebel, R., “Belief Networks for Construction Performance Diagnostics”, ASCE Journal of Computing in Civil Engineering, 1998, 12:2, pp. 93-100. 16. Moselhi, O., "Discussion of ‘Pricing Construction Risk: Fuzzy Set Application’", Journal of Construction Engineering and Management, Vol. 121, No. 1, 1995, pp. 163-164. 17. Mulholland, B., and Christian, J., "Risk Assessment in Construction Schedules", Journal of Construction Engineering and Management, Vol. 125, No. 1, January/February 1999, pp. 8-15. 18. Olesen, K. G. et al. "A MUNIN network for the median nerve – A case study on loops" Applied Artificial Intelligence, Vol. 3, 1989, pp. 385 – 404. 19. Paek, J. H., Lee, Y. W., and Ock, J. H., "Pricing Construction Risk: Fuzzy Set Application", Journal of Construction Engineering and Management, Vol. 119, No. 4, December 1993, pp. 743-756. 20. Perry, J. G., and Hayes, R. W., “Risk and its Management in Construction Projects”, Proceedings of the Institution of Civil Engineers, Part 1, Vol. 78, June 1985, pp.499-521. 21. Powell, C., "Laxton's Guide to Risk Analysis & Management", Laxton's Publishers, Jordan Hill, Oxford, 1996. 22. Pradhan, M., Henrion, M., Provan, G., del Favero, B., and Huang, K., "The Sensitivity of Belief Networks to Imprecise Probabilities: An Experimental Investigation", Artificial Intelligence, Vol. 85, 1996, pp. 363-397. 23. Shen, L. Y., Wu, G. W. C., and Ng, C. S. K. "Risk Assessment for Construction Joint Ventures in China", Journal of Construction Engineering and Management, Vol. 127, No. 1, January/February 2001, pp. 76-81. 24. Smith, G. R., and Bohn, C. M., "Small to Medium Contractor Contingency and Assumption of Risk", Journal of Construction Engineering and Management, Vol. 125, No. 2, March/April 1999, pp. 101-108. 25. Sperry, P. E., "Costing Contingencies", Civil Engineering-ASCE, Vol. 58, No. 4, April 1988, pp. 68-69. 26. Spooner, J. E., "Probabilistic Estimating", Journal of the Construction Division, Vol. 100, No.1, March 1974, pp. 65-77.
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ﺗﻘﺪﻳﺮ ﺗﻜﻠﻔﺔ اﻻﺣﺘﻴﺎﻃﺎت ﻟﻤﺸﺮوﻋﺎت اﻹﺳﻜﺎن ﺑﺎﺳﺘﺨﺪام اﻟﺸﺒﻜﺎت اﻻﻋﺘﻘﺎدﻳﻪ اﺣﻤﺪ ﺟﻼل ﺧﺎف اﷲ - ٤د .ﻣﺤﻤﻮد ﻋﺒﺪ اﻟﺴﻼم ﻃﻪ - ٥أ.د .ﻣﻬﻴﺐ اﻟﺴﻌﻴﺪ إﺑﺮاهﻴﻢ
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ﺗﻄﻮﻳﺮ ﻧﻈﺎم ﻟﺘﻘﺪﻳﺮ ﺗﻜﻠﻔﺔ اﻻﺣﺘﻴﺎﻃﺎت أﺛﻨﺎء ﺗﺠﻬﻴﺰ اﻟﻌﻄﺎءات ﺗﻢ ﻣﻌﺎﻟﺠﺘﻪ ﻓﻰ هﺬا اﻟﺒﺤﺚ .أن اﻟﻨﻈﺎم اﻟﻤﻘﺘﺮح ﻳﺤﺘﻮى ﻋﻠﻰ ﺷﺒﻜﻪ اﻋﺘﻘﺎدﻳﻪ وﻧﻤﻮذج اﻟﻤﺨﺎﻃﺮ- اﻻﺣﺘﻴﺎﻃﺎت .ﻟﻠﻮﺻﻮل ﺁﻟﻲ ذﻟﻚ ﺗﻢ ﻋﻤﻞ ﻣﺴﺢ ﻟﺘﻘﺪﻳﺮ أهﻤﻴﺔ اﺛﻨﻴﻦ وﻋﺸﺮون ﻋﺎﻣﻼ ذوى ﺗﺄﺛﻴﺮ ﻋﻠﻰ زﻳﺎدة ﺗﻜﻠﻔﺔ ﻣﺸﺮوﻋﺎت اﻹﺳﻜﺎن .ﺗﻢ اﺳﺘﺨﺪام ﻧﺘﺎﺋﺞ اﻟﻤﺴﺢ ﻟﺘﺤﺪﻳﺪ اﻟﺘﻮزﻳﻊ اﻻﺣﺘﻤﺎﻟﻲ اﻟﻤﺸﺮوط ﻟﻌﻘﺪ اﻟﺸﺒﻜﻪ اﻻﻋﺘﻘﺎدﻳﻪ .أن اﻟﻘﺎﻧﻮن اﻷﺳﺎﺳﻲ ﻟﻠﺸﺒﻜﻪ اﻻﻋﺘﻘﺎدﻳﻪ هﻮ ﺗﺤﺪﻳﺪ ﻣﺴﺘﻮى اﻟﻤﺨﺎﻃﺮ واﻻﺣﺘﻴﺎﻃﺎت اﻟﻤﺼﺎﺣﺒﺔ ﻟﻠﻤﺸﺮوع .اﻋﺘﻤﺎدا ﻋﻠﻰ هﺬا اﻟﻤﺴﺘﻮى وﻧﻤﻮذج "اﻟﻤﺨﺎﻃﺮ – اﻻﺣﺘﻴﺎﻃﺎت" واﻟﺬى ﺗﻢ ﺗﻄﻮﻳﺮﻩ اﻋﺘﻤﺎدا ﻋﻠﻰ رأى اﻟﺨﺒﺮاء ﻓﺎن ﺗﻘﺪﻳﺮ ﻧﺴﺒﺔ اﻻﺣﺘﻴﺎﻃﻲ اﻟﻤﻨﺎﺳﺒﻪ ﻳﻤﻜﻦ ﺗﻮﻗﻌﻬﺎ .أﻳﻀﺎ ﻓﺎن ﺑﺮﻧﺎﻣﺞ ﺣﺎﺳﺐ ﺁﻟﻲ ﺳﻬﻞ اﻻﺳﺘﺨﺪام ﺗﻢ ﺗﻄﻮﻳﺮﻩ ﻟﺘﺴﻬﻴﻞ اﺳﺘﺨﺪام اﻟﻨﻈﺎم.
4ﻣﺪﺭﺱ ﻣﺴﺎﻋﺪ – ﻛﻠﻴﺔ ﺍﳍﻨﺪﺳﻪ – ﺟﺎﻣﻌﺔ ﺍﻟﻘﺎﻫﺮﻩ 5ﺍﺳﺘﺎﺫ ﻣﺴﺎﻋﺪ – ﻛﻠﻴﺔ ﺍﳍﻨﺪﺳﻪ – ﺟﺎﻣﻌﺔ ﺍﻟﻘﺎﻫﺮﻩ 6ﺍﺳﺘﺎﺫ ﺍﺩﺍﺭﺓ ﻭﻫﻨﺪﺳﺔ ﺍﻟﺘﺸﻴﻴﺪ – ﻛﻠﻴﺔ ﺍﳍﻨﺪﺳﻪ – ﺟﺎﻣﻌﺔ ﺍﻟﻘﺎﻫﺮﻩ 19
