Project Cost Contingency Estimation Modeling Usinj Risk Analysis and Fuzzy Expert System

• Main Author: Rohman, Mohammad Arif
• Format: Thesis
• Language: English
• Published: 2009
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://utpedia.utp.edu.my/9390/1/2009%20-Project%20Cost%20Contingency%20Estimation%20Modeling%20Using%20Risk%20Analsis%20And%20Fuzzy%20Expert%20System.pdf; http://utpedia.utp.edu.my/9390/
• Institution: Universiti Teknologi Petronas

Determination of the appropriate project cost contingency, especially during the tendering stage is very important to ensure a successful bidding of the project. Setting too high a cost contingency will not make the tender look competitive, while putting too low will not cover risks that may cause cost overrun during the construction. Traditionally, contractors estimate cost contingency based on subjective judgment, such as 5-10% from the base cost estimated by considering past similar project. This method is typically derived from intuition, past experience and historical data. However, such method does not have a sound basis and is difficult to justify or defend. More objective methods for estimating project cost contingency have been presented. However, most of the methods still rely on formal modeling techniques which sometimes require the user to have knowledge and familiarity with statistical techniques. This research proposes a method to estimate cost contingency using a flexible and rational approach based on risk analysis andfuzzy expertsystem concept. This method could accommodate contractors' subjective judgment and also the use of risk analysis and management concept in the analysis process. The proposed method involved the development of cost contingency model for building and infrastructure projects in Malaysia. To develop the model, a number of common risk factors were identified from the literature. Data and information from the literature were also acquired to specify fuzzy expert system properties, such as membership function, rule base andfuzzy inference mechanism. The fuzzy expert system was developed using scenarios to predict percentage cost contingency allocation. The scenarios were then validated using three case projects by conducting face to face interviews with the project managers. From the validation, it was found that the predictions given by the system were within 20% accuracy compared to actual cost contingencies. A computer program was also developed using MATLAB software to demonstrate the model's application in estimating tender price during the bidding stage