A BAYESIAN ANALYSIS APPROACH TO QUANTIFY THE COST OVERRUN FACTORS IN ROAD CONSTRUCTION PROJECTS

Abstract

Almost every project in the construction industry is facing cost overrun problem due to several risks involved which results into poor performance in terms of delay and cost overrun in construction projects. In order to ensure project success by addressing those challenges, risk management is considered an integral part of project management. Although many studies have proposed a variety of processes and techniques, and Project Risk Management (PRM) is rapidly evolving as a result, handling uncertainty in complex projects still remains a challenge. Cost overrun is universal phenomena in construction industry and road projects are no exception in it. The economy of every country is measured through the development of infrastructure. Many studies have been carried out globally to identify the causes of cost overrun in road construction projects. However, these studies lack in providing a probabilistic analysis in order to capture the uncertainty of causes of cost overrun. This points toward a clear need of using a quantitative approach in road sector. The current study provides an application of an expert elicitation method based on Bayesian analysis approach to quantify the uncertainty of cost overrun causes in road construction projects of Pakistan. Bayesian method is a powerful analytical tool that will help the decision makers to understand the perception about the risks or causes that affect project performance in terms of cost. The probabilities from Bayesian analysis are simulated with the help of Monte Carlo technique which is quantitative risk analysis approach used to generate most probable outcomes. The significance of results is that they help in developing new effective strategies for risk management in road projects to enhance project performance. It is concluded that instead of using arbitrary contingency sums to account for all risks involved in road projects it is better to identify critical causes and to capture the uncertainty involved in risk identification step that will ultimately improve the efficiency of road construction industry