Project Cost Overrun Risk Prediction Using Hidden Markov Chain Analysis

Abstract

Construction project cost overrun is a common problem in the construction industry. The cost of construction projects is thought to have increased by approximately 33% on average. Several types of research on construction project cost overrun have been conducted and these generally rely on historical data. However, whilst each project has its own project characteristics and cost trend, real-time project cost data are more reliable to forecast its own cost trend. This paper proposes a real-time hidden Markov chain (HMM) model to predict cost overrun risk based on project-owned cost performance data and the corrective actions if adopted. The cost overrun events occurrence in this model was assumed to follow a Poisson arrival pattern. Real-time HMM with a particle filter was used to run the simulation. One SRC building project in Taiwan was used for model validation and comparison. The posterior probabilities from the real-time HMM model were highly consistent with the cost overrun ratios of real construction projects. The proposed cost overrun prediction model could provide an early alert of cost overruns to the project manager. Based on the survey of cost overrun risk and significantly influential factors, we propose effective cost management plans to alleviate the frequency of project cost overrun.

Keywords

• project cost overrun
• hidden Markov chain
• particle filter
• Bayesian sequential simulation