Project Cost Overrun Risk Prediction Using Hidden Markov Chain Analysis

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.

Automated summary

Construction project cost overrun is a common problem in the industry, with an average increase of approximately 33% in project costs. Previous research on cost overruns relies on historical data, but real-time project cost data that consider project characteristics and trends are more reliable for forecasting. This paper proposes a real-time hidden Markov chain (HMM) model to predict cost overrun risk based on project-owned cost performance data and corrective actions. The model assumes a Poisson arrival pattern for cost overrun events and uses a particle filter to run simulations. Validation results using a building project in Taiwan show that the model's posterior probabilities are highly consistent with real construction project cost overrun ratios. The proposed model can provide early alerts of cost overruns and effective cost management plans to mitigate the frequency of cost overruns.