Predicting the Schedule and Cost Performance in Public School Building Projects in Taiwan

The Ministry of Education (MOE) of Taiwan invests about NTD 30 billion a year in Public School Building Projects (PSBPs). However, 95% of the PSBPs have been extended and have incurred increased costs. A PSBP performance evaluation and prediction system was established by using the Fuzzy Delphi Method (FDM), association rules and an Artificial Neural Network (ANN). Sixty-two Taiwanese PSBPs were used as the samples, while eleven high correlation factors that influence the project performance of PSBPs were defined, and the reasons leading to the poor project performance were discussed in this study. Moreover, the results of the test cases operated by ANN showed that the accuracy rate for schedule and cost variability predictions can reach 84%. The high accuracy rate indicated the reliability of priority control for high-risk projects in the future. The proposed approach can be provided to clients, design and construction firms, and project managers to understand the project performance in real time and to establish a dynamic tracking review and response measures for improving the overall project satisfaction.