Wang, Y; Shao, Z and Tiong, R L K (2021) Data-driven prediction of contract failure of public-private partnership projects. Journal of Construction Engineering and Management, 147(8): 0002124, ISSN 0733-9364
Abstract
The public-private partnership (PPP) has been adopted by many governments in developing countries to provide better public services. However, PPP projects have a high risk of contract failure. To proactively predict PPP contract failure and obtain the most significant failure factors from a quantitative perspective, this research compared the performance of different combinations of machine learning models and data-balancing techniques. Forty-three project-specific and country-specific factors were examined, and the top 15 were chosen for the transportation, water and sewer, and energy sectors. The results show that the selected model can forecast contract failure with a recall of 75.9%, 73.3%, and 76.2%, respectively. This study showed the effectiveness and applicability of machine learning in predicting PPP contract failure. The results can facilitate decision making by forecasting the probability of PPP contract failure in the early planning stage.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | contract failure; decision-making process; failure factors; machine learning; public-private partnership |
| Index terms: | machine learning, decision-making, sewer, developing country, failure factor, energy sector, effectiveness, decision-making process, public services, partnership, forecasting |
| Subjects: | risk assessment, prediction and forecasting, artificial intelligence, development economics, performance management, industry analysis, infrastructure and transport systems, administrative law, decision analysis, partnership management |
| Topics: | Engineering Principles, Research Practice, Stakeholder Management, Risk Management, International Construction, Quality Management, Digital Applications, Legal Issues |
| Descriptive scope: | 3 PCA |
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