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Title: Data-driven prediction of contract failure of public-private partnership projects
Authors: Wang, Yongqi
Shao, Zhe
Tiong, Robert Lee Kong
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: Wang, Y., Shao, Z. & Tiong, R. L. K. (2021). Data-driven prediction of contract failure of public-private partnership projects. Journal of Construction Engineering and Management, 147(8), 04021089-.
Journal: Journal of Construction Engineering and Management 
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.
ISSN: 0733-9364
DOI: 10.1061/(ASCE)CO.1943-7862.0002124
Schools: School of Civil and Environmental Engineering 
Rights: © 2021 American Society of Civil Engineers. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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