Predicting the Occurrence of Construction Disputes Using Machine Learning Techniques
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 4
Abstract
The construction industry is overwhelmed by an increasing number and severity of disputes. The primary objective of this research is to predict the occurrence of disputes by utilizing machine learning (ML) techniques on empirical data. For this reason, variables affecting dispute occurrence were identified from the literature, and a conceptual model was developed to depict the common factors. Based on the conceptual model, a questionnaire was designed to collect empirical data from experts. Chi-square tests were conducted to reveal the associations between input variables and dispute occurrence. Alternative classification techniques were tested, and support vector machine (SVM) classifiers achieved the best average accuracy (90.46%). Ensemble classifiers combining the tested classification techniques were developed for enhanced prediction performance. Experimental results showed that the best ensemble classifier, obtained from the majority voting technique, can achieve 91.11% average accuracy. Based on Chi-square tests, the most influential factors on dispute occurrence were found as variations and unexpected events in projects. Other important predictors were all related to the skills of the parties involved. This study contributes to the construction dispute domain in three ways: (1) by proposing a conceptual model that combined the diverse efforts in the literature for identifying variables affecting dispute occurrence; (2) by highlighting the influential factors, such as response rate and communication skills, as indicators for potential disputes; and (3) by providing an empirical ML-based model with enhanced prediction capabilities that can function as an early-warning mechanism for decision-makers.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Company names, project names, and participant contact information cannot be provided to preserve the anonymity of participants and to comply with legal issues, such as privacy laws; instead, generic identification numbers have been assigned to each case.
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Journal of Construction Engineering and Management
Volume 147 • Issue 4 • April 2021
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© 2021 American Society of Civil Engineers.
History
Received: May 4, 2020
Accepted: Nov 9, 2020
Published online: Feb 11, 2021
Published in print: Apr 1, 2021
Discussion open until: Jul 11, 2021
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