Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan

Abstract

The type and number of defects constitute a major indicator of project quality and are thus emphasized in project management. Therefore, it is necessary to explore appropriate tools and methods to train/test/analyze defect-related big data in order to effectively explain the cause/rule/importance of the defect, to understand the focus of site management, and to effectively prevent defects. The aim of this study is to explore the capability of three kinds of decision tree algorithms, namely classification and regression tree (CART), chi-squared automatic interaction detection (CHAID) and quick unbiased efficient statistical tree algorithms (QUEST), in predicting the construction project grade given defects. Firstly, a total of 499 types of defects were identified after the analysis of the data of 990 projects obtained from the Public Construction Management Information System (PCMIS). Secondly, inspection scores and defect frequencies were estimated to perform cluster analysis for re-grouping the data to create project grade. Thirdly, decision trees were used to classify rules for defects and project grades. The results revealed that, among the three algorithms, CHAID generated the most classification rules and exhibited the highest defect prediction accuracy. The finding of this research can improve the defect prediction accuracy and management effectiveness for construction industry.