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

Chien-Liang Lin,Ching-Lung Fan

doi:10.1080/13467581.2019.1696203

Abstract

ABSTRACTThe 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.

Highlights

Webster’s Dictionary defines “defect” as a lack of something necessary for completeness, adequacy, or perfection.” In short, a defect refers to “the nonconformity of a component with a standard of specified characteristic” (Robert 2005)
The following is the sequence of defects in the order of importance: “Debris on concrete surface” (B4), “Substandard concrete pouring or ramming” (B1), and “No supervisory contractor performs site-safety-related and health-related tasks” (A48)
Decision tree (DT) algorithms were used in this study to compare the efficiency and accuracy of the classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and quick unbiased efficient statistical tree algorithms (QUEST)

Summary

Introduction

Webster’s Dictionary defines “defect” as a lack of something necessary for completeness, adequacy, or perfection.” In short, a defect refers to “the nonconformity of a component with a standard of specified characteristic” (Robert 2005). A defect is a common phenomenon in the construction industry and can have a negative effect on the cost, completion duration, and resources available in a project (Ahzahar et al 2011). Defects are the focus of quality evaluation and serve as an indicator for the performance of a construction project. Cain (2004) maintained that determining the performance of a construction team and identifying aspects that require improvement is a remarkably difficult task without a well-defined basis for evaluating project performance. Several scholars have proposed time, cost, and quality as the three most essential indicators of construction project performance (Munns and Bjeirmi 1996; Chua, Kog, and Loh 1999). A construction project is deemed successful when related tasks are completed on schedule and when budget allocation and construction quality meet the performance goals (Shenhar et al 2001; Chan, Scott, and Lam 2002)

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