Abstract
Due to the asymmetry of project features, it is difficult for project managers to make a reliable prediction of the decision-making process. Big data research can establish more predictions through the results of accurate classification. Machine learning (ML) has been widely applied for big data analytic and processing, which includes model symmetry/asymmetry of various prediction problems. The purpose of this study is to achieve symmetry in the developed decision-making solution based on the optimal classification results. Defects are important metrics of construction management performance. Accordingly, the use of suitable algorithms to comprehend the characteristics of these defects and train and test massive data on defects can conduct the effectual classification of project features. This research used 499 defective classes and related features from the Public Works Bid Management System (PWBMS). In this article, ML algorithms, such as support vector machine (SVM), artificial neural network (ANN), decision tree (DT), and Bayesian network (BN), were employed to predict the relationship between three target variables (engineering level, project cost, and construction progress) and defects. To formulate and subsequently cross-validate an optimal classification model, 1015 projects were considered in this work. Assessment indicators showed that the accuracy of ANN for classifying the engineering level is 93.20%, and the accuracy values of SVM for classifying the project cost and construction progress are 85.32% and 79.01%, respectively. In general, the SVM yielded better classification results from these project features. This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects.
Highlights
Defects are the focus of quality management and indicators of project performance.Meng [1] studied the performance information of 103 projects and found that there were 90 quality defects, 37 delayed times, and 26 overspending costs
This research was based on an machine learning (ML) algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects
The analytical methods used lack the capability of automatic data exploration, and the evaluation process is complex, time-consuming, and often requires professionals to set the appropriate parameters to obtain the correct results
Summary
Defects are the focus of quality management and indicators of project performance. Meng [1] studied the performance information of 103 projects and found that there were 90 quality defects, 37 delayed times, and 26 overspending costs. The quality defect items contributed to 87.4% of all the items. Defects are one of the main factors for poor construction performance. By using nontraditional analysis methods and using a large database for importing the ML algorithm, it is possible to classify project features and defects in a simpler model. Due to the frequent occurrence of many defects during a construction process, ML can be suitably used in the construction industry to enable a project manager to clearly determine the relationship between defects and project features
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