AI-Enhanced Defect Identification in Construction Quality Prediction: Hybrid Model of Unsupervised and Supervised Machine Learning

Abstract

In the initial phase of this study, Principal Component Analysis (PCA) was employed to meticulously select noncollinear critical defects associated with construction quality from a comprehensive dataset comprising inspections conducted on 1,015 construction projects. The identified critical components were subsequently categorized into three distinct aspects: Inspection Records and Occupational Safety and Health (Aspect A), Concrete Quality (Aspect B), and Construction Team Quality Management (Aspect C). Subsequently, in the second stage of the study, a Multilayer Perceptron (MLP) network was meticulously trained, utilizing the 23 input variables corresponding to critical defects that had been identified through the PCA process. The training regimen persisted until convergence was achieved, resulting in a well-trained model capable of transforming input data into a probability value indicative of a project's quality level. The MLP model exhibited a remarkable performance, achieving an impressive accuracy rate of 91.3%. These robust evaluation metrics affirm the model's exceptional proficiency in predicting construction quality. The hybrid machine learning approach proposed in this study has proven to be highly effective in extracting valuable insights from inspection data and identifying defects intricately linked to construction quality. This model, with its capacity to accurately forecast construction quality, holds substantial potential for practical application in the construction industry.