Data-mining-based identification of post-handover defect association rules in apartment housings

Abstract

With the increasing expectations of clients and the growing complexity of the built environment, property management teams are facing constant pressure to effectively manage and rectify defects for improved building operational efficiency and performance. This study aims to develop and validate a defect correlation evaluation model for project and property management professionals by specifically (i) examining the defect detection and management mechanisms of residential buildings and (ii) quantifying the mechanical characteristics of defects by using association rules mining (ARM) techniques. In addressing the limitations of current evaluation approaches, this study proposed an ARM evaluation model that integrated, contextualized, and operationalized building defects into work type, location, elements, and defect type. The association between these classifications was explored and mapped. Among the resulting 123 meaningful rules, rules occurred at a rate of about 62% of the same work type in the linked work type, nearly 193% of the same element in another element, and about 23% of the close location in the far location. In conclusion, this study informs project and property management professionals of the key and complex associations between defects of different characteristics and highlights the most common occurrence defects in residential apartment buildings. Thus, this helps reduce the ambiguity and subjectivity of prioritization in defect management and facilitates maintenance and repair planning.