A Hybrid BIM-Fuzzy Model for Enhanced Assessment of Building Element Conditions

Abstract

This study addresses the critical challenge of managing building element defects, such as cracks and dampness, which contribute to increased repair and maintenance costs. Highlighting the need to minimize human error and enhance the accuracy of defect classification, the research proposes a novel method integrating building information modeling (BIM) with a fuzzy algorithm, derived from extensive fieldwork and data collection. This innovative approach aims to systematize and automate the process of defect management, thus reducing the potential for human error. A BIM model tailored to asset management is developed following specific fuzzy building information modeling (FBIM) guidelines. For proper decision-making, by using a fuzzy algorithm an effective defect-based building elements condition assessment fuzzy model was produced according to field data that can show the severity of the degradation of building elements. This algorithm categorizes the condition of building elements from good to bad (C1 to C5) and damage severity from no damage to collapse (D1 to D5), providing a nuanced understanding of building elements’ health that traditional methods might overlook due to human error. Implemented on the BIM platform, this model enhances information exchange and documentation during inspections, utilizing visualization to evaluate building elements through color-coded causality analysis. A case study of an office building illustrates how the fuzzy model, underpinned by field-derived data, significantly improves asset management practices by minimizing human errors in defect identification and classification. The paper concludes by highlighting the contribution of this research to the construction industry, offering a sophisticated framework for defect management that combines detailed BIM visualization with the precision of a fuzzy algorithm informed by extensive fieldwork, ultimately leading to improved building quality and operational cost savings.