Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks: A Case Study in China

Abstract

This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform probabilistic analysis on quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the "low professional level of designers," "poor production refinement," and "poor storage conditions for finished products" have a significant impact on quality risks. This research offers a novel risk assessment tool for steel structural component production, effectively assisting enterprises in identifying potential risks, formulating risk reduction strategies, and enhancing production quality.