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Effective quality management in construction projects is relevant but quite difficult because of the dynamic nature of the industry and its complexities. Thereby, traditional quality management systems face immense integration and interpretation difficulties concerning diverse and conflicting data sets that will more likely result in inefficiencies and a heightened risk of errors. This work identifies the knowledge gap and proposes novel evidential reasoning and belief functions framework to enhance the effectiveness of quality assurance and control protocols. Based on Dempster’s rule of evidence combination, this framework systematically aggregates and processes observational data and past performance metrics to provide a more robust mathematical foundation for quality management decisions. This approach enables continuous improvements by dynamically updating belief systems with new evidence. It allows for more standardization within projects by providing a consistent method for evaluating and mitigating risks from human error or other quality-related issues. Numerical examples illustrate the framework’s functionality; hence, this may alter current quality management practices in construction by providing a more accurate and explicit view of project quality standards and results.
Alshboul et al. (Thu,) studied this question.