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A Data-Driven Optimization Framework for Project Quality Management in Construction
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DOI：10.3390/buildings16051017.png)
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Abstract
En 中文
Quality management in construction projects is critical for ensuring client satisfaction, minimizing rework, and achieving cost efficiency in an industry characterized by a long history of cost overruns. Traditional quality assurance and control (QA/QC) processes, however, are resource-intensive and often implemented without a systematic evaluation of their cost-effectiveness. Absent a systematic evaluation of the costs and benefits associated with QA/QC, stakeholders—particularly clients and contractors—are unlikely to commit resources to the implementation of quality control practices within construction projects. This paper presents a quantitative optimization framework that integrates Monte Carlo simulation of activity-level rework costs with a constrained optimization model based on a novel project-level key performance indicator, the Total Assurance on Reworks (TAR) to support data-driven decision-making in project quality management. The model enables construction managers to evaluate trade-offs between the costs of preventive quality controls and the potential consequences of non-conformities. The methodology is demonstrated through a synthetic dataset comprising 50 construction activities. Results indicate that the framework can identify optimal allocations of quality control resources, achieving up to more than 19% cost savings compared to a full-control strategy and 18% reduction in economic resources when compared with the state of the art while maintaining target quality assurance levels. This contributes to the broader discourse on quality management by offering a computationally rigorous tool for balancing cost efficiency and quality performance in complex projects.
Keywords:
project quality management
expected monetary value
Monte Carlo simulations
construction projects
data-driven decision-making
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