A Data-Driven Optimization Framework for Project Quality Management in Construction

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.