The Identification and Control Strategies of Quality Risks in Civil Engineering Projects

Dan Wang

Simen Owen Academic Proceedings Series, 2026, vol. 5, 43-51

Abstract: In civil engineering projects, maintaining high-quality standards throughout all phases, design, construction, and post-construction, is critical to ensuring safety, sustainability, and cost-effectiveness. Quality risks in these projects, including design flaws, material defects, and operational failures, can lead to project delays, cost overruns, and safety hazards. Despite the importance of quality risk management, existing research remains fragmented, focusing on isolated risks within individual stages of the project lifecycle. There is limited attention on integrating risk control measures across all phases and utilizing emerging technologies to improve risk identification and mitigation. This study addresses these gaps by proposing a comprehensive risk management framework that spans the entire lifecycle of civil engineering projects. The research employs case study analysis, literature review, and comparative research to evaluate current risk control measures and identify potential improvements. The study finds that current risk control measures, though effective in some instances, are often fragmented and reactive. It suggests that integrating advanced technologies like Building Information Modeling (BIM), real-time monitoring systems, and predictive analytics could improve risk management and mitigate quality risks across all project stages. This research contributes both theoretically and practically to the field of civil engineering. Academically, it offers a holistic approach to quality risk management. Practically, it provides actionable insights for industry professionals, suggesting improvements in technology integration and collaboration across project stages to enhance project quality, reduce costs, and improve safety.

Keywords: quality risk management; civil engineering; building information modeling; real-time monitoring; predictive analytics (search for similar items in EconPapers)
Date: 2026
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