Evaluating and predicting tunnel overbreak by integrating cloud modeling and fuzzy evaluation theory

Tunnel overbreak is a common but unfavorable phenomenon in drill-and-blast excavation, leading to increased construction costs, delayed schedules, and potential stability risks. Accurate evaluation and prediction of overbreak are therefore important for tunnel construction control. In this paper, we propose a cloud-model-based comprehensive evaluation and prediction framework developed by integrating fuzzy evaluation theory with subjective and objective weighting methods. A dataset containing 523 records from the HuXiTai (HXT) Tunnel was used, and seven routinely obtainable geological and blasting indicators were selected to construct the evaluation system. Eight weighting strategies were compared, including conventional objective methods, expert judgment, and a ridge-regression-based objective method. The best-performing objective weights were further combined with subjective weights to establish the final comprehensive evaluation model. The results show that the proposed model achieved an overbreak evaluation accuracy of 85.28%. In addition, the comprehensive evaluation score showed a strong linear relationship with the measured overbreak area, yielding a prediction coefficient of determination (R 2 ) of 0.85. The cloud model representation further enabled intuitive visualization of overbreak risk levels and transitional characteristics between adjacent grades. Overall, the proposed framework provides a practical and interpretable tool for overbreak risk evaluation and quantitative prediction in tunnel blasting construction.