Risk management plays a vital role in ensuring the safety and efficiency of tunnel construction by considering various factors, including uncertainties associated with concurrent adverse sources. One key aspect of risk management is prioritizing hazardous zones to devise an optimal countermeasure plan within time and cost constraints. This study developed an advanced tunnel risk management model, combining the analytic hierarchy process (AHP) and fuzzy set theory (FST). The model derived the impact using AHP and the probability using FST. By selectively combining causal factors that met the selection criterion, the risk of each hazardous zone was determined, enabling the prioritization of identified hazardous zones. The model application results indicated that causal combinations associated with significant tunnel convergence posed a relatively high risk. Moreover, the hazardous zones where unstable ground formations were excavated by a gripper tunnel boring machine (TBM) were revealed as the most vulnerable locations. Consequently, adopting a shield TBM or implementing ground reinforcement is recommended. Overall, the developed model effectively prioritizes identified hazardous zones and provides an optimal countermeasure plan, contributing to the overall safety and efficiency of the operations.

In order to ensure construction efficiency and stability during tunnel excavation, it is essential to predict geological risks ahead of tunnel faces. In this study, a geological risk prediction model was developed based on a machine learning (ML) algorithm. The database used to implement the ML model was synthetically acquired from a series of finite-element (FE) numerical analyses, which could simulate electrical resistivity surveys during tunnel excavation. The developed FE model helped obtain resistivity data representing various risky ground conditions (such as typical fault zones, water intrusion, mixed ground, geological transitions, and cavities) encountered during tunnel advancement. Four ML algorithms (support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting) were used to develop the prediction model. The evaluation results showed that the proposed ML prediction models produced highly accurate results. Among the ML algorithms, the prediction model based on the random forest (RF) algorithm exhibited superior performance, with an accuracy of 97.33%. Given the feasibility and efficiency of recognizing hazardous ground conditions, the proposed model is expected to serve as a reliable approach for risk management. Finally, an engineering flowchart was proposed to assist in the application of the study results to actual tunneling sites.