The present study compares and analyzes three risk analysis models that are applicable to shield tunnel boring machine (TBM) tunneling, and thus proposes an improved risk matrix model based on the causal networks applicable to sustainable tunnel projects. The advantages and disadvantages of three risk analysis models are compared, and causal networks are structured by analyzing the causal relationship between risk factors and risk events. Based on the comparison and analysis results, the causal network-based risk matrix model (CN-Matrix model), which complements the disadvantages and exploits the advantages of the three existing models, is proposed in this paper. Furthermore, this study suggests a means of modifying the weighting scores in the estimation of the risk score, which permits the CN-Matrix model to determine the risk level more reasonably. Thus, the improved CN-Matrix model is more reliable and robust compared to the three existing models.

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.

The International Roughness Index (IRI) is closely related to pavement distress. However, previous studies employing statistics and machine learning approaches would present challenges in comprehensively analyzing the influence of pavement distress on IRI considering their severities. This study introduces interpretable machine learning to investigate the influence of pavement distress on IRI, with a particular focus on the severity of pavement distress. The pavement distress and IRI data for flexible pavements obtained from the long-term pavement performance (LTPP) program were meticulously preprocessed. The developed random forest (RF) model demonstrated satisfactory predictive performance, with an RMSE of 0.2191 and an R² of 0.7874. The relationship between pavement distress and IRI, as captured by the developed model, was further analyzed using the SHapley Additive exPlanations (SHAP) method. The model interpretation identified the transverse crack, rutting, and alligator crack as the key factors influencing IRI. Notably, both transverse and alligator cracks exhibited significant contributions to IRI increment at medium and high severity levels, highlighting the importance of proactive maintenance for these distress types at lower severity levels. Additionally, a threshold in rutting depth was observed, which could increase IRI. A comparative analysis with the AASHTO MEPDG smoothness model demonstrated that the predictive performance of the RF model was notably superior.