In view of the problem that mountain tunnel construction is prone to collapse risk and disaster accidents, this paper proposes a fuzzy comprehensive evaluation method based on CRITIC and D-AHP combined weighting, which can effectively improve the accuracy of mountain tunnel collapse risk evaluation and reduce construction risks. This method combines the improved subjective weighting method D-number analytic hierarchy process and the more scientific and reasonable objective weighting method CRITIC method for combined weighting and uses game theory to optimize the weights, which improves its accuracy compared to traditional evaluation methods and makes up for the lack of subjective and objective weighting of a single evaluation method. The results show that the introduction of the D-number theory can reduce the impact of an uncertain environment on the evaluation results. The risk levels of the sample sections are all between three and four, which is consistent with the actual situation of the project, indicating that the evaluation model is feasible.

In order to reduce the risk of collapse disasters during tunnel construction in mountainous areas and to make full use of the available data, a collapse risk assessment model for highway tunnel construction was established based on the WOA–XGBOOST algorithm. Three major categories of tunnel construction risk, namely engineering geological factors, survey and design factors, and construction management factors, were selected as the first-level indicators, and 14 secondary indicators were further specified as the input variables of the collapse risk assessment model for tunnel construction. The confusion matrix and accuracy metrics were employed to evaluate the training and prediction performance of the risk assessment model on both the training set and the test set. The results show that subjective weights derived from the G1 method were integrated with objective weights generated by the WOA–XGBOOST algorithm. A game-theory-based weight integration strategy was then applied to optimize the combined weights, effectively mitigating the biases inherent in single-method weighting approaches. Risk quantification was systematically conducted using a cloud model, while spatial risk distribution patterns were visualized through graphical cloud-mapping techniques. After completion of model training, the proposed model achieved a high accuracy of over 99% on the training set and around 95% on the held-out test set based on an available dataset of 100 collapse-prone tunnel construction sections. Case-based verification further suggests that, in the studied collapse scenarios, the predicted risk levels are generally consistent with the actual engineering risks, indicating that the model is a promising tool for assisting tunnel construction risk assessment under similar conditions. The research outcomes provide an efficient and reliable approach for assessing risks in tunnel construction, thereby offering a scientific basis for engineering decision-making processes.

The selection of foundation pit support schemes and parameters for subway stations critically impacts construction safety. This study develops a deep foundation pit support evaluation system, integrating subjective weights (Analytic Hierarchy Process) and objective weights (entropy method). A game theory-based combined weighting method and joint cloud model are constructed for optimal support scheme selection. Structural redundancy theory is employed to assess safety stability. Results demonstrate that game theory-enhanced combined weighting improves multi-attribute decision-making accuracy and model stability. The proposed model identifies the optimal diaphragm wall-internal support scheme, aligning with actual engineering designs, validating method reliability. During excavation, deep horizontal wall displacement and surface settlement outside the pit exhibit parabolic and "groove-shaped" patterns, respectively. Steel support axial force initially increases then decreases. Failure of the fourth-layer steel support significantly amplifies pit displacement and settlement, with redundancy dropping below other layers, indicating the lowest-layer support as a critical "weak component" requiring prioritized design and construction attention. These findings enhance safety evaluation precision and inform risk mitigation strategies for deep foundation pit engineering.

The stability problem of high fill slopes has always been a research hotspot. Its failure mechanism is complex and prominent, featuring strong concealment, a short occurrence time and great harmfulness. In this paper, the stability of a high fill slope under rainfall conditions will be studied by using indoor tests, numerical simulations, etc. The study is based on a high fill slope in Yichang City. The evolution law of high fill slope stability under the maximum rainfall condition is revealed. The results show the following: The influence of moisture content on stress–strain curves is reflected in both the curve’s shape and the peak value of deviatoric stress. Under the constraint of confining pressure, the curve decreases and the peak value of deviatoric stress decreases with the increase of moisture content at the same confining pressure. The safety factor obtained by a rigid body limit equilibrium analysis and numerical calculation indicates that the safety factor for a 30° slope meets the requirements for slope stability evaluation and remains in a fundamentally stable state. An on-site investigation suggests that surface failure and shallow failure may be primary failure modes for this slope; therefore, it is recommended to implement slope protection measures. This study provides valuable references for similar high fill slopes.

This paper aims to address the issue of disturbance caused by excavation tunnel construction on nearby dense pipelines. Relying on the actual project of Nanchang Metro, the three-dimensional finite element numerical simulation method is used to establish a multi-condition numerical model. At the same time, key influencing factors such as the clear distance, intersection angle, surrounding rock parameter, and construction method are considered. The mechanical response of the dense pipeline adjacent to the dug tunnel was systematically studied, and the influence of key factors on the mechanical behavior of the pipeline was analyzed. The results show that when using the CRD method for construction and grouting reinforcement of the surrounding strata of the tunnel, the maximum settlement of the vault is located at the vault of the right guide tunnel, and the maximum settlement value is located at the bottom of the rainwater pipe directly above the tunnel. The vertical displacement, maximum principal stress, and minimum principal stress of the pipeline are arranged in descending order for the full-face, stepped method, and CRD method. The greater the disturbance to the soil caused by the construction method, the more unfavorable the effect on the pipeline.

The rapid development of underground engineering contributes significantly to achieving China’s “dual carbon” strategic goals. However, during the construction and operation phases, this engineering project faces diverse risks and challenges related to disasters. Consequently, enhancing the evaluation capability for underground engineering resilience is imperative. Based on the characteristics of resilience evaluation and enhancement in underground engineering, this study defines the concept and objectives of resilience evaluation for underground space engineering and analyzes corresponding enhancement methods. By considering aspects such as the magnitude of collapse disaster risk in underground engineering, its vulnerability, resistance capacity, adaptability to disasters, recovery ability, and economic feasibility, a comprehensive index system for evaluating the resilience of collapse disaster risks in underground engineering has been established. This research suggests that disaster risk management should shift from passive to active prevention. Through resilience evaluation case applications, it is possible to improve the design objectives of underground engineering towards “structural recoverability”, “ease of damage repair”, and “controllable consequences after a disaster”. The integration of intelligent static assessment models based on artificial intelligence algorithms can effectively enhance the accuracy of resilience evaluations. Furthermore, dynamic assessments using multiple data fusion techniques combined with numerical simulations represent promising directions for improving the overall resilience of underground engineering.