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 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.

Tunnel construction adjacent to a water-filled karst cave is very prone to sudden water and mud inrush and to other geological hazards. This paper presents a numerical study of the damage that the blasting of the tunnel face causes to the rock pillar between a tunnel and an adjacent water-filled karst cave. The problem is analysed using a plane-strain dynamic finite element model in LS-DYNA (version R11.0), in which the limestone is described by the RHT damage model, the explosive by the JWL equation of state and the detonation products, and the karst water by a multi-material ALE formulation with fluid–solid coupling; the static karst water pressure is applied before the detonation through an implicit–explicit stress initialisation. Thirty-six working conditions are analysed, covering explosive densities of 1300–1500 kg/m³, detonation velocities of 7250–7650 m/s, karst cave cross-sectional sizes of 3.5 m and 5.7 m, and cave water pressures of 1.0 MPa and 1.2 MPa. The findings indicate that the cavern water pressure governs whether the fissure zone becomes through-going, whereas the extent of the damage is controlled mainly by the explosive parameters. Across the 36 working conditions, the maximum length of the fissure zone varies from 206 cm to 295 cm. A quantitative comparison of the individual factors shows that the explosive density and the detonation velocity are the dominant controls: raising the density from 1300 kg/m³ to 1500 kg/m³ or the detonation velocity from 7250 m/s to 7650 m/s lengthens the fissure zone by 31–44 cm, i.e., by 13–19%. The influence of the karst cave size is secondary (enlarging the cave from 3.5 m to 5.7 m gives an increment of only 2–14 cm, i.e., 1–6%), and the influence of the cave water pressure over the engineering-relevant range of 1.0–1.2 MPa is of the same modest order (an increment of 5–12 cm, i.e., 1.9–4.8%). The role of the water pressure is therefore not to dominate the overall trend of the fissure zone but to control whether the fissure zone crosses the safety threshold: the same pressure increment pushes those combinations that already lie close to the relative-spacing threshold past it, raising the number of water-inrush conditions from 6 to 9 out of 18. Identifying the relative weight of the controlling factors, together with the threshold role played by the water pressure, provides the quantitative basis for blasting optimisation in tunnels adjacent to water-filled karst caves. For the conditions analysed, the density of the explosive in sections of high water pressure should be kept below 1400 kg/m³ and the detonation velocity below 7450 m/s.