The subterranean environment of tunnels poses considerable uncertainty as tunnel structures are ensconced in soil, unlike their above-ground counterparts. This significantly complicates tunnel risk assessment during earthquakes. This study introduces a novel method that integrates multiple damage indices to evaluate the seismic resilience of tunnels. Initially, seismic attenuation is introduced to calculate earthquake exceedance probabilities for various tunnel damage indicators, employing finite element methods (FEM). A robustness evaluation criterion scale value is established based on the amalgamation of multiple tunnel damage indices. Standard Cloud Models are then generated utilising the robustness evaluation criteria. Subsequently, the independent and correlated weights of the robustness evaluation indices are determined using the CRITIC-G1 and decision-making trial and evaluation laboratory (DEMATEL) methods, respectively. A game theory (GT) method is then utilised to amalgamate and allocate weights to these robustness evaluation indices. The evaluation Cloud Models are subsequently generated using a backward cloud generator, based on the division of damage grades for the evaluation criteria and combination weights. Finally, the robustness grade is determined by comparing the similarities between the standard and evaluation Cloud Models. The repair time of the tunnel is quantified using a repair function based on robustness grades. The efficacy of the seismic resilience assessment method is discussed based on three hypothetical cases, providing valuable guidance for assessing the seismic resilience of underground structures.

Landslides pose significant threats to mountainous regions, causing widespread damage to both property and human lives. This study seeks to enhance landslide prediction in the Aqabat Al-Sulbat Asir region of Saudi Arabia by integrating deep neural networks (DNNs), 1D convolutional neural networks (CNNs), and a combined DNN and CNN ensemble (DCN) with explainable artificial intelligence (XAI) techniques. These XAI techniques enhance the interpretability of these complex deep learning models, thereby facilitating better decision-making strategies. Furthermore, the DNN model is employed to incorporate game theory principles, assessing the individual impact of variables on landslide prediction. Our findings indicate high and very high landslide susceptibility zones covering 35.1-41.32 km2 and 15.14-16.2 km2, respectively. The DCN model boasts the highest area under the curve (AUC) at 0.97, followed by CNN (0.94) and DNN (0.9), showcasing DCN's superiority. XAI analysis exposes significant residuals in CNN's posterior despite its high AUC. Notably, precipitation, slope, soil texture, and line density emerge as pivotal parameters for accurate landslide prediction. Game theory results highlight line density's preeminence, trailed by topographic wetness index, curvature, and slope in landslide occurrence. By incorporating deep learning models, XAI, and game theory, this study presents a holistic approach to landslide management. This comprehensive framework equips authorities and stakeholders with valuable tools for informed decision-making in landslide-prone areas, delivering accurate predictions and insights into crucial parameters.