Accurate prediction of crown convergence in Tunnel Boring Machine (TBM) tunnels is critical for ensuring construction safety, optimizing support design, and improving construction efficiency. This study proposes an interpretable machine learning method based on Bayesian optimization (BO) and SHapley Additive exPlanations (SHAP) for predicting crown convergence (CC) in TBM tunnels. Firstly, a dataset comprising 1,501 samples was constructed using tunnel engineering data. Then, six classical ML models, namely, Support Vector Regression, Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting, and K-nearest neighbors—were developed, and BO was applied to tune the hyperparameters of each model to achieve accurate prediction of CC. Subsequently, the SHAP method was adopted to interpret the LightGBM model, quantifying the contribution of each input feature to the model’s predictions. The results indicate that the LightGBM model achieved the best prediction performance on the test set, with root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient values of 0.9122 mm, 0.6027 mm, 0.0644, and 0.9636, respectively; the average SHAP values for the six input features of the LightGBM model were ranked as follows: Time (0.1366) > Rock grade (0.0871) > Depth ratio (0.0528) > Still arch (0.0200) > Saturated compressive strength (0.0093) > Rock quality designation (0.0047). Validation using data from a TBM water conveyance tunnel in Xinjiang, China, confirmed the method’s practical utility, positioning it as an effective auxiliary tool for safer and more efficient TBM tunnel construction.

The geological structure of the upper reaches of the Yellow River is complex, especially in the Sigouxia-Laganxia section. It has always been a high-incidence area of landslide disasters, which poses a threat to the safe operation of the upper reaches of the Yellow River. In this study, based on the high-precision remote sensing image data, the spatial distribution of each landslide was obtained by superimposing the remote sensing image and the 1:50,000 digital elevation model (DEM). Some typical landslides were selected for detailed field investigation and field verification. The results show that the remote sensing image characteristics of landslides in the upper reaches of the Yellow River are obvious. Through remote sensing interpretation and field investigation, a total of 508 landslides of various types were found, including 24 giant landslides. The spatial spreading patterns of landslides mainly include six types: dumb-bell shape, rectangle, saddle type, long arc shape, triangle, and side-by-side shape. The length and width of the landslide deposit are mainly concentrated at 550–1500 m and 600–1500 m, and the average elevation of the sliding body is mainly concentrated between 2000 and 2800 m. The average slope of the landslide is mainly distributed between 15–20°. Giant landslides are mainly distributed in the Jianzha basin area, surrounded by the Jishishan fault and the Lajishan fault in the West Qinling Mountains. The spatial distribution characteristics of giant landslides have obvious regional differences due to different factors such as lithologic differences and riverside erosion. The research results are of great significance for the early identification, prevention, and mitigation of landslide disasters in the upper reaches of the Yellow River.

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects.

Slope instability may cause severe casualties, property losses, and ecological damage. To accurately evaluate slope stability grades and mitigate geological hazards, a dynamic stability assessment method based on variable weight theory and trapezoidal cloud model is proposed. First, an evaluation index system for slope stability is established following the principles of uniqueness, purposefulness, and scientific validity. Then, to improve the accuracy of subjective constant weights, the intuitionistic fuzzy analytic hierarchy process (IFAHP) is employed to calculate subjective constant weights. Considering the contrast intensity and conflict among indicators, an improved CRITIC method is applied to determine objective constant weights. To balance subjective and objective factors and avoid constant weight imbalance, the optimal comprehensive constant weights are computed based on game theory, effectively reducing bias caused by single weighting methods. Furthermore, to fully account for the influence of indicator state values on their weights, variable weight theory is introduced to dynamically adjust the comprehensive constant weights. Finally, based on the variable weights of evaluation indicators, a trapezoidal cloud model is utilized to construct the slope stability evaluation model, which is validated through an engineering case study. The results indicate that the stability grade of Stage 1 is assessed as basically stable, while Stages 2 and 3 are evaluated as stable. Numerical simulations show the safety factors of the three stages are 1.36, 1.83, and 2.36, respectively, verifying the correctness of the proposed model. The proposed model demonstrates practical engineering value in slope stability assessment and can be referenced for slope reinforcement and hazard prevention in later stages.

Pipe-joint misalignment in pipe-jacking construction is usually confirmed during final quality inspection, but risk signals related to guide-rail gradient deviation, jacking-force utilization, grouting-volume satisfaction, alignment-correction status, and hydrogeological disturbance accumulate during construction. Conventional inspection can determine whether a joint exceeds the allowable limit, but it cannot sufficiently explain risk sources or guide subsequent control. Taking a nominal diameter of 1500 mm (DN1500) steel pipe-jacking project in Wuhan, China, as the study object, this paper establishes a 12-indicator system covering pipe-end geometry and assembly quality, jacking-construction control, and hydrogeological and environmental response. Comprehensive weights are determined by combining a trapezoidal fuzzy analytic hierarchy process (AHP) with an improved criterion importance through inter-criterion correlation (CRITIC) approach using game-theory weighting, after which, a finite-interval cloud model is used to distinguish five risk grades. The results show that the combined weights of water head above the pipe crown, guide-rail gradient deviation, stratigraphic mutation and obstacle score, alignment-correction status, and grouting-volume satisfaction were 0.1803, 0.1737, 0.1702, 0.1027, and 0.0889, respectively, identifying these variables as comparatively influential in this case. All ten evaluated pipe joints were classified as very low risk; however, their memberships in the low-risk and medium-risk grades ranged from 0.0318 to 0.0898 and from 0.0070 to 0.0792, respectively. These differences indicate that the continuous membership vector provides additional information beyond the discrete grade label and can support differentiated post-evaluation rechecking. The proposed framework can therefore provide preliminary support for construction risk early warning and differentiated post-evaluation control, while further validation using larger samples and multiple projects is required.

Accurate characterization of salt caverns is a prerequisite for evaluating the feasibility of Compressed Air Energy Storage (CAES), yet high-noise urban environments pose challenges for conventional active-source geophysical methods. To address this, our study utilized Microtremor Array Measurements (MAMs) based on the Spatial Autocorrelation (SPAC) method to characterize deep salt caverns for a CAES project in Yunnan, China. The results reveal dissolution features and morphological asymmetry within the caverns: the upper sections are conical and brine-filled, whereas the lower sections are cylindrical, containing both brine and sediment. The identified caverns range from 45 to 190 m in height and 30–120 m in width, with an estimated total volume of approximately 4.6 × 10 ⁶ m ³ . Additionally, localized wave velocity anomalies helped map connecting channels between four brine extraction wells, which were formed by differential dissolution and piping. By delineating cavern morphology and identifying fracture zones, joint networks, and hydraulic conduits that could potentially compromise sealing integrity, the MAMs approach provides critical geophysical insights for the safety evaluation and engineering design of CAES gas storage under complex geological conditions.