Suhua Zhou

Under cyclic loading, anchorage interfaces in anti-floating anchors may degrade, weakening bond strength and reducing bearing capacity, thereby compromising structural safety. Accordingly, understanding the mechanical behavior of anti-floating anchors while accounting for interface degradation is essential for ensuring the safety of underground structures. This study employs an elastic–plastic shear stress–shear displacement model that incorporates cyclic interface degradation, in which the degradation of interface strength and stiffness is governed by the number of loading cycles and the cyclic degradation parameters. By combining the load transfer method with the finite difference method, a mechanical analysis framework was developed for anti-floating anchors subjected to cyclic loading. The model was validated against published experimental data and then used to investigate interface stress distributions, load transfer mechanisms, and the effects of the number of loading cycles, load amplitude, and mean load. The results show that (1) under initial loading, the stress distribution along the anchoring interface progressively shifts toward the anchor tip as the load increases; (2) under cyclic loading, the bearing capacity of anti-floating anchors decreases continuously with increasing cycle numbers and stabilizes after reaching a certain number of cycles; (3) different initial interface strengths significantly influence the load transfer mechanism. High-strength interfaces can complete load transfer within a shorter anchorage length with more concentrated stress distribution, while low-strength interfaces enter the stable degradation stage earlier with more uniform stress distribution. The cyclic degradation parameter determines the degradation process and ultimate strength state of the interface. These findings provide practical guidance for controlling the long-term uplift deformation of underground structures, thereby helping to reduce the risk of basement upheaval and structural cracking in coastal environments with high groundwater levels.

Horizontal runout distance prediction of potential landslides is of great significance in hazard mitigation.In this study, predictive charts of landslide horizontal runout distance were developed based on the support vector regression (SVR) algorithm and Monte Carlo (MC) modeling. An SVR-based prediction model was constructed using a dataset of 424 historical landslides, which included six parameters: triggers, mass materials, volume, slope gradient, vertical drop, and horizontal runout distance. The first five parameters were employed as predictive indicators to estimate horizontal runout distance. To investigate the optimization of the penalty factor (c) and influence parameter in the kernel function (g) and their effects on prediction accuracy, 21 conditions were tested with 7 training/testing ratios (7/1, 6/2, 5/3, 4/4, 3/5, 2/6, and 1/7) in combination with three kernel functions: linear, radial basis function (RBF), and sigmoid. Predictive charts were then created by adopting the MC method to account for uncertainties in slope volume and slope gradient parameters. The results show that (1) the coefficient of determination in each condition was greater than 0.825, with the highest value of 0.854 obtained under the condition of a 7/1 training/testing ratio in combination with the RBF kernel function; (2) increasing the training/testing ratio improved prediction accuracy; (3) the model with the RBF kernel function performed better than those with other kernel functions; and (4) the optimization of c and g parameters significantly improved prediction accuracy. The feasibility and efficiency of the proposed model were demonstrated using a practical case of the Zhonghai Village landslide, highlighting the potential of SVR combined with MC modeling for landslide runout prediction and hazard mitigation.

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies.

In predicting slope stability, updating datasets with new cases necessitates retraining traditional machine learning models, consuming substantial time and resources. This paper introduces the Incremental Learning Bayesian (ILB) model, combining incremental learning theory with the naive Bayesian model, to address this issue. Key slope parameters—height; slope angle; unit weight; cohesion; internal friction angle; and pore water ratio—are used as predictive indicators. A dataset of 242 slope cases from existing literature is compiled for training and evaluation. The ILB model’s performance is assessed using accuracy, area under the ROC curve (AUC), generalization ability, and computation time and compared to four common batch learning models: Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Variable importance and partial dependence plots are used to explore the relationship between prediction results and parameters. Validation is performed with real slope cases from the Lala Copper Mine in Sichuan Province, China. Results show that (1) The ILB model’s accuracy and AUC improve as the dataset grows. (2) The ILB model outperforms GBM, SVM, and MLP in accuracy and AUC, similar to RF. (3) It demonstrates superior generalization and lower computation time than batch learning models. (4) Internal friction angle, slope angle, and pore water ratio are the most important predictors.

The safety of existing tunnels during the entire under-crossing process of a new shield tunnel is critically important for ensuring the sustainable operation of urban transportation infrastructure. The nonlinear behavior of surrounding soils plays a significant role in the mechanical response of tunnel structures. In order to assess the mechanical response of the existing tunnel more reasonably, this study attempts to propose a novel theoretical solution and calculation method by simultaneously considering the nonlinear characteristics of surrounding soils and the tunneling effects of a new tunnel during its entire under-crossing process. Firstly, the additional stresses acting on the existing tunnel stemming from the tunneling effects of a new shield tunnel during different under-crossing stages are calculated using the typical Mindlin solution, as well as the Loganathan and Poulos solutions. The influences of the additional thrust, friction force, and grouting pressure and the loss of surrounding soils are taken into account. Then, the nonlinear Pasternak foundation model is introduced to characterize the behavior of surrounding soils, and the governing differential equation for the mechanical response of the existing tunnel is derived using the typical Euler–Bernoulli beam model. Subsequently, a novel theoretical solution and calculation approach are established using the finite difference formula and the Newton iteration method for assessing the mechanical response of the existing tunnel. Finally, one case study is performed to illustrate the mechanical behavior of the existing tunnel during the whole under-crossing process of a new shield tunnel, and the validity of the developed solution is verified against both the computed result of finite element simulation and the field measurements. In addition, the influences from the ultimate resistance and reaction coefficient of surrounding soils and those from the vertical distance and intersection angle between existing and newly constructed tunnels are analyzed and discussed in detail.

Machine learning models have been increasingly popular in landslide susceptibility mapping based on the correlations among landslides and their inducing factors. However, mislabeled data in model training sets would deteriorate model accuracy. This study employed a Bayesian network to analyze influencing factors on landslides in Fujian Province, China, prone to typhoons and landslides. An inventory of 5,992 historical landslides informs Bayesian network modeling, with ten geoenvironmental factors as predictors. We introduced a progressive noise filtering method to mitigate the mislabeling effects of non-landslide points. The results show that altitude, wind speed, and lithology are the most important factors of landslides in the study area. The accuracy of the resultant landslide susceptibility map was verified using the area under the receiver operating characteristic curve (AUC) and Moran’s I index. The AUC value was improved from 0.838 to 0.931 during the progressive noise filtering. The correlation between historical landslide number density (LND) and resultant landslide susceptibility index (LSI) was evaluated. The Local Indicators of Spatial Association based on Moran’s I index shows consistent distribution patterns for high LND and high LSI regions. This study provides a useful reference for reliable landslide susceptibility mapping in the study area and similar areas.