Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN model, consisting of a causal dilation convolution layer residual block, can flexibly increase the receptive fields and capture the global information in a deeper layer. SSA can solve the hyperparameter problem well for TCN model. The Muyubao landslide displacement collected from a professional GPS monitoring system implemented in 2006 is used to analyze the displacement features of the slope and evaluate the performance of the SSA-TCN model. The cumulative displacement time series is decomposed into trend displacement (linear part) and periodic displacement (nonlinear part) by the variational modal decomposition (VMD) method. Then, a polynomial function is used to predict the trend displacement, and the SSA-TCN model is used to predict the periodic displacement of the landslide based on considering the response relationship between periodic displacement, rainfall, and reservoir water. This research also compares the proposed approach results with the other popular machine learning and deep learning models. The results demonstrate that the proposed hybrid model is superior to and more effective and accurate than the others at predicting the landslide displacement.

The segmentation of crack detection and severity assessment in low-light environments presents a formidable challenge. To address this, we propose a novel dual encoder structure, denoted as DSD-Net, which integrates fast Fourier transform with a convolutional neural network. In this framework, we incorporate an information extraction module and an attention feature fusion module to effectively capture contextual global information and extract pertinent local features. Furthermore, we introduce a fractal dimension estimation method into the network, seamlessly integrated as an end-to-end task, augmenting the proficiency of professionals in detecting crack pathology within low-light settings. Subsequently, we curate a specialized dataset comprising instances of crack pathology in low-light conditions to facilitate the training and evaluation of the DSD-Net algorithm. Comparative experimentation attests to the commendable performance of DSD-Net in low-light environments, exhibiting superlative precision (88.5%), recall (85.3%), and F1 score (86.9%) in the detection task. Notably, DSD-Net exhibits a diminutive Model Size (35.3 MB) and elevated Frame Per Second (80.4 f/s), thereby endowing it with the potential to be seamlessly integrated into edge detection devices, thus amplifying its practical utility.

Karst landscape is a general term for earth surfaces and underground patterns that have been formed by the dissolution of soluble rock. Karst landscapes are widely distributed throughout China—particularly in the Guangxi and Guangdong provinces. The main features of karst landscapes are typically reflected in karst caverns, sinkholes, and other geographical phenomena. During tunnel construction in karst areas, various forms of karst caverns may appear on the construction route, and they can cause hazards—such as water inrush and collapse—during tunnel construction. These hazards affect the tunnel construction process. As such, it is necessary to propose a treatment for karst caverns. In this work, a case study of the tunnels on the Hechi‐Baise expressway is presented. A comprehensive pretreatment method suitable for this tunnel is proposed. On the premise of prioritizing the safety and timeline of construction, an optimized treatment scheme for the karst caverns of Hebai tunnel is followed. The optimized treatment scheme primarily includes calculation of safe thickness of tunnel face, strengthening the initial support and increasing the thickness of the second lining, increasing the reserved deformation, and grouting. The proposed scheme achieved favorable results in the treatment of a karst cavern in the Hebai tunnels.

Geotechnical engineering involves various types of uncertainties, as it always deals with highly variable natural materials. Reliability-based design/analysis can play at least a complementary role in the design approach. In this paper, reliability analysis of anti-sliding of retaining wall with the parameters treated as random variables is performed based on the concept of random fields. Both friction angle and cohesion of soils near the interface along the base of retaining wall are treated as Gaussian fields. The spatial correlation and cross correlation of the variables are calculated by a specific covariance function and the seismic loads act on retaining walls are also taken into account. Examples are illustrated to verify the accuracy of the proposed approach. It is found from the numerical results that the spatial correlation of shear strength has an important influence on the probability of the anti-sliding failure of retaining wall. Moreover, the numerical results obtained from the proposed method are in full agreement with those obtained from Monte Carlo simulations. Therefore, the proposed method provides a new view to study the stability of a retaining wall subjected to seismic loads.

Quantitative risk assessment of landslides induced by slope failure is an important precondition for formulating effective disaster prevention, mitigation measures, and establishing a landslide risk warning system. In general, the location of the critical slip surface and the failure mode is unlikely to be predicted due to the spatial variability in soil. It remains a challenging task to effectively identify the critical slip surface and conduct the efficient risk assessment based on a three‐dimensional (3‐D) slope with spatial variability. Based on Monte Carlo simulation and the random field method, a quantitative risk evaluation method for slope failure considering the spatial variability of soil parameters is proposed in the study. Compared with a uniform soil slope, the landslide volume, the critical slip surface, and the factor of safety considering the spatial variability of soil are all uncertain; thus, the soil spatial variability has a significant effect on the failure mode and stability of the slope. By using the random finite element method, the critical slip surface of the slope is accurately identified, the corresponding landslide volume and slide distance are calculated, and the modified risk index for a landslide is further enriched, which can provide the reference basis for predicting the landslide deformation, quantitatively evaluating the landslide risk, and mitigating the landslide disaster.