Yongqiang Li

To investigate the shear behavior of rock mass joint surfaces with varying roughness and lithology, this study introduces a novel experimental framework that combines high-precision 3D printing and direct shear testing. Ten artificial joint surfaces were fabricated using Barton standard profiles with different joint roughness coefficients (JRC) and were cast using two representative rock-like materials simulating soft and hard rocks. The 3D printing technique employed significantly reduced the staircase effect and ensured high geometric fidelity of the joint morphology. Shear tests revealed that peak shear strength increases with JRC, but the underlying failure mechanisms vary depending on the lithology. Experimental results were further used to back-calculate JRC values and validate the empirical JRC–JCS (joint wall compressive strength) model. Numerical simulations using FLAC3D captured the shear stress–displacement evolution for different lithologies, revealing that rock strength primarily influences peak shear strength and fluctuation characteristics during failure. Notably, despite distinct lithologies, the post-peak degradation behavior tends to converge, suggesting universal residual shear mechanisms across rock types. These findings highlight the critical role of lithology in joint shear behavior and demonstrate the effectiveness of 3D-printing-assisted model tests in advancing rock joint characterization.

To address the issues of the insufficient accuracy and weak generalization capabilities of single models in landslide displacement prediction, this paper proposes a machine learning model fusion prediction method for landslide displacement based on stacking. Taking the landslide displacement data (F) and rainfall (RAINFALL) of the Baishui River landslide in the Three Gorges Reservoir area as the research object, input sequences were constructed through data preprocessing and feature engineering. Prediction models including SVR, XGBoost, Bayesian optimization, and random forest were established. Based on the stacking framework, an integrated landslide displacement prediction model was developed by dynamically weighting the outputs of the base models using prediction accuracy and stability as fusion indicators. The Baishui River landslide, a typical colluvial landslide, was selected as a case study, with typical displacement data from monitoring points ZG118 and XD-01 from December 2006 to December 2012. The results show that the evaluation metrics (R², ERMSE, and EMAE) for ZG118 and XD-01 demonstrate satisfactory prediction performance. Compared with traditional single models such as a TCN and XGBoost, the proposed integrated model exhibits improved prediction accuracy, providing scientific support for the real-time monitoring and early warning of landslide hazards.

Accurate prediction of landslide susceptibility is a key component of disaster risk reduction and early warning systems. Traditional landslide susceptibility prediction methods often face challenges in capturing complex nonlinear and spatio-temporal relationships inherent in geospatial data. In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. The model is trained on a comprehensive dataset comprising 19,898 samples, constructed from landslide records and 16 influencing factors in Kumamoto Prefecture, Japan. The input dataset is processed in tabular format using Microsoft Excel and includes variables such as topography, meteorology, soil characteristics, and human activity. The proposed model leverages Convolutional Neural Networks (CNN) to extract spatial features, Long Short-Term Memory networks (LSTM) to model temporal dependencies, and a Spatial Attention Mechanism (SAM) to enhance feature weighting dynamically. Experimental results demonstrate that the CNN–LSTM–SAM–Attention model significantly outperforms traditional machine learning approaches in terms of accuracy, precision, recall, F1 score, ROC–AUC, and PR–AUC. This substantial improvement is attributed to the model’s enhanced capability in capturing complex spatio-temporal patterns and dynamically weighting critical spatial features through the integrated Spatial Attention Mechanism (SAM). This study highlights the potential of deep learning-based approaches for improving the reliability of spatial landslide susceptibility prediction in complex terrain and dynamic climatic conditions.