High embankment slope stability prediction using data augmentation and explainable ensemble learning

作者：Zongyu Zhang, Junjie Huang, Qian Su, Shijie Liu, Naeem Mangi, Qi Zhang, Allen A. Zhang, Yao Liu, Shengyang Wang · 发表于：Computer-Aided Civil and Infrastructure Engineering · 年份：2025 · DOI：10.1111/mice.13478 · 被引用次数：5 · 研究领域：Landslides and related hazards、Hydrology and Sediment Transport Processes、Dam Engineering and Safety

The stability of embankment slopes for heavy-haul railway foundations is essential for safe railway operations. Railway embankment slope stability datasets often rely on engineering judgment for analysis. The labor- and resource-intensive processes of data preparation result in small dataset sizes. Machine learning analysis of small-sample potential features is a key low-cost approach for slope prediction. Due to the limited availability of slope failure data, a specialized framework is required for predictive modeling. To address this challenge, the focus is placed on data augmentation and interpretability analysis. A generative adversarial model is constructed using a graph convolutional network-based generator and a discriminator based on Gated Recurrent Unit, accompanied by a quality control method for the generated samples based on maximum mean discrepancy and one-class Support Vector Machine. This approach is designed to more effectively capture the temporal and spatial features of small samples. Three ensemble learning models, namely, XGBoost, random forest, and AdaBoost, are trained with augmented data, and model interpretation is conducted using Shapley Additive exPlanations to identify key factors affecting stability and potential stability improvement strategies. Results indicate that the proposed generative adversarial model surpasses traditional models in generating adequate data; the three enhanced data-trained machine learning models in this study achieved at l...