High embankment slope stability prediction using data augmentation and explainable ensemble learning
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Le résumé fourni par la source
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 least a 12% improvement in predictive accuracy, compared to their original small-sample-trained counterparts; The proposed data augmentation method outperformed variational autoencoder and diffusion models in generating high-quality synthetic data. Additionally, the interpretability framework effectively identified primary factors influencing slope stability. These findings provide a robust framework for interpretability-driven assessments of heavy-haul railway slopes with limited sample data.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- High embankment slope stability prediction using data augmentation and explainable ensemble learning
- Date Crossref
- 01/08/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Southwest Jiaotong University pays non établi dans la noticeUniversité ou école supérieure
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Sichuan Institute of Building Research pays non établi dans la noticeStructure de recherche
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Sichuan College of Architectural Technology pays non établi dans la noticeUniversité ou école supérieure
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Chengdu University pays non établi dans la noticeUniversité ou école supérieure
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China Railway Group (China) pays non établi dans la noticeEntreprise
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China Railway Eryuan Engineering Group Co. pays non établi dans la noticeEntreprise
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School of Civil Engineering pays non établi dans la noticeUniversité ou école supérieure
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Chengdu Science City Management Committee pays non établi dans la noticeInstitution
Southwest Jiaotong University, Sichuan Institute of Building Research et Sichuan College of Architectural Technology, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.