Loess Collapsibility Prediction and Influencing Factor Analysis Using Multiple Machine Learning Algorithms in Xi’an Region
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Le résumé fourni par la source
Collapsibility is a fundamental geotechnical property of loess that critically affects its engineering behavior. In this study, a comprehensive dataset comprising 9041 experimental records on the physical properties and collapsibility of loess from the Xi’an region was compiled. Six parameters were selected as model inputs: sampling depth (H), water content (w), plastic limit (wP), plasticity index (IP), compression coefficient (a1–2), and compression modulus (Es). Based on these inputs, prediction models for the loess collapsibility coefficient (δs) were developed using Gaussian Process Regression (GPR), Gradient Boosting Machine (GBM), Support Vector Regression (SVR), Radial Basis Function Neural Network (RBFNN), Classification and Regression Tree (CART), and Feature Tokenizer Transformer (FT-Transformer). Among these, GPR demonstrated the best predictive performance, achieving the lowest error (RMSE = 9.88 × 10−3) and the highest accuracy (R2 = 0.844). Additionally, the coverage proportion of the 95% confidence interval of the GPR predictions reached 0.949. SHapley Additive exPlanations (SHAP) analysis for GPR further revealed that the compression coefficient exerted the greatest influence on δs (0.0149), followed by compression modulus (0.0080), water content (0.0068), plasticity index (0.0061), sampling depth (0.0061), and plastic limit (0.0052). The GPR-based prediction model offers significantly higher predictive accuracy than empirical models. The developed models provide a robust technical framework for the rapid estimation of loess collapsibility in the Xi’an region.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Loess Collapsibility Prediction and Influencing Factor Analysis Using Multiple Machine Learning Algorithms in Xi’an Region
- Date Crossref
- 14/11/2025
- Éditeur
- MDPI AG
- 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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Xi'an University of Science and Technology Institute of Ecological Environmental Restoration in Mine Areas of West China pays non établi dans la noticeUniversité ou école supérieure
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Beijing Municipal Ecological and Environmental Monitoring Center pays non établi dans la noticeInstitution
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State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation pays non établi dans la noticeStructure de recherche
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Ministry of Natural Resources Observation and Research Station of Ground Fissure and Land Subsidence pays non établi dans la noticeOrganisme public
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College of Geology and Environment pays non établi dans la noticeUniversité ou école supérieure
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Xi’an Municipal Geologic Environment Monitoring Station pays non établi dans la noticeInstitution
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Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation pays non établi dans la noticeStructure de recherche
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Shaanxi Institute of Geo-Environment Monitoring pays non établi dans la noticeStructure de recherche
Institute of Ecological Environmental Restoration in Mine Areas of West China — Xi'an University of Science and Technology, Beijing Municipal Ecological and Environmental Monitoring Center et State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.