{
  "abstract": "Accurate prediction of the coefficient of compressibility (av) for regional clays remains a fundamental challenge in geotechnical engineering. Focusing on Chengdu clays, this study proposes a machine learning framework that integrates physically constrained data augmentation, differentiated hyperparameter optimization, and interpretability analysis. Based on 338 experimental datasets, a hybrid optimization strategy (Grid Search + Bayesian Optimization) is employed to ensure fair model comparison. The statistical significance of performance differences is rigorously validated using 30 iterations of Monte Carlo cross-validation combined with paired t-tests. Generalization capability is assessed via spatial grouping cross-validation, accompanied by Bootstrap-based uncertainty quantification. Results demonstrate that LightGBM achieves the highest predictive accuracy (test R2 = 0.8817), significantly outperforming comparison models (p < 0.05). XGBoost exhibits comparable accuracy (R2 = 0.8805), while Random Forest demonstrates superior robustness in spatial cross-validation (R2 = 0.7273). Multi-model analysis consistently identifies water content and liquidity index as the dominant factors governing av, with cumulative contribution exceeding 95%. SHAP analysis confirms both factors exhibit monotonically positive effects with approximate additivity. Tree-based models yield Bootstrap confidence intervals with mean widths ranging from 0.0526 to 0.0653 MPa-1, providing essential risk references for engineering practice. This study not only delivers a reliable predictive tool for av of Chengdu clays but also establishes a reproducible, physically guided, and statistically rigorous paradigm for regional geotechnical machine learning research.",
  "abstract_shown": true,
  "affiliations": [
    "School of Earth Sciences, Guilin University of Technology",
    "School of Business, Guilin University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tongtong Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Hanying Bai"
    },
    {
      "affs": [
        1
      ],
      "name": "Lihong Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1c3qah0cnz1cb.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-69689-8",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-69689-8",
  "id": "1c3qah0cnz1cb",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-69689-8",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2205|Geotechnical Engineering and Soil Mechanics"
    ]
  ],
  "openalex": "W7207606296",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/1c3qah0cnz1cb.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T14:21:19Z",
      "native_id": "0c02c1b4d73a4d8abe9d719840945924",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-69689-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "An explainable ensemble machine learning approach for coefficient of compressibility prediction of Chengdu clay",
  "title_text": "An explainable ensemble machine learning approach for coefficient of compressibility prediction of Chengdu clay",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T14:20:46Z",
  "url": "https://research.draconitic.com/paper/1c3qah0cnz1cb",
  "venue": "Scientific Reports",
  "versions": []
}