{
  "abstract": "Accurate prediction of roof area-averaged external pressure coefficients ( $${\\overline{C}}_{p,A}$$ ) is crucial for low-rise building design. To isolate geometric and turbulence effects from atmospheric wind shear, wind tunnel experiments were conducted under uniform turbulent flow conditions covering roof pitch angles (0°–30°), wind directions, and turbulence intensities. To overcome the smoothing limitations of conventional regression methods, an ensemble learning framework (Random Forest, XGBoost, and LightGBM) was developed and evaluated using a rigorous Leave-One-Slope-Out (LOSO) cross-validation strategy. Results show that around the roof pitch angle of α = 18.4°, a pronounced variation in aerodynamic pressure response occurs under oblique wind conditions. XGBoost maintains robust predictive performance, outperforming conventional response surface modeling under nonlinear transition conditions. Furthermore, extrapolation failure at α = 30° delineates the practical limits of data-driven models in predicting unseen aerodynamic regimes. Finally, SHAP analysis demonstrates that the model-derived feature attribution patterns are generally consistent with previously reported aerodynamic observations, providing a physically interpretable framework for wind load prediction under complex flow transitions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Mengchang Yang"
    },
    {
      "name": "Yinghang Fu"
    },
    {
      "name": "Yangjin Yuan"
    },
    {
      "name": "Huiling Duan"
    },
    {
      "name": "Jie Wang"
    },
    {
      "name": "Il-seung Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cj4e7x6dby8dh.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-71463-9",
  "fulltext_url": null,
  "id": "cj4e7x6dby8dh",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-71463-9",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Wind and Air Flow Studies"
    ]
  ],
  "openalex": "W7203687726",
  "pdf_url": "https://www.nature.com/articles/s41598-026-71463-9.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/cj4e7x6dby8dh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.1038/s41598-026-71463-9",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-71463-9"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Wind and Air Flow Studies",
      "subfield": "environmental-science"
    }
  ],
  "title": "Machine learning prediction and analysis of area-averaged external pressure coefficients for low-rise dual-slope building roofs in a uniform turbulent flow field",
  "title_text": "Machine learning prediction and analysis of area-averaged external pressure coefficients for low-rise dual-slope building roofs in a uniform turbulent flow field",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/cj4e7x6dby8dh",
  "venue": "Scientific Reports",
  "versions": []
}