Automated ensemble machine learning models for roof snow load estimation of heated flat roofs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21610%2F25%3A00387587" target="_blank" >RIV/68407700:21610/25:00387587 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.prostr.2025.10.022" target="_blank" >http://dx.doi.org/10.1016/j.prostr.2025.10.022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.prostr.2025.10.022" target="_blank" >10.1016/j.prostr.2025.10.022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated ensemble machine learning models for roof snow load estimation of heated flat roofs

Popis výsledku v původním jazyce

Accurate prediction of roof snow loads is essential for structural safety in cold climates. This study applies an automated machine learning (ML) framework to estimate snow loads on heated flat roofs with thermal transmittance U = 1.0 W/m(2)K (no sliding) using long-term hourly climate data for Oslo, Norway. Four ensemble machine learning models—Random Forest (RF), Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), and extreme gradient boosting (XGBoost)—were trained to predict roof snow load from meteorological inputs such as temperature, precipitation, humidity, wind, and solar radiation. The models were evaluated on an independent test set, achieving high accuracy with R2 values exceeding 99.6% and root-mean-square errors below 0.05 kN/m(2). Shapley additive explanation (SHAP) analysis confirmed the dominant influence of ground snow load, snow depth, air and soil temperatures, surface pressure and thermal radiation on roof snow load formation. Local SHAP analysis also revealed the nonlinear effects and interactions between meteorological variables on roof snow load. The models effectively captured both accumulation and melt events, demonstrating their utility for fast, reliable snow load estimation without the need for complex physical simulations. The approach is scalable to other insulation levels and climates, offering a promising tool for data-driven snow load assessment and structural design optimization.

Název v anglickém jazyce

Automated ensemble machine learning models for roof snow load estimation of heated flat roofs

Popis výsledku anglicky

Accurate prediction of roof snow loads is essential for structural safety in cold climates. This study applies an automated machine learning (ML) framework to estimate snow loads on heated flat roofs with thermal transmittance U = 1.0 W/m(2)K (no sliding) using long-term hourly climate data for Oslo, Norway. Four ensemble machine learning models—Random Forest (RF), Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), and extreme gradient boosting (XGBoost)—were trained to predict roof snow load from meteorological inputs such as temperature, precipitation, humidity, wind, and solar radiation. The models were evaluated on an independent test set, achieving high accuracy with R2 values exceeding 99.6% and root-mean-square errors below 0.05 kN/m(2). Shapley additive explanation (SHAP) analysis confirmed the dominant influence of ground snow load, snow depth, air and soil temperatures, surface pressure and thermal radiation on roof snow load formation. Local SHAP analysis also revealed the nonlinear effects and interactions between meteorological variables on roof snow load. The models effectively captured both accumulation and melt events, demonstrating their utility for fast, reliable snow load estimation without the need for complex physical simulations. The approach is scalable to other insulation levels and climates, offering a promising tool for data-driven snow load assessment and structural design optimization.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Structural Integrity 73, Proceedings of the 23rd International Conference on Modelling in Mechanics 2025

ISBN

—

ISSN

2452-3216

e-ISSN

2452-3216

Počet stran výsledku

8

Strana od-do

138-145

Název nakladatele

Elsevier BV

Místo vydání

Linz

Místo konání akce

Ostravice

Datum konání akce

28. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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