Automated ensemble machine learning models for roof snow load estimation of heated flat roofs
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Automated ensemble machine learning models for roof snow load estimation of heated flat roofs
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20102 - Construction engineering, Municipal and structural engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Procedia Structural Integrity 73, Proceedings of the 23rd International Conference on Modelling in Mechanics 2025
ISBN
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ISSN
2452-3216
e-ISSN
2452-3216
Number of pages
8
Pages from-to
138-145
Publisher name
Elsevier BV
Place of publication
Linz
Event location
Ostravice
Event date
May 28, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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