Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21610%2F25%3A00380515" target="_blank" >RIV/68407700:21610/25:00380515 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.istruc.2024.108079" target="_blank" >https://doi.org/10.1016/j.istruc.2024.108079</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.istruc.2024.108079" target="_blank" >10.1016/j.istruc.2024.108079</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations
Original language description
The application of staggered slotted perforations in cold-formed steel (CFS) members is increasingly prominent in modern construction. Understanding the web-crippling strength of CFS beams, especially those with staggered slotted perforations, is crucial in structural engineering. This study employs machine learning (ML) models to predict the web-crippling strength of these beams under one-flange loading conditions, specifically interior-one- flange and end-one-flange loading. The research utilises a comprehensive dataset comprising 576 web-crippling strength results obtained through numerical modelling. The dataset includes parameters such as yield strength, thickness, corner radius, slot length, slot width, and bearing plate length. Four different ML algorithms-knearest neighbour (KNN), random forest (RF), support vector regression (SVR), and artificial neural network (ANN)-are developed and evaluated. Performance metrics, including coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean normalised bias (MNB) are used to assess model accuracy. The random forest model outperforms others in both the training and testing phases. Shapley additive explanation (SHAP) and partial dependence plots further analyse the influence of input features on web crippling strength. This study presents a robust ML-based approach for predicting web crippling strength, providing engineers with a time-efficient alternate method.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20102 - Construction engineering, Municipal and structural engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Structures
ISSN
2352-0124
e-ISSN
2352-0124
Volume of the periodical
71
Issue of the periodical within the volume
108079
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
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UT code for WoS article
001394599300001
EID of the result in the Scopus database
2-s2.0-85212876140