Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Structures

ISSN

2352-0124

e-ISSN

2352-0124

Svazek periodika

71

Číslo periodika v rámci svazku

108079

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001394599300001

EID výsledku v databázi Scopus

2-s2.0-85212876140