Machine Learning-Aided Prediction of Post-Fire Shear Resistance Reduction of Q690 HSS Plate Girders

نویسندگان

چکیده

Fire has significant effects on the residual resistance of steel structures. It is necessary to accurately clarify its Q690 HSS plate girders, which have been widely used. In this paper, ultimate and effective service girders after a fire are obtained using material tests finite element (FE) analysis including parametric studies, where data 210 models were collected. The four key parameters (hw/tw ratio, a/hw exposure temperature cooling method) post-fire shear reduction roughly investigated by individual conditional expectation (ICE), showing most important factor. popular algorithms machine learning (ML), namely artificial neural network (ANN) support vector regression (SVR) algorithms, utilized in model training predict factors both resistance. Finally, results indicate that prediction ML shows much better performance than with traditional ordinary least squares (OLS) regression, SVR genetic algorithm (GA) provides highest accuracy. paper show superiority for solving problems structures, compared conventional methods such as linear regression.

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ژورنال

عنوان ژورنال: Buildings

سال: 2022

ISSN: ['2075-5309']

DOI: https://doi.org/10.3390/buildings12091481