ESTIMATION OF MAXIMUM INTERSTORY DRIFT RATIO OFSTEEL MOMENT-RESISTING FRAMES USING MACHINELEARNING

سال انتشار: 1403
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 111

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شناسه ملی سند علمی:

SEE09_087

تاریخ نمایه سازی: 10 آبان 1403

چکیده مقاله:

This study aims to develop machine learning (ML) models that can accurately estimate themaximum interstory drift ratio (MIDR) of steel moment-resisting frames subjected to earthquakeground motions. Four boosting ML methods were applied: gradient boosting, extreme gradientboosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting(CatBoost). To train and evaluate the models, a comprehensive dataset was generated through ۹۲,۴۰۰nonlinear dynamic analyses of ۱,۸۴۸ steel moment frames with varying structural properties subjectedto ۵۰ ground motions. The selected ground motions had moment magnitudes of at least ۵.۰ and soilconditions classified as Site Class C. The results suggest that all models could reliably predict theMIDR of steel moment frames when tested on unseen data. The LightGBM model achieved the mostaccurate estimations among those considered, with a coefficient of determination (R۲) of ۰.۹۶۱, meanabsolute percentage error (MAPE) of ۰.۱۹۹%, and root mean square error (RMSE) of ۰.۱۶۵% for thetest dataset.

کلیدواژه ها:

Maximum Interstory Drift Ratio (MIDR) ، Steel Moment-Resisting Frame ، MachineLearning ، Boosting Methods

نویسندگان

Farzaneh Zareian

M.Sc. Student, Civil and Environmental Engineering Dept., Amirkabir University of Technology, Tehran,Iran,

Mehdi Banazadeh

Associate Professor, Civil and Environmental Engineering Dept., Amirkabir University of Technology,Tehran, Iran,