A MACHINE LEARNING BASED APPROACH TO ESTIMATENONLINEAR DYNAMIC RESPONSE OF REINFORCEDCONCRETE MOMENT-RESISTING FRAMES

Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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SEE09_130

تاریخ نمایه سازی: 10 آبان 1403

Abstract:

This study advances structural engineering by demonstrating the efficiency of machine learning inreducing computational costs for time history analysis while ensuring reliable predictions. The goal isto provide engineers with a dependable tool for designing structures under dynamic loads. Machinelearning models, trained on a vast dataset of ۲۱۵,۰۰۰ reinforced concrete frames and earthquakerecords, excel in predicting base shear ratios with a ۹۰% coefficient of determination. Despite a slightreduction in accuracy when predicting maximum roof drift ratios, the models still exhibitcommendable predictive capability with coefficients of determination exceeding ۸۰%. The applicationon a seven-story building's collapse reliability index shows the trained machine with a ۷% error rate,performing ۱۳۰,۰۰۰ times faster than traditional time history analysis. This highlights the machine'sremarkable efficiency and potential for widespread application in structural engineering

Authors

Mehrdad Ghafarian Tamizi

M.Sc. Graduate, Civil and Environmental Engineering Department, Amirkabir University ofTechnology, Tehran, Iran,

Mehdi Banazadeh

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