Journal Search Engine

to

Search Advanced Search Adode Reader(link)
Download PDF Export Citation Korean Bibliography
ISSN : 1226-525X(Print)
ISSN : 2234-1099(Online)
Journal of the Earthquake Engineering Society of Korea Vol.29 No.3 pp.151-161
DOI : https://doi.org/10.5000/EESK.2025.29.3.151

Machine Learning-Based Rapid Prediction Method for Seismic Performance of Reinforced Concrete Moment Frames

Hwang Heejin1), Oh Keunyeong2), Lee Kihak3), Shin Jiuk4)*
1)Ph.D. Student, Department of Architectural Engineering, Gyeongsang National University, 2)Senior Researcher, Department of Building Research,
Korea Institute of Civil Engineering and Building Technology, 3)Professor (PhD), Department of Architectural Engineering, Sejong University,
4)Associate Professor (PhD), Department of Architectural Engineering, Gyeongsang National University

Abstract

Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

초록

     

    Figure

    Table

    Reference

    Journal Abbreviation J. Earthq. Eng. Soc. Korea
    Frequency Bimonthly
    Doi Prefix 10.5000/EESK
    Year of Launching 1997
    Publisher Earthquake Engineering Society of Korea
    Indexed/Tracked/Covered By