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.30 No.3 pp.111-121
DOI : https://doi.org/10.5000/EESK.2026.30.3.111

Structural Type Classification of Domestic Reinforced Concrete Buildings Utilizing Machine-Learning Algorithms

Kim Taewan1)*
1)Professor, Department of Architectural Engineering, Kangwon National University

Abstract

This study optimizes three machine learning models—Decision Tree, Random Forest (RF), and Gradient Boosting—to classify concrete structure types (C2, C3, C4, and C5) using information from a building register. Although the initial models achieved high overall accuracy, the minority class C5 exhibited relatively low performance due to class imbalance and inherent complexity. To address this, an exhaustive grid search over discrete parameter candidates was performed, and a class-weighting strategy was integrated into the RF model to prioritize accurate classification of the minority class. The optimized RF model preserved a high overall accuracy of 94% while markedly improving C5 recall from 0.81 to 0.86 and its F1-score from 0.85 to 0.87. These results demonstrate that strategic hyperparameter tuning with class weights can effectively enhance classification reliability for rare structural types. Future research should include feature importance analysis to refine data configurations and the expansion of minority class samples to further improve model robustness in practical applications.

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