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Title |
Reduced Dataset-Based Meta Learning Model for Blast Resistance Prediction of RC Columns
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Authors |
김예은(Yeeun Kim) ; 김수빈(Subin Kim) ; 이기학(Kihak Lee) ; 신지욱(Jiuk Shin) |
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DOI |
https://doi.org/10.4334/JKCI.2025.37.2.219 |
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Keywords |
폭발손상평가; 철근콘크리트 기둥; 유한요소해석; 기계학습 machine learning; finite element analysis; blast resistance performance assessment; reinforced concrete column |
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Abstract |
This study proposes a machine learning model with a combining method capable of accurately evaluating the blastresistance performance of reinforced concrete (RC) columns using a small dataset of 200 samples. To achieve this, a blastperformance evaluation response database was established based on finite element analysis models that consider various columndetails and blast scale values. Each individual learning model applied seven classification algorithms, and the model demonstratingthe highest evaluation metrics was developed and combined. The proposed machine learning model achieved a 65.5 % reduction indata usage compared to an existing model based on 700 samples while improving performance by an average of 14.3 %. These resultsdemonstrate that the proposed method enables highly accurate and rapid evaluations even in data-limited environments.
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