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Title |
Derivation of Machine Learning-Based Equation for Shear Strength of Reinforced Concrete Beam-Column Joints
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Authors |
강진석(Jin Seok Kang) ; 김재현(Jae Hyun Kim) ; 정호성(Hoseong Jeong) ; 양은빈(Eun Bin Yang) ; 김강수(Kang Su Kim) |
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DOI |
https://doi.org/10.11112/jksmi.2025.29.6.58 |
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Keywords |
철근콘크리트; 보-기둥 접합부; 전단강도; 머신러닝; 랜덤 포레스트; 유전 프로그래밍 Reinforced concrete; Beam-column joint; Shear strength; Machine learning; Random forest; Genetic programming |
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Abstract |
Reinforced concrete (RC) beam-column joints play a critical role in ensuring the structural safety and performance of buildings. However, failures in these regions often occur in a brittle manner, potentially leading to sudden and catastrophic collapse. Existing design code equations for joint shear strength are typically simple and conservative, but they often diverge from experimental results due to their limited consideration of influential parameters such as transverse reinforcement and longitudinal beam reinforcement. To address this gap, recent studies have increasingly applied machine learning techniques to model the complex interactions among these variables. In this study, a machine learning-based approach was adopted to develop an estimation equation for the shear strength of RC beam-column joints. Preliminary variables were selected based on structural design codes and prior research, and their relative importance was assessed using the random forest method. Key variables identified through this process were then used in genetic programming to derive the final estimation equation. The performance of the proposed equation was evaluated by comparing its accuracy with that of existing design code equations and previously developed models. The results demonstrated that the proposed shear strength equation for RC beam-column joints showed superior accuracy, indicating its reliability and potential for practical application in structural design.
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