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The multi-strut model typically has two or more struts located concentric and eccentric to the infill wall corners. This model represents the interaction between walls and frames, affecting local shear and moment in the frame. It is essential to estimate the strength of the multi-strut model for accurately capturing the influence of the infill wall on the surrounding frame. However, the prediction of struts’ properties is still a problem. In this paper, we identify efficient features for estimating the strength of the multi-strut model through machine learning methods. Various feature selection techniques are applied: Sequential forward floating selection and Lasso regression. A comprehensive database of experimental studies from the literature is employed. The findings show that the compressive strength of masonry is the most important feature, while wall and column failure modes are found to be the second most important.

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Development of Multi-Strut Macro Models for Masonry Infilled RC Frames Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Eknara Junda,
  • Jarun Srechai,
  • Wongsa Wararuksajja,
  • Sutat Leelataviwat

摘要

The multi-strut model typically has two or more struts located concentric and eccentric to the infill wall corners. This model represents the interaction between walls and frames, affecting local shear and moment in the frame. It is essential to estimate the strength of the multi-strut model for accurately capturing the influence of the infill wall on the surrounding frame. However, the prediction of struts’ properties is still a problem. In this paper, we identify efficient features for estimating the strength of the multi-strut model through machine learning methods. Various feature selection techniques are applied: Sequential forward floating selection and Lasso regression. A comprehensive database of experimental studies from the literature is employed. The findings show that the compressive strength of masonry is the most important feature, while wall and column failure modes are found to be the second most important.