Scholarlink

<p>It proposes machine learning for the seismic performance estimation of FRP-retrofitted bridges using a Mayfly Optimization Algorithm (MOA) optimized Convolutional Neural Networks (CNN). A very impressive CNN model was proposed with an R<sup>2</sup> score of nearly 0.92 for Damage Index and 0.88 for Repair Costs by achieving RMSE values very low as 0.05 and 158.11, respectively. For the classification tasks, the model obtained an overall accuracy of 91% in predicting Failure Mode with a precision of 0.89, recall 0.87, and F1-score of 0.88. The most relevant factors for the prediction of the Damage Index, as determined from the feature importance analysis using SHAP, were PGA, Bridge Type, and FRP Layer Count. Pier Height and Span Length were critical features in determining which type of failure mode exists, such as Column Shear Failure. Model discriminative explainability techniques with LIME ratified such predictions' accuracy by improving the practicality of the model's handling in real engineering scenario applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning-based assessment of seismic performance in fiber-reinforced polymer (FRP) retrofitted bridges

  • 期刊论文

  • 发表日期：

    2025年1月21日

查看全文
  • Mu’taz Abuassi,
  • Majdi Bisharah

摘要

It proposes machine learning for the seismic performance estimation of FRP-retrofitted bridges using a Mayfly Optimization Algorithm (MOA) optimized Convolutional Neural Networks (CNN). A very impressive CNN model was proposed with an R2 score of nearly 0.92 for Damage Index and 0.88 for Repair Costs by achieving RMSE values very low as 0.05 and 158.11, respectively. For the classification tasks, the model obtained an overall accuracy of 91% in predicting Failure Mode with a precision of 0.89, recall 0.87, and F1-score of 0.88. The most relevant factors for the prediction of the Damage Index, as determined from the feature importance analysis using SHAP, were PGA, Bridge Type, and FRP Layer Count. Pier Height and Span Length were critical features in determining which type of failure mode exists, such as Column Shear Failure. Model discriminative explainability techniques with LIME ratified such predictions' accuracy by improving the practicality of the model's handling in real engineering scenario applications.