Scholarlink

<p>This study presents a deep learning framework for non-destructive evaluation of concrete compressive strength using high-resolution microstructural images. Unlike traditional destructive testing, this approach enables efficient large-scale and continuous strength monitoring. The proposed model combines: (1) CAE for efficient feature extraction (achieving 80% dimensionality reduction without significant information loss); (2) Transformer-based self-attention mechanisms to dynamically weight critical image regions, enhancing interpretability; and (3) LSTM networks to capture temporal strength evolution during curing, improving forecasting accuracy by 15%. The framework is trained and tested on a hybrid dataset integrating UCI concrete strength data with high-resolution microstructural images. Nested cross-validation coupled with Bayesian optimization ensures robust performance evaluation and hyperparameter tuning. Comparative analyses demonstrate superior performance over baseline CNN and traditional ML models, with 20% reduction in MAE (3.7&#xa0;MPa vs. 4.6&#xa0;MPa), 18% lower RMSE (4.9&#xa0;MPa vs. 6.1&#xa0;MPa), and 7% higher <i>R</i><sup>2</sup> (0.87 vs. 0.81). The model also reduces prediction time by approximately 20%. This scalable solution offers high accuracy, robustness, and generalizability for real-time concrete strength monitoring in infrastructure projects, advancing intelligent image-based non-destructive testing beyond conventional destructive methods.</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

Computational prediction of concrete strength via microstructure image analysis: a hybrid machine learning framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

查看全文
  • Prashant T. Dhorabe,
  • Mayuri A. Chandak,
  • Boskey V. Bahoria,
  • Tejas R. Patil,
  • Ankita Jaiswal,
  • Nilesh Shelke,
  • Vikrant S. Vairagade

摘要

This study presents a deep learning framework for non-destructive evaluation of concrete compressive strength using high-resolution microstructural images. Unlike traditional destructive testing, this approach enables efficient large-scale and continuous strength monitoring. The proposed model combines: (1) CAE for efficient feature extraction (achieving 80% dimensionality reduction without significant information loss); (2) Transformer-based self-attention mechanisms to dynamically weight critical image regions, enhancing interpretability; and (3) LSTM networks to capture temporal strength evolution during curing, improving forecasting accuracy by 15%. The framework is trained and tested on a hybrid dataset integrating UCI concrete strength data with high-resolution microstructural images. Nested cross-validation coupled with Bayesian optimization ensures robust performance evaluation and hyperparameter tuning. Comparative analyses demonstrate superior performance over baseline CNN and traditional ML models, with 20% reduction in MAE (3.7 MPa vs. 4.6 MPa), 18% lower RMSE (4.9 MPa vs. 6.1 MPa), and 7% higher R2 (0.87 vs. 0.81). The model also reduces prediction time by approximately 20%. This scalable solution offers high accuracy, robustness, and generalizability for real-time concrete strength monitoring in infrastructure projects, advancing intelligent image-based non-destructive testing beyond conventional destructive methods.