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

Abstract

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.

Keywords

Attention mechanisms / Convolutional autoencoders / Concrete strength / LSTM networks / Microstructural images / Non-destructive testing

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