Deep learning-integrated electromagnetic imaging for evaluating reinforced concrete structures in water-contact scenarios.

Assessing the resilience of overall reinforced concrete (RC) structures against micro- and macrocracks, especially in water-contact scenarios, poses a significant challenge due to the minute nature of these defects. To address this, a data-driven framework is introduced. It utilizes electromagnetic-wave (EM-wave) spectrum-encoded images alongside hybrid machine learning-deep learning (ML-DL) for predicting overall structural conditions. This methodology is reinforced by rigorous experimental validation and advanced image-processing techniques. Applied to a dataset of 7078 images, categorized into undamaged and damaged RC structures, the framework demonstrates its effectiveness by achieving an 83% prediction accuracy and an 81% F1-score. The promising results highlight the effectiveness of the presented approach in evaluating dam structures, offering a viable alternative to traditional assessment methods. • Data-driven EM-wave imaging evaluates RC structures with 83% accuracy. • Employed moisture indicators to assess reinforced concretes. • Developed a unique program for generating EM-wave spectrum datasets, enhancing structural assessment. • Attained a high level of metric precision with an 81% F1-score in evaluations. • Provided an automated framework for the assessment of RC structures exposed to water. [ABSTRACT FROM AUTHOR]