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Water that penetrates through cracks in concrete can corrode steel bars. There is a need for reliable and practical seepage sensing technology to prevent failure and determine the necessary maintenance for a concrete structure. Therefore, we propose a modified plasma-assisted electrochemical exfoliated graphite (MPGE) nanosheet smart tag. We conducted a comparative study of standard and modified RFID smart tags with sensor technology for seepage detection in concrete. The performance of both smart tags was tested and verified for seepage sensing in concrete, characterized by sensor code and frequency values. Seepage was simulated by cracking the concrete samples, immersing them for a designated time, and repeating the immersing phase with increasing durations. The test showed that the modified smart tag with 3% MPGE and an additional crosslinking agent provided the best sensitivity compared with the other nanosheet compositions. The presence of 3D segregated structures on the smart tag's sensing area successfully enhanced the sensitivity performance of seepage detection in concrete structures and is expected to benefit structural health monitoring as a novel non-destructive test method. [ABSTRACT FROM AUTHOR]

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]