Convolutional Neural Network for Specimen-Invariant Structural Health Monitoring of FRC Under Flexural Loading

Reinforced Concrete (RC) structures experience progressive degradation over their service life due to mechanical loading and environmental exposure, leading to reduced bearing capacity and compromised structural safety. Incorporating discrete fibers into concrete mitigates crack propagation and enhances ductility, resulting in fiber-reinforced concrete (FRC) with superior fracture energy, durability, and sustainability characteristics. Despite these advantages, research on Structural Health Monitoring (SHM) techniques for FRC elements remains limited. The Electromechanical Impedance (EMI) method, which exploits piezoelectric transducers as both actuators and sensors, offers high sensitivity for detecting early-stage damage by monitoring variations in local mechanical impedance. This study investigates the effectiveness of a deep learning-enabled EMI framework for assessing the structural condition of FRC beams under flexural loading. A one-dimensional convolutional neural network (1D-CNN) is proposed to automatically extract salient features from high-frequency EMI signatures and classify structural health into three predefined states. The model is rigorously evaluated using specimen-invariant validation to ensure generalization across different FRC specimens, addressing a critical limitation of conventional cross-validation approaches in SHM research. Experimental tests on FRC beams instrumented with surface-bonded PZT transducers provide a dataset of 264 EMI responses for training and validation, enabling direct comparison between common and specimen-invariant validation schemes. The results demonstrate the superior robustness of the specimen-invariant approach and confirm the capability of the proposed 1D-CNN to identify flexural damage progression in FRC elements accurately. An ablation study further highlights the contribution of each architectural component to overall model performance. The findings underscore the potential of integrating EMI-based sensing with advanced deep learning models for reliable, automated, and scalable SHM of next-generation resilient concrete infrastructures.