Smart monitoring of composite concrete damage using EMI technique with temperature effects compensation and ANN integration

Abstract The composite materials are widely used across industries however, these materials are prone to damages like cracking and delamination due to its complexity. The Electromechanical Impedance (EMI) technique offers a reliable non-destructive solution for detecting such damage using piezoelectric sensors and enabling effective structural health monitoring and enhancing safety and durability. This study explores the application of the EMI technique for monitoring damages in composite fibre concrete specimens. The specimens were prepared using Ordinary Portland Cement (OPC), fly ash, and polypropylene, glass fiber mixture, water, fine and coarse aggregates. The Piezoelectric sensors were employed to record conductance and susceptance signatures, enabling early detection and quantification of damages. The severity of damages were assessed using statistical indices such as Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), and Correlation Coefficient (CC) revealing higher sensitivity. A notable leftward shift in EMI signatures with increasing damage was confirmed progressive structural degradation. Additionally, structural parameters equivalent stiffness and equivalent damping were evaluated, demonstrating a decrease in stiffness and an increase in damping with greater damage depth. Temperature effects on EMI responses were also investigated, necessitating compensation for reliable analysis. An Artificial Neural Network (ANN) model was trained using Levenberg-Marquardt (LM) algorithm and implemented to predict conductance values and damage depth. The developed ANN showed high accuracy, with strong agreement between experimental and predicted results. Overall, the findings confirm the EMI technique’s potential for SHM of composite fiber concrete and integration with machine learning for improved predictive its durability assessment.