Enhanced diagnostic approach for multiple damage detection and severity evaluation through EMI-based sensing and artificial neural network model

Detecting and quantifying multiple damages in structures remains a significant challenge in structural health monitoring (SHM), particularly in complex civil engineering systems. This study presents an experimental approach for the detection of multiple damages and their severity using the Electromechanical Impedance (EMI) technique. The EMI method, which utilizes piezoelectric transducers, offers a sensitive and reliable means to monitor structural integrity by measuring the coupled mechanical and electrical response of structures under various damage conditions. In this research, multiple damage scenarios were simulated in concrete specimens, and the corresponding conductance signatures were recorded. Particularly shifts in conductance values were analyzed to identify and localize damages. Conventional statistical metrics such as root-mean square deviation, correlation coefficient, mean absolute percentage deviation are employed to quantify the changes in conductance signature. Additionally, a methodology for localizing the damage is presented. Additionally, a severity index based on impedance variations was developed to quantify the extent of damage. The experimental results demonstrate the effectiveness of the EMI technique in accurately detecting, locating, and assessing the severity of multiple damages in complex structural systems. Further machine learning approach viz. artificial neural network model was applied to predict the damages. The data trained an artificial neural network model, which found suitable for predicting multiple damages levels. This approach contributes to enhanced safety and reliability in structural health monitoring (SHM) and sustainable building practices by offering a scalable and sustainable approach for real-time durability assessment, performance of concrete structures, contributing to a more sustainable development. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.