Integrating electro-mechanical impedance data with machine learning for damage detection and classification of blended concrete systems

Abstract

In recent years, the assessment of structural integrity and durability of concrete systems has significantly advanced with the integration of novel sensing technologies and data-driven techniques. This study introduces an innovative approach that utilizes electro-mechanical impedance (EMI) data acquired through embedded piezo sensors (EPS) to monitor and analyze the condition of blended concrete systems. Moreover, the study involves blended concrete specimens are subjected to controlled damage scenarios under mechanical loadings to establish EPS as an early warning sensor for detecting damage in these systems. Furthermore, the extent of damage under these loadings is comprehensively evaluated using statistical indices and an equivalent stiffness parameter. Additionally, the study focuses on the application of machine learning (ML) algorithms to interpret EMI data for efficient damage classification. ML models, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and naive Bayes (NB) are trained to identify patterns correlating with different types and extents of structural damage. Among them, the RF classifier exhibits the highest accuracy of 0.91, followed by DT, KNN, SVM, and NB with accuracies of 0.88, 0.86, 0.68, and 0.17 respectively. The integration of EMI data with ML approaches demonstrates significant potential to revolutionize predictive maintenance strategies, enabling precise classification of structural damage and providing critical insights for engineers and stakeholders to proactively address issues, thereby significantly enhancing the safety, durability, and lifespan of concrete infrastructure. This study not only extends the current understanding of EMI data applications but also opens new paths for automated and real-time structural health monitoring.