Deep learning approach for early-age concrete strength monitoring using an embedded smart piezo sensor system

This study presents a novel, cost-effective approach for real-time prediction of the mechanical performance of concrete using Electro-Mechanical Impedance (EMI) profiles obtained from embedded piezo sensors (EPS). The experimental process involved embedding a smart clinker—comprising a lead zirconate titanate (PZT) sensor encapsulated in epoxy and mortar—into concrete specimens to record EMI signatures during hydration (30–500 kHz) under controlled curing conditions. This enabled continuous monitoring of stiffness evolution and strength gain. COMSOL Multiphysics simulations were employed to generate an extensive EMI dataset for training various machine learning (ML) and deep learning (DL) models. Equivalent Structural Parameter (ESP) analysis revealed a strong correlation between non-destructively derived stiffness and the percentage increase in compressive strength. Among ML models, cubic support vector machine (SVM) and medium Gaussian SVM yielded reliable strength predictions. Additionally, DL architectures, including one-dimensional convolutional neural networks (1D CNN), 1D CNN combined with Long Short-Term Memory (LSTM), and 1D CNN-Bidirectional LSTM, were further optimized using Orthogonal Matching Pursuit (OMP) for feature selection, significantly enhancing performance. The OMP-based Bi-LSTM achieved an R² of 0.9973, outperforming other models. These results confirm the efficacy of EMI-based DL frameworks for highly accurate, non-destructive estimation of concrete strength, advancing practical structural health monitoring.