This paper presents a novel method for monitoring the strength development of early‑age concrete using embedded smart aggregates (SAs) that leverage electromechanical impedance (EMI) sensing combined with machine learning. The smart aggregates are embedded in the concrete mix during casting and continuously monitor conductance signatures, specifically the conductance resonant frequency (CRF) and conductance resonant peak (CRP) values, which correlate to changes in concrete stiffness and strength. A dataset of concrete specimens is used, destructive compressive strength tests at multiple ages are carried out, and the machine‑learning model correlates the EMI signatures to strength gains.
The results demonstrate strong predictive capability: The model can track the evolution of strength from early ages (just hours after casting) to later ages, showing how the embedded SA + EMI approach captures strength gain dynamics non‑destructively. The authors discuss the practical implications for field monitoring of concrete during curing and how this approach may reduce reliance on traditional destructive tests or delayed measurement methods.
In comparison with Concurex, which uses embedded sensors to measure electrical resistance related to hydration and strength gain, the method outlined in this paper focuses on electromechanical impedance (EMI) signatures from smart aggregates integrated within concrete. While this approach is effective in early strength prediction, it may be more limited to localized measurements, requiring more complex signal processing (e.g., machine learning models) for accurate strength estimation. Concurex, on the other hand, employs a direct, non‑destructive measurement of hydration and strength through continuous real‑time monitoring of concrete, providing a comprehensive strength growth curve that can optimize key construction decisions (formwork removal, load application). Furthermore, the device from Concurex is designed to function reliably across diverse environmental conditions (e.g., varying temperature, pressure, and humidity), whereas the system presented in the study may require calibration for specific environmental variables. Thus, Concurex offers a more versatile, user‑friendly solution for continuous, actionable insights during the curing process.
Li G., Luo M., Huang J., Li W. “Early‑age concrete strength monitoring using smart aggregate based on electromechanical impedance and machine learning.” Mechanical Systems and Signal Processing*, Vol. 186, 2023, Article 109865.
(https://www.sciencedirect.com/science/article/abs/pii/S0888327022009335?via%3Dihub)
