Early-age concrete strength monitoring using smart aggregate based on electromechanical impedance and machine learning

Abstract

It is crucial to monitor the real-time strength development of early-age concrete, which is related to the structure safety and construction efficiency of concrete. This paper proposed a new method to predict and monitor concrete strength development. The basic idea is using embedded smart aggregates (SAs), combining the electromechanical impedance (EMI) technology and machine learning to monitor the strength development of early-age concrete. An EMI model of the embedded SA was firstly established and the relationship between the conductance resonant frequency (CRF) and conductance resonant peak (CRP) of the SA and the development of concrete strength was investigated. The conductance signatures were continuously monitored and the compressive strength of the specimens at different ages was tested. Hence, the EMI model results that the CRF decreases but CRP increases as the compressive strength of concrete increases were verified by analyzing the collected data. The four different models of a linear regression model (LR), a convolutional neural network (CNN), a hybrid (LR-CNN) model, and an empirical equation were established. The conductance of SAs as the developed models inputs, the compressive strength of specimens as outputs. Finally, these four models were compared with each other. Meanwhile, the performance indicators of the LR-CNN model and other hybrid models were compared. The results show that the LR-CNN model has excellent performance and successfully achieves quantitative prediction of concrete strength development. The proposed method for evaluating and predicting the compressive strength of concrete is simple, accurate, quantitative and reliable, and has promising application.