Integrated electromechanical impedance technique with convolutional neural network for concrete structural damage quantification under varied temperatures

Abstract The electromechanical impedance/admittance (inverse of impedance, EMI/EMA) technique has demonstrated its high sensitivity to structural incipient damages, however, it is also vulnerable to ambient temperature fluctuation that may adversely cause false alarms in real-life monitoring applications. This paper proposed an innovative approach integrating the EMA technique with Convolutional Neural Network (CNN) to quantify concrete structural damage severity under varied temperatures. In the approach, EMA signals were first split into multiple sub-range responses, and the corresponding statistical indices namely Pearson Correlation Coefficient (PCC), were calculated and utilized as input of one-dimensional CNN. In order to meet high accuracy for training the CNN model, an Orthogonal Matching Pursuit (OMP) algorithm based EMA data reconstruction technique was employed to generate sufficient data for the establishment of CNN data library. Effectiveness of the proposed connectionist approach was verified through crossover experiments of diagnosing crack damages on a standardized concrete cube subjected to varied temperatures. Both temperature recognition and damage quantification under elevated temperatures were accomplished via performing the EMA signal based CNN procedure. Experimental results revealed that the proposed approach was of high accuracy for temperature recognition and damage quantification, which potentially facilitates the in-situ monitoring of engineering structures under varied temperature conditions.