2D CNN-based concrete stress monitoring using impedance signals of capsule-like smart aggregate

This study aims to develop a 2D CNN deep learning model processing electromechanical impedance (EMI) responses of a capsule-like smart aggregate (CSA) sensor for monitoring stress variation in concrete structures. The following approaches are conducted to obtain the objective. Firstly, an overall scheme of the proposed method is presented. An EMI measurement model is theoretically presented for a CSA sensor embedded in a concrete cylinder under compressive loadings. A 2D CNN model is designed to learn and classify stress-sensitive features from CSA's EMI responses. Secondly, a CSAembedded concrete cylinder is experimentally investigated to record the EMI signals of the cylinder under a series of compressive stress levels. Thirdly, the performance of the 2D CNN model is investigated for noise-contaminated data sets as well as untrained stress-EMI scenarios. Finally, the accuracy of the proposed 2D CNN model is analyzed by comparatively discussing with a well-established 1D CNN model.