Prediction of unknown compressive stress/damage in concrete structure using 3D convolutional neural network-based deep learning of raw electromechanical admittance signals

Deep learning approach promotes automated identification of concrete structural stress and damage using electromechanical impedance/admittance (EMI/EMA) technique, while currently automatic identification is overly depending on the known label with certain stress level, which hinders its real-life application for those unknown loading conditions. To this end, this paper proposed a novel deep learning approach using three-dimensional convolutional neural network (3DCNN)-enhanced EMA technique to detect unknown stress and its corelated damage in concrete structures. In the approach, sub-range raw conductance signatures according to the critical root mean square deviations were selected to construct 3D input of the CNN model, and the unknown stress and its belonged range were rapidly predicted. Unknown single stress level and multiple stress range prediction were investigated via monitoring of a concrete structure subjected to uniaxial compression from initial loading to final failure. Experimental validation results demonstrated that the unknown stress level in a range as wide as 16 MPa could be rapidly predicted with high accuracy and efficiency by using the well-trained 3DCNN model, which outperformed the existed two-dimensional CNN and traditional back-propagation networks. Promising results in this study potentially provided a possible paradigm of the EMA data-driven compressive stress prediction in concrete structures with limited condition measurements.