Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements

Abstract

This paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure modes occur in a sudden and brittle way by debonding. The contribution of this work is threefold: First, for the first time, two autoencoder models, convolutional and fully connected, based on an unsupervised learning framework supplemented by appropriate pre-processing techniques, are proposed for effective tracking of FRP-strengthened RC elements from raw EMI response variations in different locations of the auscultated structure; their implementation is also extensively investigated. The proposed framework consists of two main components, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the raw EMI signal while preserving the necessary information required, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. The approach is beneficial as only the EMI spectrum from the healthy structure state is considered for the training of the autoencoders. Second, the superior performance of the proposed framework is demonstrated. The results show that the proposed technique can accurately detect minor damage in its earliest stages for this kind of strengthened structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from EMI signals for damage diagnosis. Finally, research presented in this work can potentially open up new opportunities for successful condition monitoring of this type of strengthened structures

This research was funded by the Spanish Ministry of Science and Innovation (MCIN/AEI), grants number PID2020‐119015GB‐C21 and PID2020‐119015GB‐C22