Damage Indicators for Structural Monitoring of Fiber-Reinforced Polymer-Strengthened Concrete Structures Based on Manifold Invariance Defined on Latent Space of Deep Autoencoders

Deep learning approaches based on autoencoders have been widely used for structural monitoring. Traditional approaches of autoencoders based on reconstruction errors involve limitations, since they do not exploit their hierarchical nature, and only healthy data are used for training. In this work, some health indicators, based on manifold invariance through the encoding procedure, were built for the monitoring of concrete structures strengthened with carbon fiber-reinforced polymers by directly exploring the latent space representation of the input data to a deep autoencoder. Latent representations of experimental observations of different classes were used for the learning of the network, delimiting areas in a low-dimensional space. New synthetic data with their variations, generated with a variational autoencoder, were encompassed to the trained autoencoder. The proposed method was verified on raw electromechanical impedance spectra obtained from lead zirconate titanate sensors bonded on a specimen subjected to different loading stages. The results of this research demonstrate the efficiency of the proposed approach.