Structural Damage Identification Using Autoencoders: A Comparative Study

Author:

Spínola Neto Marcos1ORCID,Finotti Rafaelle2ORCID,Barbosa Flávio2ORCID,Cury Alexandre2ORCID

Affiliation:

1. Faculty of Engineering, University of Juiz de Fora, Juiz de Fora 36036-900, MG, Brazil

2. Graduate Program in Civil Engineering, Faculty of Engineering, University of Juiz de Fora, Juiz de Fora 36036-900, MG, Brazil

Abstract

Structural health monitoring (SHM) ensures the safety and reliability of civil infrastructure. Autoencoders, as unsupervised learning models, offer promise for SHM by learning data features and reducing dimensionality. However, comprehensive studies comparing autoencoder models in SHM are scarce. This study investigates the effectiveness of four autoencoder-based methodologies, combined with Hotelling’s T2 statistical tool, to detect and quantify structural changes in three civil engineering structures. The methodologies are evaluated based on computational costs and their abilities to identify structural anomalies accurately. Signals from the structures, collected by accelerometers, feed the autoencoders for unsupervised classification. The latent layer values of the autoencoders are used as parameters in Hotelling’s T2, and results are compared between classes to assess structural changes. Average execution times of each model were calculated for computational efficiency. Despite variations, computational cost did not hinder any methodology. The study demonstrates that the best fitting model, VAE-T2, outperforms its counterparts in identifying and quantifying structural changes. While the AE, SAE, and CAE models showed limitations in quantifying changes, they remain relevant for detecting anomalies. Continuous application and development of these techniques contribute to SHM advancements, enabling the increased safety, cost-effectiveness, and long-term durability of civil engineering structures.

Funder

CAPES

Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (Brazil)—Grants CNPq/FNDCT/MCTI

Fundação de Amparo à Pesquisa do Estado de Minas Gerais—FAPEMIG—Grant

Publisher

MDPI AG

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