Novelty detection on a laboratory benchmark slender structure using an unsupervised deep learning algorithm

Rafaelle Piazzaroli Finotti; Clayton Felício da Silva; Pedro Henrique Eveling Oliveira; Flávio de Souza Barbosa; Alexandre Abrahão Cury; Rafael Cerqueira Silva

doi:10.1590/1679-78257591

Novelty detection on a laboratory benchmark slender structure using an unsupervised deep learning algorithm

Authors

Rafaelle Piazzaroli Finotti Programa de Pós-Graduação em Engenharia Civil, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brasil. https://orcid.org/0000-0002-8569-0041
Clayton Felício da Silva Programa de Pós-Graduação em Geotecnia, Universidade de Brasília, Brasília, DF, Brasil. https://orcid.org/0000-0002-6625-8165
Pedro Henrique Eveling Oliveira Programa de Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brasil. https://orcid.org/0000-0003-1463-8518
Flávio de Souza Barbosa Programa de Pós-Graduação em Engenharia Civil, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brasil. https://orcid.org/0000-0002-7991-8425
Alexandre Abrahão Cury Programa de Pós-Graduação em Engenharia Civil, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brasil. https://orcid.org/0000-0002-8860-1286
Rafael Cerqueira Silva Programa de Pós-Graduação em Geotecnia, Universidade de Brasília, Brasília, DF, Brasil. https://orcid.org/0000-0002-1673-5592

DOI:

https://doi.org/10.1590/1679-78257591

Abstract

The process that involves the continuous monitoring and analysis of a structure's behavior and performance is known as Structural Health Monitoring (SHM). SHM typically involves the use of sensors and other monitoring devices to collect data, such as displacements, strains, accelerations, among others. These data are analyzed using advanced algorithms and machine learning techniques to identify any signs of abnormal behavior or deterioration. This paper presents a numerical and experimental study of a slender frame subjected to five levels of structural changes under impact loading. The dynamic responses were recorded by four accelerometers and used to build models based on Sparse Auto-Encoders (SAE). Such models can identify each of the five structural states in an unsupervised way. A new strategy to define the hyperparameters of the SAE is presented, which proved to be adequate in the experiments conducted. Finally, the experimental data set is made available to the scientific community to serve as a benchmark for validating SHM methodologies to identify structural changes from dynamic measurements.

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Published

2023-10-04

Issue

Vol. 20 No. 9 (2023)

Section

Articles

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