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Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In...

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Autores principales: Ozelim, Luan Carlos de Sena Monteiro, Borges, Lucas Parreira de Faria, Cavalcante, André Luís Brasil, Albuquerque, Enzo Aldo Cunha, Diniz, Mariana dos Santos, Góis, Manuelle Santos, da Costa, Katherin Rocio Cano Bezerra, de Sousa, Patrícia Figuereido, Dantas, Ana Paola do Nascimento, Jorge, Rafael Mendes, Moreira, Gabriela Rodrigues, de Barros, Matheus Lima, de Aquino, Fernando Rodrigo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003076/
https://www.ncbi.nlm.nih.gov/pubmed/35408097
http://dx.doi.org/10.3390/s22072482
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author Ozelim, Luan Carlos de Sena Monteiro
Borges, Lucas Parreira de Faria
Cavalcante, André Luís Brasil
Albuquerque, Enzo Aldo Cunha
Diniz, Mariana dos Santos
Góis, Manuelle Santos
da Costa, Katherin Rocio Cano Bezerra
de Sousa, Patrícia Figuereido
Dantas, Ana Paola do Nascimento
Jorge, Rafael Mendes
Moreira, Gabriela Rodrigues
de Barros, Matheus Lima
de Aquino, Fernando Rodrigo
author_facet Ozelim, Luan Carlos de Sena Monteiro
Borges, Lucas Parreira de Faria
Cavalcante, André Luís Brasil
Albuquerque, Enzo Aldo Cunha
Diniz, Mariana dos Santos
Góis, Manuelle Santos
da Costa, Katherin Rocio Cano Bezerra
de Sousa, Patrícia Figuereido
Dantas, Ana Paola do Nascimento
Jorge, Rafael Mendes
Moreira, Gabriela Rodrigues
de Barros, Matheus Lima
de Aquino, Fernando Rodrigo
author_sort Ozelim, Luan Carlos de Sena Monteiro
collection PubMed
description Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow.
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spelling pubmed-90030762022-04-13 Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm Ozelim, Luan Carlos de Sena Monteiro Borges, Lucas Parreira de Faria Cavalcante, André Luís Brasil Albuquerque, Enzo Aldo Cunha Diniz, Mariana dos Santos Góis, Manuelle Santos da Costa, Katherin Rocio Cano Bezerra de Sousa, Patrícia Figuereido Dantas, Ana Paola do Nascimento Jorge, Rafael Mendes Moreira, Gabriela Rodrigues de Barros, Matheus Lima de Aquino, Fernando Rodrigo Sensors (Basel) Article Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow. MDPI 2022-03-24 /pmc/articles/PMC9003076/ /pubmed/35408097 http://dx.doi.org/10.3390/s22072482 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ozelim, Luan Carlos de Sena Monteiro
Borges, Lucas Parreira de Faria
Cavalcante, André Luís Brasil
Albuquerque, Enzo Aldo Cunha
Diniz, Mariana dos Santos
Góis, Manuelle Santos
da Costa, Katherin Rocio Cano Bezerra
de Sousa, Patrícia Figuereido
Dantas, Ana Paola do Nascimento
Jorge, Rafael Mendes
Moreira, Gabriela Rodrigues
de Barros, Matheus Lima
de Aquino, Fernando Rodrigo
Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
title Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
title_full Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
title_fullStr Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
title_full_unstemmed Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
title_short Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm
title_sort structural health monitoring of dams based on acoustic monitoring, deep neural networks, fuzzy logic and a cusum control algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003076/
https://www.ncbi.nlm.nih.gov/pubmed/35408097
http://dx.doi.org/10.3390/s22072482
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