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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2482 |
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| author | Luan Carlos de Sena Monteiro Ozelim Lucas Parreira de Faria Borges André Luís Brasil Cavalcante Enzo Aldo Cunha Albuquerque Mariana dos Santos Diniz Manuelle Santos Góis Katherin Rocio Cano Bezerra da Costa Patrícia Figuereido de Sousa Ana Paola do Nascimento Dantas Rafael Mendes Jorge Gabriela Rodrigues Moreira Matheus Lima de Barros Fernando Rodrigo de Aquino |
| author_facet | Luan Carlos de Sena Monteiro Ozelim Lucas Parreira de Faria Borges André Luís Brasil Cavalcante Enzo Aldo Cunha Albuquerque Mariana dos Santos Diniz Manuelle Santos Góis Katherin Rocio Cano Bezerra da Costa Patrícia Figuereido de Sousa Ana Paola do Nascimento Dantas Rafael Mendes Jorge Gabriela Rodrigues Moreira Matheus Lima de Barros Fernando Rodrigo de Aquino |
| author_sort | Luan Carlos de Sena Monteiro Ozelim |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T11:27:14Z |
| format | Article |
| id | doaj.art-2143761b278c4f918131c6abdcca64fd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:27:14Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2143761b278c4f918131c6abdcca64fd2023-11-30T23:59:30ZengMDPI AGSensors1424-82202022-03-01227248210.3390/s22072482Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control AlgorithmLuan Carlos de Sena Monteiro Ozelim0Lucas Parreira de Faria Borges1André Luís Brasil Cavalcante2Enzo Aldo Cunha Albuquerque3Mariana dos Santos Diniz4Manuelle Santos Góis5Katherin Rocio Cano Bezerra da Costa6Patrícia Figuereido de Sousa7Ana Paola do Nascimento Dantas8Rafael Mendes Jorge9Gabriela Rodrigues Moreira10Matheus Lima de Barros11Fernando Rodrigo de Aquino12Department of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, University of Brasilia, Brasilia 70910-900, BrazilInternal 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.https://www.mdpi.com/1424-8220/22/7/2482structural monitoringdamsgeotechnical engineeringdeep learningautoencoderfuzzy logic |
| spellingShingle | Luan Carlos de Sena Monteiro Ozelim Lucas Parreira de Faria Borges André Luís Brasil Cavalcante Enzo Aldo Cunha Albuquerque Mariana dos Santos Diniz Manuelle Santos Góis Katherin Rocio Cano Bezerra da Costa Patrícia Figuereido de Sousa Ana Paola do Nascimento Dantas Rafael Mendes Jorge Gabriela Rodrigues Moreira Matheus Lima de Barros Fernando Rodrigo de Aquino Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm Sensors structural monitoring dams geotechnical engineering deep learning autoencoder fuzzy logic |
| 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 | structural monitoring dams geotechnical engineering deep learning autoencoder fuzzy logic |
| url | https://www.mdpi.com/1424-8220/22/7/2482 |
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