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Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection

With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural...

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Detalles Bibliográficos
Autores principales: Li, Xiaofei, Guo, Hainan, Xu, Langxing, Xing, Zezheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255421/
https://www.ncbi.nlm.nih.gov/pubmed/37299785
http://dx.doi.org/10.3390/s23115058
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author Li, Xiaofei
Guo, Hainan
Xu, Langxing
Xing, Zezheng
author_facet Li, Xiaofei
Guo, Hainan
Xu, Langxing
Xing, Zezheng
author_sort Li, Xiaofei
collection PubMed
description With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy.
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spelling pubmed-102554212023-06-10 Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng Sensors (Basel) Article With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy. MDPI 2023-05-25 /pmc/articles/PMC10255421/ /pubmed/37299785 http://dx.doi.org/10.3390/s23115058 Text en © 2023 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
Li, Xiaofei
Guo, Hainan
Xu, Langxing
Xing, Zezheng
Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
title Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
title_full Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
title_fullStr Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
title_full_unstemmed Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
title_short Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
title_sort bayesian-based hyperparameter optimization of 1d-cnn for structural anomaly detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255421/
https://www.ncbi.nlm.nih.gov/pubmed/37299785
http://dx.doi.org/10.3390/s23115058
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