{"id":3653,"date":"2023-12-06T22:28:24","date_gmt":"2023-12-06T14:28:24","guid":{"rendered":"https:\/\/iti.team\/zhiyitang\/2023\/12\/data-anomaly-detection-for-structural-health-monitoring-by-multi-view-representation-based-on-local-binary-patterns-8\/"},"modified":"2024-12-07T11:28:41","modified_gmt":"2024-12-07T03:28:41","slug":"machine-learning-based-methods-for-output-only-structural-modal-identification","status":"publish","type":"post","link":"https:\/\/iomi.team\/en\/zhiyitang\/2023\/12\/machine-learning-based-methods-for-output-only-structural-modal-identification\/","title":{"rendered":"Machine-learning-based methods for output-only structural modal identification"},"content":{"rendered":"<p><strong>Abstract: <\/strong>In this study, we propose a machine-learning-based approach to identify the modal parameters of the output-only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex loss function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach\u2019s good capability in blindly extracting modal information from system responses.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"334\" src=\"https:\/\/iomi.team\/wp-content\/uploads\/2024\/01\/image-6-1024x334.png\" alt=\"\" class=\"wp-image-3844\" srcset=\"https:\/\/iomi.team\/wp-content\/uploads\/2024\/01\/image-6-1024x334.png 1024w, https:\/\/iomi.team\/wp-content\/uploads\/2024\/01\/image-6-300x98.png 300w, https:\/\/iomi.team\/wp-content\/uploads\/2024\/01\/image-6-768x250.png 768w, https:\/\/iomi.team\/wp-content\/uploads\/2024\/01\/image-6-18x6.png 18w, https:\/\/iomi.team\/wp-content\/uploads\/2024\/01\/image-6.png 1077w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><a href=\"https:\/\/scholar.google.hk\/citations?view_op=view_citation&amp;hl=zh-CN&amp;user=cgAplYkAAAAJ&amp;sortby=pubdate&amp;citation_for_view=cgAplYkAAAAJ:_kc_bZDykSQC\" target=\"_blank\" rel=\"noreferrer noopener\">Paper link<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>In this study, we propose a machine\u2010learning\u2010based approach to identify the modal parameters of the output\u2010only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independent feature of each mode, we use the principle of unsupervised learning, turning the training process of the neural network into the process of modal separation. A self\u2010coding neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then, we use a complex loss function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The neural \u2026<\/p>","protected":false},"author":3,"featured_media":3844,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[102,100],"tags":[153,136,137,146,150,152,148,145,149,115,114,144,147,151],"coauthors":[7],"class_list":{"0":"post-3653","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science-and-engineering","8":"category-shm","9":"tag-complex-loss-function","10":"tag-machine-learning","11":"tag-ml","12":"tag-modal-parameters","13":"tag-modal-separation","14":"tag-mode-shapes","15":"tag-neural-network","16":"tag-output-only-data","17":"tag-self-coding-neural-network","18":"tag-shm","19":"tag-structural-health-monitoring","20":"tag-structural-modal-identification","21":"tag-unsupervised-learning","22":"tag-vibration-data","23":"czr-hentry"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.7.1 (Yoast 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