Unsupervised Spatiotemporal Framework for Structural Damage Localization and Severity Quantification

Unsupervised Spatiotemporal Framework for Structural Damage Localization and Severity Quantification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unsupervised Spatiotemporal Framework for Structural Damage Localization and Severity Quantification Shuai Kang, Roshan Kumar, Houyu Lu, Menghao Jiao, Mengqian Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8769300/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The research on structural damage identification based on unsupervised learning mainly focuses on the stage of whether damage has occurred, while the research on damage localization and damage quantification is relatively scarce. Based on this, this paper proposes an unsupervised structural damage localization and quantitative identification method based on the combination of Convolutional Autoencoder (CAE) and Long Short-Term Memory (LSTM), which fully combines the advantages of the model in spatiotemporal feature extraction. First, the data is reconstructed through the established CAE-LSTM network, and then the reconstruction error between the reconstructed data and the real data is used as the damage sensitive feature to locate the damage. Subsequently, based on the normal distribution model of the damage sensitive feature, a stable damage quantification result is obtained, thereby realizing the quantitative assessment of the damage degree. The effectiveness of the proposed method was verified through the data obtained from the Qatar grandstand model and the IASC-ASCE SHM benchmark model. The results show that the proposed method is applicable to both single-damage and multi-damage scenarios. A comparative analysis was also conducted with the identification results of the traditional CAE network. The results showed that the CAE-LSTM network exhibited outstanding identification capabilities for both minor damage caused by bolt loosening and severe damage resulting from the removal of intermediate supports. Its identification results were significantly superior to those of the traditional CAE network for structural damage localization and quantification under unsupervised conditions. CAE-LSTM also holds great application potential in the damage identification of complex structures. Civil Engineering Artificial Intelligence and Machine Learning Damage location Damage quantification CAE-LSTM Unsupervised damage identification Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8769300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584641567,"identity":"826fcd5c-e11a-452e-8654-001b4c6e486a","order_by":0,"name":"Shuai Kang","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Kang","suffix":""},{"id":584641568,"identity":"b6b51355-1111-436e-ae1c-75a770d1b0c2","order_by":1,"name":"Roshan Kumar","email":"","orcid":"","institution":"Henan 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Based on this, this paper proposes an unsupervised structural damage localization and quantitative identification method based on the combination of Convolutional Autoencoder (CAE) and Long Short-Term Memory (LSTM), which fully combines the advantages of the model in spatiotemporal feature extraction. First, the data is reconstructed through the established CAE-LSTM network, and then the reconstruction error between the reconstructed data and the real data is used as the damage sensitive feature to locate the damage. Subsequently, based on the normal distribution model of the damage sensitive feature, a stable damage quantification result is obtained, thereby realizing the quantitative assessment of the damage degree. The effectiveness of the proposed method was verified through the data obtained from the Qatar grandstand model and the IASC-ASCE SHM benchmark model. The results show that the proposed method is applicable to both single-damage and multi-damage scenarios. 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