[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117846-en":3,"doc-seo-117846-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117846,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Structural Health Monitoring Using Machine Learning and Synthetic Data - Dissertation 2023","Structural health monitoring spans decades of research across engineering fields, but typical damage-detection workflows often cannot deliver real-time, fast identification of critical degradation in complex structures. A key obstacle is the volume of data that must be processed and the limited ability to relay results quickly to structural engineers. This dissertation studies machine learning as a non-invasive approach for early failure detection, reducing the dependence on real-time heavy data handling. Two cases are analyzed using synthetic data and tractable features.","Graduate Theses, Dissertations, and Problem Reports  \n2023  \nStructural Health Monitoring Using Machine Learning and Synthetic Data  \nMichail Tzimas  \nWest Virginia University, [mt0041@mix.wvu.edu](mt0041@mix.wvu.edu)  \nFollow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd)  \n Part of the Mechanical Engineering Commons  \nRecommended Citation  \nTzimas, Michail, \"Structural Health Monitoring Using Machine Learning and Synthetic Data\" (2023) . Graduate Theses, Dissertations, and Problem Reports. 11833.  \n[https://researchrepository.wvu.edu/etd/1](https://researchrepository.wvu.edu/etd/1)1833  \nThis Dissertation is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu).  \nStructural Health Monitoring Using Machine Learning and Synthetic  \nData  \nMichail Tzimas  \nDissertation submitted  \nto the Benjamin M. Statler College of Engineering and Mineral Resources  \nat West Virginia University  \nin partial ful✜llment of the requirements for the degree of  \nDoctor of Philosophy in  \nMechanical Engineering  \nEver J. Barbero, Ph.D. , Chair  \nBruce Kang, Ph.D.  \nEduardo Sosa, Ph.D.  \nVictor Mucino, Ph.D.  \nJoe Bedard, Ph.D.  \nDepartment of Mechanical Engineering  \nMorgantown, West Virginia  \n2023  \nKeywords: SHM, Buckling, Flutter, Instability, Machine Learning, Tensor✢ow,  \nNeural Networks  \nCopyright 2023 Michail Tzimas  \nAbstract  \nStructural Health Monitoring Using Machine Learning and Synthetic Data  \nMichail Tzimas  \nStructural health monitoring spans many decades of research across multiple engineering ✜elds. However, typical monitoring processes for damage detection of complex structures usually prohibit real-time or fast detection of debilitating damage to the structure. One of the major issues of real-time detection of damage is the enormity of data that needs to be processed, which is worsened by the relative inability of fast relaying of data to structural engineers. With the rapid advancement of Machine Learning, both issues can be overcome, and detection of failure is achieved with non-invasive techniques.  \nThis dissertation explores the applicability of Machine Learning as a non-invasive technique for early critical failure detection in two separate examples. The ✜rst example is a column under buckling load. The column includes various imperfections, which can cause early failure compared to theoretical solutions reporting ideal buckling load. Using synthetic data, a large data set is used to train a Machine Learning algorithm to predict which columns are imperfection sensitive. The service load on these columns does not exceed 30% of the nominal critical load.  \nThe second example considers the aeroelastic response of an aircraft and speci✜-cally the extreme case of ✢utter. A combination of inertial, aerodynamic, and elastic forces, ✢utter is a dynamic instability on a vehicle, most observed on aircraft wings. A modal analysis on a wing reveals that the leading mode shapes are susceptible to ✢utter. The mode shapes, along with air velocities, structure damping values, and various experimentally tractable features are used to train a Machine Learning algorithm to recognize possible ✢utter. Further data on a wing with di✛erent material properties are then used ","cbCaioMGz6YGCKTa","https://ap.wps.com/l/cbCaioMGz6YGCKTa","pdf",6305810,1,114,"English","en",105,"# Abstract\n## Early critical failure detection\n## Case 1: buckling under column imperfections\n## Case 2: aircraft flutter via modal features\n## Model-based damping regression","[{\"question\":\"Why is real-time structural health monitoring difficult for damage detection?\",\"answer\":\"Real-time detection is hindered by the large amount of data that must be processed and by limited rapid relay of information to structural engineers.\"},{\"question\":\"How does the dissertation approach buckling-related damage detection?\",\"answer\":\"It uses synthetic data to train a machine learning model to predict which columns are sensitive to imperfections, considering service loads up to 30% of the nominal critical load.\"},{\"question\":\"What features are used for flutter detection in the aircraft case?\",\"answer\":\"Mode shapes from modal analysis, air velocities, structure damping values, and other experimentally tractable features are used to train a machine learning algorithm for early flutter recognition.\"}]","Structural Health Monitoring Using Machine Learning and Synthetic Data - 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