[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119330-en":3,"doc-seo-119330-105":30,"detail-sidebar-cat-0-en-105":91},{"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},119330,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Machine Learning Based Analysis Of Civil Infrastructure In The Presence Of Sparse Data - Thesis Summary","High computational cost in estimating engineering demand parameters (EDPs) using finite element models constrains practical adoption of the Performance-Based Earthquake Engineering (PBEE) framework when earthquake and material uncertainties are included. The study presents a machine learning surrogate framework that predicts responses for unseen seismic events without retraining. Earthquake scenarios are generated using SVD-based projections onto an orthonormal basis and sampled scenario weights, combined with constitutive parameters. Four ML models are compared, with deep neural networks achieving highest accuracy for peak responses in one- and three-story shear frame buildings under far-field ground motions.","Doctoral Dissertations and Master's Theses  \nSummer 5-31-2024  \nMachine Learning Based Analysis Of Civil Infrastructure In The Presence Of Sparse Data  \nMegan Butcher  \nEmbry-Riddle Aeronautical University, [butchem3@my.erau.edu](butchem3@my.erau.edu)  \nFollow this and additional works at: [https://commons.erau.edu/edt](https://commons.erau.edu/edt)  \n Part of the Civil Engineering Commons, and the Structural Engineering Commons  \nScholarly Commons Citation  \nButcher, Megan, \"Machine Learning Based Analysis Of Civil Infrastructure In The Presence Of Sparse Data\" (2024) . Doctoral Dissertations and Master 's Theses. 838.  \n[https://commons.erau.edu/edt/838](https://commons.erau.edu/edt/838)  \nThis Thesis-Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Doctoral Dissertations and Master's Theses by an authorized administrator of Scholarly Commons. For more information, please [contact commons@erau.edu](contact commons@erau.edu).  \nMACHINE LEARNING BASED ANALYSIS OF CIVIL INFRASTRUCTURE IN THE  \nPRESENCE OF SPARSE DATA  \nby: Megan Lee Butcher  \n\n| Ashok Gurjar, Ph.D. Civil Engineering Department Chair |\n| --- |\n| Siddharth Parida, Ph.D.\u003Cbr>Committee Chair |\n| Dan Su, Ph.D.\u003Cbr>Committee Member |\n| Prashant Shekhar, Ph.D.\u003Cbr>Committee Member |\n\nJeff Brown, Ph.D. Committee Member  \nDate: August 7, 2024  \nMACHINE LEARNING BASED ANALYSIS OF CIVIL INFRASTRUCTURE IN THE  \nPRESENCE OF SPARSE DATA  \nMegan Lee Butcher  \nA thesis/dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering-Structures Track at Embry-Riddle Aeronautical University  \nAugust 2024  \nAcknowledgements  \nIt has been an immense privilege to undertake this research, and I owe much of its success to the support of Embry-Riddle Aeronautical University. Foremost, I extend my deepest gratitude to my thesis advisor, Dr. Siddharth Parida, Assistant Professor of Civil Engineering. Dr. Parida not only entrusted me with an impactful research topic but also guided me with profound insight and encouragement since May 2021 . His mentorship has not only shaped my academic journey but also fostered my growth as a researcher and an individual.  \nIn addition, I am indebted to my esteemed committee members: Dr. Jeff Brown, Professor of Civil Engineering and former Program Coordinator for the B.S. and M.S. in Civil Engineering; Dr. Dan Su, Assistant Professor of Civil Engineering; and Dr. Prashant Shekhar, Assistant Professor of Data Science/Math. Their collective wisdom, constructive feedback, and scholarly guidance were pivotal in navigating this thesis topic.  \nFurthermore, I extend my appreciation to all faculty members within the civil engineering department at Embry-Riddle Aeronautical University. Their unwavering support, valuable advice, and extensive knowledge significantly contributed to the development of my professional life. Their dedication to fostering academic excellence has been instrumental in my academic journey. I am also grateful to the American Society of Civil Engineers (ASCE) for providing invaluable opportunities for professional, personal, and academic growth.  \nLastly, I wish to acknowledge my family, friends, and classmates whose steadfast encouragement and unwavering belief in my abilities have been a constant source of motivation throughout my educational career. Their support has been invaluable, and I am profoundly grateful for their presence in my life. I am deeply thankful to all who have supported me on this journey. Their encouragement and guidance have been instrumental in my success, and I carry their lessons and support with me as I embark on future endeavors.  \nAbstract  \nThe high computational cost of estimating engineering demand parameters (EDPs) via finite element (FE) models, which incorporate uncertainties in earthquake events and material properties, limits the application of the Performance-Based Earthquake Engi","cbCaik8sMeg0kCbq","https://ap.wps.com/l/cbCaik8sMeg0kCbq","pdf",5030011,1,82,"English","en",105,"# Contents\n## Motivation of Research\n## Literature Review\n### Surrogate Models using ML\n### Review of machine learning algorithms","[{\"question\":\"Why is surrogate modeling needed in PBEE for structural engineering?\",\"answer\":\"Finite element models with uncertainty in earthquake loading and material properties require high computation to estimate engineering demand parameters, limiting PBEE’s practical use.\"},{\"question\":\"How does the framework generate unseen earthquake scenarios?\",\"answer\":\"Earthquakes are represented as projections on an orthonormal basis derived from SVD of a representative ground-motion suite, then scenario weights are sampled to create varied events.\"},{\"question\":\"Which machine learning model performs best and what is the key sensitivity finding?\",\"answer\":\"Among four tested models, deep neural networks provide the highest accuracy. Predictions are highly sensitive to the characterization of forcing functions.\"}]","Machine Learning Based Analysis Of Civil Infrastructure In The Presence Of Sparse Data - Thesis Summary | PDF",1785723741,207,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-analysis-of-civil-infrastructure-in-the-presence-of-sparse-data-thesis-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-analysis-of-civil-infrastructure-in-the-presence-of-sparse-data-thesis-summary/119330/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is surrogate modeling needed in PBEE for structural engineering?","Question",{"text":75,"@type":76},"Finite element models with uncertainty in earthquake loading and material properties require high computation to estimate engineering demand parameters, limiting PBEE’s practical use.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework generate unseen earthquake scenarios?",{"text":80,"@type":76},"Earthquakes are represented as projections on an orthonormal basis derived from SVD of a representative ground-motion suite, then scenario weights are sampled to create varied events.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best and what is the key sensitivity finding?",{"text":84,"@type":76},"Among four tested models, deep neural networks provide the highest accuracy. 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