[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119842-en":3,"doc-seo-119842-105":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},119842,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A STUDY OF MACHINE LEARNING APPLICATIONS FOR SOLVING PROBLEMS IN STRUCTURAL ENGINEERING - Dissertation","Structural engineering ensures designed structures remain safe, stable, and functional over their service life, while machine learning learns from data to produce predictions or decisions using statistical methods. This dissertation investigates how integrating machine learning with structural engineering can address structural problems with novel, data-driven approaches. It presents three projects: predicting steel CHS X-joint strength, performing seismic risk impact analysis for railway bridges, and detecting structural damage using wavelet scalograms with convolutional neural networks. Results support improved efficiency and accuracy across tasks.","A STUDY OF MACHINE LEARNING APPLICATIONS FOR  \nSOLVING PROBLEMS IN STRUCTURAL ENGINEERING  \nA DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR  \nTHE DEGREE OF  \nDOCTOR OF PHILOSOPHY  \nIN  \nCIVIL AND ENVIRONMENTAL ENGINEERING  \nJUNE 2023  \nBy  \nXi Song  \nThesis/Dissertation Committee:  \nChunhee Cho, Chairperson  \nIan Robertson  \nDoSoo Moon  \nLin Shen  \nLiming Guan  \nKeywords: Machine Learning, Structural Engineering  \nCOPYRIGHT  \nCopyright in this work is held by the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation.  \nABSTRACT  \nStructural engineering, a sub-discipline of civil engineering, involves the design and analysis of structures. The goal of structural engineering is to ensure that these structures are safe, stable, and capable of performing their intended functions throughout their lifespan. Meanwhile, machine learning (ML), a subset of artificial intelligence, utilizes statistical methodologies to learn and generate predictions or decisions directly from data. This dissertation explores the integration of these two fields, offering innovative approaches to solve structural engineering problems through the application of ML.  \nThe work comprises three projects that showcase the application of ML in the domain of structural engineering. The first project focuses on the predicting structural strength of steel circular hollow section (CHS) X-joints. Using support vector machines and deep neural networks, this project demonstrates how machine learning can effectively manage structural strength prediction tasks, pointing towards a promising future for ML in this field. The second project ventures into seismic risk analysis, a crucial part of structural safety evaluations. The use of advanced ML algorithms, including the discussion of hyperparameter tuning and model optimization, allowed for a more efficient and accurate prediction of seismic impact on structures such as railway bridges. The last project adopts machine learning for structural damage detection, using pre-trained convolutional neural networks tailored for image-oriented input. Key structural dynamic properties are transcribed into scalograms via wavelet transform, serving as training samples for the machine learning model. The promising outcomes from this project endorse the potential of machine learning in augmenting the efficiency and accuracy of processes for detecting and evaluating structural damage.  \nThroughout the dissertation, a progressive learning journey unfolds, detailing how the understanding and application of ML evolved from basic techniques to more advanced methodologies. Each project enhances the subsequent one, demonstrating a continuous improvement in the application and understanding of ML.  \nThis research demonstrates that machine learning can provides new perspectives and methods for tackling topics in structural engineering, greatly enhancing the efficiency and effectiveness of problem solving in the field. The integration of ML can circumvent the complex experiments, simulations, and calculations that are typically required in structural design and analysis. The work encourages future discussion in the field, refining ML applications, exploring more innovative techniques, and ultimately continuing to push the boundaries of what can be achieved in structural engineering.  \nKeywords: Structural engineering, Machine learning, Structural member strength prediction, Risk analysis, Structural damage detection  \nTABLE OF CONTENTS  \nABSTRACT............................................................................................................................................... III  \nTABLE OF CONTENTS ...........................................................................................................................V  \nLIST OF TABLES .........................................","cbCairFSdGFIFSsV","https://ap.wps.com/l/cbCairFSdGFIFSsV","pdf",4249687,1,116,"English","en",105,"# 1 INTRODUCTION\n## 1.1 MOTIVATION\n## 1.2 ORGANIZATION OF CONTENTS\n# 2 STRENGTH PREDICTION OF STEEL CHS X-JOINTS VIA LEVERAGING FINITE ELEMENT METHOD AND MACHINE LEARNING SOLUTIONS\n## 2.1 ABSTRACT\n## 2.2 INTRODUCTION\n## 2.3 STRENGTH PREDICTION OF CHS X-JOINT\n## 2.4 MACHINE LEARNING-BASED STRENGTH PREDICTIONS","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"To explore how integrating machine learning with structural engineering can solve structural problems through innovative, data-driven approaches.\"},{\"question\":\"How does the dissertation address structural strength prediction?\",\"answer\":\"It predicts the structural strength of steel circular hollow section (CHS) X-joints using support vector machines and deep neural networks, validated against numerical and experimental considerations.\"},{\"question\":\"What machine learning approach is used for seismic risk analysis and structural damage detection?\",\"answer\":\"For seismic risk analysis, advanced ML algorithms with hyperparameter tuning and model optimization improve predictions of seismic impact on structures such as railway bridges. For damage detection, pre-trained convolutional neural networks use wavelet-transform scalograms derived from structural dynamic properties as training inputs.\"}]","A STUDY OF MACHINE LEARNING APPLICATIONS FOR SOLVING PROBLEMS IN STRUCTURAL ENGINEERING - Dissertation | PDF",1785726600,292,{"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},"a-study-of-machine-learning-applications-for-solving-problems-in-structural-engineering-dissertation","",{"@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/a-study-of-machine-learning-applications-for-solving-problems-in-structural-engineering-dissertation/119842/",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},"What is the main goal of the dissertation?","Question",{"text":75,"@type":76},"To explore how integrating machine learning with structural engineering can solve structural problems through innovative, data-driven approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation address structural strength prediction?",{"text":80,"@type":76},"It predicts the structural strength of steel circular hollow section (CHS) X-joints using support vector machines and deep neural networks, validated against numerical and experimental considerations.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning approach is used for seismic risk analysis and structural damage detection?",{"text":84,"@type":76},"For seismic risk analysis, advanced ML algorithms with hyperparameter tuning and model optimization improve predictions of seismic impact on structures such as railway bridges. For damage detection, pre-trained convolutional neural networks use wavelet-transform scalograms derived from structural dynamic properties as training inputs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]