[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124150-en":3,"doc-seo-124150-105":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":4,"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},124150,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Damage identification of spatial structures based on machine learning techniques","Doctor of Philosophy thesis presenting a machine-learning framework for identifying and quantifying damage in spatial structures. The work focuses on vibration-based damage detection, proposing data-driven modeling approaches that support localization and assessment from sensor measurements. Published and in-progress contributions include PCA combined with convolutional neural networks, semirigid-joint detection using wireless measurements, and semi-supervised learning with an LSTM-VAE for multi-sensor systems, as well as CWT-based damage quantification using convolutional VAE.","Damage identification of spatial structures based on machine learning techniques  \nby Jiajia Hao  \nThesis submitted in fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nunder the supervision of A/Prof. Xinqun Zhu and Prof. Jianchun Li  \nUniversity of Technology Sydney  \nFaculty of Engineering and Information Technology  \nSeptember 2024  \nCERTIFICATE OF ORIGINAL AUTHORSHIP  \nI, Jiajia Hao declare that this thesis is submitted in fulfillment of the requirements for the award of Doctor of Philosophy, in the School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology at the University of Technology Sydney.  \nThis thesis is wholly my work unless otherwise referenced or acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis.  \nThis document has not been submitted for qualifications at any other academic institution .  \nThis research is supported by the Australian Government Research Training Program.  \nSignature  \nProduction Note:  \nSignature removed prior to publication.  \nDate: September 17, 2024  \nACKNOWLEDGEMENTS  \nFirstly, I would like to express my sincere gratitude to my principal supervisor, Prof.  \nXinqun Zhu. He is knowledgeable, patient, trustworthy, and always supportive, inviting questions in an encouraging manner. He always listens carefully to everyone's  \npresentation and provides thoughtful recommendations. He values my work, respects my time, and appreciates my progress even when there are mistakes. As I am heading to the industry soon, I look forward to potential future collaborations with him. Upon graduation, I would like to give my best wishes to him, my supervisor, my friend, and my role model.  \nI would also like to express my thanks to my co-supervisor Prof Jianchun Li. He is  \nknowledgeable and energetic, and he has a lot of passion for teaching and research. He consistently provides inspiring ideas for me to pursue. Talking to him about both research and life has always been incredibly beneficial. Many thanks to Dr. Yang Yu for his generosity in sharing his knowledge and experiences with me . The support and trust from Prof. Chunwei Zhang and Dr. Hadi Kordestani have also been invaluable to me.  \nI also had a great time with my friends Bing Zhang, Xutong Zhang, Chenguang Li, Jiahao Xia, and Saeid Talaei as well as other friends in the research group. We have supported each other like brothers and sisters. Staying with all of you for dinner at Xinqun’s home has always been one of the best moments for me.  \nA big thanks to my mentors Dr. Carmen Wong and Prof. Stuart Perry, and my friends Dimity Wehr and Stuart Allabush. I enjoyed hearing Carmen’s big laugh, Stuart talking  \nabout food, Dim sharing about her recent journey, and Stuart’s encouragement and support. It has been so good to have these nice friends around.  \nLastly, my sincere gratitude to my parents, and my sisters. They are the ones that are  \nalways by my side. If there is always a place to go, that would be my home. I could not have reached this point without their support. This thesis is dedicated to them.  \nLIST OF PUBLICATIONS  \n1. Hao, J., Zhu, X., Yu, Y., Zhang, C., & Li, J.“Damage localization and quantification of a truss bridge using PCA and convolutional neural network”, Smart Structures and Systems, 30(6), 673-686,2022 .  \n2. Hao, J., Zhu, X., & Li, J. Structural damage detection for spatial frame structures with semirigid joints using wireless measurements. (under revision by the Journal of Vibration and Control).  \n3. Hao, J., Zhu, X., & Li, J. Long short-term memory-based variational autoencoder (LSTM-VAE) based semi-supervised learning for damage identification using a multi-sensor system (to be submitted) .  \n4. Hao, J., Zhu, X., & Li, J. CWT-based damage quantification of spatial structures using convolutional VAE (under preparation).  \nTABLE OF CONTENTS  \nCERTIFICATE OF ORIGINAL AUTHORSHIP.....................","cbCaigbCNxAQkv61","https://ap.wps.com/l/cbCaigbCNxAQkv61","pdf",7088124,1,189,"English","en",105,"# Introduction\n## Background\n## Problem definition\n## Research objectives\n## Contribution of this research\n## Organisation of this thesis\n# Literature review\n## Overview\n## The Vibration-based damage detection (VBDD)\n## Model-based methods\n## Data-driven methods","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis focuses on identifying and quantifying damage in spatial structures using machine learning techniques, with emphasis on vibration-based damage detection.\"},{\"question\":\"Which types of sensor data and measurement setups are used?\",\"answer\":\"It leverages vibration and multi-sensor measurement data, including wireless measurements for certain structural setups with semirigid joints.\"},{\"question\":\"What machine learning methods are included in the thesis contributions?\",\"answer\":\"Reported contributions include PCA with convolutional neural networks, LSTM-VAE-based semi-supervised learning for multi-sensor damage identification, and CWT-based quantification using convolutional VAE.\"}]","Damage identification of spatial structures based on machine learning techniques | 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