[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124881-en":3,"doc-seo-124881-105":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},124881,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning for structural design models from the inverse problem perspective","The introduction of novel technologies has historically led to fundamental changes in structural design practices. Despite the growing prominence of machine learning, structural design models remain sparsely addressed in current engineering practice compared with structural analysis models. This thesis frames structural design as an inverse problem and proposes a methodology for accurate, generalisable, and verifiable machine learned structural design models for continuous beam systems. The work links analysis, design, and optimisation, supported by a literature review spanning blast engineering, structural health monitoring, and digital twins.","Machine learning for structural design models from the inverse problem  \nperspective  \nby  \n Adrien Gallet  \nA thesis submitted to the Department of Civil and Structural Engineering in Partial Fulfilment of the Requirements for the Degree of  \nDoctor of Philosophy  \nat the  \nUniversity of Sheffield  \nMarch 2024  \n© 2024 Adrien Gallet. All rights reserved.  \nDeclaration of Authorship  \nI, Adrien Gallet, declare that the work in this thesis, entitled,“Machine learning for structural design models from the inverse problem perspective”, is my own unless referenced to the contrary in the text.  \nSigned: Adrien Gallet  \nDate: March 8th, 2024  \nAcknowledgements  \nNo one can ever fully acknowledge all that which renders a productive venture possible. This could not be truer than of the work presented here. The following is my best attempt to acknowledge all those, which in no insignificant part, ultimately contributed to the success of this thesis. To those that expect to be mentioned, yet whom I may have missed: I thank you first. My unacknowledgement of your contribution is without doubt at the fault of my memory.  \nI would like to thank first and foremost Danny Smyl. I could not have asked for a better primary supervisor to guide and support me through the journey of this self-proposed thesis for the last three and half years. A great supervisor has the best interest of their PhD student at heart, and you, Danny, have shown that in more ways than I can remember-from being available for periodic meet-ups regardless of what time zones we happened to find ourselves in, to providing excellent feedback, giving me plenty of opportunities to develop as an independent researcher and handing out rock-solid advice based on your own research experience – this thesis would not have achieved what it did without your supervision.  \nSimilarly, I would also like to thank the rest of my supervisory team, Andrew Liew and Iman Hajirasouliha, for their continued support and enthusiasm, and for lending their ears and eyes when reviewing my works. Regardless of circumstances, the two of you found time for me when it was needed, and for that I am grateful. I would like to also thank Giacomo Torelli for the support during the closing stages of my PhD.  \nDuring my undergraduate, I was lucky to have been taught by the excellent civil and structural engineering faculty at the University of Sheffield, and I would like to give special mentions to Matthew Gilbert, Jonathan Black, Richard Collins, Colin Smith, Elisabeth Bowman, Sam Clarke, Sam Rigby, Maurizio Guadagnini, Richard Harpin, Luca Susmel, Rachel Horn and Georges Kesserwani whose teachings I especially enjoyed. I would also like to specifically mention my personal tutor Paul Hulbert, who played no insignificant part in all of this by encouraging me to continue studying civil and structural engineering during my first year of my undergraduate degree.  \nI have been fortunate enough to have been taught by incredible teachers throughout my secondary education, whose combined efforts gave me the opportunity to attend university in the first place. I would like to especially thank Arnaud Morin, David Robs, Philip Kurbis, J. Briffa, Garret Newell, Jordan Finch and Kirti Joshi for all they have taught me.  \nThe PhD would not have been nearly as enjoyable without the discussions and daily Heartspace lunches with my fellow peers including Adam Dennis, Leanna Bradbury, Isuru Nanayakkara, Oswald Li, Arthur Van Lerberghe, Emma Clarke, Wenchao Li, Edward Whiteside, Lewis Tetlow, Ross Waddoups and Ella Mendham. On this note I would also like to also acknowledge all my friends and loved ones that have made the past couple of years ever so slightly easier to bear with.  \nUltimately I would like to thank my siblings, Alexandre, Aur´elia, and Ana¨ıs, all of whom continue to inspire me in tackling whatever challenges have and may continue to lay ahead, and most important of all my parents, Wolfgang and V´eronique Gallet, ","cbCaijjuWuBdPmYG","https://ap.wps.com/l/cbCaijjuWuBdPmYG","pdf",12189756,1,200,"English","en",105,"# Abstract\n## Thesis objective and inverse-problem framing\n## Methodology for continuous beam systems\n## Literature review and domain applications","[{\"question\":\"Why does the thesis frame structural design as an inverse problem?\",\"answer\":\"It argues that structural design is fundamentally an inverse problem, enabling a shift in engineering philosophy by connecting analysis, design, and optimisation rather than treating design as a forward-oriented task.\"},{\"question\":\"What kind of machine learned structural design models does the thesis develop?\",\"answer\":\"It develops accurate, generalisable, and verifiable machine learned structural design models specifically for continuous beam systems.\"},{\"question\":\"Which application domains are covered in the thesis literature review?\",\"answer\":\"The review covers inverse problems in civil and structural engineering, including blast engineering, structural health monitoring, and digital twins, highlighting how machine learning is used in these areas.\"}]","Machine learning for structural design models from the inverse problem perspective | 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