[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124986-en":3,"doc-seo-124986-105":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},124986,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","DATA ANALYSIS AND MACHINE LEARNING FOR ENHANCING RESILIENCE TO FIRE, FROM IGNITION MAPPING TO STRUCTURAL AND SYSTEMS MODELING - Dissertation","Fire hazards threaten communities, and effective mitigation depends on understanding multi-scale processes from ignition occurrence through building structural response to the effectiveness of prevention and protection strategies. This dissertation applies Bayesian methods and machine learning to selected fire-safety challenges, targeting a fire-resilient built environment. It models fire following earthquakes and wildfires at city or regional scale, develops hierarchical approaches for scarce data, and uses ensemble learning for wildfire ignitions. It then builds surrogate models for structural fire performance and proposes a systems resilience framework for process-industry facilities.","DATA ANALYSIS AND MACHINE LEARNING FOR  \nENHANCING RESILIENCE TO FIRE, FROM IGNITION MAPPING TO STRUCTURAL AND  \nSYSTEMS MODELING  \nby  \nQi Tong  \nA dissertation submitted to Johns Hopkins University in conformity with the requirements for  \nthe degree of Doctor of Philosophy  \nBaltimore, Maryland  \nJuly 2023  \n© 2023 Qi Tong  \nAll rights reserved  \nAbstract  \nFire hazards pose significant threats to our communities. Mitigation of fire risk requires an understanding of a range of issues and processes at various scales, such as the occurrence of ignitions in a community, the performance of a building structure under fire, and the efficiency of prevention and protection strategies. In this thesis, we investigate several of these fire safety issues through the lens of data-driven methods. Bayesian methods and machine learning techniques are adopted and tailored to address selected fire hazards and provide contributions toward solving these challenges for a fire resilient built environment.  \nThe thesis focuses first on fire ignitions. It investigates the problems of fire following earthquakesand wildfires. These issues are studied at the scale of a city or a region. For fire following earthquakes, a hierarchical Bayesian method is developed to allow modeling with scarce data, while for wildfire ignitions, an ensemble-based machine learning model is adopted. Then, the thesis zooms in to the building scale to assess data-based methods for evaluation of structural fire performance. Surrogate models are derived based on machine learning to capture the capacity of slender steel members in fire. Finally, the thesis investigates a framework to assess system resilience under fire hazards. The framework is applied for resilience assessment of facilities subjected to fires in the process industry.  \nThe thesis applies different data-based and modeling approaches to deal with fire hazards from different perspectives and at different scales, with the aim to enhance fire safety and build a more resilient environment against fires for our community.  \nAdvisor: Dr. Thomas Gernay  \nReaders: Dr. Takeru Igusa  \nDr. Gonzalo Pita  \nAcknowledgments  \nI am grateful to everyone who contributed to making my Ph.D. journey at Johns Hopkins University wonderful and colorful over the past four years.  \nFirst and foremost, my heartfelt gratitude goes to my extraordinary advisor, Prof. Thomas Gernay. His guidance, patience, encouragement, and support have had a profound impact on my research and life. I am honored to have had the opportunity to work with such an amazing advisor. I extend my thanks to Carlos Couto for his kind help and guidance during our collaboration, and to everyone in the fire group for sharing valuable insights in discussions.  \nI would like to express my appreciation to Prof. Tak Igusa, Prof. Hari Rajaram, Dr. Wai Cheong Tam, and Dr. Gonzalo Pita for their service on my thesis committee. Their review and suggestions have greatly improved my work. I also want to thank Prof. Hari Rajaram for chairing my GBO committee. Special thanks to the Department of Civil and Systems Engineering at Johns Hopkins University for their support and provision of resources throughout my study.  \nI would like to express my deepest gratitude to my parents for their unwavering financial and emotional support. Their guidance and comforting presence during difficult times have been invaluable. I also want to thank my friends, colleagues, and family for their inspiration, support, and companionship.  \nTo all who have played a role in my Ph.D. journey, I sincerely thank you. Your contributions have shaped my growth and success, and I will cherish these experiences for a lifetime.  \nPreface  \nThis dissertation is submitted to the Johns Hopkins University for the degree of Doctor of Philosophy. Several supporting papers which are based on the work presented in the dissertation have been published in conjunction with Assistant Professor Thomas Gernay and other coautho","cbCaieZZDjebOk7v","https://ap.wps.com/l/cbCaieZZDjebOk7v","pdf",5879868,1,218,"English","en",105,"# Abstract\n# Acknowledgments\n# Preface\n# Table of contents","[{\"question\":\"What research problem does the dissertation address?\",\"answer\":\"It addresses how to mitigate fire risk by modeling ignition occurrence, building structural performance under fire, and the resilience of systems exposed to fire hazards.\"},{\"question\":\"Which data-driven methods are used in the study?\",\"answer\":\"Bayesian methods and machine learning techniques are tailored to different fire hazards, including hierarchical Bayesian modeling, ensemble-based learning, and machine-learning-derived surrogate models.\"},{\"question\":\"How is the dissertation organized across scales?\",\"answer\":\"It first investigates ignitions for earthquakes and wildfires at city or regional scale, then zooms in to the building scale for structural fire performance evaluation, and finally examines systems resilience for facilities in the process industry.\"}]","DATA ANALYSIS AND MACHINE LEARNING FOR ENHANCING RESILIENCE TO FIRE, FROM IGNITION MAPPING TO STRUCTURAL AND SYSTEMS MODELING - 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