[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126472-en":3,"doc-seo-126472-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126472,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Applications of Machine Learning for Modelling of Ice Flexural Strength","Design of marine vessels and ice-influenced structures must account for impact loads transferred to the structure. Ice flexural strength is a key material property that can strongly affect these loads. While flexural strength is commonly treated as depending on sample scale (beam volume), ice temperature, and sea-ice brine volume, the roles of temperature and scale remain debated. Using a large database with 2000+ freshwater and 2800 sea-ice measurements, machine learning models relate ice parameters to measured flexural strength and reveal temperature and scale-brine dependencies relevant to future design.","Applications of Machine Learning for Modelling of Ice Flexural Strength  \nBy  \n➞Robert Burton B.Eng  \nA Thesis submitted to the  \nSchool of Graduate Studies  \nIn partial fulfillment of the requirements for the degree of  \nMasters of Engineering  \nFaculty of Engineering and Applied Science  \nMemorial University  \nMay 2024  \nSt. John’s, Newfoundland and Labrador, Canada  \nThis page left blank intentionally  \nii  \nAbstract  \nThe design of marine vessels and structures operating in regions where ice is present, must consider the loads transferred to the structure upon impact with an ice feature. The flexural strength of ice is an important material property and can have significant impact on the loads transferred to a structure. Flexural strength is generally considered to be dependent on the size or scale of the sample (often reported as beam volume), ice temperature and brine volume (in the case of sea ice), however the influence of temperature and beam volume have been debated in the literature. Conventionally flexural strength was often modelled asa constant (i.e. average strength), or was modelled as a single parameter or dual parameter (sea ice only) empirical relationship. Employing an extensive database of flexural strength measurements, with over 2000 freshwater and 2800 sea ice measurements, machine learning (ML) algorithms were utilized to define a relationship between these ice parameters and the measured flexural strength. The implementation of ML algorithms was able to highlight a link between freshwater flexural strength and ice temperature, a relationship often ignored or not perceivable in existing models. When considering sea ice, the use of ML algorithms were able to highlight a dependence of flexural strength on scale, brine volume and temperature. These findings have the potential to impact the design of ice strengthened structures, and highlights the importance of accurately recording these parameters when performing tests in the either the field or laboratory.  \nKey Words  \nSea Ice, Freshwater Ice, Flexural Strength, Machine Learning, Level Ice Loads  \nAcknowledgements  \nI would like to thank Dr. Rocky Taylor and Dr. Renat Yulmetov for their continued support and guidance throughout the program. Their feedback and expert advise in the fields of ice mechanics and machine learning were indispensable.  \nI am thankful to Mohamed Aly for his efforts in digitizing the flexural strength database. This database was instrumental in the successful completion of this work.  \nFunding for this work from Innovate NL, the Natural Sciences and Engineering Research Council (NSERC) of Canada, and Hibernia Management and Development Company Ltd.(HMDC) are gratefully acknowledged. Additional thanks are extended to Memorial University’s School of Graduate Students for their financial support throughout the program.  \nA special thank-you to my parents, Everett and Irene Burton, who have wholeheartedly supported and encouraged me from day one, and for encouraging my inquisitive nature as a child.  \nFinally, I would like to express my gratitude towards to my beautiful wife Brianne, and our lovely children Zoey and Annelise for their loving support and encouragement. Your unwavering support and confidence during the many long days and nights made this possible.  \nCo-Authorship Acknowledgements  \nPart of the work presented in Chapter 5: Freshwater Ice Analysis was published in a paper entitled “Estimating Freshwater Level Ice Loads on Sloping Structures Using Machine Learning-Derived Flexural Strength”, and was published and presented at the 26th IAHR International Symposium on Ice, see Burton et al. (2022) .  \nPart of the work in Chapter 6: Sea Ice Analysis was published in a paper entitled “Estimating Level Sea Ice Loads on Sloping Structures Using Machine Learning-Derived Flexural Strength”, and was published and presented at the 27th International Conference on Port and Ocean Engineering under Arctic Conditions (POAC 2023), see B","cbCaicsuX0FPQZtE","https://ap.wps.com/l/cbCaicsuX0FPQZtE","pdf",6544220,10,1,167,"English","en",105,"# Abstract\n# Acknowledgements\n# Co-Authorship Acknowledgements\n# List of Figures\n# List of Tables\n# Nomenclature\n# Acronyms\n# 1 Introduction\n## 1.1 Overview\n## 1.2 Purpose\n## 1.3 Outline of thesis\n# 2 Literature Review\n## 2.1 Ice Failure Methods\n## 2.2 Flexural Strength\n## 2.3 Ice Properties and Ice Mechanics","[{\"question\":\"Why is ice flexural strength important for modelling ice loads on marine structures?\",\"answer\":\"Ice flexural strength governs how ice responds under impact, which directly influences the loads transferred to ice-strengthened structures.\"},{\"question\":\"Which ice parameters did the machine learning models relate to flexural strength?\",\"answer\":\"The models link measured flexural strength to ice parameters including sample scale (beam volume), ice temperature, and—specifically for sea ice—brine volume.\"},{\"question\":\"What new insights did the machine learning approach provide compared with conventional models?\",\"answer\":\"The ML results highlighted relationships that existing models often ignore, including freshwater dependence on ice temperature and sea-ice dependence on scale, brine volume, and temperature.\"}]","Applications of Machine Learning for Modelling of Ice Flexural Strength | 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is ice flexural strength important for modelling ice loads on marine structures?","Question",{"text":77,"@type":78},"Ice flexural strength governs how ice responds under impact, which directly influences the loads transferred to ice-strengthened structures.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which ice parameters did the machine learning models relate to flexural strength?",{"text":82,"@type":78},"The models link measured flexural strength to ice parameters including sample scale (beam volume), ice temperature, and—specifically for sea ice—brine volume.",{"name":84,"@type":75,"acceptedAnswer":85},"What new insights did the machine learning approach provide compared with conventional models?",{"text":86,"@type":78},"The ML results highlighted relationships that existing models often ignore, including freshwater dependence on ice temperature and sea-ice dependence on scale, brine volume, and 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