[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122577-en":3,"doc-seo-122577-105":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},122577,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","APPLICATION OF MACHINE LEARNING ALGORITHMS ON VIV FATIGUE LIFE ASSESSMENT OF MULTI - SPANNING SUBSEA PIPELINES - Master of Engineering Thesis","Subsea pipelines transport liquid, gas, and multiphase products efficiently but can lose seabed contact over an uneven seabed, creating suspended free-spans. Vortex-Induced Vibrations (VIV) drive repetitive deflection that accumulates fatigue damage and can cause premature failure. Traditional design relies on standard codes and often applies high safety factors due to uncertainty, increasing project costs. This thesis proposes machine-learning algorithms with a Python-controlled ABAQUS workflow to generate, clean, and prepare a large dataset and predict fatigue life for free- and multi-spanning pipelines with cost-effective implementation.","APPLICATION OF MACHINE LEARNING ALGORITHMS  \nON VIV FATIGUE LIFE ASSESSMENT OF MULTI  \nSPANNING SUBSEA PIPELINES  \nby  \n© Mobin Abdolalipour Miandoab  \nA Thesis submitted to the  \nSchool of Graduate Studies  \nin partial fulfillment of the requirements for the degree of  \nMaster of Engineering  \nFaculty of Engineering and Applied Science  \nMemorial University of Newfoundland  \nOct 2023  \nSt. John's Newfoundland Canada  \nABSTRACT  \nSubsea pipelines are the most efficient and reliable method of transportation of liquid, gas, and multiphase products through an aquatic medium. Pipelines laid on the uneven seabed lose contact with the seabed, leaving it suspended in some areas. The length of the pipeline hung over the seabed is called freespan. The free-spanning length of the pipeline is subjected to different static and hydrodynamic loads where the Vortex-Induced Vibrations (VIV) are among the most severe factors threatening pipeline integrity.  \nRepetitive deflection on the free-spanning length of the pipeline imposes fatigue damage on the pipeline structure. Consequently, it results in the early failure of the structure before the expected operational life. Regarding the critical role of subsea pipelines and the uncertainty of pipeline behavior against VIV damages, high safety factors are usually applied in designing subsea free-spanning pipelines, bringing unnecessary financial burdens on offshore pipeline projects.  \nDesigning subsea pipeline projects is primarily conducted based on standard codes (e.g., DNVGL RP-F105, DNVGL RP-C203, and DNVGL RP-F114) . However, accurate assessment of the free-spanning pipeline integrity against the VIV needs advanced experimental and numerical modeling with extensive cost and time impacts.  \nIn this study, machine-learning algorithms are adopted to develop a cost-effective and easyto-implement solution for VIV-induced fatigue analysis of free-spanning and multispanning pipelines. The numerical model was developed based on recommendations provided by DNVGL RP-F105 and verified by numerical results from FATFREE software, which is developed by DNV for analysis of subsea free-spanning pipelines (DET NORSKE  \nVERITAS, 2021) . The comparison conducted using the data presented by Pereira et al.(Pereira et al., 2008) . The employment of machine learning methods requires a high-quality dataset covering all of the possible cases. In this regard, a Python script was developed to control ABAQUS for creating different case studies and interacting with output results to perform data cleaning and preparation. The procedure of creating case studies, reading and analyzing outcomes, and cleaning and preparing results for machine learning was performed by a fully automated cycle managed by Python scripts. More than 200000 configurations of single free-span and multi-spanning conditions were analyzed. In order to have a more realistic study, pipeline characteristics, and soil properties were selected from available industrial products and trusted industrial data such as standard codes of DNVGL. The study showed that machine learning algorithms can effectively predict the fatigue life of the free-spanning pipelines subjected to VIV oscillations. Particularly, this can be of significant importance during the initial design projects for a fast and fairly accurate assessment of the fatigue lives.  \nACKNOWLEDGMENT  \nI would like to offer my gratitude to my supervisor and mentor, Dr. Shiri. It was a great experience and honor for me to have a chance to join his research team. Under his excellent mentorship, I could gain lots of scientific knowledge and could develop valuable skills both in academic and personal areas. Dr. Shiri offered me unconditional support and guidance. I also want to thank Mr. Hamed Azimi, my great colleague and senior researcher in Dr. Shiri's research team. He helped me by giving helpful hints derived from his excellent scientific background.  \nI gratefully thanks financial support of the Woodg","cbCaicyInCMiHjhZ","https://ap.wps.com/l/cbCaicyInCMiHjhZ","pdf",12152528,1,212,"English","en",105,"# Chapter 1: Introduction\n## 1.1 Overview\n## 1.2 Original contribution\n### 1.2.1 Organization of the Thesis\n# Chapter 2: Literature review\n## 2.1 Free-span","[{\"question\":\"What problem does the thesis address in subsea pipeline design?\",\"answer\":\"It addresses fatigue life assessment for subsea free- and multi-spanning pipelines subjected to Vortex-Induced Vibrations (VIV), which can threaten structural integrity and cause early failure.\"},{\"question\":\"Why do designers often apply high safety factors for VIV in subsea pipelines?\",\"answer\":\"Because standard-code-based assessment typically requires advanced experimental and numerical modeling for accurate VIV integrity evaluation, and uncertainty in pipeline behavior can lead to conservative design choices that increase cost burden.\"},{\"question\":\"How does the thesis develop machine-learning solutions for fatigue prediction?\",\"answer\":\"It builds a numerical model aligned with DNVGL RP-F105 recommendations, verifies it using FATFREE, and uses Python scripts to automate ABAQUS case generation, data cleaning, and preparation, analyzing over 200,000 configurations.\"}]","APPLICATION OF MACHINE LEARNING ALGORITHMS ON VIV FATIGUE LIFE ASSESSMENT OF MULTI - SPANNING SUBSEA PIPELINES - Master of Engineering Thesis | PDF",1785811413,534,{"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},"application-of-machine-learning-algorithms-on-viv-fatigue-life-assessment-of-multi-spanning-subsea-pipelines-master-of-engineering-thesis","",{"@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/application-of-machine-learning-algorithms-on-viv-fatigue-life-assessment-of-multi-spanning-subsea-pipelines-master-of-engineering-thesis/122577/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in subsea pipeline design?","Question",{"text":75,"@type":76},"It addresses fatigue life assessment for subsea free- and multi-spanning pipelines subjected to Vortex-Induced Vibrations (VIV), which can threaten structural integrity and cause early failure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do designers often apply high safety factors for VIV in subsea pipelines?",{"text":80,"@type":76},"Because standard-code-based assessment typically requires advanced experimental and numerical modeling for accurate VIV integrity evaluation, and uncertainty in pipeline behavior can lead to conservative design choices that increase cost burden.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis develop machine-learning solutions for fatigue prediction?",{"text":84,"@type":76},"It builds a numerical model aligned with DNVGL RP-F105 recommendations, verifies it using FATFREE, and uses Python scripts to automate ABAQUS case generation, data cleaning, and preparation, analyzing over 200,000 configurations.","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"]