[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126047-en":3,"doc-seo-126047-105":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},126047,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-aided risk-based inspection strategy for hydrogen technologies","Effective, safe, and economical transport is essential for a broad rollout of hydrogen technologies, yet retrofitted natural gas pipelines face hydrogen-induced material degradation. H2-rich environments reduce pipeline steels’ load-bearing capacity and accelerate crack propagation, making inspection and maintenance critical. Risk-based inspection (RBI) prioritizes high-risk components but does not account for hydrogen-driven degradations, limiting industrial use in H2 conditions. This work proposes an ad-hoc RBI methodology coupling a machine-learning fatigue crack growth model with conventional RBI planning.","Process Safety and Environmental Protection 191 (2024) 1239–1253  \nContents lists available at ScienceDirect  \nProcess Safety and Environmental Protection  \njournal [homepage:](homepage: www.journals.elsevier.com/process-safety-and-environmental-protection)[ www.journals.elsevier.com/process-safety-and-environmental-protection](homepage: www.journals.elsevier.com/process-safety-and-environmental-protection)  \n| Machine learning-aided risk-based inspection strategy for hydrogen technologies |  |  |  |\n| --- | --- | --- | --- |\n| Alessandro Camparia,*, Chiara Vianellob, Federico Ustolina, Antonio Alvaro c, Nicola Paltrinieri a\u003Cbr>a Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology NTNU, Richard Birkelands vei 2b, Trondheim 7034, Norway b Department of Industrial Engineering, University of Padova, Via Gradenigo 6a, Padova 35131, Italy\u003Cbr>c Department of Materials and Nanotechnology, SINTEF Industry, Richard Birkelands vei 2b, Trondheim 7034, Norway |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Hydrogen pipelines Component safety Hydrogen-enhanced fatigue Machine-learning\u003Cbr>Risk-based inspection Loss prevention |  | Although technically challenging, effective, safe, and economical transport is crucial for enabling a widespread rollout of hydrogen technologies. A promising option to transport large amounts of hydrogen lies in employing retrofitted natural gas pipelines. Nevertheless, H2-rich environments tend to degrade pipeline steels, reducing their load-bearing capability and accelerating crack propagation. Regular inspection and maintenance activities can preserve the pipelines’ integrity and guarantee safe operations. The risk-based inspection (RBI) approach is based on estimating the risk for each component item. It focuses most inspection activities on high-risk components to reduce costs while maximizing the plant’s safety and availability. However, the RBI standards do not consider hydrogen-induced degradations and cannot be adopted for industrial equipment operating in H2 environments. This study proposes a novel ad-hoc methodology for the risk-based inspection planning of hydrogen handling equipment. A machine-learning model to predict the fatigue crack growth in gaseous hydrogen environments is developed and integrated with the conventional RBI approach. The proposed methodology is validated on three pipelines transporting hydrogen and natural gas in different concentrations. The results show how similar operating conditions can determine different degradation rates depending on the environment and highlight how hydrogen-enhanced fatigue can reduce the pipelines’ lifetime. |  |\n\n1. Introduction  \nMitigating the human impact on the environment and moving towards a sustainable economy require a paradigm change in the energy sector (International Energy Agency, 2023). The global energy transition demands clean and affordable energy sources, and hydrogen has been primarily indicated as a versatile and potentially sustainable energy carrier (Norwegian Ministry of Petroleum and Energy, 2020). It can be produced with near-zero pollutant emissions by water electrolysis and is efficiently used in fuel cell systems or repurposed turbomachines. In addition, hydrogen is one of the few options for long-term clean energy storage, thus supporting the integration of renewable sources in the electricity system (Chatzimarkakis et al., 2021). In this context, an efficient and widespread transport infrastructure is crucial, and using the existing natural gas pipeline network to transport vast amounts of hydrogen seems a promising option (Lipia¨inen et al., 2023).  \nHowever, adapting the natural gas pipelines for transporting pure  \nhydrogen or hydrogen-natural gas blends presents several technical challenges. One of the most pressing issues is the accelerated material degradation of components exposed to H2-containing environments. The interaction of most meta","cbCaibJapSN2PEKt","https://ap.wps.com/l/cbCaibJapSN2PEKt","pdf",3364916,9,1,15,"English","en",105,"# Introduction\n# Risk-based inspection and hydrogen-induced degradation\n# Proposed machine-learning integrated RBI methodology\n# Validation on hydrogen and natural gas pipelines\n# Results and implications for pipeline lifetime","[{\"question\":\"Why do hydrogen-rich environments require new inspection planning compared with conventional RBI?\",\"answer\":\"Hydrogen environments degrade pipeline steels and accelerate crack propagation, while conventional RBI standards do not explicitly consider hydrogen-induced degradations.\"},{\"question\":\"What is the core idea of the proposed methodology in the study?\",\"answer\":\"It integrates a machine-learning model that predicts fatigue crack growth in gaseous hydrogen environments into a conventional risk-based inspection (RBI) planning framework.\"},{\"question\":\"How is the methodology validated, and what do the results indicate?\",\"answer\":\"It is validated on three pipelines transporting hydrogen and natural gas at different concentrations; similar operating conditions can yield different degradation rates, and hydrogen-enhanced fatigue can reduce pipeline lifetime.\"}]","Machine learning-aided risk-based inspection strategy for hydrogen technologies | PDF",1785902745,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-aided-risk-based-inspection-strategy-for-hydrogen-technologies","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-aided-risk-based-inspection-strategy-for-hydrogen-technologies/126047/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why do hydrogen-rich environments require new inspection planning compared with conventional RBI?","Question",{"text":77,"@type":78},"Hydrogen environments degrade pipeline steels and accelerate crack propagation, while conventional RBI standards do not explicitly consider hydrogen-induced degradations.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the core idea of the proposed methodology in the study?",{"text":82,"@type":78},"It integrates a machine-learning model that predicts fatigue crack growth in gaseous hydrogen environments into a conventional risk-based inspection (RBI) planning framework.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the methodology validated, and what do the results indicate?",{"text":86,"@type":78},"It is validated on three pipelines transporting hydrogen and natural gas at different concentrations; 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