[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118947-en":3,"doc-seo-118947-105":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},118947,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate - Research report","The study develops a fracture-analysis workflow for a 2D edge-crack semi-infinite elastic plate by combining the stress intensity factor (SIF) framework with finite element analysis. Extensive finite element simulations in ABAQUS CAE generate results across a wide range of crack lengths and mesh sizes, enabling a rich dataset for model calibration. Machine learning techniques, particularly artificial neural networks, are used to learn nonlinear relationships between inputs and crack-tip stress characteristics. Numerical outputs are compared against SIF predictions to evaluate accuracy and support reliable estimation of critical crack behavior.","S. H. Moghtaderi et alii, Frattura edIntegrità Strutturale, 68 (2024) 197-208; DOI: 10.3221/IGF-ESIS.68.13  \nApplication of machine learning in fracture analysis of edge crack semi-infinite elastic plate  \nSaeed H. Moghtaderi*  \nDepartment ofMechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia  \n[p116028@siswa.ukm.edu.my](p116028@siswa.ukm.edu.my), [https://orcid.org/0000-0002-0047-9854](https://orcid.org/0000-0002-0047-9854)  \nAlias Jedi  \nDepartment ofMechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia  \nCentre for Automotive Research, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia  \n[aliasjedi@ukm.edu.my](aliasjedi@ukm.edu.my), [https://orcid.org/0000-0002-2106-0542](https://orcid.org/0000-0002-2106-0542)  \nAhmad Kamal Ariffin, Prakash Thamburaja  \nDepartment ofMechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia  \n[kamal3@ukm.edu.my](kamal3@ukm.edu.my), [https://orcid.org/0000-0001-5098-5088](https://orcid.org/0000-0001-5098-5088)[ ](https://orcid.org/0000-0001-5098-5088)[p.thamburaja@ukm.edu.my](p.thamburaja@ukm.edu.my), [https://orcid.org/0000-0002-2639-6651](https://orcid.org/0000-0002-2639-6651)  \nCitation: Moghtaderi, S. H., Jedi, A., Ariffin, A. K., Thamburaja, P., Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate, Frattura ed Integrità Strutturale, 68 (2024) 197-208.  \nReceived: 12.12.2023  \nAccepted: 31.01.2024  \nPublished: 05.02.2024  \nIssue: 04.2024  \nCopyright: © 2024 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nKEYWORDS. Mode I fracture analysis, Machine learning, Finite element analysis, Elastic plate, Stress intensity factor.  \nS. H. Moghtaderi et alii, Frattura edIntegrità Strutturale, 68 (2024) 197-208; DOI: 10.3221/IGF-ESIS.68.13  \nINTRODUCTION  \nU nderstanding fatigue and fracture assessment of solid structures and materials such as beams [1,2], plates [3,4], and  \nbars [5,6] is critical for guaranteeing their integrity and durability, as well as building safe and resilient engineering systems. Various analytical and computational approaches have been used throughout the years to analyze fracture  \npropagation and stress distribution in such systems [7]. Among these techniques, the stress intensity factor (SIF) model has shown to be a basic tool for studying the stress field around crack tips, offering useful insights into solid component fracture mechanics [8,9] . The SIF model, which is widely used in mode I fracture analysis, has been facilitated by incorporating suitable intrinsic length scales, enabling researchers to describe size effect phenomena and determine critical conditions for crack propagation as well as evaluate the structural integrity of materials [10,11] . This model describes the stress concentration at the crack tip and is crucial in estimating the crack growth rate under various loading conditions. Numerical simulation, on the other hand, has become an essential tool in engineering methodologies, offering an efficient means for investigating complex systems. The Finite Element Analysis (FEA) approach has gained significant acceptance in this context for investigating fracture behavior, aided by software tools such as ABAQUS CAE and ANSYS Workbench to perform extensive numerical simulations of fracture propagation in complex solid structures, resulting in a rich dataset that incorporates experimental data [12-14] .  \nThe application of machine learning (ML) techniques in fracture analysis has influenced the area of material engineering","cbCaijH1RJpOuRz8","https://ap.wps.com/l/cbCaijH1RJpOuRz8","pdf",1607560,1,12,"English","en",105,"# Introduction\n## Stress intensity factor and fracture mechanics\n## Finite element analysis and data generation\n## Machine learning and artificial neural networks","[{\"question\":\"What fracture geometry and physical model are investigated in the study?\",\"answer\":\"The study focuses on a 2D edge-crack semi-infinite elastic plate and uses the stress intensity factor (SIF) model to evaluate crack-tip stress characteristics.\"},{\"question\":\"How are numerical data generated for the machine learning approach?\",\"answer\":\"Finite element analysis is performed in ABAQUS CAE across many crack lengths and mesh sizes, producing simulation results that form the data basis for learning and validation.\"},{\"question\":\"Why is an artificial neural network (ANN) used in mode I fracture analysis?\",\"answer\":\"ANN is used because it can learn complex nonlinear correlations from data, enabling predictions of crack-related quantities such as crack growth behavior and relevant fracture metrics.\"}]","Application of machine learning in fracture analysis of edge crack semi-infinite elastic plate - Research report | PDF",1785721144,30,{"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-in-fracture-analysis-of-edge-crack-semi-infinite-elastic-plate-research-report","",{"@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-in-fracture-analysis-of-edge-crack-semi-infinite-elastic-plate-research-report/118947/",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","2026-08-03",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 fracture geometry and physical model are investigated in the study?","Question",{"text":75,"@type":76},"The study focuses on a 2D edge-crack semi-infinite elastic plate and uses the stress intensity factor (SIF) model to evaluate crack-tip stress characteristics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are numerical data generated for the machine learning approach?",{"text":80,"@type":76},"Finite element analysis is performed in ABAQUS CAE across many crack lengths and mesh sizes, producing simulation results that form the data basis for learning and validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is an artificial neural network (ANN) used in mode I fracture analysis?",{"text":84,"@type":76},"ANN is used because it can learn complex nonlinear correlations from data, enabling predictions of crack-related quantities such as crack growth behavior and relevant fracture metrics.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]