[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121554-en":3,"doc-seo-121554-105":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},121554,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine learning-assisted fracture prediction - integrating synthetic and experimental data for quasi-static notch failure analysis","Machine learning-assisted fracture prediction for quasi-static notch failure is addressed by integrating synthetic and experimental data. The work targets notched members where geometrical discontinuities create complex stress and strain fields that traditional fracture mechanics and constitutive models may not capture efficiently without extensive calibration and computation. It reviews the role of Theory of Critical Distances (TCD) and strain energy approaches, then motivates AI/ML methods capable of learning nonlinear fracture–fatigue relationships. The study emphasizes using synthetic datasets from finite element simulations to reduce costly, time-consuming experiments and calibration requirements, supported by ML models such as gradient boosting.","SA. Farooq et alii, Fracture and Structural Integrity, 75 (2026) 362-372; DOI: 10.3221/IGF-ESIS.75.26  \n| Machine learning-assisted fracture prediction: integrating synthetic and experimental data for quasi-static notch failure analysis\u003Cbr>Sheikh Aamir Farooq, Danah Alajaleen\u003Cbr>Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia [g202306750@kfupm.edu.sa](g202306750@kfupm.edu.sa), [https://orcid.org/0009-0007-5821-0907](https://orcid.org/0009-0007-5821-0907)\u003Cbr>[s202170590@kfupm.edu.sa](s202170590@kfupm.edu.sa),\u003Cbr>Jafar Albinmousa\u003Cbr>Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia\u003Cbr>[binmousa@kfupm.edu.sa](binmousa@kfupm.edu.sa), [https://orcid.org/0000-0002-2395-5008](https://orcid.org/0000-0002-2395-5008) |  |\n| --- | --- |\n|  | \u003Cbr>Citation: Farooq, S. A., Alajaleen, D., Albinmousa, J., Machine learning-assisted fracture prediction: integrating synthetic and experimental data for quasi-static notch failure analysis, Fracture and Structural Integrity, 75 (2026) 362-372.\u003Cbr>Received: 28.09.2025\u003Cbr>Accepted: 11.11.2025\u003Cbr>Published: 23.11.2025\u003Cbr>Issue: 01.2026\u003Cbr>Copyright: © 2026 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. |\n| KEYWORDS. Machine Learning, Fracture, Theory of Critical Distances, PYMAPDL, Synthetic data, XGBoost. |  |\n| INTRODUCTION\u003Cbr>P olycarbonate (PC) is a high performance thermoplastic that possesses high impact resistance, excellent mechanical\u003Cbr>strength, making it suitable for high impact applications [1,2] . In addition, polycarbonate exhibits transparency due to its non-crystalline structure; nevertheless, it is almost as strong as highly crystalline nylon and is even tougher [3] . The superior mechanical and physical properties of polycarbonate have led to its widespread use in various industries including automative [4], medical [5], aerospace [6], construction [7] and electronics [8] . Given the increasing demand in |  |\n\nSA. Farooq et alii, Fracture and Structural Integrity, 75 (2026) 362-372; DOI: 10.3221/IGF-ESIS.75.26  \nthese diverse applications, particularly in load-bearing components, it is important to understand the fracture behavior under various loading conditions, especially when geometrical discontinuities such as notches or cracks are present.  \nNotches and other defects are often inherent due to design limitations, manufacturing processes or in-service damage. Geometrical discontinuities act as stress concentrators and potential crack initiation sites, which create complex stress and strain distributions near the notch tip, thereby complicating the fracture prediction. Consequently, it is essential to have a reliable design methodology for predicting the fracture load on notched members [9] . Traditional fracture mechanics methods often fall short in capturing the fracture behavior of notched ductile polymers such as polycarbonate under quasistatic loading [10] . Numerous continuum-mechanics based constitutive models have been developed over recent decades to describe the complex mechanical behavior of polycarbonate, accounting for viscoplastic behavior and notch sensitivity [11– 13] . While these models significantly enhance predictive accuracy, they require extensive experimental calibration and computational effort. Therefore, alternative methods such as Theory of Critical Distances (TCD) [14] and Strain Energy Density (SED) [15] have gained significant attention to estimate the fracture of different notch geometries and materials, without the need for extensive crack growth modeling.  \nAmong these methods, TCD is most widely used due to its simplicity","cbCaigTwGQQQ0SyA","https://ap.wps.com/l/cbCaigTwGQQQ0SyA","pdf",3819122,1,11,"English","en",105,"# Introduction\n## Notch effects and limitations of traditional fracture mechanics\n## TCD and strain-energy-based design approaches\n## AI/ML methods and the role of synthetic data","[{\"question\":\"Why are notches and other defects challenging for fracture prediction?\",\"answer\":\"Notches act as stress concentrators and potential crack initiation sites, producing complex stress and strain distributions near the notch tip. This complexity makes fracture prediction difficult for conventional approaches.\"},{\"question\":\"What is the motivation for using Theory of Critical Distances (TCD) in quasi-static notch failure?\",\"answer\":\"TCD is widely used because it is comparatively simple and requires limited computational cost. Its point method formulation supports accurate prediction without extensive nonlinear material modeling, while still relying on accurate stress fields and calibration.\"},{\"question\":\"How does integrating synthetic and experimental data improve data-driven fracture modeling?\",\"answer\":\"Synthetic datasets generated from finite element simulations can train ML models while reducing the need for large experimental campaigns. This helps address cost and time constraints and supports learning accurate nonlinear fracture–fatigue relationships.\"}]","Machine learning-assisted fracture prediction - integrating synthetic and experimental data for quasi-static notch failure analysis | PDF",1785736222,28,{"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},"machine-learning-assisted-fracture-prediction-integrating-synthetic-and-experimental-data-for-quasi-static-notch-failure-analysis","",{"@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/machine-learning-assisted-fracture-prediction-integrating-synthetic-and-experimental-data-for-quasi-static-notch-failure-analysis/121554/",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},"Why are notches and other defects challenging for fracture prediction?","Question",{"text":75,"@type":76},"Notches act as stress concentrators and potential crack initiation sites, producing complex stress and strain distributions near the notch tip. This complexity makes fracture prediction difficult for conventional approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the motivation for using Theory of Critical Distances (TCD) in quasi-static notch failure?",{"text":80,"@type":76},"TCD is widely used because it is comparatively simple and requires limited computational cost. Its point method formulation supports accurate prediction without extensive nonlinear material modeling, while still relying on accurate stress fields and calibration.",{"name":82,"@type":73,"acceptedAnswer":83},"How does integrating synthetic and experimental data improve data-driven fracture modeling?",{"text":84,"@type":76},"Synthetic datasets generated from finite element simulations can train ML models while reducing the need for large experimental campaigns. 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