[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123508-en":3,"doc-seo-123508-105":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},123508,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Recent Advances of Machine Learning in Fracture Mechanics of Quasi-Brittle Materials - A Review","The fracture mechanics of quasi-brittle materials such as concrete, ceramics, and rocks involve major modelling challenges driven by nonlinear stress–strain relations, microstructural heterogeneity, and complex damage and failure mechanisms. Traditional numerical and analytical approaches often cannot fully represent fracture propagation and evolving damage. This review synthesizes recent machine learning advances that use experimental and simulation data to improve predictive performance and enable real-time damage prediction, adaptive modelling, and stronger generalization. It emphasizes hybrid, physics-informed neural networks alongside ANN and CNN methods to enhance safety, efficiency, and accuracy in engineering applications.","Recent Advances of Machine Learning in Fracture Mechanics of Quasi-Brittle  \nMaterials: A Review  \nS. H. Moghtaderia, P. Thamburajaa,b,*, A. Jedia,c, M. Beerd,e,f, S. Abdullaha,c & A. K. Ariffina  \naDepartment of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti  \nKebangsaan Malaysia, Bangi 43600, Selangor, Malaysia  \nbDepartment of Mechanical Engineering, Texas A&M University, College Station, 77843 TX, U.S.AcCentre for Automotive Research, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi  \n43600, Selangor, Malaysia  \ndInstitute for Risk and Reliability, Leibniz University, Hannover 30167, Germany eInstitute for Risk and Uncertainty, University of Liverpool, Liverpool L69 7ZF, United Kingdom fInternational Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University, Shanghai 200092, China  \n*[Corresponding author: p](Corresponding author: p.thamburaja@ukm.edu.my)[.thamburaja@ukm.edu.my](Corresponding author: p.thamburaja@ukm.edu.my)  \nABSTRACT  \nThe fracture mechanics of quasi-brittle materials, such as concrete, ceramics, and rocks, pose significant challenges due to their nonlinear stress-strain response, microstructural heterogeneity, and complex failure mechanisms. Traditional numerical and analytical methods often fall short in capturing the full intricacies of fracture propagation and damage evolution in such materials. However, recent advances in machine learning (ML) offer promising solutions to these limitations by enabling data-driven insights and enhanced computational performance. In this review paper, we explore the growing role of ML techniques in the fracture analysis of quasibrittle materials. By leveraging large and diverse datasets from experiments and numerical simulations, ML models not only complement traditional fracture mechanics approaches but also introduce novel capabilities such as real-time damage prediction, adaptive modelling, and improved generalization across varying material conditions. The integration of data-driven models with physics-based frameworks, especially through hybrid techniques like physics-informed neural networks (PINNs), marks a significant shift in how fracture phenomena are modelled and understood. Key ML methods discussed include artificial neural networks (ANNs), convolutional neural networks (CNNs), and PINNs, with a focus on their respective advantages and implementation strategies. The review highlights how these approaches can enhance safety, efficiency, and predictive accuracy in engineering applications, ultimately making machine learning a transformative tool in the study of quasi-brittle fracture behaviour.  \nKeywords: Machine learning; Fracture mechanics; quasi-brittle materials; crack characterization;  \nINTRODUCTION  \nQuasi-brittle materials like concrete, ceramics, and composites exhibit complex fracture behavior, transitioning from microcracking to complete failure under critical load (Abuzaid et al. 2024; Lawrence et al. 2024) . This makes them essential to study in fields where durability and reliability are key, such as civil and aerospace engineering (Chin et al. 2024). Accurate prediction of crack propagation is vital for ensuring structural safety, longevity, and cost-effective maintenance.  \nClassical theoretical models, such as Linear Elastic Fracture Mechanics (LEFM) and Continuum Damage Mechanics (CDM), have served as foundational tools in understanding material failure by relating stresses, strains, and energy release rates (Piska et al. 2024). However, LEFM is based on assumptions of linear elasticity and sharp crack tips, making it inadequate for quasi-brittle materials that undergo distributed microcracking and nonlinear deformation (Da 2024) . CDM improves upon this by introducing internal state variables to capture damage evolution, yet it often lacks resolution i","cbCaitIxGVClVTKB","https://ap.wps.com/l/cbCaitIxGVClVTKB","pdf",800171,1,21,"English","en",105,"# Abstract\n# Introduction\n## Challenges in quasi-brittle fracture mechanics\n## Classical theoretical models (LEFM, CDM) and their limits\n## Nonlocal and gradient-enhanced damage models\n## Numerical method limitations (e.g., finite element)\n## Experimental investigation constraints\n## Machine learning as a new pathway","[{\"question\":\"Why are traditional fracture mechanics methods insufficient for quasi-brittle materials?\",\"answer\":\"They rely on simplifying assumptions and struggle to represent nonlinear deformation, distributed microcracking, localized fracture-zone behavior, and crack path instability. Numerical methods also face high cost and preprocessing demands for complex heterogeneous problems.\"},{\"question\":\"What kinds of data do machine learning models use in quasi-brittle fracture analysis?\",\"answer\":\"Large, diverse datasets from experiments and numerical simulations. Learning from these sources helps models complement traditional fracture mechanics while improving predictive capability.\"},{\"question\":\"Which machine learning approaches are highlighted for fracture mechanics and what is their role?\",\"answer\":\"The review discusses ANNs, CNNs, and physics-informed neural networks (PINNs). It stresses that hybrid approaches, especially PINNs, integrate data-driven learning with physics-based frameworks to better model fracture phenomena.\"}]","Recent Advances of Machine Learning in Fracture Mechanics of Quasi-Brittle Materials - A Review | PDF",1785816946,53,{"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},"recent-advances-of-machine-learning-in-fracture-mechanics-of-quasi-brittle-materials-a-review","",{"@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/recent-advances-of-machine-learning-in-fracture-mechanics-of-quasi-brittle-materials-a-review/123508/",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},"Why are traditional fracture mechanics methods insufficient for quasi-brittle materials?","Question",{"text":75,"@type":76},"They rely on simplifying assumptions and struggle to represent nonlinear deformation, distributed microcracking, localized fracture-zone behavior, and crack path instability. Numerical methods also face high cost and preprocessing demands for complex heterogeneous problems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of data do machine learning models use in quasi-brittle fracture analysis?",{"text":80,"@type":76},"Large, diverse datasets from experiments and numerical simulations. Learning from these sources helps models complement traditional fracture mechanics while improving predictive capability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are highlighted for fracture mechanics and what is their role?",{"text":84,"@type":76},"The review discusses ANNs, CNNs, and physics-informed neural networks (PINNs). 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