[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122845-en":3,"doc-seo-122845-105":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},122845,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Microcapsule Triggering Mechanics in Cementitious Materials - A Modelling and Machine Learning Approach","Self-healing cementitious materials with microcapsules carrying healing agents can seal cracks and help restore structural integrity. Optimising microcapsule mechanics so capsules survive concrete mixing yet rupture at the cracked interface for controlled release remains difficult. This work presents an integrated numerical modelling and machine-learning framework to tailor acrylate-based microcapsules for triggering inside cementitious matrices. Microfluidics enables systematic variation of capsule shell thickness, strength, and cement compatibility; continuum damage mechanics models simulate cracking and are linked to machine learning predictions using artificial neural networks trained on capsule properties. Design curves relate capsule strength, toughness, and interfacial bonding to fracture propensity for robust self-healing performance.","materials   \nArticle  \nMicrocapsule Triggering Mechanics in Cementitious Materials: A Modelling and Machine Learning Approach  \nEvan John Ricketts 1, *, Lívia Ribeiro de Souza 2,†, Brubeck Lee Freeman 1,3, Anthony Jefferson 1 and Abir Al-Tabbaa 2  \nCitation: Ricketts, E.J.; de Souza, L.R.; Freeman, B.L.; Jefferson, A.;  \nAl-Tabbaa, A. Microcapsule Triggering Mechanics in Cementitious Materials: A Modelling and Machine Learning Approach. Materials 2024, 17, 764. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ma17030764  \nAcademic Editors: Lizhi Sun and Samir Chidiac  \nReceived: 19 December 2023  \nRevised: 14 January 2024  \nAccepted: 28 January 2024  \nPublished: 5 February 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Engineering, Cardiff University, 3-5 The Walk, Cardiff CF24 3AA, UK; [freemanbl@cardiff.ac.uk or](freemanbl@cardiff.ac.uk or)[ ](freemanbl@cardiff.ac.uk or)[brubeck.freeman@lusas.com](brubeck.freeman@lusas.com) (B.L.F.); [jeffersonad@cardiff.ac.uk](jeffersonad@cardiff.ac.uk) (A.J.)  \n2 Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK; [livia@mimicrete.com](livia@mimicrete.com) (L.R.d.S.); [aa22@eng.cam.ac.uk](aa22@eng.cam.ac.uk) (A.A.-T.)  \n3 LUSAS, Forge House, 66 High Street, Kingston upon Thames KT1 1HN, UK  \n* Correspondence: [rickettse1@cardiff.ac.uk](rickettse1@cardiff.ac.uk)  \n† Current address: Mimicrete Ltd., 95 Regent Street, Cambridge CB2 1AA, UK.  \nAbstract: Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems.  \nKeywords: self-healing concrete; microcapsules; triggering mechanics; continuum damage modelling; finite element modelling; microfluidics; machine learning; neural networks; design curves; interfacial properties  \n1. Introduction  \nSelf-healing technologies aim to impart materials, such as polymers and cementitious composites, with the ability to autonomously repair damage. Microcapsules are a critical component of many self-healing systems, enabling the incorporation and release of healing agents into structural materials to seal cracks [1–3] . Different healing agents have been encapsulated, including polymers, bacteria, and chemicals, such as sodium silicate and dicyclopentadiene, that can react with the host material or each other to re-bond crack faces [2–4] ","cbCaihZCIhVRWTEU","https://ap.wps.com/l/cbCaihZCIhVRWTEU","pdf",4701914,1,15,"English","en",105,"# Introduction\n## Background: self-healing systems and microcapsules\n## Fabrication challenges and property tailoring\n## Modelling approaches and research gaps\n## Objectives and proposed framework","[{\"question\":\"Why is microcapsule triggering mechanics difficult to optimise in cementitious materials?\",\"answer\":\"Microcapsules must withstand concrete mixing while still rupturing specifically at the cracked interface to release the healing agent. Achieving this balance requires precise control over shell mechanics and cement compatibility.\"},{\"question\":\"How are the microcapsules produced for systematic property variation?\",\"answer\":\"Microfluidics is used to fabricate microcapsules with systematically varied shell thickness, shell strength, and cement compatibility, enabling controlled studies of triggering behaviour.\"},{\"question\":\"How does the machine learning model predict microcapsule triggering behaviour?\",\"answer\":\"Continuum damage mechanics simulations generate results based on capsule and interfacial properties, and these simulation outputs train an artificial neural network. The trained model rapidly predicts triggering behaviour and yields design curves linking capsule properties to fracture propensity.\"}]","Microcapsule Triggering Mechanics in Cementitious Materials - A Modelling and Machine Learning Approach | PDF",1785813230,38,{"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},"microcapsule-triggering-mechanics-in-cementitious-materials-a-modelling-and-machine-learning-approach","",{"@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/microcapsule-triggering-mechanics-in-cementitious-materials-a-modelling-and-machine-learning-approach/122845/",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 is microcapsule triggering mechanics difficult to optimise in cementitious materials?","Question",{"text":75,"@type":76},"Microcapsules must withstand concrete mixing while still rupturing specifically at the cracked interface to release the healing agent. Achieving this balance requires precise control over shell mechanics and cement compatibility.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the microcapsules produced for systematic property variation?",{"text":80,"@type":76},"Microfluidics is used to fabricate microcapsules with systematically varied shell thickness, shell strength, and cement compatibility, enabling controlled studies of triggering behaviour.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine learning model predict microcapsule triggering behaviour?",{"text":84,"@type":76},"Continuum damage mechanics simulations generate results based on capsule and interfacial properties, and these simulation outputs train an artificial neural network. 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