[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117833-en":3,"doc-seo-117833-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},117833,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Probabilistic selection and design of concrete using machine learning","Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and the combinatorial space of mix proportions. Reliable property prediction via machine learning can support performance-based concrete specification, reducing material inefficiencies and improving sustainability. The study develops a machine learning algorithm that leverages intermediate target variables and their associated noise to predict a final target. It specifies one mix with high resistance to carbonation and another with low environmental impact, meeting strength, density, and cost targets, and validates the mixes experimentally against predictions. The framework enables practical exploitation of noise for accelerated materials design.","Data-Centric Engineering (2023), 4: e9  \ndoi:10.1017/dce.2023.5  \nRESEARCH ARTICLE   \nProbabilistic selection and design of concrete using machine learning  \nJessica C. Forsdyke 1 , Bahdan Zviazhynski2 , Janet M. Lees 1  and Gareth J. Conduit2   \n1Department of Engineering, University of Cambridge, Cambridge, United Kingdom 2Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom Corresponding author: Jessica C. Forsdyke; Email: [jf580@cam.ac.uk](jf580@cam.ac.uk)  \nReceived: 15 August 2022; Revised: 01 March 2023; Accepted: 24 March 2023  \nKeywords: Carbonation; concrete; machine learning; performance-based specification  \nAbstract  \nDevelopment of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variablesand their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond.  \nImpact Statement  \nThis article demonstrates that machine learning can be used to predict the properties of concrete even with a sparse and noisy dataset. This has important applications to performance-based specification of concrete mixes—enabling appropriately durable and strong concretes to be specified while minimizing embodied carbon or cost. In cases where time-consuming and costly trials are required, this is particularly beneficial. The machine learning methodology developed and demonstrated in this article has application to the broader field of accelerated materials design, allowing bespoke materials to be designed rapidly for each particular application. Furthermore, there are many examples of other verticals where information is embedded in noise, including autonomous vehicles, additive manufacturing, and information engineering, where machine learning offers the opportunity to accelerate development, understanding, and impact.  \n1. Introduction  \nConcrete is the most heavily used construction material in the world. The only substance consumed in greater quantities is water (Sedgwick, 1991) . Concrete is ideal for construction because it is readily  \nJ.C.F. and B.Z. contributed equally to this work.  \n This research article was awarded an Open Data badge for transparent practices. See the Data Availability Statement for details.  \n©The Author(s), 2023. Published by Cambridge University Press. This is an Open Access article, distributed under the terms ofthe Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.  \n[https://doi.org/10.1017/dce.2023.5](https://doi.org/10.1017/dce.2023.5) Published online by Cambridge University Press  \ne9-2 Jessica C. Forsdyke et al.  \nadaptable—material properties including density, strength, durability, and appearance can be manipulated by adjusting the proportions and materials in the mix design.  \nConventional concrete mixes comprise three primary constituents: cement, water, and aggregate. Cement acts as the binder in concrete and is both economically and environmentally costly (Wassermannet al., 2009). Production of cement is responsible","cbCaikVCUb3KVZuO","https://ap.wps.com/l/cbCaikVCUb3KVZuO","pdf",1330261,1,18,"English","en",105,"# Introduction\n## Concrete variability and mix design challenge\n## Machine learning for property prediction\n## Carbonation and durability relevance\n## Performance-based vs prescriptive specification","[{\"question\":\"Why is designing lower-impact concrete mixes challenging?\",\"answer\":\"Natural variability in constituent materials and the large number of possible mix proportion combinations make reliable design difficult.\"},{\"question\":\"How does the proposed machine learning method improve prediction?\",\"answer\":\"It uses intermediate target variables together with their associated noise to predict a final target variable.\"},{\"question\":\"What concrete mixes were specified and how were they validated?\",\"answer\":\"One mix targets high resistance to carbonation, and another targets low environmental impact; both satisfy strength, density, and cost targets and are experimentally validated against model predictions.\"}]","Probabilistic selection and design of concrete using machine learning | PDF",1785679891,45,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"probabilistic-selection-and-design-of-concrete-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/probabilistic-selection-and-design-of-concrete-using-machine-learning/117833/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is designing lower-impact concrete mixes challenging?","Question",{"text":76,"@type":77},"Natural variability in constituent materials and the large number of possible mix proportion combinations make reliable design difficult.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed machine learning method improve prediction?",{"text":81,"@type":77},"It uses intermediate target variables together with their associated noise to predict a final target variable.",{"name":83,"@type":74,"acceptedAnswer":84},"What concrete mixes were specified and how were they validated?",{"text":85,"@type":77},"One mix targets high resistance to carbonation, and another targets low environmental impact; 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