[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123923-en":3,"doc-seo-123923-105":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},123923,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete - Article","Geopolymer concrete (GPC) offers environmentally friendly attributes compared with ordinary Portland cement, yet accurate prediction of the CO2 footprint of its constituent materials remains limited. This study develops a CO2 footprint prediction approach for ground granulated blast-furnace slag (GGBFS)-based GPC using advanced AI, combining multiple machine learning models through stacking ensembles. Key drivers of CO2 emissions are analyzed, including superplasticizer content, initial curing temperature, and NaOH (dry) content, with attention to optimizing mixture design via NaOH ratios. After evaluating 12 models, the stacking configuration labeled M4 is selected as the most effective ensemble model, supported by metrics such as MSE 88.8, RMSE 9.42, and multiple performance scores around 0.95, with additional validation using Euclidean distance and Taylor diagrams.","Journal of Cleaner Production 472 (2024) 143463  \nContents lists available at ScienceDirect Journal of Cleaner Production  \njournal [homepage: www.elsevier.com/locate/jclepro](homepage: www.elsevier.com/locate/jclepro)  \n| Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete\u003Cbr>Amin Al-Fakiha,b,****, Ebrahim Al-wajihc,***, Radhwan A.A. Salehd,e,**, Imrose B. Muhitf,*\u003Cbr>a Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia\u003Cbr>b Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia c University Schools, King Fahd University of Petroleum & Minerals, Dhahran, 34463, Saudi Arabia\u003Cbr>d VARPA Group, CITIC, INIBIC, University ofA Coru˜na, A Coru˜na, Spain\u003Cbr>e Mechatronics Engineering Department, Kocaeli University, Umuttepe, Izmit, 41001, Kocaeli, Turkiye\u003Cbr>f School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, TS1 3BX, United Kingdom |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Handling Editor: Jian Zuo |  | While geopolymer concrete (GPC) has gained popularity for its environmentally friendly attributes compared to ordinary Portland cement, the absence of a prediction model for the carbon footprint of its constituents presents challenges for optimization within the evolving concrete industry. This study offers a thorough CO2 footprint prediction for ground granulated blast-furnace slag (GGBFS)-based GPC, utilizing advanced AI techniques, including a combination of machine learning models and stacking ensembles. This research statistically examines crucial parameters responsible for CO2 emissions in GGBFS-based GPC production, identifying factors like superplasticizer content, initial curing temperature, and NaOH (dry) content as significant contributors. Emphasizing sustainability, the study advocates optimizing concrete mixtures by considering factors like the NaOH ratio to other activator materials. After rigorously evaluating 12 machine learning models, including ensemble techniques, this study identified M4—a stacking model of Support Vector Regression (SVR) and Neural Network (NN)—as weak models, and Decision Tree (DT) as a meta-model, as the most effective ensemble model for predicting CO2 footprints. The choice of M4 is supported by various performance metrics such as the lowest Mean Squared Error of 88.8 and Root Mean Squared Error of 9.42, alongside the highest R2, Adjusted R2, and Explained Variance scores, all approximately 0.95. Additional analyses, such as Euclidean distance and Taylor diagrams, further substantiate the selection of M4. The findings have practical implications for sustainable and cleaner concrete production, enabling businesses to optimize the CO2 footprint of GGBFS-based GPC. |\n| Keywords:\u003Cbr>CO2 footprint Geopolymer concrete Industrial waste materials GGBFS\u003Cbr>Sustainability\u003Cbr>Cleaner production Ensemble machine learning |  |  |\n\n1. Introduction  \nIt is well known that concrete is widely used in construction due to its numerous advantages. However, the main ingredient in producing concrete is ordinary Portland cement (OPC) where its manufacturing contributes primarily about 7% to the global CO2 emission (Benhelalet al., 2013; He et al., 2019; Gartner, 2004). To create more sustainable concrete and to reduce the CO2 emission associated with the production of OPC, researchers have conducted studies to replace OPC partially or fully with alternative materials called supplementary cementitious  \nmaterials (SCMs) such as ground granulated blast-furnace slag (GGBS) and fly ash (FA), which are used as precursors (Chand, 2021; Chandet al., 2021; Al-Fakih et al., 2023; Muhit et al., 2018). These materials not only improve the mechanical and rheological properties of concrete but also contribute to reducing CO2 emissions","cbCaijjBrhSyWZhI","https://ap.wps.com/l/cbCaijjBrhSyWZhI","pdf",14288499,1,19,"English","en",105,"# Introduction\n## Background: OPC CO2 and SCM substitution\n## Research gap in CO2 footprint prediction\n## Study aim and methodology overview","[{\"question\":\"Why is predicting the CO2 footprint of GGBFS-based geopolymer concrete important?\",\"answer\":\"The study addresses the optimization challenge caused by the lack of robust predictive models for CO2 emissions from GPC constituents. Accurate prediction supports cleaner, more sustainable concrete design.\"},{\"question\":\"Which parameters are identified as significant contributors to CO2 emissions?\",\"answer\":\"The research highlights superplasticizer content, initial curing temperature, and NaOH (dry) content as significant factors. It also emphasizes optimizing mixture design through NaOH ratios relative to other activators.\"},{\"question\":\"What ensemble model is reported as the most effective for CO2 footprint prediction?\",\"answer\":\"The stacking ensemble M4, combining Support Vector Regression (SVR) and Neural Network (NN) with a Decision Tree meta-model, is selected as the best-performing approach. It achieves strong accuracy across multiple metrics and validation plots.\"}]","Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete - Article | PDF",1785819266,48,{"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},"ensemble-machine-learning-models-for-predicting-the-co2-footprint-of-ggbfs-based-geopolymer-concrete-article","",{"@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/ensemble-machine-learning-models-for-predicting-the-co2-footprint-of-ggbfs-based-geopolymer-concrete-article/123923/",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","2026-08-04",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 predicting the CO2 footprint of GGBFS-based geopolymer concrete important?","Question",{"text":76,"@type":77},"The study addresses the optimization challenge caused by the lack of robust predictive models for CO2 emissions from GPC constituents. Accurate prediction supports cleaner, more sustainable concrete design.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which parameters are identified as significant contributors to CO2 emissions?",{"text":81,"@type":77},"The research highlights superplasticizer content, initial curing temperature, and NaOH (dry) content as significant factors. It also emphasizes optimizing mixture design through NaOH ratios relative to other activators.",{"name":83,"@type":74,"acceptedAnswer":84},"What ensemble model is reported as the most effective for CO2 footprint prediction?",{"text":85,"@type":77},"The stacking ensemble M4, combining Support Vector Regression (SVR) and Neural Network (NN) with a Decision Tree meta-model, is selected as the best-performing approach. 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