[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119120-en":3,"doc-seo-119120-105":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},119120,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Concrete aging factor prediction using machine learning","Accurate prediction of concrete aging factor is pivotal for performance-based durability design of reinforced concrete. The study presents a machine-learning workflow using seven algorithms—Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, CatBoost, and LightGBM. A dataset of 130 instances describes cement type, cement content, pozzolan type, pozzolan content, w/b ratio, exposure condition, and concrete age. Models are trained in five scenarios and evaluated by MAE, MSE, RMSE, and R², showing stronger performance when engineered features are used, with LightGBM achieving the best results in Scenario III.","This is a self-archived version of the original publication  \nThe self-archived version is a publisher’s pdf of the original publication. Please note that the self-archived version may differ from the original in pagination, typographical details and illustrations.  \nTo cite this, use the original publication:  \nTaffese, W. Z. , Wally, G. B. , Magalhães, F. C. , & Espinosa-Leal, L. (2024) . Concrete aging factor prediction using machine learning. Materials Today Communications, 40(august) , 109527.  \nDOI: 10. 1016/j. mtcomm.2024.109527  \nAll material supplied via Arcada’s self-archived publications collection in Theseus repository is protected by copyright laws. Use of all or part of any of the repository collections is permitted only for personal non-commercial, research or educational purposes in digital and print form. You must obtain permission for any other use.  \nThis is a self-archived version of the original publication  \nThe self-archived version is a publisher’s pdf of the original publication. Please note that the self-archived version may differ from the original in pagination, typographical details and illustrations.  \nTo cite this, use the original publication:  \nTidskrift:  \nTaffese, W. Z. , Wally, G. B. , Magalhães, F. C. , & Espinosa-Leal, L. (2024) . Concrete aging factor prediction using machine learning. Materials Today Communications, 40(august) , 109527.  \nDOI: 10. 1016/j. mtcomm.2024.109527  \nAll material supplied via Arcada’s self-archived publications collection in Theseus repository is protected by copyright laws. Use of all or part of any of the repository collections is permitted only for personal non-commercial, research or educational purposes in digital and print form. You must obtain permission for any other use.  \nMaterials Today Communications 40 (2024) 109527  \nContents lists available at ScienceDirect  \nMaterials Today Communications  \njournal [homepage: www.elsevier.com/locate/mtcomm](homepage: www.elsevier.com/locate/mtcomm)  \n| Concrete aging factor prediction using machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Woubishet Zewdu Taffesea, *, Gustavo Bosel Wallyb, c, F´abio Costa Magalh˜aes c, Leonardo Espinosa-Leala\u003Cbr>a School of Research and Graduate Studies, Arcada University of Applied Sciences, Helsinki, Finland b Catholic University of Pelotas, Pelotas, RS, Brazil\u003Cbr>c Structures and Building Materials Laboratory (LEMCC), Federal Institute of Rio Grande do Sul, Rio Grande, RS, Brazil |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Concrete aging factor Chloride diffusion coefficient Machine learning Ensemble Methods Concrete durability design |  | Accurate prediction of concrete aging factor is pivotal for performance-based durability reinforced concrete design. This study introduces an innovative method leveraging machine learning techniques, employing seven algorithms: Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, CatBoost, and LightGBM. The dataset comprises 130 instances with seven input features describing cement type, cement content, pozzolan type, pozzolan content, w/b ratio, exposure condition, and age of concrete. Seventy models were trained across five scenarios, categorized into two groups: Group I using all features of the raw dataset, and Group II incorporating engineered features. Model performance, assessed by mean-absolute error (MAE), mean-square error (MSE), root-mean-square error (RMSE), and coefficient of determination (R2), reveals superior performance in Group II compared to Group I. Notably, the LightGBM algorithm in Scenario III outperforms all models with a remarkable MAE of 0.110, MSE of 0.018, RMSE of 0.133, and R2 of 0.818. Subsequently, models from Scenarios V and IV exhibit strong performance. The implemented machine learning models demonstrate notable generalizability, effectively capturing feature interrelations without the need for resource-intensive experimental testing. |  |\n\n1. Introduction  \nRei","cbCaigySUdtvZALu","https://ap.wps.com/l/cbCaigySUdtvZALu","pdf",8809839,1,18,"English","en",105,"# Introduction\n## Background: durability and chloride penetration\n## Aging factor concept and influencing parameters\n## Modeling objective: service-life prediction","[{\"question\":\"What does the concrete aging factor represent in chloride-durability modeling?\",\"answer\":\"The concrete aging factor accounts for how chloride diffusivity changes over time, reflecting refinement of the pore structure during cement hydration and potential pozzolanic reactions.\"},{\"question\":\"Which machine-learning algorithms are used to predict the aging factor?\",\"answer\":\"The study employs Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, CatBoost, and LightGBM.\"},{\"question\":\"How is model performance evaluated, and which approach performs best?\",\"answer\":\"Performance is assessed using MAE, MSE, RMSE, and R². Engineered-feature scenarios perform better overall, and LightGBM in Scenario III achieves the strongest results (MAE 0.110, R² 0.818).\"}]","Concrete aging factor prediction using machine learning | PDF",1785722492,45,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"concrete-aging-factor-prediction-using-machine-learning","",{"@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/concrete-aging-factor-prediction-using-machine-learning/119120/",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},"What does the concrete aging factor represent in chloride-durability modeling?","Question",{"text":75,"@type":76},"The concrete aging factor accounts for how chloride diffusivity changes over time, reflecting refinement of the pore structure during cement hydration and potential pozzolanic reactions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning algorithms are used to predict the aging factor?",{"text":80,"@type":76},"The study employs Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, CatBoost, and LightGBM.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated, and which approach performs best?",{"text":84,"@type":76},"Performance is assessed using MAE, MSE, RMSE, and R². Engineered-feature scenarios perform better overall, and LightGBM in Scenario III achieves the strongest results (MAE 0.110, R² 0.818).","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]