[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-125468-105":59,"doc-detail-125468-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","analysis-of-strength-prediction-models-using-machine-learning-for-mongolian-fly-ash-concrete-artificial-intelligence-based-applications-in-engineering-science","Analysis of Strength Prediction Models using Machine Learning for Mongolian Fly Ash Concrete - Artificial Intelligence Based Applications in Engineering Science","","This paper investigates machine learning methods for predicting the compressive strength of Mongolian fly ash concrete. Four models—Multiple Linear Regression, Ridge Regression, Lasso Regression, and Decision Tree Regression—are trained and compared using experimental data alongside a UCI benchmark dataset. The Decision Tree model achieves the best performance (R²=0.95, RMSE=3.71 N/mm², MAPE=8.00%), outperforming linear approaches (R²≈0.57). The work targets Mongolian high-calcium Class C fly ash, whose CaO-rich composition yields different hydration kinetics and strength development patterns, supporting safer, more efficient local design.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/analysis-of-strength-prediction-models-using-machine-learning-for-mongolian-fly-ash-concrete-artificial-intelligence-based-applications-in-engineering-science/125468/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/analysis-of-strength-prediction-models-using-machine-learning-for-mongolian-fly-ash-concrete-artificial-intelligence-based-applications-in-engineering-science/125468.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-26","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which machine learning models are used to predict compressive strength in Mongolian fly ash concrete?","Question",{"text":112,"@type":113},"The study develops and compares Multiple Linear Regression, Ridge Regression, Lasso Regression, and Decision Tree Regression models.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the Decision Tree model perform compared with linear regression-based models?",{"text":117,"@type":113},"The Decision Tree model delivers the highest accuracy (R²=0.95, RMSE=3.71 N/mm², MAPE=8.00%), while linear regression-based approaches show much lower performance (R²≈0.57).",{"name":119,"@type":110,"acceptedAnswer":120},"Why does the paper emphasize Mongolian high-calcium (Class C) fly ash?",{"text":121,"@type":113},"Its elevated CaO content leads to hydration kinetics and strength development behavior that differ from the more commonly studied Class F fly ashes, making region-specific prediction necessary.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},125468,1785899176,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":52},962085564807,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Issue Topic: “Artificial Intelligence Based Applications in Engineering Science”  \nAnalysis of Strength Prediction Models using Machine Learning for Mongolian Fly Ash Concrete  \nBulgan Daalkhai  \nMongolian University of Science and Technology [E-mail:](E-mail: Bulgan.dkh@gmail.com)[ ](E-mail: Bulgan.dkh@gmail.com)[Bulgan.dkh@gmail.com](E-mail: Bulgan.dkh@gmail.com)  \nUranchimeg Tudevdagva 1,2  \n1Mongolian University of Science and Technology  \n2Citi University  \n[E-mail:](E-mail: uranchimeg@must.edu.mn)[ ](E-mail: uranchimeg@must.edu.mn)[uranchimeg@must.edu.mn](E-mail: uranchimeg@must.edu.mn)  \nAbstract — This paper investigates the application of machine learning (ML) methods for predicting the compressive strength of Mongolian fly ash concrete. Four predictive models—Multiple Linear Regression (MLR), Ridge Regression, Lasso Regression, and Decision Tree Regression—were developed and compared using both experimental data and a benchmark dataset from the UCI repository. The Decision Tree model demonstrated the highest predictive accuracy (R² = 0.95, RMSE = 3.71 N/mm², MAPE = 8.00%), outperforming all linear regression-based approaches (R² ≈ 0.57). The novelty of this work lies in its focus on Mongolian high-calcium (Class C) fly ash, whose elevated CaO content and distinct hydration kinetics differ from globally studied Class F fly ashes. These variations in chemical composition and mineral structure result in distinct patterns of strength development, highlighting the importance of developing region-specific prediction models to ensure structural safety, cost efficiency, and sustainability in Mongolian construction practices. The study further provides a foundation for integrating ML-based predictive tools into local engineering practice and design standards.  \nKeywords—Concrete, Fly Ash, Compressive Strength, Machine Learning, Decision Tree  \nI. INTRODUCTION  \nConcrete plays a central role in modern civil engineering, providing the backbone for infrastructure such as bridges, highways, buildings, and power facilities. Its compressive strength is the most critical parameter determining safety, durability, and long-term serviceability of structures [1] . With rapid urbanization and industrial development, demand for concrete has surged globally, and especially in emerging economies such as Mongolia. Mongolia is undergoing a construction boom driven by housing projects, infrastructure expansion, and policy reforms. This rapid growth intensifies the need for efficient quality control systems that can reliably  \npredict the mechanical properties of concrete before structural application [2] .  \nFly ash, a by-product of coal-fired power plants, has become a widely recognized supplementary cementitious material (SCM) . Globally, fly ash is known to enhance workability, durability, and sustainability of concrete by reducing cement consumption and associated CO₂ emissions [3] . In Mongolia, where approximately 93% of electricity is generated from coal, fly ash is produced in large quantities, predominantly of the high-calcium (Class C) type [2] . The chemical composition of Mongolian fly ash, particularly its elevated CaO content, distinguishes it from the low-calcium (Class F) fly ashes commonly reported in international literature [1] . This compositional difference results in distinct hydration kinetics and strength development behaviors, necessitating region-specific research.  \nTraditional prediction methods for compressive strength—such as empirical equations and regression-based models—have long been employed in construction practice [4] . While these approaches offer interpretability and simplicity, they struggle with non-linear material interactions, multicollinearity, and variability in local raw materials [2] . As a result, inaccurate predictions may occur: overestimating strength in the early stages can lead to premature demolding and structural failures, while underestimation in later stages may cause conservative mix de","cbCairH221Eh0Gak","https://ap.wps.com/l/cbCairH221Eh0Gak","pdf",317955,"English","# Introduction\n## Role of compressive strength in civil engineering\n## Fly ash as a supplementary cementitious material\n## Limitations of traditional prediction methods\n## AI/ML approaches for concrete property prediction\n# Study objectives and model comparison","[{\"question\":\"Which machine learning models are used to predict compressive strength in Mongolian fly ash concrete?\",\"answer\":\"The study develops and compares Multiple Linear Regression, Ridge Regression, Lasso Regression, and Decision Tree Regression models.\"},{\"question\":\"How does the Decision Tree model perform compared with linear regression-based models?\",\"answer\":\"The Decision Tree model delivers the highest accuracy (R²=0.95, RMSE=3.71 N/mm², MAPE=8.00%), while linear regression-based approaches show much lower performance (R²≈0.57).\"},{\"question\":\"Why does the paper emphasize Mongolian high-calcium (Class C) fly ash?\",\"answer\":\"Its elevated CaO content leads to hydration kinetics and strength development behavior that differ from the more commonly studied Class F fly ashes, making region-specific prediction necessary.\"}]","Analysis of Strength Prediction Models using Machine Learning for Mongolian Fly Ash Concrete - Artificial Intelligence Based Applications in Engineering Science | PDF"]