[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126950-en":3,"doc-seo-126950-105":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},126950,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improving Aggregate Abrasion Resistance Prediction via Micro-Deval Test Using Ensemble Machine Learning Techniques","Aggregate is the most extracted material from mines and is widely used in civil and construction projects, where abrasion resistance strongly affects pavement and concrete durability. The Micro-Deval abrasion test (MD) is a key laboratory method for evaluating aggregate resistance to mechanical abrasive actions under repeated impact. Limited datasets can reduce prediction accuracy, so the study applies ensemble machine learning to estimate MD abrasion values from diverse aggregate properties, achieving an R2 of 0.95 with stacking and identifying influential features.","Missouri University of Science and Technology  \nScholars' Mine  \n\n| Civil, Architectural and Environmental\u003Cbr>Engineering Faculty Research & Creative Works | Civil, Architectural and Environmental Engineering |\n| --- | --- |\n| 01 Jan 2024\u003Cbr>Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test using Ensemble Machine Learning Techniques\u003Cbr>Alireza Roshan\u003Cbr>Magdy Abdelrahman\u003Cbr>Missouri University of Science and Technology, [abdelrahmanm@mst.edu](abdelrahmanm@mst.edu)\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/civarc_enveng_facwork](https://scholarsmine.mst.edu/civarc_enveng_facwork)\u003Cbr> Part of the Civil Engineering Commons, and the Structural Engineering Commons |  |\n\nRecommended Citation  \nA. Roshan and M. Abdelrahman, \"Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test using Ensemble Machine Learning Techniques,\" Engineering Journal, vol. 28, no. 3, pp. 15-24, Chulalongkorn University, Jan 2024.  \nThe definitive version is available at [https://doi.org/10.4186/ej.2024.28.3.15](https://doi.org/10.4186/ej.2024.28.3.15)  \nThis Article-Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Civil, Architectural and Environmental Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nArticle  \nImproving Aggregate Abrasion Resistance Prediction via Micro-Deval Test Using Ensemble Machine Learning Techniques  \nAlireza Roshan1,a,* and Magdy Abdelrahman2,b  \n1 Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA  \n2 Missouri Asphalt Pavement Association (MAPA) Endowed Professor, Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA E-mail: a,*[alireza.roshan@mst.edu](alireza.roshan@mst.edu) (Corresponding author), [b](bm.abdelrahman@mst.edu)[m.abdelrahman@mst.edu](bm.abdelrahman@mst.edu)  \n[Abstract.](Abstract. Aggregate is the most extracted material from the world)[ Aggregate is the most extracted material from the world](Abstract. Aggregate is the most extracted material from the world)'[s](s) mines and widely used in civil and construction projects. The Micro-Deval abrasion test (MD) is one of the most important tests that provides characteristics of crushed aggregates that show their resistance against mechanical abrasive factors such as repeated impact loading. The impact of various factors on abrasive resistance properties of aggregates has led researchers to seek correlations, often focusing on limited data samples, leading to reduced accuracy. This study employs machine learning (ML) methods to predict MD abrasion values, considering diverse aggregate properties. Various ensemble ML methods were applied, revealing the exceptional performance of the stacking model, which achieved an R2 score of 0.95 in predicting aggregate abrasion resistance. The feature importance analysis highlights the influence offactors such as Magnesium Sulfate Soundness (MSS), Water Absorption (ABS), and Los Angeles Abrasion (LAA) on aggregate abrasion values, suggesting that the use of multiple test methods could yield a more dependable assessment of aggregate durability.  \nKeywords: Aggregate abrasion resistance and durability, micro-Deval abrasion test, friction assessment, ensemble machine learning.  \nENGINEERING JOURNAL Volume 28 Issue 3 Received 16 November 2023  \nAccepted 15 March 2024 Published 31 March 2024 Online at [https://engj.org/](https://engj.org/)  \n[DOI:10.4186/ej.2024.28.3.15](DOI:10.4186/ej.2024.28.3.15)  \n1. Introduction  \nAggregates, a blend of fine and coarse particles includin","cbCaig5v1xcMdqZw","https://ap.wps.com/l/cbCaig5v1xcMdqZw","pdf",1089268,1,11,"English","en",105,"# Introduction\n## Aggregate materials and their role in civil infrastructure\n## Pavement friction and the need for abrasion/durability evaluation\n## Laboratory tests: Micro-Deval and Los Angeles abrasion\n# Study approach (machine learning prediction of MD abrasion values)\n## Ensemble methods and model performance\n## Feature importance and influential aggregate properties","[{\"question\":\"What is the goal of the study regarding the Micro-Deval test?\",\"answer\":\"To predict Micro-Deval abrasion (MD) values using machine learning models based on diverse aggregate properties.\"},{\"question\":\"Which ensemble machine learning approach performed best?\",\"answer\":\"The stacking model showed the exceptional performance, reaching an R2 score of 0.95 for predicting aggregate abrasion resistance.\"},{\"question\":\"Which aggregate properties were most influential for abrasion values?\",\"answer\":\"Feature importance analysis highlighted the impact of Magnesium Sulfate Soundness (MSS), Water Absorption (ABS), and Los Angeles Abrasion (LAA).\"}]","Improving Aggregate Abrasion Resistance Prediction via Micro-Deval Test Using Ensemble Machine Learning Techniques | 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is the goal of the study regarding the Micro-Deval test?","Question",{"text":75,"@type":76},"To predict Micro-Deval abrasion (MD) values using machine learning models based on diverse aggregate properties.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which ensemble machine learning approach performed best?",{"text":80,"@type":76},"The stacking model showed the exceptional performance, reaching an R2 score of 0.95 for predicting aggregate abrasion resistance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which aggregate properties were most influential for abrasion values?",{"text":84,"@type":76},"Feature importance analysis highlighted the impact of Magnesium Sulfate Soundness (MSS), Water Absorption (ABS), and Los Angeles Abrasion 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