[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126439-en":3,"doc-seo-126439-105":29,"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":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126439,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Improving Concrete Mix Design Predictions with Machine Learning Algorithms","The construction industry significantly drives environmental pollution and climate change, largely through concrete production. Cement, which represents only 10%–15% of mix mass, can contribute up to 90% of greenhouse gas emissions, while production is inherently complex and uncertain. This work applies machine learning to predict concrete mechanical properties, using mix design data and weather-related inputs from batching plants. Regression and classification models target resistance and workability to reduce cement consumption without compromising safety.","POLITECNICO DI TORINO Repository ISTITUZIONALE  \nImproving Concrete Mix Design Predictions with Machine Learning Algorithms  \nOriginal  \nImproving Concrete Mix Design Predictions with Machine Learning Algorithms / Melchiorre, Jonathan; Anerdi, Costanza; Randazzo, Vincenzo; Marano, Giuseppe Carlo. -ELETTRONICO. - (2025), pp. 1-7. ( International Joint Conference on Neural Networks (IJCNN) Roma (Italy) 30 June-5 July 2025) [10 . 1109/ijcnn64981 .2025. 11228960] .  \nAvailability:  \nThis version is available at: 11583/3005221 since: 2025-11-17T17:16:41Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/ijcnn64981.2025.11228960  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n21 February 2026  \nImproving Concrete Mix Design Predictions with Machine Learning Algorithms  \nAbstract—The construction industry has a crucial impact on environmental pollution and on the phenomenon of climate change.  \nOne of the most impactful activities in this sector, is the concrete production. The concrete industry alone is a significant source of CO2 emissions, with concrete being the most widely used construction material. Concrete is a material mainly composed of cement, water, aggregates and additives. Despite the percentage of cement in concrete mixtures is comprised between 10% and 15% by weight, this component contributes up to 90% of the associated greenhouse gas emissions.  \nThe process involved in concrete production is complex and involve many uncertainties. To compensate for these unknownsand ensure structural safety, cement is frequently added in excess to improve mechanical performance. However, reducing the cement content in concrete formulations can have considerable environmental benefits. Therefore, enhancing the precision of predicting concrete’s mechanical characteristics is essential for lowering cement consumption without compromising safety.  \nIn this paper, the application of machine learning techniques to forecast the mechanical properties of concrete is presented. The main goal is to use the concrete mix design data to accurately predict the properties related to the resistance and the workability of concrete. To this extent, a dataset comprising roughly 1100 mix designs was utilized, combined with weather-related data from concrete batching plants. The study emphasizes regression and classification models to predict concrete properties, taking into account environmental and meteorological factors at the production site.  \nIndex Terms—Concrete, Mix Design, Machine Learning, Sustainability, Cost reduction, Artificial Intelligence  \nI. INTRODUCTION  \nThe construction industry is a crucial sector with both significant economic and environmental implications. It contributes approximately 10% to the global gross domestic product (GDP) [1], making it one of the largest industries worldwide. However, its rapid expansion, especially in developing regions, raises concerns about its long-term sustainability [2] .  \nOne of the most pressing issues associated with this sector is its environmental footprint. The industry is responsible for nearly 30% of global greenhouse gas emissions [3] and consumes approximately 50% of the world’s raw materials and 40% of total energy resources [4] . As the demand for infrastructure and urban development grows, mitigating these environmental effects becomes increasingly urgent.  \nIn recent years, the entire construction industry has been increasingly adopti","cbCaikSHB8XwHAza","https://ap.wps.com/l/cbCaikSHB8XwHAza","pdf",3246980,1,"English","en",105,"# Abstract\n## Introduction\n## Problem Context and Sustainability Goals\n## Concrete Production and Cement Emissions\n## Machine Learning for Property Prediction","[{\"question\":\"Why is predicting concrete mechanical properties important in this research?\",\"answer\":\"More accurate predictions support reducing cement content in mixes, improving sustainability while maintaining structural safety.\"},{\"question\":\"What data sources are used to train the machine learning models?\",\"answer\":\"The study uses a dataset of roughly 1100 concrete mix designs combined with weather-related data from concrete batching plants.\"},{\"question\":\"Which concrete properties are targeted for prediction?\",\"answer\":\"The models focus on resistance and workability-related properties, accounting for environmental and meteorological factors at the production site.\"}]","Improving Concrete Mix Design Predictions with Machine Learning Algorithms | 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