[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120487-en":3,"doc-seo-120487-105":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":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},120487,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Design of concrete mixtures and prediction of their compressive strength using machine learning","The work explores machine learning and neural networks for predicting concrete compressive strength to improve accuracy and reliability in concrete mixture design and optimization. Rapid progress in algorithms and network architectures enables models to learn from expanding experimental datasets and represent complex links between input parameters and mechanical performance. A regression model is developed using compressive-strength results from mixtures with known composition from prior experiments and validated by testing machine-designed mixtures at 28 days for compressive strength.","Design of concrete mixtures and prediction of their compressive strength using machine learning  \nRadoslav Gandel1*, Jan Jerabek 1, Petr Cmiel2, and Oldrich Sucharda 1  \n1VSB – Technical University of Ostrava, Faculty of Civil Engineering, Department of Building Materials and Diagnostics of Structures, Ludvika Podeste 1875/17, 708 00 Ostrava-Poruba, Czech Republic  \n2 TESTSTAV, [spol. s r.o](spol. s r.o)., Františka Lyska 1599/6, 700 30 Ostrava – Belsky Les,Czech Republic  \nAbstract. The use of machine learning and neural networks in predicting the compressive strength of concrete promises to significantly improve the accuracy and reliability of models for the design and optimization of concrete mixtures. With rapid advances in this field, computational models will be able to handle even larger amounts of experimental data, increasing their ability to capture the complex relationships between input parameters and the mechanical properties of concrete. With the development of new neural network architectures and machine learning algorithms, it will be possible to create highly adaptive predictive models that can better respond to variability in concrete composition and production conditions, leading to more efficient and sustainable design in the construction industry. The submitted paper deals with the design of concrete mixtures and prediction of their compressive strength based on the compressive strength results of mixtures of known composition from other experiments using machine learning. Practical validation of the developed regression model will be carried out by testing the machine-designed mixtures for compressive  \nstrength after 28 days.  \n1 Introduction  \nConcrete is one of the most widely used building materials in the world due to its availability, properties and wide range of applications [1, 2]. Despite its widespread use, the proper design of concrete mixture is a challenge because its properties depend not only on the nature of the input materials and the production technology, but also on the maturation time of the mixture. In the construction industry, accurate prediction of the key properties of concrete is important not only for the safety and reliability of structures [3], but also for reducing costs and carbon footprint [4, 5] . The methods used to predict strength characteristics, such as experimental tests, are time and cost consuming. Therefore, many researches are currently exploring the use of modern, advanced methods such as machine learning [6-8] or deep learning using neural networks [9, 10] to predict concrete properties. Machine learning, as one of the most  \n* Corresponding author: [radoslav.gandel@vsb.cz](radoslav.gandel@vsb.cz)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nfundamental tools in the world of artificial intelligence, represents an innovative and relatively simple approach to solving civil engineering problems. Its main advantage, based on the analysis and processing of large amounts of data, is the ability to identify relationships and patterns obscure to conventional methods. In the practice of concrete mixture design, this means eliminating experimental testing while reducing financial costs and time, which is achieved by predicting the desired properties based on the composition of the concrete and its eventual subsequent optimization. Although machine learning is easier to understand compared to more advanced artificial intelligence methods, its effectiveness is largely influenced by several negative factors: the quality of the dataset, the time and cost associated with creating the model and the limited ability to handle complex relationships. Nevertheless, it has several advantages over deep learning, such as lower computational requirements orthe suitability of using eve","cbCaiofL1b5ovXDZ","https://ap.wps.com/l/cbCaiofL1b5ovXDZ","pdf",2782439,1,7,"English","en",105,"# Introduction\n# Experimental program\n## Training dataset and input materials\n## Prediction app environment and user interface\n# Model development and validation\n## Regression model testing at 28 days","[{\"question\":\"What is the paper’s main goal for using machine learning in concrete?\",\"answer\":\"To validate how accurately a developed machine learning regression model can predict concrete compressive strength for mixtures designed from data reported in other experiments.\"},{\"question\":\"What data and inputs are used to train the computational model?\",\"answer\":\"The training dataset contains more than 1000 compressive-strength values for cylinders, using concrete mixtures with variable composition including cement type I, slag, fly ash, water, aggregates, and a plasticizer, measured across ages from 1 to 365 days.\"},{\"question\":\"How is the developed model practically validated?\",\"answer\":\"The model-designed mixtures are produced and tested for compressive strength after 28 days to check the regression model’s predictive performance.\"}]","Design of concrete mixtures and prediction of their compressive strength using machine learning | 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is the paper’s main goal for using machine learning in concrete?","Question",{"text":75,"@type":76},"To validate how accurately a developed machine learning regression model can predict concrete compressive strength for mixtures designed from data reported in other experiments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and inputs are used to train the computational model?",{"text":80,"@type":76},"The training dataset contains more than 1000 compressive-strength values for cylinders, using concrete mixtures with variable composition including cement type I, slag, fly ash, water, aggregates, and a plasticizer, measured across ages from 1 to 365 days.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the developed model practically validated?",{"text":84,"@type":76},"The model-designed mixtures are produced and tested for compressive strength after 28 days to check the regression model’s predictive 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