{"title":"Artificial Intelligence in Materials Science and Modern Concrete Technologies: Analysis of Possibilities and Prospects","authors":"V. A. Poluektova, M. A. Poluektov","doi":"10.1134/S2075113324700783","DOIUrl":null,"url":null,"abstract":"<p><b>Abstract</b>—An analysis of current trends and opportunities for the application of artificial intelligence (AI) in materials science and concrete technology, including 3D printing in construction, is presented. The key role of AI in predicting material properties, developing new materials, and quality control is highlighted. By analyzing large volumes of data collected from numerous studies, AI can suggest optimal parameters to achieve desired material properties, thereby reducing costs and increasing production efficiency. Existing rheological models, such as the Bingham–Shvedov model or the Herschel–Bulkley model, describe material behavior based on specific equations and parameters. These models can be useful in predicting concrete properties, especially when data on its component composition is available. However, these models may be limited in their predictive accuracy, particularly for nonstandard or novel materials. It has been found that machine learning and neural networks have the potential to provide accurate predictions of rheological and physicomechanical properties of concrete materials, considering multiple parameters that influence material characteristics, including chemical and mineralogical composition, as well as structural features. The combination of experimental data and AI can successfully optimize compositions and properties during production, reducing costs and research/testing time, and opening new opportunities for researchers and engineers in the field of materials science. Machine-learning algorithms such as XGBoost, LightGBM, Catboost, and NGBoost demonstrate high predictive accuracy and have become powerful tools in the design of concrete compositions and innovative technologies. The analysis of Shapley additive explanations allows us to understand which parameters of a concrete mixture have the greatest influence on its characteristics.</p>","PeriodicalId":586,"journal":{"name":"Inorganic Materials: Applied Research","volume":"15 5","pages":"1187 - 1198"},"PeriodicalIF":0.5000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inorganic Materials: Applied Research","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1134/S2075113324700783","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract—An analysis of current trends and opportunities for the application of artificial intelligence (AI) in materials science and concrete technology, including 3D printing in construction, is presented. The key role of AI in predicting material properties, developing new materials, and quality control is highlighted. By analyzing large volumes of data collected from numerous studies, AI can suggest optimal parameters to achieve desired material properties, thereby reducing costs and increasing production efficiency. Existing rheological models, such as the Bingham–Shvedov model or the Herschel–Bulkley model, describe material behavior based on specific equations and parameters. These models can be useful in predicting concrete properties, especially when data on its component composition is available. However, these models may be limited in their predictive accuracy, particularly for nonstandard or novel materials. It has been found that machine learning and neural networks have the potential to provide accurate predictions of rheological and physicomechanical properties of concrete materials, considering multiple parameters that influence material characteristics, including chemical and mineralogical composition, as well as structural features. The combination of experimental data and AI can successfully optimize compositions and properties during production, reducing costs and research/testing time, and opening new opportunities for researchers and engineers in the field of materials science. Machine-learning algorithms such as XGBoost, LightGBM, Catboost, and NGBoost demonstrate high predictive accuracy and have become powerful tools in the design of concrete compositions and innovative technologies. The analysis of Shapley additive explanations allows us to understand which parameters of a concrete mixture have the greatest influence on its characteristics.
期刊介绍：
Inorganic Materials: Applied Research contains translations of research articles devoted to applied aspects of inorganic materials. Best articles are selected from four Russian periodicals: Materialovedenie, Perspektivnye Materialy, Fizika i Khimiya Obrabotki Materialov, and Voprosy Materialovedeniya and translated into English. The journal reports recent achievements in materials science: physical and chemical bases of materials science; effects of synergism in composite materials; computer simulations; creation of new materials (including carbon-based materials and ceramics, semiconductors, superconductors, composite materials, polymers, materials for nuclear engineering, materials for aircraft and space engineering, materials for quantum electronics, materials for electronics and optoelectronics, materials for nuclear and thermonuclear power engineering, radiation-hardened materials, materials for use in medicine, etc.); analytical techniques; structure–property relationships; nanostructures and nanotechnologies; advanced technologies; use of hydrogen in structural materials; and economic and environmental issues. The journal also considers engineering issues of materials processing with plasma, high-gradient crystallization, laser technology, and ultrasonic technology. Currently the journal does not accept direct submissions, but submissions to one of the source journals is possible.
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