Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition
ABSTRACTThis study focuses on the optimization of geopolymer composites, considering the parameters of composition and performance. The research explores the integration of algorithm-built hybrid implementations and a hybrid intelligent system to solve complex optimization problems in geopolymer com...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Natural Fibers |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2023.2293047 |
| _version_ | 1797387707311194112 |
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| author | Katarzyna Ewa Buczkowska Petr Louda Artem Sharko Oleksandr Sharko Dmitry Stepanchikov Ludek Jancik Piotr Los Kinga Plawecka Van Su Le |
| author_facet | Katarzyna Ewa Buczkowska Petr Louda Artem Sharko Oleksandr Sharko Dmitry Stepanchikov Ludek Jancik Piotr Los Kinga Plawecka Van Su Le |
| author_sort | Katarzyna Ewa Buczkowska |
| collection | DOAJ |
| description | ABSTRACTThis study focuses on the optimization of geopolymer composites, considering the parameters of composition and performance. The research explores the integration of algorithm-built hybrid implementations and a hybrid intelligent system to solve complex optimization problems in geopolymer composite materials. Firstly, an algorithm-built hybrid implementation is proposed, combining experimental results with various data processing methods. This approach enables the utilization of composite algorithms, offering several advantages, such as scalability and adaptability to different loads. The models developed in this study provide a flexible and extensible architecture, allowing for efficient problem-solving in optimization tasks. Secondly, a hybrid intelligent system is introduced, comprising statistical simulation models that combine different control and design problem-solving approaches. Markov chains are employed to address the quantitative aspects of loosely structured tasks and process performance evaluation. Criterion methods are utilized for quantitative conclusions, ensuring the optimal adaptation of the results from both applications. The research culminates in the identification of the optimal composition, denoted as G + FC + CFI, with specific weight content. This composition consists of cement, activator, fireclay, and carbon fiber I, with 100 g, 90 g, 100 g, and 2.5 g, respectively. The findings from this study provide valuable insights into the optimization of geopolymer composites, employing algorithm-built hybrid implementations and a hybrid intelligent system. The proposed approaches offer enhanced efficiency and accuracy in solving complex optimization problems in the field of geopolymer composite materials. The identified optimal composition demonstrates the potential for improving performance in composition and weight content. |
| first_indexed | 2024-03-08T22:29:57Z |
| format | Article |
| id | doaj.art-0299afc6368f41baae7d402d57ff796a |
| institution | Directory Open Access Journal |
| issn | 1544-0478 1544-046X |
| language | English |
| last_indexed | 2024-03-08T22:29:57Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj.art-0299afc6368f41baae7d402d57ff796a2023-12-18T06:05:28ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2024-12-0121110.1080/15440478.2023.2293047Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content CompositionKatarzyna Ewa Buczkowska0Petr Louda1Artem Sharko2Oleksandr Sharko3Dmitry Stepanchikov4Ludek Jancik5Piotr Los6Kinga Plawecka7Van Su Le8Department of Material Science, Faculty of Mechanical Engineering, Technical University of Liberec, Liberec, Czech RepublicFaculty of Mechanical Engineering, University of Kalisz, Kalisz, PolandDepartment of Material Science, Faculty of Mechanical Engineering, Technical University of Liberec, Liberec, Czech RepublicDepartment of Transport Technology and Mechanical Engineering, Kherson State Marine Academy, Kherson, UkraineDepartment of Energetics, Electrical Engineering and Physics, Kherson National Technical University, Kherson, UkraineDepartment of Power Engineering Equipment, Faculty of Mechanical Engineering, TU Liberec, Liberec, Czech RepublicDepartment of Material Science, Faculty of Mechanical Engineering, Technical University of Liberec, Liberec, Czech RepublicFaculty of Material Engineering and Physics, Cracow University of Technology, Cracow, PolandDepartment of Material Science, Faculty of Mechanical Engineering, Technical University of Liberec, Liberec, Czech RepublicABSTRACTThis study focuses on the optimization of geopolymer composites, considering the parameters of composition and performance. The research explores the integration of algorithm-built hybrid implementations and a hybrid intelligent system to solve complex optimization problems in geopolymer composite materials. Firstly, an algorithm-built hybrid implementation is proposed, combining experimental results with various data processing methods. This approach enables the utilization of composite algorithms, offering several advantages, such as scalability and adaptability to different loads. The models developed in this study provide a flexible and extensible architecture, allowing for efficient problem-solving in optimization tasks. Secondly, a hybrid intelligent system is introduced, comprising statistical simulation models that combine different control and design problem-solving approaches. Markov chains are employed to address the quantitative aspects of loosely structured tasks and process performance evaluation. Criterion methods are utilized for quantitative conclusions, ensuring the optimal adaptation of the results from both applications. The research culminates in the identification of the optimal composition, denoted as G + FC + CFI, with specific weight content. This composition consists of cement, activator, fireclay, and carbon fiber I, with 100 g, 90 g, 100 g, and 2.5 g, respectively. The findings from this study provide valuable insights into the optimization of geopolymer composites, employing algorithm-built hybrid implementations and a hybrid intelligent system. The proposed approaches offer enhanced efficiency and accuracy in solving complex optimization problems in the field of geopolymer composite materials. The identified optimal composition demonstrates the potential for improving performance in composition and weight content.https://www.tandfonline.com/doi/10.1080/15440478.2023.2293047Geopolymer compositesoptimizationalgorithm-built hybrid implementationhybrid intelligent systemcomposite algorithmsperformance evaluation |
| spellingShingle | Katarzyna Ewa Buczkowska Petr Louda Artem Sharko Oleksandr Sharko Dmitry Stepanchikov Ludek Jancik Piotr Los Kinga Plawecka Van Su Le Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition Journal of Natural Fibers Geopolymer composites optimization algorithm-built hybrid implementation hybrid intelligent system composite algorithms performance evaluation |
| title | Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition |
| title_full | Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition |
| title_fullStr | Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition |
| title_full_unstemmed | Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition |
| title_short | Maximizing Performance of Geopolymer Mortar: Optimizing Basalt and Carbon Fiber Content Composition |
| title_sort | maximizing performance of geopolymer mortar optimizing basalt and carbon fiber content composition |
| topic | Geopolymer composites optimization algorithm-built hybrid implementation hybrid intelligent system composite algorithms performance evaluation |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2023.2293047 |
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