Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model
In this paper, the aim is to propose a new model to obtain the mechanical properties of sand/glass polymeric concrete including modulus of elasticity and the ultimate tensile stress. The neural network soft computation, support vector machine (SVM), and active learning method (ALM) that is a fuzzy r...
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| Format: | Article |
| Language: | English |
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Semnan University
2021-11-01
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| Series: | Mechanics of Advanced Composite Structures |
| Subjects: | |
| Online Access: | https://macs.semnan.ac.ir/article_4713_9dd9ca0ad672d83272fe734c22ce325f.pdf |
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| author | Mahmood Heshmati Sajad Hayati Saeed Javanmiri Mohammad Javadian |
| author_facet | Mahmood Heshmati Sajad Hayati Saeed Javanmiri Mohammad Javadian |
| author_sort | Mahmood Heshmati |
| collection | DOAJ |
| description | In this paper, the aim is to propose a new model to obtain the mechanical properties of sand/glass polymeric concrete including modulus of elasticity and the ultimate tensile stress. The neural network soft computation, support vector machine (SVM), and active learning method (ALM) that is a fuzzy regression model are all used to construct a simple and reliable model based on experimental datasets. The experimental data are obtained via the tensile and bending tests of sand/glass reinforced polymer with different weight percentages of sand and chopped glass fibers. The extracted results are then used for training and testing of the neural network models. Two different types of neural networks including feed-forward neural network (FFNN) and radial basis neural network (RBNN) are employed for connecting the properties of the sand/glass reinforced polymer to the properties of the resin and weight percentages of sand and glass fibers. Besides the neural network models, the SVM and ALM models are applied to the problem. The models are compared with each other with respect to the statistical indices for both train and test datasets. Finally, to obtain the properties of the sand/glass reinforced polymer, the most accurate model is presented as an FFNN model. |
| format | Article |
| id | doaj-art-99486a9b47f1488eae9a419dc46d13f3 |
| institution | Kabale University |
| issn | 2423-4826 2423-7043 |
| language | English |
| publishDate | 2021-11-01 |
| publisher | Semnan University |
| record_format | Article |
| series | Mechanics of Advanced Composite Structures |
| spelling | doaj-art-99486a9b47f1488eae9a419dc46d13f32024-12-16T21:03:20ZengSemnan UniversityMechanics of Advanced Composite Structures2423-48262423-70432021-11-018224526810.22075/macs.2020.20699.12684713Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New ModelMahmood Heshmati0Sajad Hayati1Saeed Javanmiri2Mohammad Javadian3Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, 67156-85420, IranDepartment of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, 67156-85420, IranDepartment of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, 67156-85420, IranDepartment of Computer Engineering, Kermanshah University of Technology, Kermanshah, 67156-85420, IranIn this paper, the aim is to propose a new model to obtain the mechanical properties of sand/glass polymeric concrete including modulus of elasticity and the ultimate tensile stress. The neural network soft computation, support vector machine (SVM), and active learning method (ALM) that is a fuzzy regression model are all used to construct a simple and reliable model based on experimental datasets. The experimental data are obtained via the tensile and bending tests of sand/glass reinforced polymer with different weight percentages of sand and chopped glass fibers. The extracted results are then used for training and testing of the neural network models. Two different types of neural networks including feed-forward neural network (FFNN) and radial basis neural network (RBNN) are employed for connecting the properties of the sand/glass reinforced polymer to the properties of the resin and weight percentages of sand and glass fibers. Besides the neural network models, the SVM and ALM models are applied to the problem. The models are compared with each other with respect to the statistical indices for both train and test datasets. Finally, to obtain the properties of the sand/glass reinforced polymer, the most accurate model is presented as an FFNN model.https://macs.semnan.ac.ir/article_4713_9dd9ca0ad672d83272fe734c22ce325f.pdfreinforced polymermechanical propertiesa new modelactive learning methodneural networks |
| spellingShingle | Mahmood Heshmati Sajad Hayati Saeed Javanmiri Mohammad Javadian Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model Mechanics of Advanced Composite Structures reinforced polymer mechanical properties a new model active learning method neural networks |
| title | Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model |
| title_full | Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model |
| title_fullStr | Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model |
| title_full_unstemmed | Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model |
| title_short | Artificial Intelligence Method for Predicting Mechanical Properties of Sand/Glass Reinforced Polymer: a New Model |
| title_sort | artificial intelligence method for predicting mechanical properties of sand glass reinforced polymer a new model |
| topic | reinforced polymer mechanical properties a new model active learning method neural networks |
| url | https://macs.semnan.ac.ir/article_4713_9dd9ca0ad672d83272fe734c22ce325f.pdf |
| work_keys_str_mv | AT mahmoodheshmati artificialintelligencemethodforpredictingmechanicalpropertiesofsandglassreinforcedpolymeranewmodel AT sajadhayati artificialintelligencemethodforpredictingmechanicalpropertiesofsandglassreinforcedpolymeranewmodel AT saeedjavanmiri artificialintelligencemethodforpredictingmechanicalpropertiesofsandglassreinforcedpolymeranewmodel AT mohammadjavadian artificialintelligencemethodforpredictingmechanicalpropertiesofsandglassreinforcedpolymeranewmodel |