Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers
The use of natural fibers as a reinforcing product in the production of compressed earth blocks can be considered as an effective means for the environment and savings. This study presents a prediction a model- and simulation-based approach using artificial neural networks (ANN) to predict tensile...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Viçosa (UFV)
2023-07-01
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| Series: | The Journal of Engineering and Exact Sciences |
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| Online Access: | https://periodicos.ufv.br/jcec/article/view/15910 |
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| author | Bentegri Houcine Rabehi Mohamed Kherfane Samir Boukansous Sarra |
| author_facet | Bentegri Houcine Rabehi Mohamed Kherfane Samir Boukansous Sarra |
| author_sort | Bentegri Houcine |
| collection | DOAJ |
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The use of natural fibers as a reinforcing product in the production of compressed earth blocks can be considered as an effective means for the environment and savings. This study presents a prediction a model- and simulation-based approach using artificial neural networks (ANN) to predict tensile strength and compressive strength. of earth-friendly concrete containing different types of natural fibers. A group of data with eight influencing characteristics; cement, fiber, sand, fiber length, fiber tensile strength, clay, silt, age used for model formation and validation were collected from the literature. The output was compressive strength and tensile strength. The combination of root mean square propagation and stochastic propagation gradient descent with the momentum method is used to train the ANN. Using various validation criteria such as coefficient of determination (R), root mean squared error (RMSE) and mean absolute error (MAE), the ANN model was validated and compared to two machine learning (ML) Random Forest (RF) techniques and Multilayer Perceptron (MLP). A sensitivity analysis was also performed to validate the robustness and stability of these models. The experimental results showed that the ANN model performed better than other models and, therefore, it can be used as a suitable approach to predict the compressive strength of environmentally friendly earth concrete.
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| format | Article |
| id | doaj-art-f57096663fb948ee82be0e3a1a1db999 |
| institution | Kabale University |
| issn | 2527-1075 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Universidade Federal de Viçosa (UFV) |
| record_format | Article |
| series | The Journal of Engineering and Exact Sciences |
| spelling | doaj-art-f57096663fb948ee82be0e3a1a1db9992025-02-02T19:54:58ZengUniversidade Federal de Viçosa (UFV)The Journal of Engineering and Exact Sciences2527-10752023-07-019410.18540/jcecvl9iss4pp15910-01eArtificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibersBentegri Houcine0Rabehi Mohamed 1Kherfane Samir2Boukansous Sarra3Civil Engineering and Sustainable Development Laboratory, Faculty of Sciences and Technology, Ziane Achour University of Djelfa, 17000, Algeria Civil Engineering and Sustainable Development Laboratory, Faculty of Sciences and Technology, Ziane Achour University of Djelfa, 17000, Algeria Civil Engineering and Sustainable Development Laboratory, Faculty of Sciences and Technology, Ziane Achour University of Djelfa, 17000, Algeria School Of Computer Science And Information Engineering, Zhejiang Gongshan University,Hagzhou310018, China, The use of natural fibers as a reinforcing product in the production of compressed earth blocks can be considered as an effective means for the environment and savings. This study presents a prediction a model- and simulation-based approach using artificial neural networks (ANN) to predict tensile strength and compressive strength. of earth-friendly concrete containing different types of natural fibers. A group of data with eight influencing characteristics; cement, fiber, sand, fiber length, fiber tensile strength, clay, silt, age used for model formation and validation were collected from the literature. The output was compressive strength and tensile strength. The combination of root mean square propagation and stochastic propagation gradient descent with the momentum method is used to train the ANN. Using various validation criteria such as coefficient of determination (R), root mean squared error (RMSE) and mean absolute error (MAE), the ANN model was validated and compared to two machine learning (ML) Random Forest (RF) techniques and Multilayer Perceptron (MLP). A sensitivity analysis was also performed to validate the robustness and stability of these models. The experimental results showed that the ANN model performed better than other models and, therefore, it can be used as a suitable approach to predict the compressive strength of environmentally friendly earth concrete. https://periodicos.ufv.br/jcec/article/view/15910Compressed earth block .Artificial neural networks .Fibers. Cement. Prediction. |
| spellingShingle | Bentegri Houcine Rabehi Mohamed Kherfane Samir Boukansous Sarra Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers The Journal of Engineering and Exact Sciences Compressed earth block .Artificial neural networks .Fibers. Cement. Prediction. |
| title | Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers |
| title_full | Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers |
| title_fullStr | Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers |
| title_full_unstemmed | Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers |
| title_short | Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers |
| title_sort | artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers |
| topic | Compressed earth block .Artificial neural networks .Fibers. Cement. Prediction. |
| url | https://periodicos.ufv.br/jcec/article/view/15910 |
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