Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence
Purpose: This study was conducted to predicte the resistance properties of concrete with different types of neural networks. The studied data was collected from the database of 127 mixing plans. The input data included the age of concrete in day, the amount of coarse grain, fine grain, cement, water...
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| Format: | Article |
| Language: | fas |
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Ayandegan Institute of Higher Education, Tonekabon,
2024-05-01
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| Series: | مدیریت نوآوری و راهبردهای عملیاتی |
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| Online Access: | http://www.journal-imos.ir/article_195786_94407c5286370fb2f4afa625e654c94d.pdf |
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| _version_ | 1832577825627963392 |
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| author | Hadi Faghihmaleki Mehdi Ghadiri |
| author_facet | Hadi Faghihmaleki Mehdi Ghadiri |
| author_sort | Hadi Faghihmaleki |
| collection | DOAJ |
| description | Purpose: This study was conducted to predicte the resistance properties of concrete with different types of neural networks. The studied data was collected from the database of 127 mixing plans. The input data included the age of concrete in day, the amount of coarse grain, fine grain, cement, water and concrete plasticizer. The target data included compressive strength.Methodology: In this research, an attempt has been made to make models for different projects by statistical study of laboratory samples of concrete in order to have a suitable prediction for estimating the resistance properties of concrete. The use of artificial intelligence as a modern method has a special place in engineering sciences. In this research, the data used were first normalized and then the desired data were trained using the Lorenberg Marquardt algorithm.Findings: The evaluation criteria of artificial neural network models were obtained using evaluation and error and the results showed that the use of 10 hidden layers had the highest correlation coefficient and the lowest error. The structure of this network was multi-layered perceptron.Originality/Value: The results showed that for the constructed neural network, the value of correlation coefficient, mean root, error square and mean absolute error of the artificial neural network were 0.94 and 1.9, respectively. |
| format | Article |
| id | doaj-art-8b505b8c68954844b3c16254a1bfefc4 |
| institution | Kabale University |
| issn | 2783-1345 2717-4581 |
| language | fas |
| publishDate | 2024-05-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | مدیریت نوآوری و راهبردهای عملیاتی |
| spelling | doaj-art-8b505b8c68954844b3c16254a1bfefc42025-01-30T14:56:37ZfasAyandegan Institute of Higher Education, Tonekabon,مدیریت نوآوری و راهبردهای عملیاتی2783-13452717-45812024-05-0151799210.22105/imos.2024.453210.1346195786Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligenceHadi Faghihmaleki0Mehdi Ghadiri1Department of Civil Engineering, Faculty of Civil Engineering, Ayandegan Institute of Higher Education, Tonkabon, Iran.Department of Civil Engineering, Faculty of Civil Engineering, Ayandegan Institute of Higher Education, Tonkabon, Iran.Purpose: This study was conducted to predicte the resistance properties of concrete with different types of neural networks. The studied data was collected from the database of 127 mixing plans. The input data included the age of concrete in day, the amount of coarse grain, fine grain, cement, water and concrete plasticizer. The target data included compressive strength.Methodology: In this research, an attempt has been made to make models for different projects by statistical study of laboratory samples of concrete in order to have a suitable prediction for estimating the resistance properties of concrete. The use of artificial intelligence as a modern method has a special place in engineering sciences. In this research, the data used were first normalized and then the desired data were trained using the Lorenberg Marquardt algorithm.Findings: The evaluation criteria of artificial neural network models were obtained using evaluation and error and the results showed that the use of 10 hidden layers had the highest correlation coefficient and the lowest error. The structure of this network was multi-layered perceptron.Originality/Value: The results showed that for the constructed neural network, the value of correlation coefficient, mean root, error square and mean absolute error of the artificial neural network were 0.94 and 1.9, respectively.http://www.journal-imos.ir/article_195786_94407c5286370fb2f4afa625e654c94d.pdfmechanical performance of concreteartificial neural networklunberg marquardt algorithmconcrete quality control |
| spellingShingle | Hadi Faghihmaleki Mehdi Ghadiri Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence مدیریت نوآوری و راهبردهای عملیاتی mechanical performance of concrete artificial neural network lunberg marquardt algorithm concrete quality control |
| title | Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence |
| title_full | Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence |
| title_fullStr | Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence |
| title_full_unstemmed | Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence |
| title_short | Operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence |
| title_sort | operational strategies to the mechanical performance assessment of concrete with the neural network and artificial intelligence |
| topic | mechanical performance of concrete artificial neural network lunberg marquardt algorithm concrete quality control |
| url | http://www.journal-imos.ir/article_195786_94407c5286370fb2f4afa625e654c94d.pdf |
| work_keys_str_mv | AT hadifaghihmaleki operationalstrategiestothemechanicalperformanceassessmentofconcretewiththeneuralnetworkandartificialintelligence AT mehdighadiri operationalstrategiestothemechanicalperformanceassessmentofconcretewiththeneuralnetworkandartificialintelligence |