An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event
Experiment design is believed to be an important part of investigating an engineering phenomenon for characterizing and optimizing the process. In this study, the Taguchi method (TM) reduced the number of experiments and was used to analyze the results of an artificial neural network (ANN) and find...
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| Format: | Article |
| Language: | English |
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Shahid Chamran University of Ahvaz
2021-05-01
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| Series: | Journal of Hydraulic Structures |
| Subjects: | |
| Online Access: | https://jhs.scu.ac.ir/article_16904_1aaa03583ec54b5375176e37fbb698b4.pdf |
| _version_ | 1797465886885412864 |
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| author | Sara Esfandmaz Atabak Feizi Mojtaba Karimaei Tabarestani Saeed Rasi Nezami |
| author_facet | Sara Esfandmaz Atabak Feizi Mojtaba Karimaei Tabarestani Saeed Rasi Nezami |
| author_sort | Sara Esfandmaz |
| collection | DOAJ |
| description | Experiment design is believed to be an important part of investigating an engineering phenomenon for characterizing and optimizing the process. In this study, the Taguchi method (TM) reduced the number of experiments and was used to analyze the results of an artificial neural network (ANN) and find the optimal combination of the relevant parameters in the ANN. Accordingly, the phenomenon of the local scour depth around the bridge during flood events was considered as a case study. The study results indicated that TM could reduce the number of experiments compared to the previous original study and the full factorial method by 28% and 67%, respectively. According to TM, the flow intensity at the hydrograph peak was the most effective parameter providing the optimal state (minimum scour depth). Additionally, an ANN with three hidden layers and the main parameters, including several neurons in the first and second hidden layers, training function, and transfer function, was introduced. Adjusting the input parameters of the ANN, TM led to the emergence of networks with a reasonable correlation coefficient of R= 0.952. Finally, the results demonstrated that the transfer function had the most significant effect on the results of the ANN. |
| first_indexed | 2024-03-09T18:28:13Z |
| format | Article |
| id | doaj.art-4f6b673287654d659d4da44cc10b77e2 |
| institution | Directory Open Access Journal |
| issn | 2345-413X 2345-4156 |
| language | English |
| last_indexed | 2024-03-09T18:28:13Z |
| publishDate | 2021-05-01 |
| publisher | Shahid Chamran University of Ahvaz |
| record_format | Article |
| series | Journal of Hydraulic Structures |
| spelling | doaj.art-4f6b673287654d659d4da44cc10b77e22023-11-24T07:43:46ZengShahid Chamran University of AhvazJournal of Hydraulic Structures2345-413X2345-41562021-05-01719811310.22055/jhs.2021.37443.117216904An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood EventSara Esfandmaz0Atabak Feizi1Mojtaba Karimaei Tabarestani2Saeed Rasi Nezami3Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.Experiment design is believed to be an important part of investigating an engineering phenomenon for characterizing and optimizing the process. In this study, the Taguchi method (TM) reduced the number of experiments and was used to analyze the results of an artificial neural network (ANN) and find the optimal combination of the relevant parameters in the ANN. Accordingly, the phenomenon of the local scour depth around the bridge during flood events was considered as a case study. The study results indicated that TM could reduce the number of experiments compared to the previous original study and the full factorial method by 28% and 67%, respectively. According to TM, the flow intensity at the hydrograph peak was the most effective parameter providing the optimal state (minimum scour depth). Additionally, an ANN with three hidden layers and the main parameters, including several neurons in the first and second hidden layers, training function, and transfer function, was introduced. Adjusting the input parameters of the ANN, TM led to the emergence of networks with a reasonable correlation coefficient of R= 0.952. Finally, the results demonstrated that the transfer function had the most significant effect on the results of the ANN.https://jhs.scu.ac.ir/article_16904_1aaa03583ec54b5375176e37fbb698b4.pdftaguchi methodartificial neural networkscour depthbridge piersflood flow |
| spellingShingle | Sara Esfandmaz Atabak Feizi Mojtaba Karimaei Tabarestani Saeed Rasi Nezami An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event Journal of Hydraulic Structures taguchi method artificial neural network scour depth bridge piers flood flow |
| title | An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event |
| title_full | An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event |
| title_fullStr | An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event |
| title_full_unstemmed | An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event |
| title_short | An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event |
| title_sort | artificial neural network and taguchi method integrated approach to predicting the local scour depth around the bridge pier during flood event |
| topic | taguchi method artificial neural network scour depth bridge piers flood flow |
| url | https://jhs.scu.ac.ir/article_16904_1aaa03583ec54b5375176e37fbb698b4.pdf |
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