TY - JOUR ID - 16904 TI - An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event JO - Journal of Hydraulic Structures JA - JHS LA - en SN - 2345-413X AU - Esfandmaz, Sara AU - Feizi, Atabak AU - Karimaei Tabarestani, Mojtaba AU - Rasi Nezami, Saeed AD - Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran. AD - Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran. Y1 - 2021 PY - 2021 VL - 7 IS - 1 SP - 98 EP - 113 KW - Taguchi method KW - Artificial neural network KW - Scour depth KW - Bridge piers KW - Flood flow DO - 10.22055/jhs.2021.37443.1172 N2 - 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. UR - https://jhs.scu.ac.ir/article_16904.html L1 - https://jhs.scu.ac.ir/article_16904_1aaa03583ec54b5375176e37fbb698b4.pdf ER -