Journal of Hydraulic Structures

Journal of Hydraulic Structures

An Artificial Neural Network and Taguchi Method Integrated Approach to Predicting the Local Scour Depth around the Bridge Pier during Flood Event

Document Type : Research Paper

Authors
Sara Esfandmaz 1
Atabak Feizi 1
Mojtaba Karimaei Tabarestani 2
Saeed Rasi Nezami 1
1 Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
2 Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
10.22055/jhs.2021.37443.1172
Abstract
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.
Keywords
Taguchi method
Artificial neural network
Scour depth
Bridge piers
Flood flow
  1. Melville, B. W., & Chiew, Y.-M. (1999). Time Scale for Local Scour at Bridge Piers. Journal of Hydraulic Engineering, 125(1), 59–65.http://dx.doi.org/doi:10.1061/(asce)0733-9429(1999)125:1(59).
  2. Karimaee Tabarestani, M., & Zarrati, A. R. (2015 a). Design of Riprap Stone around Bridge Piers Using Empirical and Neural Network Method. Civil Engineering Infrastructures Journal, 48(1), 175-188.
  3. Najafzadeh, M., Barani, G.-A., & Azamathulla, H. M. (2013). GMDH to predict scour depth around a pier in cohesive soils. Applied Ocean Research, 40, 35–41. http://dx.doi.org/doi:10.1016/j.apor.2012.12.004.
  4. Dang, N.M., Anh, D.T. and Dang, T.D., (2019). ANN optimized by PSO and Firefly algorithms for predicting scour depths around bridge piers. Engineering with Computers, pp.1-11.https://doi.org/10.1007/s00366-019-00824-y.
  5. Atarodi, A., Karami, H., Ardeshir, A., Hosseini, K., & Lampert, D. (2020). Experimental Investigation of Scour Reduction Around Spur Dikes by Collar Using Taguchi Method. Iranian Journal of Science and Technology, Transactions of Civil Engineering.http://dx.doi.org/doi:10.1007/s40996-020-00373-1.
  6. Samadi M, M.H. Afshar, E. Jabbari E and Sarkardeh, H., (2020). Prediction of current-induced scour depth around pile groups using MARS, CART, and ANN approaches. Marine Georesources & Geotechnology: 1-2.https://doi.org/10.1080/1064119x.2020.1731025.
  7. Madić, M. J., Radovanović, M.R. “Optimal selection of ANN training and architectural parameters using Taguchi method: A case study”, FME Transactions, 39(2): 79-86, 2011.
  8. Chen, M.Y., M.H. Fan, C.C. Chen and S.Y. Jhong, “ Integrating Artificial Neural Network and Taguchi Method on Constructing the Real Estate Appraisal Model”. Network, 8(9): 3010-18. 2014.http://dx.doi.org /10.5281/zenodo.1096141.
  9. Kant, S., 2017. Application of Taguchi OA array and Artificial Neural Network for Optimizing and Modeling of Drilling Cutting Parameters. International Journal of Theoretical and Applied Mechanics, 12(1): 1-2.http://www.ripublication.com.
  10. Kavimani, V., & Prakash, K. S. (2017). Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach. Journal of Physics and Chemistry of Solids, 110, 409–419. http://dx.doi.org /doi:10.1016/j.jpcs.2017.06.028.
  11. Beeravelli, V. N., Chanamala, R., Rayavarapu, U. M. R., & Kancherla, P. R. (2018). An Artificial Neural Network and Taguchi Integrated Approach to the Optimization of Performance and Emissions of Direct Injection Diesel Engine. European Journal of Sustainable Development Research, 2(2). http://dx.doi.org/doi:10.20897/ejosdr/85412
  12. Alam, M. T., Arif, S., Ansari, A. H., & Alam, M. N. (2019). Optimization of wear behaviour using Taguchi and ANN of fabricated aluminium matrix nanocomposites by two-step stir casting. Materials Research Express, 6(6), 065002. http://dx.doi.org/doi:10.1088/2053-1591/ab0871.
  13. Ajith Arul Daniel, S., Pugazhenthi, R., Kumar, R., & Vijayananth, S. (2019). Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi -grey relational analysis. Defence Technology, 15(4), 545–556.http://dx.doi.org/doi:10.1016/j.dt.2019.01.001.
  14. Ranjbar-Zahedani, M.; Keshavarzi, A.; Khabbaz, H and Ball, J.E. (2021). Optimizing flow diversion structure as an effective pier-scour countermeasure. Journal of Hydraulic Research: 1-14. https://doi.org/10.1080/00221686.2020.1862321
  15. Roy, R., 1990. A primer on the Taguchi method. Society of Manufacturing Engineers New York.
  16. Rostamabadi, M, 2017. Determining the optimal amount of height and position of sedative blocks using studies designed by Taguchi and full factorial methods. Journal of Hydraulic Research, 12(2), 35-44, (in Persian).
  17. Yao, Albert W.L., and S.C. Chi.. 2004. Analysis and design of a Taguchi–Grey based electricity demand predictor for energy management systems. Energy Conversion and Management, 45(7-8), 1205–1217. http://dx.doi.org/doi:10.1016/j.enconman.2003.08.008.
  18. Demuth, H. and M. Beale, 1992. Neural Network Toolbox: User's Guide: for Use with Matlab. MathWorks Incorporated.
  19. Braddock, R.D., M.L. Kremmer and L. Sanzogni, (1998). Feed‐forward artificial neural network model for forecasting rainfall run‐off. Environmetrics: The official journal of the International Environmetrics Society, 9(4): 419-32.http://dx.doi.org/doi.org/10.1002/(sici)1099-095x(199807/08)9:43E3.0.co;2-d.
  20. Hong C-W. (2012). Using the Taguchi method for effective market segmentation. Expert Systems with Applications [Internet]. Elsevier BV; 2012 Apr;39(5):5451–9. Available http://dx.doi.org/10.1016/j.eswa.2011.11.040
  21. Karimaee Tabarestani, M. & Zarrati, A. R. (2016). Local scour calculation around bridge pier during flood event. KSCE Journal of Civil Engineering, 21(4), 1462–1472.http://dx.doi.org/doi:10.1007/s12205-016-0986-3.
  22. Karimaee Tabarestani, M., and Zarrati, A. R. (2014). Sediment transport during flood event: a review. International Journal of Environmental Science and Technology, 12(2), 775–788.http://dx.doi.org/doi:10.1007/s13762-014-0689-6.