Journal of Hydraulic Structures

Journal of Hydraulic Structures

Estimation of the Local Scour from a Cylindrical Bridge Pier Using a Compilation Wavelet Model and Artificial Neural Network

Document Type : Research Paper

Authors
Mehran Seifollahi 1
‪Mohammad Ali Lotfollahi-Yaghin 1
Farhoud Kalateh 1
Rasoul Daneshfaraz 2
Salim Abbasi 3
John Abraham 4
1 Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
2 Department of Civil Engineering, University of Maragheh, Maragheh, Iran.
3 Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
4 School of Engineering, University of St. Thomas, St Paul, MN, USA.
10.22055/jhs.2021.38300.1187
Abstract
In the present study, an artificial neural network and its combination with wavelet theory are used as the computational tool to predict the depth of local scouring from the bridge pier. The five variables measured are the pier diameter of the bridge, the critical and the average velocities, the average diameter of the bed aggregates, and the flow depth. In this study, the neural wavelet method is used as a preprocessor. The data was passed through the wavelet filter and then passed to the artificial neural network. Among the various wavelet functions used for preprocessing, the dmey function results in the highest correlation coefficient and the lowest RMSE and is more efficient than other functions. In the wavelet-neural network compilation method, the neural network activator function was replaced by different wavelet functions. The results show that the neural network method with the Polywog4 wavelet activator function with a correlation coefficient of 87% is an improvement of 8.75% compared to the normal neural network model. By performing data filtering by wavelet and using the resulting coefficients in the neural network, the resulting correlation coefficient is 82%, only a 2.5% improvement compared to the normal neural network. By analyzing the results obtained from neural network methods, the wavelet-neural network predicted errors compared to experimental observations were 8.26, 1.56, and 1.24%, respectively. According to the evaluation criteria, combination of the best effective hydraulic parameters, the combination of wavelet function and neural network, and the number of neural network neurons achieved the best results.
Keywords
Artificial Neural Network
Bridge Cylindrical Pier
Scour Depth
Wavelet Theory
  1. Firat, M., and Gungor, M. (2009). Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge pier, Advances in Engineering Software, 40, 731-737.
  2. Guven, A., Azamathulla, H.M., and Zakaria, N.A. (2009). Linear genetic programming for prediction of circular pile scour. Journal of Ocean Engineering, 36, 985–991.
  3. Azamathulla, H.M., Ghani, A.A., Zakaria, N.A., and Aytac, G. (2010). Genetic programming to predict bridge pier scour. Journal of Hydraulic Engineering, 136(3), 165–169.
  4. Toth, E., and Brandimarte, L. (2011). Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks, Journal of Hydroinformatics, 13(4), 812-824.
  5. Mostaghimzadeh, E., Ashrafi, S.M., & Adib, A. (2021). Estimation of accuracy of forecast-based policies on reservoir operation using discrete wavelet transformation and ensemble learning methods. Iranian Water Researches Journal, 15(2), 1-15.
  6. Karimaee Tabarestani, M., and Zarrati, A.R. (2015). Design of riprap stone around bridge piers using empirical and neural network method, Civil Engineering Infrastructures Journal, 48(1), 175-188.
  7. Najafzadeh, M., Barani, G.A., and Azamathulla, H.M. (2013). GMDH to predict scour depth around a pier in cohesive soils. Applied ocean research, 40, 35-41.
  8. Chen, B.F., Wang, H.D. and Chu, C.C., (2007). Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea. Ocean Engineering, 34(16), 2161-2175.
  9. Postalcioglu, S. and Becerikli, Y., (2007). Wavelet networks for nonlinear system modeling. Neural Computing and Applications, 16(4), 433-441.
  10. Mayorga, C.R.D., Rivera, M.A.E., Velasco, L.E.R., Fernández, J.C.R. and Hernández, E.E., (2011). November. Wavelet neural network algorithms with applications in approximation signals. In Mexican International Conference on Artificial Intelligence (374-385). Springer, Berlin, Heidelberg.
  11. Zhong, W., and Song, Y. (2009). Wavelet neural networks model used for runoff forecast based on fuzzy C-means clustering. In 2009 2nd International Conference on Biomedical Engineering and Informatics, 1-5. IEEE.
  12. Nazaruddin, Y.Y. (2006). Wavenet based modeling of vehicle suspension system. In IECON 2006-32nd Annual Conference on IEEE Industrial Electronics (144-149). IEEE.
  13. Fang, Y., and Chow, T.W. (2006). Wavelets based neural network for function approximation. In International symposium on neural networks, 80-85. Springer, Berlin, Heidelberg.
  14. Roshangar, K. (2013). Evaluation of ANFIS machine learning approach for predicting of a local scour. In: International Conference on Civil, Transport and Environment Engineering (ICCTEE'2013) August 28-29, 2013 Penang (Malaysia).
  15. Sarshari, E., and Mullhaupt, P. (2015). Application of artificial neural networks in assessing the equilibrium depth of local scour around bridge piers. In: International Conference on Offshore Mechanics and Arctic Engineering, (Vol. 56550, p. V007T06A061). American Society of Mechanical Engineers.
  16. Bonakdari, H., and Ebtehaj, I. (2017). Scour depth prediction around bridge piers using neuro-fuzzy and neural network approaches. International Journal of Civil and Environmental Engineering, 11(6), 835-839.
  17. Esmaeili-Varaki, M., Kanani, A., and Jamali, A. (2017). Prediction of scour depth around inclined bridge piers using optimized ANFIS with GA. Journal of Hydrosciences and Environment, 1(2), 34-45.
  18. Ismail, A. (2018). Prediction of Scour Depth Around Bridge Piers Using Evolutionary Neural Network, Journal of Mathematical Modelling in Civil Engineering, 14(2), 26‐36.
  19. Sreedhara, B.M., Rao, M., and Mandal, S. (2018). Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers, Journal of Neural Computing and Applications, https://doi.org/10.1007/s00521-018-3570-6.
  20. Sadeghfam, S., Daneshfaraz, R., Khatibi, R., and Minaei, O. (2019). Experimental studies on scour of supercritical flow jets in upstream of screens and modelling scouring dimensions using artificial intelligence to combine multiple models (AIMM). Journal of Hydroinformatics, 21(5), 893-907.
  21. Daneshfaraz, R., Aminvash, E., Ghaderi, A., Abraham, J., and Bagherzadeh, M. (2021). SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop. Applied Sciences, 11(9), 4238.
  22. Ashrafi, S.M., Mostaghimzadeh, E., & Adib, A. (2020). Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves. Hydrological Sciences Journal, 65(12), 2007-2021.
  23. Daneshfaraz, R., Abam, M., Heidarpour, M., Abbasi, S., Seifollahi, M., and Abraham, J. (2021). The impact of cables on local scouring of bridge piers using experimental study and ANN, ANFIS algorithms. Water Supply. https://doi.org/10.2166/ws.2021.215.
  24. Ghoushchi, S.J., Manjili, S., Mardani, A. and Saraji, M.K. (2021). An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant. Energy, 223, p.120052.
  25. Anshuka, A., Buzacott, A.J., Vervoort, R.W., & van Ogtrop, F.F. (2021). Developing drought index–based forecasts for tropical climates using wavelet neural network: an application in Fiji. Theoretical and Applied Climatology, 143(1), 557-569.
  26. Ouma, Y.O., Cheruyot, R., & Wachera, A.N. (2021). Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: a case study of Nzoia hydrologic basin. Complex & Intelligent Systems, 1-24.
  27. El‑Hady Rady, A. (2020). Prediction of local scour around bridge piers: artificial‑intelligence‑based modeling versus conventional regression methods, Journal of Applied Water Science, 10 (57), 1-11. https://doi.org/10.1007/s13201-020-1140-4.
  28. Zarbazoo Siahkali, M., Ghaderi, A. A., Bahrpeyma, A. H., Rashki, M., & Safaeian Hamzehkolaei, N. (2021). Estimating pier scour depth: Comparison of empirical formulations with ANNs, GMDH, MARS, and Kriging. Journal of AI and Data Mining, 9(1), 109-128.
  29. Daneshfaraz, R., Bagherzadeh, M., Esmaeeli, R., Norouzi, R., and Abraham, J. (2021). Study of the performance of support vector machine for predicting vertical drop hydraulic parameters in the presence of dual horizontal screens. Journal of Water Supply, 21(1), 217-231.
  30. Fuladipanah, M., & Majediasl, M. (2021). Assessment of the geometric shape of bridge pier on the scour depth using the support vector machine.
  31. Norouzi, R., Sihag, P., Daneshfaraz, R., Abraham, J., and Hasannia, V. (2021). Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques. Journal of Water Supply.
  32. Chatterjee, P. (2017). Wavelet Analysis in Civil Engineering. ISBN 9781138893955, Published July 26, 2017 by CRC Press, 224 Pages 90 B/W Illustrations.
  33. Adamowski, J., and Sun, (2010). Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390(1-2), 85-91.
  34. Coleman, N.L. (1971). Analyzing laboratory measurements of scour at cylindrical piers in sand beds. Proc. 14th IAHR Congress, Paris, France, 3, 307-313.
  35. Norman, V.W. (1975). Scour at selected bridge sites in Alaska. US Geological Survey, Water Resources Division. 75, 32-75. https://DOI.org/10.3133/wri7532.
  36. Jain, S.C. and Fischer, E.E. (1980). Scour around bridge piers at high flow velocities. Journal of Hydraulic Engineering, ASCE, 106(11), 1827-1842.
  37. Samaga, B.R. Ranga Raju, K.G. and Garde, R.J. (1985). Concentration distribution of sediment mixtures in open-channel flow. Journal of hydraulic research, 23(5), 467-483.
  38. G¨unyaktı, A. A., “Graphical Procedure for the Determination of Local Scour Around Bridge Piers”, Journal of Engineering and Environmental Sciences, Scientific and Technical Research Council Turkey, 12(1):96-108, 1988, (in Turkish).
  39. Melville, B.W. and Sutherland, A.J. (1988). Design method for local scour at bridge piers. Journal of Hydraulic Engineering, 114(10), 1210-1226.
  40. S. DOT. (1993). Evaluating scour at bridges. Hydraul. Eng. Circular No.18, FHWA-IP-90- 017, Fed. Hwy. Admin., U.S. Dept. of Transp., McLean, Va.
  41. Melville, B.W. and Coleman, S.E. (2000). Bridge scour. Water Resources Publication.
  42. Daneshfaraz, R., Ghaderi, A., Sattariyan, M., Alinejad, B., Majedi-Asl, M., and Di Francesco, S. (2021). Investigation of local scouring around hydrodynamic and circular pile groups under the influence of river material harvesting pits. Journal of Water, 13(16), 2192. https://doi.org/10.3390/w13162192.
  43. Jeng, D.S., Bateni, S.M., and Lockett, E. (2005). Neural network assessment for scour depth around bridge piers. Civil Engineering Research Rep (No. R855), 1-89.
  44. Kothyari, U.C. Ranga Raju, K.G. and Garde, R.J. (1992). Live-bed scour around cylindrical bridge piers. Journal of Hydraulic Research, 30(5), 701-715.
  45. Melville, B.W. and Raudkivi, A.J. (1977). Flow characteristics in local scour at bridge piers. Journal of Hydraulic Research, 15(4), 373-380.
  46. Chabert, J. and Engeldinger, P. (1956). Etude des affouillements autour des piles des ponts. Laboratoire d` Hydraulique, Chatou, France (in French).
  47. Chee, R.K.W. (1982). Live-bed scour at bridge piers. Publication of: Auckland University, New Zealand, (290).
  48. Chiew, Y. M. (1984). Local Scour at Bridge Piers. Rep No.355, School of Engineering, The Univ. of Auckland, Auckland, New Zealand.
  49. Ettema, R.E. (1980). Scour at bridge piers. Rep. No. 216, Auckland, New Zealand: University of Auckland.
  50. Graf, W.H. (1995). Load scour around piers. Annu. Rep., Laboratoire de Recherches. Hydrauliques, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, B.33.1 – B.33.8.
  51. Hancu, S. (1971). Sur le calcul des affouillements locaux dams la zone des piles des ponts. Proc. 14th IAHR Congress. Paris, France, 3, 299-313.
  52. Kwan, T.F. (1988). A study of abutment scour.
  53. Melville, B.W. (1997). Pier and Abutment Scour – An Integrated Approach. Journal of Hydraulic Engineering, 123(2), 125- 136.
  54. Oliveto, G. and Hager, W.H. (2001). Clear-water pier and abutment scour. In XXIX IAHR Congress Proceedings. Theme D. Hydraulics of Rivers, Water Works and Machinery (1, 7-12). Tsinghua University Press.
  55. Choi, S.U. & Cheong, S. (2006). Prediction of local scour around bridge piers using artificial neural networks, J. of the American Water Resources Association (JAWRA), 42, 487-494, https://doi.org/10.1111/j.1752-1688.2006.tb03852.x.
  56. Laursen, E.M., & Toch, A. (1956). Scour around bridge piers and abutments (Vol. 4). Ames, IA: Iowa Highway Research Board.
  57. Neill, C.R. (1973). Guide to bridge hydraulics. roads and transportation Assoc. of Canada, University of Toronto Press, Toronto, Canada.
  58. Lee, T.L., Jeng, D.S., Zhang, G.H., Hong, J.H. (2007). Neural network modeling for estimation of scour depth around bridge piers, J. of Hydrodynamics, 19 :378-386.
  59. Shen, H. W. (1971). Scour near piers. In: River Mechanics, II, Chap. 23, Ft. Collins, Colo.
  60. Ettema R.E., Melville B.W., and Barkdoll, B. (1999). Closure. A scale effect in pier-scour experiments. Journal of Hydraulic Engineering, ASCE, 125(8), 895-896.
  61. Bateni, S. M., Borghei, S. M., & Jeng, D. S. (2007). Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, 20(3), 401-414.
  62. Breusers, H.N.C., Nicollet, G., Shen, H.W. (1977). Local scour around cylindrical piers. Journal of Hydraul Res, 15(3), 211–52.
  63. Melville, B.W., and Chiew, Y.M. (1999). Time scale for local scour depth at bridge piers. Journal of Hydraul Eng, 125(1), 59–65.
  64. Ismail, A., Jeng, D.S., Zhang, L.L., and Zhang, J.S. (2013). Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function. Engineering Applications of Artificial Intelligence, 26(5-6), 1540-1549.
  65. Zounemat-Kermani, M., and Teshnehlab, M. (2008). Using adaptive neuro-fuzzy inference system for hydrological time series prediction, Applied soft computing, 8 (2), 928-936.
  66. Salim, M., and Jones, J.S. (1980). Scour around exposed pile foundations, ASCE, Comp. of Conf. Scour Papers (1991–1998), Reston, VA, 1998.
  67. Max Sheppard, D., and Glasser, T. (2004). Sediment scour at piers with complex geometries, in: Proceedings of the 2nd International Conference on Scour and Erosion, World Scientific, Singapore.
  68. Ataie-Ashtiani, B., Beheshti, A.A. (2006). Experimental investigation of clear-water local scour at pile groups, J. Hydraul. Eng., ASCE, 132(10), 1100–1104.
  69. Farokhnia, A., and Morid, S. (2010). Uncertainty analysis of artificial neural networks and neuro-fuzzy models in river flow forecasting, Iran-Water Resources Research Journal. 5(3), 14-27. (In Persian)
  70. Khashei, A., Shahidi, A., pourrezabilondi, M., Amirabadizadeh, M., jafarzadeh, A. (2018). Performance Assessment of ANN and SVR for downscaling of daily rainfall in dry regions, Iranian Journal of Soil and Water Research, 49(4), 781-793. (In Persian)
  71. Lan, S., Li, S., Shahbaba, B. (2021). Scaling up bayesian uncertainty quantification for inverse problems using deep neural networks. Arxiv preprint arxiv: 2101. 3906 (2021).
  72. Pourreza Bilondi, M., and Khashei-Siuki, A. (2015). Uncertainty analysis of artificial neural networks in simulation of saturated hydraulic conductivity using Monte-Carlo simulation. Iranian Journal of Irrigation and Drainage, 9(4), 655-664. (In Persian)
  73. Duflot, L.A., Reisenhofer, R., Tamadazte, B., Andreff, N., and Krupa, A. (2019). Wavelet and shearlet-based image representations for visual servoing. The International Journal of Robotics Research, 38(4), 422-450.
  74. Lekutai, G. (1997). Adaptive self-tuning neuro wavelet network controllers (Doctoral dissertation, Virginia Polytechnic Institute and State University).