Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill
- 1
- 2
- 3
Abstract
This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.
- D. S. Jeng, S. M. Bateni and E. Lockett, “Neural Network Assessment for Scour Depth Around Bridge,” Department of Civil Engineering, the University of Sydney, Sydney, NSW, Australia, Environmental Fluids/Wind Group, Research Report No R855, 2006.
- N. E Yankielun and L. Zabilansky, “Laboratory Investigation of Time-Domain Reflectometry System for Monitoring Bridge Scour,” Journal of Hydraulic Engineering, ASCE, Vol. 125, No. 12, 1999, pp. 1279-1284.
- P. A. Johnson, “Comparison of Pier-Scour Equations Using Field Data,” Journal of Hydraulic Engineering, ASCE, Vol. 121, No. 8, 1995, pp. 626-629.
- R. Trent, N. Gagarain and J. Rhodes, “Estimating Pier Scour with Artificial Neural Networks,” Proceedings of ASCE Conference on Hydraulic Engineering, San Francisco, CA, 1993a, pp. 1043-1048.
- R. Trent, A. Molinas and N. Gagarain, “An Artificial Neural Networks for Computing Sediment Estimation of Scour Below Spillways Using Neural Networks,” Proceedings of ASCE Conference on Hydraulic Engineering, San Francisco, CA, 1993b, pp. 1049-1054.
- S. Choi and S. Cheong, “Prediction of Local Scour around Bridge Piers Using Artificial Neural Networks,” Journal of the American Water Resources Association, Vol. 42, No 2, 2006, pp. 487-494.
- J. B. Butcher, “Co-kriging to Incorporate Screening Data: Hudson River Sediment,” Journal of the American Water Resources Association, Vol. 32, No. 2, 1996, pp. 349- 356.
- B. Biglary and T. W. Sturm, “Numerical Modeling of Flow around Bridge Abutments in Compound Channel,” Journal of Hydraulic Engineering, ASCE, Vol. 124, 1998, pp. 156-164.
- S. L. Liriano and R. A. Day, “Prediction of Scour Depth at Culvert Outlets Using Neural Networks,” Journal of Hydroinformatics, Vol. 3, 2001, pp. 231-238.
- A. R. Kambekar and M. C. Deo, “Estimation of Group Pile Scour Using Neural Networks,” Applied Ocean Research, Vol. 25, No. 4, 2003, pp. 225-234.
- S. M. Bateni, S. M. Borghei and D. S. Jeng, “Neural Network and Neuro-Fuzzy Assessments for Scour Depth around Bridge Piers,” Engineering Applications of Artificial Intelligence, Vol. 20, No. 3, 2007a, pp. 401-414.
- T. L. Lee, D. S. Jeng, G. H. Zhang and I. H. Hong, “Neural Network Modeling for Estimation of Scour Depth around Bridge Piers,” Journal of Hydrodynamic, Vol. 19, No. 3, 2007, pp. 378-386.
- S. M. Betani, D. S. Jeng and B. W. Melville, “Bayesian Neural Networks for Prediction of Equilibrium and Time-Dependent Scour Depth around Bridge Piers,” Advances in Engineering Software, Vol. 38, No. 2, 2007b, pp. 102-111.