Prediction of discharge flow rate beneath sheet piles using scaled boundary finite element modeling database

Document Type : Article

Authors

Department of Civil and Environmental Engineering, Shiraz University of Technology, Shiraz, P.O. Box 71557-13876, Iran

Abstract

Sheet piles are of general water retaining structures. The discharge flow rate beneath sheet plies is an important parameter in the design of these structures. In this study, the Gene Expression Programming (GEP) as an Artificial Intelligence (AI) method is used for developing a model to predict the discharge flow rate. The input parameters include the sheet pile height, upstream head and hydraulic conductivity anisotropy ratio. In order to achieve better performance, the flow rate is normalized and selected as an output of the model. A database including 1000 cases are created from the Scaled Boundary Finite Element Method (SBFEM) for the seepage beneath sheet plies is employed to develop the model. The GEP-based model predictions demonstrate a reasonable agreement with the simulated data, which indicates the efficiency of the developed model. The results of the sensitivity analysis indicate that the upstream head is the most influential parameter in the discharge flow rate beneath the sheet piles. Furthermore, the outputs of the parametric analysis show the reasonable performance of the model in the prediction of normalized discharge flow rate.

Keywords


References
[1] He, J.H. “Approximate analytical solution for seepage flow with fractional derivatives in porous media”, Computer Methods in Applied Mechanics and Engineering, 167, pp. 57-68 (1998).
[2] Jie, Y., Jie, G., Mao, Z., and Li, G. “Seepage analysis based on boundary-fitted coordinate transformation method”, Computers and Geotechnics, 31, pp. 279-283 (2004).
[3] Fukuchi, T. “Numerical analyses of steady-state seepage problems using the interpolation finite difference method”, Soils and Foundations, 56, pp. 608-626 (2016).
[4] Bresciani, E., Davy, P., and Dreuzy, J.R. “A finite volume approach with local adaptation scheme for the simulation of free surface flow in porous media”, International Journal for Numerical and Analytical Methods in Geomechanics, 36, pp. 1574-1591 (2012).
[5] Ouria, A., Toufigh, M.M., and Nakhai, A. “An Investigation on the effect of the coupled and uncoupled formulation on transient seepage by the finite element method”, American Journal of Applied Sciences, 12, pp. 950-956 (2007).
[6] Kazemzadeh-Parsi, M.J. and Daneshmand, F. “Three dimensional smoothed fixed grid finite element method for the solution of unconfined seepage problems”, Finite Elements in Analysis and Design, 64, pp. 24-35 (2013).        
[7] Rafiezadeh, K. and Ataie-Ashtiani, B. “Transient free-surface seepage in three-dimensional general anisotropic media by BEM”, Engineering Analysis with Boundary Elements, 46, pp. 51-66 (2014).            
[8] Jie, Y.X., Liu, L.Z., Xu, W.J., and Li, G.X. “Application of NEM in seepage analysis with a free surface”, Mathematics and Computers in Simulation, 89, pp. 23-37 (2013).
[9] Zhang, W., Dai, B., Liu, Z., and Zhou, C. “Unconfined seepage analysis using moving Kriging mesh-free method with Monte Carlo integration”, Transport in Porous Media, 116, pp. 163-180 (2017).
[10] Song, C. and Wolf, J.P. “The scaled boundary finite-element method—alias consistent infinitesimal finite-element cell method—for elastodynamics”, Computer Methods in applied mechanics and engineering, 147, pp. 329-355 (1997).
[11] Bazyar, M.H. and Graili, A. “A practical and efficient numerical scheme for the analysis of steady-state unconfined seepage flows”, International Journal for Numerical and Analytical Methods in Geomechanics, 36, pp. 1793-1812 (2012).
[12] Bazyar, M.H. and Talebi, A. “Transient seepage analysis in zoned anisotropic soils based on the scaled boundary finiteā€element method”, International Journal for Numerical and Analytical Methods in Geomechanics, 39, pp. 1-22 (2015).
[13] Johari, A. and Heydari, A. “Reliability analysis of seepage using an applicable procedure based on stochastic scaled boundary finite element method”, Engineering Analysis with Boundary Elements, 94, pp. 44-59 (2018).
[14] Su, H., Li, J., Wen, Z., Guo, Z., and Zhou, R. “Integrated certainty and uncertainty evaluation approach for seepage control effectiveness of a gravity dam”, Applied Mathematical Modelling, 65, pp. 1-22 (2019).
[15] Xie, J.X., Cheng, C.T., Chau, K.W., and Pei, Y. Z. “A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity”, Int. J. Env. Poll., 28, pp. 364-381 (2006).
[16] Taormina, R., Chau, K.W., and Sethi, R. “Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon”, Eng. Appl. Artif. Intell., 25, pp. 1670-1676 (2012).
[17] Azamathullah, H.M.D., Chang, C.K., Ghani, A.A., Ariffin, J., Zakaria, N.A., and Abu Hasan, Z. “An ANFIS-based approach for predicting the bed load for moderately-sized rivers”, J. Hydro-environ. Res., 3, pp. 35-44 (2009).
[18] Azamathulla, H.M.D. and Ghani, A.A., “An ANFIS-based approach for predicting the scour depth at culvert outlet”, J. Pipeline Syst. Eng. Pract., 2, pp. 35-40 (2011).
[19] Gao, W. “Premium-penalty ant colony optimization and its application in slope stability analysis”, Applied Soft Computing, 43, pp. 480-488 (2016).
[20] Ahangar-Asr, A., Javadi, A.A., Johari, A., and Chen, Y. “Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions: An intelligent evolutionary approach”, Applied Soft Computing, 24, pp. 822-828 (2014).
[21] Cheng, C.T., Wang, W.C., Xu, D.M., and Chau, K.W. “Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos”, Water Resources Management, 22, pp. 895-909 (2008).
[22] Johari, A., Javadi, A.A., and Habibagahi, G. “Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network”, Computers and Geotechnics, 38, pp. 2-13 (2011).
[23] Yalcin, Y., Orhon, M., and Pekcan, O. “An automated approach for the design of Mechanically Stabilized Earth Walls incorporating metaheuristic optimization algorithms”, Applied Soft computing, 74, pp. 547-566 (2019).
[24] Vardhan, H., Garg, A., Li, J., and Garg, A. “Measurement of stress dependent permeability of unsaturated clay”, Measurement, 91, pp. 371-376 (2016).
[25] Zhou, W.H., Garg, A., and Garg, “A. Study of the volumetric water content based on density, suction and initial water content”, Measurement, 94, pp. 531-537 (2016).
[26] Mishra, A.K., Kumar, B., and Vadlamudi, S. “Prediction of hydraulic conductivity for soil–bentonite mixture”, Int. J. Environ. Sci. Technol., 14, pp. 1625-1634 (2017).
[27] Gandomi, A.H., Alavi, A.H., Mohammadzadeh-Shadmehri, D., and Sahab, M. G. “An empirical model for shear capacity of RC deep beams using genetic simulated annealing”, Arch Civil Mech. Eng., 13, pp. 354-69 (2013).
[28] Johari, A., Habibagahi, G., and Ghahramani, A. “Prediction of SWCC using artificial intelligent systems: A comparative study”, Scientia Iranica, 18, pp. 1002-1008 (2011).
[29] Johari, A., Habibagahi, G., and Ghahramani, A. “Prediction of a Soil-Water Characteristic Curve using a Genetic-Based Neural Network”, Scientia Iranica., 13, pp. 284-294 (2006).
[30] Azamathulla, H.M.D., “Gene-expression programming to predict friction factor of Southern Italian Rivers”, Neural Comput. Appl., 23, pp. 1421-1426 (2013).
[31] Guven, A. and Azamathulla, H.M.D. “Gene-expression programming for flip bucket spillway scour”, Water Sci. Technol., 65, pp. 1982-1987 (2012).
[32] Azamathulla, H.M.D. “Gene expression programming for prediction of scour depth downstream of sills”, J. Hydrol., 460, pp. 156-159 (2012).
[33] Marino, M.A. and Luthin, J.N. “Seepage and groundwater”, Elsevier (1982).
[34] Wolf, J.P. “The scaled boundary finite element method”, John Wiley & Sons (2003).
[35] Wolf, J.P. and Song, C. “The scaled boundary finite-element method–a fundamental solution-less boundary-element method”, Computer Methods in Applied Mechanics and Engineering, 190, pp. 5551-5568 (2001).
[36] Song, C. and Wolf, J.P. “The scaled boundary finite-element method: analytical solution in frequency domain”, Computer Methods in Applied Mechanics and Engineering, 164, pp. 249-264 (1998).
[37] Ferreira, C. “Gene expression programming: A new adaptive algorithm for solving problems”, Complex Syst., 13, pp. 87-129 (2001).
[38] GEPSOFT. GeneXproTools. Version 4.0, http://www.gepsoft.com
Volume 28, Issue 2
Transactions on Civil Engineering (A)
March and April 2021
Pages 645-655
  • Receive Date: 06 April 2019
  • Revise Date: 23 June 2019
  • Accept Date: 12 January 2020