Survey the suitable approach to predict the local scour depth around a bridge pier
Article Sidebar
Main Article Content
Abstract
Local scouring around the piers of bridges is one of the main reasons for bridge failure. The main purpose of this study was to survey the suitable empirical equation to predict local scour depth around pier bridges, by applying various approach techniques to achieve more effective predictions. These approaches include gene expression programming (GEP), artificial neural networks (ANN) and statistic non-linear regression (NLR) methods. The empirical equations derived were based on shape of pier, intensity of flow, flow depth ratio, pier width ratio and attack angle. The data set, a total of 729 data points obtained from numerical simulations using Flow-3D, were divided into training and validation datasets (test). A functional relationship was created using GEP, its performance compared to ANN and NLR. Identification of the best techniques to predict scour depth, was achieved using three statistical parameters: R2, RMSE and MAE. The equation obtained using GEP, performed better than the conventional regression NLR model, but slightly poorer than that of ANN (R2 = 0.89, RMSE=0.152 and MAE= 0.118). Even though ANN performed better than GEP (R2 = 0.93, RMSE=0.129 and MAE=0.088), the latter is preferred because of its ability to provide compressed and explicit arithmetic expressions. The GEP model equation has been verified with laboratory data, predicting a good result.
Article Details
References
Shepherd R, Frost JD. Failures in civil engineering: structural, foundation and geoenvironmental case studies. New York: ASCE Publishing; 1995.
Muzzammil M, Gangadharaiah T, Gupta AK. An experimental investigation of a horseshoe vortex induced by a bridge pier. Water Manag. 2004;157(2):109-119.
Jalal HK, Hassan WH. Effect of bridge pier shape on depth of scour. In: Nile K. Basim, editor. 3rd International Conference on Engineering Sciences; 2019 Nov 4-6; Kerbala, Iraq. Bristol: IOP Publishing; 2020. p. 1-16.
Melville BW. Pier and abutment scour: integrated approach. J Hydraul Eng. 1997;123(2):125-136.
Ansari SA, Kothyari UC, Raju KV. Influence of cohesion on scour around bridge piers. J Hydraul Res. 2002;40(6):717-729.
Huang W, Yang Q, Xiao H. CFD modeling of scale effects on turbulence flow and scour around bridge piers. Comput Fluids. 2009;38(5):1050-1058.
Hassan WH, Jassem MH, Mohammed SS. A GA-HP model for the optimal design of sewer networks. Water Resour Manag. 2018;32(3):865-879.
Azamathulla HM, Ghani AA, Zakaria NA, Guven A. Genetic programming to predict bridge pier scour. J Hydraul Eng. 2010;136(3):165-169.
Mohamed TA, Noor MJ, Ghazali AH, Huat BB. Validation of some bridge pier scour formulae using field and laboratory data. Am J Environ Sci. 2005;1(2):119-125.
Arneson LA, Zevenbergen LW, Lagasse PF, Clopper PE. Evaluating scour at bridges 5th ed. Final report. Virginia; National Highway Institute (US); 2012 Apr. Report No.: FHWA-HIF-12-003 HEC-18.
Jain SC, Fischer EE. Scour around circular bridge piers at high Froude numbers. Final report. Iowa: Department of Transportation, Federal Highway Administration, Office of Research, Environmental Division;1976 Apr. Report No.:FHWA-RD-79-104.
Melville BW, Sutherland AJ. Design method for local scour at bridge piers. J Hydraul Eng. 1988;114(10):1210-1226.
Muzzammil M, Alama J, Danish M. Scour prediction at bridge piers in cohesive bed using gene expression programming. Aquat Procedia. 2015;4:789-796.
Hassan WH. Application of a genetic algorithm for the optimization of a location and inclination angle of a cut-off wall for anisotropic foundations under hydraulic structures. Geotech Geol Eng. 2019;37(2):883-895.
Baghban A, Jalali A, Shafiee M, Ahmadi MH, Chau KW. Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids. Eng Appl Comput Fluid Mech. 2019;13(1):26-39.
Govindaraju RS. Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng. 2000;5(2):115-123.
Azmathullah HM, Deo MC, Deolalikar PB. Neural networks for estimation of scour downstream of a ski-jump bucket. J Hydraul Eng. 2005;131(10):898-908.
Khan M, Azamathulla HM, Tufail M. Gene-expression programming to predict pier scour depth using laboratory data. J Hydroinformatics. 2012;14(3):628-645.
Jalal HK. Numerical study of optimum pier shape for safe bridge. [Thessis]. Karbala:University of Kerbala; 2019.
Bobrowsky PT, Marker B, editors. Encyclopedia of engineering geology. Cham: Springer Nature Switzerland AG; 2018.
Melville BW. Local scour at bridge sites [PhD Thesis] Auckland: University of Auckland; 1994.
Ferreira C. Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 2001;13(2):87-129.
Koza JR. Genetic programming: on the programming of computers by means of natural selection. 1st ed. Massachusetts: MIT press; 1992.
Ferreira C. Automatically defined functions in gene expression programming. In: Ajith A, Nedjah N, Mourelle LM, editors. Genetic systems programming: theory and experiences. 1st ed. Berlin: Springer; 2006. p. 21-56.
Hassan WH. Application of a genetic algorithm for the optimization of a cutoff wall under hydraulic structures. J Appl Water Eng Res. 20172;5(1):22-30.
Onen F. Prediction of scour at a side-weir with GEP, ANN and regression models. Arab J Sci Eng. 2014 ;39(8):6031-6041.
Hassan WH. Climate change impact on groundwater recharge of Umm er Radhuma unconfined aquifer Western desert, Iraq. IJHST. 2020;10(4):392-412.
Azamathulla HM, Ghani AA, Leow CS, Chang CK, Zakaria NA. Gene-expression programming for the development of a stage-discharge curve of the Pahang river. Water Resour Manag. 2011;25(11):2901-2916.
Fattah MY, Hassan WH, Rasheed SE. Effect of geocell reinforcement above buried pipes on surface settlement. Int Rev Civ Eng. 2018;9(2):86-90.
Fattah MY, Hassan WH, Rasheed SE. Behavior of flexible buried pipes under geocell reinforced subbase subjected to repeated loading. Int J Geotech Earthq Eng. 2018;9(1):22-41.