عنوان مقاله [English]
The phenomenon of scouring around bridge piers is one of the important issues in river engineering science. Scouring creates a trench around the bridge piers and ultimately destroys them. So far, many studies have been done in this regard and numerous relationships have been proposed to estimate the scour depth parameter. However, due to the influence of various parameters and uncertainty in the scouring phenomenon, existing relationships do not lead to accurate and comprehensive results. In the present study, using a series of experimental data, the efficiency of the Gaussian Process Regression (GPR) method was evaluated to estimate the scour depth of bridge piers in cohesive and grainy beds. Various models were developed and the impacts of hydraulic parameters were evaluated. The results proved the high efficiency of the applied method in the research in estimating the scour depth compared to the semi-empirical equations. It was observed that the defined models for cohesive soils are more successful in estimating the scour depth than grainy and non- cohesive soil. The best result for test series was obtained in the state of soil with clay and sand with the values of CC=0.952, DC=0.801, RMSE=0.132 and MAPE=15.23%; in clay soil state with the values of CC=0.889, DC=0.883, RMSE=0.054 and MAPE=11.82% and in non-cohesive soil state with the values of CC=0.988, DC=0.796, RMSE=0.191 and MAPE=18.21%. The results showed that soil moisture and clay soil density were effective in predicting scour depth. Based on the results of sensitivity analysis, in the state of soil with clay and sand moisture with mean absolute percentage error of 24.42%, in the state of clay soil shear stresses and the percentage of soils density with errors of 43.42% and 47.04%, respectively, and in non-cohesive soil state pier Froude number with error of 28.65% were respectively the most effective parameters in estimating the scour depth in cohesive and non-cohesive soils.
شفاعی بجستان،م. 1387. مبانی نظری و عملی هیدرولیک انتقال رسوب .انتشارات دانشگاه شهید چمران اهواز، چاپ اول.
Ansari,S.A., Kothyari,U.C and Ranga Raju,K.G. 2002. Influence of cohesion on scour around bridge piers. Journal of Hydraulic Research. 40.6: 717–729.
ASCE, Task Committee on Application of Artificial Neural Networks in Hydrology. 2000. Artificial Neural Networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering, ASCE. 5.2: 115-123.
Bateni,S.M., Borghei,S.M and 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.
Depnath,K and Chaudhuri,S. 2010. Laboratory experimental on local scour around cylinder for clay – sand mixed beds. Journal of Geology. 111: 51 – 61.
Ettema,R.E. 1980. Scour at Bridge Piers. Department of Civil Engineering, University of Auckland, Auckland, New Zealand, 216.
Federal Highway Administration 2003. Bridge scour in non-uniform sediment mixtures and in cohesive materials. No. FHWA-RD – 03 - 083.
Federal Highway Administration. 1999. Exprimenal study of scour around circular pier in cohesive soils. 4. No. FHWA- RD – 99 - 186.
Firat,M and Gungor,M. 2009. Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software. 40: 731–737.
Froehlich,D.C. 1987. Local Scour at Bridge Piers from Onsite Measurements, U.S. Geological Survey, Water Resources Division. 11: 534-539.
Lee,T.L., Jeng,D.S., Zhang,G.H and Hong,J.H. 2007. Neural network modeling for estimation of scour depth around bridge piers. Hydrodynamics. 19.3: 378–386.
Mohammad,Z.K., Beheshti,A.A., Behzad,A.A and Sabbagh-Yazdi,S.R. 2009. Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Applied soft computing. 9.2: 746–755.
Mueller,D.S and Wagner,C.R. 2005. Field observations and evaluations of streambed scour at bridges. Rep. No. FHWA-RD-03-052, Office of Engineering Research and Development, Federal Highway Administration, 134.
Najafzadeh,M., Etemad-Shahidi,A and Lim,S.Y. 2016. Scour prediction in long contractions using ANFIS and SVM. Ocean engineering. 111: 128-135.
Pal,M., Singh,N.K and Tiwari,N.K. 2012. M5 model tree for pier scour prediction using field dataset. Civil Engineering. 16.6: 1079–1084.
Richardson,E.V and Davis,S.R. 2001. Evaluating Scour at Bridges. 4th edn. Hydraulic Engineering Circular No. 18, Rep. No. FHWA NHI 01-001,Federal Highway Administration, Washington, DC.
Shen,H.W., Schneider,V.R and Karaki,S.S. 1969. Local scour around bridge piers. Hydraulic Division ASCE. 95.6: 1919-1940.