Prediction and determination of effective parameters of local energy loss in culvert systems, using intelligent evolutionary Algorithm

Document Type : Original Article

Authors

1 Associate Professor, Department of Civil Engineering, University of Tabriz, Tabriz, Iran

2 M.sc student, Department of Civil Engineering, University of Tabriz, Tabriz, Iran

Abstract

A culvert is a hydraulically short conduit which conveys stream flow through a roadway or embankment. Culverts are available in many different shapes and in spite of their simple structure, the process of designing requires accurate calculations and they are controlled by many variables. Accurate estimation of the energy loss in culverts plays an important role in optimum designing of culvert systems. In this paper, in order to simulate the local energy loss coefficient in culverts with various geometries (circular and rectangular), several models with different inputs were defined. The optioned results proved the capability of GEP in predicting local energy loss coefficient in culvert systems. Also it was found that in order to estimate the bend loss coefficient, model with input parameters of (Fr, θ) and to estimate the entrance loss coefficient, models with input parameters of (Fr, ) led to more accurate outcomes. Sensitivity analyses showed that θ and had the key role in estimating bend loss and entrance loss coefficient.

Keywords

References

کاوه­کار،ش.، قربانی،م.، اشرف­زاده،ا.، دربندی،ص. 1392. شبیه­سازی نوسانات تراز آب با استفاده از برنامه­ریزی بیان ژن. نشریه مهندسی عمران و محیط زیست دانشگاه تبریز. 43. 72: 75-69
Anderson,D.S. 2006. Inlet Loss Coefficients and Inlet Control Head-Discharge Relationships for Buried-Invert Culverts and Slip-Lined Culverts. Ph.D. Thesis: Utah State University, Logan, UT 113 pp.
Ferreira,C. 2001. Algorithm for solving gene expression programming: a new adaptive problems. Complex systems. 13.2:87-129.
Ferreira,C. 2004. Gene expression programming and the evolution of computer programs. Recent developments in biologically inspired computing. 82-103.
Graziano,F., Martin,B., Stein,S., Umbrell,E. 2001. South Dakota culvert inlet design coefficients Turner-Fairbank Highway Research Center. (No. FHWA-RD-01-076).
Jones,J.S., Kerenyi,K., Stein,S. 2006. Effects of inlet geometry on hydraulic performance of box culverts. Federal Highway Administration (No. FHWA-HRT-06-138).
Kisi,O., Shiri,J., Tombul,M. 2013. Modeling rain fall-runoff process using soft computing techniques. Computers geosciences, 51, 1018-117.
Kotowski,A., Szewczyk,H., Ciezak,W. 2011. Entrance loss coefficients in pipe hydraulic systems. Environment Protection Engineering. 37.4: 105-117.
Lopes,H.S., Weinert,W.R. 2004. EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems. International Journal of Applied Mathematics and Computer Science. 14.3: 375-384.
Malone,T.R., Parr,A.D. 2008. Bend losses in rectangular culverts. Kansas Department of Transportation. (No. K-TRAN: KU-05-5)
Robinson,S.C. 2005. Hydraulic characteristics of a buried invert elliptical culvert inlet and quantification of culvert exit loss (Doctoral dissertation, Utah State University, Department of Civil and Environmental Engineering.
Roushangar,K., Ghasempour,R. 2017. Estimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithm. International Journal of Sediment Research..32.4:564_574
Shiri,J., Kisi,O. 2011, Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers and geosciences. 37.10: 1692-1701.
Tullis,B.P. 2012. Hydraulic loss coefficients for culverts (R. 734). Transportation Research Board.123p
 Tullis,B., Robinson,S., Young,J. 2005. Part 3: Hydrology, Hydraulics, and Water Quality: Hydraulic Characteristics of Buried-Invert Elliptical Culverts. Transportation Research Record: Journal of the Transportation Research Board.1904: 104-112.
Wang,W.C., Chau,K.W., Cheng,C.T., Qiu,L. 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journals of Hydrology. 374.3-4: 294-306.
Iranian Journal of Irrigation & Drainage
Volume 12, Issue 5 - Serial Number 71
January and February 2019
Pages 1073-1085

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