Investigating the Effect of Flow and Sediment Particles Characteristics on Sandy Sediments Transport in Circular Sections using Data Driven Methods

Author

department of civil engineering,Islamic azad university, Tabriz Branch

Abstract

Sedimentation is one of the important phenomena in water engineering due to its effect on the transport capacity and hydraulic performance of water transport structures. As there are numerous factors that affect this phenomenon, it is difficult to accurately determine the most influential parameters. In the present study, the efficiencies of intelligent Gaussian Process Regression (GPR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) approaches in the prediction of sandy sediments in circular pipes with rough and smooth beds have been evaluated. Using several series of laboratory data, different models were defined with considering the impacts of hydraulic parameters and sediment particle characteristics and evaluated for rough and smooth pipes. The results showed a high accuracy of the methods used in the present research. According to the results in estimating the sediment load in circular pipes using only hydraulic parameters did not lead to accurate results, and the properties of sediment particles also affect the estimation process of this parameter. With performing sensitivity analysis, it was observed that the particle's Froude number is the most important parameter in the estimation of sediment load in circular pipes. Also, the results showed that the bed and walls roughness of pipes was effective in sediment transport and the increase of roughness values reduced the accuracy of the results.

Keywords


 
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.
Bertrand JL.2010. Sewer sediment production and transport modeling: A literature review. Journal of Hydraulic
 Research. 5: 24-32.
Bong CH, 2014. Self-cleansing design of rectangular open storm sewer. 13th International Conference on Urban
 Drainage, Sarawak, Malaysia.
El-Zaemey AK.1991. Sediment transport over deposited beds in sewers. Ph. D Thesis, University of Newcastle
 Upon Tyne, UK.
Falamaki A, Eskandari M, Baghlani A, Ahmadi SA,2013. Modeling total sediment load in rivers using artificial neural
 networks. Water and Soil Science - University of Tabriz 2(3),13-26 (In Farsi) 
Ghani A.1993.Sediment Transport in Sewers. Ph. D Thesis, University of Newcastle Upon Tyne, UK.
Jang JR, 1993.ANFIS: Adaptive Network-Based Fuzzy Inference System, Proc., IEEE Conf. Transactions on Systems Man and Cybernetics 23: 665-685.
Laursen EM.1956. The Hydraulics of a Storm-Drain System for Sediment Transporting Flow. Bull. No 5, Lowa
 Highway Research Board.
May RW.1982. Sediment transport in sewers. Hydraulic Research Station, Wallingford, England, Report IT 222.
Mayerle R.1988. Sediment transport in rigid boundary channels. PhD thesis. University of Newcastle upon Tyne
 England.
Ota JJ, Perrusquia GS. 2013.Particle velocity and sediment transport at the limit of deposition in sewers. Water
 Science and Technology 67(5): 959-967.
Renaat D.S. 2013.Validation of existing bed load transport formulas using In-Sewer sediment. Journal of
 Hydraulic Engineering 12(1):325-338.
Rezazadeh-Joudi, A, Sattari, MT, 2016. Estimation of scour depth of piers in hydraulic structures using gaussian process Regression, Journal of Applied Research in Irrigation and Drainage Structures Engineering 65(16): 19-36(In Persian)
Roushangar K.2014. Modeling river total bed material load discharge using artificial intelligence approaches
(based on conceptual inputs). Journal of Hydrology 514: 114-122.
Siviapragasam C, Liong S.2001. Rainfall and runoff forecasting with SSA-SVM approach. Journal of
Hydroinformation 3:141-152.
Vongvisessomjai N.2010. Non-deposition design criteria for sewers with part-full flow. Urban Water Journal 7(1):
61–77.