Abstract:
Many reservoirs in the world are aging, and dredging has been the most common method to maintain the function of the reservoirs. The main problem affecting the useful life of the reservoirs is sediment deposition. Knowledge of both the rate and pattern of sediment deposition in a reservoir is required to predict the types of service impairments that would occur, the time frame in which these impairments would occur, and the types of remedial strategies that could be applied. The present analysis detailed the importance of physical properties to the total load sediment fluxes using 22 equations. The study measured gravel particles and suggested properties that have more control on the final result by providing insight into the relative strengths and weaknesses. The authors concentrated on available field and flume datasets gathered from different sources rather than focusing entirely on total load equations. The artificial neural network (ANN) was used to validate this study. The results emphasized the influence of the parameters detected by ANN and showed that the parameters were directly controlling the error in the total load sediment flux using the measured gravel particle datasets. This research had theoretical and practical significance for the future investigations concerning the fundamentals of total load sediment transport in reservoir engineering.
Description:
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