Abstract
Large amounts of fine sediment infiltration into void spaces of coarse bed material have the ability to alter the morphodynamics of rivers and their aquatic ecosystems. Modelling the mechanisms of fine sediment infiltration in gravel-bed is therefore of high significance. We proposed a framework for calculating the sediment exchange in two layers. On the basis of the conventional approaches, we derived a two-layer fine sediment sorting, which considers the transportation of fine sediment in the form of infiltration into the void spaces of the gravel-bed. The relationship between the fine sediment exchange and the affected factors was obtained by using the discrete element method (DEM) in combination with feedforward neural networks (FNN). The DEM model was validated and applied for gravel-bed flumes with different sizes of fine sediments. Further, we developed algorithms for extracting information in terms of gravel-bed packing, grain size distribution, and porosity variation. On the basis of the DEM results with this extracted information, we developed an FNN model for fine sediment sorting. Analyzing the calculated results and comparing them with the available measurements showed that our framework can successfully simulate the exchange of fine sediment in gravel-bed rivers.
Highlights
Fine sediment infiltration, where sand or silt fills a previously vacant gravel substrate, has important ecological and river engineering implications [1,2,3,4]
On the basis of the discrete element method (DEM) results with this extracted information, we developed an feedforward neural network (FNN) model for fine sediment sorting
We compared the results of the FNN, DEM, and the experiment cited to verify the framework of DEM and FNN in simulating exchange rate and predicting fine sediment fraction in gravel-bed rivers
Summary
Fine sediment infiltration, where sand or silt fills a previously vacant gravel substrate, has important ecological and river engineering implications [1,2,3,4]. In the study by [15], the authors found that the coarse bed porosity together with the roughness Reynolds number, a combination that indicates the pore water velocity distribution is that of the initially un-infiltrated bed, was a significant factor of the maximum bridging depth. These investigations have been useful in describing and quantifying several traits of the infiltration process. We compared the results of the FNN, DEM, and the experiment cited to verify the framework of DEM and FNN in simulating exchange rate and predicting fine sediment fraction in gravel-bed rivers
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