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Nonequilibrium Sorting of Nonuniform Gravel in a Steep Channel
|關鍵字:||steep channel;陡坡渠槽;non-uniform gravel;non-equilibrium sediment transport;grain-sorting;artificial neural network;混合礫石;非平衡輸沙;顆粒篩選;類神經網路||出版社:||土木工程學系||摘要:||
Mountain rivers such as those in the upland areas of Taiwan usually have gravel with wide gradations. The sorting phenomenon frequently occurs in the bed material during the floods.. After the ghastly 921 earthquake, the mountainous region contains a large quantity of loose sediment. In addition, with the advent of global warming, the torrential rain carried by typhoons tends to fall intensively within a specific area. For example, the heavy rainfall on July 2, 2004 caused the Da-Chia River in the central Taiwan to overload with gravel, and subsequently affected the national economy and the people's livelihood enormously.
In this study, the selective transport phenomenon of the non-uniform gravel under the non-equilibrium condition was investigated according to the equilibrium condition data from the author's Master's thesis. With the geometric standard deviations of 2 and 1.5, the same size ranging from 2.36 to 38.1 mm, and the same median size of 7.5 mm, two kinds of particle size distributions were prepared by sieve analysis of the natural gravel. As a result, forty non-equilibrium tests (including the total runs and the subruns) were conducted under both the underloading and overloading conditions with a slope ranging from 2% to 5%.
According to the experimental results, the grain-sorting mechanism includes both the gravity sorting along the longitudinal direction and the vertical sorting by the hiding phenomenon. As the non-equilibrium experiments were carried out under the conditions of larger geometric standard deviation and steeper bed slope, phenomena such as the mobility reversal due to the gravity effect, and the flow intensity influenced by the fining of bed material, became more obvious. The downstream coarsening of the median size in both the bed load and the deposited layer occurred mainly due to gravity effect in aggrading laboratory deposits. In addition, the lower limit of the coarse grains affected by the gravity effect decreased with an increase of bed slope (mobility reversal), so the upward fining of the deposited material also occurred due to the hiding phenomenon.
The sorting phenomenon was verified by the analysis of the roughness of the aggrading deposits in the subruns. In addition, developed from the ANN (artificial neural network) model for predicting the bed load with non-uniform gravel in steep slopes in this study, the BPN (back-propagation network) scheme was also proved to be reasonably accurate with flume data collected under the equilibrium condition.
|Appears in Collections:||土木工程學系所|
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