Title

高含砂水流流動室內試驗及類神經網路於流變參數推估之應用

Translated Titles

Laboratory Experiments of Hyper-concentrated Flow and Application of ANN to Rheological Parameters Prediction

DOI

10.6845/NCHU.2011.00987

Authors

莊巧巧

Key Words

高含砂水流 ; 流變 ; 類神經網路 ; Hyper-concentrated flow ; Rheology ; Artifical neural networks

PublicationName

中興大學土木工程學系所學位論文

Volume or Term/Year and Month of Publication

2011年

Academic Degree Category

碩士

Advisor

盧昭堯

Content Language

繁體中文

Chinese Abstract

台灣為一狹長島嶼,由於地形特殊,河川特徵多為坡度陡、長度短且流況湍急,使河川水流蘊藏較高的沖刷趨勢。再加上台灣位於歐亞大陸板塊與菲律賓海板塊之間,板塊活動頻繁,自1999年921集集大地震後,部分山區地質變得較鬆軟,加上居民對山坡地不當使用,地表覆蓋因而遭受破壞。每逢颱風暴雨侵襲,因雨量強度高且集中,大量土壤沖蝕及崩塌土石被挾帶而下,對河道屢生衝擊,高含砂水流之災情時有所聞。鑒於高含砂水流對河川輸砂之衝擊,王嘉筠(2008)與經濟部水利署水利規劃試驗所(2006)等曾針對濁水溪、東埔蚋溪進行流變及動床物理模型試驗,並驗證高含砂水流模式。 本研究藉由濁水溪十八重溪橋、人倫橋與太麻里溪拉灣橋採集之土樣進行物理試驗,以暸解其土壤特性,並將採集之漿體分別調配適當濃度之試樣,以Brookfield水平旋轉式流變計,進行流變試驗。其次,配合國內外現有相關資料,利用類神經網路(ANN)進行流變參數之推估預測。另外,本研究以現場量測土樣作為水槽試驗粒料選擇之依據,利用動床渠槽進行高含砂流動試驗,採不同坡度與單寬流量,進行垂向流速與濃度剖面之水理與泥砂量測。 經與文獻經驗式計算之流變參數推估比較顯示,本研究之類神經網路模式推估效果較佳。本研究除分析實測流速與濃度剖面外,亦嘗試擬合流速與濃度剖面。此外,文中並分析水流挾砂力與水流強度之關係,納入前人相關資料,推導含粒徑因子之高含砂水流挾砂力關係。

English Abstract

Taiwan is a long and narrow island. Due to the special terrain, common river characteristics include steep slope, short length and rapid flow, resulting in a high erosion potential. Morever, Taiwan is located between the Eurasia and Philippine Sea plates with frequent plate movements. After the 921 Chi-Chi earthquake, the land covers are destroyed because of the fragile mountain geology, and the improper use of the hillslopes. The hyper-concentrated flow disasters frequently occur during the typhoons and heavy rain storms because of the severe soil erosion and landslides. In view of the impact of hyper-concentrated flows on the river system, Wang (2008) and the Water Resources Planning Institute (2006) have conducted the rheological and physical mobile bed tests for the Cho-Shui River and Ton-Pu-Rey Creek to verify a hyper-concentrated flow model. In this study, the soil samples collected at the Shih-Ba-Chung Creek Bridge and Ren-Luen Bridge on the Cho-Shui River, and the La-Wan Bridge on the Taimali River were used to conduct the physical tests to increase our understanding of the soil characteristics. Samples with proper concentrations were prepared, and rheological tests were performed using the Brookfield rheometer. Furthermore, an ANN (Artifical Neural Networks) model was established for estimating the rheological parameters. It was found that the ANN model gave more accurate parameter estimations as compared with the empirical formulas. Finally, a series of hyper-concentrated flow experiments was conducted in a re-circulation flume with mobile bed. Both vertical velocity and concentration profiles were measured and compared with Chiu’s theory for tests with different slope gradients and unit flow discharges. An empirical formula was also derived based on the existing laboratory data for predicting the sediment transport capacity of the hyper-concentrated flow.

Topic Category 工學院 > 土木工程學系所
工程學 > 土木與建築工程
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