NETSTARS 模式加入橋墩沖刷功能之研究─以八掌溪為例

This study applies NETSTARS V3.0 by adding the calculation functions of eighteen pier scour formulas based on a comprehensive literature review to demonstrate local scour mechanisms. The study area is a reach of the Pachang Creek from the Housheng Bridge to the Chukou Bridge. We do not set the structures and weirs in the river to be scoured. Simulations are conducted by setting boundary conditions and importing information about nineteen bridges, and validations are separated into two steps as: general scouring and bridge local scouring. The best parameters are qualified by computing error evaluated parameter to fit the changing tendencies of the Pachang Creek. Finally, long-term riverbed evolution is simulated. The results show that there are 5 bridges with erosion trends. The results can be used as a reference for one-dimensional numerical models with pier scouring functions.本研究以NETSTARS V3.0 功能為基礎，根據橋墩沖刷研究，撰寫橋墩沖刷功能並新增18 個常用的橋墩沖刷公式於模式中。研究區域為八掌溪厚生橋至觸口橋河段。在輸砂模式建置上，將結構物設定為不可沖刷，並輸入邊界條件與現有19 座橋樑資訊，完成一般沖刷與局部沖刷階段之參數檢定，並利用誤差評估參數檢視最佳參數以反應八掌溪流域河床變遷趨勢。最後對未來十年河床沖淤進行預測，推測有沖刷趨勢的橋樑共5 座，研究成果可作為一維數值模式新增橋墩沖刷功能之參考

Simulation of Rock Bed Scours in the Upper Reach of Pachang River

八掌溪仁義潭攔河堰下游有軟岩沖刷問題，特別是斷面94到9732（心上橋）之間的河床，雖然有設置固床工抗沖刷，仍然有些河段有岩盤裸露的問題，岩床經過一段時間風化後導致下次洪水後又加深侵蝕河床，為了模擬此狀況，本研究利用NETSTARS模式新增之兩種岩盤沖刷功能，以八掌溪15年的水文資料及實測河床斷面進行模擬。發現其模擬結果確實比一般沖刷模擬結果更符合實測河床變動數據。本文採用誤差評估參數評估兩者精度，模擬結果發現考慮懸浮載的公式組合時最佳。Soft-rock erosion in the lower reaches of the Renyitan weir in the Pachang River is a problem, particularly in the riverbed between cross-sections 94 and 9732 (Xinshang Bridge). Anti-scouring check dams had been built, but exposed rock plates in some river sections remain problematic. Erosion of the rock riverbed could deepen after the next flood and subsequent period of weathering. To simulate this situation, this study used two types of rock bed scouring function in the NETSTARS (Network of Sediment Transport Model for Alluvial River Simulation) model. This special erosion-deposition simulation was executed with 15 years of hydrological data and riverbed sections of the Pachang River. The simulation results of both functions were better fitted to the riverbed variation of field data than were the general erosion-deposition simulation results. In this study, an error evaluation parameter was used to evaluate the accuracy of both functions, and the simulation that considered the suspension composition in the formula produced better results

NETSTARS 模式參數最佳化之研究

本研究以離散參數試誤法、人工經驗調整法與倒傳遞類神經網路法，優選NETSTARS 模式參數，並評估其成效。所用參數為河道曼寧n 值及可沖刷厚度參數Alt 值，模擬對應的成果分別為水位歷程及河床縱斷面高程變化。第一法的推估成果被當成近似理論解，做為評估標準。由水位變動成果發現，以倒傳遞類神經網路法與離散參數試誤法得到的曼寧n 值較為一致，平均值也與人工經驗調整法接近，均適用於NETSTARS 模式，但不同事件所得之最佳參數值仍有些許差異；在河床變動成果的部分，後兩法成果均與第一法差異頗大，由於Alt 值無法由最佳化方法獲得相近的參數成果，因此這些最佳化方法均不適用於此參數之推估。Methods, such as the experience-based artificial adjustment, back-propagation neural network, and discrete-parameter trial-and-error, were used to investigate the optimal performances of these parameters of the NETSTARS model. The parameters adopted in this study include Manning's n value of channels and Alt value of scouring thickness, and the corresponding results are water level hydrograph and longitudinal riverbed profile. The results of the discrete-parameter trial-and-error method are regarded as approximate theoretical solutions, and it is regarded as a evaluation criteria. The simulation regarding water level change reveals that the Manning's n values resulting from the back-propagation neural network and the discrete-parameter trial-and-error method show the consistency, and the average of these values is also close to the result of experience-based artificial adjustment method. So, those optimization methods are suitable to NETSTRAS model for Manning's n estimation, but the optimized parameters show the significant discrepancy in different events. For the riverbed change, the results of the last two methods vary considerably with the result of the first method. Because similar Alt values cannot be obtained by the optimization methods, these methods are not applicable to this parameter's estimation