Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/16303
標題: 以類神經網路預測斜面海堤越波量之研究
Determination of Wave Overtopping Rates at Sloping Seawalls Using Neural Network
作者: Lee, Yi-Ting
李宜庭
關鍵字: neural network
類神經網路
overtopping
sloping seawalls
越波量
斜面海堤
出版社: 土木工程學系所
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摘要: 設置沿海結構物的重要目的之ㄧ是防止及減少波浪越波以降低海岸溢淹災害。往昔學者提出簡化之經驗公式來預測波浪越波量。然而影響越波量之因子較為複雜時,經驗公式常無法做有效的越波量計算,因此近幾年已有許多文獻以類神經網路預測不同型式結構物之越波量。 本文旨以類神經網路對斜面海堤結構物預測波浪越波量。本文以CLASH之資料庫中選取不同斜面海堤坡度的越波量資料,經由類神經網路模式訓練及測試過程,得出以輸入海堤相對出水高、無因次碎波參數及相對水深時,可得斜面海堤越波量的最佳預測模式。在具相同條件下,由個別坡度所建立的預測模式之精度較所有坡度建立的預測模式之精度為高。 本研究顯示,斜面海堤之重要物理參數於不同坡度之預測上,與Van der Meer et al. (1994) 比較結果,本模式的預測效果可獲得較佳之準確度。在斜面海堤波浪越波量的預測上,倒傳遞類經網路模式亦有良好的預測表現。
The determination of wave overtopping rate is an essential in the design of a coastal structure. An exact mathematical description of the wave overtopping process for coastal dikes or seawalls seems not possible due to the stochastic nature of wave breaking, wave run-up and the various factors influencing on the wave overtopping process. Therefore, wave overtopping rates for coastal dikes or seawalls were mainly determined by empirical formulas derived from experimental or field investigations. In this paper, the artificial neural network model (ANN) is applied to the prediction of overtopping rates at sloped seawalls. The data of wave overtopping rates of sloped seawalls selected from the database of CLASH are adopted for the learning and testing in the present ANN model. This paper first configures the optimum architecture of the ANN using different combinations of input factors. The results show that the ANN can achieve satisfactory prediction of the dimensionless wave overtopping rate at a sloped seawall from three input parameters; they are the relative freeboard, the surf similarity parameter and the relative water depth at toe. It is found that the accuracy is better if the learning of the ANN is based on the overtopping at the individual slope of seawall, rather than learning from all slopes of seawalls. As comparing with the prediction using empirical formula by Van der Meer et al. (1994), the results show that the present ANN model obtains better prediction.
URI: http://hdl.handle.net/11455/16303
其他識別: U0005-2308201017571900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2308201017571900
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