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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
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
引用: 1.Daemrich, K. -F., Meyering, J., Ohle, N. and Zimmermann, C. (2006) “Irregular wave overtopping at vertical walls – Learning from regular wave tests,” Proceedings of the 30th International Conference on Coastal Engineering, ASCE, San Diego. 2.Daemrich, K. -F., Tack, G., Zimmermann, C. and Cheng, H. -Y. (2006) “ Irregular wave overtopping based on regular wave tests,” Proceedings of the 3rd Chinese-German Joint Symposium on Coastal and Ocean Engineering, National Cheng Kung University, Tainan. 3.Eds Pullen, T., Allsop, N. W. H., Bruce, T., Kortenhaus, A., Schüttrumpf, H. and van der Meer, J. W. (2007) Wave Overtopping of Sea Defences and Related Structures: Assessment Manual, EurOtop, 4.Endo, S. and Miura, A. (1983) “Experimental study on sea wave movement facing a vertical wall,” Rept. Res. Inst. Of Industrial Tech., Nihon University, Japan, pp. 20. 5.Franco, L., de Gerloni, M. and Van der Meer, J. W. (1994) “Wave Overtopping on Vertical and Composite Breakwaters,” Proceedings of the 24th International Conference on Coastal Engineering, American Society of Civil Engineers, Vol. 1, pp 1030-1045. 6.Goda, Y. and Tsuruta, S. (1968) “Expected discharge of irregular wave overtopping,” Proceedings of the 11th International Conference on Coastal Engineering, ASCE, pp.833-852. 7.Goda, Y. (1970) “A synthese of breaker indices,” Tranaction of Japan Society of Civil Engineers,Vol. 2, Part 2, pp. 227 - 230. 8.Goda, Y. (1970) ”Estimation of the rate of irregural wave overtopping of seawalls,” Rept. Port and Harbour Res. Inst., Vol. 9, No. 4, pp. 3-41. 9.Goda, Y. (1985) Random seas and design of marine structures, University of Tokyo Press, pp. 323. 10.Goda, Y. (2000) Random seas and design of marine structures, World Scientific, Advanced Series on Ocean Engineering, Vol. 15. 11.Iwagaki, Y., Shima, A. and Inoue, M. (1965) “Effects of Wave Height and Sea Water Level on Wave Overtopping and Wave Run-up,” Coastal Engineering in Japan, Vol. 8, pp. 141-154. 12.Kikkawa, H. Shi-Igai, H. and Kono, T. (1968) “Fundamental study of water over-topping on levees”, Coastal Engineering in Japan, Vol. 11, pp. 107-115. 13.Lee, T. L. (2004) “Back-propagation neural network for long-term tidal predictions,” Ocean Engineering, Vol. 31, pp. 225 - 238. 14.Owen M. W. (1980) “Design of Seawalls Allowing for Wave Overtopping,” Report No. Ex 924, Hydraulics Research Station, Wallingford, U. K. 15.Saville, T. and Caldwell, J. M. (1953) “Experimental Study of Wave Overtopping on Shore Structures,” Proc. IAHR., pp. 261 - 269. 16.Sibul, O. J. (1955) “Flow over Structure by Wave Action,” Trans .A. G. U., Vol. 36, No. 1, pp. 61 - 69. 17.Takada, A. (1970) “On relations among wave run-up, overtopping and reflection”, Proc. Japan Soc. Civil Eng., No. 182, pp. 19-30. 18.Tang, Z., Almeida, C. D. and Fishwick, P. A. (1991) “Time series forcasting using neural networks vs. Box-Jenkins methodology,” Simulation, pp. 303-310. 19.Tsai, C. P., Lee, T. L. and Chu, L. H. (1999) “Forecasting of wave time series using back propagation neural network,” Journal of the Chinese Institute of Civil and Hydraulic Engineering, Vol.11, pp.589-596. 20.Tsai, C. P., Lin, C. and Shen, J. N. (2002) “Neural network for wave forecasting among multi-stations,” Ocean Engineering, Vol. 29, pp.1683-1695. 21.Tsai, C. P., Lee, T. L., Yang, T.J. and Hsu, Y. J. (2005) “Back-propagation neural networks for Prediction of Storm Surge,” Structural and Environmental Engineering, Civil-comp Press. Vol. 11, pp. 589–596. 22.Tsai, C. P., Daemrich, K. -F. and Ho, C. L. (2008) “Determination of wave overtopping using neural network,” Chinese-German Joint Symposium on Coastal and Ocean Engineering, Darmstadt, pp. 519 - 523. 23.Umeyama, m. (1993) “Wave overtopping on vertical boundary and water-surface displacement,” Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, Vol. 119, No. 3, pp. 243-260. 24.Van der Meer, J., Verhaeghe, H. and Steendam, G. J. (2005) “Database on wave overtopping at coastal structure,” CLASH WP2 database, Infram, Marknesse, The Netherlands. (database freely available on: ). 25.Van der Meer, J. W. and Janssen, J. P. F.M. (1994) “Wave run-up and wave overtopping at dikes and revetments,” Delft Hydraulics, No. 485, pp. 1-22. 26.Van der Meer, J. W., Verhaeghe, H. and Steendam, G. J. (2009) “The new wave overtopping database for coastal structures,” Coastal Engineering, Vol. 56, pp. 108-120. 27.Van Gent, M. R. A., Van den Boogaard, H. F. P., Pozueta, B. and Medina, J. R. (2007) “Neural network modeling of wave overtopping at coastal structures,” Coastal Engineering, Vol. 54, No. 8, pp. 586-593. 28.Verhaeghe, H., De Rouck, J. and Van der Meer, J. (2008) “Combined classifier-quantifier model : A 2-phases neural model for prediction of wave overtopping at coastal structures,” Coastal Engineering, Vol. 55, pp. 357-374. 29.田中浩生,水口 優 (1995) 「不規則波 Ueh 越波」,海岸工學講演會論文集,第42卷,第 796 - 800 頁。 30.葉怡成 (1998) 類神經網路模式應用與實作,儒林圖書有限公司。 31.李宗霖、S. Rajasekaran、徐月娟、楊宗儒 (2003) 「序列學習神經網路在颱風期間之潮位預測」,第 25 屆海洋工程研討會論文集,第 275-279 頁。 32.郭ㄧ羽 (2003) 海岸工程學,文山書局,台南。 33.張斐章、張麗秋 (2005) 類神經網路,台灣東華書局,台北。 34.陳大煒、臧效義 (2007) 「斜面提越波量計算公式與潰堤影響評估」,第 29 屆海洋工程研討會論文集,第 409 - 414 頁。 35.黃正欣、林西川、林嘉應 (2008) 「斜面提越波量及反射率之研究」,第 30 屆海洋工程研討會論文集,第 367 - 372 頁。 36.蔡清標、游智宇 (2009)「類神經網路在暴潮偏差預測之研究-以淡水河口為例」,第 31 屆海洋工程研討會論文集,第 133 - 137 頁。
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.
其他識別: U0005-2308201017571900
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