Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/10748
標題: 類神經網路在堤趾沖刷深度預測之研究
Prediction of Toe Scour Depth at Seawall Using Artificial Neural Network
作者: 張庭瑞
Chang, Ting-Jui
關鍵字: 類神經網路;ANN;堤趾沖刷;BPN;Toe scouring
出版社: 土木工程學系所
引用: 參考文獻 1. Arneborg, L., Hansen, E.A., and Juhl, J. (1995). “Numerical modeling of local scour at partially reflective structures,” Final Proceedings of the Project Rubble-Mound Breakwater Failure Models, MAST Contract No. MAS2-CT92-0047, Vol. 2. Commission of the European Communities, Directorate General for Science, Research and Development. 2. Eckert, J.W. (1983). “Design of toe protection for coastal structures,” Proceedings, Coastal Structures 83, American Society of Civil Engineers, pp. 331-341. 3. Fredsoe, J., and Sumer, B.M. (1997). “Scour at the round head of a rubble-mound breakwater,” Coastal Engineering, Vol. 29, pp. 231-262. 4. Hales, L.Z. (1980). “Erosion control of scour during construction: Report 2. Literature survey of theoretical, experimental and prototype investigations,” Technical Report HL-80-3. US Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS. 5. Herbich, J., and Ko, B. (1968). “Scour of sand beaches in front of seawalls,” Proceedings of 7th International Conference on Coastal Engineering, ASCE, pp. 622-643. 6. Irie, I., and Nadaoka, K. (1984). “Laboratory reproduction of seabed scour in front of breakwaters,” Proceedings of 19th International Conference on Coastal Engineering, ASCE, Vol. 2, pp. 1715-1731. 7. Kuo, C.T., and Hwung, H.H. (1975). “Study on the scouring of seawall,” International Association of Hydraulic Research. 8. Lee, T.L. (2004). “Back-propagation neural network for long-term tidal predictions,” Ocean Engineering, Vol. 31, pp. 225-238. 9. Sato, S., Tanaka, N., and Irie, I. (1969). “ Study on scouring at the foot of coastal structures,” Coastal Engineering in Japan, Vol. 37, pp. 579-598. 10. Sumer, B.M., and Fredsoe, J. (2000). “Experimental study of 2D scour and its protection at a rubble-mound breakwater,” Coastal Engineering, Vol. 40, pp. 59-87. 11. Sutherland, J., Brampton, A., Motyka, G., Blanco, B., and Whitehouse, R. (2003). “Beach lowering in front of coastal structures,” Research Scoping Study Rep. No. FD1916/TR1, <http://sciencesearch.defra.gov.uk/> (May 4, 2006) 12. Tsai, C.P., Chen, H.B., and You, S.S. (2009). “Toe scour of seawall on a steep seabed by breaking waves,” Journal of Waterway. Port. Coast. Ocean Eng., ASCE, Vol. 135, No. 2, pp. 61-68. 13. 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. 14. 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. 15. 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. 16. Tsai, C.P., Wang, J.S., and Lin, C. (1998). “Down-rush flow waves on sloping seawalls,” Ocean Engineering, Vol. 25, No. 4-5, pp. 295-308. 17. Twu, S.W., and Liao, W.M. (1999). “Effects of seawall slopes on scour depth,” Journal of Coastal Research, Vol. 15, No. 4, pp. 985-990. 18. Xie, S.L. (1981). “Scouring patterns in front of vertical breakwaters and their influence on the stability of the foundations of the breakwaters,” Report, Department of Civil Engineering, Delft University of Technology, Delft, The Netherlands, pp. 61. 19. 李宗霖、Rajasekaran, S.、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。 20. 涂盛文、邱惠萍(1994),「海堤最佳面坡之二維試驗研究」,第十六屆海洋工程研討會論文集,第C-17~C-30頁。 21. 涂盛文、劉正琪、李宜靜(1995),「海堤最佳面坡之二維試驗研究-碎波試驗」,第十七屆海洋工程研討會論文集,第1139-1153 頁。 22. 陳鴻彬、蔡清標、陳文喜、游永傑(2003),「碎波作用下斜面及階梯海堤之堤趾沖刷研究」,中國海事商業專科學校學報,第17-46 頁。 23. 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。 24. 簡仲和、黃俊維、郭晉安(2003),「不規則波作用下海堤堤趾沖淤特性之初步研究」,第二十五屆海洋工程研討會論文集,第421-428頁。
摘要: 
海堤為保護海岸的重要結構物,其最常受到破壞的原因為堤趾沖刷。目前許多文獻已提出堤趾沖刷的相關經驗公式,但簡化之經驗公式常無法有效的堤趾沖刷深度之預測。
本研究係利用倒傳遞類神經網路進行對海堤堤趾沖刷深度的預測。採用前人實驗數據並先將資料依照其底床坡度分為陡坡與緩坡情況,再經由類神經網路訓練及測試,透過探討其輸入參數之過程求得各情況最佳預測模式。最後得到輸入堤前相對水深、入射波波浪尖銳度、底床坡度及堤面坡度等四項輸入因子來建立堤趾沖刷深度的最佳預測模式,其相關係數皆達0.95以上。並嘗試將陡坡緩坡數據混合進行預測,發現預測結果之相關係數亦可達0.8960。藉由本研究之結果可知,本模式緩坡情況下預測堤趾沖刷之成果較Sumer and Fredsoe (2000)所提出之經驗公式為佳。而陡坡情況其預測之沖刷深度有著沖刷深度會隨著波浪尖銳度增加而變深,隨著堤前相對水深增加而變淺之特性與Tsai et al. (2009)實驗的特性一致。
URI: http://hdl.handle.net/11455/10748
其他識別: U0005-0108201212000900
Appears in Collections:土木工程學系所

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