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http://dx.doi.org/10.9765/KSCOE.2011.23.1.109

Prediction of Stability Number for Tetrapod Armour Block Using Artificial Neural Network and M5' Model Tree  

Kim, Seung-Woo (Department of Civil and Environmental Engineering, Seoul National University)
Suh, Kyung-Duck (Department of Civil and Environmental Engineering, Seoul National University)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.23, no.1, 2011 , pp. 109-117 More about this Journal
Abstract
It was calculated using empirical formulas for the weight of Tetrapod, which was a representative armor unit in the rubble mound breakwater in Korea. As the formulas were evaluated from a curve-fitting with the result of hydraulic test, the uncertainty of experimental error was included. Therefore, the neural network and M5' model tree were used to minimize the uncertainty and predicted the stability number of armor block. The index of agreement between the predicted and measured stability number was calculated to assess the degree of uncertainty for each model. While the neural network with the highest index of agreement have an excellent prediction capability, a significant disadvantage exists that general designers can not easily handle the method. However, although M5' model tree has a lower prediction capability than the neural network, the model tree is easily used by the designers because it has a good prediction capability compared with the existing empirical formula and can be used to propose the formulas like an empirical formula.
Keywords
Tetrapod; Artificial neural network; M5' model tree; Index of agreement;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Kaku, S., Kobayashi, N. and Ryu, C. R. (1991). Design formulas for hydraulic stability of rock slopes under irregular wave attack, Proc. 38th Japanese Conf. on Coast. Engrg., JSCE, Tokyo, Japan, 661-665 (in Japanese)
2 Kim, D. H. and Park, W. S. (2005). Neural network for design and reliability analysis of rubble mound breakwaters, Ocean Engineering, 32, 1332-1349.   DOI   ScienceOn
3 Kim, D., Kim, D. H. and Chang, S. (2008). Application of probabilistic neural network to design breakwater armor blocks, Ocean Engineering, 35, 294-300.   DOI   ScienceOn
4 Mase, H., Sakamoto, M. and Sakai, T. (1995). Neural network for stability analysis of rubble-mound breakwaters, J. Wtrwy. Port. Coast. Ocean Eng., 121(6), 294-299.   DOI   ScienceOn
5 Quinlan, J. R. (1992). Learning with continuous classes. In: Adams, Sterling, editors. Proceedings of AI'92. World Scientific, 343-348.
6 Smith, W. G., Kobayashi, N. and Kaku, S. (1992). Profile changes of rock slopes by irregular waves, Proc. 23rd Int. Conf. on Coast. Engrg., ASCE, New York, 1559-1572.
7 US Army Corps of Engineers (1987). Shore Protection Manual, U.S. Army Corps of Engineers.
8 van der Meer, J. W. (1988). Stability of Cubes, Tetrapods and Accropods, Proc. of the Breakwaters '88 Conference; Design of Breakwaters, Institution of Civil Engineering, Thomas Telford, London, UK, 71-80.
9 Willmott, C. J. (1981). On the validation of models, Phys. Geogr., 2(2), 184-194.
10 김승우, 서경덕 (2009). 국내에서 시공된 Tetrapod 피복재에 대한 Hudson 공식의 부분안전계수 산정, 한국해안해양공학회논문집, 21(5), 345-356.
11 해양수산부 (2001a). 경사식 방파제의 최적설계 기술개발 (I), 한국해양연구원.
12 해양수산부 (2001b). 경사식 방파제의 최적설계 기술개발 (II), 한국해양연구원.
13 Burcharth, H. F. and Sorenson, J. D. (2000). The PIANC safety factor system for breakwaters, Proc. Coastal Structures '99, Spain, 1125-1144.
14 Delft hydraulics (1987). Stability of rubble mound breakwaters. Stability formula for breakwaters armoured with Tetrapods, Report on basic research, H462 Volume II.
15 Jekabsons, G. (2010). M5' regresion tree and model tree toolbox for Matlab, 2010, avaiable at http://www.cs.rtu.lv/jekabsons, M5PrimeLab.
16 De Jong, R. J. (1996). Wave transmission at low-crested structures. Stability of tetrapods at front, crest and rear of a low-crested breakwater, MSc-thesis, Delft University of Technology.
17 Erdik, T. (2009). Fuzzy logic approach to conventional rubble mound structures design, Expert Systems with Applications, 36, 4162-4170.   DOI   ScienceOn
18 Etemad-Shahidi, A. and Bonakdar, L. (2009). Design of rubblemound breakwaters using M5' machine learning method, Applied Ocean Research, 31, 197-201.   DOI   ScienceOn