Journal of Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회논문집)

Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회)
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Prediction of Stability Number for Tetrapod Armour Block Using Artificial Neural Network and M5' Model Tree

인공신경망과 M5' model tree를 이용한 Tetrapod 피복블록의 안정수 예측

  • Kim, Seung-Woo (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Suh, Kyung-Duck (Department of Civil and Environmental Engineering, Seoul National University)
  • 김승우 (서울대학교 건설환경공학부) ;
  • 서경덕 (서울대학교 건설환경공학부)
  • Received : 2010.11.26
  • Accepted : 2011.02.14
  • Published : 2011.02.28
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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.

국내 경사식 방파제의 대표적인 피복재인 Tetrapod는 대부분 경험식을 사용하여 중량을 산정한다. 경험식은 수리 실험의 결과를 곡선맞춤(curve-fitting)하여 제안되기 때문에 실험 오차에 따른 불확실성이 내포되어 있다. 이런 불확실성을 최소화하기 위해 인경신경망과 M5' model tree를 사용하여 피복재 안정수를 예측하였다. 각 모형의 불확실성의 정도는 예측된 안정수와 수리실험의 안정수 사이의 일치지수(index of agreement)를 사용하여 평가하였다. 일치지수가 가장 큰 인공신경망은 우수한 예측 능력을 가지고 있지만 일반 설계자들이 쉽게 사용할 수 없는 큰 단점이 있다. 반면에 M5' model tree는 인공신경망보다는 예측 능력이 조금 떨어지지만 기존의 경험식보다는 예측능력이 우수하고 또한 일반 설계자들이 쉽게 사용할 수 있는 공식의 형태로 주어지는 장점이 있다.

Keywords

References

  1. 김승우, 서경덕 (2009). 국내에서 시공된 Tetrapod 피복재에 대한 Hudson 공식의 부분안전계수 산정, 한국해안해양공학회논문집, 21(5), 345-356.
  2. 해양수산부 (2001a). 경사식 방파제의 최적설계 기술개발 (I), 한국해양연구원.
  3. 해양수산부 (2001b). 경사식 방파제의 최적설계 기술개발 (II), 한국해양연구원.
  4. Burcharth, H. F. and Sorenson, J. D. (2000). The PIANC safety factor system for breakwaters, Proc. Coastal Structures '99, Spain, 1125-1144.
  5. Delft hydraulics (1987). Stability of rubble mound breakwaters. Stability formula for breakwaters armoured with Tetrapods, Report on basic research, H462 Volume II.
  6. 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.
  7. Erdik, T. (2009). Fuzzy logic approach to conventional rubble mound structures design, Expert Systems with Applications, 36, 4162-4170. https://doi.org/10.1016/j.eswa.2008.06.012
  8. Etemad-Shahidi, A. and Bonakdar, L. (2009). Design of rubblemound breakwaters using M5' machine learning method, Applied Ocean Research, 31, 197-201. https://doi.org/10.1016/j.apor.2009.08.003
  9. Jekabsons, G. (2010). M5' regresion tree and model tree toolbox for Matlab, 2010, avaiable at http://www.cs.rtu.lv/jekabsons, M5PrimeLab.
  10. 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)
  11. Kim, D. H. and Park, W. S. (2005). Neural network for design and reliability analysis of rubble mound breakwaters, Ocean Engineering, 32, 1332-1349. https://doi.org/10.1016/j.oceaneng.2004.11.008
  12. Kim, D., Kim, D. H. and Chang, S. (2008). Application of probabilistic neural network to design breakwater armor blocks, Ocean Engineering, 35, 294-300. https://doi.org/10.1016/j.oceaneng.2007.11.003
  13. 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. https://doi.org/10.1061/(ASCE)0733-950X(1995)121:6(294)
  14. Quinlan, J. R. (1992). Learning with continuous classes. In: Adams, Sterling, editors. Proceedings of AI'92. World Scientific, 343-348.
  15. 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.
  16. US Army Corps of Engineers (1987). Shore Protection Manual, U.S. Army Corps of Engineers.
  17. 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.
  18. Willmott, C. J. (1981). On the validation of models, Phys. Geogr., 2(2), 184-194.

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