Skip to main content
Log in

Stability number prediction for breakwater armor blocks using Support Vector Regression

  • Published:
KSCE Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

This paper presents the Support Vector Regression (SVR) to predict the stability number of armor blocks of breakwaters. The experimental data of van der Meer are used as the training and test data for the SVR in this study. Estimated results of SVR are compared with those of the empirical formula and a previous Artificial Neural Network (ANN) model. The comparison of results shows the efficiency of the proposed method in the prediction of the stability numbers. The proposed method proves to be an effective tool for designers of rubble mound breakwaters to support their decision process and to improve design efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Arora, J. S. (1989). Introduction to optimum design, McGraw-Hill, New York.

    Google Scholar 

  • Bazaraa, M. S., Sherali, H. D., and Shetty, C. M. (1993). Nonlinear programming: Theory and algorithms, 2nd Ed., John Wiley & Sons, Inc., New York.

    MATH  Google Scholar 

  • Hanzawa, M., Sato, H., Takahashi, S., Shimosako, K., Takayama, T., and Tanimoto, K. (1996). “New stability formula for wave-dissipating concrete blocks covering horizontally composite breakwaters.” ASCE 25th Coastal Engineering Conference, pp. 1665–1678.

  • Hudson, R. Y. (1958). Design of quarry stone cover layer for rubble mound breakwaters, Waterways Experiment Station, Coastal Engineering Research Centre, Research Report No.2-2, Vicksburg, Miss.

    Google Scholar 

  • Kaku, S. (1990). Hydraulic stability of rock slopes under irregular wave attack, Master Thesis, University of Delaware, Newark, Del.

    Google Scholar 

  • Kaku, S., Kobayashi, N., and Ryu, C. R. (1991). “Design formulas for hydraulic stability of rook slopes under irregular wave attack.” 38th Japanese Conference Coastal Engineering, pp. 661–665.

  • Kim, D. K., Kim, D. H., Chang, S. K., and Chang, S. K. (2007). “Modified probabilistic neural network considering heterogeneous probabilistic density functions in the design of breakwater.” KSCE Journal of Civil Engineering, KSCE, Vol. 11, No. 2, pp. 65–71.

    Article  MathSciNet  Google Scholar 

  • Kim, D. H. and Park, W. S. (2005). “Neural network for design and reliability analysis of rubble mound breakwaters.” Ocean Engineering, Vol. 32, No. 11/12, pp. 1332–1349.

    Article  Google Scholar 

  • Lee, J. J., Kim, D. K., Chang, S. K., and Lee, J. H. (2007). “Application of support vector regression for the prediction of concrete strength.” Computers and Concrete, Vol. 4, No. 4, pp. 299–316.

    Google Scholar 

  • Mase, H., Sakamoto, M., and Sakai, T. (1995). “Neural network for stability analysis of rubble-mound breakwater.” ASCE Journal of Waterway, Port, Coastal, & Ocean Engineering, Vol. 121, No. 6, pp. 294–299.

    Article  Google Scholar 

  • Mita, A. and Hagiwara, H. (2003). “Quantitative damage diagnosis of shear structures using support vector machine.” KSCE Journal of Civil Engineering, Vol. 7, No. 6, pp. 683–689.

    Google Scholar 

  • Mukherjee, S., Osuna, E., and Girosi, F. (1997). “Nonlinear prediction of chaotic time series using support vector machines.” Proceedings of IEEE NNSP, Amelia Island, FL.

  • Muller, K. R., Smola, A., Ratsch, G., Scholkopf, B., Kohlmorgen, J., and Vapnik, V. (1997). “Predicting time series with support vector machines.” Proceedings of ICANN, Lausanne.

  • Samui, P. (2000). “Support vector machine applied to settlement of shallow foundations on cohesionless soils.” Computers and Goetechnics, Vol. 35, No. 3, pp. 419–427.

    Article  Google Scholar 

  • Samui, P., Pradeep, K., and Sitharam, T. G. (2008). “OCR prediction using support vector machine based on piezocone data.” Journal of Geotechnical and GeoEnvironmental Engineering, Vol. 134, No. 6, pp. 894–898.

    Article  Google Scholar 

  • Smith, W. G., Kobayashi, N., and Kaku, S. (1992). “Profile changes of rock slopes by irregular waves.” ASCE 23rd Coastal Engineering Conference, pp. 1559–1572.

  • Steve, G. (1998). Support vector machines for classification and regression, ISIS Technical Report, University of Southampton.

  • Stitson, M. O., Weston, J. A. E., Gammerman, A., Vork, V., and Vapnik, V. (1996). Theory of support vector machines, Technical Report CSD-TR-96-17, Department of Computer Science, Royal Holloway College, University of London.

  • van der Meer, J. W. (1988a). “Deterministic and probabilistic design of breakwater armor layers.” ASCE Journal of Waterway, Port, Coastal, & Ocean Engineering, Vol. 14, No. 1, pp. 66–80.

    Google Scholar 

  • van der Meer, J. W. (1998b). Rock slopes and gravel beaches under wave attack, PhD Thesis, Delft Univ. of Technology, Delft, The Netherlands.

    Google Scholar 

  • Vapnik, V. (1995). The nature of statistical learning theory, Springer Berlin, New York.

    MATH  Google Scholar 

  • Vapnik, V. (1999). The nature of statistical learning theory, 2nd Edition, Springer Berlin, New York.

    Google Scholar 

  • Willmott, C. J. (1981). “On the validation of models.” Physical Geography, Vol. 2, No. 2, pp. 184–194.

    Google Scholar 

  • Ye, Q., Huang, Q., Gao, W., and Zhao, D. (2005). “Fast and robust text detection in images and video frames.” Image and Vision Computing, Vol. 23, No. 6, pp. 565–576.

    Article  Google Scholar 

  • Yu, P. S., Chen, S. T., and Chang, I. F. (2006). “Support vector regression for the real-time flood stage forecasting.” Journal of Hydrology, Vol. 328, Nos. 3–4, pp. 704–716.

    Article  Google Scholar 

  • Zhang, J., Sato, T., and Iai, S. (2006). “Support vector regression for online health monitoring of large-scale structures.” Structural Safety, Vol. 28, No. 4, pp. 392–406.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dookie Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, D., Kim, D.H., Chang, S. et al. Stability number prediction for breakwater armor blocks using Support Vector Regression. KSCE J Civ Eng 15, 225–230 (2011). https://doi.org/10.1007/s12205-011-1031-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-011-1031-1

Keywords

Navigation