Skip to main content

Using PSO and RST to Predict the Resistant Capacity of Connections in Composite Structures

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 284))

Abstract

In this paper, a method is proposed that combines the methaheuristic Particle Swarm Optimization (PSO) with the Rough Set Theory (RST) to carry out the prediction of the resistant capacity of connectors (Q) in the branch of Civil Engineering. The k-NN method is used to calculate this value. A feature selection process is performed in order to develop a more efficient process to recover the similar cases; in this case, the feature selection is done by finding the weights to be associated with the predictive features that appear in the weighted similarity function used for recovering. In this paper we propose a new alternative for calculating the weights of the features based on extended RST to the case of continuous decision features. Experimental results show that the algorithm k-NN, PSO and the method for calculating the weight of the attributes constitute an effective technique for the function approximation problem.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Eurocode 4 (EN 1994-1-1). Desing of Composite Steel and Concrete Structures Part 1.1. European Committee for Standardization, Brussels (2004)

    Google Scholar 

  2. Load and Resistance Factor Design (LRFD) Specification for Structural Steel Building. American Institute of Steel Construction (AISC), Inc., Chicago (2005)

    Google Scholar 

  3. NR 080-2007, Calculation of between floors made up of concrete and steel with soul beams full subjected to load static. Code of good practical: Brunch Norma of the Ministry of the Construction of Cuba (2007)

    Google Scholar 

  4. Stahl, A., Gabel, T.: Using evolution programs to learn local similarity measures. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 537–551. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. García Martíez, C., et al.: Global and local real-coded genetic algorithms based on parent-centric crossover operators. European Journal of Operational Research 185, 1088–1113 (2008)

    Article  Google Scholar 

  6. Dasarathy, B.V., Sánchez, J.S.: Nearest neighbour editing and condensing. tools - synergy exploitation. Pattern Analysis Applications (2000)

    Google Scholar 

  7. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)

    MathSciNet  Google Scholar 

  8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  9. Kennedy, J., Eberhart, R.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  10. Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Natural Computing 1, 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  11. Reyes-Sierra, M., Coello Coello, C.: Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  12. Mitchell, T.: Machine learning, p. 414. McGraw Hill, New York (1997)

    MATH  Google Scholar 

  13. Pawlak, Z.: Rough sets. International Journal of Information Computer Sciences 11, 145–172 (1982)

    MathSciNet  Google Scholar 

  14. Lopez, R.L., Armengol, E.: Machine learning from examples: Inductive and lazy methods. Data Knowlege Engineering 25, 99–123 (1998)

    Article  MATH  Google Scholar 

  15. Herrera, F., Lozano, M., Sánchez, A.: A taxonomy for the crossover operator for real coded genetic algorithms: An experimental study. International Journal of Intelligent Systems 18, 309–338 (2003)

    Article  MATH  Google Scholar 

  16. Gabel, T., Riedmiller, M.: CBR for state value function approximation in reinforcement learning. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 206–221. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Filiberto, Y., Bello, R., Caballero, Y., Larrua, R. (2010). Using PSO and RST to Predict the Resistant Capacity of Connections in Composite Structures. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12538-6_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12537-9

  • Online ISBN: 978-3-642-12538-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics