Table of Contents
Journal of Construction Engineering
Volume 2014, Article ID 109184, 9 pages
http://dx.doi.org/10.1155/2014/109184
Research Article

An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Department of Technology and Construction Management, Faculty of Building and Industrial Construction, National University of Civil Engineering, Room 307, A1 Building, No. 55 Giai Phong Road, Hanoi 10000, Vietnam

Received 23 December 2013; Revised 27 February 2014; Accepted 20 March 2014; Published 10 April 2014

Academic Editor: Eric Lui

Copyright © 2014 Hong-Hai Tran and Nhat-Duc Hoang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Zebovitz, R. J. Krizek, and D. K. Atmatzidis, “Injection of fine sands with very fine cement grout,” Journal of Geotechnical Engineering, vol. 115, no. 12, pp. 1717–1733, 1989. View at Google Scholar · View at Scopus
  2. C. Butrón, G. Gustafson, Å. Fransson, and J. Funehag, “Drip sealing of tunnels in hard rock: a new concept for the design and evaluation of permeation grouting,” Tunnelling and Underground Space Technology, vol. 25, no. 2, pp. 114–121, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Perret, K. H. Khayat, E. Gagnon, and J. Rhazi, “Repair of 130-year old masonry bridge using high-performance cement grout,” Journal of Bridge Engineering, vol. 7, no. 1, pp. 31–38, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Tekin and S. O. Akbas, “Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts,” Bulletin of Engineering Geology and the Environment, vol. 70, no. 1, pp. 153–161, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Incecik and I. Ceren, “Cement grouting model tests,” Bulletin of the Technical University of Istanbul, vol. 48, pp. 305–317, 1995. View at Google Scholar
  6. E. B. Burwell, “Cement and clay grouting of foundations: practice of the corps of engineering,” Journal of the Soil Mechanics and Foundations Division, vol. 84, pp. 1551/1–1551/22, 1958. View at Google Scholar
  7. R. J. Krizek, H.-J. Liao, and R. H. Borden, “Mechanical properties of microfine cement/sodium silicate grouted sand,” in Proceedings of the ASCE Specialty Conference on Grouting, Soil Improvement and Geosynthetics, pp. 688–699, February 1992. View at Scopus
  8. C. L. Huang, J. C. Fan, and W. J. Yang, “A study of applying microfine cement grout to sandy silt soil,” Sino-Geotech, vol. 111, pp. 71–82, 2007. View at Google Scholar
  9. S. Akbulut and A. Saglamer, “Estimating the groutability of granular soils: a new approach,” Tunnelling and Underground Space Technology, vol. 17, no. 4, pp. 371–380, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. H. G. Ozgurel and C. Vipulanandan, “Effect of grain size and distribution on permeability and mechanical behavior of acrylamide grouted sand,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 131, no. 12, pp. 1457–1465, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. K.-W. Liao, J.-C. Fan, and C.-L. Huang, “An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts,” Computers and Geotechnics, vol. 38, no. 8, pp. 978–986, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. Y.-L. Chen, R. Azzam, T. M. Fernandez-Steeger, and L. Li, “Studies on construction pre-control of a connection aisle between two neighbouring tunnels in Shanghai by means of 3D FEM, neural networks and fuzzy logic,” Geotechnical and Geological Engineering, vol. 27, no. 1, pp. 155–167, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Kalinli, M. C. Acar, and Z. Gündüz, “New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization,” Engineering Geology, vol. 117, no. 1-2, pp. 29–38, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Samarasinghe, Neural Networks for Applied Sciences and Engineering, Taylor and Francis, 2006.
  15. S. Kiranyaz, T. Ince, A. Yildirim, and M. Gabbouj, “Evolutionary artificial neural networks by multi-dimensional particle swarm optimization,” Neural Networks, vol. 22, no. 10, pp. 1448–1462, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998.
  17. K. Gopalakrishnan and S. Kim, “Support vector machines approach to HMA stiffness prediction,” Journal of Engineering Mechanics, vol. 137, no. 2, pp. 138–146, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M.-Y. Cheng, N.-D. Hoang, and Y.-W. Wu, “Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: a Taiwan case study,” Automation in Construction, vol. 35, pp. 306–313, 2013. View at Google Scholar
  19. P. Samui, “Slope stability analysis: a support vector machine approach,” Environmental Geology, vol. 56, no. 2, pp. 255–267, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. K. C. Lam, E. Palaneeswaran, and C.-Y. Yu, “A support vector machine model for contractor prequalification,” Automation in Construction, vol. 18, no. 3, pp. 321–329, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution a Practical Approach to Global Optimization, Springer, 2005.
  22. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Google Scholar · View at Scopus
  23. H.-L. Chen, B. Yang, G. Wang et al., “A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method,” Knowledge-Based Systems, vol. 24, no. 8, pp. 1348–1359, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. M.-Y. Cheng, A. F. V. Roy, and K.-L. Chen, “Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction,” Expert Systems with Applications, vol. 39, no. 2, pp. 1737–1746, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” Tech. Rep., Department of Computer Science, National Taiwan University, 2010. View at Google Scholar
  26. C. Bishop, Pattern Recognition and Machine Learning, Springer Science+Business Media, Singapore, 2006.
  27. S. Arlot and A. Celisse, “A survey of cross-validation procedures for model selection,” Statistics Surveys, vol. 4, pp. 40–79, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Zhang, “Model selection via multifold cross validation,” The Annals of Statistics, vol. 21, pp. 299–313, 1993. View at Google Scholar
  29. J.-S. Chou, C.-K. Chiu, M. Farfoura, and I. Al-Taharwa, “Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques,” Journal of Computing in Civil Engineering, vol. 25, no. 3, pp. 242–253, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. S. J. Russell and P. Norvig, Artificial Intelligence a Modern Approach, Prentice Hall, Person Education, 2nd edition, 2003.