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Mathematical Problems in Engineering
Volume 2017, Article ID 5450297, 11 pages
https://doi.org/10.1155/2017/5450297
Research Article

Time-Varying Identification Model for Crack Monitoring Data from Concrete Dams Based on Support Vector Regression and the Bayesian Framework

Bo Chen,1,2,3,4 Zhongru Wu,1,3,4 Jiachen Liang,1,3,4 and Yanhong Dou4

1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2Key Laboratory of Earth-Rock Dam Failure Mechanism and Safety Control Techniques, Ministry of Water Resources, Nanjing 210029, China
3National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China
4College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China

Correspondence should be addressed to Bo Chen; moc.621@uhhobnehc

Received 26 October 2016; Revised 20 December 2016; Accepted 19 January 2017; Published 19 February 2017

Academic Editor: Salvatore Caddemi

Copyright © 2017 Bo Chen et al. 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. ICOLD, World Register of Dams, International Commission on Large Dams, Paris, France, 1988.
  2. L. M. Feng, O. A. Pekau, and C. H. Zhang, “Cracking analysis of arch dams by 3D boundary element method,” Journal of Structural Engineering, vol. 122, no. 6, pp. 691–699, 1996. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Li, C. Gu, and Z. Wu, “Nonparametric change point diagnosis method of concrete dam crack behavior abnormality,” Mathematical Problems in Engineering, vol. 2013, Article ID 969021, 13 pages, 2013. View at Google Scholar · View at MathSciNet
  4. C. Gu, Z. Li, and B. Xu, “Abnormality diagnosis of cracks in the concrete dam based on dynamical structure mutation,” Science China Technological Sciences, vol. 54, no. 7, pp. 1930–1939, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  5. Z. Li, C. Gu, Z. Wang, and Z. Wu, “On-line diagnosis method of crack behavior abnormality in concrete dams based on fluctuation of sequential parameter estimates,” Science China Technological Sciences, vol. 58, no. 3, pp. 415–424, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Yao, S.-T. E. Tung, and B. Glisic, “Crack detection and characterization techniques—an overview,” Structural Control and Health Monitoring, vol. 21, no. 12, pp. 1387–1413, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. O. V. Shiryayev and J. C. Slater, “Detection of fatigue cracks using random decrement signatures,” Structural Health Monitoring, vol. 9, no. 4, pp. 347–360, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. M. I. Friswell and J. E. T. Penny, “Crack modeling for structural health monitoring,” Structural Health Monitoring, vol. 1, no. 2, pp. 139–148, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Bao, F. Tang, Y. Chen, W. Meng, Y. Huang, and G. Chen, “Concrete pavement monitoring with PPP-BOTDA distributed strain and crack sensors,” Smart Structures and Systems, vol. 18, no. 3, pp. 405–423, 2016. View at Publisher · View at Google Scholar
  10. S. Ritdumrongkul and Y. Fujino, “Identification of the location and size of cracks in beams by a piezoceramic actuator-sensor,” Structural Control and Health Monitoring, vol. 14, no. 6, pp. 931–943, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Hehr, M. Schulz, V. Shanov, and Y. Song, “Micro-crack detection and assessment with embedded carbon nanotube thread in composite materials,” Structural Health Monitoring, vol. 13, no. 5, pp. 512–524, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Rangaraj, B. Pokale, A. Banerjee, and S. Gupta, “Investigations on a particle filter algorithm for crack identification in beams from vibration measurements,” Structural Control and Health Monitoring, vol. 22, no. 8, pp. 1049–1067, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Omondi, D. G. Aggelis, H. Sol, and C. Sitters, “Improved crack monitoring in structural concrete by combined acoustic emission and digital image correlation techniques,” Structural Health Monitoring, vol. 15, no. 3, pp. 359–378, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Kumar and S. V. Barai, “Equivalence between stress intensity factor and energy approach based fracture parameters of concrete,” Engineering Fracture Mechanics, vol. 76, no. 9, pp. 1357–1372, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Kumar, S. R. Pandey, and A. K. L. Srivastava, “Determination of double-K fracture parameters of concrete using peak load method,” Engineering Fracture Mechanics, vol. 131, pp. 471–484, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Ince, “Determination of the fracture parameters of the Double-K model using weight functions of split-tension specimens,” Engineering Fracture Mechanics, vol. 96, pp. 416–432, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Bang, S.-K. Lee, C. Cho, and J. U. Cho, “Study on crack propagation of adhesively bonded DCB for aluminum foam using energy release rate,” Journal of Mechanical Science and Technology, vol. 29, no. 1, pp. 45–50, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Yang, J. S. Tomblin, and L. Salah, “Stress model and strain energy release rate of a prescribed crack in scarf joint/repair of composite panels,” Journal of Composite Materials, vol. 49, no. 29, pp. 3635–3663, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Bao, D. Qin, X. Zhou, and G. Wu, “Abnormality monitoring model of cracks in concrete dams,” Science China Technological Sciences, vol. 54, no. 7, pp. 1914–1922, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. S. Xu and H. W. Reinhardt, “Determination of double-K criterion for crack propagation in quasi-brittle fracture, Part III: compact tension specimens and wedge splitting specimens,” International Journal of Fracture, vol. 98, no. 2, pp. 179–193, 1999. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Li, C. Gu, and Z. Wu, “Abnormality diagnosis of cracks in the concrete based on double crack tip opening displacement criterion,” Science China Technological Sciences, vol. 56, no. 8, pp. 1915–1928, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Xu, W. G. Lu, Z. C. Li et al., “Order typical small probability diagnosis method of concrete dam crack behavior abnormality,” Science China Technological Sciences, vol. 44, pp. 1035–1042, 2014 (Chinese). View at Google Scholar
  23. Z. R. Wu, Safety Monitoring Theory and Its Application of Hydraulic Structures, Higher Education Press, Beijing, China, 2003 (Chinese).
  24. Z. Dongjian, H. Zhongyan, and L. Bo, “Arch-dam crack deformation monitoring hybrid model based on XFEM,” Science China Technological Sciences, vol. 54, no. 10, pp. 2611–2617, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  25. Z. R. Wu, C. S. Shen, and H. X. Ruan, “Factor selection of displacement statistical models for concrete dams,” Journal of Hohai University, vol. 16, pp. 1–9, 1988. View at Google Scholar
  26. C. Gu, D. Qin, Z. Li, and X. Zheng, “Study on semi-parametric statistical model of safety monitoring of cracks in concrete dams,” Mathematical Problems in Engineering, vol. 2013, Article ID 874629, 9 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Panizzo and A. Petaccia, Analysis of Monitoring Data for the Safety Control of Dams Using Neural Networks, Tsinghua University Press, 2007.
  28. W. C. Chan, C. W. Chan, K. C. Cheung, and C. J. Harris, “On the modelling of nonlinear dynamic systems using support vector neural networks,” Engineering Applications of Artificial Intelligence, vol. 14, no. 2, pp. 105–113, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. X. Zhang, P. Wang, D. Liang, C. Fan, and C. Li, “A soft self-repairing for FBG sensor network in SHM system based on PSO-SVR model reconstruction,” Optics Communications, vol. 343, pp. 38–46, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Gretton, A. Doucet, R. Herbrich, P. J. W. Rayner, and B. Schölkopf, “Support vector regression for black-box system identification,” in Proceedings of the IEEE Workshop on Statitical Signal Processing, pp. 341–344, IEEE, Singapore, August 2001. View at Scopus
  31. M. Martínez-Ramón, J. L. Rojo-Álvarez, G. Camps-Valls et al., “Support vector machines for nonlinear Kernel ARMA system identification,” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1617–1622, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. Z. Lu and J. Sun, “Non-Mercer hybrid kernel for linear programming support vector regression in nonlinear systems identification,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 94–99, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Balasundaram, D. Gupta, and Kapil, “Lagrangian support vector regression via unconstrained convex minimization,” Neural Networks, vol. 51, pp. 67–79, 2014. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  34. V. Ranković, N. Grujović, D. Divac, and N. Milivojević, “Development of support vector regression identification model for prediction of dam structural behaviour,” Structural Safety, vol. 48, pp. 33–39, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. J. J. Lee, D. K. Kim, S. K. Chang, and J.-H. Lee, “Application of support vector regression for the prediction of concrete strength,” Computers and Concrete, vol. 4, no. 4, pp. 299–316, 2007. View at Publisher · View at Google Scholar · View at Scopus
  36. H. Su, Z. Wen, X. Sun, and M. Yang, “Time-varying identification model for dam behavior considering structural reinforcement,” Structural Safety, vol. 57, pp. 1–7, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge, UK, 2000. View at Publisher · View at Google Scholar
  38. H. Su, Z. Wen, and Z. Wu, “Early-warning model of dam safety based on SVM theory,” Journal of Basic Science and Engineering, vol. 17, no. 1, pp. 40–48, 2009. View at Google Scholar · View at Scopus
  39. C. M. Bishop, Neural networks for pattern recognition, The Clarendon Press, Oxford University Press, New York, NY, USA, 1995. View at MathSciNet
  40. R. Wang, F. Luo, H. Yang et al., “Observer design for nonlinear systems based on regression LS-SVM with Bayesian methods,” Automation & Instrumentation, vol. 7, pp. 5–9, 2011. View at Google Scholar
  41. J. T.-Y. Kwok, “The evidence framework applied to support vector machines,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1162–1173, 2000. View at Publisher · View at Google Scholar · View at Scopus