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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 985659, 15 pages
http://dx.doi.org/10.1155/2014/985659
Research Article

Data Mining of the Thermal Performance of Cool-Pipes in Massive Concrete via In Situ Monitoring

1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2Planning and Design Institute of Water Transportation, Beijing 100007, China

Received 27 December 2013; Revised 30 January 2014; Accepted 4 February 2014; Published 5 May 2014

Academic Editor: Ting-Hua Yi

Copyright © 2014 Zheng Zuo 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. G.-D. Zhou and T.-H. Yi, “Thermal load in large-scale bridges: a state-of-the-art review,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 217983, 17 pages, 2013. View at Publisher · View at Google Scholar
  2. H.-S. Shanga and T.-H. Yi, “Behavior of HPC with fly ash after elevated temperature,” Advances in Materials Science and Engineering, vol. 2013, Article ID 478421, 7 pages, 2013. View at Publisher · View at Google Scholar
  3. F.-J. Ulm and O. Coussy, “What is a “massive” concrete structure at early ages? Some dimensional arguments,” Journal of Engineering Mechanics, vol. 127, no. 5, pp. 512–522, 2001. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Zuo, Y. Hu, Q. Li et al., “User-friendly thermal-stress coupled simulating platform of mass concrete,” Chinese Journal of Computational Mechanics, vol. 30, supplement 1, pp. 1–6, 2013 (Chinese). View at Google Scholar
  5. Wikipedia, “Hoover dam,” 2011, http://en.wikipedia.org/wiki/Hoover_dam.
  6. D. o. t. I. U.S, “Concrete,” 2005, http://www.usbr.gov/lc/hooverdam/History/essays/concrete.html.
  7. B. o. R. United States Department of the Interior, Cooling of Concrete Dams: Final Reports, Boulder Canyon project: Final Reports. Part VII, Cement and Concrete Investigations, United States Department of the Interior, Washington, DC, USA, 1949.
  8. Y. Hu, Z. Zuo, Q. Li, and Y. Duan, “Boolean-based surface procedure for the external heat transfer analysis of dams during construction,” Mathematical Problems in Engineering, vol. 2013, Article ID 175616, 17 pages, 2013. View at Publisher · View at Google Scholar
  9. L. Zhang, Y. Hu, Q. Li et al., “Hex-meshing method for gravity dam based on planar boolean operations,” Journal of Hyderoelectric Engineering, vol. 31, no. 5, pp. 209–215, 2012 (Chinese). View at Google Scholar
  10. H. Xie and Y. Chen, “Influence of the different pipe cooling scheme on temperature distribution in RCC arch dams,” Communications in Numerical Methods in Engineering, vol. 21, no. 12, pp. 769–778, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Zuo, Y. Hu, Y. Duan, and J. Yang, “Simulation of the temperature field in mass concrete with double layers of cooling pipes during construction,” Journal of Tsinghua University, vol. 52, no. 2, pp. 186–189, 2012 (Chinese). View at Google Scholar · View at Scopus
  12. B. F. Zhu and J. B. Cai, “Finite element analysis of effect of pipe cooling in concrete dams,” Journal of Construction Engineering and Management, vol. 115, no. 4, pp. 487–498, 1989. View at Google Scholar · View at Scopus
  13. J. Yang, Y. Hu, Z. Zuo, F. Jin, and Q. Li, “Thermal analysis of mass concrete embedded with double-layer staggered heterogeneous cooling water pipes,” Applied Thermal Engineering, vol. 35, no. 1, pp. 145–156, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Zhu, “Effect of cooling by water flowing in nonmetal pipes embedded in mass concrete,” Journal of Construction Engineering and Management, vol. 125, no. 1, pp. 61–68, 1999. View at Google Scholar · View at Scopus
  15. T. Myers and J. Charpin, “Modelling the temperature, maturity and moisture content in a drying concrete block,” Mathematics-in-Industry Case Studies, vol. 1, pp. 24–48, 2008. View at Google Scholar
  16. M. Jiaxuan, “A combined method of theoretic and numerical solutions for pipe cooling in concrete dams,” Journal of Hydroelectric Engineering, vol. 29, no. 4, pp. 31–41, 1998 (Chinese). View at Google Scholar
  17. X. Wu, V. Kumar, J. R. Quinlan et al., “Top 10 algorithms in data mining,” Knowledge and Information Systems, vol. 14, no. 1, pp. 1–37, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. D. H. Dan, Y. M. Zhao, T. Yang, and X.-F. Yan, “Health condition evaluation of cable-stayed bridge driven by dissimilarity measures of grouped cable forces,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 818967, 12 pages, 2013. View at Publisher · View at Google Scholar
  19. J. M. Ko and Y. Q. Ni, “Technology developments in structural health monitoring of large-scale bridges,” Engineering Structures, vol. 27, no. 12, pp. 1715–1725, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Ou and H. Li, “Structural health monitoring in mainland china: review and future trends,” Structural Health Monitoring, vol. 9, no. 3, pp. 219–231, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Dan, Y. Zhao, T. Yang et al., “Vibration measurement based cable transion identification method for cable-damper systerm,” Journal of Vibration and Shock, vol. 41, no. 6, pp. 123–127, 2013 (Chinese). View at Google Scholar
  22. S. D. Glaser, M. Li, M. L. Wang, J. Ou, and J. Lynch, “Sensor technology innovation for the advancement of structural health monitoring: a strategic program of US-China research for the next decade,” Smart Structures and Systems, vol. 3, no. 2, pp. 221–244, 2007. View at Google Scholar · View at Scopus
  23. A. K. Choudhary, J. A. Harding, and M. K. Tiwari, “Data mining in manufacturing: a review based on the kind of knowledge,” Journal of Intelligent Manufacturing, vol. 20, no. 5, pp. 501–521, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. S. J. Lee and K. Siau, “A review of data mining techniques,” Industrial Management & Data Systems, vol. 101, no. 1, pp. 41–46, 2001. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Baker and K. Yacef, “The state of educational data mining in 2009: a review and future visions,” Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009. View at Google Scholar
  26. I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, San Diego, Calif, USA, 2011.
  27. X. Ye, L. Ran, T. H. Yi, and X. B. Dong, “Intelligent risk assessment for dewatering of metro-tunnel deep excavations,” Mathematical Problems in Engineering, vol. 2012, Article ID 618979, 13 pages, 2012. View at Publisher · View at Google Scholar
  28. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Liu, T.-H. Yi, and Z.-J. Xu, “Safety early warning research for highway construction based on case-based reasoning and variable fuzzy sets,” The Scientific World Journal, vol. 2013, Article ID 178954, 10 pages, 2013. View at Publisher · View at Google Scholar
  30. Y. B. Dibike, S. Velickov, D. Solomatine, and M. B. Abbott, “Model induction with support vector machines: introduction and applications,” Journal of Computing in Civil Engineering, vol. 15, no. 3, pp. 208–216, 2001. View at Publisher · View at Google Scholar · View at Scopus
  31. V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, Cambridge, Mass, USA, 2001.
  32. 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, Taipei, Taiwan, 2003. View at Google Scholar
  33. R. M. Balabin and E. I. Lomakina, “Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data,” Analyst, vol. 136, no. 8, pp. 1703–1712, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. C. H. Q. Ding and I. Dubchak, “Multi-class protein fold recognition using support vector machines and neural networks,” Bioinformatics, vol. 17, no. 4, pp. 349–358, 2001. View at Google Scholar · View at Scopus
  35. Y. Hu, Z. Zuo, Q. Li et al., “Study on abrupt gape of transverse joints and its cause during construction of high arch dams,” Journal of Hydroelectric Engineering, vol. 32, no. 5, pp. 218–225, 2013 (Chinese). View at Google Scholar
  36. H. Yu, J. Vaidya, and X. Jiang, “Privacy-preserving svm classification on vertically partitioned data,” in Advances in Knowledge Discovery and Data Mining, vol. 3918 of Lecture Notes in Computer Science, pp. 647–656, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  37. S. C. Larson, “The shrinkage of the coefficient of multiple correlation,” Journal of Educational Psychology, vol. 22, no. 1, pp. 45–55, 1931. View at Publisher · View at Google Scholar · View at Scopus
  38. S. Geisser, “The predictive sample reuse method with applications,” Journal of the American Statistical Association, vol. 70, no. 350, pp. 320–328, 1975. View at Publisher · View at Google Scholar
  39. F. Mosteller and J. W. Tukey, Data Analysis, including Statistics, 1968.
  40. P. Burman, “A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods,” Biometrika, vol. 76, no. 3, pp. 503–514, 1989. View at Publisher · View at Google Scholar · View at Scopus
  41. T. M. Khoshgoftaar and N. Seliya, “Fault prediction modeling for software quality estimation: comparing commonly used techniques,” Empirical Software Engineering, vol. 8, no. 3, pp. 255–283, 2003. View at Publisher · View at Google Scholar · View at Scopus
  42. L. Yang and M. Song, “Coal mine safety evaluation with V-fold cross-validation and BP neural network,” Journal of Computers, vol. 5, no. 9, pp. 1364–1371, 2010. View at Publisher · View at Google Scholar · View at Scopus