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Advances in Civil Engineering
Volume 2018, Article ID 5140610, 10 pages
https://doi.org/10.1155/2018/5140610
Research Article

Prediction of the Strength Properties of Carbon Fiber-Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Department of Civil Engineering, Technology Faculty, Firat University, Elaziğ, Turkey

Correspondence should be addressed to Harun Tanyildizi; rt.ude.tarif@izidliynath

Received 24 August 2017; Accepted 9 November 2017; Published 31 January 2018

Academic Editor: Cumaraswamy Vipulanandan

Copyright © 2018 Harun Tanyildizi. 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. J. A. Bogas and A. Gomes, “Compressive behavior and failure modes of structural lightweight aggregate concrete characterization and strength prediction,” Materials & Design, vol. 46, pp. 832–841, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Kılıç, C. D. Atis, E. Yaşar, and F. Özcan, “High-strength lightweight concrete made with scoria aggregate containing mineral admixtures,” Cement and Concrete Research, vol. 33, no. 10, pp. 1595–1599, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. I. B. Topcu, “Semi-lightweight concretes produced by volcanic slags,” Cement and Concrete Research, vol. 27, no. 1, pp. 15–21, 1997. View at Publisher · View at Google Scholar
  4. H. Al-Khaiat and M. N. Haquet, “Effect of initial curing on early strength and physical properties of lightweight concrete,” Cement and Concrete Research, vol. 28, no. 6, pp. 859–866, 1998. View at Publisher · View at Google Scholar
  5. S. P. Shah and S. H. Ahmad, High Performance Concrete, Properties and Applications, McGraw-Hill, New York, NY, USA, 1994.
  6. C. S. Poon, S. Azhar, M. Anson, and Y. L. Wong, “Performance of metakaolin concrete at elevated temperatures,” Cement and Concrete Composites, vol. 25, no. 1, pp. 83–89, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. L. T. Phan, Fire Performance of High Strength Concrete, A Report of the State-of-the-Art, Building and Fire Research Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA, 1996.
  8. M. M. Shoaib, S. A. Ahmed, and M. M. Balaha, “Effect of fire and cooling mode on the properties of slag mortars,” Cement and Concrete Research, vol. 31, no. 11, pp. 1533–1538, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Wang, S. Wu, and H. Dai, “Fatigue behavior and life prediction of carbon fiber reinforced concrete under cyclic flexural loading,” Materials Science and Engineering A, vol. 434, no. 1-2, pp. 347–351, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. P. W. Chen, X. Fu, and D. D. L. Chung, “Microstructural and mechanical effects of latex, methylcellulose and silica fume on carbon fiber reinforced cement,” ACI Materials Journal, vol. 94, no. 2, pp. 147–155, 1997. View at Publisher · View at Google Scholar
  11. Y. Xu and D. D. L. Chung, “Improving silica fume cement by using silane,” Cement Concrete Research, vol. 30, no. 8, pp. 1305–1311, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Zu, Z. Li, X. Song, and D. D. L. Chung, “Deformation adjustment of concrete beams laminated with carbon fiber mats,” Construction and Building Materials, vol. 21, no. 3, pp. 621–625, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Mohammadi, S. P. Singh, and S. K. Kaushik, “Properties of steel fibrous concrete containing mixed fibers in fresh and hardened states,” Construction and Building Materials, vol. 22, no. 5, pp. 956–965, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. F. Altun and B. Aktaş, “Investigation of reinforced concrete beams behavior of steel fiber added lightweight concrete,” Construction and Building Materials, vol. 38, pp. 575–581, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Hassanpour, P. Shafigh, and H. B. Mahmud, “Lightweight aggregate concrete fiber reinforcement–a review,” Construction and Building Materials, vol. 37, pp. 452–461, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. A. C. Aydin, “Self compactability of high volume hybrid fiber reinforced concrete,” Construction and Building Materials, vol. 21, no. 6, pp. 1149–1154, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. U. S. Camli and B. Binici, “Strength of carbon fiber reinforced polymers bonded to concrete and masonry,” Construction and Building Materials, vol. 21, no. 7, pp. 1431–1446, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Cevik and I. H. Guzelbey, “Neural network modeling of strength enhancement for CFRP confined concrete cylinders,” Building and Environment, vol. 43, no. 5, pp. 751–763, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. I. B. Topcu and M. Canbaz, “Effect of different fibers on the mechanical properties of concrete containing fly ash,” Construction and Building Materials, vol. 21, no. 7, pp. 1486–1491, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. M. H. F. Zarandi, I. B. Türksen, J. Sobhani, and A. A. Ramezanianpour, “Fuzzy polynomial neural networks for approximation of the compressive strength of concrete,” Applied Soft Computing, vol. 8, no. 1, pp. 488–498, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. I. B. Topcu and M. Sarıdemir, “Prediction of properties of waste AAC aggregate concrete using artificial neural network,” Computational Materials Science, vol. 41, no. 1, pp. 117–125, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. I. B. Topcu and M. Sarıdemir, “Prediction of rubberized mortar properties using artificial neural network and fuzzy logic,” Journal of Materials Processing Technology, vol. 199, no. 1–3, pp. 108–118, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. I. B. Topcu and M. Sarıdemir, “Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic,” Computational Materials Science, vol. 42, no. 1, pp. 74–82, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. I. C. Yeh, “Modeling slump flow of concrete using second-order regressions and artificial neural networks,” Cement and Concrete Composites, vol. 29, no. 6, pp. 474–480, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Demir, “Prediction of elastic modulus of normal and high strength concrete by artificial neural networks,” Construction and Building Materials, vol. 22, no. 7, pp. 1428–1435, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Altun, O. Kişi, and K. Aydin, “Predicting the compressive strength of steel fiber added lightweight concrete using neural network,” Computational Materials Science, vol. 42, no. 2, pp. 259–265, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. G. İnan, A. B. Göktepe, K. Ramyar, and A. Sezer, “Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology,” Building and Environment, vol. 42, no. 3, pp. 1264–1269, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. V. Ranković, N. Grujović, D. Divac, and N. Milivojević, “Development of support vector regression identification model for prediction of dam structural behavior,” Structural Safety, vol. 48, pp. 33–39, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. R. Wang, C. Y. Yu, and H. H. Chan, “Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models,” International Journal of Project Management, vol. 30, no. 4, pp. 470–478, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. 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 Concrete, vol. 4, no. 4, pp. 299–316, 2007. View at Publisher · View at Google Scholar
  31. B. T. Chen, T. P. Chang, J. Y. Shih, and J. J. Wang, “Estimation of exposed temperature for fire-damaged concrete using support vector machine,” Computational Materials Science, vol. 44, no. 3, pp. 913–920, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. X. C. Shi and Y. F. Dong, “Support vector machine applied to prediction strength of cement,” in Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 1585–1588, IEEE, Zhengzhou, China, August 2011.
  33. G. T. G. Mohamedbhai, “Effect of exposure time and rates of heating and cooling on residual strength of heated concrete,” Magazine of Concrete Research, vol. 38, no. 136, pp. 151–158, 1986. View at Publisher · View at Google Scholar
  34. Y. N. Chan, G. F. Peng, and M. Anson, “Residual strength and pore structure of high-strength concrete and normal-strength concrete after exposure to high temperatures,” Cement and Concrete Composites, vol. 21, no. 1, pp. 23–27, 1999. View at Publisher · View at Google Scholar
  35. K. M. A. Hossain, “High strength blended cement concrete incorporating volcanic ash: performance at high temperatures,” Cement and Concrete Composites, vol. 28, no. 6, pp. 535–545, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. B. M. Luccioni, M. I. Figueroa, and R. F. Danesi, “Thermo-mechanic model for concrete exposed to elevated temperatures,” Engineering Structures, vol. 25, no. 6, pp. 729–742, 2003. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. N. Chan, X. Luo, and W. Sun, “Compressive strength and pore structure of high-performance concrete after exposure to high temperature up to 800°C,” Cement and Concrete Research, vol. 30, no. 2, pp. 247–251, 2000. View at Publisher · View at Google Scholar · View at Scopus
  38. K. Sakr and E. E. Hakim, “Effect of high temperature or fire on heavy weight concrete properties,” Cement and Concrete Research, vol. 35, no. 3, pp. 590–596, 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. T. R. Naik and R. N. Kraus, Temperature Effects on High-Performance Concrete, Report no. CBU-2002-07 REP-460, Department of Civil Engineering and Mechanics, College of Engineering and Applied Science, University of Wisconsin–Milwaukee, Milwaukee, WI, USA, 2002.
  40. B. Chen and J. Liu, “Residual strength of hybrid-fiber-reinforced high-strength concrete after exposure to high temperatures,” Cement and Concrete Research, vol. 34, no. 6, pp. 1065–1069, 2004. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Çavdar, “A study on the effects of high temperature on mechanical properties of fiber reinforced cementitious composites,” Composites Part B: Engineering, vol. 43, no. 5, pp. 2452–2463, 2012. View at Publisher · View at Google Scholar · View at Scopus
  42. H. Tanyildizi, “Effect of temperature, carbon fibers, and silica fume on the mechanical properties of lightweight concretes,” New Carbon Materials, vol. 23, no. 4, pp. 339–344, 2008. View at Publisher · View at Google Scholar
  43. J. Sharma, S. Chawla, and S. Dalhotra, “A research agenda on artificial neural network topologies & data mining in neural network,” International Journal of Data & Network Security, vol. 1, pp. 41–47, 2013. View at Google Scholar
  44. Z. H. Duan, S. C. Kou, and C. S. Poon, “Prediction of compressive strength of recycled aggregate concrete using artificial neural networks,” Construction and Building Materials, vol. 40, pp. 1200–1206, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. D. Hanbay, I. Turkoglu, and Y. Demir, “An expert system based on wavelet decomposition and neural network for modeling Chua’s circuit,” Expert Systems with Applications, vol. 34, no. 4, pp. 2278–2283, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. S. Haykin, Neural Networks, a Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, USA, 1994.
  47. D. Hanbay, I. Turkoglu, and Y. Demir, “Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks,” Expert Systems with Applications, vol. 34, no. 2, pp. 1038–1043, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. H. Erdem, “Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks,” Advances in Engineering Software, vol. 41, no. 2, pp. 270–276, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. L. Bal and F. Buyle-Bodin, “Artificial neural network for predicting drying shrinkage of concrete,” Construction and Building Materials, vol. 38, pp. 248–254, 2013. View at Publisher · View at Google Scholar · View at Scopus
  50. M. Jalal and A. A. Ramezanianpour, “Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks,” Composites Part B: Engineering, vol. 43, no. 8, pp. 2990–3000, 2012. View at Publisher · View at Google Scholar · View at Scopus
  51. E. M. Golafshani, A. Rahai, M. H. Sebt, and H. Akbarpour, “Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic,” Construction and Building Materials, vol. 36, pp. 411–418, 2012. View at Publisher · View at Google Scholar · View at Scopus
  52. E. Deniz, “ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array,” Neural Computing and Applications, vol. 28, no. 10, pp. 3061–3072, 2017. View at Publisher · View at Google Scholar · View at Scopus
  53. O. Karahan, H. Tanyildizi, and C. D. Atis, “An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash,” Journal of Zhejiang University-Science A, vol. 9, no. 11, pp. 1514–1523, 2008. View at Publisher · View at Google Scholar · View at Scopus
  54. H. Tanyildizi, “Prediction of compressive strength of lightweight mortar exposed to sulfate attack,” Computers and Concrete, vol. 19, no. 2, pp. 217–226, 2017. View at Publisher · View at Google Scholar · View at Scopus
  55. M. Sarıdemir, I. B. Topcu, F. Ozcan, and M. H. Severcan, “Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic,” Construction and Building Materials, vol. 23, no. 3, pp. 1279–1286, 2009. View at Publisher · View at Google Scholar · View at Scopus
  56. P. Yuvaraj, A. R. Murthy, N. R. Iyer, S. K. Sekar, and P. Samui, “Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams,” Engineering Fracture Mechanics, vol. 98, pp. 29–43, 2013. View at Publisher · View at Google Scholar · View at Scopus
  57. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY, USA, 1995.
  58. V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Network and Fuzzy Logic Models, MIT Press, Cambridge, MA, USA, London, England, 2001.
  59. T. M. Cover, “Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition,” IEEE Transactions on Electronic Computers, vol. 14, no. 3, pp. 326–334, 1965. View at Publisher · View at Google Scholar · View at Scopus
  60. K. Yan and C. Shi, “Prediction of elastic modulus of normal and high strength concrete by support vector machine,” Construction and Building Materials, vol. 24, no. 8, pp. 1479–1485, 2010. View at Publisher · View at Google Scholar · View at Scopus