Table of Contents Author Guidelines Submit a Manuscript
Advances in Civil Engineering
Volume 2016, Article ID 2861380, 8 pages
http://dx.doi.org/10.1155/2016/2861380
Research Article

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

1Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang 550000, Vietnam
2Faculty of Project Management, The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang, Danang 550000, Vietnam
3Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang, Vietnam

Received 3 June 2016; Revised 22 September 2016; Accepted 26 September 2016

Academic Editor: Ghassan Chehab

Copyright © 2016 Nhat-Duc Hoang 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. B.-I. Bae, H.-K. Choi, and C.-S. Choi, “Flexural strength evaluation of reinforced concrete members with ultra high performance concrete,” Advances in Materials Science and Engineering, vol. 2016, Article ID 2815247, 10 pages, 2016. View at Publisher · View at Google Scholar
  2. O. Gunes, S. Yesilmen, B. Gunes, and F.-J. Ulm, “Use of UHPC in bridge structures: material modeling and design,” Advances in Materials Science and Engineering, vol. 2012, Article ID 319285, 12 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Ahmadi-Nedushan, “An optimized instance based learning algorithm for estimation of compressive strength of concrete,” Engineering Applications of Artificial Intelligence, vol. 25, no. 5, pp. 1073–1081, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. B. A. Omran, Q. Chen, and R. Jin, “Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete,” Journal of Computing in Civil Engineering, 2016. View at Publisher · View at Google Scholar
  5. A. Pham, N. Hoang, and Q. Nguyen, “Predicting compressive strength of high-performance concrete using metaheuristic-optimized least squares support vector regression,” Journal of Computing in Civil Engineering, vol. 30, no. 3, Article ID 06015002, 2016. View at Publisher · View at Google Scholar
  6. C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, Adaptive Computation and Machine Learning, The MIT Press, Cambridge, Mass, USA, 2006. View at MathSciNet
  7. J. Hu and J. Wang, “Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression,” Energy, vol. 93, part 2, pp. 1456–1466, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Zhou, J. Chen, and Z. Song, “Recursive gaussian process regression model for adaptive quality monitoring in batch processes,” Mathematical Problems in Engineering, vol. 2015, Article ID 761280, 9 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Suykens, J. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, Least Square Support Vector Machines, World Scientific, Singapore, 2002.
  10. S. Chithra, S. R. R. S. Kumar, K. Chinnaraju, and F. Alfin Ashmita, “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks,” Construction and Building Materials, vol. 114, pp. 528–535, 2016. View at Publisher · View at Google Scholar
  11. R. Gupta, M. A. Kewalramani, and A. Goel, “Prediction of concrete strength using neural-expert system,” Journal of Materials in Civil Engineering, vol. 18, no. 3, pp. 462–466, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. I.-C. Yeh and L.-C. Lien, “Knowledge discovery of concrete material using Genetic Operation Trees,” Expert Systems with Applications, vol. 36, no. 3, pp. 5807–5812, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. 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
  14. M. Słoński, “A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks,” Computers and Structures, vol. 88, no. 21-22, pp. 1248–1253, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. M.-Y. Cheng, P. M. Firdausi, and D. Prayogo, “High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT),” Engineering Applications of Artificial Intelligence, vol. 29, pp. 104–113, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. M.-Y. Cheng, J.-S. Chou, A. F. V. Roy, and Y.-W. Wu, “High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model,” Automation in Construction, vol. 28, pp. 106–115, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Chen and T.-S. Wang, “Modeling strength of high-performance concrete using an improved grammatical evolution combined with macrogenetic algorithm,” ASCE Journal of Computing in Civil Engineering, vol. 24, no. 3, pp. 281–288, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. S. M. Mousavi, P. Aminian, A. H. Gandomi, A. H. Alavi, and H. Bolandi, “A new predictive model for compressive strength of HPC using gene expression programming,” Advances in Engineering Software, vol. 45, no. 1, pp. 105–114, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Castelli, L. Vanneschi, and S. Silva, “Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators,” Expert Systems with Applications, vol. 40, no. 17, pp. 6856–6862, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. H. I. Erdal, O. Karakurt, and E. Namli, “High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform,” Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1246–1254, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. J.-S. Chou, C.-F. Tsai, A.-D. Pham, and Y.-H. Lu, “Machine learning in concrete strength simulations: multi-nation data analytics,” Construction and Building Materials, vol. 73, pp. 771–780, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. M.-Y. Cheng, C.-C. Huang, and A. F. V. Roy, “Predicting project success in construction using an evolutionary gaussian process inference model,” Journal of Civil Engineering and Management, vol. 19, supplement 1, pp. S202–S211, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Pal and S. Deswal, “Modelling pile capacity using Gaussian process regression,” Computers and Geotechnics, vol. 37, no. 7-8, pp. 942–947, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Ebden, “Gaussian processes: a quick introduction,” https://arxiv.org/abs/1505.02965
  25. Mathworks, Statistics and Machine Learning Toolbox, The MathWorks, 2016.
  26. M. H. Beale, M. T. Hagan, and H. B. Demuth, Neural Network Toolbox User's Guide, The MathWorks, 2012.
  27. K. De Brabanter, P. Karsmakers, F. Ojeda et al., “LS-SVMlab toolbox user's guide version 1.8,” Internal Report 10-146, ESAT-SISTA, KU Leuven, Leuven, Belgium, 2010. View at Google Scholar
  28. D.-T. Vu and N.-D. Hoang, “Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach,” Structure and Infrastructure Engineering, vol. 12, no. 9, 2016. View at Publisher · View at Google Scholar · View at Scopus
  29. W. Sun and M. Liu, “Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China,” Journal of Cleaner Production, vol. 122, pp. 144–153, 2016. View at Publisher · View at Google Scholar
  30. M.-Y. Cheng, N.-D. Hoang, and Y.-W. Wu, “Cash flow prediction for construction project using a novel adaptive time-dependent least squares support vector machine inference model,” Journal of Civil Engineering and Management, vol. 21, no. 6, pp. 679–688, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Cheng and N. Hoang, “A self-adaptive fuzzy inference model based on least squares SVM for estimating compressive strength of rubberized concrete,” International Journal of Information Technology & Decision Making, vol. 15, no. 3, pp. 603–619, 2016. View at Publisher · View at Google Scholar
  32. M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994. View at Publisher · View at Google Scholar · View at Scopus
  33. T.-H. Tran and N.-D. Hoang, “Predicting colonization growth of algae on mortar surface with artificial neural network,” Journal of Computing in Civil Engineering, 2016. View at Publisher · View at Google Scholar
  34. 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 Zentralblatt MATH · View at MathSciNet · View at Scopus
  35. P. Zhang, “Model selection via multifold cross validation,” The Annals of Statistics, vol. 21, no. 1, pp. 299–313, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet