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Advances in Materials Science and Engineering
Volume 2018, Article ID 6387930, 16 pages
https://doi.org/10.1155/2018/6387930
Research Article

Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences

State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Xuehui An; nc.ude.auhgnist.liam@euxna

Received 23 May 2018; Accepted 25 October 2018; Published 25 November 2018

Academic Editor: Barbara Liguori

Copyright © 2018 Zhongcong Ding and Xuehui An. 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. H. Okamura and M. Ouchi, “Self-compacting concrete,” Journal of Advanced Concrete Technology, vol. 1, no. 1, pp. 5–15, 2003. View at Publisher · View at Google Scholar
  2. X. An, Q. Wu, F. Jin et al., “Rock-filled concrete, the new norm of SCC in hydraulic engineering in China,” Cement and Concrete Composites, vol. 54, pp. 89–99, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Coppola, T. Cerulli, and D. Salvioni, “Sustainable development and durability of self-compacting concretes,” in Proceedings of 11th International Conference on Fracture 2005 (ICF11), pp. 2226–2241, Turin, Italy, March 2005.
  4. Q. Wu and X. An, “Development of a mix design method for SCC based on the rheological characteristics of paste,” Construction and Building Materials, vol. 53, pp. 642–651, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. C. F. Ferraris, L. Browner, C. Ozyildirim, and J. Daczko, “Workability of self-compacting concrete,” in Proceedings of International Symposium on High Performance Concrete, pp. 398–407, Orlando, FL, USA, 2000.
  6. D. Chopin, B. Cazacliu, F. De Larrard, and R. Schell, “Monitoring of concrete homogenisation with the power consumption curve,” Materials and Structures, vol. 40, pp. 897–907, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Daumann and H. Nirschl, “Assessment of the mixing efficiency of solid mixtures by means of image analysis,” Powder Technology, vol. 182, no. 3, pp. 415–423, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Li and X. An, “Method for estimating workability of self-compacting concrete using mixing process images,” Computers and Concrete, vol. 13, no. 6, pp. 781–798, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Ding and X. An, “A method for real-time moisture estimation based on self-compacting concrete workability detected during the mixing process,” Construction and Building Materials, vol. 139, pp. 123–131, 2017. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” in Lecture Notes in Computer Science, pp. 346–361, Springer Verlag, Berlin, Germany, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Jia, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: a large-scale hierarchical image database,” in Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, Miami, FL, USA, June 2009. View at Publisher · View at Google Scholar
  13. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Affonso, A. L. D. Rossi, F. H. A. Vieira, and A. C. P. de L. F. de Carvalho, “Deep learning for biological image classification,” Expert Systems with Applications, vol. 85, pp. 114–122, 2017. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, Columbus, OH, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Gidaris and N. Komodakis, “Object detection via a multi-region and semantic segmentation-aware U model,” in Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1134–1142, Santiago, Chile, December 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Donahue, L. A. Hendricks, M. Rohrbach et al., “Long-term recurrent convolutional networks for visual recognition and description,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 677–691, 2017. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. View at Publisher · View at Google Scholar · View at Scopus
  19. X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W. Wong, and W. Woo, Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, 2015, http://arxiv.org/abs/1506.04214.
  20. A. Zhang, K. C. P. Wang, B. Li et al., “Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network,” Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 10, pp. 805–819, 2017. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Yokoyama and T. Matsumoto, “Development of an automatic detector of cracks in concrete using machine learning,” Procedia Engineering, vol. 171, pp. 1250–1255, 2017. View at Publisher · View at Google Scholar · View at Scopus
  22. U. Reuter, A. Sultan, and D. S. Reischl, “A comparative study of machine learning approaches for modeling concrete failure surfaces,” Advances in Engineering Software, vol. 116, pp. 67–79, 2018. View at Publisher · View at Google Scholar · View at Scopus
  23. 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
  24. A. M. Abd and S. M. Abd, “Modelling the strength of lightweight foamed concrete using support vector machine (SVM),” Case Studies in Construction Materials, vol. 6, pp. 8–15, 2017. View at Publisher · View at Google Scholar · View at Scopus
  25. Z. M. Yaseen, R. C. Deo, A. Hilal et al., “Predicting compressive strength of lightweight foamed concrete using extreme learning machine model,” Advances in Engineering Software, vol. 115, pp. 112–125, 2017. View at Publisher · View at Google Scholar · View at Scopus
  26. W. Z. Taffese and E. Sistonen, “Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions,” Automation in Construction, vol. 77, pp. 1–14, 2017. View at Publisher · View at Google Scholar · View at Scopus
  27. M. A. Hariri-ardebili and F. Pourkamali-anaraki, “Support vector machine based reliability analysis of concrete dams,” Soil Dynamics and Earthquake Engineering, vol. 104, pp. 276–295, 2018. View at Publisher · View at Google Scholar · View at Scopus
  28. W. Z. Taffese, E. Sistonen, and J. Puttonen, “CaPrM: carbonation prediction model for reinforced concrete using machine learning methods,” Construction and Building Materials, vol. 100, pp. 70–82, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. C. Kanan and G. W. Cottrell, “Color-to-grayscale: does the method matter in image recognition?” PLoS One, vol. 7, no. 1, Article ID e29740, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. B. Cazacliu and N. Roquet, “Concrete mixing kinetics by means of power measurement,” Cement and Concrete Research, vol. 39, no. 3, pp. 182–194, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2323, 1998. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Schlüter and T. Grill, “Exploring data augmentation for improved singing voice detection with neural networks,” in Proceedings of 16th International Society for Music Information Retrieval Conference, pp. 121–126, Malaga, Spain, 2015, http://www.ofai.at/∼jan.schlueter/pubs/2015_ismir.pdf.
  33. P. Golik, P. Doetsch, and H. Ney, “Cross-entropy vs. Squared error training: a theoretical and experimental comparison,” in Proceedings of the 22nd international conference on Machine learning (ICML’05), Bonn, Germany, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Wiesler, J. Li, and J. Xue, “Investigations on hessian-free optimization for cross-entropy training of deep neural networks,” in Proceedings of 14th Annual Conference of the International Speech Communication Association, Lyon, France, August 2013.