딥러닝(Deep learning) 기술을 기반한 건설재료 특성 예측

  • Published : 2024.02.28

Abstract

Keywords

References

  1. Fischer, H.R.; Dillingh, E.C.; Hermse, C.G.M. On the micro-structure of bituminous binders. Road Mater. Pavement Des. 2014, 15, 1-15. https://doi.org/10.1080/14680629.2013.837838
  2. Kim, H.H. ; Mazumder, M. ; Lee, S.J. Micromorphology and rheology of warm binders depending on aging. J. Mater. Civil Eng. 2017, 29, 04017226.
  3. Kim, H.H.; Mazumder, M.; Torres, A.; Lee, S.J.; Lee, M.S. Characterization of CRM binders with wax additives using an atomic force microscopy (AFM) and an optical microscopy. Adv. Civil Eng. Mater. 2017, 6, 504-525. https://doi.org/10.1520/ACEM20160071
  4. Jager, A.; Lackner, R.; Eisenmenger-Sittner, C.; Blab, R. Identification of microstructural components of bitumen by means of atomic force microscopy (AFM). PAMM 2004, 4, 400-401. https://doi.org/10.1002/pamm.200410181
  5. Chan, T.H.; Jia, K.; Gao, S.; Lu, J.; Zeng, Z.; Ma, Y. PCANet: A simple deep learning baseline for image classification? IEEE Trans. Image Process. 2015, 24, 5017-5032. https://doi.org/10.1109/TIP.2015.2475625
  6. Li, G.; Yu, Y. Deep contrast learning for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June-1 July 2016; pp. 478-487.
  7. Liu, X.; Liang, W.; Wang, Y.; Li, S.; Pei, M. 3D head pose estimation with convolutional neural network trained on synthetic images. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25-28 September 2016; pp. 1289-1293.
  8. Perez, L.; Wang, J. The effectiveness of data augmentation in image classification using deep learning. arXiv 2017, arXiv:1712.04621
  9. Buetti-Dinh, A.; Galli, V.; Bellenberg, S.; Ilie, O.; Herold, M.; Christel, S.; Mariia, B.; Igor, P.; Paul, W.; Sand, W.; et al. Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition. Biotechnol. Rep. 2019, 22, e00321.