journal article·2024-01

BallastGAN: Random generation of ballast particle contour based on generative adversarial networks

Yang Wang, Hong Xiao, Yihao Chi, Zhihai Zhang, Zhongxia Qian

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage · Image and Object Detection Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (43)

Show source-provided references
1. Exploring the effects of particle shape and content of fines on the shear behavior of sand-fines mixtures via the DEM10.1016/j.compgeo.2018.10.021 ↗
2. Simple particle shapes for DEM simulations of railway ballast: influence of shape descriptors on packing behaviour10.1007/s10035-020-1009-0 ↗
3. Three‐dimensional quantitative analysis on granular particle shape using convolutional neural network10.1002/nag.3296 ↗
4. Shakedown behaviors of railway ballast under cyclic loading10.1016/j.conbuildmat.2017.07.225 ↗
5. Evaluation of morphological properties of railway ballast particles by image processing method10.1016/j.trgeo.2017.07.001 ↗
6. Development and validation of an online analyzer for particle size distribution in conveyor belts10.3390/min11060581 ↗
7. A discrete numerical model for granular assemblies10.1680/geot.1979.29.1.47 ↗
8. Discrete element modelling of large scale particle systems—I: exact scaling laws10.1007/s40571-014-0010-y ↗
9. K. Gregor, I. Danihelka, A. Graves, D. Rezende, D. Wierstra, Draw: A recurrent neural network for image generation, International conference on machine learning, PMLR, 2015, pp. 1462–1471.
10. Unsupervised cross-domain image generation
11. Influence of particle shape on engineering properties of assemblies of two-dimensional polygon-shaped particles10.1680/geot.2002.52.3.209 ↗
12. The importance of modelling ballast particle shape in the discrete element method10.1007/s10035-006-0021-3 ↗
13. Ballast degradation: effect of particle size and shape using Los Angeles Abrasion test and image analysis10.1016/j.conbuildmat.2018.02.170 ↗
14. Three-dimensional laser scanning technique to quantify aggregate and ballast shape properties10.1016/j.conbuildmat.2013.02.062 ↗
15. Simple shear simulation of 3D irregularly-shaped particles by image-based DEM10.1007/s10035-010-0207-6 ↗
16. Modelling realistic shape and particle inertia in DEM10.1680/geot.9.T.015 ↗
17. Image-aided element shape generation method in discrete-element modeling for railroad ballast10.1061/(ASCE)MT.1943-5533.0000839 ↗
18. Micromechanical particle interactions in railway ballast through DEM simulations of direct shear tests10.1061/(ASCE)GM.1943-5622.0001403 ↗
19. Image analysis for morphology, rheology and degradation study of railway ballast: a review10.1016/j.trgeo.2018.12.001 ↗
20. Packing of discrete and irregular particles10.1016/j.compgeo.2018.03.011 ↗
21. An optimization-based approach for modeling of complex particles10.1016/j.powtec.2019.08.027 ↗
22. Application of Fourier analysis to digital imaging for particle shape analysis10.1061/(ASCE)0887-3801(2004)18:1(2) ↗
23. 3D generation of realistic granular samples based on random fields theory and Fourier shape descriptors10.1016/j.cma.2014.06.022 ↗
24. Three-dimensional sphericity, roundness and fractal dimension of sand particles10.1680/jgeot.16.P.207 ↗
25. A stochastic multiscale algorithm for modeling complex granular materials10.1007/s10035-018-0816-z ↗
26. Random generation method of ballast 2D topology based on particle characteristics10.1016/j.conbuildmat.2019.06.131 ↗
27. Generative adversarial networks: an overview10.1109/MSP.2017.2765202 ↗
28. Generative adversarial network: an overview of theory and applications
29. Generative adversarial nets
30. Progressive growing of gans for improved quality, stability, and variation
31. A style-based generator architecture for generative adversarial networks
32. Analyzing and improving the image quality of stylegan
33. Training generative adversarial networks with limited data
34. Alias-free generative adversarial networks
35. S. Nowozin, B. Cseke, R. Tomioka, f-gan: Training generative neural samplers using variational divergence minimization, Advances in neural information processing systems 29 (2016).
36. Fractal functions and interpolation10.1007/BF01893434 ↗
37. The effect of morphological characteristic of coarse aggregates measured with fractal dimension on asphalt mixture’s high-temperature performance10.1155/2016/6264317 ↗
38. A review of methods used to determine the fractal dimension of linear features10.1007/BF02065874 ↗
39. Le Méhauté · 1991
40. A digitalized 2D particle database for statistical shape analysis and discrete modeling of rock aggregate10.1016/j.conbuildmat.2019.117906 ↗
41. Reconstruction of granular railway ballast based on inverse discrete Fourier transform method
42. 2D ballast particle contour generation based on the random midpoint displacement algorithm10.1007/s40571-022-00526-1 ↗
43. Discrete element analysis of elastic modulus of railway ballasts in wind with different sand contents