3D Visualization Study on Microstructure Variation of Dredger Fill by Step Vacuum Preloading

  • Xiao-qing Yuan
  • Man-lei Wang
  • Huan Yan
  • Jun-qi Chen
  • Bao-ming Tan
Conference paper

Abstract

Step vacuum preloading (SVP) can effectively solve the problem of sludge plugging on prefabricated vertical drains in the process of the traditional vacuum preloading method for the consolidation of high-clay content dredger fill. The microstructure of dredger fill in the consolidation process of step vacuum preloading is constantly changing. To reveal the consolidation mechanism of SVP, the microstructural changes of dredger fill under the step vacuum pressure were studied at the microscopic level. However, most studies on the microstructure of soil concentrate on two-dimensional analysis which cannot accurately reflect the real situation of the microstructure. Three-dimensional studies do not only reflect the surface variations of the soil, but also greatly improve the utilization of other spatial information. SEM images of dredger fill at each vacuum preloading stage were obtained under a SVP indoor simulation test. The SEM images were modeled and displayed using ArcGis software. The 3D porosity of the dredger fill at each vacuum preloading stage was calculated using the area and volume method provided by geographic information system (GIS). The results show that 3D images of dredger fill based on GIS can significantly improve the fluctuations in microstructure than those achieved using 2D images. 3D porosity analysis is more realistic and reliable than the 2D method by which calculates porosity directly from the grayscale image of the dredger fill and avoids calculation error by threshold selection from inaccurate 2D analysis.

Keywords

High-clay content dredger fill SVP Microstructure 3D visualization 3D porosity 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of China (grant numbers: 41602285, 41430642 and 41172236). These supports are greatly appreciated.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xiao-qing Yuan
    • 1
  • Man-lei Wang
    • 1
  • Huan Yan
    • 2
  • Jun-qi Chen
    • 1
  • Bao-ming Tan
    • 1
  1. 1.Jilin UniversityChangchunChina
  2. 2.Changchun Institute of TechnologyChangchunChina

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