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Bibliothèque A Novel Method for Obtaining the Loess Structural Index from Computed Tomography Images: A Case Study from the Lvliang Mountains of the Loess Plateau (China)

A Novel Method for Obtaining the Loess Structural Index from Computed Tomography Images: A Case Study from the Lvliang Mountains of the Loess Plateau (China)

A Novel Method for Obtaining the Loess Structural Index from Computed Tomography Images: A Case Study from the Lvliang Mountains of the Loess Plateau (China)
Volume 10 Issue 3

Resource information

Date of publication
Mars 2021
Resource Language
ISBN / Resource ID
10.3390/land10030291
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The structural index is an important quantitative parameter for revealing the structural properties of loess. However, there is no a widely accepted measurement method for structural index at present. This study aims at presenting a novel method for obtaining the loess structural index (LSI), based on the application of computed tomography (CT) scanning techniques and laboratory physico-mechanical tests. The mountainous area of Lvliang in northwest China was taken as the study area, and Late Pleistocene loess samples were taken from various sites in the region. Several physical parameters were first measured using laboratory tests, including dry density, pore ratio, and liquidity index. CT scanning was used to observe sample microstructures, and a mathematical relationship was established between CT image parameters and the physical property indices, through three dimensions (3D) reconstruction and slice porosity analysis. The results revealed that LSI can be expressed as a non-linear function related to CT image parameters, dry density, and the liquidity index of the loess. Compared with traditional calculation methods, this novel technique calculates the LSI by using an empirical formula, which is less labor-intensive. Such results indicate that the method warrants wide application in the future.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Tang, Yaming
Bi, Yinqiang
Guo, Zizheng
Li, Zhengguo
Feng, Wei
Wang, Jiayun
Li, Yane
Ma, Hongna

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