On the fractal characteristics of loess subsidence

Mokritskaya, Т. P.; Tushev, A. V.; Nikulchev, Evgeny; Samoylich, Ksenia A.

doi:10.12988/ces.2016.6687

Cited by 4 publications

(3 citation statements)

References 11 publications

(13 reference statements)

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“…It needs to be pointed out that the values of dry density and particle size distribution index employed for experiments fall into the normal range of loess in the studied area, and that the employed illuminance values are typical of light conditions controlled in the laboratory studies of the image analysis method. Considering the fractal characteristics of soil particle size distribution of natural loess [38][39][40], the mass fractal dimension D is used as a target indicator when preparing specimens with different particle size distribution. The mass fractal dimension D is calculated using the following equation:…”

Section: Sample Preparationmentioning

confidence: 99%

“…All of the specimens have a diameter of 61.8 mm and a height of 5 mm and are fully saturated using the vacuum saturation method prior to the testing. Considering the fractal characteristics of soil particle size distribution of natural loess [38][39][40], the mass fractal dimension D is used as a target indicator when preparing specimens with different particle size distribution. The mass fractal dimension D is calculated using the following equation:…”

Section: Sample Preparationmentioning

confidence: 99%

“…Considering the fractal characteristics of soil particle size distribution of natural loess [ 38 , 39 , 40 ], the mass fractal dimension D is used as a target indicator when preparing specimens with different particle size distribution. The mass fractal dimension D is calculated using the following equation:

where M ( r < R ) represents the cumulative mass of particles with a size below an upper limit R ; M T is the total mass of soil particles; R L is the upper size limit for fractal scaling; D is the mass fractal dimension or termed fractal dimension of particle mass size distribution.…”

Section: Test Materialsmentioning

confidence: 99%

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Characterization of the Relationship between the Loess Moisture and Image Grayscale Value

Liu

Wang

Zheng

et al. 2021

Sensors

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This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.

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Section: Sample Preparationmentioning

confidence: 99%

Section: Sample Preparationmentioning

confidence: 99%