Effect of Water Content Variation on the Tensile Characteristic of Clayey Loess in Ili Valley, China

Zheng, Penglin; Wang, Jinge; Wu, Zihao; Huang, Wei; Li, Changdong; Liu, Qingbing

doi:10.3390/app12178470

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…The contact between particles shifts from face-to-face contact to point-to-point contact, leading to an increase in total pore volume and inter-aggregate pore volume, while intra-aggregate pore volume decreases. Zheng et al [27] investigated the influence of water content on the tensile properties of remolded loess. They determined that the tensile strength of loess is predominantly governed by the hydration/cementation of clay, development of capillary suction, and microstructural evolution associated with changes in water content.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Overburden Stress and Molding Water Content on the Microstructure of Remolded Loess

2023

Sustainability

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This study aims to reveal the mechanisms of the microstructural evolution of remolded loess under different molding water contents and overburden stresses. Utilizing L6 loess from Yan’an, remolded soil specimens were fabricated under various pressures and moisture contents, followed by conducting one-dimensional consolidation tests. The macroscopic porosity, pore size distribution curves (PSD), and microstructure of these remolded loess samples were examined. Experimental findings indicate that an increase in molding water content leads to an augmentation in macroscopic pore volume and elongated pore shapes, concurrently exerting substantial influence on the primary pore size and pore volume of both macropores (>0.4 μm) and minipores (0.4–4 μm), with minimal impact on micropores (<0.4 μm). The escalation of overburden stress solely contributes to the reduction in pore size and pore volume of macropores. Variations in the Menger fractal dimension occur only beyond the optimal water content, while overburden stress exhibits a minimal effect on the Menger fractal dimension. Furthermore, remolded loess exhibited a certain yield stress, and when the overburden stress was lower than the yield stress, there was almost no change in various types of pores. Finally, a microstructural evolution model of remolded loess under different molding water contents and overburden stresses was proposed. These findings are expected to provide new insights into the microstructural evolution and deformation mechanisms of loess in high embankment construction sites.

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Section: Introductionmentioning

confidence: 99%

The Influence of Overburden Stress and Molding Water Content on the Microstructure of Remolded Loess

2023

Sustainability

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“…Zhou [18] established a matrix calculation equation for the collapsibility coefficient through multiple quadratic non-linear regression using MATLAB and achieved acceptable prediction accuracy. Zheng et al [19] analysed the correlation between moisture content and the mechanical properties of loess through microscopic means. Sun [20] analysed the correlation between moisture content and loess collapsibility when identifying the main causes of collapse diseases of masonry structures in loess areas.…”

Section: Introductionmentioning

confidence: 99%

Research on the Prediction Model of Loess Collapsibility in Xinyuan County, Ili River Valley Area

Chen,

et al. 2023

Water

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Collapsibility is a unique engineering geological property of loess. Choosing appropriate parameters to build the prediction model of loess collapsibility is an essential step toward solving the loess collapsibility problem. A case study was performed for the loess in Xinyuan County of the Yili River Basin, China. A large amount of data was collected from preliminary geotechnical tests in this region. Mathematical statistics were applied to analyse the correlations between the loess collapsibility and soil parameters. Multiple linear regression and neural network theories were adopted to build this region’s prediction model of loess collapsibility. The results showed that microscopically, the soils in this region were predominantly flocculated structures. The soil particles were flaky and in bracket contact, and the pores were round or irregularly shaped. Regarding the material composition, the soils were primarily composed of quartz and albite, with a low hematite content. In the study area, the correlation coefficients between the collapsibility coefficient of the loess vs. the density, dry density, saturation, porosity ratio, and porosity varied between 0.628 and 0.857, indicating a strong or very strong correlation. In terms of predicting loess collapsibility, the effectiveness of neural networks based on RBF (radial basis function) and multiple linear regression models was contrasted. The latter was discovered to be more appropriate, dependable, and accurate, with an accuracy percentage of 94.42%. Simultaneously, the model’s assessment index is 0.014 for the root mean squared error (RMSE), 0.962 for the correlation coefficient (CC), 0.919 for the Nash–Sutcliffe efficiency coefficient (NSE), and −1.494 percent for the percent bias (PBIAS). It works well for estimating whether local loess may collapse. Therefore, the RBF neural network model built in the present study has adequate precision and meets the engineering requirements. Our research sheds new light on loess collapsibility assessment in this region.

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Monitoring Creeping Landslides with InSAR in a Loess-covered Mountainous Area in the Ili Valley, Central Asia

Fan,

Luo,

Hellwich

et al. 2024

PFG

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Loess landslides in mountainous regions of the Ili Valley have resulted in numerous casualties as well as huge economic losses. However, the characteristics and driving mechanisms of surface deformation related to loess landslides in mountainous areas remain unclear, thus limiting our ability to identify, monitor, and warn populations of potential catastrophic events. This study was conducted in a typical mountainous area of the Ili Valley, where landslides have been documented by field investigations, unmanned aerial vehicle images, and light detection and ranging data. With ascending and descending Sentinel‑1 time series synthetic aperture radar images, acquired using the small baselines subset method, surface deformation was observed for the period from October 2014 to October 2021, and loess landslides were concurrently mapped to delineate hazardous areas. Using the methods of this study, we were able to identify 74.4% of previously documented landslides. Additionally, we observed a seasonal time-series of deformation that had a time delay of less than one month and was responsive to rainfall. Our analysis of the characteristics and driving mechanisms of creeping landslides in the Ili Valley led to the compilation of a new inventory of active slopes that will offer valuable guidance for land managers tasked with implementing disaster prevention measures.

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Effect of Water Content Variation on the Tensile Characteristic of Clayey Loess in Ili Valley, China

Cited by 4 publications

References 42 publications

The Influence of Overburden Stress and Molding Water Content on the Microstructure of Remolded Loess

The Influence of Overburden Stress and Molding Water Content on the Microstructure of Remolded Loess

Research on the Prediction Model of Loess Collapsibility in Xinyuan County, Ili River Valley Area

Monitoring Creeping Landslides with InSAR in a Loess-covered Mountainous Area in the Ili Valley, Central Asia

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