Preprints
https://doi.org/10.5194/egusphere-2025-4768
https://doi.org/10.5194/egusphere-2025-4768
20 Oct 2025
 | 20 Oct 2025
Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Experimental Investigation of the Direct Shear Strength Parameters of Compacted Snow

Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao, and Xueyuan Tang

Abstract. Compacted snow is utilized as a building material in various construction and engineering applications across global high-latitude regions. For the safety assessment of snow and ice structures in cold regions, cohesion and internal friction angle are key shear strength parameters for compacted snow. This study investigates 69 test conditions, considering variations in initial density, sintering time, and sintering temperature. Using direct shear tests, the variation patterns of the shear strength of compacted (machine-made) snow under normal pressures below 100 kPa were analyzed. Results show that under high sintering degree conditions and low normal pressures, the shear stress–displacement curve tends to exhibit strain softening. As initial density increases from 450 to 650 kg·m-3;, both cohesion and internal friction angle increase linearly. With sintering time increasing from 0 to 60 days, cohesion first rises and then falls, while the internal friction angle steadily decreases. As sintering temperature decreases from -5 to -25 °C, cohesion decreases, whereas the internal friction angle increases slightly. A Genetic Algorithm-Back Propagation (GA-BP) neural network was employed to develop a predictive model for shear strength parameters, providing benchmark values for cohesion and internal friction angle under various conditions. These benchmarks can be adaptively adjusted when additional influencing factors require consideration. This study provides essential strength parameters for the design and construction of compacted snow structures and offers a framework for accounting for the influence of other factors on these parameters.

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Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao, and Xueyuan Tang

Status: open (until 01 Dec 2025)

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Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao, and Xueyuan Tang
Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao, and Xueyuan Tang
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Latest update: 20 Oct 2025
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Short summary
Through a series of direct shear tests, this study analyzes the variation of shear strength parameters (cohesion and internal friction angle) in compacted snow under different conditions of density, sintering time, and temperature. A Genetic Algorithm-Back Propagation neural network model was subsequently developed to establish systematic benchmark values for these parameters. This work provides essential data and a predictive framework for the reliable design of snow structures in cold regions.
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