| 20 Oct 2025
Experimental Investigation of the Direct Shear Strength Parameters of Compacted Snow
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.
Experimental Investigation of the Direct Shear Strength Parameters of Compacted Snow
By Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao and Xueyuan Tang
Summary
This paper presents an experimental setup to investigate the shear strength parameters of compacted snow. It also proposes a methodology for preparing compacted snow samples with distinct parameters, including initial density, sintering time, and sintering temperature. Using their apparatus, the authors obtained shear stress–displacement curves for samples under different normal loads. From these curves, they derived the shear strength based on the peak shear stress when present, or at a fixed displacement of 4 mm when no peak was observed. They also calculated the internal friction angle and cohesion using a Mohr–Coulomb relationship. Finally, the authors proposed a neural network to predict the shear parameters based on the four tested variables.
The paper is generally well written, and the methodology appears appropriate for the research question. While the novelty of the work is limited, the measurements and dataset produced in this study are valuable and warrant publication. My main concern is the choice of a neural network to predict a relatively simple relationship using only four variables. This choice seems driven more by popularity than by scientific necessity, especially since it is neither justified nor discussed. In addition, the discussion section would benefit from deeper analysis, as it currently reads like a list of bullet points. It may also be useful to add a dedicated section addressing the limitations and biases of the study, and how these may influence the results.
Major Comment:
Specific comments (line number)
63: Which studies? A few references are required here as it is the base of the novelty of the study.
Specific comments (line numbers)
74–75: Is there any reference for this process?
89: What is the resulting sow grain type, I’m guessing rounding grains or facets but was this observed? What exactly is meant by a natural sintering environment? Does this refer to isolating the sample from the surrounding air in the cold room? If possible, add a photo to Figure 1.
Table 1: Please define σ as the other variables.
Figure 6: Please add the symbol definitions so the figure can be understood on its own.
157: This result is interesting, as lower densities closer to natural compacted snow often exhibit peak strain-softening. Why was 650 kg/m³ used for sintering time tests and 550 kg/m³ for sintering temperature tests?
177: “…and increases again after 250 kg/m³.”
Figure 11: Why is the density plotted with a dashed line while the other curves are solid? You should consider using dashed lines for all curves, since no observations exist outside the measured points and no analytical fit is provided.
Section 4: Why use a neural network? It seems excessive for a four-variable input and a relatively simple interaction between variables.
284: Rephrase the sentence so it is clear that you compared the shear strength with all the other input variables.
337-339: Can you compare with other studies for your “low density”? It is surprising to me that you have no softening peak with lower densities that are closer to natural snow. Maybe discuss the influence of the other parameters on that matter (sintering time chose = 5 days).