Junping Ren

and 4 more

The increasing use of the seasonally frozen and permafrost regions for civil engineering constructions and the effects of global warming on these regions have stimulated research on the behaviors of frozen soils. In the present study, the frost heave characteristics of a coarse-grained soil with volcanic nature was experimentally investigated. A large soil tank model was established in laboratory for this purpose. The effects of temperature boundary, external water supply, and water transfer type on the frost heave characteristics of the volcanic soil were studied, through a series of frost heave tests. The particle image velocimetry (PIV) technique was used to quantify the full field deformation of the soil specimen. The results suggest that temperature gradient inside the soil specimen is the driving force for the migration of pore water and vapor. The largest increment in water content generally agrees well with the frost penetration depth. The contribution of vapor to the frost heave of the Komaoka soil specimen is typically small. The applied seeding method, selected subset size, image-object space calibration, and calculation processes ensured accurate PIV results. Discussions regarding the presented experimental investigation and the employment of PIV technique for quantifying frozen soil deformation are summarized. These findings and discussions can provide valuable insights into the frost heave behavior of the studied soil in particular, as well as promote the application of PIV for frozen soil engineering.

Junping Ren

and 4 more

A portion of pore water is typically in a state of unfrozen condition in frozen soils due to the complex soil-water interactions. The variation of the amount of unfrozen water and ice has a significant influence on the physical and mechanical behaviors of the frozen soils. Several empirical, semi-empirical, physical and theoretical models are available in the literature to estimate the unfrozen water content (UWC) in frozen soils. However, these models have limitations due to the complex interactions of various influencing factors that are not well understood or fully established. For this reason, in the present study, an artificial neural network (ANN) modeling framework is proposed and the PyTorch package is used for predicting the UWC in soils. For achieving this objective, extensive UWC data of various types of soils tested under various conditions were collected through an extensive search of the literature. The developed ANN model showed good performance for the test dataset. In addition, the model performance was compared with two traditional statistical models for UWC prediction on four additional types of soils and found to outperform these traditional models. Detailed discussions on the developed ANN model, and its strengths and limitations in comparison to different other models are provided. The study demonstrates that the proposed ANN model is simple yet reliable for estimating the UWC of various soils. In addition, the summarized UWC data and the proposed machine learning modeling framework are valuable for future studies related to frozen soils.