Evaluation of the Strength Characteristics of Silty-Clayey Soils during Freezing-Thawing Cycles

Destructuring settlements due to frost heave during the structures’ exploitation are often not taken into account at the designing stage, although they are indirectly related to the bearing capacity of the soils. The objective of this research was analyzing the effect of the number of freezing-thawing cycles on the strength characteristics of soils. A paired experiment with various initial parameters (void ratio, initial moisture content, and the number of freezing-thawing cycles) was carried out. According to the experimental results, the cohesion largely depends on the above parameters which might lead to its decrease by up to three times. The angle of internal friction demonstrated an indefinite behavior during the freeze-thaw cycles, which is confirmed by a literature review. Freezing–thawing cycles significantly decrease the soil bearing capacity: up to 44% after 10 freezing-thawing cycles for soil with and . However, in the case of and it increased by 33%. A program based on the least-squares method was used to calculate the approximation coefficients of the dependence describing the changes in strength characteristics from the abovementioned parameters. Changes in strength characteristics must be taken into account when designing structures, as they can lead to additional settlement or even subsidence of the foundations.

Stability of the Foundation of Buried Energy Pipeline in Permafrost Region

During operation, a buried pipeline is threatened by a variety of geological hazards, particularly in permafrost regions, where freezing-thawing disasters have a significant influence on the integrity and safety of the buried pipelines. The topographical environmental conditions along the pipeline, as well as the influence of frost heave and thaw settlement on the pipeline’s foundation soil, must be considered in the design and construction stage. Theoretical analysis, numerical modeling, field testing, and mitigation measures on vital energy pipelines in permafrost have been widely documented, but no attempt has been made to review the freezing-thawing disasters, current research methodologies, and mitigation strategies. This article reviews the formation mechanisms and mitigation measures for frost hazards (e.g., differential frost heave, thaw settlement, slope instability, frost mounds, icing, river ice scouring, and pipeline floating) along buried pipelines in permafrost regions and summarizes and prospects the major progress in the research on mechanisms, analysis methods, model test, and field monitoring based on publications of studies of key energy pipelines in permafrost regions. This review will provide scholars with a basic understanding of the challenging freezing-thawing hazards encountered by energy pipelines in permafrost regions, as well as research on the stability and mitigation of pipeline foundation soils plagued by freezing-thawing hazards in permafrost regions under a warming climate and degrading permafrost environment.

Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.