Calibration of a simple cone-penetration model for snow. (April 2016)

• Record Type: Journal Article
• Authors: Lee, Jonah H.
• Is Part Of: Journal of terramechanics. Volume 64(2016:Apr.)
• Page Start: 36
• Page End: 45
• Publication Date: 2016-04
• Subjects: Cone penetration -- Snow -- Drucker–Prager -- Validation -- Calibration -- Sensitivity -- Metrics -- Hardness -- Cavity expansion -- Gaussian process; Trafficability -- Periodicals; Praticabilité (Routes) -- Périodiques; Trafficability; Periodicals; 629.222
• DOI: 10.1016/j.jterra.2016.01.001
• Languages: English
• ISSNs: 0022-4898

Abstract

Highlights: Calibration of a physically-based snow penetration model was conducted. Four validation metrics were used to show that the model performs well. Sensitivity studies revealed the top model parameters. Abstract: Numerical studies using the Material Point Method (MPM) have been conducted recently to model snow penetration tests for fine-grained and coarse-grained snows using small cones with diameters ranging from 2.5 mm to 4 mm, and cone half-angles between 15° and 45°. Although numerical studies have gained physical insight of these tests, due to the lengthy computation time needed for the MPM simulations, it is not feasible to use these simulations to develop a stochastic model to assess the large variations of the mechanical properties of snow typically shown in tests. In this paper, we present a simple and efficient physics-based analytical model based on equilibrium and a cavity expansion solution upon which a stochastic model is built to obtain calibrated material parameters for a Drucker–Prager (DP) model such that prediction of the model can be made. Sensitivity analysis of the analytical model indicates that cohesion and interfacial shear (friction) factor contribute significantly to the penetration hardness whereas the friction angle has little contribution. The calibrated material parameters are similar to those estimated via the MPM simulations. The quality of the stochastic model, when compared with test data, was assessed using four interval-based validation metrics with good results.