This paper describes and benchmarks a new implementation of image-based deformation measurement for geotechnical applications. The updated approach combines a range of advances in image analysis algorithms and techniques best suited to geotechnical applications. Performance benchmarking of the new approach has used a series of artificial images subjected to prescribed spatially varying displacement fields. An improvement by at least a factor of 10 in measurement precision is achieved relative to the most commonly used particle image velocimetry (PIV) approach for all deformation modes, including rigid-body displacements, rotations, and strains (compressive and shear). Lastly, an example analysis of a centrifuge model test is used to demonstrate the capabilities of the new approach. The strain field generated by penetration of a flat footing and an entrapped sand plug into an underlying clay layer is computed and compared for both the current and updated algorithms. This analysis demonstrates that the enhanced measurement precision improves the clarity of the interpretation.Key words: image analysis, model tests, particle image velocimetry, digital image correlation.
Assessment of the risk of punch-through failure of spudcan foundations on sand overlying clay requires prediction of the full penetration resistance profile, from touchdown and through punch-through to equilibrium of the vertical resistance at depth in the underlying clay layer. This study uses the Coupled Eulerian–Lagrangian approach, a large deformation finite element analysis method, to model the complete penetration resistance profile of a spudcan on sand overlying clay. The sand is modeled using the Mohr–Coulomb model, while the clay is modeled using a modified Tresca model to account for strain softening. The numerical method is then used to simulate a series of spudcan penetration tests, performed in a geotechnical centrifuge, on medium dense sand overlying clay. The punch-through behavior observed in the experiments is replicated, and the penetration resistance profiles from numerical analyses are generally a reasonable match to the experimental measurements. The influences of the sand layer height to foundation diameter ratio, sand–clay interface shear strength, and strength gradient in clay on the penetration resistance profiles are explored in a complementary parametric study. The penetration resistance in the underlying clay layer is well predicted using a simple linear expression for the bearing capacity factor for the spudcan and underlying sand plug. This expression is combined with an existing failure stress dependent model for predicting peak resistance to form a simplified method for prediction of the full penetration resistance profile. This new method provides estimates of the vertical penetration that the spudcan will run during the punch-through event. It is validated against both medium dense and dense sand centrifuge tests.
A complete analytical method to describe the full load-penetration resistance profile of a mobile jackup spudcan footing penetrating a sand over clay stratigraphy is described. It is based on both large deformation finite-element analyses and geotechnical centrifuge experiments. The coupled Eulerian-Lagrangian (CEL) approach is used to accommodate the large deformations of a spudcan footing penetrating sand overlying clay. Modified Mohr-Coulomb and Tresca models describe the sand and clay behaviour, with modifications accounting for the effects of strain softening on the response of the soil. The CEL results are shown to match centrifuge tests well, allowing the numerical study to be extended parametrically, and with confidence, to cover the range of layer geometries, sand relative densities and footing shapes that are of practical interest to offshore jack-ups. The results are used to (a) assess the performance of an existing model to predict the peak resistance in the sand layer (extending its range of application to medium dense to dense sands and to conical footings of angle 0°t o 21°), and (b) develop an expression for the bearing capacity factor when the footing penetrates into the underlying clay. Using the analytical formulas proposed, retrospective simulations of centrifuge tests show that the method provides a reasonable estimate of the peak punch-through load, the behaviour in the underlying clay, as well as the punch-through distance; the latter being a basic reflection of the severity of a potential punch-through failure.
This paper describes a new apparatus and techniques for performing deformation measurements using particle image velocimetry in the centrifuge environment. The new system includes camera, lighting, and control equipment that facilitates image capture at least 30 times faster than that in legacy systems. Methods for optimizing the addition of artificial seeding on the exposed plane of a geotechnical model are also set out. These techniques ensure that the precision of the deformation calculations is optimized even in models with multiple layers of different soils, fully harnessing the method's capabilities. An example application of a flat footing penetrating sand overlying clay is used to illustrate the performance of the equipment and the artificial seeding optimization technique. Deformation fields at the point of peak resistance during punch-through are presented in the form of vector fields, normalized displacement contours, and shear strain contours. It is shown that the advances in equipment and artificial seeding allow both macroscopic and grain-scale deformation features to be identified. These analyses highlight not only the benefits of the new technology, but also the need for carefully optimized experimental procedures to maximize the measurement precision.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.