Optical Flow Method for Measuring Deformation of Soil Specimen Subjected
to Torsional Shearing
- URL: http://arxiv.org/abs/2101.07005v2
- Date: Tue, 19 Jan 2021 08:46:18 GMT
- Title: Optical Flow Method for Measuring Deformation of Soil Specimen Subjected
to Torsional Shearing
- Authors: Piotr E. Srokosz, Marcin Bujko, Marta Boche\'nska and Rafa{\l}
Ossowski
- Abstract summary: The main objective was to observe how the deformation distributes along the whole height of cylindrical soil specimen subjected to torsional shearing (TS test)
The experiments were conducted on dry non-cohesive soil specimens under two values of isotropic pressure.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this study optical flow method was used for soil small deformation
measurement in laboratory tests. The main objective was to observe how the
deformation distributes along the whole height of cylindrical soil specimen
subjected to torsional shearing (TS test). The experiments were conducted on
dry non-cohesive soil specimens under two values of isotropic pressure.
Specimens were loaded with low-amplitude cyclic torque to analyze the
deformation within the small strain range (0.001-0.01%). Optical flow method
variant by Ce Liu (2009) was used for motion estimation from series of images.
This algorithm uses scale-invariant feature transform (SIFT) for image feature
extraction and coarse-to-fine matching scheme for faster calculations. The
results were validated with the Particle Image Velocimetry (PIV). The results
show that the displacement distribution deviates from commonly assumed
linearity. Moreover, the observed deformation mechanisms analysis suggest that
the shear modulus $G$ commonly determined through TS tests can be considerably
overestimated.
Related papers
- Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids [0.0]
This work presents a novel approach for estimating fluid flow fields using neural networks and the optical flow equation.
The methodology was validated on synthetic and experimental images.
arXiv Detail & Related papers (2025-01-28T20:40:15Z) - ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle [49.61982102900982]
A LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains.
A global-scale factor graph is established to aid in the reduction of cumulative errors.
The results demonstrate that ROLO-SLAM excels in pose estimation of ground vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks.
arXiv Detail & Related papers (2025-01-04T02:44:27Z) - Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters.
We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
arXiv Detail & Related papers (2024-11-17T17:45:37Z) - Superresolution in separation estimation between two dynamic incoherent sources using spatial demultiplexing [0.0]
Recently, perfect measurement based on spatial mode demultiplexing (SPADE) in Hermite-Gauss modes allowed one to reach the quantum limit of precision for estimation of separation between two weak incoherent stationary sources.
In this paper, we consider another deviation from the perfect setup by discarding the assumption about the stationarity of the sources.
We formulate a measurement algorithm that allows for the reduction of one parameter for estimation in the stationary sources scenario.
arXiv Detail & Related papers (2024-07-15T07:57:57Z) - Characterizing segregation in blast rock piles a deep-learning approach leveraging aerial image analysis [7.211586388797869]
This study introduces an advanced image analysis methodology to characterize such segregation of rock fragments.
The accurate delineation of detailed rock fragment size distributions was achieved through the analysis of drone-captured imagery.
arXiv Detail & Related papers (2024-06-06T15:13:56Z) - Vision-Informed Flow Image Super-Resolution with Quaternion Spatial
Modeling and Dynamic Flow Convolution [49.45309818782329]
Flow image super-resolution (FISR) aims at recovering high-resolution turbulent velocity fields from low-resolution flow images.
Existing FISR methods mainly process the flow images in natural image patterns.
We propose the first flow visual property-informed FISR algorithm.
arXiv Detail & Related papers (2024-01-29T06:48:16Z) - Machine learning enabled experimental design and parameter estimation
for ultrafast spin dynamics [54.172707311728885]
We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED)
Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED.
Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
arXiv Detail & Related papers (2023-06-03T06:19:20Z) - DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic
Correlation Diffusion Model [46.68717345017946]
Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing.
We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD)
Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
arXiv Detail & Related papers (2023-05-21T09:21:41Z) - Evaluation of particle motions in stabilized specimens of transparent
sand using deep learning segmentation [1.8047694351309205]
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants.
The Cellpose U-Net model, originally developed to segment biological cells, was trained to segment images of fused quartz particles.
arXiv Detail & Related papers (2022-12-06T12:53:22Z) - Retrieving space-dependent polarization transformations via near-optimal
quantum process tomography [55.41644538483948]
We investigate the application of genetic and machine learning approaches to tomographic problems.
We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time.
We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
arXiv Detail & Related papers (2022-10-27T11:37:14Z) - Two-dimensional Multi-fiber Spectrum Image Correction Based on Machine
Learning Techniques [8.754036933225398]
We propose a novel method to solve the problem of spatial variation PSF through image aberration correction.
When CCD image aberration is corrected, PSF, the convolution kernel, can be approximated by one spatial invariant PSF only.
arXiv Detail & Related papers (2020-02-16T15:39:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.