Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentation
David Marx, Krishna Kumar, Jorge Zornberg

TL;DR
This study introduces an experimental method combining transparent sand, deep learning image segmentation, and particle tracking to quantify the lateral restraint effect of geogrids at the particle level, validated against DIC.
Contribution
It presents a novel particle-level measurement technique for geogrid restraint quantification using deep learning and Fourier shape analysis, validated with experimental data.
Findings
Geosynthetic inclusions significantly restrict particle displacements and rotations.
Particle displacements are restrained more than rotations.
A relationship exists between permanent strains and the zone of probable motions.
Abstract
An experimental technique is presented to quantify the lateral restraint provided by a geogrid embedded in granular soil at the particle level. Repeated load triaxial tests were done on transparent sand specimens with geosynthetic inclusions simulating geogrids. Particle outlines on laser illuminated planes through the specimens were segmented using a deep learning-based segmentation algorithm. The particle outlines were characterized in terms of Fourier shape descriptors and tracked across sequentially captured images. The accuracy of the particle displacement measurements was validated against Digital Image Correlation (DIC) measurements. In addition, the method's resolution and repeatability is presented. Based on the measured particle displacements and rotations, a state boundary line between probable and improbable particle motions was identified for each test. The size of the zone…
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Taxonomy
TopicsGeotechnical Engineering and Soil Stabilization · Geotechnical Engineering and Soil Mechanics · Geotechnical Engineering and Underground Structures
MethodsMax Pooling · Convolution · *Communicated@Fast*How Do I Communicate to Expedia? · Concatenated Skip Connection · U-Net
