Ben Leshchinsky is an assistant professor in geotechnical engineering. His research focuses on basic and applied soil mechanics, particularly focused towards geohazards.

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Leshchinsky Research Group Welcomes New Students

We welcome Nicolas Mathews, Ezra Stockton (coadvised with Michael Olsen), and Andrew Senogles (coadvised with Michael Olsen), to the team!

Leshchinsky Research Group, Graduate Students and Collaborators Publish Letter in Science

The letter, ttilted Mitigating Coastal Landslide Damage, was published in the September 2017 issue of the magazine. It outlines the relevance of the recent Big Sur Landslide to coastal resilience with increasing seacliff erosion and dependence on vulnerable lifelines. This is a product of ongoing research focusing on coastal landslides in collaboration with ODOT.

Sagar Satyal successfully Defends M.S.

Sagar defended his M.S. thesis, titled "Use of Cellular Confinement for Improved Railway Performance on Soft Subgrades ", earning an M.S. in Civil Engineering.

Linghui Wang successfully Defends M.S., Publishes research in Computers and Geotechnics

Linghui defended her M.S. thesis, titled "Active and passive arching stresses in c'-ϕ' soils: A sensitivity study using computational limit analysis", earning her M.S. in Civil engineering. Her work was published in the peer reviewed journal, Computers and Geotechnics.

Daniel Hess successfully Defends M.S., Publishes research in Landslides

Danny defended his M.S. thesis, titled "A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity", earning his M.S. in Civil engineering. His work was published in the peer reviewed journal, Landslides.

Erica Kemp successfully Defends M.S.

Erica defended her M.S. thesis, titled "Sediment Transport Prototypes: Novel Methods to Disconnect Roads from Streams", earning her M.S. in water resources engineering.

Spencer Ambauen successfully Defends M.S., Publishes research in Geosynthetics International, Joins Aspect Engineering in Seattle

Spencer defended his M.S. thesis, titled "Numerical simulation of mechanically stabilized earth walls for parametric evaluation of behavior under surcharge loading", earning his M.S. in geotechnical engineering. Find his thesis here. His work was published in He accepted a position as a staff engineer at Aspect Engineering in Seattle, WA, where he currently performs a variety of geotechnical duties.

Landslide Inventorying Algorithm Featured in the Media - Contour Connection Algorithm

The Contour Connection Algorithm, a collaborative effort between OSU and George Mason University (recently published in Computers and Geosciences), was featured in several media outlets, including Photonics, KGW Portland, TerraDaily, LiDAR News and Oregon State University.

Contour Connection Method - Landslide Inventorying Algorithm published in Computers and Geosciences

Abstract: Landslides are a common hazard worldwide that result in major economic, environmental and social impacts. Despite their devastating effects, inventorying existing landslides, often the regions at highest risk of reoccurrence, is challenging, time-consuming, and expensive. Current landslide mapping techniques include field inventorying, photogrammetric approaches, and use of bare-earth (BE) lidar digital terrain models (DTMs) to highlight regions of instability. However, many techniques do not have sufficient resolution, detail, and accuracy for mapping across landscape scale with the exception of using BE DTMs, which can reveal the landscape beneath vegetation and other obstructions, highlighting landslide features, including scarps, deposits, fans and more. Current approaches to landslide inventorying with lidar to create BE DTMs include manual digitizing, statistical or machine learning approaches, and use of alternate sensors (e.g., hyperspectral imaging) with lidar.

This paper outlines a novel algorithm to automatically and consistently detect landslide deposits on a landscape scale. The proposed method is named as the Contour Connection Method (CCM) and is primarily based on bare earth lidar data requiring minimal user input such as the landslide scarp and deposit gradients. The CCM algorithm functions by applying contours and nodes to a map, and using vectors connecting the nodes to evaluate gradient and associated landslide features based on the user defined input criteria. Furthermore, in addition to the detection capabilities, CCM also provides an opportunity to be potentially used to classify different landscape features. This is possible because each landslide feature has a distinct set of metadata – specifically, density of connection vectors on each contour – that provides a unique signature for each landslide. In this paper, demonstrations of using CCM are presented by applying the algorithm to the region surrounding the Oso landslide in Washington (March 2014), as well as two 14,000 ha DTMs in Oregon, which were used as a comparison of CCM and manually delineated landslide deposits. The results show the capability of the CCM with limited data requirements and the agreement with manual delineation but achieving the results at a much faster time.

Find the publication here.

Yonggui Xie is awarded the GSI Fellowship for the 2014-2015 Academic Year

Yonggui, a current Ph.D. student was awarded the prestigious GSI fellowship, generously provided by the Geosynthetic Institute of Kendron, PA. This fellowship will assist Yonggui in pursuing research related to serviceability of surcharge supporting MSE Walls.