Predicting Soil Type from Non-destructive Geophysical Data using Bayesian Statistical Methods

The goal of the original research project was to develop a rapid, non-destructive geophysical testing program that can be used to proactively evaluate levees. A series of geophysical field trials were conducted to determine the most accurate and efficient methods and the best parameters for detecting various features or defects within levees. Of the available techniques, electrical resistivity measurements and surface wave methods were determined to be the most advantageous in terms of capturing features of interest. While these are the best indicators of a subsurface condition, neither method was able to provide a confident prediction of soil type when used alone. For resistivity in particular, a wide range of predictor values was found associated to a given soil type, leading to poor uncertainty quantification. Even though a laboratory study was conducted to better understand the influence that geotechnical parameters have on a soil’s measured electrical resistivity, the low sample size made it difficult to predict soil type using a traditional statistical regression or classification framework with sufficient power. A lower sample size can also lead to biased parameter estimates inhibiting a study of their relative importance.

    Project

    • Status: Completed
    • Sponsor Organizations:

      Department of Transportation

      1200 New Jersey Avenue, SE
      Washington, DC  United States  20590

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Start Date: 20180101
    • Expected Completion Date: 20180831
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01661363
    • Record Type: Research project
    • Source Agency: Maritime Transportation Research and Education Center
    • Files: UTC, RiP
    • Created Date: Feb 26 2018 10:30AM