详情

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

项目名称：	Predicting Soil Type from Non-destructive Geophysical Data using Bayesian Statistical Methods
摘要：	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.
状态：	Completed
资助组织：	Office of the Assistant Secretary for Research and Technology
开始时间：	20180101
预计完成日期：	20180831
实际结束时间：	0