Determination of efficient sampling locations in geotechnical site characterization using information entropy and Bayesian compressive sampling

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1622-1637
Journal / PublicationCanadian Geotechnical Journal
Issue number11
Online published29 Nov 2018
Publication statusPublished - Nov 2019


Site characterization is indispensable in geotechnical practice, and measurements on soil properties are performed through in-situ tests, laboratory tests, or other methods. However, due to time or budget limit, technical or access constraints etc., the measurements are usually taken at a limited number of locations. This leads to a question of how to select the efficient locations for measurements/sampling such that as much as possible information on the spatial variability of soil properties can be obtained from a given number of measurements. In addition, site characterization is a multi-stage process, and additional measurements might be required at a later stage of site characterization. In this case, how to efficiently select the additional sampling locations such that the pre-existing measurements obtained from the preliminary stages of site characterization can be best used and as much as possible information on soil properties can be further obtained? This paper aims to address these two problems using information entropy and Bayesian compressive sampling (BCS). Real cone penetration test data along vertical and horizontal directions are used to illustrate and validate the proposed methods. The results show that the proposed methods are very effective and robust in selecting efficient sampling locations for geotechnical site characterization.

Research Area(s)

  • site investigation, spatial data, spatial variability, compressive sensing, Bayesian method