LAUSR

Site characterization is indispensable in geotechnical engineering 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 or sampling such that as much information as possible on the spatial variability of soil properties can be obtained from a given number of measurements. In addition, geotechnical site characterization is a multi-stage process, and additional measurements might be required at a later stage of site characterization. In such a case, how the additional sampling locations can be selected efficiently such that the pre-existing measurements obtained from the preliminary stages of site characterization can be best used and how can as much information as possible on soil properties be further obtained are problems engineers face. This paper aims to address these two problems using information entropy and Bayesian compressive sampling. Real cone penetration test data along both vertical and horizontal directions are used to illustrate and validate the proposed methods. Results show that the proposed methods are very effective and robust in selecting efficient sampling locations for geotechnical site characterization.