Data-driven determination of sample number and efficient sampling locations for geotechnical site investigation of a cross-section using Voronoi diagram and Bayesian compressive sampling

Abstract

Geotechnical analyses and designs in practice are often performed using a two-dimensional (2D) cross-section, information of which is obtained from site investigation. The quality of site investigation results depends greatly on the number and locations of sampling during site investigation. However, increasing the sample number requires additional expenditure, human resources, and time. In addition, geotechnical site investigation is a multi-stage process, and the measurements at preliminary stage are often sparse and limited, hence additional samples might be needed in later stages. This study develops a smart sampling strategy for planning of multistage geotechnical site investigation of a cross-section using Voronoi diagram, Bayesian compressive sampling (BCS), and information entropy. The proposed method is non-parametric and data-driven, and it can determine both the necessary sample number and their corresponding optimal sampling locations. The proposed smart sampling strategy applies Voronoi diagram to determine the efficient sampling locations of measurements at preliminary stage of site investigation, and uses BCS and information entropy to automatically decide whether or not additional samples are needed and their efficient sampling locations in a self-adaptive and data-driven manner. The proposed method is illustrated using real soil data and showed to perform well and robustly.