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.

中文翻译：

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使用 Voronoi 图和贝叶斯压缩采样进行横截面岩土现场调查的样本数量和有效采样位置的数据驱动确定

摘要 实际中的岩土分析和设计通常使用二维 (2D) 横截面进行，其信息是从现场调查中获得的。现场调查结果的质量在很大程度上取决于现场调查期间采样的数量和位置。但是，增加样本数量需要额外的支出、人力资源和时间。此外，岩土现场勘察是一个多阶段的过程，前期的测量往往是稀疏和有限的，因此后期可能需要额外的样本。本研究开发了一种智能采样策略，用于使用 Voronoi 图、贝叶斯压缩采样 (BCS) 和信息熵来规划横截面的多阶段岩土工程现场调查。该方法是非参数和数据驱动的，它可以确定必要的样本数量及其相应的最佳采样位置。所提出的智能采样策略应用Voronoi图在现场调查的初步阶段确定测量的有效采样位置，并使用BCS和信息熵来自适应和自适应地自动决定是否需要额外的样本及其有效采样位置。数据驱动的方式。所提出的方法使用真实的土壤数据进行了说明，并表明其性能良好且稳健。所提出的智能采样策略应用Voronoi图在现场调查的初步阶段确定测量的有效采样位置，并使用BCS和信息熵来自适应和自适应地自动决定是否需要额外的样本及其有效采样位置。数据驱动的方式。所提出的方法使用真实的土壤数据进行了说明，并表明其性能良好且稳健。所提出的智能采样策略应用Voronoi图在现场调查的初步阶段确定测量的有效采样位置，并使用BCS和信息熵来自适应和自适应地自动决定是否需要额外的样本及其有效采样位置。数据驱动的方式。所提出的方法使用真实的土壤数据进行了说明，并表明其性能良好且稳健。