Estimating the standard deviation of soil properties with limited samples through the Bayesian approach

Characterizing the standard deviation of soil properties is important to a geotechnical probabilistic analysis, and the task is usually achieved with sufficient samples. However, sometimes soil samples or soil tests in a project could be limited (e.g., only one sample), making classical statistics approaches less applicable to the estimating. In this technical note, we introduce a new Bayesian algorithm to estimate the standard deviation of soil properties, using limited project-specific samples along with relevant prior information from the literature. In addition to the methodology, a few demonstrations are also given in the paper, to re-evaluate the standard deviation of soil properties with the new algorithm. Like many Bayesian algorithms, the new application could be useful for site characterizations when samples are limited.