Limit state design, incorporated into many recent geotechnical design codes, introduces the application of partial or resistance factors to selected characteristic values. Partial or resistance factors are usually set by national standard organizations, while characteristic values of geotechnical parameters are selected by engineers, often based on sparse measurement data combined with subjective engineering experience and judgment. Due to this subjective selection and individual judgment, the characteristic value derived by different engineers from the same dataset may vary greatly, especially when the test data contain significant variability. To address this issue, a new method based on Bayesian compressive sampling (BCS) is proposed in this study. BCS is able to reconstruct a high-resolution geotechnical property profile from sparse measurement data and quantify the uncertainty, e.g., confidence interval (CI) associated with the interpreted profile. The quantified uncertainty in the BCS has a clear statistical meaning: the corresponding confidence level for a CI from the BCS is the expected coverage proportion (i.e., fraction) of the complete profile that falls within the CI, if all data points along depth can be measured to provide the complete profile. This statistical meaning can be used to facilitate objective determination of characteristic values for geotechnical properties.
Interpolating spatially varying soil property values from sparse data for facilitating characteristic value selection
