Author Description

"Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. Geodata is distinctively multivariate, unique, sparse, incomplete, possibly corrupted, and spatial variable, so conventional methods may deliver unreliable results. The powerful methods can be widely applied, as with site characterization, foundation design, underground stratification, soil property estimation, inverse method, observational method, and liquefaction estimation, and are espcially useful for large and complex projects where cost is a major factor"--