Optimization of site investigation program for improved statistical characterization of geotechnical property based on random field theory

This paper presents a framework for optimization of site investigation program, within which the robustness of the site investigation program and the investigation effort are optimized. A site investigation program is judged robust if the derived statistics of the geotechnical property of interest are robust against the uncertainties caused by limited data availability and test error. In this study, a Markov chain Monte Carlo simulation-based Bayesian inference approach was used to characterize the statistics of the intended geotechnical property. The robustness of the site investigation program was formulated as a byproduct of the Bayesian inference of the geotechnical property statistics. The proposed framework for optimization of the site investigation program was implemented as a bi-objective optimization problem that considers both robustness and investigation effort. The concepts of Pareto Front and knee point were employed to aid in making an informed decision regarding selection of site investigation program. The effectiveness and significance of the proposed framework were demonstrated through a simulation study.