Using Sigma-Points to Identify Optimal Experimental Design for Dike Monitoring

Abstract

In geotechnical engineering, using monitored data for model validation is common practice. However, a model-based optimal experimental design for parameter identification is unusual when planning a monitoring set-up. As soils are subjected to large parameter uncertainties, model validation is of high interest to enable a precise prediction of the system behaviour. Due to the complexity of considered cases and employed FE-models, time-efficient solutions are of interest. For the case of a dike subjected to a rapid drawdown of the current water level, a Monte-Carlo based approach was previously employed. In the present study, it is intended to improve the efficiency by applying the so-called sigma-Points method that substitutes random sampling by defined characteristics of model response distribution to set-up a monitoring design that allows to identify the relevant system parameters.