Rob Barthorpe


Structural Health Monitoring, Uncertainty Analysis

Rob’s research covers the topics of structural health monitoring, model validation and uncertainty quantification. An underlying theme of his work is the integration of model predictions and experimental data to support decision-making. His work includes the application of multiclass machine learning algorithms for detection and localisation of damage within structures based upon measured dynamic data. These techniques are very powerful but are reliant upon the availability of appropriate damaged-state data. This issue has motivated work in the field of Finite Element model prediction in the presence of random and systematic uncertainties, and in the broader fields of Model Validation and Uncertainty Quantification. The domain of applicability is broad, as the issues of uncertainty quantification, parameter estimation and handling of test-model discrepancy are pervasive in many branches of science and engineering. He is the Facility Manager for the LVV.