Lizzy’s main research interests span the fields of structural health monitoring (SHM), machine learning and nonlinear system identification. Her specific areas of interest include the development of robust indicators for structural performance and condition and the importation of sophisticated mathematical techniques for use in the discipline of structural dynamics. Most of her research projects focus on the analysis of large datasets from monitored structures, where she employs data-driven algorithms to extract useful information. For SHM, these efforts attempt to address the problem of confounding influences – where benign changes in the measurements of structural parameters caused by the environment mask the detectability of damage.
Lizzy Cross
Head of Department, Professor of Structural Dynamics
