I am a PhD student supervised by Dr R.J. Barthorpe and Dr C. Lord. I obtained a first class MEng degree in Mechanical Engineering with a Year in Industry in 2015 from the University of Sheffield before joining the Dynamics Research Group in the same year.
My research interests include using numerical models to aid structural health monitoring (SHM) methodologies, uncertainty quantification with an importance on understanding model-test discrepancies and the application of machine learning technologies in SHM.
My current research is in novel approaches to model-based SHM. Currently there are two main categories of approaches to SHM. The first are data-driven approaches that use data obtained from the structure combined with machine learning techniques to detect changes due to damage. The second are model-driven techniques which use numerical models that are updated using an inverse approach as data is obtained from the structure. These methods both have several challenges to their implementation, the former being restricted by the availability of appropriate data and the latter from the effect of uncertainties associated with non-calibrated model parameters and model-form errors.
The intention of my current research is to integrate simulated statistically representative damage state data from numerical models into the data-driven approach to SHM therefore improving the problems associated with a lack of experimental data. The work involves the use of Bayesian calibration and bias correction techniques in order to overcome model-test discrepancies and to capture associated uncertainties.
- Gardner P, Barthorpe RJ & Lord C, (2016) The Development of a Damage Model for the use in Machine Learning Driven SHM and Comparison with Conventional SHM Methods, Conference Proceedings of ISMA2016 (pp 3333-3346)
- Gardner P, Barthorpe RJ & Lord C, (2016) Quantification of uncertainty for experimentally obtained modal parameters in the creation of a robust damage model, Conference Proceedings of EACS2016