Multitask learning for improved scour detection: A dynamic wave tank study

Multitask learning for improved scour detection: A dynamic wave tank study Simon M. Brealy, Aidan J. Hughes, Tina A. Dardeno, Lawrence A. Bull, Robin S. Mills, Nikolaos Dervilis, Keith Worden Bayesian hierarchical models help reduce uncertainty of foundation model parameters in populations of wind-turbines Reduced foundation parameter uncertainty aids detection of anomalies in dynamic behaviour during operation Future design of turbines may also be improved through reducing the likelihood and severity of fatigue damage Abstract Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a Bayesian hierarchical model as a means of multitask learning, to infer foundation stiffness distribution parameters at both population and local levels. To do this, observations of natural frequency from populations of structures were first generated from both numerical and experimental models. These observations were then used in a partially-pooled Bayesian hierarchical model in tandem with surrogate FE models of the structures to infer foundation stiffness parameters.