The value of structural health monitoring (SHM) is realised through analysis of structural response measurements. Such analyses can include structural characterisation, identification of trends and divergence from trends, calculation of cumulative usage indicators, predictive estimation of remaining structural life and more. These data analytics quantify the overall structural condition to inform decision making for continual safe operation, predictive maintenance planning, and replacement planning
Successful and efficient implementation of SHM data analytics requires many steps from measurement data acquisition, validation & cleaning, database ingress, calculating operational parameters from physical and statistical models, investigative and retrospective analysis, visualisation and reporting.
These presentations show applications of such models with machine learning for SHM of bridges and civil infrastructure, certification of machine learning for remaining useful life of aircraft landing gear, and SHM of railway infrastructure and rail vehicles.