A simple LMS-based approach to the structural health monitoring benchmark problem.
Thesis DisciplineMechanical Engineering
Degree GrantorUniversity of Canterbury
Degree NameMaster of Engineering
A civil structure’s health or level of damage can be monitored by identifying changes in structural or modal parameters. However, modal parameters can be less sensitive to (localised) damage than directly identifying the changes in physical parameters of a structure. This research directly identifies changes in structural stiffness due to modelling error or damage, when the structural state and a reasonable estimate of the input force are available, such as earthquake or roof loading of a sensored steel frame structure. This thesis presents the development and implementation of a health monitoring method based on Adaptive Least Mean Square (LMS) filtering theory. The focus in developing these methods is on simplicity to enable real-time implementation with minimal computation. Several adaptive LMS filtering approaches are used to analyse the data from the International Association for Structural Control and American Society of Civil Engineers Structural Health Monitoring (SHM) Task Group Benchmark problem. Results are compared with those from the Task Group and other published results. The proposed methods are shown to be very effective, accurately identifying damage to within 1%, with convergence times of 0.4 – 13.0 seconds for the twelve different 4 and 12 degrees-offreedom SHM Benchmark problems. The resulting modal parameters match to within 1% those from the SHM Benchmark problem definition. Finally, the method presented is computationally extremely simple, requiring no more than 1.4 Mega-cycles of computation, and therefore could easily be implemented in real-time.