LMS-based structural health monitoring methods for the ASCE benchmark problem

Abstract

A structure's health or level of damage can be monitored by identifying changes in structural or modal parameters. This research directly identifies changes in structural stiffness due to modelling error or damage using a structural 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. An LMS filtering based approach is used to analyze the data from the IASC-ASCE structural health monitoring task group benchmark problem. The proposed methods accurately identify damage to within 1%, with convergence times of 0.4 - 13.0 seconds for the twelve different 4 and 12 degree of freedom benchmark problems and modal parameters match to within 1%. Finally, the method presented is computationally simple, requiring no more than 1.4 Mcycles of computation.