LMS-based method for damage detection applied to Phase II of Structural Health Monitoring benchmark problem

Abstract

Structural Health Monitoring (SHM) is the process of monitoring the state of a structure to determine the existence, location, and degree of damage that may exist within the entire structure. A structure’s health or level of damage can be monitored by identifying changes in structural or modal parameters. In this research, the structure’s health is monitored by identifying changes in structural stiffness. The Adaptive Least Mean Square (LMS) filtering approach is used to directly identify changes in structural stiffness for the IASC-ASCE Structural Health Monitoring Task Group Benchmark problem for both Phase I and II. The research focuses primarily on Phase II of the benchmark problem. In Phase II, modeling error and noise is introduced to the problem making the problem more realistic. The research found that the LMS filter approach can be used to detect damage and distinguish relative severity of the damage in Phase II of the benchmark problem in real time. Even though the LMS filter approach identified damage, a threshold below which damage is hard to identify exists. If the overall stiffness changes less than 10%, then identifying the presence and location of damage is difficult. But if the time of damage is known, then the presence and location can be determined. The research is of great interest to those in the structural health monitoring community, structural engineers, and inspection practitioners who deal with structural damage identification problems.