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 structureÂs health or level of damage can be monitored by
identifying changes in structural or modal parameters. In this research, the structureÂs
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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3728 |
| Date | 16 August 2006 |
| Creators | Preston, Robin Huckaby |
| Contributors | Barroso, Luciana |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 9083697 bytes, electronic, application/pdf, born digital |
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