- author = "Colin C. Olson",
- title = "Evolutionary algorithms, chaotic excitations, and structural health monitoring: On global search methods for improved damage detection via tailored inputs",
- school = "Structural Engineering, University of California, San Diego",
- year = "2008",
- address = "USA",
- keywords = "genetic algorithms, genetic programming, differential evolution, gene expression programming, GEP/DE, Applied science, Chaos theory, Civil engineering, Damage detection, Evolutionary Algorithm, Nonlinear dynamic, Structural health monitoring",
- isbn13 = "9780549423881",
-
URL = "
http://search.proquest.com/docview/304659192",
- size = "294 pages",
-
abstract = "In a vibration-based damage detection paradigm, a
structural health monitoring (SHM) system is designed
to acquire data from which damage-sensitive features
will be observed in order to classify the damage state
of the structure without providing false positive
indications of damage. An optimization routine is
introduced that exploits the concept of SHM as a
pattern recognition problem for which there is a space
of solutions that can be searched by global
optimization algorithms to improve damage detection
sensitivity.
The optimization procedure is general in that it can be used to improve a number of elements that are particular to the problem of damage detection including: the excitation, method of data conditioning, detection features, and statistical metrics of comparison. In particular, this work focuses on tailoring excitations for global, active sensing SHM applications via evolutionary algorithms. Indications of damage in the response are shown to be more detectable in both computational and experimental platforms using excitations that have been tailored to the application via the optimization routine.
Using the routine, a class of excitations that significantly enhance damage detection relative to untailored inputs is discovered and shown to do so because of a change in the state-space representation of structural response. When state-space features are employed, the relative power of structural response frequencies is shown, in the most basic case, to significantly affect the 2-torus representation of the response. The routine is also used to show that sensitivity is significantly affected by the employed detection feature. In particular, the introduction of temporal information in the feature formulation dictates that improved detection occurs for a class of chaotic excitations.
In addition, a method is introduced that allows tailored inputs to be generated for more complicated structures by training on a simple infinite impulse response filter. Design of the filter only requires identification of two structural resonant frequencies. A 6percent reduction in bolt stiffness in an experimental structure is clearly classified as damaged using state-space features and excitations that have been trained on the filter. The reduction in stiffness is not visible using traditional modal methods.",
-
notes = "Supervisor Michael D. Todd Dissertation/thesis number:
3296835 ProQuest document ID: 304659192
Source DAI-B 69/01, Jul 2008",
