Evolutionary algorithms, chaotic excitations, and structural health monitoring: On global search methods for improved damage detection via tailored inputs

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  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
  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://phdtree.org/pdf/25321145-evolutionary-algorithms-chaotic-excitations-and-structural-health-monitoring-on-global-search-methods-for-improved-damage-detection-via-tailo/",
  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

                 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",

Genetic Programming entries for Colin C Olson