Affiliations: Graduate Program in Computational Modeling,
Polytechnic Institute, State University of Rio de Janeiro, Nova Friburgo, RJ, Brazil | Instituto de Ciências Ambientais e Desenvolvimento
Sustentável, Federal University of Bahia, Salvador, Brazil
Note: [] Corresponding author: R.A. Tenenbaum, Graduate Program in
Computational Modeling, Polytechnic Institute, State University of Rio de
Janeiro, Rua Bonfim, 25, Lagoinha, Nova Friburgo, RJ, Brazil. E-mail: [email protected]
Abstract: The formulation and solution of the inverse problem of damage
identification based on an one-dimensional wave propagation approach are
presented in this paper. Time history responses, obtained from pulse-echo
synthetic experiments, are used to damage identification. The identification
process is built on the minimization of the squared residue between the
synthetic experimental echo, obtained by using a sequential algebraic
algorithm, and the corresponding analytical one. Five different hybrid
optimization methods are investigated. The hybridization is performed combining
the deterministic Levenberg-Marquardt method and each one of the following
stochastic techniques: The Particle Swarm Optimization; the Luus-Jaakola
optimization method; the Simulated Annealing method; the Particle Collision
method; and a Genetic Algorithm. A performance comparison of the five hybrid
techniques is presented. Different damage scenarios are considered and, in
order to account for noise corrupted data, signals with 10 dB of signal to
noise ratio are also considered. It is shown that the damage identification
procedure built on the Sequential Algebraic Algorithm yielded to very fast and
successful solutions. In the performance comparison, it is also shown that the
hybrid technique combining the Luus-Jaakola and the Levenberg-Marquardt
optimization methods provides the faster damage recovery.
