Genetic Algorithm Based Objective Functions Comparative Study for Damage Detection and Localization in Beam Structures (original) (raw)
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In this paper a procedure for the static identification and reconstruction of concentrated damage distribution in beam-like structures, implemented in a dedicated software, is presented. The proposed damage identification strategy relies on the solution of an optimisation problem, by means of a genetic algorithm, which exploits the closed form solution based on the distribution theory of multi-cracked beams subjected to static loads. Precisely, the adoption of the latter closed-form solution allows a straightforward evolution of an initial random population of chromosomes, representing different damage distributions along the beam axis, towards the fittest and selected as the sought solution. This method allows the identification of the position and intensity of an arbitrary number of cracks and is limited only by the amount of data experimentally measured. The proposed procedure, which has the great advantage of being robust and very fast, has been implemented in the powerful agent based software environment NetLogo, and is here presented and validated with reference to several benchmark cases of single and multi-cracked beams considering different load scenarios and boundary conditions. Sensitivity analyses to assess the influence of instrumental errors are also included in the study.
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The problem of damage identification in framed structures using vibrational data is considered. The identification problem is modelled as an optimization task and the use of measured natural frequencies as well as modeshape information in the construction of objective functions is discussed. In a first attempt, a standard genetic algorithm is shown to be ineffective in obtaining the correct damage distribution in test problems. Using domain knowledge, modifications are introduced in the coding process, in the initial population generation, in the fitness function, and in the genetic operators, leading to a promising tool to solve this class of problems. Synthetic problems, with the addition of noise in the simulated measured data associated with the damaged structure, are analysed in order to assess the capability of the proposed technique.
Structural Damage Detection using Genetic Algorithm by Static Measurements
Damage detection techniques have been recognized as an important non-destructive tool for the identification of damage in structures for several years. This paper proposed a method of locating and quantifying the damage in structural members using the concept of prediction algorithm along with normalized residual function employed to formulate the objective function. Two-point crossover binary coded genetic algorithm (GA) combined with artificial Intelligence continuing search algorithms, in minimum constraint function (Fmin.con.). Sequential Quadratic Programming (SQP), Interior-point, Active set, each one alone or hybrid with GA, used in minimizing the objective function and optimum set of stiffness reduction parameters. The optimum sensors locations were determined using strain energy equation. The proposed technique was incorporated into MATLAB code and applied to selected problems. Results of actual supposed damage using site full measurements and limited measurements, using th...
AN APPLICATION OF GENETIC ALGORITHMS TO IDENTIFY DAMAGE IN ELASTIC STRUCTURES
Journal of Sound and Vibration, 1996
A technique currently under development for the detection of macroscopic structural damage in elastic structures is described. The location and quantification of the extent of the damage is performed with genetic algorithms implemented by using the residual force method which is based on conventional modal analysis theory. A brief survey of the literature published on the subject, the theory of genetic algorithms and the residual forces method are included and a new formulation of an objective function for the genetic search optimization procedure is presented. The results of test cases are included in order to illustrate the techniques presented. In these examples, experimental data were simulated numerically by using finite element models of structural systems and, as demonstrated, it has been possible to identify the damage introduced to a reasonable level of accuracy.