An Application of Genetic Algorithms to Identify Damage in Elastic Structures

Mares, C.; Surace, Cecilia

doi:10.1006/jsvi.1996.0416

Cited by 203 publications

(98 citation statements)

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“…The predicted stiffness factors by the CCGA are almost the same as the actual stiffness factors for the cases without noise in the vibration characteristics as shown in Tables 4-7. Although there is the noise in natural frequencies and mode shapes, Tables 8-11 Predicted Figures 8,9,10,11,12,13,14, and 15 plot the predicted stiffness factors obtained from the CCGA versus the number of generated solutions for cases (a)-(d) without noise and with noise in the vibration characteristics, respectively. Similar to the first test problem, the stiffness factors searched by the CCGA are quickly converged to the good solutions including the cases that consider the noise in the vibration characteristics.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Boonlong

2014

Advances in Mechanical Engineering

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Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA), which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…A number of works employed GAs to solve the structural damage detection problems such as [5,6,[11][12][13][14][15][16]. Rao et al [5] used a two-point crossover binary coded GA with tournament selection for reproduction of population.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Boonlong

2014

Advances in Mechanical Engineering

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“…On the other hand, Araújo et al (2006); Araújo et al (2002) proposed an inverse problem to obtain the constitutive properties of composite plate specimens with surface bonded piezoelectric patches or layers, where the cost functional was the difference between the experimental and FEM-predicted eigen-frequencies and its minimization was carried out using two strategies: a gradient-based method, and neural networks. A genetic algorithm was applied by Chou & Ghaboussi (2001) and Mares & Surace (1996) to solve the IP in elastic structures. Based on crystallographic criteria by Russell & Ghomshei (1997) a cost functional was formulated as the difference in the orientation distribution function, which provides a statistical description of the orientation.…”

Section: Characterization Of Propertiesmentioning

confidence: 99%

Characterization of Properties and Damage in Piezoelectrics

Rus¹,

Palma²,

Suárez³

2010

Piezoelectric Ceramics

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“…Cunha et al (1999) use genetic algorithm to estimate the stiffness coefficients by minimizing a residual formed from the Eigen solutions. Another example of the use of genetic algorithm for the determination of stiffness reduction is Mares and Surace (1996). The main drawback of the above methodologies is that causes of structural behavior cannot be easily determined using the values of stiffness coefficients.…”

Section: Introductionmentioning

confidence: 99%

Model Identification of Bridges Using Measurement Data

Robert-Nicoud¹,

Raphael²,

Burdet³

et al. 2005

Computer aided Civil Eng

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Measurements are increasingly used to augment traditional assessments of structural state. Measurements of deflections, rotations and strain provide indications of damages as well as changes in the values of parameters such as Young's modulus. Finite element model updating methods have been developed in the 1990s for identifying structural state from measurements. Currently most methods aim to determine the values of stiffness coefficients that result in measured responses. In the present work, a model calibration method identifies causes of structural behaviour such as support conditions and material properties. Static measurement data is employed for model calibration. A case study of the Lutrive bridge in Switzerland illustrates the methodology. Candidate models whose responses reasonably match measured data are identified. These models are then examined in order to determine whether the calibrated values are physically possible. Such examinations lead to either model rejection or further measurements.

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An Application of Genetic Algorithms to Identify Damage in Elastic Structures

Cited by 203 publications

References 0 publications

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Characterization of Properties and Damage in Piezoelectrics

Model Identification of Bridges Using Measurement Data

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