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Application of genetic algorithm in crack detection of beam-like structures using a new cracked Euler-Bernoulli beam element

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TLDR
It is shown that the present algorithm is able to identify various crack configurations in a cracked beam, and an inverse problem is established in which the cracks location and depth are identified.
Abstract
In this paper, a crack identification approach is presented for detecting crack depth and location in beam-like structures. For this purpose, a new beam element with a single transverse edge crack, in arbitrary position of beam element with any depth, is developed. The crack is not physically modeled within the element, but its effect on the local flexibility of the element is considered by the modification of the element stiffness as a function of crack's depth and position. The development is based on a simplified model, where each crack is substituted by a corresponding linear rotational spring, connecting two adjacent elastic parts. The localized spring may be represented based on linear fracture mechanics theory. The components of the stiffness matrix for the cracked element are derived using the conjugate beam concept and Betti's theorem, and finally represented in closed-form expressions. The proposed beam element is efficiently employed for solving forward problem (i.e., to gain accurate natural frequencies of beam-like structures knowing the cracks' characteristics). To validate the proposed element, results obtained by new element are compared with two-dimensional (2D) finite element results as well as available experimental measurements. Moreover, by knowing the natural frequencies, an inverse problem is established in which the cracks location and depth are identified. In the inverse approach, an optimization problem based on the new beam element and genetic algorithms (GAs) is solved to search the solution. The proposed approach is verified through various examples on cracked beams with different damage scenarios. It is shown that the present algorithm is able to identify various crack configurations in a cracked beam.

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Journal ArticleDOI

A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates

TL;DR: The proposed sunflower optimization algorithm (SFO) technique is a population-based iterative heuristic global optimization algorithm for multi-modal problems that employs terms as root velocity and pollination providing robustness.
Journal ArticleDOI

Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review

TL;DR: The state of the art of five AI methods which are used in the field of fracture mechanics, which include artificial neural networks, Bayesian networks, genetic algorithms, fuzzy logic and case-based reasoning are surveyed.
Journal ArticleDOI

Cyclical Parthenogenesis Algorithm for guided modal strain energy based structural damage detection

Ali Kaveh, +1 more
TL;DR: The results indicate that the proposed method, named Cyclical Parthenogenesis Algorithm, is capable of locating and quantifying structural damage using only the first few modes of the structure.
Journal ArticleDOI

Inverse problem based differential evolution for efficient structural health monitoring of trusses

TL;DR: DE search performance for structural damage detection can be considerably improved by integrating RBF into its procedure, and the new algorithm is the best for all test problems.
Journal ArticleDOI

Crack Assessment in Frame Structures Using Modal Data and Unified Particle Swarm Optimization Technique

TL;DR: The developed UPSO technique for solving crack assessment problems in frame like structures indicates that the developed method is capable for crack detection and quantification with satisfactory precision.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

Genetic algorithms in search, optimization and machine learning

TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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TL;DR: In this article, the methodes are numeriques and the fonction de forme reference record created on 2005-11-18, modified on 2016-08-08.
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TL;DR: Introduction to Optimization The Binary genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.
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Vibration problems in engineering

TL;DR: In this article, the Probleme dynamique and Vibration were used for propagation of ondes reference records created on 2004-09-07, modified on 2016-08-08.
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