Semnan University Press Journal of Modeling in Engineering 2008-4854 7 18 2009 11 22 Crack Detection in Plane Structures by Genetic Algorithm Crack Detection in Plane Structures by Genetic Algorithm 23 37 1537 10.22075/jme.2017.1537 FA Journal Article 2017 01 28 Crack changes the static and the dynamic characteristics, and as a result, the structural stiffness decreases and flexibility increase. The location and size estimation of cracks are inverse of analysis process. Most of the damage assessments are based on minimization of error function, that is difference between experimental and analytical responses. In this paper, a triangular element with through crack is introduced and non-destructive method is applied for crack detection in plane structures that uses changes in the natural frequencies of a structure. The genetic algorithm is used for optimization of the error function and the size of crack is obtained. Unlike the traditional mathematical methods, which guide the direction of hill climbing by the derivatives of objective functions, GA searches the problem domain by the objective function itself at multiple points. This method is able to detect the location of the cracks. Finally, crack detection procedure is evaluated by numerical examples. The results show that Several parameters affect on the crack detection accuracy and some of them are: size of mesh, number of generations and population, genes length, crossover and mutation operations. Crack changes the static and the dynamic characteristics, and as a result, the structural stiffness decreases and flexibility increase. The location and size estimation of cracks are inverse of analysis process. Most of the damage assessments are based on minimization of error function, that is difference between experimental and analytical responses. In this paper, a triangular element with through crack is introduced and non-destructive method is applied for crack detection in plane structures that uses changes in the natural frequencies of a structure. The genetic algorithm is used for optimization of the error function and the size of crack is obtained. Unlike the traditional mathematical methods, which guide the direction of hill climbing by the derivatives of objective functions, GA searches the problem domain by the objective function itself at multiple points. This method is able to detect the location of the cracks. Finally, crack detection procedure is evaluated by numerical examples. The results show that Several parameters affect on the crack detection accuracy and some of them are: size of mesh, number of generations and population, genes length, crossover and mutation operations. https://modelling.semnan.ac.ir/article_1537_99bd78a04cf3aa2b2f878208fbe9e90d.pdf