Identification of crack-like defects in elastic structural elements on the basis of evolution algorithms
Source abstract
Abstract
A method for the identification of defects in elastic structural elements (SEs) on the basis of artificial neural network (ANN) tools was proposed. The problems of constructing a simulation model of an object, the optimal arrangement of transducers with the use of a genetic algorithm, and the subsequent identification of a defect were solved. The application of different learning architectures and algorithms to feed-forward neural networks (FFNs) was studied and the influence of errors on the estimation accuracy of the parameters of a defect was analyzed.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Engineering Diagnostics and Reliability · Metallurgy and Material Forming
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (10)
Show source-provided references
1. Vatul’yan, A.O., Obratnye zadachi v mekhanike deformiruemogo tverdogo tela (Inverse Problems in Mechanics of Deformable Solids), Moscow: Fizmatlit, 2007.
2. Vatul’yan, A.O., Anikina, T.A., and Bogachev, I.V., Identification of the Inhomogeneous Characteristics of Viscoelastic Rods under Bending Vibrations, Mekh. Komp. Mater. Konstr., 2011, no. 1 (in press).
3. Waszczyszyn, Z. and Zeimianski, L., Neural Networks in Mechanics of Structures and Materials. New Results and Prospects of Applications, Comp. Struct., 2001, vol. 79, nos. 22–25, pp. 2261–2276.10.1016/S0045-7949(01)00083-9 ↗
4. Solov’ev, A.N., Kurbatova, P.S., Saprunov, N.I., and Shevtsov, S.N., On the Application of Neural Networks in the Problems of the Identification of Defects in Elastic Bodies, Materialy X mezhdunarodnoi konferentsii “Sovremennye problemy mekhaniki sploshnoi sredy (Proc. X Int. Conf. “Contemporary Problems of Continuum Mechanics”), Rostov-on-Don, 2006, pp. 175–180.
5. Fang, X., Luo, H., and Tang, J., Structural Damage Detection Using Neural Network with Learning Rate Improvement, Comp. Struct., 2005, vol. 83, nos. 25–26, pp. 2150–2161.10.1016/j.compstruc.2005.02.029 ↗
6. LeClerc, J.R., Worden, K., Staszewski, W.J., and Haywood, J., Impact Detection in an Aircraft Composite Panel. A Neural-Network Approach, J. Sound Vibr., 2007, vol. 299, no. 3, pp. 672–682.10.1016/j.jsv.2006.07.019 ↗
7. Xu, Y.G., Liu, G.R., Wu, Z.P., and Huang, X.M., Adaptive Multilayer Perceptron Networks for Detection of Cracks in Anisotropic Laminated Plates, Int. J. Solids Struct., 2001, vol. 38, nos. 32–33, pp. 5625–5645.10.1016/S0020-7683(00)00368-1 ↗
8. Worden, K. and Burrows, A.P., Optimal Sensor Placement for Fault Detection, Eng. Struct., 2001, vol. 23, no. 8, pp. 885–901.10.1016/S0141-0296(00)00118-8 ↗
9. Emel’yanov, V.V., Kureichik, V.V., and Kureichik, V.M., Teoriya i praktika evolutsionnogo modelirovaniya (Theory and Practice of Evolutionary Modeling), Moscow: Fizmatlit, 2003.
10. Haykin, S., Neural Networks: A Comprehensive Foundation, New York: Prentice Hall, 1998.