Prediction of buckling instability of perfect and defective carbon nanotubes

This paper deals with axial, torsional and bending buckling instabilities of CNTs with different geometries and boundary conditions. At first, the results are obtained for some cases using structural mechanics and due to the uneven trends of the results, they are fed into an adaptive neurofuzzy inference system to predict the buckling loads of the unmodeled CNTs. Some special cases are used to validate the capability of the predictive tool and the accuracy of the predicted results. Moreover, the effects of various defects including single, double and triple vacancies as well as Stone-Wales defect with different numbers and positions on the buckling loads of the CNTs are analyzed in detail.