Abstract
Anchoring technology is widely used in slope, tunnels and underground engineering. However, the rock bolt quality is still a hot problem which is difficult to solve. Considering the shortcoming of pull-out testing, defect identification in a non-destructive way is necessary. In this paper, the signal decomposition is obtained by rock bolt quality detector and wavelet packet transform and energy feature is extracted; the normalised energy eigenvector is converted as input of probabilistic neural network (PNN); the smoothing factor in PNN is optimised based on particle swarm optimisation algorithm and the defect identification rate of PNN is improved. With a higher accuracy than radial basis functions (RBF) neural network and PNN, the improved PNN can provide a reference for defect identification of rock bolt in engineering without destruction.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Modelling, Identification and Control
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
