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.
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