Title: Identification of rock bolt quality based on improved probabilistic neural network

Authors: Weiguo Di; Mingming Wang; Xiaoyun Sun; Fengning Kang; Hui Xing; Haiqing Zheng; Jianpeng Bian

Addresses: School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China ' School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

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.

Keywords: rock bolt; non-destructive testing; wavelet packet; probabilistic neural network; PNN; particle swarm optimisation; PSO; feature extraction; quality identification; classification.

DOI: 10.1504/IJMIC.2018.094206

International Journal of Modelling, Identification and Control, 2018 Vol.30 No.2, pp.105 - 117

Received: 08 Mar 2017
Accepted: 22 Sep 2017
Published online: 22 Aug 2018 *

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