Application of pruning type neural network in defect identification of blot anchoring systems
SUN Xiaoyun1，WU Shixing1, HAN Guang1，TIAN Jun2，CHENG Qi1
1.School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
2.Hebei Provincial Development And Reform Commission, Shijiazhuang 050043, China
Abstract：Rock bolts are widely applied in bridges, tunnels, and buildings etc. In construction processes, due to influences of geological conditions, materials, architectures and other factors, there are many defects in anchoring systems. All these defects affect the life and safety of rock bolts, so it is very valuable to identify defects of anchor bolts. Artificial neural network can be used as an intelligent classifier to identify and classify defects of anchor bolts. Here, an adaptive threshold feed-forward neural network pruning algorithm was proposed, its essence was to judge the contribution value of hidden layer neurons in their learning processes to output, take the significant exponent as an index to delete redundant nodes of the network, and realize dynamic optimization and adjustment of the network structure. The simulation results showed that the proposed method can not only reduce the complexity of the network structure, but also improve classification and identification accuracies of anchor bolt defects.
孙晓云1，吴世星1，韩 广1,田 军2，成 琦1. 修剪型神经网络在锚杆锚固缺陷识别中的应用[J]. 振动与冲击, 2018, 37(5): 221-227.
SUN Xiaoyun1，WU Shixing1, HAN Guang1，TIAN Jun2，CHENG Qi1. Application of pruning type neural network in defect identification of blot anchoring systems. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(5): 221-227.
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