Journal of Northeastern University(Natural Science) ›› 2024, Vol. 45 ›› Issue (1): 101-110.DOI: 10.12068/j.issn.1005-3026.2024.01.013

• Resources & Civil Engineering • Previous Articles     Next Articles

Intelligent Identification and Parameter Extraction of Key Joints in Rock (Mass) Based on AttentionR2U-net

Hao SUN1,2, Zong-sheng DAI1,2, Ai-bing JIN1,2, Yan CHEN3   

  1. 1.Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines,University of Science and Technology Beijing,Beijing 100083,China
    2.School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China
    3.School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China. Corresponding author: JIN Ai-bing,E-mail: jinaibing@ustb. edu. cn
  • Received:2022-08-16 Online:2024-01-15 Published:2024-04-02

Abstract:

Aiming at the problem of intelligent identification and parameter extraction of key joints in complex joint networks on surfaces of rock (mass), a method based on AttentionR2U-net network and joint geometric feature model is proposed. The attention gate was introduced on the basis of R2U-net network to improve the network, and then the accuracy test and generalization ability study of the recognition results of slope joint images and fracture images in concrete, cracked soil and common brittle rocks were carried out by qualitative and quantitative methods. Finally, the AttentionR2U-net network was used to identify the key joints by coupling the geometric features of the joints. The geometric parameters of the original and key joints are extracted, and then the trace length, area and inclination angle are analyzed. The results show that for joint identification of rock (mass), the Dice similarity coefficient of the proposed algorithm is increased from 0.965 to 0.990 in U-net network, outperforming the traditional algorithm significantly. Therefore, the proposed algorithm has stronger reliability and superiority in joint identification of rock (mass). For crack identification in concrete, cracked soil and brittle rocks such as marble, granite and sandstone, the similarity coefficientDice of the proposed algorithm is above 0.953, so the proposed algorithm has strong generalization ability. Compared with the original joint network, the dominant trace length of the key joint network increased significantly from 0.732 m to 1.835 m, the distribution pattern of joint dip angle and the dominant dip angle group remained unchanged, and the proportion of joints with dominant trace length and dip angle increased significantly.

Key words: rock (mass), key joint, AttentionR2U-net network, intelligent recognition, parameter extraction

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