Experimental study on shear mechanical characteristics and its size effect of concrete joints based on BP neural network method

被引:0
|
作者
Zhang, Zhezhe [1 ]
Guo, Baohua [1 ,2 ]
Zhu, Chuangwei [1 ]
Zhong, Pengbo [1 ]
Rong, Tenglong [1 ,2 ]
机构
[1] Henan Polytech Univ, Sch Energy Sci & Engn, Jiaozuo 454000, Henan, Peoples R China
[2] Synergism Innovat Ctr Coal Safety Prod Henan Prov, Jiaozuo 454000, Henan, Peoples R China
关键词
Rock mechanics; Size effect; Shearing strength; Pre-peak shear stiffness; BP neural network; ROCK JOINTS; STRENGTH; ROUGHNESS; BEHAVIOR; DEFORMABILITY;
D O I
10.1016/j.conbuildmat.2024.137583
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In order to study the shear mechanical characteristics and its size effect on concrete joints, this paper uses the RDS-200 shear test system developed by GCTS company to carry out the indoor direct shear tests on concrete joint specimens with five kinds of JRC and five grades of joint surface size under five normal stress conditions. The results show that the pre-peak shear stiffness of concrete joints is not significantly affected by JRC and normal stress, but is significantly affected by the joint surface size. The pre-peak shear stiffness decreases with the increase of joint surface size, when the joint surface size increases from 35 mm to 95 mm, the pre-peak shear stiffness of specimens with JRC being 0 2, 4 6,8 10, 12 14 and 16 18 decreased by 66.96%, 63.87%, 71.67 %, 64.57 % and 66.32 %, respectively, with an average of 66.68 %. Moreover, with the increase of joint surface size, the decrease rate of pre-peak shear stiffness decreases gradually, which is basically exponential, that is because the pre-peak shear stiffness has negative size effect. In addition, the peak shear strength of concrete joints increases with the increase of normal stress and JRC, and the relationship between peak shear strength and normal stress conforms to the Mohr-Coulomb criterion. Furthermore, there is no obvious size effect on the peak shear strength of concrete joints in the range of 35-95 mm. An intelligent prediction method for predicting the peak shear strength and pre-peak shear stiffness of concrete joints considering JRC, joint surface size and normal stress was established. The prediction results show that the BP Neural Network optimized by genetic algorithm can effectively predict the peak shear strength and pre-peak shear stiffness of concrete joints, the average error of peak shear strength is about 4.91 %, and the average error of pre-peak shear stiffness is 9.37 %. The conclusions can provide some reference for the evaluation of shear instability of rock joints in surface and underground rock engineering.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Experimental study on the dip characteristics of key joints in rock mass based on improved mechanical equivalence
    Jin A.
    Lu T.
    Wang B.
    Chen S.
    Zhang J.
    Su N.
    Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, 2023, 42 (01): : 76 - 87
  • [32] Experimental prediction model for the running-in state of a friction system based on chaotic characteristics and BP neural network
    Ding, Cong
    Feng, Shiqing
    Qiao, Zhizhao
    Zhu, Hua
    Zhou, Zhenyu
    Piao, Zhongyu
    TRIBOLOGY INTERNATIONAL, 2023, 188
  • [33] Study on structural damage identification method with fiber Bragg grating based on BP neural network
    Zhou Xuefang
    Liang Lei
    Proceedings of the First International Symposium on Test Automation & Instrumentation, Vols 1 - 3, 2006, : 757 - 760
  • [34] Back analysis method of foundation pit soil mechanical parameters based on GA-BP neural network
    Ping, J., 1600, Asian Network for Scientific Information (13): : 3099 - 3103
  • [35] Experimental study on shear behavior and size effect of high strength lightweight aggregate concrete deep flexural members
    Wu T.
    Liu X.
    Wei H.
    Huang H.
    1600, Science Press (41): : 119 - 132
  • [36] Analysis of size effect for shear characteristics of rock mass based on 3D fracture network
    Song S.
    Huang D.
    Sui J.
    Tao Y.
    Ma M.
    Li H.
    Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2024, 56 (03): : 9 - 18
  • [37] Study-on automatic creating method of public transportation dispatching form based on BP neural network
    Hu, JM
    Song, JY
    Zhang, Y
    Yang, ZS
    IEEE 5TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, PROCEEDINGS, 2002, : 863 - 867
  • [38] A Mechanical Treatment Method for Recycled Aggregates and Its Effect on Recycled Aggregate-Based Concrete
    Savva, Pericles
    Ioannou, Socrates
    Oikonomopoulou, Konstantina
    Nicolaides, Demetris
    Petrou, Michael Frixos
    MATERIALS, 2021, 14 (09)
  • [39] Evaluation Method of Vocal Music Teaching Effect based on Computer-Aided Technology and BP Neural Network
    Li X.
    Bian J.
    Computer-Aided Design and Applications, 2022, 19 (S7): : 79 - 89
  • [40] Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network
    Fang, Xinxin
    Feng, Hong
    Wang, Hao
    PETROLEUM SCIENCE AND TECHNOLOGY, 2022, 40 (17) : 2141 - 2162