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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (11): 2303-2312    DOI: 10.3785/j.issn.1008-973X.2022.11.021
    
Discrete element study on effect of cyclic loading strengthening on meso-destruction of granite
Xiao ZHANG1(),Hao YU1,Zhuang LI1,Yan-shun LIU1,Zi-dong ZHANG1,Xin-yu JI1,Xiang-hui LI2
1. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, China
2. School of Civil Engineering, Shandong University, Jinan 250061, China
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Abstract  

A grain-based model (GBM) was established based on the results of uniaxial compression test by discrete element method. The intragranular and intergranular contact model of GBM was improved according to the results of indoor cyclic loading and unloading tests. A grain-based reinforcement model was established, which accurately characterized the cyclic loading strengthening effect. The grain-based reinforcement model was used to reveal the influence mechanism of cyclic loading strengthening on the mesoscopic failure of granite during uniaxial compression. The results showed that the stress distribution by self-locking effect was different in the pre-peak stress stage, which led to tensile microcracks in intergranular and intragranular contact. The Quartz and feldspar became load-bearing bodies in turn. The shear energy by the earlier strengthening was released in the post-peak stress stage, leading to the intensive intergranular microcracks of feldspar. The phenomenon was the main sign of the instability of the specimen. The differential failure of the minerals around the feldspar caused the change of the feldspar destruction pathways and the peak stress of test block increased with the enlargement of strengthening factor. The grain-based reinforcement model provides a new method, which is used to study the mesoscopic failure mechanism of brittle rocks with different loading paths on cyclic loading strengthening.

Key words： granite      cyclic loading strengthening effect      discrete element      grain-based reinforcement model      micro-cracking behavior      mesoscopic failure mechanism     
Received: 27 December 2021      Published: 02 December 2022
CLC:  TU 452  
Fund:  中央高校基本科研业务费资助项目（2019GN079）
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Xiao ZHANG
Hao YU
Zhuang LI
Yan-shun LIU
Zi-dong ZHANG
Xin-yu JI
Xiang-hui LI
Cite this article:

Xiao ZHANG,Hao YU,Zhuang LI,Yan-shun LIU,Zi-dong ZHANG,Xin-yu JI,Xiang-hui LI. Discrete element study on effect of cyclic loading strengthening on meso-destruction of granite. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2303-2312.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.11.021     OR     https://www.zjujournals.com/eng/Y2022/V56/I11/2303


循环加载强化作用对花岗岩细观破坏影响的离散元研究

基于室内单轴压缩试验结果，采用离散元方法建立等效晶质模型（GBM）. 根据室内循环加卸载试验结果改进GBM模型晶内、晶间接触模型，建立能够准确表征循环加载强化作用的GBM强化模型,借此GBM强化模型揭示循环加载强化作用对花岗岩单轴压缩过程细观破坏的影响机制. 结果表明，在峰前应力阶段，自锁效应造成的应力分布不同，导致晶内/晶间接触出现以张拉为主的裂纹，石英、长石依次成为承载主体；在峰后应力阶段，前期强化作用所积蓄的剪切能量得到释放，导致长石出现密集的晶内裂纹，是试块失稳的主要标志；长石周边矿物差异性失效引起长石矿物破坏路径改变，造成试块峰值应力随强化系数增大呈现波动性增长. 构建的GBM强化模型为研究循环加载强化作用对脆性岩石不同加载路径细观破坏机制提供新方法.


关键词： 花岗岩,  循环加载强化作用,  离散元,  GBM强化模型,  微裂纹特征,  细观破坏机理 
Fig.1 White linen granite
矿物成分 ωc/% 矿物成分 ωc/%
石英 45.64 云母 6.75
斜长石 23.02 其他矿物 0.40
钾长石 24.19 — —
Tab.1 Mass fraction of different mineral compositions
Fig.2 Process of constructing GBM
种类 颗粒基本参数 晶体内部细观参数 晶体边界细观参数
ωc/
% 		Rmin/
mm 		Rmax /
mm 		ρ/
（kg·m?3） 		E/
GPa 		${\bar {\boldsymbol{k}}_{\text{n} } }/{\bar {\boldsymbol{k}}_{\text{s} } }$ $ {\overline \sigma _{\text{c}}} $/
MPa 		c/
MPa 		φ/
（°） 		μ ${\bar {\boldsymbol{k}}_{\text{n} } }$/
（GPa·m?1） 		${\bar {\boldsymbol{k}}_{\text{s} } }$/
（GPa·m?1） 		$ {\overline \sigma _{\text{t}}} $/
MPa 		$ {\overline \sigma _{\text{c}}} $/
MPa 		φ/
（°） 		μ
石英 46 0.2 1.8 2 650 6.64 1 82.67 82.67 50 0.5 3×1011 6.25×1010 72.4 123.6 20 0.4
长石 47 0.5 1.8 2 600 5.13 2 103.6 103.60 35 0.5
云母 7 0.5 1.8 3 050 3.14 2 62.00 62.00 30 0.5
Tab.2 Meso-scopic parameters of GBM
项目 σu/MPa E/GPa εu
原状试块 160.1 16.0 0.012 5
GBM模型 163.6 15.7 0.011 6
Tab.3 Macroscopic parameters between undisturbed specimens and GBM
Fig.3 Comparison of σ-ε curves between undisturbed specimens and GBM
Fig.4 Residual deformation at each cycle loading stage
Fig.5 Stress-strain curves between undisturbed specimens and pretreatment specimens
Fig.6 Strengthened comparison of linear parallel bond model
Fig.7 Sf -strain relationship graph
Fig.8 Stress-strain curve of grain-based reinforcement model with different $S_{\text{f}}^ * $
Fig.9 Variation curves of peak strength and elastic modulus of grain-based reinforcement model with $S_{\text{f}}^ * $ and residual deformation
项目 原状试块 预处理试块
σ0u /MPa E0 /GPa $\sigma _{{\rm{Iu}}}^\prime$ /MPa ${E_1}'$ /GPa
试验值 160.1 16.0 174.6 18.3
模拟值 159.9 16.0 175.3 18.6
Tab.4 Macroscopic parameter test value and simulation value of undisturbed specimens and pretreatment specimens
Fig.10 Stress-strain curves between GBM and grain-based reinforcement model
Fig.11 Macroscopic failure characteristics of pretreatment specimens and grain-based reinforcement model
Fig.12 Initial stage microcrack distribution
Fig.13 Distribution of pre-peak microcracks at different stress levels in GBM
Fig.14 Distribution of pre-peak microcracks at different stress levels in grain-based reinforcement model
Fig.15 GBM peak stress and post-peak microcrack distribution
Fig.16 Grain-based reinforcement model peak stress and post-peak microcrack distribution
Fig.17 Variation curve of grain-based reinforcement model crack number with strain
Fig.18 Grain-based reinforcement model crack distribution characteristics
$\sigma _{{\rm{1}}}^\prime$ na ne
nt ns nt ns
石英 长石 云母 石英 长石
0.5 30 4 0 0 1 0 0
0.6 65 25 3 0 2 0 0
0.7 101 73 15 1 3 3 0
0.8 133 165 36 4 4 16 0
0.9 182 268 75 11 7 31 1
1.0 243 418 224 71 15 73 6
Tab.5 Statistics of number of cracks in grain-based reinforcement model when $S_{\text{f}}^ * $=0.35
$\sigma _{{\rm{1}}}^\prime$ na ne
nt ns nt ns
石英 长石 云母 石英 长石
0.5 30 4 0 0 1 0 0
0.6 66 26 3 0 2 0 0
0.7 102 74 18 2 2 3 0
0.8 130 160 37 4 4 13 0
0.9 185 269 89 11 8 32 1
1.0 243 441 239 80 25 77 4
Tab.6 Statistics of number of cracks in grain-based reinforcement model when $S_{\text{f}}^ * $=0.4
Fig.19 Variation of nc and $ {\sigma' _{{\text{1u}}}}$ with $S_{\text{f}}^ * $
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