考虑承载-破坏全过程的钢筋网力学行为分析与预测

孙克国; 张宇; 许炜萍; 赵旭伟; 秦晋行; 刘欢; 曹勇

doi:10.6052/j.issn.1000-4750.2021.09.0724

考虑承载-破坏全过程的钢筋网力学行为分析与预测

孙克国^1, ,,
张宇^1,,
许炜萍^1,,
赵旭伟^{1, 2,},
秦晋行^1,,
刘欢^1,,
曹勇^2,

1.
西南交通大学土木工程学院，交通隧道工程教育部重点实验室，四川，成都 610031
2.
中铁上海设计院集团有限公司，上海 200070

基金项目: 国家自然科学基金项目(52178396，51678495)

详细信息

作者简介:
张　宇(1997−)，男，河南商丘人，硕士生，主要从事隧道主动支护研究(E-mail: 2931800764@qq.com)

许炜萍(1981−)，女，山东德州人，副教授，博士，硕导，主要从事隧道工程动力学研究(E-mail: xwp1981@126.com)

赵旭伟(1981−)，男，吉林辽源人，高工，博士生，主要从事隧道工程的设计与研究工作(E-mail: zxw@sty.sh.cn)

秦晋行(1997−)，男，河北邯郸人，硕士生，主要从事寒区隧道冻害发生机理与防控研究(E-mail: 1249771942@qq.com)

刘　欢(1998−)，男，山西大同人，硕士生，主要从事隧道震动作用下机理的研究(E-mail: 2813774923@qq.com)

曹　勇(1983−)，男，山东郓城人，高工，硕士，主要从事隧道工程的设计与研究工作(E-mail: caoyong@sty.sh.cn)

通讯作者:
孙克国(1981−)，男，山东青岛人，副教授，博士，博导，主要从事隧道结构设计与灾害防控研究(E-mail: sunkeg@126.com)

中图分类号: U25；O39
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出版历程
- 收稿日期: 2021-09-17
- 修回日期: 2022-03-30
- 录用日期: 2022-04-07
- 网络出版日期: 2022-04-07
- 刊出日期: 2023-02-28

ANALYSIS AND PREDICTION OF MECHANICAL BEHAVIOR OF STEEL MESH CONSIDERING THE WHOLE LOADING-FAILURE PROCESS

SUN Ke-guo^1, ,,
ZHANG Yu^1,,
XU Wei-ping^1,,
ZHAO Xu-wei^{1, 2,},
QIN Jin-hang^1,,
LIU Huan^1,,
CAO Yong^2,

1.
Southwest Jiaotong University, School of Civil Engineering, Key Laboratory of Transportation Tunnel Engineering of Ministry of Education, Chengdu, Sichuan 610031, China
2.
China Railway Shanghai Design Institute Group Co., Ltd, Shanghai 200070, China

摘要

摘要: 考虑钢筋网承载-破坏全过程，探明锚网喷结构中钢筋网的力学行为，可切实指导钢筋网选型与参数设计。采用已有的试验结果对计算模型与参数的合理性进行验证，在此基础上完成承载-破坏全过程的钢筋网力学行为分析，并利用多元非线性回归和BP神经网络方法对钢筋网的最大应力和竖向位移进行预测与分析。研究发现：单一荷载作用下钢筋网的应力分布呈现分区现象，1区和2区特定范围的钢筋应力基本处于单轴拉伸状态，2区其余区域的应力处于拉-剪复合状态；钢筋网的力学行为受钢材型号影响显著：由HPB300、HPB400、HPB500钢筋制成的钢筋网存在流幅现象，由1650级、1770级高强不锈钢丝制成的钢筋网无流幅现象；BP神经网络模型预测精度明显优于多元非线性回归方法，对于最大应力和竖向位移两个指标，采用BP神经网络预测的平均相对误差分别为1.31%和1.67%，而多元非线性回归方法的预测误差为7.39%和8.19%。
- 隧道工程 /
- 联合支护 /
- 钢筋网 /
- 承载特性 /
- 应力分区
Abstract: Considering the whole loading-failure process of a steel mesh, the mechanical behavior of the steel mesh in the bolt-mesh-shotcrete structure can be verified, which can guide the selection and parameter design of the steel mesh practically. The existing experimental results were used to verify the rationality of the calculation model and parameters. On this basis, the mechanical behavior analysis of the steel mesh in the whole process of loading-failure was completed. The maximum stress and vertical displacement of the steel mesh were predicted and analyzed by multivariate nonlinear regression and BP neural network method. It is found that: The stress distribution of the steel mesh under a single load presents a zoning phenomenon. The steel stress in the specific range of area 1 and area 2 is basically in the uniaxial tensile state, and the stress in the other areas of area 2 is in a tensile-shear composite state. The mechanical behavior of the steel mesh is significantly affected by the steel type such as the steel mesh made of HPB300, HPB400 and HPB500 bars has plastic flow range phenomenon. There is no plastic flow range in the steel mesh made of 1650 grade and of 1770 grade high strength stainless steel wire. The prediction accuracy of BP neural network model is better than that of multivariate nonlinear regression method obviously. The average relative errors of maximum stress and vertical displacement predicted by BP neural network are 1.31% and 1.67%, respectively. The prediction errors of multivariate nonlinear regression method are 7.39% and 8.19%.
- tunnel engineering /
- combined support /
- steel mesh /
- bearing characteristics /
- stress partition

HTML全文

图 1 对称约束、荷载情况下的钢筋网变形和受力

Figure 1. Deformation and force of steel mesh under symmetrical restraint and load

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图 2 数据对比分析曲线

Figure 2. Data comparative analysis curve

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图 3 隧道初期支护布置

Figure 3. Primary support arrangement of tunnel

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图 4 简化后模型及边界条件

Figure 4. Simplified model and boundary conditions

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图 5 网片测试设备布置

Figure 5. Mesh test equipment layout

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图 6 试验模型图　/mm

Figure 6. The model of experiment

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图 7 8 kN荷载作用下钢筋网位移　/mm

Figure 7. Displacement of steel mesh under 8 kN load

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图 8 数值计算与试验数据对比

Figure 8. Comparison of numerical calculation and experimental data

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图 9 材料本构模型

Figure 9. Material constitutive model

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图 10 均布荷载作用下钢筋网应力分布　/MPa

Figure 10. Stress distribution of steel mesh under uniform load

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图 11 均布荷载作用下钢筋网位移　/mm

Figure 11. Displacement of steel mesh under uniform load

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图 12 不同路径的竖向位移

Figure 12. Vertical displacement of different paths

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图 13 不同荷载作用下的钢筋网应力及位移云图

Figure 13. Stress and displacement contour of steel mesh under different loads

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图 14 荷载作用下钢筋网最大Mises应力

Figure 14. Maximum Mises stress of steel mesh under load

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图 15 荷载作用下钢筋网竖向位移

Figure 15. Vertical displacement of steel mesh under load

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图 16 钢筋网应力传递

Figure 16. Stress transfer of steel mesh

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图 17 100 kPa均布荷载下钢筋网的应力三轴度

Figure 17. Stress triaxiality of steel mesh under 100 kPa

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图 18 最大应力随荷载变化曲线

Figure 18. Curve of maximum stress varying with load

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图 19 竖向位移随荷载变化曲线

Figure 19. Curve of vertical displacement versus load

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图 20 拟合结果曲面图

Figure 20. Curved surface of fitting results

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图 21 BP神经网络拓扑图

Figure 21. BP neural network topology

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图 22 训练结果图

Figure 22. Training result chart

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图 23 BP神经网络迭代训练寻优过程图

Figure 23. BP neural network iterative training optimization process diagram

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表 1 结构材料参数

Table 1 Structural material parameters

结构	材料类型	弹性模量/ MPa	屈服强度/ MPa	极限强度/ MPa	泊松比	密度/ (kg·m⁻³)
钢筋网	300	210 000	300	420	0.3	7800
	400	200 000	400	540
	500	200 000	500	630
	1650	110 000	−	1650
	1770	110 000	−	1770
千斤顶	钢材	210 000	−	−

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A1 多元非线性回归模型检验结果

A1 Test results of multivariate nonlinear regression model

序号	钢筋等级/ MPa	荷载/ kPa	最大应力/ MPa	竖向位移/ mm	模型计算的最大应力/MPa	模型计算的竖向位移/mm	最大应力相对误差/(%)	竖向位移相对误差/(%)
1	300	49.51	254.60	18.45	219.77	15.91	13.68	13.78
2	300	53.76	285.60	18.97	225.63	16.55	21.00	12.74
3	300	99.03	303.70	23.63	283.35	23.26	6.70	1.56
4	300	780.00	455.70	116.70	486.72	95.51	6.81	18.16
5	400	282.94	427.50	39.60	442.74	46.87	3.56	18.37
6	400	495.15	471.20	64.21	516.97	68.91	9.71	7.32
7	400	565.88	484.00	71.80	524.34	75.33	8.33	4.91
8	400	990.30	581.00	109.80	545.18	108.15	6.17	1.50
9	500	84.88	293.90	22.06	255.94	21.72	12.92	1.55
10	500	565.88	567.40	60.45	546.28	73.47	3.72	21.54
11	500	1131.77	674.00	105.90	673.15	114.12	0.13	7.77
12	1650	14.15	59.70	14.99	63.80	18.25	6.87	21.72
13	1650	353.68	555.70	43.47	577.33	43.06	3.89	0.95
14	1650	424.41	629.60	46.14	655.21	46.52	4.07	0.83
15	1650	919.56	1311.00	59.53	1190.82	61.32	9.17	3.00
16	1650	1202.50	1658.00	64.75	1689.33	67.46	1.89	4.19
17	1770	28.29	97.44	18.98	83.10	20.22	14.72	6.52
18	1770	113.18	254.40	29.97	240.57	27.24	5.44	9.10
19	1770	282.94	477.00	40.40	497.04	38.33	4.20	5.13
20	1770	848.83	1101.00	57.97	1154.32	56.16	4.84	3.13

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A2 BP神经网络模型检验结果

A2 Test results of BP neural network model

序号	钢筋等级/ MPa	荷载/ kPa	最大应力/ MPa	竖向位移/ mm	模型计算的最大应力/MPa	模型计算的竖向位移/mm	最大应力相对误差/(%)	竖向位移相对误差/(%)
1	300	49.51	254.60	18.45	251.18	18.76	1.34	1.66
2	300	53.76	285.60	18.97	267.17	19.29	6.45	1.68
3	300	99.03	303.70	23.63	301.02	23.85	0.88	0.95
4	300	780.00	455.70	116.70	444.90	113.22	2.37	2.98
5	400	282.94	427.50	39.60	425.77	39.32	0.40	0.70
6	400	495.15	471.20	64.21	468.39	64.22	0.60	0.02
7	400	565.88	484.00	71.80	478.16	72.05	1.21	0.35
8	400	990.30	581.00	109.80	585.25	113.28	0.73	3.17
9	500	84.88	293.90	22.06	311.91	22.41	6.13	1.58
10	500	565.88	567.40	60.45	571.77	60.50	0.77	0.09
11	500	1131.77	674.00	105.90	674.25	105.33	0.04	0.54
12	1650	14.15	59.70	14.99	58.32	13.46	2.30	10.18
13	1650	353.68	555.70	43.47	555.79	43.74	0.02	0.63
14	1650	424.41	629.60	46.14	629.96	46.67	0.06	1.14
15	1650	919.56	1311.00	59.53	1307.91	59.71	0.24	0.30
16	1650	1202.50	1658.00	64.75	1662.22	64.56	0.25	0.29
17	1770	28.29	97.44	18.98	95.40	19.90	2.10	4.83
18	1770	113.18	254.40	29.97	253.88	29.76	0.20	0.70
19	1770	282.94	477.00	40.40	477.16	40.61	0.03	0.51
20	1770	848.83	1101.00	57.97	1099.28	57.29	0.16	1.17

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