爆炸作用下地铁盾构隧道韧性评估与预测

doi:10.16265/j.cnki.issn1003-3033.2026.04.0085

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (4): 123-131.doi: 10.16265/j.cnki.issn1003-3033.2026.04.0085

爆炸作用下地铁盾构隧道韧性评估与预测

王睿¹(), 张讯¹, 邓祥辉¹^,^**(), 王平安², 王旭¹, 张巍³

¹ 西安工业大学建筑工程学院, 陕西西安 710021
² 中铁二十局集团有限公司, 陕西西安 710016
³ 中铁建城建交通发展有限公司, 江苏苏州 215000

收稿日期:2025-11-14 修回日期:2026-02-05 出版日期:2026-04-28
通信作者:
**邓祥辉(1976—),男,四川德阳人,博士,教授,主要从事隧道韧性安全评估等方面的研究。E-mail:xianghuideng@xatu.edu.cn。
作者简介:
王睿 (1981—),男,河北饶阳人,博士,副教授,主要从事隧道与城市地下工程施工技术及风险评估方面的研究。E-mail:wangrui@xatu.edu.cn。
王平安, 教授级高级工程师
张巍, 高级工程师
基金资助:
陕西省自然科学基础研究计划项目(2023-JC-YB-327); 陕西省教育厅服务地方专项计划项目(22JC040); 陕西省重点研发计划(2025SF-YBXM-151); 陕西省交通运输厅交通运输科研项目计划(25-56X)

Resilience assessment and prediction of metro shield tunnels under explosive conditions

Wang Rui¹(), Zhang Xun¹, Deng Xianghui¹^,^**(), Wang Ping'an², Wang Xu¹, Zhang Wei³

¹ Civil and Architecture Engineering, Xi'an Technology University, Xi'an Shaanxi 710021, China
² China Railway 20^th Bureau Group Corporation Limited, Xi'an Shaanxi 710016, China
³ China Railway Construction Urban Construction Transportation Development Corporation Limited, Suzhou Jiangsu 215000, China

Received:2025-11-14 Revised:2026-02-05 Published:2026-04-28

摘要/Abstract

摘要：

为保障地铁运行及结构安全,开展爆炸作用下地铁盾构隧道韧性评估。建立爆炸作用下地铁盾构隧道的韧性评估体系和韧性等级划分标准,提出基于反向传播(BP)神经网络的3输入层-5隐藏层-单输出层的地铁盾构隧道韧性预测模型,并依托西安地铁一号线完成韧性评估和预测。结果表明:爆心距减小、炸药当量增大、爆炸次数增加均会加速地铁盾构隧道韧性下降;首次爆炸后地铁盾构隧道韧性降幅最为明显,之后韧性降速较为缓慢,直至第5次爆炸后,韧性降低速度大大加快,地铁盾构隧道进入低韧性,需及时修复以满足运行要求,而第7次爆炸后降为极低韧性,已不能满足运行安全。所建立的韧性评估体系和预测模型可用于连续外爆作用下地铁盾构隧道安全状况的评估。

关键词: 爆炸作用, 地铁盾构隧道, 韧性评估, 反向传播(BP)神经网络, 韧性预测

Abstract:

To ensure the operational and structural safety of the subway system, an assessment of the resilience of metro shield tunnels under explosive loading was conducted. A resilience assessment framework and grading criteria for shield tunnels under blast loading were established, and a resilience prediction model was developed based on a backpropagation (BP) neural network with a three-input, five-hidden-layer, single-output architecture. This model and evaluation approach were applied in a case study on the Xi'an Metro Line 1 to assess and predict the tunnel's resilience. The results indicate that a shorter standoff distance, a higher explosive yield, and a higher number of explosions each accelerate the decline in the tunnel's resilience. The resilience exhibited the most pronounced drop after the first explosion; the subsequent rate of decline was relatively gradual until the fifth explosion, after which it increased significantly. After the fifth explosion, the tunnel entered a low-resilience state requiring prompt repairs to meet operational requirements, and by the seventh explosion, the resilience had fallen to an extremely low level that could no longer ensure operational safety. The resilience assessment framework and prediction model developed in this study can be used to assess the safety status of metro shield tunnels under repeated external explosions.

Key words: explosions effect, metro shield tunnels, resilience assessment, back propagation(BP) neural network, resilience prediction

中图分类号:

王睿, 张讯, 邓祥辉, 王平安, 王旭, 张巍. 爆炸作用下地铁盾构隧道韧性评估与预测[J]. 中国安全科学学报, 2026, 36(4): 123-131.

Wang Rui, Zhang Xun, Deng Xianghui, Wang Ping'an, Wang Xu, Zhang Wei. Resilience assessment and prediction of metro shield tunnels under explosive conditions[J]. China Safety Science Journal, 2026, 36(4): 123-131.

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指标名称	表达式	注释
直径变形	${q}_{1}\left(t\right)=\frac{{\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}-\lambda \left(t\right)}{{\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}}$	λ_max为隧道结构允许的最大直径变形率,取为9‰;λ(t)为随时间隧道结构直径变形率
竖向位移	${q}_{2}\left(t\right)=\frac{{V}_{\mathrm{m}\mathrm{a}\mathrm{x}}-V\left(t\right)}{{V}_{\mathrm{m}\mathrm{a}\mathrm{x}}}$	V_max为隧道结构允许的最大竖向位移,取为20 mm;V(t)为随时间隧道结构竖向位移量
水平位移	${q}_{3}\left(t\right)=\frac{{H}_{\mathrm{m}\mathrm{a}\mathrm{x}}-H\left(t\right)}{{H}_{\mathrm{m}\mathrm{a}\mathrm{x}}}$	H_max为隧道结构允许的最大水平位移,取为20 mm;H(t)为随时间隧道结构水平位移
接缝张开量	${q}_{4}\left(t\right)=\frac{\mathrm{\Delta }{L}_{1\mathrm{m}\mathrm{a}\mathrm{x}}-\Delta {L}_{1}\left(t\right)}{2\mathrm{\Delta }{L}_{1\mathrm{m}\mathrm{a}\mathrm{x}}}+\frac{\mathrm{\Delta }{L}_{2\mathrm{m}\mathrm{a}\mathrm{x}}-\Delta {L}_{2}\left(t\right)}{2\mathrm{\Delta }{L}_{2\mathrm{m}\mathrm{a}\mathrm{x}}}$	ΔL_1max为隧道结构允许的最大纵缝张开量;ΔL_2max为隧道结构允许的最大环缝张开量,均取3 mm;ΔL₁(t)为随时间隧道结构纵缝张开量;ΔL₂(t)为随时间隧道结构环缝张开量
错台量	${q}_{5}\left(t\right)=\frac{\mathrm{\Delta }{S}_{1\mathrm{m}\mathrm{a}\mathrm{x}}-\Delta {S}_{1}\left(t\right)}{2\mathrm{\Delta }{S}_{1\mathrm{m}\mathrm{a}\mathrm{x}}}+\frac{\mathrm{\Delta }{S}_{2\mathrm{m}\mathrm{a}\mathrm{x}}-\Delta {S}_{2}\left(t\right)}{2\mathrm{\Delta }{S}_{2\mathrm{m}\mathrm{a}\mathrm{x}}}$	ΔS_1max为隧道结构允许的最大纵缝错台量;ΔS_2max为隧道结构允许的最大环缝错台量,均取10 mm;ΔS₁(t)为随时间隧道结构纵缝错台量;ΔS₂(t)为随时间隧道结构环缝错台量

管片	Z	经验公式预测值/MPa	数值模拟值/MPa	误差/ %
H1	0.2	23.508	21.206	-9.79
	0.4	3.494	3.169	-9.30
	0.6	1.146	1.041	-9.16
	0.8	0.519	0.468	-9.83
H2	0.2	23.508	21.351	-9.18
	0.4	3.494	3.189	-8.73
	0.6	1.146	1.037	-9.51
	0.8	0.519	0.472	-9.06
H3	0.2	23.508	21.428	-8.85
	0.4	3.494	3.291	-5.81
	0.6	1.146	1.248	8.90
	0.8	0.519	0.482	-7.13
H4	0.2	23.508	21.917	-6.77
	0.4	3.494	3.765	7.76
	0.6	1.146	1.206	5.24
	0.8	0.519	0.479	-7.71
H5	0.2	23.508	25.641	9.07
	0.4	3.494	3.798	8.70
	0.6	1.146	1.247	8.81
	0.8	0.519	0.533	2.70
H6	0.2	23.508	25.846	9.95
	0.4	3.494	3.822	9.39
	0.6	1.146	1.254	9.42
	0.8	0.519	0.566	9.06

爆炸次数	炸药当量/kg	管片编号
爆炸次数	炸药当量/kg	H1	H2	H3	H4	H5	H6
1	500	0.951(I)	0.952(I)	0.947(I)	0.947(I)	0.928(I)	0.937(I)
	1 000	0.942(I)	0.942(I)	0.931(I)	0.931(I)	0.907(I)	0.913(I)
	1 500	0.939(I)	0.939(I)	0.923(I)	0.908(I)	0.897(I)	0.902(I)
	2 000	0.941(I)	0.939(I)	0.921(I)	0.913(I)	0.903(I)	0.901(I)
	2 500	0.934(I)	0.928(I)	0.911(I)	0.902(I)	0.874(I)	0.881(I)
2	500	0.922(I)	0.921(I)	0.913(I)	0.914(I)	0.884(I)	0.895(I)
	1 000	0.910(I)	0.907(I)	0.889(I)	0.890(I)	0.852(I)	0.861(I)
	1 500	0.906(I)	0.904(I)	0.880(I)	0.856(I)	0.840(I)	0.847(I)
	2 000	0.909(I)	0.905(I)	0.878(I)	0.864(I)	0.852(I)	0.846(I)
	2 500	0.900(I)	0.887(I)	0.860(I)	0.845(I)	0.804(I)	0.813(I)
3	500	0.890(I)	0.887(I)	0.875(I)	0.876(I)	0.839(I)	0.848(I)
	1 000	0.875(I)	0.867(I)	0.844(I)	0.844(I)	0.797(Ⅱ)	0.804(I)
	1 500	0.870(I)	0.863(I)	0.834(I)	0.803(I)	0.782(Ⅱ)	0.787(Ⅱ)
	2 000	0.874(I)	0.863(I)	0.831(I)	0.811(I)	0.794(Ⅱ)	0.784(Ⅱ)
	2 500	0.863(I)	0.840(I)	0.805(I)	0.779(Ⅱ)	0.730(Ⅱ)	0.742(Ⅱ)
4	500	0.863(I)	0.857(I)	0.839(I)	0.839(I)	0.797(Ⅱ)	0.803(I)
	1 000	0.844(I)	0.830(I)	0.803(I)	0.799(Ⅱ)	0.745(Ⅱ)	0.751(Ⅱ)
	1 500	0.834(I)	0.822(I)	0.790(Ⅱ)	0.751(Ⅱ)	0.725(Ⅱ)	0.728(Ⅱ)
	2 000	0.833(I)	0.819(I)	0.785(Ⅱ)	0.757(Ⅱ)	0.733(Ⅱ)	0.720(Ⅱ)
	2 500	0.821(I)	0.790(Ⅱ)	0.748(Ⅱ)	0.714(Ⅱ)	0.660(Ⅱ)	0.670(Ⅱ)
5	500	0.837(I)	0.829(I)	0.806(I)	0.804(I)	0.757(Ⅱ)	0.760(Ⅱ)
	1 000	0.816(I)	0.797(Ⅱ)	0.764(Ⅱ)	0.756(Ⅱ)	0.695(Ⅱ)	0.701(Ⅱ)
	1 500	0.797(Ⅱ)	0.782(Ⅱ)	0.746(Ⅱ)	0.702(Ⅱ)	0.671(Ⅱ)	0.671(Ⅱ)
	2 000	0.789(Ⅱ)	0.774(Ⅱ)	0.738(Ⅱ)	0.704(Ⅱ)	0.670(Ⅱ)	0.655(Ⅱ)
	2 500	0.777(Ⅱ)	0.740(Ⅱ)	0.691(Ⅱ)	0.650(Ⅱ)	0.592(Ⅲ)	0.599(Ⅲ)

数据集	R²	MAE	MBE	RMSE
训练集	0.975 5	0.009 113 2	0.000 678 49	0.011 486
测试集	0.977 5	0.011 143	0.002 151 6	0.013 799

管片标号	爆炸次数	预测韧性	爆炸次数	预测韧性	爆炸次数	预测韧性
1	6	0.713	7	0.608	8	0.494
2	6	0.661	7	0.557	8	0.444
3	6	0.619	7	0.491	8	0.382
4	6	0.555	7	0.426	8	0.309
5	6	0.490	7	0.352	8	0.248
6	6	0.504	7	0.361	8	0.255

爆炸作用下地铁盾构隧道韧性评估与预测

Resilience assessment and prediction of metro shield tunnels under explosive conditions

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管片编号	爆炸次数
管片编号	1	2	3	4	5	6	7	8
H1	I	I	I	I	Ⅱ	Ⅱ	Ⅱ	Ⅲ
H2	I	I	I	Ⅱ	Ⅱ	Ⅱ	Ⅲ	Ⅲ
H3	I	I	I	Ⅱ	Ⅱ	Ⅱ	Ⅲ	Ⅳ
H4	I	I	Ⅱ	Ⅱ	Ⅱ	Ⅲ	Ⅲ	Ⅳ
H5	I	I	Ⅱ	Ⅱ	Ⅲ	Ⅲ	Ⅳ	Ⅳ
H6	I	I	Ⅱ	Ⅱ	Ⅲ	Ⅲ	Ⅳ	Ⅳ

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