Article(id=1281608083059225313, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.0085, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763049600000, receivedDateStr=2025-11-14, revisedDate=1770220800000, revisedDateStr=2026-02-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488778127, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488778127, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488778127, creator=13701087609, updateTime=1783488778127, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=123, endPage=131, ext={EN=ArticleExt(id=1281608083264746210, articleId=1281608083059225313, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Resilience assessment and prediction of metro shield tunnels under explosive conditions, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Rui Wang1, Xun Zhang1, Xianghui Deng**, 1, Ping'an Wang2, Xu Wang1, Wei Zhang3, authorsList=Rui Wang, Xun Zhang, Xianghui Deng, Ping'an Wang, Xu Wang, Wei Zhang, authorCompany=null, correspAuthors=Xianghui Deng, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281608085261234917, articleId=1281608083059225313, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=爆炸作用下地铁盾构隧道韧性评估与预测, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

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。

, authors=王睿副教授1, 张讯1, 邓祥辉教授**, 1, 王平安教授级高级工程师2, 王旭1, 张巍高级工程师3, authorsList=王睿副教授, 张讯, 邓祥辉教授, 王平安教授级高级工程师, 王旭, 张巍高级工程师, authorCompany=null, correspAuthors=邓祥辉教授, authorNote=

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:

, correspAuthorsNote=
** 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:
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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:

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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:

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爆炸作用下地铁盾构隧道韧性评估与预测
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王睿副教授 1 , 张讯 1 , 邓祥辉教授 **, 1 , 王平安教授级高级工程师 2 , 王旭 1 , 张巍高级工程师 3
中国安全科学学报 | 安全技术与工程 2026,36(4): 123-131
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中国安全科学学报 |安全技术与工程 2026 , 36 (4) : 123 -131
爆炸作用下地铁盾构隧道韧性评估与预测
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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:

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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:

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王睿副教授1 , 张讯1, 邓祥辉教授**, 1 , 王平安教授级高级工程师2, 王旭1, 张巍高级工程师3
作者信息
  • 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
通讯作者:
** 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:
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:

Resilience assessment and prediction of metro shield tunnels under explosive conditions
Rui Wang1 , Xun Zhang1, Xianghui Deng**, 1 , Ping'an Wang2, Xu Wang1, Wei Zhang3
Affiliations
  • 1Civil and Architecture Engineering, Xi'an Technology University, Xi'an Shaanxi 710021, China
  • 2China Railway 20th Bureau Group Corporation Limited, Xi'an Shaanxi 710016, China
  • 3China Railway Construction Urban Construction Transportation Development Corporation Limited, Suzhou Jiangsu 215000, China
出版时间: 2026-04-28 doi: 10.16265/j.cnki.issn1003-3033.2026.04.0085
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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。

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

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.

explosions effect  /  metro shield tunnels  /  resilience assessment  /  back propagation(BP) neural network  /  resilience prediction
王睿副教授, 张讯, 邓祥辉教授, 王平安教授级高级工程师, 王旭, 张巍高级工程师. 爆炸作用下地铁盾构隧道韧性评估与预测. 中国安全科学学报, 2026 , 36 (4) : 123 -131 . DOI: 10.16265/j.cnki.issn1003-3033.2026.04.0085
Rui Wang, Xun Zhang, Xianghui Deng, Ping'an Wang, Xu Wang, Wei Zhang. Resilience assessment and prediction of metro shield tunnels under explosive conditions[J]. China Safety Science Journal, 2026 , 36 (4) : 123 -131 . DOI: 10.16265/j.cnki.issn1003-3033.2026.04.0085
  • 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)
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doi: 10.16265/j.cnki.issn1003-3033.2026.04.0085
  • 接收时间:2025-11-14
  • 首发时间:2026-07-08
  • 出版时间:2026-04-28
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  • 收稿日期:2025-11-14
  • 修回日期:2026-02-05
基金
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)
作者信息
    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

通讯作者:

** 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:
参考文献
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2种不同金属材料的力学参数

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种数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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