Article(id=1149738959208624965, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, articleNumber=1003-3033(2024)04-0175-08, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.04.1526, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1702137600000, receivedDateStr=2023-12-10, revisedDate=1704902400000, revisedDateStr=2024-01-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048728992, onlineDateStr=2025-07-09, pubDate=1714233600000, pubDateStr=2024-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048728992, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048728992, creator=13701087609, updateTime=1752048728992, updator=13701087609, issue=Issue{id=1149738954913661267, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='4', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048727968, creator=13701087609, updateTime=1756468927830, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1168278616925286857, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1168278616925286858, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=175, endPage=182, ext={EN=ArticleExt(id=1149738959401562954, articleId=1149738959208624965, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Influence of snowfall weather on resilience recovery of airport infrastructure system, columnId=1149735802789348081, journalTitle=China Safety Science Journal, columnName=Technology and engineering of disaster prevention and mitigation, runingTitle=null, highlight=null, articleAbstract=

To quantitatively evaluate the influence of snowfall on the resilience of airport infrastructure system,the airport infrastructure system was divided into four first-level indicators,including physical system,functional system,economic system and organizational system. The damage intensity coefficient of snowfall weather to the second-level indicators was put forward,and a quantitative analysis model of airport infrastructure system resilience was established. The variation of resilience level of airport infrastructure system was simulated by the Monte Carlo Method under four different levels of snowfall. The simulation result show that the functional damage degree and recovery time of the airport infrastructure system are positively correlated with snowfall intensity. The system recovery time is 13,14,16 and 23 respectively under the conditions of light snow,moderate snow,heavy snow and blizzard. The resilience of the airport infrastructure system under the condition of blizzard is 9.65%,17.01% and 20.27% lower than that under the conditions of heavy,medium and light snow,respectively. The recovery speed of the airport infrastructure system increases from 0.053%/h to 0.061%/h,and the recovery time reduces from 12 h to 9 h when the failure strength coefficient reduces by 20%.

, correspAuthors=Kun WU, 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, authorCompany=null, fund=null, authors=null, authorsList=Xin HUANG, Ping XU, Kun WU), CN=ArticleExt(id=1149738970260615382, articleId=1149738959208624965, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=降雪天气对机场基础设施系统韧性恢复的影响, columnId=1149735802885817075, journalTitle=中国安全科学学报, columnName=防灾减灾技术与工程, runingTitle=null, highlight=null, articleAbstract=

为量化评估降雪天气对机场基础设施系统韧性影响,将机场基础设施系统分为物理、功能、经济和组织4个一级指标,提出降雪天气对各二级指标的破坏强度系数,建立机场基础设施系统韧性量化分析模型;采用蒙特卡罗方法分析4种不同等级降雪天气下机场基础设施系统韧性水平变化特征。结果表明:机场基础设施系统功能受损程度和恢复时间与降雪强度呈正相关;小雪、中雪、大雪和暴雪天气下,系统恢复时间分别为13、14、16和23 h;暴雪天气下机场基础设施系统韧性相对于大、中、小雪下分别降低9.65%、17.01%和20.27%;当破坏强度系数降低20%时,机场基础设施系统的恢复速度从0.053%/h提升至0.061%/h,恢复时间由12 h降低至9 h。

, correspAuthors=吴堃, authorNote=null, correspAuthorsNote=
**吴堃(1991—),男,安徽巢湖人,博士,副教授,主要从事工程结构防灾减灾研究。E-mail:
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黄信 (1983—),男,安徽六安人,博士,教授,主要从事机场基础设施与工程结构等方面的研究。E-mail:

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黄信 (1983—),男,安徽六安人,博士,教授,主要从事机场基础设施与工程结构等方面的研究。E-mail:

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黄信 (1983—),男,安徽六安人,博士,教授,主要从事机场基础设施与工程结构等方面的研究。E-mail:

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Journal of Tsinghua University: Science and Technology, 2022, 62(6): 1016-1022., articleTitle=Urban public transport system resilience evaluation based on a system function curve, refAbstract=null)], funds=[Fund(id=1168150821247132110, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, awardId=2021YFB2600500, language=CN, fundingSource=国家重点研发计划项目(2021YFB2600500), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1168150817715528064, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, xref=null, ext=[AuthorCompanyExt(id=1168150817719722369, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, companyId=1168150817715528064, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=College of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China), AuthorCompanyExt(id=1168150817728110978, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, companyId=1168150817715528064, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国民航大学 交通科学与工程学院,天津 300300)])], figs=[ArticleFig(id=1168150819477135790, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Fig.1, caption=Resilience conceptual curve, figureFileSmall=4/YQOrwlMYpkrqNMeiUcCA==, figureFileBig=2Fx+7DnWBtyxSgpCPP7+NA==, tableContent=null), ArticleFig(id=1168150819577799088, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=图1, caption=韧性曲线, figureFileSmall=4/YQOrwlMYpkrqNMeiUcCA==, figureFileBig=2Fx+7DnWBtyxSgpCPP7+NA==, tableContent=null), ArticleFig(id=1168150819766542772, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Fig.2, caption=Changes in functional level of airport infrastructure systems, figureFileSmall=sO3CoNnn9ZxXa1Bu0Ubp2w==, figureFileBig=6yYeqa6kDC+46ud3uPrjdg==, tableContent=null), ArticleFig(id=1168150819837845940, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=图2, caption=机场基础设施系统功能水平变化, figureFileSmall=sO3CoNnn9ZxXa1Bu0Ubp2w==, figureFileBig=6yYeqa6kDC+46ud3uPrjdg==, tableContent=null), ArticleFig(id=1168150819921732022, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Fig.3, caption=Resilience level of airport infrastructure system, figureFileSmall=vhLNtJRPGVCUCTMGKzCcIw==, figureFileBig=2FreX827a+t//CFP9m013A==, tableContent=null), ArticleFig(id=1168150820005618104, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=图3, caption=机场基础设施系统韧性水平, figureFileSmall=vhLNtJRPGVCUCTMGKzCcIw==, figureFileBig=2FreX827a+t//CFP9m013A==, tableContent=null), ArticleFig(id=1168150820093698490, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Fig.4, caption=Cumulative density curve of resilience level of airport infrastructure system, figureFileSmall=Hfp5uKKJ47f1CznW4Kodkw==, figureFileBig=ZQzuL6HaDPOZN+xln9XbaQ==, tableContent=null), ArticleFig(id=1168150820144030139, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=图4, caption=机场基础设施系统韧性水平累计密度曲线, figureFileSmall=Hfp5uKKJ47f1CznW4Kodkw==, figureFileBig=ZQzuL6HaDPOZN+xln9XbaQ==, tableContent=null), ArticleFig(id=1168150820223721917, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Fig.5, caption=Functional level curve of airport infrastructure system under different failure intensity, figureFileSmall=D2CLgsZ4rNe1YC/S9cjRDg==, figureFileBig=4MtLZAGeFr9npKAWPNg/mQ==, tableContent=null), ArticleFig(id=1168150820278247870, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=图5, caption=不同破坏强度下机场基础设施系统功能水平曲线, figureFileSmall=D2CLgsZ4rNe1YC/S9cjRDg==, figureFileBig=4MtLZAGeFr9npKAWPNg/mQ==, tableContent=null), ArticleFig(id=1168150820328579520, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Tab.1, caption=

Definition of snowfall grade

, figureFileSmall=null, figureFileBig=null, tableContent=
降雪等级 降雪量/mm
小雪 [0.1,2.5)
中雪 [2.5,5.0)
大雪 [5.0,10)
暴雪 [10,+∞)
), ArticleFig(id=1168150820387299778, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=表1, caption=

降雪等级定义

, figureFileSmall=null, figureFileBig=null, tableContent=
降雪等级 降雪量/mm
小雪 [0.1,2.5)
中雪 [2.5,5.0)
大雪 [5.0,10)
暴雪 [10,+∞)
), ArticleFig(id=1168150820492157380, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Tab.2, caption=

Damage intensity coefficient of snow weather on second-level indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 小雪 中雪 大雪 暴雪
物理 核心飞行区道面 U(0,0.25) U(0.25,0.5) U(0.75,1) U(0.75,1)
空管设施 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
目视助航及灯光设施 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
机场排水系统 B(1,0.26) B(1,0.42) B(1,0.48) B(1,0.72)
机场供电设施 B(1,0.28) B(1,0.38) B(1,0.52) B(1,0.7)
功能 保障设施 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
航站区陆侧通行区 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
航站区空侧通行区 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
经济 机场盈利 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
除雪设备及物资 B(1,0.28) B(1,0.46) B(1,0.7) B(1,0.88)
组织 机场人员工作效率 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
场监系统 B(1,0.28) B(1,0.4) B(1,0.6) B(1,0.78)
), ArticleFig(id=1168150820580237766, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=表2, caption=

降雪天气对各二级指标的破坏强度系数

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 小雪 中雪 大雪 暴雪
物理 核心飞行区道面 U(0,0.25) U(0.25,0.5) U(0.75,1) U(0.75,1)
空管设施 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
目视助航及灯光设施 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
机场排水系统 B(1,0.26) B(1,0.42) B(1,0.48) B(1,0.72)
机场供电设施 B(1,0.28) B(1,0.38) B(1,0.52) B(1,0.7)
功能 保障设施 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
航站区陆侧通行区 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
航站区空侧通行区 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
经济 机场盈利 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
除雪设备及物资 B(1,0.28) B(1,0.46) B(1,0.7) B(1,0.88)
组织 机场人员工作效率 U(0,0.25) U(0.25,0.5) U(0.5,0.75) U(0.75,1)
场监系统 B(1,0.28) B(1,0.4) B(1,0.6) B(1,0.78)
), ArticleFig(id=1168150820664123847, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Tab.3, caption=

Minimum functional level and recovery time of airport infrastructure under snowfall

, figureFileSmall=null, figureFileBig=null, tableContent=
一级
指标
最低功能水平 恢复时间/h
小雪 中雪 大雪 暴雪 小雪 中雪 大雪 暴雪
物理 0.823 0.587 0.426 0.374 13 14 16 23
功能 0.869 0.709 0.507 0.388 13 14 15 22
经济 0.909 0.708 0.523 0.418 9 11 13 23
组织 0.871 0.633 0.451 0.346 13 14 16 22
整体 0.819 0.531 0.281 0.153 13 14 16 23
), ArticleFig(id=1168150820764787145, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=表3, caption=

降雪天气下机场基础设施最低功能水平及恢复时间

, figureFileSmall=null, figureFileBig=null, tableContent=
一级
指标
最低功能水平 恢复时间/h
小雪 中雪 大雪 暴雪 小雪 中雪 大雪 暴雪
物理 0.823 0.587 0.426 0.374 13 14 16 23
功能 0.869 0.709 0.507 0.388 13 14 15 22
经济 0.909 0.708 0.523 0.418 9 11 13 23
组织 0.871 0.633 0.451 0.346 13 14 16 22
整体 0.819 0.531 0.281 0.153 13 14 16 23
), ArticleFig(id=1168150820894810571, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=EN, label=Tab.4, caption=

Recovery of airport infrastructure system under different damage intensities

, figureFileSmall=null, figureFileBig=null, tableContent=
破坏强度系数
降低工况/%
0 5 10 20
V/(%·h-1) 0.053 0.056 0.062 0.061
rb 0.352 0.368 0.374 0.432
), ArticleFig(id=1168150820978696651, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738959208624965, language=CN, label=表4, caption=

不同破坏强度系数下机场基础设施系统恢复情况

, figureFileSmall=null, figureFileBig=null, tableContent=
破坏强度系数
降低工况/%
0 5 10 20
V/(%·h-1) 0.053 0.056 0.062 0.061
rb 0.352 0.368 0.374 0.432
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降雪天气对机场基础设施系统韧性恢复的影响
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黄信 , 徐平 , 吴堃 **
中国安全科学学报 | 防灾减灾技术与工程 2024,34(4): 175-182
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中国安全科学学报 | 防灾减灾技术与工程 2024, 34(4): 175-182
降雪天气对机场基础设施系统韧性恢复的影响
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黄信 , 徐平, 吴堃**
作者信息
  • 中国民航大学 交通科学与工程学院,天津 300300
  • 黄信 (1983—),男,安徽六安人,博士,教授,主要从事机场基础设施与工程结构等方面的研究。E-mail:

通讯作者:

**吴堃(1991—),男,安徽巢湖人,博士,副教授,主要从事工程结构防灾减灾研究。E-mail:
Influence of snowfall weather on resilience recovery of airport infrastructure system
Xin HUANG , Ping XU, Kun WU**
Affiliations
  • College of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China
出版时间: 2024-04-28 doi: 10.16265/j.cnki.issn1003-3033.2024.04.1526
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为量化评估降雪天气对机场基础设施系统韧性影响,将机场基础设施系统分为物理、功能、经济和组织4个一级指标,提出降雪天气对各二级指标的破坏强度系数,建立机场基础设施系统韧性量化分析模型;采用蒙特卡罗方法分析4种不同等级降雪天气下机场基础设施系统韧性水平变化特征。结果表明:机场基础设施系统功能受损程度和恢复时间与降雪强度呈正相关;小雪、中雪、大雪和暴雪天气下,系统恢复时间分别为13、14、16和23 h;暴雪天气下机场基础设施系统韧性相对于大、中、小雪下分别降低9.65%、17.01%和20.27%;当破坏强度系数降低20%时,机场基础设施系统的恢复速度从0.053%/h提升至0.061%/h,恢复时间由12 h降低至9 h。

降雪天气  /  机场基础设施  /  系统韧性  /  一级指标  /  恢复时间

To quantitatively evaluate the influence of snowfall on the resilience of airport infrastructure system,the airport infrastructure system was divided into four first-level indicators,including physical system,functional system,economic system and organizational system. The damage intensity coefficient of snowfall weather to the second-level indicators was put forward,and a quantitative analysis model of airport infrastructure system resilience was established. The variation of resilience level of airport infrastructure system was simulated by the Monte Carlo Method under four different levels of snowfall. The simulation result show that the functional damage degree and recovery time of the airport infrastructure system are positively correlated with snowfall intensity. The system recovery time is 13,14,16 and 23 respectively under the conditions of light snow,moderate snow,heavy snow and blizzard. The resilience of the airport infrastructure system under the condition of blizzard is 9.65%,17.01% and 20.27% lower than that under the conditions of heavy,medium and light snow,respectively. The recovery speed of the airport infrastructure system increases from 0.053%/h to 0.061%/h,and the recovery time reduces from 12 h to 9 h when the failure strength coefficient reduces by 20%.

snowfall  /  airport infrastructure  /  system resilience  /  first-level indicators  /  recovery time
黄信, 徐平, 吴堃. 降雪天气对机场基础设施系统韧性恢复的影响. 中国安全科学学报, 2024 , 34 (4) : 175 -182 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1526
Xin HUANG, Ping XU, Kun WU. Influence of snowfall weather on resilience recovery of airport infrastructure system[J]. China Safety Science Journal, 2024 , 34 (4) : 175 -182 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1526
机场基础设施是保证飞机安全运行的基础。我国气候条件复杂,尤其是北方地区和高原地区,冰雪等恶劣天气对飞行安全和航班延误造成严重隐患[1]。降雪天气对机场基础设施的正常运行有巨大影响。韧性是系统抵御灾害的能力,基于韧性视角研究降雪天气对机场基础设施的影响,对保障机场安全运行具有重要意义。
目前,在公路、轨道及航运等领域,学者们基于韧性理念研究了系统的抗灾性能和恢复能力,主要是从网络层面提出系统的韧性评价方法和恢复策略,如伍静[2]从复杂网络理论角度分析了海上丝绸之路航运网络的拓扑结构和港口重要度;唐少虎等[3]采用模糊层次分析法,评估了暴雨灾害下城市道路交通系统韧性;张雯婕等[4]以网络平均效率度量城市轨道交通网络韧性,并提出一种新的恢复策略。在基础设施韧性评价方面,BRUNEAU等[5-6]从工程韧性、组织韧性、经济韧性及社会韧性4个角度分析了韧性系统;毕玮等[7]指出基础设施系统韧性研究主要集中在公路交通、能源和水资源基础设施;李瑞奇等[8]通过建模方法开展了城市在地震灾害下韧性定量分析研究;LABAKA等[9]考虑了核电站基础设施内部和外部因素,建立了韧性框架模型;FRANCHIN等[10]基于基础设施网络,建立了一种地震灾害下基础设施韧性评估框架。已有韧性研究主要从基础设施网络层面分析,针对基础设施本体韧性研究较为分散。
在民航韧性评估方面,ZHOU Lei等[11]用航班恢复时间衡量机场整体在各种恶劣天气条件下的韧性;BAO Danwen等[12]基于历史数据建立了大型机场在突发事件下韧性的评估框架;王兴隆等[13]基于机场航班运行情况分析了机场韧性;GUO Jiuxia等[14]基于功能共振分析法,提出一种半定量的机场运行系统安全韧性评价的方法;李龙海等[15]基于构建的韧性指标评估机场整体运行韧性。综上可知:在民航韧性研究方面,现有研究主要针对天气灾害对航班和机场整体的影响,而针对降雪天气对机场基础设施韧性影响的研究还较少。
鉴于此,笔者拟构建机场基础设施的韧性指标体系,提出降雪天气对各系统指标的坡坏强度系数,建立机场基础设施系统的韧性量化分析模型;基于蒙特卡罗方法,研究不同等级降雪下机场基础设施系统的韧性水平变化特征;分析物理、功能、经济和组织4个一级指标对韧性的影响,以期提升机场基础设施韧性水平,为量化分析机场基础设施韧性提供参考。
建立降雪天气下机场基础设施系统的韧性分析模型,分析模型包括机场系统模型、韧性函数模型、降雪灾害模型和恢复函数模型[16-17]
将机场基础设施系统定义为一级指标和二级指标2个层级指标体系,其中,一级指标包括物理系统、功能系统、经济系统和组织系统。各二级指标如下:
1) 物理系统。指降雪天气下受到影响的机场重要基础设施。根据《民用机场工程项目建设标准》(建标105—2008)中的机场基础设分类,设定二级指标为主体设施和其他设施。主体设施即核心飞行区道面,包括跑道、停机坪、滑行道、联络道等;其他设施包括空管设施、目视助航及灯光设施、机场排水系统及机场供电设施。
2) 功能系统。指保障旅客通行效率并能够为飞机提供补给和维护的设施。其二级指标主要包括驱鸟车、行李传送车和摆渡车等保障设施、航站区路侧通行区、航站区空侧通行区。
3) 经济系统。指机场运行产生的经济效益及运维费用。在降雪天气下会因航班延误和物资使用产生直接或间接经济损失,其二级指标主要包括机场客货运盈利、除雪设备及物资。
4) 组织系统。指能够及时应对各种灾害和事件引起的内外部环境变化的系统。保障飞机进离场服务和地面保障服务效率;参考城市韧性中组织韧性,即管理网络和制度体系[18],二级指标主要包括机场人员工作效率、场监系统,场监系统是机场灾害预警系统,通过实时监控为人员提供处理灾害的能力。
机场基础设施系统韧性水平由各一级指标的功能水平确定,系统韧性函数定义为:
R ( t ) = R ( F ( t ) ) = R ( F ( f 1 ( t ) f 2 ( t ) f 4 ( t ) ) )
f i ( t ) = f ( f i 1 ( t ) f i 2 ( t ) f i j ( t ) f i m ( t ) )
式中:R(t)为机场基础设施系统韧性函数;F(t)为机场基础设施系统的整体功能水平;fi(t)为第i(i=1,2,…,4)个一级指标的功能水平;fij(t)为第i个一级指标的第j (j=1,2,…,m)个二级指标的功能水平。
机场的物理、功能、经济和组织4个一级指标的功能水平分别为各二级指标功能水平的平均值,见下式:
f i (t) = j = 1 m f i j ( t ) m
式中m为第i个一级指标下的二级指标数。
机场基础设施系统的整体功能水平见下式,其中,一级指标的权重系数由民航基础设施韧性水平量化分析得到,物理系统对整体系统的功能水平权重系数w1=0.254,功能系统权重系数w2=0.265,组织系统权重系数w3=0.238,经济系统权重系数w4=0.243[19]
F (t) = i = 1 4 w i · f i (t)
式中wi为一级指标的权重系数。
采用CIMELLARO等[20]提出的韧性量化方法计算机场基础设施系统韧性水平,其物理意义为图1 中阴影部分面积占矩形面积比例,见下式:
R (t) = t 0 t F ( t ) d t F ( t 0 ) · ( t - t 0 )  
式中F(t0)为初始时刻t0时机场基础设施系统的功能水平,韧性曲线如图1所示,图中t1为灾害开始时刻;t2为系统功能水平降至最低值时刻;t3为恢复到正常水平时刻;tE为模拟时间段结束时刻。
恢复力定义为恢复阶段的系统机能累积量与总时间内系统正常运转时的机能累积量之比[21],见下式:
r b = t 2 t 3 F ( t ) d t t 1 t E F s ( t ) d t
式中:Fs(t)表示系统正常运转情况下的功能曲线;F(t)表示系统在灾害下的功能曲线。
灾害下系统平均恢复速度定义为单位时间内功能恢复水平,见下式:
v = Δ F ( t ) Δ t = F ( t y ) - F ( t x ) t y - t x  
式中:v为恢复速度;txty分别为恢复计算的起始时刻和截止时刻;F(tx)为tx时刻的功能水平;F(ty)为ty时刻的功能水平 ; Δ F ( t )表示时间间隔 Δ t内功能变化情况。
不同等级降雪对机场基础设施影响方式和程度有所差别。依据中国气象局对降雪等级的划分标准,降雪等级定义见表1
为确定降雪天气下机场基础设施二级指标的破坏强度系数,向民航从业人员发放降雪天气对机场基础设施韧性影响的调查问卷,问卷采用李克特5级量表方法。根据各二级指标在不同降雪等级下的影响程度评价和频数结果,确定不同等级降雪对各系统二级指标的破坏强度系数,见表2。其中,U(xy)代表取值为[xy]的均匀分布;B(1,p)表示概率为p的0~1分布。
定义降雪灾害事件eN二级指标功能的累计破坏强度函数Dij(t)与dij(t)关系,见下式:
D i j ( t ) = t 1 t d i j ( t ) d t
式中:Dij(t)为eN从灾害发生时刻 t 1到时刻t对机场基础设施第 i个一级指标下的第j个二级指标的累积破坏强度函数;dij(t)为在时刻t降雪灾害对机场基础设施第 i个一级指标的第j个二级指标的破坏强度函数。
考虑降雪演进过程特征为降雪、积雪和融雪3个阶段,选取双指数信号函数作为降雪的累积灾害能量表征函数,构建eN对二级指标的破坏强度函数[8]:
d i j ( t e N ) = c i j N · σ ( t ) · f i j ( t 0 )
式中:N=1,2,3,4分别对应4种不同降雪等级; c i j N为破坏强度系数,取值见表2; σ(t)为累积灾害能量函数。由于累积破坏强度函数D(t)的取值范围为[0,1],所以,降雪事件的累积灾害能量函数如下:
σ (t) = e x p ( 2 t - 12 ) 0 < t < 6   1 t = 6   e x p ( - 2 t + 12 ) t > 6
暴雪灾害发生后,为缩短航班起飞延迟时间,减少机场基础设施系统损失,应及时采取恢复措施,因此,定义t时刻机场基础设施系统功能恢复函数为B(t)满足下式:
F ( t ) = F ( t 1 ) - D ( t ) + t 1 t B ( t ) d t
式中B(t)为机场基础设施系统功能恢复函数。
机场基础设施系统功能恢复函数B(t)受系统功能水平影响,则B(t)可表示为:
B ( t ) = B ( f 1 ( t ) f 2 ( t ) f 4 ( t ) )
4个一级指标之间的功能水平恢复相互依赖,物理系统是机场的核心设施,受损后修复依赖于辅助设施和工作人员,涉及到功能和组织系统功能水平;功能系统主要考虑保障设施的功能和航站区的通行效率,依赖于物理和组织系统;由于降雪可能造成航班延误,除雪也会消耗设备及物资,使得经济系统受损较大,其恢复依赖于其他3个系统;组织系统与人员和设备的工作效率有关,考虑其恢复依赖于经济系统。因此,将各一级指标功能恢复函数定义如下:
B 1 ( t ) = r 1 · f 2 ( t - 1 ) · f 4 ( t - 1 ) f 2 ( t 0 ) · f 4 ( t 0 ) · f 1 ( t 0 )
B 2 ( t ) = r 2 · f 1 ( t - 1 ) · f 4 ( t - 1 ) f 1 ( t 0 ) · f 4 ( t 0 ) · f 2 ( t 0 )
B 3 ( t ) = r 3 · f 1 ( t - 1 ) · f 2 ( t - 1 ) · f 4 ( t - 1 ) f 1 ( t 0 ) · f 2 ( t 0 ) f 4 ( t 0 ) · f 2 ( t 0 )
B 4 ( t ) = r 4 · f 3 ( t - 1 ) f 3 ( t 0 ) · f 4 ( t 0 )
式中ri(i=1,2,3,4)为各一级指标的功能恢复系数,将机场基础设施系统视为一个整体,考虑物理系统恢复效果最为直接,故设定各系统的功能恢复系数为r1=0.3,r2=0.2,r3=0.1,r4=0.4。
为分析降雪天气对机场基础设施韧性恢复的影响,设定研究时间为40 h,初始时刻t0=0,降雪灾害发生时间为t1=1,对该机场基础设施系统功能水平函数F(t)、各系统功能水平函数fi(t)、二级指标功能水平函数fij(t)的取值归一化处理,取值范围为[0,1],F(t0)=1,fi(t0)=1,fij (t0)=1。
由于机场基础设施二级指标的破坏强度系数服从均匀概率分布和0-1概率分布,每次模拟计算结果具有一定随机性,故采用蒙特卡罗方法统计模拟,针对小雪、中雪、大雪和暴雪4种等级降雪情景进行10 000次模拟计算,分析得到4种等级降雪下的机场基础设施的功能水平和韧性水平。
为分析机场基础设施一级指标在不同降雪等级下的功能水平变化情况,将10 000次模拟结果求均值得到各一级指标及整体系统的功能水平变化曲线,如图2所示。
各一级指标受到降雪灾害后,功能水平逐渐降低至最低值,随后在互相影响的恢复机制下恢复到灾前正常水平,最低功能水平及恢复时间见表3
图2表3可知:随着降雪强度增加,机场基础设施系统的功能水平逐渐降低,恢复时间逐渐增加,说明机场基础设施系统功能受损程度和恢复时间与降雪强度呈正相关,如小雪、中雪、大雪和暴雪下物理系统功能最低水平分别为0.823、0.587、0.426和0.374,功能水平降低幅值为28.7%、27.4%和12.0%,可以看出,相对大雪至暴雪而言,小雪至中雪、中雪至大雪的过程中系统功能水平降低更为明显;在小雪、中雪、大雪和暴雪下物理系统恢复时间分别为13、14、15和22h,增加幅值为7.7%、7.1%和46.7%,说明相对小雪至中雪、中雪至大雪的过程,大雪至暴雪恢复时间明显增加。
图2e可知:小雪、中雪、大雪和暴雪下,整体系统功能最低水平分别为0.819、0.531、0.281和0.153,恢复时间分别为13、14、16和23 h。
不同降雪等级下,由于降雪强度不同,各二级指标的破坏强度系数不同,各一级指标受影响程度不同。在小雪和大雪天气下,各一级指标对机场基础设施功能影响程度由大至小分别为物理、组织、功能和经济;在暴雪天气下,各一级指标对机场基础设施功能影响程度由大至小分别为组织、物理、功能和经济。说明降雪天气下,相对功能和经济一级指标而言,物理和组织一级指标对机场基础设施韧性影响更大。
同一降雪等级下,各一级指标受破坏强度不同,并且在恢复过程中各一级指标会相互影响,各一级指标受影响程度也存在差异。例如:在暴雪天气下,组织、物理、功能和经济功能受损程度大小分别为0.654,0.626,0.612和0.582,恢复时间均大于22 h,说明在后续恢复措施中,在有限恢复资源条件下,应优先考虑组织系统和物理系统的恢复。
机场基础设施系统韧性水平在不同降雪等级下模拟结果如图3图4所示,其中,累计密度是韧性水平的累积频数与总模拟次数的比值。
图3图4可知:小雪天气下,机场基础设施系统韧性平均值为0.983 9,最大值为0.999 2,最小值为0.912 1,韧性水平值超过0.966 1的概率为81.45%;中雪天气下,机场基础设施系统的韧性平均值为0.945 3,最大值为0.979 4,最小值为0.864 8,韧性水平值超过0.924 4的概率为80.56%。可以看出,小雪和中雪下机场基础设施系统仍保持较好的韧性水平。大雪天气下,机场基础设施系统的韧性平均值为0.868 3,最大值为0.92 27,最小值为0.822 9,韧性水平值超过0.848 9的概率为81.01%,可见:大雪天气对机场基础设施韧性功能影响明显。暴雪天气下机场基础设施系统的韧性平均值为0.784 5,最大值为0.826,最小值为0.605 4,韧性水平值超过0.768 4的概率为82.49%,说明暴雪天气对机场基础设施系统的韧性影响较大。
综上可知:暴雪天气下机场基础设施系统韧性受损程度显著,相对大雪、中雪和小雪天气而言,其韧性水平分别降低9.65%、17.01%和20.27%。
考虑优化除雪时间和经济成本等措施,将二级指标在暴雪天气下的破坏强度系数分别降低5%、10%和20%,对功能水平变化情况进行10 000次模拟,分析优化措施对机场基础设施韧性提升效果,结果如图5所示,图中破坏强度系数为0%时表示未采取任何优化措施。
图5可知:当考虑优化措施将破坏强度系数分别降低0、5%、10%和20%后,暴雪天气下机场基础设施系统功能水平由最低水平恢复到灾前80%水平所用时间分别为12、11、10和9 h,说明通过优化恢复措施可以缩短机场基础设施系统功能恢复时间。不同破坏强度下机场基础设施系统恢复速度和恢复力见表4
表4可知:破坏强度系数降低5%、10%和20%后,平均恢复速度v分别为0.056、0.062和0.061,相对于破坏强度系数未降低而言,恢复速度增幅为5.7%、17.0%和15.1%,说明破坏强度降低10%后,恢复效果最佳;同样,破坏强度系数降低5%、10%和20%后,恢复力rb有较明显的提升,分别为0.368、0.374和0.432,相对于未降低破坏强度系数而言,恢复力增幅为4.5%、6.3%和22.7%,说明从韧性角度考虑,破坏强度系数降低20%时恢复力最高。
1) 暴雪天气下机场基础设施系统韧性受损程度显著,机场基础设施系统功能受损程度和恢复时间都与降雪强度呈正相关,小雪、中雪、大雪和暴雪下机场基础设施的恢复时间分别为13、14、16和23 h;相对大雪、中雪和小雪天气而言,暴雪下机场韧性水平分别降低9.65%、17.01%和20.27%。
2) 物理系统、功能系统、经济系统和组织系统对机场基础设施的韧性影响存在差异,如暴雪下物理、功能、经济和组织系统对应的功能水平分别为0.374、0.388、0.418和0.346;机场基础设施系统在同等级降雪下,物理系统和组织系统受损程度大于功能系统和经济系统,说明降雪天气下应优先对物理系统和组织系统采取恢复措施。
3) 考虑优化除雪时间等措施,可以提升暴雪天气下机场基础设施系统韧性的恢复速度,如当破坏强度系数降低20%可使系统恢复速度从0.053%/h提升至0.061%/h,恢复力从0.352提升至0.432,对应恢复时间由12 h降低至9 h。文中重点研究了降雪天气下机场韧性变化特征,未来在各指标下具体恢复措施方面,将进行更深入地探索。
  • 国家重点研发计划项目(2021YFB2600500)
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2024年第34卷第4期
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doi: 10.16265/j.cnki.issn1003-3033.2024.04.1526
  • 接收时间:2023-12-10
  • 首发时间:2025-07-09
  • 出版时间:2024-04-28
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  • 收稿日期:2023-12-10
  • 修回日期:2024-01-11
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国家重点研发计划项目(2021YFB2600500)
作者信息
    中国民航大学 交通科学与工程学院,天津 300300

通讯作者:

**吴堃(1991—),男,安徽巢湖人,博士,副教授,主要从事工程结构防灾减灾研究。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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