Article(id=1149745447826273064, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1149745442428203486, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2024.04.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1702656000000, receivedDateStr=2023-12-16, revisedDate=1710777600000, revisedDateStr=2024-03-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1752050275998, onlineDateStr=2025-07-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752050275998, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752050275998, creator=13701087609, updateTime=1752050275998, updator=13701087609, issue=Issue{id=1149745442428203486, tenantId=1146029695717560320, journalId=1146123302524792850, year='2024', volume='37', issue='4', pageStart='1', pageEnd='141', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1752050274711, creator=13701087609, updateTime=1753780137206, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1157001011664605316, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1149745442428203486, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1157001011664605317, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1149745442428203486, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=24, endPage=31, ext={EN=ArticleExt(id=1149745448228926282, articleId=1149745447826273064, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Measured Analysis of Vibration Source Strength in Metro Tunnels Influenced by Vehicle Condition Factors, columnId=1152669340382966151, journalTitle=Urban Rapid Rail Transit, columnName=Forum of Rapid Rail Transit, runingTitle=null, highlight=null, articleAbstract=

This study focuses on a straightline section of a subway line to analyze the factors influencing the vibration source strength in subway tunnels. Vehicleinduced vibration signals and velocity signals were collected synchronously, and the timedomain and frequencydomain characteristics of allday test samples were statistically analyzed. The results showed that during peak hours in the morning and evening, the vibration response inside the tunnel may not be completely maximized, and the degree of dispersion of the vibration source intensity is directly related to the vehicle state. The study found an average difference of 6.7 dB in the VLZ of the tunnel wall measurement points caused by V₁ and V2 vehicles. Using more than 40 sets of test samples to calculate the vibration source intensity yielded results closer to the daily sample mean. The main frequency of tunnel vibration caused by the wheelrail system shifts with changes in vehicle speed. A strong linear positive correlation was observed between vehicle speed and the vibration acceleration levels of the track bed and tunnel wall. Specifically, for every 10 km/h increase in vehicle speed, the vibration increases by about 1.7 dB and 2.6 dB, respectively. When the speed difference is within 10 km/h, the vibration source intensity changes within 1 dB; when the speed difference is between 10~20 km/h, the increase is about 1.5~2.2 dB. The coefficient of speedcorrected CV was calculated to be 18.5, aligning closely with the coefficient used for train speed correction in the vibration prediction formula. Tunnel wall vibration was more discrete in the lowfrequency band below 40 Hz, but this band contributed minimally to the source strength of vibration, accounting for 15.29%. The dominant frequency band was 5063 Hz, contributing 59.55% to the source strength of vibration. These research findings can provide a reference for further refining the vibration prediction formula and offer a theoretical basis for improving the accuracy of metro tunnel source strength test results.

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为分析地铁隧道振动源强的影响因素,以某地铁线路直线段为研究对象,同步采集车致振动信号和速度信号,统计分析全天测试样本的时域、频域特征。结果表明:早晚高峰时段,隧道内振动响应不一定完全最大,振动源强的离散程度与车辆状态直接相关,V1、V2两车所引起的隧道壁测点Z振级的均值相差6.7 dB;取40组以上的测试样本计算振动源强,其结果更接近全天样本均值;由轮轨系统引起的隧道振动主频随车速的变化而偏移,车速与道床、隧道壁的振动加速度级呈现较强的线性正相关,车速每提高10km/h,振动分别增加约1.7、2.6 dB;速度差在10km/h以内时,振动源强在1dB以内变化,速度差在10~20km/h时,振动增幅为1.5~2.2 dB;计算得到车速修正C的系数为18.5,与环评振动预测公式中列车速度修正所使用系数接近;隧道壁振动在40 Hz 以下的低频段离散较大,但该频段对振动源强的贡献度较小,占15.29%,优势频段为50~63Hz,该频段对振动源强的贡献度达 59.55%。研究成果可为环评振动预测公式的进一步精确化提供参考,为提高地铁隧道源强测试结果的准确性提供理论依据。

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刘文武,男,本科,高级工程师,从事轨道工程设计及咨询工作,

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刘文武,男,本科,高级工程师,从事轨道工程设计及咨询工作,

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刘文武,男,本科,高级工程师,从事轨道工程设计及咨询工作,

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language=CN, orderNo=3, keyword=列车车速), Keyword(id=1154040203464397459, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, orderNo=4, keyword=离散特征), Keyword(id=1154040203539894933, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, orderNo=5, keyword=统计分析)], refs=[Reference(id=1154040207746781923, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, doi=null, pmid=null, pmcid=null, year=2020, volume=20, issue=3, pageStart=1, pageEnd=16, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=马蒙, 刘维宁, 刘卫丰, journalName=交通运输工程学报, refType=null, unstructuredReference=马蒙, 刘维宁, 刘卫丰. 列车引起环境振动预测方法与不确定性研究进展[J]. 交通运输工程学报, 2020, 20(3): 1-16., articleTitle=列车引起环境振动预测方法与不确定性研究进展, refAbstract=null), Reference(id=1154040207826473702, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, doi=null, pmid=null, 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caption=${V}{L}_{\text{Zmax }}$ of the rail of the train when the train passes, figureFileSmall=+GOyXFOX40e92g+mtd6mWQ==, figureFileBig=mTv44756rylF43Xg0VXIrA==, tableContent=null), ArticleFig(id=1154040205033067188, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, label=图 2, caption=列车通过时钢轨最大 $\mathrm{Z}$ 振级, figureFileSmall=+GOyXFOX40e92g+mtd6mWQ==, figureFileBig=mTv44756rylF43Xg0VXIrA==, tableContent=null), ArticleFig(id=1154040205095981749, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=EN, label=Figure 3, caption=Rail vibration acceleration time history of ${\mathrm{V}}_{1}$ and ${\mathrm{V}}_{2}$, figureFileSmall=+/MvY7Da+MmWebUXPa/B3Q==, figureFileBig=SXeF6osrsmvGpcbUwyKqBA==, tableContent=null), ArticleFig(id=1154040205158896311, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, label=图 3, caption=典型列车 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value of $1/3$ octave spectrum of tunnel wall measuring points of ${\mathrm{V}}_{1}$ and ${\mathrm{V}}_{2}$ trains in ${T}_{1}\sim {T}_{8}$ period, figureFileSmall=nEcdkEHmTFs4KLsSw3lMfw==, figureFileBig=tZLt/o6LpiGzgRcDXwrGVg==, tableContent=null), ArticleFig(id=1154040205460886207, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, label=图 5, caption=${T}_{1}\sim {T}_{8}$ 时间段内 ${\mathrm{V}}_{1}\text{、}{\mathrm{\;V}}_{2}$ 车的隧道壁测点 $1/3$ 倍频程谱均值, figureFileSmall=nEcdkEHmTFs4KLsSw3lMfw==, figureFileBig=tZLt/o6LpiGzgRcDXwrGVg==, tableContent=null), ArticleFig(id=1154040205519606465, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=EN, label=Figure 6, caption=VAL of all the trains passing through the test section in a whole day of $1/3$ octave spectrum, figureFileSmall=mA1H0KuJwPuxdZOBpIBveA==, figureFileBig=MJLzgexJXMDsM/VTvNnJ8Q==, tableContent=null), 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figureFileBig=null, tableContent=
测点 车辆 时段
7:00—9:00 17:00—19:00
钢轨 正常车 21.83 27.46
${\mathrm{V}}_{2}$ 25.04 32.81
道床 正常车 0.164 0.164
${\mathrm{V}}_{2}$ 0.187 0.201
隧道壁 正常车 0.103 0.120
${\mathrm{V}}_{2}$ 0.107 0.133
), ArticleFig(id=1154040206845006548, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, label=表 1, caption=早晚高峰时段加速度有效值统计, figureFileSmall=null, figureFileBig=null, tableContent=
测点 车辆 时段
7:00—9:00 17:00—19:00
钢轨 正常车 21.83 27.46
${\mathrm{V}}_{2}$ 25.04 32.81
道床 正常车 0.164 0.164
${\mathrm{V}}_{2}$ 0.187 0.201
隧道壁 正常车 0.103 0.120
${\mathrm{V}}_{2}$ 0.107 0.133
), ArticleFig(id=1154040207004390103, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=EN, label=Table 2, caption=Definition of test sample number when calculating $V{L}_{\mathrm{Z}}$ with different standards, figureFileSmall=null, figureFileBig=null, tableContent=
规范/标准 内容
GB 10070—1988 [ 11 ] 每个测点连续测量 20 次列车, 取 20 次测量值的算术平均值
GB/T 19846—2005 [ 12 ] 至少记录列车 3 次且测出的 3 个值 相差不超过 $3\mathrm{\;{dB}}$
JGJ/T 170—2009[ 13 ] 通过列车数量不少于 5 列
HJ 453—2018 [ 14 ] 取运营高峰期不少于 5 次测量值的 算术平均值
DB11/T 838—2019[ 15 ] 测量车次不少于 100 列, 取 100 列 列车的均值作为源强, 不足 100 列 则取全天运营车次的均值
T/CAMET 03001—2020 [ 16 ] 宜测量全天通过测量断面的车次
), ArticleFig(id=1154040207088276183, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, label=表 2, caption=不同标准计算 $V{L}_{\mathrm{Z}}$ 时对测试样本取值的定义, figureFileSmall=null, figureFileBig=null, tableContent=
规范/标准 内容
GB 10070—1988 [ 11 ] 每个测点连续测量 20 次列车, 取 20 次测量值的算术平均值
GB/T 19846—2005 [ 12 ] 至少记录列车 3 次且测出的 3 个值 相差不超过 $3\mathrm{\;{dB}}$
JGJ/T 170—2009[ 13 ] 通过列车数量不少于 5 列
HJ 453—2018 [ 14 ] 取运营高峰期不少于 5 次测量值的 算术平均值
DB11/T 838—2019[ 15 ] 测量车次不少于 100 列, 取 100 列 列车的均值作为源强, 不足 100 列 则取全天运营车次的均值
T/CAMET 03001—2020 [ 16 ] 宜测量全天通过测量断面的车次
), ArticleFig(id=1154040207163773657, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=EN, label=Table 3, caption=${V}{L}_{\text{Zmax }}$ during morning and evening peak hours, figureFileSmall=null, figureFileBig=null, tableContent=
车速/(km/h) 速度差/(km/h) VLZmax/dB VLZmax差/dB 车辆编号
75.93 2.59 65.8 0.7 No.A1
73.34 65.1
75.84 1.76 66.9 0.6 No.A3
74.08 66.3
74.99 3.14 65.2 0.3 No.A4
71.85 65.5
74.57 2.18 67.5 0.3 No.A4
72.39 67.2
74.99 5.61 69.3 0.4 No.A2
80.60 69.7
96.14 8.8 69.9 0.4 No.A2
87.34 69.5
96.28 13.99 67.9 1.5 No.A3
82.29 66.4
96.28 15.88 67.3 1.6 No.A5
80.40 65.7
96.55 20.79 67.0 2.2 No.A6
75.76 64.8
96.28 21.79 66.2 2.1 No.A1
74.49 64.1
), ArticleFig(id=1154040207247659737, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149745447826273064, language=CN, label=表 3, caption=早晚高峰时段振动源强, figureFileSmall=null, figureFileBig=null, tableContent=
车速/(km/h) 速度差/(km/h) VLZmax/dB VLZmax差/dB 车辆编号
75.93 2.59 65.8 0.7 No.A1
73.34 65.1
75.84 1.76 66.9 0.6 No.A3
74.08 66.3
74.99 3.14 65.2 0.3 No.A4
71.85 65.5
74.57 2.18 67.5 0.3 No.A4
72.39 67.2
74.99 5.61 69.3 0.4 No.A2
80.60 69.7
96.14 8.8 69.9 0.4 No.A2
87.34 69.5
96.28 13.99 67.9 1.5 No.A3
82.29 66.4
96.28 15.88 67.3 1.6 No.A5
80.40 65.7
96.55 20.79 67.0 2.2 No.A6
75.76 64.8
96.28 21.79 66.2 2.1 No.A1
74.49 64.1
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地铁隧道振动源强受车辆状态影响因素实测分析
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刘文武 1 , 冯青松 2 , 罗信伟 1 , 齐英睿 2
都市快轨交通 | 快轨论坛 2024,37(4): 24-31
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都市快轨交通 | 快轨论坛 2024, 37(4): 24-31
地铁隧道振动源强受车辆状态影响因素实测分析
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刘文武1 , 冯青松2, 罗信伟1, 齐英睿2
作者信息
  • 1 广州地铁设计研究院股份有限公司 广州 510010
  • 2 华东交通大学 轨道交通基础设施性能监测与保障国家重点实验室 南昌 330013
  • 刘文武,男,本科,高级工程师,从事轨道工程设计及咨询工作,

Measured Analysis of Vibration Source Strength in Metro Tunnels Influenced by Vehicle Condition Factors
Wenwu LIU1 , Qingsong FENG2, Xinwei LUO1, Yingrui QI2
Affiliations
  • 1 Guangzhou Metro Design and Research Institute Co., Ltd. Guangzhou 510010
  • 2 State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure East China Jiaotong University Nanchang 330013
doi: 10.3969/j.issn.1672-6073.2024.04.004
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为分析地铁隧道振动源强的影响因素,以某地铁线路直线段为研究对象,同步采集车致振动信号和速度信号,统计分析全天测试样本的时域、频域特征。结果表明:早晚高峰时段,隧道内振动响应不一定完全最大,振动源强的离散程度与车辆状态直接相关,V1、V2两车所引起的隧道壁测点Z振级的均值相差6.7 dB;取40组以上的测试样本计算振动源强,其结果更接近全天样本均值;由轮轨系统引起的隧道振动主频随车速的变化而偏移,车速与道床、隧道壁的振动加速度级呈现较强的线性正相关,车速每提高10km/h,振动分别增加约1.7、2.6 dB;速度差在10km/h以内时,振动源强在1dB以内变化,速度差在10~20km/h时,振动增幅为1.5~2.2 dB;计算得到车速修正C的系数为18.5,与环评振动预测公式中列车速度修正所使用系数接近;隧道壁振动在40 Hz 以下的低频段离散较大,但该频段对振动源强的贡献度较小,占15.29%,优势频段为50~63Hz,该频段对振动源强的贡献度达 59.55%。研究成果可为环评振动预测公式的进一步精确化提供参考,为提高地铁隧道源强测试结果的准确性提供理论依据。

地铁隧道  /  振动源强  /  列车车速  /  离散特征  /  统计分析

This study focuses on a straightline section of a subway line to analyze the factors influencing the vibration source strength in subway tunnels. Vehicleinduced vibration signals and velocity signals were collected synchronously, and the timedomain and frequencydomain characteristics of allday test samples were statistically analyzed. The results showed that during peak hours in the morning and evening, the vibration response inside the tunnel may not be completely maximized, and the degree of dispersion of the vibration source intensity is directly related to the vehicle state. The study found an average difference of 6.7 dB in the VLZ of the tunnel wall measurement points caused by V₁ and V2 vehicles. Using more than 40 sets of test samples to calculate the vibration source intensity yielded results closer to the daily sample mean. The main frequency of tunnel vibration caused by the wheelrail system shifts with changes in vehicle speed. A strong linear positive correlation was observed between vehicle speed and the vibration acceleration levels of the track bed and tunnel wall. Specifically, for every 10 km/h increase in vehicle speed, the vibration increases by about 1.7 dB and 2.6 dB, respectively. When the speed difference is within 10 km/h, the vibration source intensity changes within 1 dB; when the speed difference is between 10~20 km/h, the increase is about 1.5~2.2 dB. The coefficient of speedcorrected CV was calculated to be 18.5, aligning closely with the coefficient used for train speed correction in the vibration prediction formula. Tunnel wall vibration was more discrete in the lowfrequency band below 40 Hz, but this band contributed minimally to the source strength of vibration, accounting for 15.29%. The dominant frequency band was 5063 Hz, contributing 59.55% to the source strength of vibration. These research findings can provide a reference for further refining the vibration prediction formula and offer a theoretical basis for improving the accuracy of metro tunnel source strength test results.

subway tunnel  /  vibration source strength  /  train speed  /  discrete characteristics  /  statistical analysis
刘文武, 冯青松, 罗信伟, 齐英睿. 地铁隧道振动源强受车辆状态影响因素实测分析. 都市快轨交通, 2024 , 37 (4) : 24 -31 . DOI: 10.3969/j.issn.1672-6073.2024.04.004
Wenwu LIU, Qingsong FENG, Xinwei LUO, Yingrui QI. Measured Analysis of Vibration Source Strength in Metro Tunnels Influenced by Vehicle Condition Factors[J]. Urban Rapid Rail Transit, 2024 , 37 (4) : 24 -31 . DOI: 10.3969/j.issn.1672-6073.2024.04.004
近年来, 城市轨道交通因具有运量大、能耗低、 便捷性等优势得到了高速发展, 但其产生的振动和噪声问题也日益突出。新线开通后振动噪声超标以及运营一段时间后居民投诉的问题常有发生, 造成这一现象的原因与环境影响评价初期对地铁振动源强预测的准确度和可靠性直接相关 [ 1 ]
国内外学者通过理论分析、现场试验等方式对地铁列车振源特性 [ 2 ] 、传播规律 [ 3 ] 、预测方法 [ 4 ] 和减振措施 [ 5 ] 进行了一系列研究。目前,地铁隧道振动源强取值方法得到的是单一具体值, 而实际情况中振动源强与车辆-轨道系统的车辆状态、列车车速、轮轨粗糙度、 线路结构等因素密切相关, 具有时间和空间不确定性。 李明航等 [ 6 ] 通过原位测试,发现同一列车多次经过固定断面引起的振动差异主要受 ${6.3}\mathrm{\;{Hz}}$ 以下频段的离散性影响。张凌等 [ 7 ] 对 200 趟车通过邻近减振与非减振断面的源强进行统计分析, 发现隧道壁测点越高, 测试数据的离散性越大。葛辉等 [ 8 ] 实测不同车速下地铁多种轨道结构的振动响应, 结果表明车速增加对低频振动影响较大。杜林林等 [ 9 ] 针对城市轨道交通振动评价指标计算存在的问题, 详细计算了重叠系数及计权因子对评价指标的影响。
上述研究为地铁列车振动源强特性分析提供了大量理论依据, 然而源强测试结果受众多因素影响, 如测试样本数量、源强离散特性、车轮损伤、车速等。 本文从时域、频域多角度出发, 统计分析全天测试样本的离散特征, 并就以上因素对源强测试结果的影响进行分析, 结合现行标准进行评价。研究成果可为环评振动预测公式的进一步细化提供参考, 提高地铁隧道源强测试结果的准确性, 并为地铁隧道环境振动概率预测和分频振动控制提供理论依据。
测试断面选择在某地铁线路直线区间段, 轨道结构为普通整体道床, 扣件类型为弹条III型扣件, 地铁列车为 8 辆编组 $\mathrm{A}$ 型车,最高运行车速为 ${100}\mathrm{\;{km}}/\mathrm{h}$图 1 为隧道内振动测点的布置图, 共 3 个测点, 其中钢轨测点位于轨底, 道床测点位于道床中心, 隧道壁测点距离轨面高度 ${1.25}\mathrm{\;m}$ 。测试采用 NI-9031 数据采集仪, 钢轨垂向振动采用 PCB-352 加速度传感器; 道床和隧道壁垂向振动采用 PCB-393 加速度传感器, 采样频率为 ${3200}\mathrm{\;{Hz}}$ ;列车车速测试采用激光雷达传感器 TF03, 采样频率设置为 ${100}\mathrm{\;{Hz}}$
图 2 为列车通过测试断面时钢轨的最大 $\mathrm{Z}$ 振级,由图可知, ${T}_{1}\sim {T}_{8}$ 所在的 8 个时间段内均有 7 组测试结果,且钢轨测点最大 $\mathrm{Z}$ 振级 $\left({V{L}_{\mathrm{{Zmax}}}}\right)$ 均呈现相同的波动规律, 可以判定这 8 个时间段内的振动信号为同种编组的 7 列车通过测试断面时触发的。
此外,早晚高峰(7:00-9:00; 17:00-19:00)时段隧道内的振动响应不一定完全最大,在 ${T}_{1}\sim {T}_{8}$ 时间段内运营的 7 列车中存在 1 列车, 使得隧道内振动响应较其他列车更大,现定义该趟车为 ${\mathrm{V}}_{2}$ 车, 表 1 将给出早晚高峰时段轮轨状态正常的 6 辆车与 ${\mathrm{V}}_{2}$ 车通过测试断面时的有效值统计。由 表 1 可知, 与正常车辆相比, ${\mathrm{V}}_{2}$ 车通过时钢轨、道床、隧道壁测点的加速度有效值均值分别增大 17.1%、3.7%和 7.4%。
选取 图 2 中晚高峰 ${T}_{7}$ 时段内 $V{L}_{\text{Zmax }}$ 差异显著的 ${\mathrm{V}}_{1}$${\mathrm{V}}_{2}$ 两车进行时程分析,如 图 3 所示,两车车速分别为 ${96.3}\text{、}{96.6}\mathrm{\;{km}}/\mathrm{h}$ 。由 图 3 可知, ${\mathrm{V}}_{1}\text{、}{\mathrm{\;V}}_{2}$ 两列车的钢轨加速度时程曲线基本一致,但 ${\mathrm{V}}_{2}$ 车在 ${10}\sim {11}\mathrm{\;s}$ 内 (第四节车厢) 钢轨加速度峰值达到 ${626.5}\mathrm{\;m}/{\mathrm{s}}^{2}$ ,比 ${\mathrm{V}}_{1}$ 车增大 28.9%。经后期技术人员对车辆的车轮检测发现, 该车部分车轮存在车轮多边形, 由于测试时间段内钢轨粗糙度基本保持不变, 并且列车行车速度也基本一致, 因此可以发现, 列车车轮多边形磨损对隧道内振动影响显著。
图 4${\mathrm{V}}_{1}\text{、}{\mathrm{V}}_{2}$ 两车的钢轨振动时频分析, $7 \sim {14}\mathrm{s}$ 内明暗相间的条纹共有 9 条, 对应列车的 8 辆编组, 其中在 ${10}\sim {11}\mathrm{\;s}$ 内, ${\mathrm{V}}_{2}$ 车第四节车厢的振动峰值达到 ${300}\sim {400}\mathrm{\;m}/{\mathrm{s}}^{2}$ ,较其他车厢和 ${\mathrm{V}}_{1}$ 车的振动能量都更高,由此也可看出 ${\mathrm{V}}_{2}$ 车车轮存在多边形磨损,导致列车驶过测试断面时产生了冲击振动。
1/3 倍频程谱能够描述不同频率下的振动水平, 各中心频率的分频振动加速度级反映该频段内的振动能量大小。 图 5 给出了 ${T}_{1}\sim {T}_{8}$ 时间段内,由列车 ${\mathrm{V}}_{1}\text{、}{\mathrm{V}}_{2}$ 所引起的隧道壁测点的 $1/3$ 倍频程谱均值。由 图 5 可知, ${\mathrm{V}}_{2}$ 车所引起的隧道壁振动响应几乎在全频段内均高于 ${\mathrm{V}}_{1}$ 车所引起的隧道壁振动响应,当频率范围处在 $4 \sim {40}\mathrm{\;{Hz}}$${80}\sim {125}\mathrm{\;{Hz}}$ 时差异较为明显,在 $4 \sim {40}\mathrm{\;{Hz}}$ 范围内差值为 $3 \sim 6\mathrm{\;{dB}}$ ,在 ${80}\sim {125}\mathrm{\;{Hz}}$ 范围内差值为 $4 \sim {11}\mathrm{\;{dB}}$${\mathrm{V}}_{1}$${\mathrm{V}}_{2}$ 两车所引起的隧道壁测点 $\mathrm{Z}$ 振级的均值分别为 ${66.1}\text{、}{72.8}\mathrm{\;{dB}}$ ,二者相差 ${6.7}\mathrm{\;{dB}}$
图 6 为剔除异常车辆 $\left({\mathrm{V}}_{2}\right.$ 车) 数据后,测试断面全天 95 组样本的隧道壁振动加速度 $1/3$ 倍频程谱,由 图 6 可知, 尽管列车的车辆参数、车轮状态存在差异, 且一天内不同时段的载运量、列车车速随时间变化,但隧道内车致振动响应的主频均在 ${63}\sim {100}\mathrm{\;{Hz}}$ 范围内。 该频段内的振动主频由列车的动态激励控制, 车辆的簧下质量会激发列车动态激励, 由簧下质量、钢轨、 扣件形成的轮轨系统在该频段内产生共振频率 [ 10 ] 。此外,隧道壁的振动响应在 ${40}\mathrm{\;{Hz}}$ 以下的低频段离散更大,分频振动加速度级相差达 ${30}\mathrm{\;{dB}}$
为定量讨论样本数对振动源强离散特性的影响, 采用移动平均法分析振动源强大小与样本数之间的关系, 移动平均法能有效地消除测试过程中的随机误差。
国内外标准 [ 11 - 16 ] 大都以铅垂向的 $\mathrm{Z}$ 振级 $V{L}_{\mathrm{Z}}$ 评价地铁环境振动对人体的影响, 并以多组测量数据的算术平均值作为评价量, 其中文献[ 11 - 16 ]对测试样本取值的定义如 表 2 所示。
图 7 为按照车辆通过测试断面的时间,采用移动平均法分别取 5、10、20、30 组源强样本计算得到的振动源强移动平均值。 图 7 显示, 随着源强样本数的增加,振动源强计算结果的离散程度从 ${3.68}\mathrm{\;{dB}}$ 迅速降低至 ${1.05}\mathrm{\;{dB}}$ ,多组源强样本的平均值可以有效降低测试过程中随机因素对源强不确定性的影响。
图 8 为振动源强均值随源强样本数变化的离散程度, 分别计算了两天内的测试数据。可以看出, 当样本数由 1 增加至 40 时, 振动源强的计算结果逐渐趋于稳定,离散程度在 $1\mathrm{\;{dB}}$ 左右。因此在对地铁振动源强评价时, 为减小测试样本数对源强不确定性的影响, 取 40 组以上的计算结果更接近全天测试样本均值。
从节约能耗的角度出发,运营组织会根据客流强度, 全天候采用不同的运行模式, 形成最高速度运行和正常速度运行。因此利用此测试线路, 采集到不同速度下的测试数据。
为分析车速变化对轮轨系统共振频率的影响, 分别将 ${\mathrm{V}}_{1}\text{、}{\mathrm{\;V}}_{2}$ 车在全天不同时段以车速 ${65}\sim {100}\mathrm{\;{km}}/\mathrm{h}$ 运行时的钢轨振动加速度 $1/3$ 倍频程谱进行对比分析, 如 图 9 所示, 在轮轨粗糙度保持不变的情况下, 隧道内的振动主频随着列车车速的变化产生明显偏移。当车速在 ${65}\sim {80}\mathrm{\;{km}}/\mathrm{h}$ 时,振动主频在 ${63}\sim {80}\mathrm{\;{Hz}}$ 之间; 当车速在 ${80}\sim {100}\mathrm{\;{km}}/\mathrm{h}$ 时,振动主频偏移至 ${100}\mathrm{\;{Hz}}$ 附近。
文献[ 14 ]中定义振动源强的预测公式为
$ V{L}_{\mathrm{Z}\max }= V{L}_{\mathrm{Z}0\max }+ {C}_{\mathrm{{VB}}}$
式中, $V{L}_{\text{Zmax }}$ 为预测点处的最大 $\mathrm{Z}$ 振级; $V{L}_{\text{Z0max }}$ 为列车实测振动源强; ${C}_{\mathrm{{VB}}}$ 为振动修正,包含车速、隧道型式等条件修正。对于列车速度修正 ${C}_{\mathrm{V}}$ 而言,当运行速度 $v \leq {100}\mathrm{\;{km}}/\mathrm{h}$ 时,修正公式为
${C}_{\mathrm{V}}= {20}\lg v -{20}\lg {v}_{0}$
式中, $v$ 为列车通过预测点的运行速度; ${v}_{0}$ 为源强列车的参考速度, 参考速度不低于预测点设计速度的 75%。
为分析车速对隧道内振动响应的影响, 采用最小二乘法对车速与振动加速度级、最大 $\mathrm{Z}$ 振级的相关性进行回归分析,用决定系数 ${R}^{2}$ 评价两变量间的相关性, ${R}^{2}$ 介于 $0 \sim 1$ 之间,越接近 1,拟合效果越好,超过 0.8 时拟合度比较高, 为高度线性相关。 图 10 为车速与道床、隧道壁测点的相关性分析。由 图 10 可知, 车速与道床、隧道壁测点的振动加速度级拟合效果较好, ${R}^{2}$ 分别为 0.942、0.915,相关性明显; 车速与最大 $\mathrm{Z}$ 振级的拟合效果较差, ${R}^{2}$ 低于 0.8,但从整体趋势上看也呈现非线性正相关性。车速与道床和隧道壁测点的振动加速度级拟合公式分别为
${VAL}_{\mathrm{{TB}}}= {0.167v}+ {91.130}\;{70}\mathrm{\;{km}}/\mathrm{h}\leq v \leq {100}\mathrm{\;{km}}/\mathrm{h}\;\left( 3\right)$
${VAL}_{\mathrm{{TW}}}= {0.262v}+ {78.398}\;{70}\mathrm{\;{km}}/\mathrm{h}\leq v \leq {100}\mathrm{\;{km}}/\mathrm{h}\;(4 $
式中, ${VA}{L}_{\mathrm{{TB}}}$ 为道床测点的振动加速度级; ${VA}{L}_{\mathrm{{TW}}}$ 为隧道壁测点的振动加速度级。
随着车速的增加, 道床和隧道壁测点的振动加速度级呈线性递增,车速每提高 ${10}\mathrm{\;{km}}/\mathrm{h}$ ,振动分别增加约 1.7、2.6 dB。
进一步分析车速变化对振动源强的影响规律, 减少车辆状态、各时段载运量差异等随机因素对振动源强的影响, 选取同一辆车早晚高峰时段连续通过测试断面时的振动源强进行对比, 如 表 3 所示。由 表 3 可知,当速度差在 $0 \sim {10}\mathrm{\;{km}}/\mathrm{h}$ 时,振动源强变化在 $1\mathrm{\;{dB}}$ 以内; 速度差在 ${10}\sim {20}\mathrm{\;{km}}/\mathrm{h}$ 时,递增规律明显,增幅为 ${1.5}\sim {2.2}\mathrm{\;{dB}}$
由公式(2)可知,文献[ 14 ]中列车速度修正 ${C}_{\mathrm{V}}$ 的系数 ${K}_{\mathrm{{cv}}}$ 为 20,其计算公式为
${K}_{\mathrm{{cv}}}= \frac{V{L}_{Z\max 1}- V{L}_{Z\max 2}}{\lg {v}_{1}- \lg {v}_{2}}$
式中, ${v}_{1},{v}_{2}$ 为列车通过断面的车速 $\mathrm{{km}}/\mathrm{h};V{L}_{\mathrm{{Zmax}}1}$ , $V{L}_{\mathrm{Z}\max 2}$ 为对应车速过车时的源强, $\mathrm{{dB}}$
选取循环组中的同一辆车连续两次通过测试断面时的振动源强值计算列车速度修正 ${C}_{\mathrm{V}}$ 的系数 ${K}_{\mathrm{{cv}}}$ ,如 图 11 所示,经计算,20 组列车速度修正 ${C}_{\mathrm{V}}$ 的系数在 ${K}_{\mathrm{{cv}}}= {20}$ 上下波动,样本均值为 18.5,利用文献[ 14 ] 的车速修正公式在车速 ${70}\mathrm{\;{km}}/\mathrm{h}\leq v \leq {100}\mathrm{\;{km}}/\mathrm{h}$ 范围内进行修正, 符合工程预测需要。
计算列车过车时段的 $\mathrm{Z}$ 振级时,需将原始加速度信号进行 1/3 倍频程谱分析,转换为频域内各个中心频率的振动加速度级 ${VA}{L}_{i}$ ,并对其进行 ${W}_{\mathrm{k}i}\left(\mathrm{\;{dB}}\right)$ 计权, 得到计权后的振动加速度级 $V{L}_{i}$ ,再逆变换为各个中心频率修正后的振动加速度 ${a}_{i}$
${a}_{i}= {10}^{\left(\frac{V{L}_{i}}{20}\right)} \times {a}_{0}$
${a}_{\mathrm{{rms}}}{}^{2}= \sum {a}_{i}{}^{2}$
式中, $V{L}_{i}$ 为计权后第 $i$ 个中心频率的振动加速度级; ${a}_{i}$ 为频域计权后第 $i$ 个中心频率的振动加速度; ${a}_{0}$ 为基准加速度,取 $1 \times {10}^{-6}\mathrm{\;m}/{\mathrm{s}}^{2};{a}_{\mathrm{{rms}}}^{2}$ 为各中心频率频域计权振动加速度的平方和。研究采用 ISO 2631-1: 1997 [ 17 ] 中规定的计权因子,时间积分常数为 $1\mathrm{\;s}$
由式(6)、式(7)可知, $Z$ 振级计算结果与 $1/3$ 倍频程各中心频率计权后的加速度大小直接相关。为定量分析不同频段对振动源强的贡献度,依次计算 $1 \sim {400}\mathrm{\;{Hz}}$ 范围内各中心频率频域计权后的振动加速度平方值。 图 12 为隧道壁全天测试样本 ${W}_{\mathrm{k}}$ 计权后的振动加速度 1/3 倍频程谱,与 图 6 中隧道壁未计权的 $1/3$ 倍频程谱在 $1 \sim {400}\mathrm{\;{Hz}}$ 范围内呈现持续递增趋势不同,计权后的分频振级在 ${63}\sim {400}\mathrm{\;{Hz}}$ 频段内整体上呈现递减趋势。
图 13 分析了测试断面全天通过列车 $1/3$ 倍频程各中心频率对振动源强的贡献度。结合 图 12图 13 可知,虽然 ${40}\mathrm{\;{Hz}}$ 以下的低频段离散性比较大,分频振级的离散程度超过 ${30}\mathrm{\;{dB}}$ ,但该频段对振动源强的实际贡献度较小, 累计占比为 15.29%。与振动源强大小密切相关的优势频段为 ${50}\sim {63}\mathrm{\;{Hz}}$ ,该频段对振动源强贡献度的占比达 59.55%。
本文以某地铁线路直线段为研究对象, 轨道结构为普通整体道床, 扣件类型为弹条III型扣件, 地铁列车为 8 辆编组 $\mathrm{A}$ 型车,最高运行车速 ${100}\mathrm{\;{km}}/\mathrm{h}$ 。通过现场实测, 对全天测试样本的时域和频域特征进行统计分析, 探讨车轮损伤、测试样本数量、车速等因素对源强测试结果的影响。主要结论如下:
1) 早晚高峰时段(7:00-9:00; 17:00-19:00), 隧道内的振动响应不一定完全最大, 在评价环境振动影响时需均衡考虑其他时段影响。振动源强离散程度与车辆状态直接相关, ${\mathrm{V}}_{1}\text{、}{\mathrm{V}}_{2}$ 两车所引起的隧道壁测点 $\mathrm{Z}$ 振级的均值分别为 ${66.1}\text{、}{72.8}\mathrm{\;{dB}}$ ,二者相差 ${6.7}\mathrm{\;{dB}}$
2) 在分析源强样本数量取值的影响时发现, 样本数量取值超过 40 组后能降低随机因素对振动源强不确定性的影响, 计算结果更接近全天测试样本均值。
3) 在分析列车车速对源强的影响时发现, 轮轨系统引起的隧道振动主频随列车车速的变化而偏移。当车速在 ${65}\sim {80}\mathrm{\;{km}}/\mathrm{h}$ 时,振动主频在 ${63}\sim {80}\mathrm{\;{Hz}}$ 之间, 车速在 ${80}\sim {100}\mathrm{\;{km}}/\mathrm{h}$ 时,偏移至 ${100}\mathrm{\;{Hz}}$ 附近。车速与道床、隧道壁的振动加速度级呈现较强的线性正相关,车速每提高 ${10}\mathrm{\;{km}}/\mathrm{h}$ ,振动加速度级分别增加 1.7、 ${2.6}\mathrm{\;{dB}}$ 。当速度差在 $0 \sim {10}\mathrm{\;{km}}/\mathrm{h}$ 时,振动源强在 $1\mathrm{\;{dB}}$ 以内变化; 速度差 ${10}\sim {20}\mathrm{\;{km}}/\mathrm{h}$ 时,其增幅为 ${1.5}\sim {2.2}\mathrm{\;{dB}}$ 。 计算得到列车速度修正 ${C}_{\mathrm{V}}$ 的系数均值为 18.5,与环评振动预测公式车速修正所使用的系数相近。
4) 隧道壁振动在 ${40}\mathrm{\;{Hz}}$ 以下的低频段离散较大, 但该频段对振动源强的贡献度较小, 累计占比 15.29%, 优势频段为 ${50}\sim {63}\mathrm{\;{Hz}}$ ,该频段对振动源强的贡献度达 59.55%。
  • 国家自然科学基金(52178423)
  • 国家自然科学基金(52068029)
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2024年第37卷第4期
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doi: 10.3969/j.issn.1672-6073.2024.04.004
  • 接收时间:2023-12-16
  • 首发时间:2025-07-09
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  • 收稿日期:2023-12-16
  • 修回日期:2024-03-19
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国家自然科学基金(52178423)
国家自然科学基金(52068029)
作者信息
    1 广州地铁设计研究院股份有限公司 广州 510010
    2 华东交通大学 轨道交通基础设施性能监测与保障国家重点实验室 南昌 330013
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2种不同金属材料的力学参数

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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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