Article(id=1228805184718041669, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228805175335383281, articleNumber=null, orderNo=null, doi=10.16385/j.cnki.issn.1004-4523.2025.04.019, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1691337600000, receivedDateStr=2023-08-07, revisedDate=1695052800000, revisedDateStr=2023-09-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1770899586133, onlineDateStr=2026-02-12, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770899586133, onlineIssueDateStr=2026-02-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770899586133, creator=13701087609, updateTime=1770899586133, updator=13701087609, issue=Issue{id=1228805175335383281, tenantId=1146029695717560320, journalId=1225147924628267009, year='2025', volume='38', issue='4', pageStart='663', pageEnd='888', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1770899583895, creator=13701087609, updateTime=1770901458539, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1228813038325789525, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228805175335383281, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1228813038329983830, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228805175335383281, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=838, endPage=848, ext={EN=ArticleExt(id=1228805185011642965, articleId=1228805184718041669, tenantId=1146029695717560320, journalId=1225147924628267009, language=EN, title=Existing continuous beam bridges seismic vulnerability assessment method considering with model modification based on influence line, columnId=null, journalTitle=Journal of Vibration Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The influence line is an important parameter of the elastic mechanical state of the bridge structure,which can effectively reflect the resistance and deformation resistance of the structure,and is expected to be used to evaluate and predict the elastic-plastic response during earthquakes. Taking the influence line of a three-span steel plate composite continuous beam bridge as the model correction target,the bridge model correction research is carried out based on BP neural network. With the expectation of Beta distribution as the earthquake damage index,the overtaking probability expression of the bridge model under various performance levels is fitted,and the seismic vulnerability of the continuous beam bridge structure before and after the finite element modification is analyzed and compared. The results show that the relative error between the measured value and the calculated value can be reduced from 38% to less than 10%,and the earthquake damage index of the modified finite element model is lower than that of the initial model. By incorporating the Beta distribution to weight and integrate different performance levels,the structural vulnerability matrix can be transformed into a seismic damage index,thereby accounting for the damage consequences of different failure levels and providing a more comprehensive representation of the seismic performance of the bridge structure.

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影响线是桥梁结构弹性力学状态的重要表征参数,能够有效反映结构的抗力与抗变形性能,并有望用于地震期间弹塑性响应的评估与预测。以某实测三跨钢板组合连续梁桥影响线为模型修正目标,基于BP神经网络开展桥梁模型修正研究;进一步构建不同峰值地面加速度下的墩柱弯矩-曲率指标,以Beta分布的期望为震害指数,拟合桥梁模型在多种性能水平下结构的超越概率表达式,分析对比连续梁桥结构有限元修正前、后的地震易损性。结果表明,有限元模型修正理论可使实测值与计算值间的相对误差从38%降低至10%以内,有限元修正模型比初始模型的震害指数更低;通过引入Beta分布对不同性能水平进行加权融合,可将结构的易损性矩阵转化为震害指数,从而综合考虑不同破坏等级的危害后果,更为全面地展现桥梁结构的抗震性能。

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周宇(1989—),男,博士,讲师。E-mail:
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ArticleFig(id=1229121427450942412, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=图15, caption=震害对比曲线, figureFileSmall=jPjjXjIyNKwxuYvfmN6O8g==, figureFileBig=IAJCxDLkqw84NDRecZpDaA==, tableContent=null), ArticleFig(id=1229121427559994318, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 1, caption=

Nonlinear material parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
材料参数取值
无约束混凝土抗压强度/(N·mm-240
约束混凝土峰值压应变0.002
无约束混凝土屈服应变0.0014
无约束混凝土极限应变0.02
钢材双折线模型弹性模量/(N·mm-2200000
钢材双折线模型斜率0.01
), ArticleFig(id=1229121427639686096, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表1, caption=

非线性材料参数

, figureFileSmall=null, figureFileBig=null, tableContent=
材料参数取值
无约束混凝土抗压强度/(N·mm-240
约束混凝土峰值压应变0.002
无约束混凝土屈服应变0.0014
无约束混凝土极限应变0.02
钢材双折线模型弹性模量/(N·mm-2200000
钢材双折线模型斜率0.01
), ArticleFig(id=1229121427769709527, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 2, caption=

Error formulas

, figureFileSmall=null, figureFileBig=null, tableContent=
误差类别计算公式
绝对误差
百分比误差
相对误差
相关系数
), ArticleFig(id=1229121427887150043, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表2, caption=

误差公式

, figureFileSmall=null, figureFileBig=null, tableContent=
误差类别计算公式
绝对误差
百分比误差
相对误差
相关系数
), ArticleFig(id=1229121428042339297, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 3, caption=

Sample fitting results

, figureFileSmall=null, figureFileBig=null, tableContent=
类别回归系数R
训练集0.9673
验证集0.9502
测试集0.9315
总体0.9559
), ArticleFig(id=1229121428109448165, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表3, caption=

样本拟合结果

, figureFileSmall=null, figureFileBig=null, tableContent=
类别回归系数R
训练集0.9673
验证集0.9502
测试集0.9315
总体0.9559
), ArticleFig(id=1229121428214305770, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 4, caption=

Physical parameters after model modification

, figureFileSmall=null, figureFileBig=null, tableContent=
类别弹性模量E/ MPa顶板厚度T1/mm底板厚度T2/mm腹板厚度Tw/ mm
初始值206000304016
修正值29024828.2536.4120.39
修正程度40%6%9%27%
), ArticleFig(id=1229121428293997549, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表4, caption=

模型修正后的物理参数

, figureFileSmall=null, figureFileBig=null, tableContent=
类别弹性模量E/ MPa顶板厚度T1/mm底板厚度T2/mm腹板厚度Tw/ mm
初始值206000304016
修正值29024828.2536.4120.39
修正程度40%6%9%27%
), ArticleFig(id=1229121428377883633, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 5, caption=

Error comparison before and after model modification

, figureFileSmall=null, figureFileBig=null, tableContent=
类别绝对误差/με百分比误差/%相对误差/%相关系数
修正前3.3416.4938.000.99669
修正后0.830.899.430.99671
), ArticleFig(id=1229121428440798197, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表5, caption=

模型修正前后误差对比

, figureFileSmall=null, figureFileBig=null, tableContent=
类别绝对误差/με百分比误差/%相对误差/%相关系数
修正前3.3416.4938.000.99669
修正后0.830.899.430.99671
), ArticleFig(id=1229121428528878586, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 6, caption=

Frequency comparison before and after model modification

, figureFileSmall=null, figureFileBig=null, tableContent=
阶数实测频率/Hz有限元计算频率/Hz相对误差/%
修正前修正后修正前修正后
1阶3.7183.2796623.788414-11.791.89
2阶4.3063.5587444.045485-17.35-6.05
3阶4.5043.6150874.108515-19.74-8.78
), ArticleFig(id=1229121428633736188, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表6, caption=

模型修正前后频率对比

, figureFileSmall=null, figureFileBig=null, tableContent=
阶数实测频率/Hz有限元计算频率/Hz相对误差/%
修正前修正后修正前修正后
1阶3.7183.2796623.788414-11.791.89
2阶4.3063.5587444.045485-17.35-6.05
3阶4.5043.6150874.108515-19.74-8.78
), ArticleFig(id=1229121428713426944, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 7, caption=

Ground motions recording

, figureFileSmall=null, figureFileBig=null, tableContent=
编号地震名称震级KT/KN
RSN6ImperialValley-026.950.96
RSN9Borrego6.50.87
RSN10ImperialValley-035.61.10
RSN15KernCounty7.360.99
RSN18ImperialValley-045.51.03
RSN21ImperialValley-055.41.02
RSN22ElAlamo6.80.99
RSN24CentralCalif-0250.91
RSN26Hollister-015.60.97
RSN28Parkfield6.190.90
RSN35NorthernCalif-065.21.11
RSN59SanFernando6.610.97
RSN67SanFernando6.610.85
RSN76SanFernando6.610.98
RSN77SanFernando6.611.07
RSN79SanFernando6.610.93
RSN84SanFernando6.611.03
RSN86SanFernando6.611.05
RSN96Managua_Nicaragua-025.21.12
RSN97PointMugu5.651.06
T1人工地震波1.01
), ArticleFig(id=1229121428780535812, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表7, caption=

地震动记录

, figureFileSmall=null, figureFileBig=null, tableContent=
编号地震名称震级KT/KN
RSN6ImperialValley-026.950.96
RSN9Borrego6.50.87
RSN10ImperialValley-035.61.10
RSN15KernCounty7.360.99
RSN18ImperialValley-045.51.03
RSN21ImperialValley-055.41.02
RSN22ElAlamo6.80.99
RSN24CentralCalif-0250.91
RSN26Hollister-015.60.97
RSN28Parkfield6.190.90
RSN35NorthernCalif-065.21.11
RSN59SanFernando6.610.97
RSN67SanFernando6.610.85
RSN76SanFernando6.610.98
RSN77SanFernando6.611.07
RSN79SanFernando6.610.93
RSN84SanFernando6.611.03
RSN86SanFernando6.611.05
RSN96Managua_Nicaragua-025.21.12
RSN97PointMugu5.651.06
T1人工地震波1.01
), ArticleFig(id=1229121428851838985, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 8, caption=

Performance levels and limits

, figureFileSmall=null, figureFileBig=null, tableContent=
性能水平限值
基本完好μdμcy1
轻微破坏μcy1 < μdμcy
中等破坏μcy < μdμc4
严重破坏μc4 < μdμc max
倒塌μd >μc max
), ArticleFig(id=1229121428965085195, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表8, caption=

性能水平及限值

, figureFileSmall=null, figureFileBig=null, tableContent=
性能水平限值
基本完好μdμcy1
轻微破坏μcy1 < μdμcy
中等破坏μcy < μdμc4
严重破坏μc4 < μdμc max
倒塌μd >μc max
), ArticleFig(id=1229121429061554190, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 9, caption=

Bending moment-curvature results of pier column

, figureFileSmall=null, figureFileBig=null, tableContent=
参数取值
初始屈服曲率0.002328
等效屈服曲率ϕy/ m-10.003186
极限屈服曲率ϕu/ m-10.044000
), ArticleFig(id=1229121429141245967, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表9, caption=

墩柱弯矩-曲率结果

, figureFileSmall=null, figureFileBig=null, tableContent=
参数取值
初始屈服曲率0.002328
等效屈服曲率ϕy/ m-10.003186
极限屈服曲率ϕu/ m-10.044000
), ArticleFig(id=1229121429233520660, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 10, caption=

Damage indices

, figureFileSmall=null, figureFileBig=null, tableContent=
破坏等级延性比
μcy11.0000
μcy1.3686
μc44.4273
μc max7.4273
), ArticleFig(id=1229121429304823829, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表10, caption=

损伤指数

, figureFileSmall=null, figureFileBig=null, tableContent=
破坏等级延性比
μcy11.0000
μcy1.3686
μc44.4273
μc max7.4273
), ArticleFig(id=1229121429384515606, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 11, caption=

Vulnerability probability of the model optimization model(Unit:%)

, figureFileSmall=null, figureFileBig=null, tableContent=
PGA/g性能水平区间
D1(0~0.1)D2(0.1~0.3)D3(0.3~0.5)D4(0.5~0.7)D5(0.7~1.0)
0.199.970.030.000.000.00
0.269.9324.725.350.000.00
0.311.8934.4953.620.000.00
1.00.000.0014.2362.4123.36
), ArticleFig(id=1229121429476790296, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表11, caption=

优化模型易损性概率(单位:%)

, figureFileSmall=null, figureFileBig=null, tableContent=
PGA/g性能水平区间
D1(0~0.1)D2(0.1~0.3)D3(0.3~0.5)D4(0.5~0.7)D5(0.7~1.0)
0.199.970.030.000.000.00
0.269.9324.725.350.000.00
0.311.8934.4953.620.000.00
1.00.000.0014.2362.4123.36
), ArticleFig(id=1229121429585842203, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 12, caption=

Probability density of the optimization model

, figureFileSmall=null, figureFileBig=null, tableContent=
PGA/g性能水平区间
D1(0~0.1)D2(0.1~0.3)D3(0.3~0.5)D4(0.5~0.7)D5(0.7~1.0)
0.110.000.000.000.000.00
0.26.991.240.270.000.00
0.31.191.722.680.000.00
1.00.000.000.713.120.78
), ArticleFig(id=1229121429665533983, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表12, caption=

优化模型概率密度

, figureFileSmall=null, figureFileBig=null, tableContent=
PGA/g性能水平区间
D1(0~0.1)D2(0.1~0.3)D3(0.3~0.5)D4(0.5~0.7)D5(0.7~1.0)
0.110.000.000.000.000.00
0.26.991.240.270.000.00
0.31.191.722.680.000.00
1.00.000.000.713.120.78
), ArticleFig(id=1229121429745225762, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=EN, label=Tab. 13, caption=

Beta distribution function parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
PGA/g期望(震害指数)方差形状参数a形状参数b
0.10.05000.0292.77952.745
0.20.10580.1031.8407.099
0.30.28940.1382.8166.913
1.00.62990.1545.5663.270
), ArticleFig(id=1229121429841694759, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805184718041669, language=CN, label=表13, caption=

Beta分布函数参数

, figureFileSmall=null, figureFileBig=null, tableContent=
PGA/g期望(震害指数)方差形状参数a形状参数b
0.10.05000.0292.77952.745
0.20.10580.1031.8407.099
0.30.28940.1382.8166.913
1.00.62990.1545.5663.270
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考虑影响线模型修正的既有连续梁桥地震易损性评价方法
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周宇 1, 2, 3 , 甘露一 1, 4 , 狄生奎 2 , 陈东 1 , 方登甲 2
振动工程学报 | 2025,38(4): 838-848
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振动工程学报 | 2025, 38(4): 838-848
考虑影响线模型修正的既有连续梁桥地震易损性评价方法
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周宇1, 2, 3 , 甘露一1, 4, 狄生奎2, 陈东1, 方登甲2
作者信息
  • 1.安徽建筑大学土木工程学院,安徽 合肥 230601
  • 2.兰州交通大学土木工程学院,甘肃 兰州 730070
  • 3.安徽建筑大学建筑健康监测与灾害预防技术国家地方联合工程实验室,安徽 合肥 230601
  • 4.合肥市城市生命线工程安全运行监测中心,安徽 合肥 230601

通讯作者:

周宇(1989—),男,博士,讲师。E-mail:
Existing continuous beam bridges seismic vulnerability assessment method considering with model modification based on influence line
Yu ZHOU1, 2, 3 , Luyi GAN1, 4, Shengkui DI2, Dong CHENG1, Dengjia FANG2
Affiliations
  • 1.College of Civil Engineering,Anhui Jianzhu University,Hefei 230601,China
  • 2.School of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
  • 3.National-local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology,Anhui Jianzhu University,Hefei 230601,China
  • 4.Operation and Monitoring Center for Hefei Urban Safety & Security,Hefei 230601,China
出版时间: 2025-04-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.04.019
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影响线是桥梁结构弹性力学状态的重要表征参数,能够有效反映结构的抗力与抗变形性能,并有望用于地震期间弹塑性响应的评估与预测。以某实测三跨钢板组合连续梁桥影响线为模型修正目标,基于BP神经网络开展桥梁模型修正研究;进一步构建不同峰值地面加速度下的墩柱弯矩-曲率指标,以Beta分布的期望为震害指数,拟合桥梁模型在多种性能水平下结构的超越概率表达式,分析对比连续梁桥结构有限元修正前、后的地震易损性。结果表明,有限元模型修正理论可使实测值与计算值间的相对误差从38%降低至10%以内,有限元修正模型比初始模型的震害指数更低;通过引入Beta分布对不同性能水平进行加权融合,可将结构的易损性矩阵转化为震害指数,从而综合考虑不同破坏等级的危害后果,更为全面地展现桥梁结构的抗震性能。

桥梁工程  /  地震易损性  /  增量动力分析  /  应变影响线  /  模型修正

The influence line is an important parameter of the elastic mechanical state of the bridge structure,which can effectively reflect the resistance and deformation resistance of the structure,and is expected to be used to evaluate and predict the elastic-plastic response during earthquakes. Taking the influence line of a three-span steel plate composite continuous beam bridge as the model correction target,the bridge model correction research is carried out based on BP neural network. With the expectation of Beta distribution as the earthquake damage index,the overtaking probability expression of the bridge model under various performance levels is fitted,and the seismic vulnerability of the continuous beam bridge structure before and after the finite element modification is analyzed and compared. The results show that the relative error between the measured value and the calculated value can be reduced from 38% to less than 10%,and the earthquake damage index of the modified finite element model is lower than that of the initial model. By incorporating the Beta distribution to weight and integrate different performance levels,the structural vulnerability matrix can be transformed into a seismic damage index,thereby accounting for the damage consequences of different failure levels and providing a more comprehensive representation of the seismic performance of the bridge structure.

bridge engineering  /  seismic vulnerability  /  incremental dynamic analysis  /  strain influence line  /  model modification
周宇, 甘露一, 狄生奎, 陈东, 方登甲. 考虑影响线模型修正的既有连续梁桥地震易损性评价方法. 振动工程学报, 2025 , 38 (4) : 838 -848 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.04.019
Yu ZHOU, Luyi GAN, Shengkui DI, Dong CHENG, Dengjia FANG. Existing continuous beam bridges seismic vulnerability assessment method considering with model modification based on influence line[J]. Journal of Vibration Engineering, 2025 , 38 (4) : 838 -848 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.04.019
中国作为地震多发国家,超过Ⅶ度的地震多发区占国土面积的40%以上。桥梁作为“交通生命线”的关键设施,在地震发生后的紧急救援、物资运输、震后重建中发挥了重要作用[1]。传统抗震设计以生命安全为唯一设防目标,无法全面评估桥梁结构在不同地震动强度下的损伤概率,难以满足当前社会对结构多性能水平抗震能力的更高要求[2]。对于那些在使用功能或其他方面有专门要求的工程建筑,在采用抗震性能设计时,应当设定更为具体的或更高的抗震设防目标。因此,基于桥梁性能的抗震设计思想应运而生,且发展态势良好[3-4]
MAHMOUDI等[5]分析远场地震对素混凝土拱桥抗震性能的影响,绘制结构易损性曲线并评估桥梁各性能水平的超越概率;XING等[6]分析地震作用对高速铁路桥梁的影响,研究了不同地震激励下钢轨垂向位移的特点,并计算了钢轨垂向位移超过允许标准的概率;LU等[7]考虑构件劣化对结构抗震性能的影响,构建考虑环境腐蚀的桥梁在不同损伤状态下的地震易损性曲线,以此评价桥梁结构在不同损伤水平下的破坏概率。但上述研究依据增量动力方法(incremental dynamic analysis,IDA)分析结构在各性能水平下的地震易损性,仅考虑桥梁在各个破坏等级下的超越概率,未综合地对桥梁结构在不同性能水平下的震害表达关系开展系统研究。
LI等[8]通过建立三跨连续公路桥梁有限元模型,研究新型桥墩体系对连续梁桥抗震性能的影响,并得到桥梁在不同桥墩体系下结构的地震易损性曲线;MORTAGI等[9]通过建立三跨连续梁桥模型,分析了桥梁在不同气候变化下的结构地震脆弱性;SHEKHAR等[10]考虑支座类型对多跨简支(MSSS)公路桥梁抗震性能的影响,建立三维非线性模型,研究桥梁结构非延性破坏模式。以上桥梁地震易损性研究均以有限元模型计算为基础,然而依据设计图纸展开的建模工作未考虑模型的简化或几何材料的不确定性,此类问题会导致所建有限元模型无法高精度反映桥梁实际结构特性[11],其分析结果的准确性与科学性尚有挖掘空间与提升潜力。
桥梁影响线可以通过单点输出的响应数据反映桥梁结构全局刚度,是桥梁固有力学特征,基于桥梁影响线的结构模型修正研究已取得阶段性进展[12-14]。本文以某既有三跨钢板组合连续梁桥为背景,选取实测的桥梁结构应变影响线作为模型修正的目标参数,利用人工神经网络开展桥梁有限元模型修正研究。对模型修正后的优化模型开展地震易损性分析,导入20条地震动记录及1条人工地震波,提取结构在不同峰值地面加速度(peak ground acceleration,PGA)下的位移响应,计算结构超越概率与易损性矩阵,进而基于Beta分布构建结构震害指数表达式,以震害指数期望值绘制震害对比曲线(seismic damage comparison curve,SDCC),研究既有桥梁结构抗震评价方法。
结构有限元建模往往仅依靠工程设计图纸,所建模型并未考虑结构材料强度偏差、实际施工误差与实际边界特性,并不能有效地反映真实结构特性[15],因此,有必要利用实测数据开展有限元模型修正研究。技术路线如图1所示。
以某既有三跨钢板组合梁桥为研究对象,该桥梁为双幅桥,左右两幅结构参数相同,本文选取右幅开展试验测试与模型修正研究。全桥结构总长为105 m,单孔跨径为35 m,单幅桥宽为12 m,上部结构采用工字形钢和混凝土面板组合梁,主梁采用Q345钢材,下部结构采用圆形截面墩柱式桥墩,采用C40抗硫混凝土,设计抗震设防等级为8度(0.2g),场地类别为Ⅱ类,桥梁尺寸信息如图2所示,其中2%表示桥面板的横坡。为模拟桥梁结构在地震作用下的非线性特性(如表1所示),所建有限元模型的混凝土采用Mander本构模型[16],钢材采用双折线本构模型,墩柱采用纤维截面分割分配塑性铰。
试验通过车辆准静态影响线加载方法,将表面式应变计安装在中跨跨中梁底,加载路线选取距桥梁中心线6.375 m,令满载37 t的试验车辆按加载路线行进。由于荷载效率过大易导致荷载试验的安全事故,过小则会导致结构响应不充分,故本次试验荷载效率设为0.2~0.5。
测得桥梁在准静态影响线加载试验下结构的应变响应数据,并识别桥梁单位荷载作用下结构的应变影响线[17-18],测点布置如图3所示。
模型修正理论是一种通过对比理论计算结果与试验结果,构建误差评价函数,并优化模型参数以提高结构模型精度的分析方法[19],主要由以下三部分组成:
(1)修正方法:BP神经网络具有很好的非线性映射能力,能够有效地将模型修正的逆问题转换为正问题[20]。通过MATLAB构建两层前馈BP神经网络,输入层选取5个节点,输出层选取4个节点,隐含层节点数由以下经验公式得出:
式中,m为输入层节点数;n为输出层节点数;a为常数,a=1,2,…,10。本文隐含层节点数P为12。
依据所确立的各层节点数量,构建5-12-4-4两层前馈BP神经网络,传递函数采用Tansig及Pureline函数[21],反向传播算法采用L-M(Levenberg-Marquardt)优化算法[22],拓扑结构如图4所示。
(2)目标函数:选取实测应变影响线为模型修正目标,以实测影响线与有限元计算影响线间的4种误差为目标函数,以此来综合评估真实结构与有限元模型间的吻合情况,相关公式如表2所示。
(3)修正参数:考虑到钢梁与混凝土面板连接的剪力键影响,本文选取弹性模量E为待修正参数之一;试验桥梁支座处钢梁截面与跨中处钢梁截面存在尺寸差别,两截面在过渡区为线性变化的变截面。考虑到施工工艺的影响,本文选取钢梁顶板厚度、底板厚度及腹板厚度为待修正参数。本文最终选取以下4个物理参数进行修正[23-25],即纵向主梁的弹性模量E、顶板厚度T1、底板厚度T2和腹板厚度Tw
对初始有限元模型结构参数进行30次随机缩放,提取对应结构参数下桥梁的应变影响线作为样本集。为验证网络模型的泛化能力,将上述30个样本集随机划分为训练集(70%)、测试集(15%)和验证集(15%),进而导入所建立的神经网络,对网络展开训练、测试及验证。
通过34次迭代后,样本均方误差达到预期值。基于训练后的网络进行样本测试,神经网络训练结果如图5所示,样本拟合结果如表3所示。
表3图5可知,所构建的BP神经网络在第34次迭代训练后达到允许误差要求,结束迭代计算,保存权值结果。其中,BP神经网络训练到第28代时的训练结果最理想,此时均方误差仅为4.69×10-4,满足计算误差要求。此时训练集、验证集及测试集样本拟合回归系数均在0.93以上,总体回归拟合评价达0.9559,易知该神经网络具有较好的映射能力,可基于该神经网络对模型参数进行修正。
将上述实测影响线数据导入训练后的神经网络中,对有限元模型修正参数进行预测,得到修正后的物理参数,如表4所示。将修正后的物理参数重新代入有限元模型,计算桥梁结构应变影响线,并与实测影响线进行对比,修正前后结果如表5图6所示。
为进一步分析模型修正对桥梁模态频率的影响,对模型修正后的桥梁优化模型开展自振频率分析,结果如表6图7所示。
表4可知,修正后的结构弹性模量为2.90248×105 MPa,比初始值增大40%,这是由于建模过程中尚未考虑混凝土面板与钢主梁间的剪力键作用;修正后的结构顶板厚度为28.25 mm,底板厚度为36.41 mm,略薄于设计值,其中腹板厚度为20.39 mm,比设计值厚27%,该差异可能是由于实际桥梁存在装饰架等附属结构,其等效作用在建模过程中反映为腹板厚度的增大。
表5图6可知,修正后的优化模型比初始模型更贴合实际桥梁结构,绝对误差从3.34 με降至0.83 με,百分比误差及相对误差均小于10%,其中百分比误差小于1%,相关系数从0.99669提高至0.99671。
表6图7可知,模型修正后的结构自振频率相较于修正前均增加,且更加贴近桥梁实测频率,桥梁结构实测频率与计算频率误差从修正前的20%以内降低至10%以内,且一阶频率误差减小至1.89%,模型精度存在显著提升。
桥梁地震易损性分析通常用易损性曲线来表示,以反映给定地震动强度下既有桥梁结构超过极限状态的条件概率[26],地震易损性分析方法同样也是基于性能的抗震设防思想中重要的决策支持工具[27]。因此在实际工程中,可利用地震易损性曲线辅助优化结构设计,从而实现结构“小震不坏、中震可修、大震不倒”的抗震目标[28]
选取合适的地震波是地震易损性分析中的重要环节,所选取的地震动记录需能够充分反映桥梁所处地区地震危险的不确定性[29]。为确保所选地震波反应谱与规范谱的谱型一致,可用反应谱曲线与周期坐标所围面积评价反应谱吻合程度:
式中,ST1为地震波反应谱0.1 s至特征周期之间的谱值所围面积;ST2为地震波反应谱在0.7倍特征周期至1.3倍特征周期之间的谱值所围面积;SN1为规范反应谱在0.1 s至特征周期之间的谱值所围面积;SN2为规范反应谱在0.7倍特征周期至1.3倍特征周期之间的谱值所围面积。
根据钢板组合梁桥所在地的抗震设防等级,绘制当地规范反应谱,从太平洋地震工程研究中心数据库中选取20条天然地震动记录,依据规范反应谱生成1条人工地震波[30]。将21条地震波依据式(2)和(3)计算吻合程度,结果如图8表7所示。
图8表7可知,KT/KN值在0.8~1.2之间,即所选地震波反应谱与规范谱高频段所围面积与低频段所围面积比值差异在±20%以内,因此21条地震波的选取满足抗震分析要求。
在地震作用下,主桥往往趋于弹性,但桥梁墩柱易发生塑性变形导致结构坍塌,因此将墩柱设为易损性构件,将结构损伤状态对应的屈服曲率ϕ作为墩柱损伤评价依据,由下式进行转换[31]
式中,Δcy1为钢筋首次屈服时墩顶最大位移;Δy为墩顶弹性位移;Δu为出现塑性铰时墩顶最大位移;为初始屈服曲率;ϕy为等效屈服曲率;ϕu为墩柱极限屈服曲率;θu为出现塑性铰时墩顶的塑性转角;l为桥墩高度;lp为等效塑性铰长度;ds为内纵筋直径;fy为钢筋屈服应力;K为安全系数,取值为2。
基于上述转换,选用墩柱位移延性比μd作为损伤指数,将桥梁破坏等级μcμcy1μcyμc4μc max)定义为5种状态[32-33],如表8所示。
表8中,μd为墩柱位移延性比;μcy1为钢筋首次屈服时桥墩的位移延性比,取值为1;μcy为等效屈服点桥墩的位移延性比;μc4为墩柱保护层混凝土压应变达0.004时桥墩的位移延性比;μc max为桥墩极限位移延性比,以上参数由下式计算:
式中,Δ表示有限元计算所得的墩顶最大位移。
通过Xtract程序将墩柱弯矩-曲率曲线等效成双折线模型,如图9所示;提取墩柱初始、等效与极限屈服曲率,如表9所示,代入式(5)~(13)计算结构损伤指数,结果如表10所示。
基于性能的地震易损性分析是将结构在地震动作用下的反应量值转变为损伤指标量值,再将损伤指标量值与结构各性能水平下损伤指标的阈值取比值[34],通过线性回归分析求解结构超越某性能水平的概率。基于Beta分布将各性能水平的超越概率转换为结构整体震害表达式,对模型修正后的钢板组合梁桥展开地震易损性分析。
假设桥梁墩柱延性比μd与损伤指标μc服从对数正态分布,通过回归分析求得均值λ与峰值地面加速度PGA的关系表达式为:
式中,q为回归斜率;p为回归斜距。
其中ln(μx/μc)的标准差σ为:
式中,Sr为残差平方和;n为样本数。
结构超越概率为:
式中,Φ为标准正态分布概率函数。
基于超越概率构建结构易损性矩阵,进而基于Beta分布将易损性矩阵转换为震害指数-峰值地面加速度的表达式,从而全面、系统地评价结构地震易损性。现定义随机变量x的Beta概率密度[35]
式中,ab为Beta分布函数的形状参数,a>0,b>0。
且期望及方差分别为:
基于上述分析,对既定PGA条件下结构易损性矩阵进行拟合,令易损性矩阵中破坏等级用震害等级Dr表示,结构破坏等级的概率密度值为:
式中,i为既定峰值地面加速度;j为破坏等级;和ΔDrij分别为地震强度i等级下发生j级破坏的概率密度、概率值和区间段。五种性能水平(基本完好、轻微破坏、中等破坏、严重破坏和倒塌)对应区间段为:0~0.1、0.1~0.3、0.3~0.5、0.5~0.7和0.7~1.0,分别用D1、D2、D3、D4和D5表示。
此时地震动强度i等级下震害等级的期望Ei和方差σi2分别为:
联合式(19)~(23),得到地震动强度i下Beta分布函数的形状参数aibi分别为:
将计算得到的形状参数代入式(17)和(18),得到在地震动强度i下桥梁结构的连续型Beta概率密度函数。
IDA作为一种参数分析方法,近年来被广泛用于结构的抗震性能评估中[36]。该方法对所选地震波按照特定比例调整振幅,将调幅后的地震波作为输入导入结构中展开非线性时程分析[37]。本文采用等步调幅,设置步长为0.1g。将调幅后的210个地震波样本导入有限元中,进行非线性时程分析,时程曲线如图10所示;绘制IDA曲线簇,如图11所示。
依据式(10)计算得到各工况下墩柱的位移延性比μd,将地震动强度指标PGAμd/μc取对数,绘制散点图并代入式(14)计算回归关系,结果如图12所示;依据式(15)和(16)计算结构各性能水平的超越概率,结果如图13所示。根据结构超越概率提取易损性矩阵[38],如表11所示;根据式(21)计算结构某一破坏等级的概率密度值,如表12所示,将概率密度值代入式(22)和(23)求得震害指数期望及方差,依据式(17)、(18)、(24)和(25)得到Beta分布函数的形状参数,如表13所示;并绘制概率密度图,如图14所示。以期望为震害指数,联合Beta分布概率密度曲线构建震害对比曲线,如图15所示,以此评价模型修正前、后桥梁结构抗震能力。
图11可知,随着PGA的增加,桥梁墩顶位移增大,当PGA达到0.3g时,结构由超越弹性阶段进入塑性阶段,产生塑性铰,故墩顶位移增加速率也增大。根据图12可知,ln(PGA)与ln(μd/μc)线性拟合效果较好,可由此计算既有桥梁超越概率。分析图13可知,优化模型超越概率随PGA的增大而增加,随着PGA增加到0.4g时,结构超越轻微破坏等级的概率达到约100%;当PGA达到0.5g时,桥梁结构主要呈现中等破坏现象;当PGA达到0.6g时,倒塌概率首次开始出现;当PGA达到1.0g时,结构倒塌概率达到65%。
相较于未进行模型修正的初始模型,两者在轻微破坏、中等破坏及严重破坏的超越概率接近,但在PGA达到0.6g以上时,优化模型比初始模型的倒塌概率增长速率更缓慢,优化模型抗震性能表现更佳,且PGA为1.0g时,优化模型比初始模型的倒塌超越概率小5%。基于设计图纸建立的初始模型结构抗震表现较差,模型修正使得既有桥梁结构地震易损性评价结果更为精确,且既有桥梁实际抗震性能优于初始模型。
图14可知,基于Beta分布将易损性矩阵转换为震害指数下表达的方法有效,随着PGA的增加,概率密度曲线峰值点右移,桥梁进入高破坏等级阶段。由图15可知,随着PGA增加,桥梁震害指数增加,且增加趋势加快,当PGA达到0.6g以上时,初始模型震害指数增长速度更快,震害表现更明显;PGA为1.0g时,优化模型震害指数为0.62,比初始模型大2.5%。
基于既有三跨钢板组合梁桥应变影响线实测数据,提取有限元模型移动工况下应变影响线作为计算值,以两者误差为目标函数,利用BP神经网络模型修正算法,开展既有桥梁有限元模型修正研究。以峰值地面加速度作为修正后模型的地震动强度指标,开展桥梁地震易损性分析,得到地震易损性矩阵,基于Beta分布构建震害指数表达式,绘制震害指数曲线,评价既有桥梁地震易损性。研究提出了一种基于影响线模型修正的既有桥梁地震易损性评价方法,得到结论如下:
(1)提出了基于影响线模型修正的既有桥梁结构地震易损性评价方法,基于Beta分布函数构建的结构震害对比曲线,可用于快速量化既有桥梁结构震害指数,评价在役桥梁结构的抗震性能。
(2)基于应变影响线的BP神经网络能够很好地处理既有桥梁模型修正中的非线性映射问题,可快速、准确地开展目标函数寻优,经过28次迭代,既有桥梁模型修正误差可降至10%以内。
(3)模型修正可以使桥梁有限元模型尽可能地逼近实际既有桥梁,对比发现,依据设计图纸所建桥梁初始模型抗震能力更弱,修正后桥梁优化模型的震害指数较低,实际抗震性能更佳。
(4)通过Beta分布构建的概率密度曲线可在较高峰值地面加速度(>1.0g)下对桥梁结构的震害指数进行预测,这为桥梁生命线设施在巨震下的结构抗震性能预测提供了新思路。
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2025年第38卷第4期
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doi: 10.16385/j.cnki.issn.1004-4523.2025.04.019
  • 接收时间:2023-08-07
  • 首发时间:2026-02-12
  • 出版时间:2025-04-10
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  • 收稿日期:2023-08-07
  • 修回日期:2023-09-19
基金
安徽省高校科学研究重点项目(2022AH050248)
甘肃省建设科技项目(JK2023-03)
安徽省自然科学基金青年项目(2008085QE247)
安徽省高校优秀拔尖人才培育项目(gxgnfx2022021)
企业委托技术开发课题(HYB20220240)
企业委托技术开发课题(HYB20230001)
建筑健康监测与灾害预防技术国家地方联合工程实验室主任基金资助项目(GG22KF002)
甘肃省重点研发计划-工业类项目(23YFGA0043)
作者信息
    1.安徽建筑大学土木工程学院,安徽 合肥 230601
    2.兰州交通大学土木工程学院,甘肃 兰州 730070
    3.安徽建筑大学建筑健康监测与灾害预防技术国家地方联合工程实验室,安徽 合肥 230601
    4.合肥市城市生命线工程安全运行监测中心,安徽 合肥 230601

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周宇(1989—),男,博士,讲师。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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