Article(id=1225751357131502008, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1225751351125263080, articleNumber=null, orderNo=null, doi=10.16385/j.cnki.issn.1004-4523.202307065, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1690128000000, receivedDateStr=2023-07-24, revisedDate=1703692800000, revisedDateStr=2023-12-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1770171496898, onlineDateStr=2026-02-04, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770171496898, onlineIssueDateStr=2026-02-04, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770171496898, creator=13701087609, updateTime=1770171496898, updator=13701087609, issue=Issue{id=1225751351125263080, tenantId=1146029695717560320, journalId=1225147924628267009, year='2025', volume='38', issue='10', pageStart='2205', pageEnd='2462', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1770171495466, creator=13701087609, updateTime=1774228911890, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1242769389133611807, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1225751351125263080, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1242769389133611808, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1225751351125263080, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2405, endPage=2415, ext={EN=ArticleExt(id=1225751358960218619, articleId=1225751357131502008, tenantId=1146029695717560320, journalId=1225147924628267009, language=EN, title=Multi‑factor analysis of buffeting response in cable‑stayed bridge with Π‑shaped main beam, columnId=null, journalTitle=Journal of Vibration Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to obtain the main influencing factors of buffeting response calculation of Π‑shaped main girder cable‑stayed bridge, taking Qingzhou Minjiang River Bridge as the engineering background, the buffeting response characteristics of main girder displacement were analyzed by using three‑dimensional multi‑modal coupling buffeting calculation method on the basis of wind tunnel test. The significance of nine factors in buffeting response was tested by uniform experimental design and regression analysis method. The results show that the aerodynamic admittance, fluctuating wind correlation coefficient, vertical wind spectrum, average wind profile index, surface roughness height and air density have significant influence on the buffeting response of cable‑stayed bridge in the common range of values. The influence of structural mass and damping ratio is not significant, and its value deviation can be ignored in buffeting calculation. The horizontal wind spectrum only has a significant effect on the lateral buffeting response.

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为获得影响Π型主梁斜拉桥抖振响应计算的主要因素,以青洲闽江大桥为工程背景,在风洞试验的基础上,采用三维多模态耦合抖振计算方法对主梁位移抖振响应特性进行了分析,并利用均匀试验设计和回归分析方法对9项因素在抖振响应中的显著性进行了检验。结果表明:在常用取值范围内,气动导纳、脉动风相关系数、竖向风谱、平均风剖面指数、地表粗糙高度、空气密度对斜拉桥抖振响应的影响显著;结构质量和阻尼比的影响不显著,抖振响应计算中可忽略其取值偏差;水平风谱仅对侧向抖振响应影响显著。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
王琳凯(1998—),男,硕士研究生。E‑mail:
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董锐(1982—),男,博士,副研究员。E‑mail:

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董锐(1982—),男,博士,副研究员。E‑mail:

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注:各因素检验结果具有阴影填充的表示检验t值大于0,无填充的表示检验t值小于0。

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Main component material characteristics

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部位材料类型E/GPaG/GPaρ/(kg⋅m-3)νΘ/(℃)-1
主梁C60砼36.015.302.6×1030.171.0×10-5
桥塔C50砼34.514.66
桥墩C40砼32.513.80
边梁Q345B钢210797.9×1030.301.2×10-5
斜拉索钢绞线190798.3×1030.301.2×10-5
), ArticleFig(id=1225751372038058142, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表1, caption=

主要构件材料特性

, figureFileSmall=null, figureFileBig=null, tableContent=
部位材料类型E/GPaG/GPaρ/(kg⋅m-3)νΘ/(℃)-1
主梁C60砼36.015.302.6×1030.171.0×10-5
桥塔C50砼34.514.66
桥墩C40砼32.513.80
边梁Q345B钢210797.9×1030.301.2×10-5
斜拉索钢绞线190798.3×1030.301.2×10-5
), ArticleFig(id=1225751372189053095, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 2, caption=

The value ranges and corresponding levels of influencing factors are summarized

, figureFileSmall=null, figureFileBig=null, tableContent=
序号因素取值范围类型取值范围对应水平
123456
1Su集合型{Si, V1, Ka, Ha, Da, So}SiV1K1HaDaSo
2Sw集合型{LP, V2, K2}LPV2K2
3λ区间型[7, 21]71013161921
4α区间型[0.136, 0.16]0.1360.140.1450.150.1550.16
5z0区间型[0.01, 0.05]0.010.020.0250.030.040.05
6ρ区间型[1.20, 1.28]1.201.221.2241.2251.251.28
7km区间型[0.90, 1.10]0.900.940.981.021.061.10
8ζ区间型[0.01, 0.05]0.010.0150.020.030.040.05
9|χ|2集合型{1, Sears函数}1Sears函数
), ArticleFig(id=1225751372335853746, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表2, caption=

影响因素取值范围及对应水平汇总

, figureFileSmall=null, figureFileBig=null, tableContent=
序号因素取值范围类型取值范围对应水平
123456
1Su集合型{Si, V1, Ka, Ha, Da, So}SiV1K1HaDaSo
2Sw集合型{LP, V2, K2}LPV2K2
3λ区间型[7, 21]71013161921
4α区间型[0.136, 0.16]0.1360.140.1450.150.1550.16
5z0区间型[0.01, 0.05]0.010.020.0250.030.040.05
6ρ区间型[1.20, 1.28]1.201.221.2241.2251.251.28
7km区间型[0.90, 1.10]0.900.940.981.021.061.10
8ζ区间型[0.01, 0.05]0.010.0150.020.030.040.05
9|χ|2集合型{1, Sears函数}1Sears函数
), ArticleFig(id=1225751372486848698, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 3, caption=

The mid‑span buffeting responses of the main girder corresponding to different factor values

, figureFileSmall=null, figureFileBig=null, tableContent=
因素水平竖向/m侧向/m扭转向/(°)
水平风谱类型Si0.27040.01620.2308
V10.26940.01250.2291
K10.27010.01510.2303
Ha0.26940.01250.2291
Da0.27010.01500.2302
So0.26940.01280.2292
竖向风谱类型LP0.23460.01140.2059
V20.26940.01250.2291
K20.21820.01110.1892
脉动风相关系数λ=70.26940.01250.2291
λ=100.25390.01200.2160
λ=130.24100.01160.2055
λ=160.23030.01120.1969
λ=190.22110.01090.1896
λ=210.21570.01080.1853
平均风剖面指数α=0.1360.24820.01130.2114
α=0.140.25160.01150.2142
α=0.1450.25580.01180.2177
α=0.150.26010.01200.2213
α=0.1550.26440.01230.2250
α=0.160.26940.01250.2291
地表粗糙高度/mz0=0.010.21850.01020.1859
z0=0.020.23780.01110.2023
z0=0.0250.24480.01140.2083
z0=0.030.25080.01170.2134
z0=0.040.26090.01210.2220
z0=0.050.26940.01250.2291
空气密度/(kg⋅m-3)ρ=1.200.26390.01220.2244
ρ=1.220.26830.01250.2282
ρ=1.2240.26910.01250.2289
ρ=1.2250.26940.01250.2291
ρ=1.250.27480.01280.2338
ρ=1.280.28140.01320.2394
结构质量系数km=0.900.26700.01240.2300
km=0.940.26800.01210.2244
km=0.980.26890.01250.2293
km=1.020.26980.01260.2289
km=1.060.27070.01250.2286
km=1.100.27150.01250.2282
阻尼比ζ=0.010.26940.01250.2291
ζ=0.0150.26900.01250.2291
ζ=0.020.26870.01250.2291
ζ=0.030.26810.01250.2291
ζ=0.040.26740.01250.2291
ζ=0.050.26680.01240.2291
气动导纳10.34000.01520.2968
Sears函数0.26940.01250.2291
), ArticleFig(id=1225751372633649347, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表3, caption=

不同因素取值对应的主梁跨中抖振响应

, figureFileSmall=null, figureFileBig=null, tableContent=
因素水平竖向/m侧向/m扭转向/(°)
水平风谱类型Si0.27040.01620.2308
V10.26940.01250.2291
K10.27010.01510.2303
Ha0.26940.01250.2291
Da0.27010.01500.2302
So0.26940.01280.2292
竖向风谱类型LP0.23460.01140.2059
V20.26940.01250.2291
K20.21820.01110.1892
脉动风相关系数λ=70.26940.01250.2291
λ=100.25390.01200.2160
λ=130.24100.01160.2055
λ=160.23030.01120.1969
λ=190.22110.01090.1896
λ=210.21570.01080.1853
平均风剖面指数α=0.1360.24820.01130.2114
α=0.140.25160.01150.2142
α=0.1450.25580.01180.2177
α=0.150.26010.01200.2213
α=0.1550.26440.01230.2250
α=0.160.26940.01250.2291
地表粗糙高度/mz0=0.010.21850.01020.1859
z0=0.020.23780.01110.2023
z0=0.0250.24480.01140.2083
z0=0.030.25080.01170.2134
z0=0.040.26090.01210.2220
z0=0.050.26940.01250.2291
空气密度/(kg⋅m-3)ρ=1.200.26390.01220.2244
ρ=1.220.26830.01250.2282
ρ=1.2240.26910.01250.2289
ρ=1.2250.26940.01250.2291
ρ=1.250.27480.01280.2338
ρ=1.280.28140.01320.2394
结构质量系数km=0.900.26700.01240.2300
km=0.940.26800.01210.2244
km=0.980.26890.01250.2293
km=1.020.26980.01260.2289
km=1.060.27070.01250.2286
km=1.100.27150.01250.2282
阻尼比ζ=0.010.26940.01250.2291
ζ=0.0150.26900.01250.2291
ζ=0.020.26870.01250.2291
ζ=0.030.26810.01250.2291
ζ=0.040.26740.01250.2291
ζ=0.050.26680.01240.2291
气动导纳10.34000.01520.2968
Sears函数0.26940.01250.2291
), ArticleFig(id=1225751372776255685, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 4, caption=

The maximum variation of RMS value corresponding to each factor level

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素极差变化幅度/%
竖向侧向扭转向
水平风谱0.3722.840.75
竖向风谱23.4513.0021.13
脉动风相关系数24.8616.4723.66
平均风剖面指数8.5210.448.37
地表粗糙高度23.2723.2723.27
空气密度6.638.206.68
结构质量1.703.612.48
阻尼比0.960.810.03
气动导纳26.2321.1129.54
), ArticleFig(id=1225751372902084806, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表4, caption=

各因素水平对应RMS值的极差变化幅度

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素极差变化幅度/%
竖向侧向扭转向
水平风谱0.3722.840.75
竖向风谱23.4513.0021.13
脉动风相关系数24.8616.4723.66
平均风剖面指数8.5210.448.37
地表粗糙高度23.2723.2723.27
空气密度6.638.206.68
结构质量1.703.612.48
阻尼比0.960.810.03
气动导纳26.2321.1129.54
), ArticleFig(id=1225751373027913936, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 5, caption=

Mix level uniform design table U30(67×3×2)

, figureFileSmall=null, figureFileBig=null, tableContent=
试验编号SuSwλαz0ρkmζ|χ|2
15(Da)1(LP)1(7)6(0.16)2(0.02)4(1.225)4(1.02)5(0.04)1(1)
22(V1)2(V2)6(21)5(0.155)1(0.01)3(1.224)5(1.06)4(0.03)2(Sears函数)
31(Si)3(K2)5(19)6(0.16)3(0.025)4(1.225)6(1.10)3(0.02)1(1)
44(Ha)1(LP)4(16)6(0.16)4(0.03)6(1.28)3(0.98)6(0.05)2(Sears函数)
53(K1)3(K2)2(10)4(0.15)1(0.01)1(1.20)3(0.98)6(0.05)1(1)
61(Si)1(LP)5(19)4(0.15)4(0.03)1(1.20)2(0.94)1(0.01)2(Sears函数)
74(Ha)2(V2)6(21)4(0.15)6(0.05)5(1.25)2(0.94)4(0.03)1(1)
82(V1)3(K2)1(7)2(0.14)3(0.025)2(1.22)2(0.94)3(0.02)2(Sears函数)
94(Ha)2(V2)2(10)1(0.136)3(0.025)1(1.20)6(1.10)5(0.04)2(Sears函数)
102(V1)2(V2)5(19)2(0.14)2(0.02)5(1.25)1(0.90)6(0.05)1(1)
116(So)2(V2)2(10)2(0.14)6(0.05)3(1.224)3(0.98)1(0.01)1(1)
126(So)2(V2)5(19)5(0.155)4(0.03)2(1.22)5(1.06)6(0.05)1(1)
135(Da)3(K2)3(13)6(0.16)5(0.04)2(1.22)1(0.90)4(0.03)2(Sears函数)
146(So)1(LP)2(10)4(0.15)5(0.04)5(1.25)6(1.10)3(0.02)2(Sears函数)
151(Si)3(K2)1(7)3(0.145)6(0.05)4(1.225)4(1.02)6(0.05)2(Sears函数)
161(Si)2(V2)2(10)5(0.155)1(0.01)6(1.28)2(0.94)3(0.02)2(Sears函数)
176(So)1(LP)3(13)3(0.145)1(0.01)3(1.224)1(0.90)5(0.04)2(Sears函数)
181(Si)1(LP)3(13)1(0.136)5(0.04)2(1.22)3(0.98)4(0.03)1(1)
195(Da)2(V2)1(7)3(0.145)4(0.03)6(1.28)5(1.06)2(0.015)1(1)
203(K1)1(LP)1(7)5(0.155)3(0.025)3(1.224)1(0.90)2(0.015)1(1)
215(Da)1(LP)6(21)2(0.14)2(0.02)1(1.20)4(1.02)3(0.02)1(1)
223(K1)1(LP)3(13)1(0.136)2(0.02)5(1.25)5(1.06)1(0.01)2(Sears函数)
234(Ha)2(V2)5(19)1(0.136)5(0.04)4(1.225)1(0.90)2(0.015)2(Sears函数)
243(K1)2(V2)4(16)6(0.16)6(0.05)1(1.20)4(1.02)2(0.015)2(Sears函数)
253(K1)3(K2)6(21)2(0.14)4(0.03)6(1.28)5(1.06)5(0.04)2(Sears函数)
264(Ha)3(K2)4(16)3(0.145)1(0.01)2(1.22)6(1.10)2(0.015)1(1)
275(Da)3(K2)6(21)4(0.15)2(0.02)4(1.225)3(0.98)1(0.01)2(Sears函数)
286(So)3(K2)4(16)1(0.136)3(0.025)6(1.28)2(0.94)4(0.03)1(1)
292(V1)1(LP)4(16)3(0.145)6(0.05)3(1.224)6(1.10)5(0.04)1(1)
302(V1)3(K2)3(13)5(0.155)5(0.04)5(1.25)4(1.02)1(0.01)1(1)
), ArticleFig(id=1225751373141160146, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表5, caption=

混合水平均匀设计表U30(67×3×2)

, figureFileSmall=null, figureFileBig=null, tableContent=
试验编号SuSwλαz0ρkmζ|χ|2
15(Da)1(LP)1(7)6(0.16)2(0.02)4(1.225)4(1.02)5(0.04)1(1)
22(V1)2(V2)6(21)5(0.155)1(0.01)3(1.224)5(1.06)4(0.03)2(Sears函数)
31(Si)3(K2)5(19)6(0.16)3(0.025)4(1.225)6(1.10)3(0.02)1(1)
44(Ha)1(LP)4(16)6(0.16)4(0.03)6(1.28)3(0.98)6(0.05)2(Sears函数)
53(K1)3(K2)2(10)4(0.15)1(0.01)1(1.20)3(0.98)6(0.05)1(1)
61(Si)1(LP)5(19)4(0.15)4(0.03)1(1.20)2(0.94)1(0.01)2(Sears函数)
74(Ha)2(V2)6(21)4(0.15)6(0.05)5(1.25)2(0.94)4(0.03)1(1)
82(V1)3(K2)1(7)2(0.14)3(0.025)2(1.22)2(0.94)3(0.02)2(Sears函数)
94(Ha)2(V2)2(10)1(0.136)3(0.025)1(1.20)6(1.10)5(0.04)2(Sears函数)
102(V1)2(V2)5(19)2(0.14)2(0.02)5(1.25)1(0.90)6(0.05)1(1)
116(So)2(V2)2(10)2(0.14)6(0.05)3(1.224)3(0.98)1(0.01)1(1)
126(So)2(V2)5(19)5(0.155)4(0.03)2(1.22)5(1.06)6(0.05)1(1)
135(Da)3(K2)3(13)6(0.16)5(0.04)2(1.22)1(0.90)4(0.03)2(Sears函数)
146(So)1(LP)2(10)4(0.15)5(0.04)5(1.25)6(1.10)3(0.02)2(Sears函数)
151(Si)3(K2)1(7)3(0.145)6(0.05)4(1.225)4(1.02)6(0.05)2(Sears函数)
161(Si)2(V2)2(10)5(0.155)1(0.01)6(1.28)2(0.94)3(0.02)2(Sears函数)
176(So)1(LP)3(13)3(0.145)1(0.01)3(1.224)1(0.90)5(0.04)2(Sears函数)
181(Si)1(LP)3(13)1(0.136)5(0.04)2(1.22)3(0.98)4(0.03)1(1)
195(Da)2(V2)1(7)3(0.145)4(0.03)6(1.28)5(1.06)2(0.015)1(1)
203(K1)1(LP)1(7)5(0.155)3(0.025)3(1.224)1(0.90)2(0.015)1(1)
215(Da)1(LP)6(21)2(0.14)2(0.02)1(1.20)4(1.02)3(0.02)1(1)
223(K1)1(LP)3(13)1(0.136)2(0.02)5(1.25)5(1.06)1(0.01)2(Sears函数)
234(Ha)2(V2)5(19)1(0.136)5(0.04)4(1.225)1(0.90)2(0.015)2(Sears函数)
243(K1)2(V2)4(16)6(0.16)6(0.05)1(1.20)4(1.02)2(0.015)2(Sears函数)
253(K1)3(K2)6(21)2(0.14)4(0.03)6(1.28)5(1.06)5(0.04)2(Sears函数)
264(Ha)3(K2)4(16)3(0.145)1(0.01)2(1.22)6(1.10)2(0.015)1(1)
275(Da)3(K2)6(21)4(0.15)2(0.02)4(1.225)3(0.98)1(0.01)2(Sears函数)
286(So)3(K2)4(16)1(0.136)3(0.025)6(1.28)2(0.94)4(0.03)1(1)
292(V1)1(LP)4(16)3(0.145)6(0.05)3(1.224)6(1.10)5(0.04)1(1)
302(V1)3(K2)3(13)5(0.155)5(0.04)5(1.25)4(1.02)1(0.01)1(1)
), ArticleFig(id=1225751373258600667, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 6, caption=

The assignment of categorical variables

, figureFileSmall=null, figureFileBig=null, tableContent=
因素变量类型分类赋值说明变量表示
水平风谱参照变量Simiu谱(1)0、0、0、0、0、0
虚拟变量Von Karman谱(2)0、1、0、0、0、0V1
虚拟变量Kaimal谱(3)0、0、1、0、0、0K1
虚拟变量Harris谱(4)0、0、0、1、0、0Ha
虚拟变量Davenport谱(5)0、0、0、0、1、0Da
虚拟变量Solari谱(6)0、0、0、0、0、1So
竖向风谱参照变量Lumley‑Panofsky谱(1)0、0、0
虚拟变量Von Karman谱(2)0、1、0V2
虚拟变量Kaimal谱(3)0、0、1K2
气动导纳参照变量1(1)0、0
虚拟变量Sears函数(2)0、1气动导纳
), ArticleFig(id=1225751373380235484, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表6, caption=

分类变量赋值情况

, figureFileSmall=null, figureFileBig=null, tableContent=
因素变量类型分类赋值说明变量表示
水平风谱参照变量Simiu谱(1)0、0、0、0、0、0
虚拟变量Von Karman谱(2)0、1、0、0、0、0V1
虚拟变量Kaimal谱(3)0、0、1、0、0、0K1
虚拟变量Harris谱(4)0、0、0、1、0、0Ha
虚拟变量Davenport谱(5)0、0、0、0、1、0Da
虚拟变量Solari谱(6)0、0、0、0、0、1So
竖向风谱参照变量Lumley‑Panofsky谱(1)0、0、0
虚拟变量Von Karman谱(2)0、1、0V2
虚拟变量Kaimal谱(3)0、0、1K2
气动导纳参照变量1(1)0、0
虚拟变量Sears函数(2)0、1气动导纳
), ArticleFig(id=1225751373518647525, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 7, caption=

Related parameters of buffeting response regression equation

, figureFileSmall=null, figureFileBig=null, tableContent=
相关参数竖向侧向扭转向
复相关系数R0.9890.9890.989
决定系数R20.9780.9780.979
调整决定系数Ra20.9570.9570.960
标准估计的误差0.008260.000460.00758
海宾‑沃森值2.3262.3542.225
F47.61547.21142.997
), ArticleFig(id=1225751373652865259, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表7, caption=

抖振响应回归方程相关参数

, figureFileSmall=null, figureFileBig=null, tableContent=
相关参数竖向侧向扭转向
复相关系数R0.9890.9890.989
决定系数R20.9780.9780.979
调整决定系数Ra20.9570.9570.960
标准估计的误差0.008260.000460.00758
海宾‑沃森值2.3262.3542.225
F47.61547.21142.997
), ArticleFig(id=1225751373753528560, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=EN, label=Tab. 8, caption=

Comparison of significance of factors influencing chattering response between multi‑factor analysis and single factor analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
方向因素单因素分析①单因素分析②多因素分析
竖向Su不显著不显著不显著
Sw显著显著显著
λ显著显著显著
α显著不显著显著
z0显著显著显著
ρ显著不显著显著
km不显著不显著不显著
ζ不显著不显著不显著
|χ|2显著显著显著
侧向Su显著显著显著
Sw显著显著显著
λ显著显著显著
α显著显著显著
z0显著显著显著
ρ显著不显著显著
km不显著不显著不显著
ζ不显著不显著不显著
|χ|2显著显著显著
扭转向Su不显著不显著不显著
Sw显著显著显著
λ显著显著显著
α显著不显著显著
z0显著显著显著
ρ显著不显著显著
km不显著不显著不显著
ζ不显著不显著不显著
|χ|2显著显著显著
), ArticleFig(id=1225751373900329209, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1225751357131502008, language=CN, label=表8, caption=

多因素与单因素分析结果比较

, figureFileSmall=null, figureFileBig=null, tableContent=
方向因素单因素分析①单因素分析②多因素分析
竖向Su不显著不显著不显著
Sw显著显著显著
λ显著显著显著
α显著不显著显著
z0显著显著显著
ρ显著不显著显著
km不显著不显著不显著
ζ不显著不显著不显著
|χ|2显著显著显著
侧向Su显著显著显著
Sw显著显著显著
λ显著显著显著
α显著显著显著
z0显著显著显著
ρ显著不显著显著
km不显著不显著不显著
ζ不显著不显著不显著
|χ|2显著显著显著
扭转向Su不显著不显著不显著
Sw显著显著显著
λ显著显著显著
α显著不显著显著
z0显著显著显著
ρ显著不显著显著
km不显著不显著不显著
ζ不显著不显著不显著
|χ|2显著显著显著
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Π型主梁斜拉桥抖振响应多因素分析
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董锐 1, 2, 3 , 王琳凯 1 , 郭宇 1 , 翁祥颖 4
振动工程学报 | 2025,38(10): 2405-2415
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振动工程学报 | 2025, 38(10): 2405-2415
Π型主梁斜拉桥抖振响应多因素分析
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董锐1, 2, 3 , 王琳凯1 , 郭宇1, 翁祥颖4
作者信息
  • 1.福州大学土木工程学院,福建 福州 350108;
  • 2.招商局重庆交通科研设计院有限公司桥梁工程结构动力学国家重点实验室,重庆 400067;
  • 3.福建省土木建筑学会,福建 福州 350001;
  • 4.福建理工大学土木工程学院,福建 福州 350118
  • 董锐(1982—),男,博士,副研究员。E‑mail:

通讯作者:

王琳凯(1998—),男,硕士研究生。E‑mail:
Multi‑factor analysis of buffeting response in cable‑stayed bridge with Π‑shaped main beam
Rui DONG1, 2, 3 , Linkai WANG1 , Yu GUO1, Xiangying WENG4
Affiliations
  • 1.College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
  • 2.State Key Laboratory of Bridge Engineering Structural Dynamics, China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd., Chongqing 400067, China
  • 3.Fujian Civil Engineering & Architectural Society, Fuzhou 350001, China
  • 4.College of Civil Engineering, Fujian University of Technology, Fuzhou 350118, China
doi: 10.16385/j.cnki.issn.1004-4523.202307065
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为获得影响Π型主梁斜拉桥抖振响应计算的主要因素,以青洲闽江大桥为工程背景,在风洞试验的基础上,采用三维多模态耦合抖振计算方法对主梁位移抖振响应特性进行了分析,并利用均匀试验设计和回归分析方法对9项因素在抖振响应中的显著性进行了检验。结果表明:在常用取值范围内,气动导纳、脉动风相关系数、竖向风谱、平均风剖面指数、地表粗糙高度、空气密度对斜拉桥抖振响应的影响显著;结构质量和阻尼比的影响不显著,抖振响应计算中可忽略其取值偏差;水平风谱仅对侧向抖振响应影响显著。

斜拉桥  /  抖振  /  Π型主梁  /  风洞试验  /  均匀试验设计  /  回归分析

In order to obtain the main influencing factors of buffeting response calculation of Π‑shaped main girder cable‑stayed bridge, taking Qingzhou Minjiang River Bridge as the engineering background, the buffeting response characteristics of main girder displacement were analyzed by using three‑dimensional multi‑modal coupling buffeting calculation method on the basis of wind tunnel test. The significance of nine factors in buffeting response was tested by uniform experimental design and regression analysis method. The results show that the aerodynamic admittance, fluctuating wind correlation coefficient, vertical wind spectrum, average wind profile index, surface roughness height and air density have significant influence on the buffeting response of cable‑stayed bridge in the common range of values. The influence of structural mass and damping ratio is not significant, and its value deviation can be ignored in buffeting calculation. The horizontal wind spectrum only has a significant effect on the lateral buffeting response.

cable‑stayed bridge  /  buffeting  /  Π‑shaped main beam  /  wind tunnel tests  /  uniform experimental design  /  regression analysis
董锐, 王琳凯, 郭宇, 翁祥颖. Π型主梁斜拉桥抖振响应多因素分析. 振动工程学报, 2025 , 38 (10) : 2405 -2415 . DOI: 10.16385/j.cnki.issn.1004-4523.202307065
Rui DONG, Linkai WANG, Yu GUO, Xiangying WENG. Multi‑factor analysis of buffeting response in cable‑stayed bridge with Π‑shaped main beam[J]. Journal of Vibration Engineering, 2025 , 38 (10) : 2405 -2415 . DOI: 10.16385/j.cnki.issn.1004-4523.202307065
Π型主梁一般采用型钢与混凝土组合断面,具有重量轻,构造简单,受力合理,制作、施工和养护方便,造价相对较低等优点,被广泛应用于斜拉桥主梁[1],如加拿大Annacis桥、中国南浦大桥、杨浦大桥、青洲闽江大桥等。与流线型闭口箱型主梁相比,Π型主梁属于典型的钝体断面,在自然风作用下的抖振响应敏感性更加突出[2]。抖振虽然不会引发桥梁结构的毁灭性破坏,但具有发生频率高、幅值大等特点,对桥梁施工阶段的安全性、运营阶段的行车舒适性和结构疲劳安全性等均产生重要影响。作为设计阶段的一项重要工作,斜拉桥的抖振响应计算非常复杂,其影响因素主要包括3类[3]:(1)来流风特性因素,主要表现为桥梁所在工程场地的风场特性;(2)结构因素,主要表现为桥梁结构的静、动力特性;(3)气动力因素,主要表现为风与桥梁结构的相互作用机理。关于各因素对抖振响应计算精度的影响,国内外学者开展了广泛的研究。胡钢[4]采用数值模拟的方法,研究了紊流强度、紊流积分尺度、平均风速等来流风特性因素对桥梁抖振响应的影响,结果表明紊流强度对斜拉桥竖向和侧向抖振位移响应的影响很小,抖振响应随紊流积分尺度的增大出现先增大后减小的趋势;李利孝等[5]、焦丽娜[6]进一步采用二维抖振理论分析主梁节段模型抖振响应的影响因素,发现常见水平风谱对抖振响应的影响很小,而竖向风谱对其影响相对较大;李鹏飞[7]利用节段模型风洞试验和抖振响应数值分析研究了气动导纳、自激力、抖振力互谱等因素对主梁抖振响应的影响,结果表明气动导纳对主梁抖振响应的影响较大,自激力对主梁抖振响应起抑制作用,抖振力互谱对抖振响应的影响可忽略不计;JAIN等[8]采用多模态耦合分析方法,发现了抖振响应的多模态参与和耦合效应,即当水平和竖向脉动风速完全相关时,忽略水平‑竖向脉动风速互谱会得到偏危险的结果;钟应子[9]采用现场实测、风洞试验和抖振响应时域分析相结合的研究方法,发现,不同于文献[4]桥梁竖向抖振位移随着紊流强度的增大而减小。马婷婷[10]采用二维和三维抖振频域计算方法,研究了结构阻尼比、结构质量、结构刚度等结构参数的不确定性对主梁抖振响应的影响,结果表明主梁刚度增大会使得抖振响应明显减小;然而,WANG等[11]研究发现主梁竖向和扭转向刚度对抖振响应的影响较小;赵林等[1213]采用数值计算和风洞试验相结合的方法,从概率评价的角度,研究了结构频率、结构质量、阻尼比等参数取值不确定性对抖振响应的影响。CARACOGLIA[14]基于多模态抖振频域分析方法,发现气动力特性对大跨度桥梁抖振响应影响显著。其他国内外学者对抖振影响因素进行的研究[1519],限于篇幅,不再逐一介绍。
国内外学者对斜拉桥抖振响应影响因素的研究,基本上都是单因素分析,即只考虑一个因素变化时抖振响应的变化规律,具有很大的局限性。实际上,当一个影响因素变化时,其他因素的取值也可能随之调整,只有将斜拉桥抖振响应视为一个系统,采用多因素分析才能获得更加合理的结果。斜拉桥设计阶段,其抖振响应估算涉及大量参数,这些参数的取值存在不同程度的不确定性,使得结构的抖振响应也存在较大的不确定性。明确斜拉桥抖振响应计算中的主要影响因素,并给予重点关注,对于该类桥梁的建设具有重要意义。
为探究Π型主梁斜拉桥抖振响应主要影响因素,本文以青洲闽江大桥为分析对象,在风洞试验的基础上,采用三维多模态耦合抖振计算方法对主梁位移抖振响应特性进行了分析,并利用均匀试验设计和回归分析方法对9项因素在抖振响应中的影响显著性进行了检验。
青洲闽江大桥横跨闽江,连接福州马尾和长乐。该桥为双塔双索面叠合梁斜拉桥,跨径组合为250 m+605 m+250 m=1105 m,纵坡为1%,竖曲线半径R=30251.512 m,桥型布置如图1所示。
主梁采用Π型开口断面,梁宽29 m,两边梁中心距为27 m,边梁高2.45 m,如图2所示。桥面板采用C60混凝土,桥塔和桥墩分别采用C50和C40混凝土,工字型边梁采用Q345B钢,斜拉索采用标准强度为1860 MPa的镀锌高强度钢绞线,主要构件材料特性如表1所示。
本文采用的有限元分析程序为ANSYS,其中,主梁采用BEAM189单元模拟,桥塔和桥墩采用BEAM4单元模拟,斜拉索采用经过Ernst公式修正的LINK10单元模拟。
动力特性作为桥梁抖振响应计算的基础,是桥梁的固有属性,反映桥梁的刚度和质量分布情况。通过对青洲闽江大桥有限元模型进行模态分析得到桥梁的前50阶振型和频率,主梁1阶振型及对应频率如图3所示。
主梁在竖向、侧向、扭转3个方向的第1阶振型均为1阶正对称,对应频率分别为0.206、0.246、0.929 Hz,表明该桥主梁竖弯刚度最小,侧弯刚度次之,扭转刚度相对最大。在前50阶振型中,以竖弯为主的振型占绝大多数,共有28阶,以扭转为主的振型12阶,以侧弯为主的振型6阶。
根据《公路桥梁抗风设计规范》(JTG/T 3360‑01—2018)[20],青洲闽江大桥处于B类地貌,风剖面指数α=0.16,地表粗糙高度为0.05 m,基本风速取为34 m/s,抗风风险系数为1.05,主梁高度50.5 m处的计算风速为46.3 m/s,钢混组合梁桥各阶模态阻尼比为0.01,空气密度为1.225 kg/m3,抖振响应积分频率范围取0.001~1.5 Hz,间隔步长设为0.002 Hz。
抖振响应计算时,同时考虑水平、竖向和交叉风谱。水平风谱取Simiu谱[21],如下式所示:
nSu(z,n)u*2=200f(1+50f)5/3
竖向风谱取Lumley‑Panofsky谱[22],如下式所示:
nSw(z,n)u*2=3.36f1+10f5/3
水平和竖向风谱的交叉风谱仅考虑余谱[23],采用工程中常用的形式,如下式所示:
nCuw(z,n)u*2=-14f1+9.6f2.4
式中,n为脉动风频率;Su(z,n)Sw(z,n)Cuw(z,n)分别表示水平风谱、竖向风谱和共相谱;f=nz/U(z),其中U(z)表示桥面高度z处的平均风速;u*表示气流摩阻速度,且u*=kU(z)/ln(z/z0),其中,k为冯卡门常数,取为0.4,z0为地表粗糙高度。
本文脉动风相关系数采用文献[20]中建议的形式,计算中风场相关系数偏安全地取为7。
青州闽江大桥气动参数通过主梁节段模型风洞试验获得,试验在同济大学土木工程防灾国家实验室TJ‑2大气边界风洞中完成,标准主梁断面在-6°~6°风攻角范围内的静风三分力系数,以及3°、0°和-3°时的颤振导数如图4所示。
采用上述相关参数,考虑斜拉桥前50阶振型,通过三维多模态耦合抖振计算方法,获得主梁不同工况下的抖振位移响应RMS值随主梁坐标x的变化情况,如图5所示。计算中,抖振力采用Davenport模型[24],自激力采用Scanlan模型[25]
图5可知,斜拉桥主梁在3个方向的抖振响应RMS值均以主梁跨中节点为中心,大致呈对称分布,且最大值均发生在主梁跨中位置处。进一步观察可以发现,主梁竖向抖振响应RMS值相对最大,0°风攻角时最大值为0.2694 m,3°风攻角时最大值达到0.3222 m;主梁侧向和扭转向的抖振响应RMS值相对较小,侧向最大值仅为0.0221 m,发生在-3°风攻角时,扭转向最大值仅为0.2291°,发生在0°风攻角时。根据本节斜拉桥抖振响应分析结果,本文将选取主梁跨中处的位移抖振响应RMS值即主梁最大抖振位移响应RMS值,作为抖振响应影响显著性分析的目标响应。
在对桥梁抖振响应主要影响因素及其国内外研究情况进行了详细调研的基础上,结合现有条件,本文选取水平风谱、竖向风谱、脉动风相关系数、平均风剖面指数、地表粗糙高度、空气密度、结构质量、阻尼比、气动导纳共9个影响因素作为分析对象,并依据8部现行世界主要风荷载规范及相关文献[202632]对其常用取值范围进行界定。斜拉桥抖振响应影响因素及取值范围如表2所示。
单独改变某一因素的取值,其余因素按水平1进行取值,计算主梁各节点的抖振响应RMS值,不同因素取值对应的主梁跨中抖振响应RMS值汇总于表3
表3可知,在0°风攻角时,不同水平风谱对主梁竖向和扭转向抖振响应的影响非常小,而对侧向抖振响应有显著影响;不同竖向风谱对主梁竖向、侧向和扭转向抖振响应的影响均较大,差异十分明显;脉动风相关系数对主梁3个方向抖振响应均有一定影响,主梁3个方向抖振响应RMS值均随着脉动风相关系数λ的增大而减小;主梁3个方向抖振响应RMS值均随着平均风剖面指数α、地表粗糙高度z0和空气密度ρ的增大而增大;结构质量和阻尼比对主梁3个方向抖振响应的影响均非常小,可忽略不计;气动导纳对主梁3个方向抖振响应均有一定影响,主梁3个方向抖振响应RMS值在气动导纳取1时比取Sears函数要大得多,表明不考虑气动导纳得到的主梁抖振响应偏保守。
为了衡量单个因素对主梁跨中抖振位移响应RMS值的影响程度,本文引入极差变化幅度s0,表示该因素在常用取值水平范围内的影响,如下式所示:
s0=xmax-xminxstandard×100%
式中,xmaxxmin分别表示单个因素不同水平对应的主梁跨中抖振响应RMS值的最大值和最小值;xstandard表示主梁跨中抖振响应RMS值基准值,计算中统一取1.2节0°风攻角时的对应值。
各因素对应的主梁跨中抖振响应RMS值的极差变化幅度汇总于表4
表4可知:对于竖向抖振响应RMS值,气动导纳、脉动风相关系数、竖向风谱、地表粗糙高度的影响较大,其极差变化幅度分别为26.23%、24.86%、23.45%、23.27%;平均风剖面指数、空气密度影响相对较小,极差变化幅度分别为8.52%和6.63%;其余因素极差变化幅度均小于5%,计算中可忽略。对于侧向抖振响应RMS值,水平风谱、地表粗糙高度、气动导纳、脉动风相关系数、竖向风谱、平均风剖面指数影响较大,极差变化幅度分别为22.84%、23.27%、21.11%、16.47%、13.00%、10.44%;空气密度影响相对较小,极差变化幅度为8.20%;其余因素极差变化幅度均小于5%,计算中可以忽略。对于扭转向抖振响应RMS值,气动导纳、脉动风相关系数、地表粗糙高度、竖向风谱影响较大,极差变化幅度分别为29.54%、23.66%、23.27%、21.13%;平均风剖面指数、空气密度影响相对较小,极差变化幅度分别为8.37%和6.68%;其余因素极差变化幅度均小于5%,计算中可以忽略。
在抖振响应多因素分析中,有2个问题需要解决:(1)每个因素的改变都会影响抖振响应的大小,当一个因素变化时,其他因素也可能随之变化,单因素分析不能够有效反映整个系统特性;(2)全面试验通常工作量太大,需采用恰当的试验设计方法进行简化。以本文为例,9个因素水平数不全相同,其中脉动风相关系数、平均风剖面指数、地表粗糙高度、空气密度、结构质量、阻尼比和水平风谱7个因素对应的水平数均为6个,竖向风谱和气动导纳对应的水平数分别为3个和2个,若进行全面试验则需要进行67×3×2=1679616次试验。鉴于此,本文采用均匀试验设计和多元线性回归分析方法进行青洲闽江大桥主梁抖振响应多因素分析,探究斜拉桥抖振响应的主要影响因素。
均匀试验设计的基本思想是以回归分析为统计模型,考虑试验点在试验范围内均匀散布,利用均匀性选出具有代表性的水平组合。相较于多因素、多水平下的正交试验设计,均匀试验设计在保证分析结果可靠的前提下,试验次数和计算量显著减少。均匀试验设计流程如图6所示。
本文以9因素6水平做30次试验的等水平均匀设计表U30(69)为基础[33],采用拟水平法[34]将U30(69)表中的任意一列按照3水平进行合并:{1,2}⇒1,{3,4}⇒2,{5,6}⇒3;再将剩余8列中的任意一列按照2水平进行合并:{1,2,3}⇒1,{4,5,6}⇒2。采用中心化L2‑偏差CD2度量其均匀性,比较发现,当把U30(69)的第6列和第8列分别按照3水平和2水平进行合并时,计算得到的中心化偏差CD2=0.3488最小,即得到的混合水平均匀设计表U30(67×3×2)的均匀性最好。本文分析采用混合水平均匀设计表U30(67×3×2)及其水平对应情况,如表5所示。
多元线性回归分析包括确认回归模型类型、估计回归系数、回归方程与回归系数检验3步,具体流程如图7所示。本文采用最小二乘法进行回归系数估计;采用决定系数R2或调整决定系数Ra2进行回归方程的拟合优度检验,其中R2Ra2的取值范围为[0,1],取值越接近1,回归方程的拟合效果越好;通过F检验(回归方程检验p值)对回归方程的显著性进行检验,当回归模型F值大于临界值Fαsn-s-1)时,或检验p值小于显著性水平时,则表明回归方程在此显著性水平上显著;通过t检验(回归系数检验p或|t|值)对回归系数的显著性进行检验,当回归系数的检验p值小于显著性水平或检验|t|值大于显著性水平下的临界|t|值时,则表明回归系数在此显著性水平下影响显著。
对于表5中的9个因素,脉动风相关系数、平均风剖面指数、地表粗糙高度、空气密度、结构质量系数、阻尼比6个因素为数值型变量,可以直接代入回归方程;而水平风谱、竖向风谱和气动导纳3个因素为分类变量,首先需要确定其中一个水平作为参照变量,而其他的水平则需要转化为0‑1类型的虚拟变量。水平风谱有6个水平,取Simiu谱为参照组,需要另外设置5个虚拟变量;竖向风谱有3个水平,取Lumley‑Panofsky谱为参照组,需要另外设置2个虚拟变量;气动导纳有2个水平,以不考虑气动导纳(取1)为参照组,需要设置1个虚拟变量。虚拟变量的赋值情况如表6所示。
根据表5的因素水平组合,计算出0°风攻角下各样本的跨中抖振响应RMS值。
以主梁跨中抖振响应RMS值为因变量,将6个数值型变量和8个虚拟变量作为自变量,对其进行多元线性回归分析,相关参数如表7所示。竖向、侧向和扭转向的调整决定系数Ra2均不小于0.978,说明该模型可以解释主梁跨中抖振响应RMS值的97.8%以上变异,拟合效果较好。德宾‑沃森值用于检验残差是否独立,当该值处于[1.5,2.5]时,可以认为残差相互独立。该模型的德宾‑沃森值分别为2.326、2.354和2.225,说明残差相互独立,满足独立性假定。各个方向抖振响应回归模型F值均大于F0.05(14,15)=2.424,表明回归方程有统计学意义。本文采用t检验对回归方程中各系数的显著性进行检验,如图8所示。
在回归系数的检验中,通常取显著性水平为0.05,对应检验临界值|t|=1.70,当检验值大于临界值时,表示该因素影响“显著”;否则,认为“不显著”。由图8可知,气动导纳、脉动风相关系数、竖向风谱、地表粗糙高度、平均风剖面指数、空气密度对斜拉桥主梁3个方向的抖振响应均存在显著性影响,且t值越大,对应的影响程度越大。其中t值为正或负时,代表与参照组相比较,该因素取值对抖振响应存在增强或抑制作用。水平风谱仅对斜拉桥侧向抖振响应的影响显著,存在显著影响的为Von Karman谱、Kaimal谱和Davenport谱。结构质量和阻尼比对斜拉桥3个方向的抖振响应的影响均不显著,在常用取值范围内,可以任意选取。
本节对单因素与多因素分析结果进行比较。在单因素分析中,使用者需要通过设定极差变化幅度临界值后才能对某项因素的影响显著性作出判断,存在主观性。为了体现单因素分析中人为主观性的影响,本文同时给出了以5%和10%作为临界值时的分析结果,分别对应单因素分析结果①和②。即当极差变化幅度小于临界值时,认为该因素影响不显著,在常用取值范围内可以任意取值;否则,认为该因素影响显著。多因素分析中,取显著性水平为0.05,对应t分布的临界值为1.7。0°风攻角时,斜拉桥主梁抖振响应单因素和多因素比较汇总于表8
表8可知,当极差变化幅度临界值为5%时,单因素分析①的结果与多因素分析一致;当极差变化幅度临界值为10%时,单因素分析②的结果发生变化,且与多因素分析存在一定差别,主要表现为:单因素分析中空气密度的极差变化幅度为5%~10%,其影响显著性受极差变化幅度临界值的影响;对于竖向和扭转抖振响应的单因素分析,平均风剖面指数的极差变化幅度也为5%~10%,当极差变化幅度临界值改变时,其影响显著性也会随之改变。单因素分析中临界值的确定存在较大的主观性,使得各因素显著的判断存在很大的不确定性;而多因素分析中判断单个因素影响程度时具有明确的判断依据和标准,当1个因素影响显著时,表明该因素与系统的随机误差相比较,表现出明显的系统性影响,且检验结果的稳定性通常较好。以本文为例,在多因素分析中无论取显著性水平为0.05(对应t值为1.7)或为0.01(对应t值为2.95),各因素的影响显著性均表现出很好的稳定性。
本文以青洲闽江大桥斜拉桥为分析对象,采用均匀设计和回归分析的手段,对9项影响因素的合理取值、影响显著性等进行了研究。主要结论如下:
(1)Π型主梁斜拉桥主梁基频仅0.2063 Hz,为典型的风敏感结构,其在3°、0°和-3°风攻角下的抖振响应RMS值分布形状基本相同,均以跨中节点为中心大致对称分布,跨中位置处的竖向、侧向和扭转向抖振响应RMS最大值分别为:0.3222 m、0.0221 m和0.2291°;
(2)抖振响应单因素分析表明:气动导纳、脉动风相关系数、竖向风谱、地表粗糙高度对主梁3个方向的抖振响应均存在较大影响,极差变化幅度均大于13%;结构质量和阻尼比的影响很小,极差变化幅度均小于5%;平均风剖面指数、空气密度存在一定影响,极差变化幅度介于5%~10%;水平风谱仅对侧向存在较大影响,对于竖向和扭转向的影响很小;
(3)抖振响应多因素分析表明:竖向风谱、脉动风相关系数、平均风剖面指数、地表粗糙高度、空气密度、气动导纳对抖振响应的影响显著,是斜拉桥抖振响应的主要影响因素;结构质量和阻尼比对抖振响应的影响不显著;水平风谱仅对侧向抖振响应存在显著性影响;
(4)与单因素分析相比较,多因素分析判断单个因素影响程度时具有明确的判断依据和标准,能够定量给出不同因素影响程度的相对大小,且检验结果的稳定性较好,具有显著优势。
  • 桥梁工程结构动力学国家重点实验室开放基金资助项目(201502)
  • 桥梁结构抗震技术交通行业重点实验室开放基金资助项目(201502)
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doi: 10.16385/j.cnki.issn.1004-4523.202307065
  • 接收时间:2023-07-24
  • 首发时间:2026-02-04
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  • 收稿日期:2023-07-24
  • 修回日期:2023-12-28
基金
桥梁工程结构动力学国家重点实验室开放基金资助项目(201502)
桥梁结构抗震技术交通行业重点实验室开放基金资助项目(201502)
作者信息
    1.福州大学土木工程学院,福建 福州 350108;
    2.招商局重庆交通科研设计院有限公司桥梁工程结构动力学国家重点实验室,重庆 400067;
    3.福建省土木建筑学会,福建 福州 350001;
    4.福建理工大学土木工程学院,福建 福州 350118

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王琳凯(1998—),男,硕士研究生。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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