Article(id=1228295395562943460, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228295387077866291, articleNumber=null, orderNo=null, doi=10.16385/j.cnki.issn.1004-4523.2025.01.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1672934400000, receivedDateStr=2023-01-06, revisedDate=1678809600000, revisedDateStr=2023-03-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1770778042926, onlineDateStr=2026-02-11, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770778042926, onlineIssueDateStr=2026-02-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770778042926, creator=13701087609, updateTime=1770778042926, updator=13701087609, issue=Issue{id=1228295387077866291, tenantId=1146029695717560320, journalId=1225147924628267009, year='2025', volume='38', issue='1', pageStart='1', pageEnd='222', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1770778040904, creator=13701087609, updateTime=1770949073977, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1229012751838802169, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228295387077866291, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1229012751838802170, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228295387077866291, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=144, endPage=153, ext={EN=ArticleExt(id=1228295396246615029, articleId=1228295395562943460, tenantId=1146029695717560320, journalId=1225147924628267009, language=EN, title=Influence line identification method of beam bridge based on empirical mode decomposition and Tikhonov regularization, columnId=null, journalTitle=Journal of Vibration Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The deflection influence line and strain influence line can integrally reflect the flexural stiffness of beam bridge section. In the process of obtaining the measured time-history response of beam bridge, the response of beam bridge involves the influence line information and structural dynamic components, and is interfered by the multi-axis effect of loading vehicle under vehicle moving load. In order to identify the influence line of beam bridge structure accurately, the empirical mode decomposition was proposed to eliminate the dynamic component in the measured data of beam bridge, and the quasi-static response data of beam bridge containing the multi-axis effect was obtained. Combined with the sampling frequency and vehicle wheelbase, a mathematical model was established to identify the influence line, and the multi-axis effect of vehicle was converted into unit concentrated load. Tikhonov regularization method was used to accurately solve the stable solution of the influence line of the beam bridge. Through the establishment of numerical simulation models of 1/2 two-axle vehicle-crossing simply-supported beam bridge and three-span continuous beam bridge with variable section, the deflection and strain time-history responses of simply-supported beam bridge and three-span continuous beam bridge at different vehicle speeds were extracted, and the feasibility and effectiveness of identifying influence lines of beam bridge based on empirical mode decomposition and Tikhonov regularization method were verified. The deflection influence lines and strain influence lines of the structural examples of the beam bridge were identified accurately, and the identification effect of the influence lines was evaluated quantitatively by establishing error index. The research also found that the identification effect of the influence lines of the beam bridge decreased with the increase of the loading vehicle speed.

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挠度影响线、应变影响线能够完整地反映梁桥截面抗弯刚度,在获取梁桥实测时程响应过程中,车辆移动荷载作用下的梁桥响应掺杂了影响线信息和结构动力成分,并受到加载车辆的多轴效应干扰。为准确识别梁桥结构影响线,通过经验模态分解剔除梁桥实测数据中的动力成分,得到含有车辆多轴效应的梁桥准静态响应数据,结合采样频率与车辆轴距,建立了影响线识别的数学模型,将车辆多轴效应转化为单位集中荷载,进而采用Tikhonov正则化方法准确解得梁桥影响线的稳定解。通过建立1/2双轴车过简支梁桥与三跨变截面连续梁桥的数值仿真模型,提取车辆不同移动速度下简支梁桥跨中和三跨连续梁桥中跨跨中的挠度、应变时程响应,验证了基于经验模态分解与Tikhonov正则化识别梁桥影响线方法的可行性与有效性,准确地识别了梁桥结构算例的挠度影响线、应变影响线,并通过建立误差指标定量评价了影响线识别效果。研究还发现,梁桥影响线的识别效果随加载车辆速度的增大而降低。

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周宇(1989—),男,博士,副教授。E-mail:
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A non-stationary signal analysis approach——the local characteristic-scale decomposition method[J]. 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Vehicle dynamics model parameters

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参数数值参数数值
m1/kg224Ka1/(N·m-1875668
m2/kg197Ka2/(N·m-1788100
m3/kg2451Ca1/(N·s·m-14000
J/(kg·m325000Ca2/(N·s·m-12000
Kb1/(N·m-11138368Cb1/(N·s·m-115000
Kb2/(N·m-11982510Cb2/(N·s·m-115000
), ArticleFig(id=1228299360824389911, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228295395562943460, language=CN, label=表1, caption=

车辆动力学模型参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值参数数值
m1/kg224Ka1/(N·m-1875668
m2/kg197Ka2/(N·m-1788100
m3/kg2451Ca1/(N·s·m-14000
J/(kg·m325000Ca2/(N·s·m-12000
Kb1/(N·m-11138368Cb1/(N·s·m-115000
Kb2/(N·m-11982510Cb2/(N·s·m-115000
), ArticleFig(id=1228299360950219036, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228295395562943460, language=EN, label=Tab. 2, caption=

Error analysis of influence line identification under different structure

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结构形式影响线30 km/h60 km/h90 km/h120 km/h
OREPREOREPREOREPREOREPRE
简支梁桥DIL1.34%0.77%1.70%4.08%0.22%0.31%4.25%4.80%
SIL1.59%1.60%1.87%6.56%0.30%0.34%4.53%4.91%
三跨连续梁桥DIL2.98%2.73%2.06%7.09%0.65%0.94%4.55%5.60%
SIL4.91%3.97%2.51%8.23%0.92%0.98%6.32%7.58%
), ArticleFig(id=1228299361063465250, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228295395562943460, language=CN, label=表2, caption=

不同结构下的影响线识别误差分析

, figureFileSmall=null, figureFileBig=null, tableContent=
结构形式影响线30 km/h60 km/h90 km/h120 km/h
OREPREOREPREOREPREOREPRE
简支梁桥DIL1.34%0.77%1.70%4.08%0.22%0.31%4.25%4.80%
SIL1.59%1.60%1.87%6.56%0.30%0.34%4.53%4.91%
三跨连续梁桥DIL2.98%2.73%2.06%7.09%0.65%0.94%4.55%5.60%
SIL4.91%3.97%2.51%8.23%0.92%0.98%6.32%7.58%
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基于经验模态分解与Tikhonov正则化的梁桥影响线识别方法
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周宇 1, 2, 3 , 尚稳齐 1, 2 , 吴德义 1, 2 , 狄生奎 3 , 郑旭 4
振动工程学报 | 2025,38(1): 144-153
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振动工程学报 | 2025, 38(1): 144-153
基于经验模态分解与Tikhonov正则化的梁桥影响线识别方法
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周宇1, 2, 3 , 尚稳齐1, 2, 吴德义1, 2, 狄生奎3, 郑旭4
作者信息
  • 1.安徽建筑大学土木工程学院,安徽 合肥 230601
  • 2.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
  • 3.兰州交通大学土木工程学院,甘肃 兰州 730070
  • 4.大连理工大学土木工程学院,辽宁 大连 116023

通讯作者:

周宇(1989—),男,博士,副教授。E-mail:
Influence line identification method of beam bridge based on empirical mode decomposition and Tikhonov regularization
Yu ZHOU1, 2, 3 , Wenqi SHANG1, 2, Deyi WU1, 2, Shengkui DI3, Xu ZHENG4
Affiliations
  • 1.School of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China
  • 2.National-local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology, Anhui Jianzhu University, Hefei 230601, China
  • 3.College of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • 4.School of Civil Engineering, Dalian University of Technology, Dalian 116023, China
出版时间: 2025-01-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.01.016
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挠度影响线、应变影响线能够完整地反映梁桥截面抗弯刚度,在获取梁桥实测时程响应过程中,车辆移动荷载作用下的梁桥响应掺杂了影响线信息和结构动力成分,并受到加载车辆的多轴效应干扰。为准确识别梁桥结构影响线,通过经验模态分解剔除梁桥实测数据中的动力成分,得到含有车辆多轴效应的梁桥准静态响应数据,结合采样频率与车辆轴距,建立了影响线识别的数学模型,将车辆多轴效应转化为单位集中荷载,进而采用Tikhonov正则化方法准确解得梁桥影响线的稳定解。通过建立1/2双轴车过简支梁桥与三跨变截面连续梁桥的数值仿真模型,提取车辆不同移动速度下简支梁桥跨中和三跨连续梁桥中跨跨中的挠度、应变时程响应,验证了基于经验模态分解与Tikhonov正则化识别梁桥影响线方法的可行性与有效性,准确地识别了梁桥结构算例的挠度影响线、应变影响线,并通过建立误差指标定量评价了影响线识别效果。研究还发现,梁桥影响线的识别效果随加载车辆速度的增大而降低。

桥梁工程  /  移动荷载  /  影响线识别  /  经验模态分解  /  Tikhonov正则化

The deflection influence line and strain influence line can integrally reflect the flexural stiffness of beam bridge section. In the process of obtaining the measured time-history response of beam bridge, the response of beam bridge involves the influence line information and structural dynamic components, and is interfered by the multi-axis effect of loading vehicle under vehicle moving load. In order to identify the influence line of beam bridge structure accurately, the empirical mode decomposition was proposed to eliminate the dynamic component in the measured data of beam bridge, and the quasi-static response data of beam bridge containing the multi-axis effect was obtained. Combined with the sampling frequency and vehicle wheelbase, a mathematical model was established to identify the influence line, and the multi-axis effect of vehicle was converted into unit concentrated load. Tikhonov regularization method was used to accurately solve the stable solution of the influence line of the beam bridge. Through the establishment of numerical simulation models of 1/2 two-axle vehicle-crossing simply-supported beam bridge and three-span continuous beam bridge with variable section, the deflection and strain time-history responses of simply-supported beam bridge and three-span continuous beam bridge at different vehicle speeds were extracted, and the feasibility and effectiveness of identifying influence lines of beam bridge based on empirical mode decomposition and Tikhonov regularization method were verified. The deflection influence lines and strain influence lines of the structural examples of the beam bridge were identified accurately, and the identification effect of the influence lines was evaluated quantitatively by establishing error index. The research also found that the identification effect of the influence lines of the beam bridge decreased with the increase of the loading vehicle speed.

bridge engineering  /  moving load  /  influence line identification  /  empirical mode decomposition  /  Tikhonov regularization
周宇, 尚稳齐, 吴德义, 狄生奎, 郑旭. 基于经验模态分解与Tikhonov正则化的梁桥影响线识别方法. 振动工程学报, 2025 , 38 (1) : 144 -153 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.01.016
Yu ZHOU, Wenqi SHANG, Deyi WU, Shengkui DI, Xu ZHENG. Influence line identification method of beam bridge based on empirical mode decomposition and Tikhonov regularization[J]. Journal of Vibration Engineering, 2025 , 38 (1) : 144 -153 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.01.016
在役梁桥由于材料劣化、环境侵蚀、异常荷载等因素,会不可避免地发生截面刚度退化或约束边界弱化[1]现象。影响线是描述梁桥在单位移动荷载作用下结构的固有静力特征,综合反映梁桥结构边界条件、截面抗弯特性的重要参数。已有研究表明,挠度影响线、应变影响线能够在桥梁损伤识别[2-3]、模型修正[4-5]、承载能力评估[6-7]等方面得到有效应用,但准确识别并提取桥梁影响线是上述研究的重要基础与先决条件。
国内外学者围绕上述问题开展了具有一定深度的研究,王宁波等[8]证实多项式分段拟合可应用于不同边界条件桥梁应变影响线的提取,该方法可有效剔除桥梁的动力效应,并对该方法在理想支承条件下进行了验证。陈志为等[9]建立桥梁静力影响线识别的数学模型,提出采用Tikhonov正则化方法解决影响线识别中的不适定问题,采用三次B样条曲线重构出桥梁准静态影响线。李东平等[10]分别利用分段多项式与可调幅函数描述了实测桥梁响应中的静力部分与动力部分,利用最小二乘法计算分段多项式模型,进而获得桥梁准静态影响线。MUSTAFA等[11]证明通过时域法和频域法识别影响线的基本原理相同,但由于离散傅里叶变换的固有假设,导致两种影响线识别结果不同,同时提出利用低通滤波获取桥梁准静态影响线的方法。OBRIEN等[12]建立了与加载车辆相关的矩阵,通过求矩阵的逆并利用最小二乘法从桥梁实测响应中直接提取桥梁影响线。IENG[13]结合桥梁动态称重系统获取桥梁时程响应,证实了基于最大似然估计法识别桥梁影响线具有较好的鲁棒性。综上,既有影响线识别方法已取得了较大突破,但在影响线识别方法层面还有研究空间,例如,桥梁实测响应数学表达式中并未考虑结构动力成分的干扰,仅依据桥梁准静态响应建立影响线识别模型会影响识别结果的准确性,此外,利用常规分段拟合等方法来剥离剔除桥梁实测时程响应中的结构动力成分,其过程复杂且计算量大,可操作性仍可改进。
鉴于此,为剥离梁桥时程响应中的结构动力成分,从而较精确、快速地识别出桥梁准静态影响线,本研究采用经验模态分解(empirical mode decomposition,EMD)方法剔除梁桥结构时程响应信号中的动力成分,以获得梁桥结构的准静态时程响应,根据采样频率与车辆轴距建立梁桥准静态影响线识别的数学模型,将车辆多轴效应转化为单位集中荷载。修正影响线识别数学模型的误差项会引起数学模型矩阵不适定现象,故采用Tikhonov正则化方法求解病态线性方程组,从而准确解得梁桥影响线的稳定解。建立简支梁桥与三跨变截面连续梁桥的数值仿真模型,提取1/2双轴车辆在不同行驶速度下简支梁桥跨中和三跨连续梁桥中跨跨中挠度、应变时程响应,进而验证基于EMD与Tikhonov正则化识别梁桥影响线方法的可行性和有效性。
EMD方法可在不同时间尺度将含有强噪声干扰的时程响应信号分解成若干本征模态函数(intrinsic mode function,IMF)和残余项,同时利用快速傅里叶变换将时域信号转换为频域信号,并获取各IMF的主频率,以桥梁结构的基频为阈值,主频率大于结构基频的IMF可被当作动力扰动剔除,以此获取桥梁的准静态时程响应,其计算效率高且具有理想的信噪比[14]。根据车辆轴重、轴距与采样频率构建车辆信息矩阵,通过建立影响线识别数学模型,将实测响应中含有的车辆多轴荷载转化为单位集中力,进而利用Tikhonov正则化求解梁桥影响线,结合EMD方法与Tikhonov正则化优势能够实现梁桥结构影响线的高效、精准识别。
HUANG等[15]认为任何信号均可由若干本征模态函数IMF组成,EMD分解的目的是寻找原始信号中的IMF。基于EMD方法剔除桥梁响应中结构动力成分的计算流程为:
1.输入桥梁原始时程响应yt),寻找局部极大值点和极小值点;
2.通过三次样条插值计算局部极大值函数与极小值函数,并绘制极大、极小值函数包络线emaxt),emint[16]
3.计算局部均值:m(t)=[emax(t)+emin(t)]/2;
4.用原始时程响应减去局部均值得到第一个振荡函数,即h1(t)=y(t)-m(t);
5.当h1t)满足IMF预设条件时,h1t)成为第一个IMFt),否则,用h1t)代替步骤(1)中的yt)并重复以上步骤;
6.当筛选出第一个IMF1t),对应的余量为r1t)=yt)−IMF1t),令r1t)作为新的原始时程响应重复上述过程找出IMF2t),依此类推,r2t)=r1t)-IMF2t),…,rnt=rn−1t)-IMFnt)。当满足截止条件ε时或原始信号中再无IMF时筛选结束,截止条件如下式所示:
式中,hprevious(t)和hcurrent(t)分别为信号分解中的两相邻振荡函数。通过快速傅里叶变换获取各IMF主频率,当IMF主频率小于结构基频时筛选终止,将小于结构基频的kIMF函数与余量进行重构。经过步骤1~6分解,原始时程响应可表示为下式,重构后的余量rnt)即为桥梁的准静态时程响应:
基于EMD预处理后的桥梁准静态响应中仍存在车辆多轴效应,假设车辆各轴对桥梁的影响相互独立[17],即实测桥梁响应是车辆各轴引起桥梁响应叠加的结果,具体见下式:
式中,Rst)为移动车辆荷载作用所引起的桥梁响应;N为车辆轴数;Ai为车辆轴重;为第i个轴对应的影响线系数;为第i个轴与桥梁相互作用所产生的结构动力成分。
式(3)中,Ci为第i轴与第1轴之间的采样差:
式中,Di为第i轴与第1轴之间的距离;f为采样频率;v为车辆行驶速度。
为简化计算,将连续影响线离散成结构节点上的离散影响线系数[18],建立梁桥时程响应经EMD预处理后的影响线识别模型,如下式所示:
式中,R为准静态时程响应;L为车辆信息矩阵;Φ为桥梁影响线系数。车辆信息矩阵与采样频率、车辆轴距有关,以N轴车辆为例,车辆信息矩阵采用车辆前轴上桥、后轴下桥的车辆行驶模型来构建,L矩阵表达式如下:
式中,nm分别为矩阵的行和列数,其值由信号采集设备的频率和加载车辆轴数决定。
经EMD预处理得到准静态时程响应R,因原始时程响应分解时设置了计算截止条件,时程响应R中仍可能存在未被有效识别的IMF信息,识别出的准静态时程响应与桥梁静态时程存在偏差,现引入识别误差项η对理论公式进行修正,得到下式:
识别影响线系数Φ属于反问题求解范畴,式(7)引入的误差项η将导致影响线求解方程出现不适定问题。Tikhonov正则化(L2型)方法是解决不适定问题的经典方法,能够通过L2范数作为罚函数来限制最小二乘表达式,进而求解病态线性方程组,采用该方法建立影响线求解的数学模型如下式所示:
常用的正则化矩阵T如下式所示:
对式(8)中影响线系数Φ求一阶导,则影响线系数可通过Tikhonov正则化方法求解,表达式如下:
采用L2范数作为罚函数,使得该不适定问题的近似解在较小范围内波动,并呈现平滑滤波的效果,进而通过L-curve法确定正则化系数λ,使式(8)中两个Euclidean范数最小[19],从而得到式(8)最小值。本文所采用的影响线识别流程如图1所示,λ的取值直接决定了影响线识别的精度,L-curve法示意图如图2所示。
通过建立1/2双轴车过简支梁桥仿真算例来验证本文方法的有效性,利用EMD将算例中提取的桥梁时程响应转化为准静态时程响应,通过建立车辆信息矩阵,构造影响线识别模型剔除准静态时程响应中的车辆多轴效应,进而基于Tikhonov正则化方法识别算例梁桥结构影响线。
为准确获得车辆激励下桥梁的动力响应,通过建立四自由度1/2双轴车辆以多种车速驶过简支梁桥的仿真模型,提取梁桥模型跨中的挠度与应变时程响应并展开分析。算例选用跨径为30 m的简支梁桥模型,共划分300个梁单元,梁单元选用Beam188,梁桥基频为10.396 Hz,材料弹性模量为2.46×1011 N/m2、泊松比取0.3、密度为2000 kg/m3,1/2双轴车轴距为3 m,假定车辆系统为刚体,仅在平衡位置产生小变形振动,悬挂系统阻尼通过弹簧模拟,车辆行驶通过平顺桥面,并与桥梁始终接触,算例模型如图3所示,其中,1/2双轴车动力学模型如图4所示。
图4中,m1m2分别为车辆前、后轴质量,m3为车身质量;J为车体点头刚度;Ka1Ka2分别为车辆模型的前、后轴一系悬挂刚度,Kb1Kb2分别为前、后轴二系悬挂刚度;Ca1Ca2分别为车辆模型的前、后轴一系悬挂阻尼系数,Cb1Cb2分别为前、后轴二系悬挂阻尼系数;Z1Z2分别为车辆模型前、后转向架自由度,Z3为车体沉浮自由度;θ为车体点头自由度。车辆动力学模型参数如表1所示。
车辆分别以0.001、30、60、90与120 km/h速度过桥,其中车辆以0.001 km/h速度过桥产生的时程响应被视为桥梁静态响应。为充分获取桥梁时程响应中的IMF,利用EMD对简支梁桥跨中挠度响应进行预处理,设置EMD最大迭代次数为5000次,截止条件设为1×10−7,来分解含动力扰动的梁桥挠度、应变时程响应。以车辆前轴作用位置为横坐标,不同速度下简支梁桥跨中测点的挠度曲线(deflection curve,DC)、应变曲线(strain curve,SC)如图56所示。
因采用车辆前轴上桥、后轴下桥的行驶过桥模型,故图56中横坐标范围为0~33 m。由于考虑车辆多轴效应,因此1/2双轴车位于简支梁桥跨中附近的时程响应曲线存在“平台”,其长度等于车辆轴距。随着车辆速度的增大,桥梁挠度与应变响应中动力波动愈加明显。简支梁桥跨中挠度与应变响应经EMD预处理后如图7~10所示。
图7~10可知,基于EMD的桥梁时程响应预处理方法可有效剔除各车辆速度下简支梁桥跨中挠度与应变响应中的动力成分,得到较为光滑的准静态挠度与准静态应变曲线。随着车辆速度增大,桥梁挠度与应变位置响应中的动力波动成分逐渐增大,经EMD预处理得到的挠度与应变响应峰值随车速的增大而减小。当车辆移动速度为90 km/h时,经EMD处理的简支梁跨中应变曲线峰值处仍存在波动成分,该现象由EMD初设条件中本征模态函数IMF的筛选过程与时程响应曲线形状导致。此外,由于EMD在剥离桥梁时程响应中的结构动力成分时存在端点效应[20],导致预处理得到的桥梁准静态时程响应端点不为0。
经EMD预处理得到含有车辆多轴效应的简支梁桥跨中准静态挠度、应变响应,采用本文提出的影响线识别方法来剔除简支梁桥准静态响应中的车辆多轴效应,并将多轴车辆移动荷载转化为单位集中荷载,进而利用Tikhonov正则化方法求解简支梁桥挠度、应变影响线。以车速为0.001 km/h识别的简支梁桥影响线作为结构影响线基线(baseline)。绘制挠度影响线(deflection influence line,DIL)识别结果如图1112所示,绘制应变影响线(strain influence line,SIL)识别结果如图1314所示。
图1113可知,利用基于EMD与Tikhonov正则化的梁桥影响线识别方法可识别出各速度下的简支梁桥跨中挠度与应变影响线,影响线识别效果随车辆移动速度的增大而变差。由图11可知,简支梁桥跨中挠度影响线随车速增加,其峰值绝对值逐渐减小,影响线两端影响线系数与结构基线间误差逐渐增大。由图1214可知,利用EMD预处理得到的准静态时程响应来识别桥梁挠度、应变影响线均存在“削峰”现象,即除静态响应以外的简支梁响应识别出的影响线绝对值峰值均小于结构影响线基线绝对值峰值,随着车速的增加,“削峰”现象愈加明显。其中,识别得到的应变影响线两端系数与结构基线较吻合,挠度影响线与影响线基线吻合较差。
综上,基于EMD与Tikhonov正则化可有效识别简支梁桥结构的挠度影响线与应变影响线,识别效果会随车速的增大而变差,且车速越快识别出的应变影响线“削峰”愈明显。
建立1/2双轴车以不同车速行驶通过三跨变截面连续梁桥的仿真算例,来研究本文影响线识别方法的适用性,三跨连续梁桥模型跨径组合为15 m+30 m+15 m,共划分成200个梁单元,建模采用Beam188单元,0#台、3#台及中跨跨中梁截面高度为0.75 m,1#墩与2#墩的墩顶截面高1.25 m,建模材料弹性模量为2.46×1011 N/m2、泊松比取0.3、密度为2000 kg/m3,桥梁基频为9.047 Hz;车辆模型与简支梁算例中车辆相同,算例模型如图15所示。
以三跨变截面连续梁桥的中跨跨中截面为测点,移动车辆加载工况与简支梁算例相同,利用EMD对连续梁桥中跨跨中挠度响应进行预处理,设置EMD最大迭代次数为5000次,截止条件ε设为1×10−7,来分解含动力扰动的梁桥挠度、应变时程响应。进而获取连续梁中跨跨中测点的DC和SC曲线,分别绘制于图1617
分析可知,随着车辆速度的增大,桥梁挠度与应变响应中动力波动愈加明显,且由于行驶车辆的双轴效应,在车辆行驶通过中跨跨中测点,应变响应曲线在测点处出现“平台”段,该现象同简支梁桥算例一致。连续梁桥中跨跨中挠度与应变响应经EMD预处理后如图18~21所示。
图18~21可知,基于EMD的桥梁动力响应预处理方法可有效剔除连续梁桥中跨跨中测点的挠度、应变响应中的动力成分。利用EMD预处理得到的连续梁桥准静态挠度曲线与桥梁静态挠度曲线吻合较好,但应变响应曲线随加载车辆移动速度的增大与桥梁静态曲线吻合程度降低。随加载车辆移动速度的增大,基于EMD预处理得到的挠度时程响应曲线峰值的绝对值增大,应变曲线峰值整体呈现“削峰”趋势。当移动加载车辆速度为30 km/h时,因时程响应数据中动力成分较小,经EMD预处理得到的准静态曲线峰值较大。当车辆速度为90,120 km/h时,经EMD预处理的连续梁桥应变时程响应曲线仍存在动力波动是由EMD初设筛选条件与应变时程响应曲线形状引起的,导致应变时程响应中的IMF未被完全识别。
经EMD预处理得到含有车辆多轴效应的连续梁桥中跨跨中准静态挠度、应变时程响应曲线,影响线识别方法同简支梁桥算例。绘制连续梁桥DIL,SIL曲线于图22~25
图22~25可知,基于EMD与Tikhonov正则化的方法能够有效用于连续梁桥的影响线识别。其中连续梁桥中跨跨中的挠度影响线识别效果较好,识别出各速度工况下的影响线与结构影响线基线较吻合。当车速v≤60 km/h时,应变影响线峰值与结构影响线基线吻合程度高,应变影响线识别效果随加载车辆速度的增大而逐渐变差。
综上所述,基于EMD与Tikhonov正则化的梁桥影响线识别方法适用于连续梁桥中跨跨中的挠度与应变影响线,且该方法对连续梁桥挠度影响线识别效果较好。
为进一步定量评价基于EMD与Tikhonov正则化的梁桥影响线识别方法对梁桥挠度与应变影响线的识别效果,考查所提方法的识别精度,引入整体相对误差(overall relative error,ORE)与峰值相对误差(peakvalue relative error,PRE)两个指标对本文提出的影响线识别方法的精确性进行综合评价:
式中,φmi为识别影响线;φsi为准静态影响线。
表2可知,基于EMD与Tikhonov正则化方法识别出的影响线,与对应影响线基线间的误差均随着加载车辆移动速度的增大而增大。当车速v≤120 km/h,简支梁桥跨中挠度影响线与结构基线间的误差均小于5%,应变影响线误差均小于9%;连续梁桥中跨跨中挠度影响线与结构基线间误差均小于1%,应变影响线误差均小于8%,挠度影响线识别效果普遍优于应变影响线。
研究发现,当车辆模型高速(v≤120 km/h)行驶通过梁桥时,基于EMD与Tikhonov正则化的影响线识别方法仍可有效识别简支梁桥跨中与连续梁桥中跨跨中的挠度影响线、应变影响线,且加载车辆在低速(v≤30 km/h)行驶下能够获得更高精度的识别结果,加载车辆缓慢匀速过桥是保证梁桥结构影响线识别精度的前提条件。
通过模拟1/2双轴车行驶通过简支梁桥与三跨连续梁桥的两个仿真算例,验证了基于EMD与Tikhonov正则化的梁桥结构影响线识别方法的可行性、适用性与精确性,得到如下结论:
利用EMD能够剔除车辆行驶通过简支梁桥、连续梁桥的挠度与应变时程响应中的动力成分,获取梁桥结构准静态时程响应,准静态挠度时程曲线与结构静态挠度曲线的吻合效果优于准静态应变时程曲线,基于EMD预处理的简支梁桥与连续梁桥应变时程响应峰值均大于相应结构的静态应变响应峰值。
通过建立车辆信息矩阵构建影响线识别的数学模型,可有效剔除梁桥结构时程响应中的车辆多轴效应,从而将作用于桥梁的多轴车辆荷载转化为单位集中荷载,进而利用Tikhonov正则化求得简支梁桥与连续梁桥的结构影响线稳定解。
经误差指标分析,当车辆移动速度v≤60 km/h时,挠度影响线识别误差不大于2%,挠度影响线与应变影响线识别效果随车辆移动速度的增大而变差,且连续梁桥结构的挠度影响线识别精度更高。
在实际工程中,为确保精确识别梁桥结构影响线,建议加载车辆缓慢匀速过桥,本文研究为进一步开展梁桥结构影响线的应用奠定了方法基础。
  • 安徽省高校自然科学研究项目-重点项目(2022AH050248)
  • 建筑健康监测与灾害预防技术国家地方联合工程实验室开放课题(GG22KF002)
  • 企业委托技术开发课题(HYB20220240)
  • 企业委托技术开发课题(HYB20230001)
  • 安徽省高校优秀拔尖人才培育项目(gxgnfx2022021)
  • 甘肃省建设科技项目计划(JK2023-03)
  • 国家自然科学基金资助项目(51868045)
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2025年第38卷第1期
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doi: 10.16385/j.cnki.issn.1004-4523.2025.01.016
  • 接收时间:2023-01-06
  • 首发时间:2026-02-11
  • 出版时间:2025-01-10
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  • 收稿日期:2023-01-06
  • 修回日期:2023-03-15
基金
安徽省高校自然科学研究项目-重点项目(2022AH050248)
建筑健康监测与灾害预防技术国家地方联合工程实验室开放课题(GG22KF002)
企业委托技术开发课题(HYB20220240)
企业委托技术开发课题(HYB20230001)
安徽省高校优秀拔尖人才培育项目(gxgnfx2022021)
甘肃省建设科技项目计划(JK2023-03)
国家自然科学基金资助项目(51868045)
作者信息
    1.安徽建筑大学土木工程学院,安徽 合肥 230601
    2.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
    3.兰州交通大学土木工程学院,甘肃 兰州 730070
    4.大连理工大学土木工程学院,辽宁 大连 116023

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周宇(1989—),男,博士,副教授。E-mail:
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2种不同金属材料的力学参数

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属数
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genus
种数
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species
占总种数比例
Percentage of
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种数
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Percentage of total
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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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