CN101782372B - An Intelligent Method for Damage Diagnosis of Bridge Expansion Joints Based on Longitudinal Displacement of Beam Ends - Google Patents

An Intelligent Method for Damage Diagnosis of Bridge Expansion Joints Based on Longitudinal Displacement of Beam Ends Download PDF

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CN101782372B
CN101782372B CN201019026007XA CN201019026007A CN101782372B CN 101782372 B CN101782372 B CN 101782372B CN 201019026007X A CN201019026007X A CN 201019026007XA CN 201019026007 A CN201019026007 A CN 201019026007A CN 101782372 B CN101782372 B CN 101782372B
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temperature
displacement
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longitudinal displacement
vehicular load
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丁幼亮
邓扬
李爱群
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Southeast University
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Abstract

The present invention relates to an intelligent diagnosis method for bridge telescopic seam injury based on girder end longitudinal displacement. The long-term monitoring data of main girder longitudinal displacement, temperature and vertical acceleration after a bridge is built are obtained by mounting a small number of sensors on the bridge; a relevance model of the main girder longitudinal displacement, the bridge temperature and the vertical acceleration in a healthy state is established step by step; the influence of the main girder temperature and the vertical acceleration on the longitudinal displacement can be eliminated on the basis of the relevance model, and the environment condition uniformization displacement which can reflect a working state of a telescopic seam is obtained. When the method is applied to the injury diagnosis of the telescopic seam, the monitoring data in an unknown state are processed just by adopting the established relevance model, and finally, the environment condition uniformization displacement of the healthy state and the unknown state is simultaneously input into a mean value control map; and if the telescopic seam generates injury, a sample point of the control map can exceed a control line, thereby realizing the intelligent recognition on the injury of the telescopic seam.

Description

基于梁端纵向位移的桥梁伸缩缝损伤诊断智能方法An Intelligent Method for Damage Diagnosis of Bridge Expansion Joints Based on Longitudinal Displacement of Beam Ends

技术领域technical field

本发明是一种应用于桥梁结构伸缩缝损伤诊断的智能方法,涉及桥梁工程的无损检测领域。The invention is an intelligent method applied to damage diagnosis of expansion joints of bridge structures, and relates to the field of non-destructive testing of bridge engineering.

背景技术Background technique

桥梁在温度变化时,桥面有膨胀和收缩的纵向变形,另外,在车辆荷载的作用下,桥面也会产生纵向位移。为满足这种变形,就要在桥梁的梁端与桥台之间设置伸缩缝。在桥梁结构中,伸缩缝不仅要满足由于温度变化和车辆荷载所引起的主梁的纵向变形,还要满足混凝土收缩徐变引起的位移,并消减由于不均匀沉降所引起的上部结构的二次应力[1],因此,伸缩缝是桥梁中很重要的结构构件,其状态安全与否不仅关系到整个桥梁结构的运营,还直接影响到车辆通过桥梁时的行车状态。桥梁伸缩缝受力复杂,一直是桥梁的薄弱环节,但是由于存在着诸如设计施工缺陷、管理维护不善及车辆超载加剧等情况,使得实际工程中的伸缩缝发生损坏的情况较为严重,因此需要对伸缩缝的状态进行监测和评估,以便准确地发现其损伤的发生,并及时地对伸缩缝进行修复或者进行更换。When the temperature of the bridge changes, the bridge deck has longitudinal deformation of expansion and contraction. In addition, under the action of vehicle load, the bridge deck will also produce longitudinal displacement. In order to meet this deformation, it is necessary to set expansion joints between the beam end of the bridge and the abutment. In the bridge structure, expansion joints must not only meet the longitudinal deformation of the main beam caused by temperature changes and vehicle loads, but also meet the displacement caused by concrete shrinkage and creep, and reduce the secondary deformation of the superstructure caused by uneven settlement. Stress [1] , therefore, expansion joints are very important structural components in bridges, and whether their state is safe or not is not only related to the operation of the entire bridge structure, but also directly affects the driving state of vehicles passing through the bridge. Bridge expansion joints bear complex forces and have always been the weak link of bridges. However, due to defects in design and construction, poor management and maintenance, and vehicle overloading, damage to expansion joints in actual engineering is more serious. Therefore, it is necessary to The status of expansion joints is monitored and evaluated in order to accurately detect the occurrence of damage and repair or replace expansion joints in a timely manner.

目前,伸缩缝损伤检测主要采用人工定期检测的方式,这种方式存在以下的问题:(1)人工检测的主观性较强,不能对伸缩缝的损伤状态做出定量的判断;(2)人工检测通常会影响交通,并且缺乏历史数据的积累;(3)实时性较差,不能及时地发现伸缩缝损伤的发生,有可能影响到桥梁结构和行车的安全;(4)由于需要长期定期的指派维护工人进行现场查看,总体费用较高。因此,针对上述人工检测的缺点,迫切需要发展一种智能方法对桥梁伸缩缝的状态进行实时地损伤诊断。桥梁结构健康监测技术的发展为实现上述目的提供了契机[2-3],工程建设过程中可以在桥梁结构上敷设传感器,在桥梁运营期间长期地记录梁端的纵向变位、结构温度、交通荷载等数据,通过这些监测数据,桥梁管理人员就可以对伸缩缝的“健康”状态进行评估,为伸缩缝的维护、维修和管理决策提供依据和指导。At present, the damage detection of expansion joints mainly adopts manual periodic detection, which has the following problems: (1) manual detection is highly subjective, and cannot make quantitative judgments on the damage state of expansion joints; (2) manual Detection usually affects traffic and lacks the accumulation of historical data; (3) the real-time performance is poor, and the occurrence of expansion joint damage cannot be found in time, which may affect the safety of bridge structures and traffic; (4) due to the need for long-term and regular Assign maintenance workers to conduct on-site inspections, and the overall cost is higher. Therefore, in view of the above-mentioned shortcomings of manual detection, it is urgent to develop an intelligent method for real-time damage diagnosis of the state of bridge expansion joints. The development of bridge structure health monitoring technology provides an opportunity to achieve the above goals [2-3] . During the construction process, sensors can be laid on the bridge structure, and the longitudinal displacement of the beam end, the structure temperature, and the traffic load can be recorded for a long time during the bridge operation period. Through these monitoring data, bridge managers can evaluate the "health" status of expansion joints, and provide basis and guidance for maintenance, repair and management decisions of expansion joints.

参考文献references

[1]Chen Wai-Fah,Duan Lian.Bridge engineering handbook[M].Boca Raton:CRC Press,2000.[1] Chen Wai-Fah, Duan Lian. Bridge engineering handbook [M]. Boca Raton: CRC Press, 2000.

[2]李爱群,缪长青,李兆霞.润扬长江公路大桥结构健康监测系统研究[J].东南大学学报(自然科学版),2003,33(5):544-548.[2] Li Aiqun, Miao Changqing, Li Zhaoxia. Research on Structural Health Monitoring System of Runyang Yangtze River Highway Bridge [J]. Journal of Southeast University (Natural Science Edition), 2003, 33(5): 544-548.

[3]Ko J M,Ni Y Q.Technology developments in structural health monitoring of large-scalebridges[J].Engineering Structures,2005,27(12):1715-1725.[3] Ko J M, Ni Y Q. Technology developments in structural health monitoring of large-scale bridges [J]. Engineering Structures, 2005, 27(12): 1715-1725.

发明内容Contents of the invention

技术问题:本发明的目的是提供一种基于梁端纵向位移异常监测的伸缩缝损伤诊断方法,解决现有检测技术的不足。Technical problem: The purpose of the present invention is to provide a method for diagnosing expansion joint damage based on abnormal monitoring of beam end longitudinal displacement, so as to solve the shortcomings of existing detection technologies.

技术方案:本发明的基本思想是:由于在桥梁主梁的端部设置伸缩缝的目的就是为了满足主梁纵向变位的需要,因此,梁端纵向位移的变化规律就隐含了伸缩缝的状态信息,当伸缩缝发生损伤时,梁端纵向位移就会发生异常变化,基于这种位移的变化,就可以对桥梁伸缩缝的损伤进行识别。Technical solution: The basic idea of the present invention is: since the purpose of setting the expansion joints at the end of the main girder of the bridge is to meet the needs of the longitudinal displacement of the main girder, the change law of the longitudinal displacement of the beam end implies the expansion joints. State information, when the expansion joint is damaged, the longitudinal displacement of the beam end will change abnormally. Based on this displacement change, the damage of the bridge expansion joint can be identified.

但是,如背景技术中所述,在桥梁的运营状况下,温度、车辆荷载的作用将会引起梁端纵向位移在一个较宽的范围内波动(温度、车辆荷载引起梁端位移的原理见说明书附图1和图2),这种波动将淹没或掩盖结构因伸缩缝损伤所造成的位移变化。因此,本发明的方法是:建立伸缩缝正常状态下的梁端纵向位移与温度、车辆荷载等环境条件的数学模型,在此基础上消除环境条件对纵向位移的影响,得到能够真正反映伸缩缝健康状态的纵向位移,并采用均值控制图的方法识别由伸缩缝损伤所引起的纵向位移异常变化,从而建立了能够对伸缩缝状态进行实时在线损伤诊断的智能方法。However, as described in the background technology, under the operating conditions of the bridge, the effects of temperature and vehicle load will cause the longitudinal displacement of the beam end to fluctuate in a wide range (the principle of the temperature and vehicle load causing the beam end displacement can be found in the specification sheet Figure 1 and Figure 2), this fluctuation will submerge or cover up the displacement change of the structure caused by the expansion joint damage. Therefore, the method of the present invention is to establish a mathematical model of the longitudinal displacement of the beam end under the normal state of the expansion joint and the environmental conditions such as temperature and vehicle load, and eliminate the influence of the environmental condition on the longitudinal displacement on this basis, so as to obtain a model that can truly reflect the expansion joint. The longitudinal displacement of the healthy state, and the method of mean value control chart is used to identify the abnormal change of the longitudinal displacement caused by the damage of the expansion joint, thus establishing an intelligent method for real-time online damage diagnosis of the expansion joint state.

本发明提出的基于梁端纵向位移的桥梁伸缩缝损伤诊断的智能方法为:The intelligent method of bridge expansion joint damage diagnosis based on beam end longitudinal displacement proposed by the present invention is:

1)主梁梁端的纵向位移及桥梁环境条件传感器的设置1) The longitudinal displacement of the girder end of the main beam and the setting of the environmental condition sensor of the bridge

桥梁施工建设时,在主梁梁端位置设置纵向位移传感器,同时,在主梁的跨中位置安装温度传感器和加速度传感器,用以监测主梁的温度和由于车辆荷载引起的主梁竖向加速度;During bridge construction, a longitudinal displacement sensor is installed at the girder end of the main girder. At the same time, a temperature sensor and an acceleration sensor are installed at the mid-span position of the main girder to monitor the temperature of the main girder and the vertical acceleration of the main girder caused by the vehicle load. ;

2)监测数据的处理2) Processing of monitoring data

以10-min为计算区间,对传感器获取的原始数据进行处理,计算梁端纵向位移、温度和车辆荷载的代表值;With 10-min as the calculation interval, the raw data acquired by the sensor are processed, and the representative values of longitudinal displacement, temperature and vehicle load at the beam end are calculated;

3)完好状态下纵向位移和环境条件的数学相关模型3) Mathematical correlation model of longitudinal displacement and environmental conditions in intact state

a)选取桥梁施工建成后n天的监测数据来建立相关模型,纵向位移D、温度T和车辆荷载代表值R,a) Select the monitoring data n days after the bridge construction is completed to establish the relevant model, the longitudinal displacement D, the temperature T and the representative value R of the vehicle load,

b)采用线性回归的方法建立温度T和梁端纵向位移D之间的关系,回归模型参数由最小二乘方法计算得到,b) The linear regression method is used to establish the relationship between the temperature T and the longitudinal displacement D of the beam end, and the regression model parameters are calculated by the least square method,

c)在建立车辆荷载代表值R与位移的相关模型之前,先消除温度对纵向位移的影响,选取参考温度为Tr,将位移原始测试值D“归一化”至参考温度Tr,得到消除温度影响的梁端纵向位移值D1c) Before establishing the correlation model between the vehicle load representative value R and the displacement, the influence of temperature on the longitudinal displacement is first eliminated, the reference temperature is selected as T r , and the original test value D of the displacement is "normalized" to the reference temperature T r , to obtain The longitudinal displacement value D 1 of the beam end to eliminate the influence of temperature,

d)采用线性回归建立D1和车辆荷载代表值R的相关性模型,然后与步骤c)类似,选取车辆荷载的参考值为Rr,将步骤c)中得到的D1“归一化”至车辆荷载的参考值Rr,得到消除车辆荷载影响的位移值D2d) Use linear regression to establish a correlation model between D 1 and the representative value R of vehicle load, and then similar to step c), select the reference value of vehicle load R r , and "normalize" D 1 obtained in step c) To the reference value R r of the vehicle load, the displacement value D 2 to eliminate the influence of the vehicle load is obtained;

4)控制图显著性水平的确定4) Determination of the significance level of the control chart

将步骤3)计算出的位移值D2取日平均值,记为D2,将其输入均值控制图,调整控制图的显著性水平,使得上述n个样本点全部落在控制图的上、下控制线之内;Take the daily average value of the displacement value D 2 calculated in step 3), record it as D 2 , input it into the mean value control chart, and adjust the significance level of the control chart so that the above n sample points all fall on the top of the control chart, within the Lower Line of Control;

5)伸缩缝损伤的智能诊断5) Intelligent diagnosis of expansion joint damage

对未知状态的m天监测数据,采用伸缩缝完好状态下的相关模型消除温度和车辆荷载的影响,在此基础上得到m个日平均位移值,记为D3。保持步骤4)确定的显著性不变,将D2和D3同时输入均值控制图,此时,若所有n+m个样本仍全部位于上、下控制线内,则说明伸缩缝状态为正常,若有样本落在了控制线以外,则说明伸缩缝状态异常,可作出伸缩缝发生损伤的预警。For the m-day monitoring data in the unknown state, the correlation model under the intact state of the expansion joint is used to eliminate the influence of temperature and vehicle load, and m daily average displacement values are obtained on this basis, which is recorded as D 3 . Keep the significance determined in step 4) unchanged, and input D 2 and D 3 into the mean value control chart at the same time. At this time, if all n+m samples are still within the upper and lower control lines, it means that the state of the expansion joint is normal , if any sample falls outside the control line, it indicates that the state of the expansion joint is abnormal, and an early warning of damage to the expansion joint can be given.

有益效果:针对运营状态下桥梁伸缩缝极易发生损坏的工程实际,本发明综合采用现场测试、线性回归、均值控制图等手段提出了基于梁端纵向位移的损伤诊断的智能方法,具有以下有益效果:Beneficial effects: Aiming at the engineering reality that bridge expansion joints are extremely prone to damage under operating conditions, the present invention proposes an intelligent method for damage diagnosis based on longitudinal displacement of beam ends by comprehensively using on-site testing, linear regression, and mean value control charts, which has the following benefits Effect:

(1)本发明所需安装的传感器数量较少,仅需要位移传感器、温度传感器和加速度传感器。同时,本发明采用的方法简单易行,可以在计算机中较为方便地编程实现,方便实际工程的应用。(1) The number of sensors to be installed in the present invention is relatively small, and only displacement sensors, temperature sensors and acceleration sensors are needed. At the same time, the method adopted by the present invention is simple and easy to implement, and can be programmed and implemented in a computer more conveniently, which is convenient for the application in actual engineering.

(2)本发明全面考虑了环境因素对梁端纵向位移所产生的影响,分步消除了结构温度、车辆荷载对位移测试值的影响,消除环境因素影响后的位移值可以准确地反映运营状态下桥梁伸缩缝的健康状态。(2) The present invention fully considers the influence of environmental factors on the longitudinal displacement of the beam end, and eliminates the influence of structural temperature and vehicle load on the displacement test value step by step, and the displacement value after eliminating the influence of environmental factors can accurately reflect the operating state Health status of lower bridge expansion joints.

(3)本发明引入均值控制图的方法对梁端纵向位移的异常变化进行多样本的假设检验,可以减少误判的可能性。(3) The method of introducing the mean value control chart in the present invention performs multi-sample hypothesis testing on abnormal changes in the longitudinal displacement of the beam end, which can reduce the possibility of misjudgment.

(4)本发明能够对伸缩缝状态进行在线监测,方法的实现过程中无需人工干预,减少了人力劳动的支出,可以实现桥梁伸缩缝的无人值守的智能监测,具有广阔的工程应用前景。(4) The present invention can monitor the state of expansion joints on-line, without manual intervention in the implementation process of the method, which reduces the expenditure of human labor, can realize unattended intelligent monitoring of bridge expansion joints, and has broad engineering application prospects.

附图说明Description of drawings

图1是温度升高时简支梁纵向位移变形图,图中T↑表示温度升高,dt表示温度引起的梁端纵向位移;Figure 1 is the longitudinal displacement and deformation diagram of a simply supported beam when the temperature rises. In the figure, T↑ represents the temperature rise, and dt represents the longitudinal displacement of the beam end caused by the temperature;

图2是车辆荷载作用下简支梁纵向位移变形图,图中F表示车辆荷载,df表示车辆荷载引起的梁端纵向位移;Figure 2 is the longitudinal displacement and deformation diagram of simply supported beam under the action of vehicle load, in which F represents the vehicle load, and df represents the longitudinal displacement of the beam end caused by the vehicle load;

图3为主梁北端纵向位移和温度的相关性散点图;Fig. 3 The correlation scatter diagram of longitudinal displacement and temperature at the north end of the main girder;

图4为主梁南端纵向位移和温度的相关性散点图;Fig. 4 The correlation scatter diagram of longitudinal displacement and temperature at the south end of the main girder;

图5为主梁北端纵向位移和加速度均方根的相关性散点图,图中的加速度RMS表示加速度均方根;Figure 5 is a scatter diagram of the correlation between the longitudinal displacement and the root mean square acceleration of the north end of the main girder, and the acceleration RMS in the figure means the root mean square acceleration;

图6为主梁南端纵向位移和加速度均方根的相关性散点图,图中的加速度RMS表示加速度均方根;Figure 6 is a scatter diagram of the correlation between the longitudinal displacement and root mean square acceleration at the south end of the main girder. The acceleration RMS in the figure means the root mean square acceleration;

图7为主梁北端纵向位移的日平均的实测值和消除环境影响后的归一化值;Figure 7 is the daily average measured value of the longitudinal displacement at the north end of the main girder and the normalized value after eliminating the environmental impact;

图8为主梁南端纵向位移的日平均的实测值和消除环境影响后的归一化值;Figure 8 is the daily average measured value of the longitudinal displacement at the south end of the main girder and the normalized value after eliminating the environmental impact;

图9为伸缩缝正常状态下的位移均值控制图,图中的UCL表示上控制线,LCL表示下控制线,CL为控制图的中线;Figure 9 is the average displacement control diagram of expansion joints under normal conditions, UCL in the diagram represents the upper control line, LCL represents the lower control line, and CL is the center line of the control diagram;

图10伸缩缝损伤状态下的位移均值控制图,图中的UCL表示上控制线,LCL表示下控制线,CL为控制图的中线。Figure 10 is the average displacement control diagram of expansion joints under damaged state. UCL in the figure represents the upper control line, LCL represents the lower control line, and CL is the center line of the control diagram.

具体实施方式Detailed ways

下面对本发明的具体实施方案进行进一步的描述:Specific embodiments of the present invention are further described below:

(1)在梁端纵向位移及桥梁环境条件传感器的设置过程中,传感器的布置数量、位置及参数的设置可视桥的类型、跨径、桥面宽度以及桥址的环境等具体情况而定,通常在主梁梁端各设置一个纵向位移传感器,在主梁跨中设置一个温度传感器和一个加速度传感器,即可满足本发明的需要。(1) During the setting process of the longitudinal displacement of the girder end and the environmental condition sensors of the bridge, the number, position and parameter setting of the sensors can be determined according to the specific conditions such as the type of bridge, the span diameter, the bridge deck width and the environment of the bridge site. , usually a longitudinal displacement sensor is set at each end of the main girder, and a temperature sensor and an acceleration sensor are set in the middle of the main girder span, which can meet the needs of the present invention.

(2)将原始监测数据作如下处理:梁端纵向位移和结构温度数据以10-min为区间计算其平均值,以此作为这一时间区段内的梁端纵向位移和结构温度的代表值,以10-min为区间计算主梁竖向加速度的均方根(Root Mean Square,简记为RMS),以此作为这一时间段内的车辆荷载的强度代表值。(2) The original monitoring data is processed as follows: the longitudinal displacement of the beam end and the structural temperature data are calculated in a 10-min interval, and the average value is used as the representative value of the longitudinal displacement of the beam end and the structural temperature in this time period , and calculate the root mean square (Root Mean Square, abbreviated as RMS) of the vertical acceleration of the main beam in a 10-min interval, and use it as the representative value of the strength of the vehicle load in this period of time.

(3)选择桥梁施工完成后n天的监测数据来建立相关模型,这是因为这段时间内的伸缩缝可认为处于完好的状态,以D、T和R分别表示位移、温度和车辆荷载代表值,样本总数为144×n。(3) Select the monitoring data of n days after the completion of the bridge construction to establish the relevant model, because the expansion joints during this period can be considered to be in a good state, and D, T and R represent the displacement, temperature and vehicle load respectively value, the total number of samples is 144×n.

(4)采用线性回归的方法建立温度T和梁端纵向位移D之间的关系,模型表达式为:(4) The linear regression method is used to establish the relationship between the temperature T and the longitudinal displacement D of the beam end, and the model expression is:

D=β01T                                      (1)D=β 01 T (1)

式中,β0和β1为回归系数,可通过最小二乘的方法得到:In the formula, β 0 and β 1 are the regression coefficients, which can be obtained by the method of least squares:

β 1 = S DT S TT , β0=D-β1T                            (2) β 1 = S DT S TT , β 0 =D-β 1 T (2)

式中,SDT为位移与温度的协方差;STT为温度的方差;D和T分别为位移和温度的均值。In the formula, S DT is the covariance of displacement and temperature; S TT is the variance of temperature; D and T are the mean values of displacement and temperature, respectively.

(5)选取参考温度为Tr,将其代入式(1),得到位移的参考值为Dr,同时将温度代表值T也代入式(1),得到位移的计算值Dt,于是可计算消除温度影响的梁端纵向位移值D1(5) Select the reference temperature as T r , and substitute it into formula (1) to obtain the reference value of displacement D r , and substitute the representative value of temperature T into formula (1) to obtain the calculated value of displacement D t , then Calculate the longitudinal displacement value D 1 of the beam end to eliminate the influence of temperature:

D1=D-(Dt-Dr)                                    (3)D 1 =D-(D t -D r ) (3)

(6)建立消除了温度影响的位移值D1和车辆荷载代表值R的线性回归模型:(6) Establish the linear regression model of the displacement value D 1 and the vehicle load representative value R that have eliminated the influence of temperature:

D1=β23R                                     (4)D 123 R (4)

式中,β2和β3为回归系数,可类似式(2)计算。选取车辆荷载的参考值为Rr,将其代入式(4),得到位移的参考值Drr,将车辆荷载代表值R也代入式(4),得到位移的计算值Dtt,可计算消除了车辆荷载影响的位移值D2In the formula, β 2 and β 3 are regression coefficients, which can be calculated similarly to formula (2). Select the reference value of the vehicle load R r and substitute it into formula (4) to obtain the reference value D rr of the displacement, and substitute the representative value R of the vehicle load into formula (4) to obtain the calculated value D tt of the displacement, which can be calculated and eliminated Displacement value D 2 affected by vehicle load:

D2=D1-(Dtt-Drr)                                (5)D 2 =D 1 -(D tt -D rr ) (5)

(7)将消除了温度和车辆荷载影响的144×n个位移值D2取日平均值,得到了n个位移样本,记为D2,将D2输入均值控制图,调整控制图的显著性水平,使得上述n个样本点全部落在上、下控制线(UCL、LCL)之内。(7) Take the daily average of 144×n displacement values D 2 that have eliminated the influence of temperature and vehicle load, and obtain n displacement samples, which are recorded as D 2 , input D 2 into the mean control chart, and adjust the significance of the control chart level, so that the above n sample points all fall within the upper and lower control lines (UCL, LCL).

(8)对未知状态的m天监测数据,首先将其处理为10-min为计算区间的代表值,然后采用伸缩缝完好状态下的相关模型消除温度和车辆荷载的影响,计算出144×m个消除温度和车辆荷载影响的位移值D3,然后再取日平均值,得到m个位移样本,记为D3。保持控制图的显著性水平与完好状态下的一致,将D2和D3同时输入均值控制图,此时,若所有n+m个样本仍全部位于上、下控制线内,则说明伸缩缝状态为正常,若有样本落在了控制线以外,则说明伸缩缝状态异常,可作出伸缩缝发生损伤的预警。(8) For the m-day monitoring data in the unknown state, first process it into 10-min as the representative value of the calculation interval, and then use the relevant model under the intact state of the expansion joint to eliminate the influence of temperature and vehicle load, and calculate 144×m A displacement value D 3 that eliminates the influence of temperature and vehicle load, and then take the daily average value to obtain m displacement samples, which are recorded as D 3 . Keep the significance level of the control chart consistent with that in the intact state, and input D 2 and D 3 into the mean control chart at the same time. At this time, if all n+m samples are still within the upper and lower control lines, it means that the expansion joint The state is normal. If any sample falls outside the control line, it indicates that the state of the expansion joint is abnormal, and an early warning of damage to the expansion joint can be given.

下面以润扬大桥南汊悬索桥为例,说明本发明的具体实施过程:Take the South Branch Suspension Bridge of Runyang Bridge as an example below to illustrate the specific implementation process of the present invention:

选取2006年一月到六月100天的监测数据来建立梁端纵向位移和温度及车辆荷载的相关性,传感器选用的是主梁两端的位移传感器、主梁跨中的温度传感器和竖向加速度传感器,以10-min为时距,计算了纵向位移、温度和车辆荷载的代表值,共144×100=14400个样本。The monitoring data of 100 days from January to June 2006 were selected to establish the correlation between the longitudinal displacement of the beam end and the temperature and the vehicle load. The sensors used were the displacement sensors at both ends of the main beam, the temperature sensor in the middle of the main beam span and the vertical acceleration The sensors calculated representative values of longitudinal displacement, temperature and vehicle load at 10-min time intervals, totaling 144×100=14400 samples.

图3和图4分别给出了主梁北端、南端的位移与温度的相关性散点图,从图这两幅图可以看出,梁端纵向位移与温度之间存在较强的线性相关性,并表现出“温度高位移大、温度低位移小”的特征。同时由图3发现主梁北端位移的变化区间为[-23.0cm,28.7cm],由图4发现南端的变化区间为[-20.6cm,33.0cm],由此可得梁端纵向位移的变化幅度为51.7cm和53.6cm。Figure 3 and Figure 4 show the correlation scatter diagrams of displacement and temperature at the north end and south end of the main beam respectively. From these two figures, it can be seen that there is a strong linear correlation between the longitudinal displacement and temperature at the beam end , and show the characteristics of "large displacement at high temperature and small displacement at low temperature". At the same time, it is found from Fig. 3 that the variation range of the displacement at the north end of the main beam is [-23.0cm, 28.7cm], and from Fig. 4 that the variation range at the south end is [-20.6cm, 33.0cm]. From this, the variation of the longitudinal displacement at the beam end can be obtained The amplitude is 51.7cm and 53.6cm.

表1给出了采用线性回归分析建立的位移D和温度T的相关性模型。Table 1 shows the correlation model between displacement D and temperature T established by linear regression analysis.

表1温度-位移的线性回归模型Table 1 Linear regression model of temperature-displacement

  位置 Location   回归函数(位移:D(cm)温度:T(℃))Regression function (displacement: D(cm) temperature: T(℃))   主梁北端North end of main beam   D=-17.6681+1.0032TD=-17.6681+1.0032T   主梁南端South end of main beam   D=-15.5170+1.0097TD=-15.5170+1.0097T

选取参考温度Tr为20℃,将参考温度值代入表1的线性模型,得到梁端的参考位移Dr,同时将温度代表值T也代入表1的模型,得到位移的计算值Dt,得到消除温度影响的梁端纵向位移值D1Select the reference temperature T r as 20°C, and substitute the reference temperature value into the linear model in Table 1 to obtain the reference displacement D r of the beam end. At the same time, substitute the temperature representative value T into the model in Table 1 to obtain the calculated value of displacement D t , and obtain The longitudinal displacement value D 1 of the beam end that eliminates the influence of temperature.

图5和图6分别给出了主梁北端和南端的纵向位移D1与车辆荷载代表值R(加速度RMS)的相关性散点图,从图中可以看出数据点的分布较分散,但是仍然可以看到R和D1之间具有明显的相关性,表现出“荷载大位移小,荷载小位移大”的特征。Figure 5 and Figure 6 show the correlation scatter diagrams of the longitudinal displacement D 1 at the north end and the south end of the main girder and the representative value R (acceleration RMS) of the vehicle load. It can be seen from the figure that the distribution of data points is scattered, but It can still be seen that there is an obvious correlation between R and D1 , showing the characteristics of "large load with small displacement, small load with large displacement".

同样,表2给出了采用回归分析建立的位移D1和车辆荷载代表值R的相关性模型。Similarly, Table 2 shows the correlation model of the displacement D1 and the representative value R of the vehicle load established by regression analysis.

表2车辆荷载-位移的线性回归模型Table 2 Linear regression model of vehicle load-displacement

  位置 Location   回归函数(位移:D1(cm)加速度RMS:R(cm/s2))Regression function (displacement: D 1 (cm) acceleration RMS: R (cm/s 2 ))   北段 North Section   D1=2.8834-0.5391RD 1 =2.8834-0.5391R   南端south end   D1=5.6170-0.6646RD 1 =5.6170-0.6646R

选取参考车辆荷载代表值Rr为1cm/s2,将车辆荷载代表值代入表2的线性模型,得到梁端的参考位移Drr,同时将车辆荷载代表值R也代入表2的模型,得到位移的计算值Dtt,得到消除温度和车辆荷载影响的梁端纵向位移值D2Select the representative value R r of the reference vehicle load as 1cm/s 2 , and substitute the representative value of the vehicle load into the linear model in Table 2 to obtain the reference displacement D rr of the beam end. At the same time, substitute the representative value R of the vehicle load into the model in Table 2 to obtain the displacement The calculated value D tt of D tt is obtained to obtain the longitudinal displacement value D 2 of the beam end that eliminates the influence of temperature and vehicle load.

将D2取日平均值,得到100个样本,记为D2Take the daily average value of D 2 to obtain 100 samples, which are recorded as D 2 .

再取另外48天监测数据作为伸缩缝状态未知时的数据,采用本发明的方法得到48个日平均位移样本,记为D3Take another 48 days of monitoring data as the data when the state of the expansion joint is unknown, and use the method of the present invention to obtain 48 daily average displacement samples, which are recorded as D 3 .

图7和图8分别给出了主梁北端和南端的纵向位移日平均的实测值和消除环境影响后的归一化值。图中实线表示位移的日平均实测值,虚线的前100个样本即表示D2,后48个样本则表示D3,从这两幅图可以看出南北端的位移归一化值曲线变化都很平稳,且幅度很小,这说明本发明的方法有效地去除了环境因素对梁端纵向位移的影响,Fig. 7 and Fig. 8 show the daily average longitudinal displacement measured value and the normalized value after eliminating the environmental influence, respectively, at the north end and south end of the main girder. The solid line in the figure represents the daily average measured value of the displacement, the first 100 samples of the dotted line represent D 2 , and the last 48 samples represent D 3 . From these two figures, it can be seen that the normalized value curves of the displacement at the north and south ends are all the same. very stable, and the amplitude is very small, which shows that the method of the present invention effectively removes the influence of environmental factors on the longitudinal displacement of the beam end,

将D2和D3同时输入均值控制图,图9给出了伸缩缝正常状态下的位移均值控制图。图中前100个数据表述伸缩缝完好状态,即D2;后48个数据表示伸缩缝未知状态,即D3。通过调整控制图的显著性水平使前100个样本正好位于上下控制线之内,同时,可以发现,后48个样本同样也位于控制线之内,这说明此时伸缩缝处于健康状态。Input D 2 and D 3 into the mean value control chart at the same time, and Figure 9 shows the mean value control chart of the expansion joint under normal conditions. The first 100 data in the figure represent the intact state of the expansion joint, that is, D 2 ; the last 48 data represent the unknown state of the expansion joint, that is, D 3 . By adjusting the significance level of the control chart, the first 100 samples are just within the upper and lower control lines. At the same time, it can be found that the last 48 samples are also within the control line, which shows that the expansion joints are in a healthy state at this time.

为了检验本发明对伸缩缝进行损伤评估的效果,将上述另取的48天的10-min平均的位移代表值施加一定的变化,以此来模拟伸缩缝损伤对位移的影响:In order to check the effect of the present invention on the damage assessment of expansion joints, a certain change is applied to the above-mentioned 10-min average displacement representative value of another 48 days, so as to simulate the impact of expansion joint damage on displacement:

Dm=D-εΔD                                        (6)D m =D-εΔD (6)

式中,D为后48天的10-min位移实测值;Dm为伸缩缝损伤状态下后48天的10-min位移模拟值;ε表示损伤水平,这里取为1.0%;ΔD为南北端梁端纵向位移的年变化幅度,即以上介绍的53.6cm和51.7cm。In the formula, D is the measured value of 10-min displacement in the last 48 days; D m is the simulated value of 10-min displacement in the last 48 days of expansion joint damage; ε represents the damage level, which is taken as 1.0% here; The annual variation range of the longitudinal displacement of the beam end is 53.6cm and 51.7cm introduced above.

再将Dm按照本发明的方法计算得到48个日平均位移样本,记为D3。然后将D2和D3也同时输入均值控制图,保持控制图的显著性水平不变,图10给出了伸缩缝模拟损伤状态下的位移均值控制图,从图中可以看出当伸缩缝损伤引起位移发生1.0%的变化时,后48个样本明显地趋近下控制线,且有部分样本已超出控制范围,此时,可以判定伸缩缝发生损伤。Then D m is calculated according to the method of the present invention to obtain 48 daily average displacement samples, which are recorded as D 3 . Then input D 2 and D 3 into the mean value control chart at the same time, keeping the significance level of the control chart unchanged. Figure 10 shows the mean displacement control chart of the expansion joint under simulated damage state. When the damage causes a 1.0% change in displacement, the last 48 samples obviously approach the lower control line, and some samples have exceeded the control range. At this time, it can be judged that the expansion joint is damaged.

以上算例表明本发明所提出的方法能够有效地消除环境条件对梁端纵向位移变化的影响,提取出能够反映伸缩缝状态的纵向位移归一化值,可以应用于桥梁伸缩缝的长期在线监测和损伤诊断。The above calculation examples show that the method proposed by the present invention can effectively eliminate the influence of environmental conditions on the longitudinal displacement of the beam end, extract the normalized value of the longitudinal displacement that can reflect the state of the expansion joint, and can be applied to the long-term online monitoring of the expansion joint of the bridge and injury diagnosis.

Claims (1)

1. bridge telescopic seam injury intelligent diagnosing method based on girder end longitudinal displacement is characterized in that this damage intelligent diagnosing method is:
1) setting of the length travel of girder beam-ends and bridge environmental conditions ensor:
When bridge construction is built, the length travel sensor is set in girder beam-ends position, simultaneously, at the span centre position mounting temperature sensor and the acceleration transducer of girder, in order to the temperature of monitoring girder with because the vertical acceleration of girder that vehicular load causes;
2) processing of Monitoring Data:
With 10min is computation interval, and the raw data that sensor obtains is handled, and calculates the typical value of girder end longitudinal displacement, temperature and vehicular load; The typical value of described vehicular load is to be the interval root mean square that calculates the vertical acceleration of girder with 10min, with this intensity typical value as the vehicular load in this time period;
3) the mathematics correlation model of length travel and environmental baseline under the serviceable condition:
A) choose bridge construction and build up n days the Monitoring Data in back and set up correlation model, length travel D, temperature T and vehicular load typical value R,
B) method of employing linear regression is set up the relation between temperature T and the girder end longitudinal displacement D, and the regression model parameter is calculated by least square method,
C) before the correlation model of setting up vehicular load typical value R and displacement, eliminate the influence of temperature earlier to length travel, choosing reference temperature is T r, with the original test value D of displacement " normalization " to reference temperature T r, the girder end longitudinal displacement value D of the temperature effect that is eliminated 1,
Concrete grammar is: the method for employing linear regression is set up the relation between temperature T and the girder end longitudinal displacement D, and the model tormulation formula is:
D=β 01T (1)
In the formula, β 0And β 1Be regression coefficient, can obtain by the method for least square:
Figure FSB00000469620300011
Figure FSB00000469620300012
In the formula, S DTCovariance for displacement and temperature; D TTVariance for temperature;
Figure FSB00000469620300013
With
Figure FSB00000469620300014
Be respectively the average of displacement and temperature, choosing reference temperature is T r, with its substitution formula (1), the reference value that obtains displacement is D r,, obtain calculation of displacement value D simultaneously with also substitution formula of temperature typical value T (1) tSo, can calculate the girder end longitudinal displacement value D that eliminates temperature effect 1:
D 1=D-(D t-D r) (3)
D) adopt linear regression to set up D 1With the correlation models of vehicular load typical value R, similar with step c) then, the reference value of choosing vehicular load is R r, with the D that obtains in the step c) 1" normalization " is to the reference value R of vehicular load r, the shift value D of the vehicular load that is eliminated influence 2
Concrete grammar is: set up the shift value D that has eliminated temperature effect 1Linear regression model (LRM) with vehicular load typical value R:
D 1=β 23R (4)
In the formula, β 2And β 3Be regression coefficient, can similar formula (2) calculate that the reference value of choosing vehicular load is R r,, obtain the reference value D of displacement with its substitution formula (4) Rr,, obtain calculation of displacement value D with also substitution formula of vehicular load typical value R (4) Tt, can calculate the shift value D that has eliminated the vehicular load influence 2:
D 2=D 1-(D tt-D rr) (5)
4) determining of control chart level of significance:
The shift value D that step 3) is calculated 2Get daily mean, be designated as , with its input mean chart, adjust the level of significance of control chart, make a said n sample point all drop within the upper and lower control line of control chart;
5) intelligent diagnostics of telescopic seam injury:
To m days monitoring data of unknown state, the correlation model under the serviceable condition of employing expansion joint is eliminated the influence of temperature and vehicular load, obtains m per day shift value on this basis, is designated as
Figure FSB00000469620300022
, the conspicuousness that keeps step 4) to determine is constant, will With
Figure FSB00000469620300024
Import mean chart simultaneously, at this moment, if all n+m sample still all is positioned at upper and lower control line, illustrate that then the expansion joint state is for normal, if there is sample to drop on beyond the control line, the expansion joint abnormal state then is described, can make the early warning that damage takes place at the expansion joint.
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