CN113868749A - Vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data - Google Patents
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Abstract
The invention belongs to the technical field of structural safety detection, and discloses a vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data, which comprises the following steps: (1) inverting the fatigue strain influence line; (2) simulating random traffic load of each lane; (3) virtual loading of strain influence lines; (4) and (4) evaluating uncertainty of fatigue damage. Compared with the existing general fatigue vehicle model, the vehicle load characteristics under the real operation condition of the bridge can be more accurately reflected by analyzing the actual vehicle measurement data and considering to distinguish different traffic operation states, and meanwhile, the influence of the traffic growth rate can be considered. Meanwhile, fatigue loading is carried out on a strain influence line obtained by inversion of actually measured strain data, and fatigue strain response which is more in line with the actual stress condition of the structure than a finite element model can be obtained. Therefore, the analyzed structural fatigue damage analysis result and the predicted residual life are more reasonable and reliable.
Description
Technical Field
The invention belongs to the technical field of bridge structure performance evaluation, and particularly relates to a vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data.
Background
In the case of highway and municipal bridges, the vehicle load is one of the most important live load forms of the bridge structure, and the vehicle load directly acts on the bridge deck through the wheels and is transmitted to other load-bearing members. In actual operation engineering, a large amount of automobile loads form random traffic flows through bridges, repeated loading and unloading conditions can occur in structural members of the bridges under the long-term action of the random traffic flows, and the problem of high cycle fatigue of the structural members such as steel bridge deck plates, guys, suspenders and the like caused by the repeated loading and unloading conditions is very prominent. The fatigue damage assessment and prediction of the bridge member under the action of the automobile load are carried out, and the method has very important engineering significance for ensuring the long-term operation safety of the bridge and providing a basis for daily management and maintenance.
The existing bridge structure fatigue damage assessment method generally comprises the following three methods: the first method is to carry out fatigue test on the local bridge component through a laboratory model test, and ensure that the structural design has enough fatigue safety reserve in the actual operation process. Due to the limitation of model size, test means and the like, the model test often cannot accurately reproduce the actual stress characteristics of the structural member, the accuracy of fatigue analysis is greatly limited, and the fatigue damage condition of the structural member in actual operation cannot be evaluated. The second method is to analyze fatigue damage by a method of simulating fatigue loading through a finite element model. In such methods, various simplifications and assumptions exist for the structural finite element model and the loading model, and the fatigue analysis results also lack reliability. The third method is fatigue damage assessment based on structural field strain monitoring data. Compared with the former two methods, the method can obtain the fatigue stress characteristics reflecting the actual operation condition of the bridge, and has obvious advantages in the aspects of evaluating the accuracy of structural fatigue damage and residual fatigue life. However, direct monitoring to obtain structural strain is difficult to separate the vehicle-mounted fatigue effect from various environments such as vehicles, wind, temperature and the like and under the load coupling effect, and the vehicle-induced fatigue evaluation cannot be realized.
The vehicle dynamic weighing system is one of important components of a bridge structure health monitoring system, and can realize real-time online acquisition and storage of vehicle load parameters such as vehicle axle weight, axle number, axle distance, traffic flow and the like in random traffic flow. The information provides a data basis for accurately acquiring the vehicle load model. In addition, the bridge structure response data under the loading of the test calibration vehicle can obtain a strain influence line reflecting the real stress characteristics of the structure, and a solution is provided for solving the problem that the stress characteristics of the finite element model and the actual structure do not accord with each other. Therefore, the invention provides a vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data, which utilizes vehicle data and structural response test data of a dynamic weighing system to obtain a vehicle fatigue load model accurately reflecting real vehicle loading characteristics and an influence line model reflecting real stress characteristics of a structure, and realizes the evaluation of the existing fatigue damage of the structure and the prediction of the residual fatigue life.
Disclosure of Invention
The invention provides a vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data, and aims to realize the assessment of vehicle-induced fatigue damage and the prediction of residual fatigue life of bridge members by monitoring vehicle-mounted and structural response data.
The technical scheme of the invention is as follows:
a vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data comprises the following steps:
And a two-axle or multi-axle heavy vehicle is used as a loading vehicle, and the axle load and the axle distance of the loading vehicle are obtained through calibration. Enabling the loaded vehicle to sequentially drive each lane at a constant speed of not more than 10km/h, and synchronously recording strain data of the position of the vehicle and structural fatigue details caused by vehicle loading. Defining a vehicle loading matrix obtained in the process that the loaded vehicle runs through the ith lane as AiThe structural fatigue detail strain vector is BiThe bridge influence coefficient vector is Ci. According to strain influence line definitionEquation (1) can be established. And (3) solving the formula (1) by adopting a least square method to obtain a strain influence line of the structural fatigue detail corresponding to the ith lane. Similarly, the strain influence lines of all lanes can be obtained through the measured data.
AiCi＝Bi (1)
(2.1) acquiring four vehicle load characteristic monitoring data of the vehicle load on each lane, namely the traffic flow, the vehicle weight and the vehicle speed of the vehicle load on each lane and the time interval of the two adjacent vehicles to reach the same position through a dynamic weighing system, calculating the average value of the vehicle load parameters every half hour to form a vehicle-mounted characteristic data sample set, and recording the vehicle-mounted characteristic data set on the ith lane as an expression (2).
Xi＝{x1(v1,w1,s1,d1),x2(v2,w2,s2,d2),...,xN(vN,wN,sN,dN)} (2)
Wherein: v, w, s and d are average values of the traffic flow, the weight, the speed and the distance of a certain lane on the bridge within half an hour. And taking the 4 parameters as clustering indexes, dividing the running state of the random traffic flow data set in one day on each lane by adopting a two-step clustering method, and determining the optimal clustering number m. And finally, dividing the vehicle load data into m sample sets corresponding to different running states.
And (2.2) respectively carrying out statistical analysis on the vehicle load data corresponding to different running states in each lane, fitting the probability distribution of the vehicle distance, the vehicle weight and the vehicle speed by adopting a normal distribution model, a logarithmic normal distribution model, an extreme value distribution model, a Weibull distribution model, a gamma distribution model and a Gaussian mixed distribution model, and carrying out K-S test. And finally, determining the optimal distribution closest to the actual data according to the difference value between the vehicle load parameter test statistic value and the assumed critical value.
And (2.3) sequentially and randomly sampling the traffic flow, the vehicle type proportion, the vehicle weight, the vehicle speed and the vehicle distance respectively by adopting a Monte Carlo random sampling method according to the optimal probability distribution model of the vehicle-mounted data obtained by fitting on each lane, and generating a random vehicle flow consisting of a random sequence of the vehicle speed, the vehicle weight and the vehicle distance on the lane.
And (3.1) virtually loading each lane strain influence line obtained in the step 1 by adopting the random traffic flow obtained in the step 2. And synchronously applying the simulated random traffic flow corresponding to each lane to the strain influence line of the corresponding lane, acquiring a stress time-course curve of the fatigue details of the bridge structure under the action of the random traffic flow, and further acquiring the fatigue stress amplitude and the stress cycle times by adopting a rain flow counting method. The influence line virtual loading schematic diagram is shown in fig. 1.
(3.2) simultaneously adopting the simulated random traffic flow to virtually load the theoretical strain influence line of the bridge, obtaining the minimum value of the structural fatigue stress amplitude caused by the random traffic flow in an ideal state, and taking 10 percent of the minimum vehicle-caused fatigue stress amplitude as a cut-off stress sigmaL. And (3.1) when the fatigue stress obtained by the virtual loading of the actual measurement influence line in the step (3.1) is greater than the cut-off stress, considering the effective fatigue damage stress amplitude, or considering the interference component caused by the inversion error of the actual measurement influence line and the noise in the data field acquisition process, and not counting the contribution of the interference component to the fatigue damage in the analysis process.
Step 4. fatigue damage uncertainty assessment
And (4.1) calculating the equivalent fatigue stress and stress cycle number of each day through formulas (3) and (4) according to a linear accumulated fatigue damage criterion. On the basis, a daily equivalent fatigue stress amplitude and stress cycle frequency probability distribution model is fitted.
Wherein: d0Caused by random traffic loadingDaily cumulative fatigue damage; siAnd SjGreater and less than constant amplitude fatigue limit delta sigma in stress spectrum caused by random traffic loadingDThe stress amplitude of (a); n isiAnd njGreater and less than constant amplitude fatigue limit delta sigma in stress spectrum caused by random traffic loadingDThe number of cycles corresponding to the stress amplitude of (a); kCAnd KDStress amplitude greater than and less than delta sigma caused by random traffic loadingDFatigue strength, coefficient of
ΔσCThe fatigue life for a component detail type is a fatigue stress amplitude of two million times.
And (4.2) establishing a fatigue damage limit state function of the fatigue details of the bridge structure, as shown in the formula (5). And acquiring the fatigue life of the bridge structure conforming to the reliability beta by adopting a Monte Carlo method according to the established probability distribution model of each parameter in the extreme state function.
β＝Φ-1(1-Pf)＝Φ-1(Pf) (6)
Wherein: dfCritical damage to the bridge member; n is the service life of the bridge; a is the annual increase coefficient of the traffic flow; pfIs the probability of fatigue failure.
The invention has the beneficial effects that:
1. and carrying out statistical analysis and reconstruction on the vehicle dynamic weighing data to obtain random traffic flow as a vehicle fatigue load model. Compared with the existing general fatigue vehicle model, the vehicle load characteristics under the real operation condition of the bridge can be more accurately reflected by analyzing the actual vehicle measurement data and considering to distinguish different traffic operation states, and meanwhile, the influence of the traffic growth rate can be considered.
2. Fatigue loading is carried out on a strain influence line obtained by inversion of the actually measured strain data, and fatigue strain response which is more in line with the actual stress condition of the structure than a finite element model can be obtained. Therefore, the analyzed structural fatigue damage analysis result and the predicted residual life are more reasonable and reliable.
Drawings
FIG. 1 is a schematic diagram of the virtual loading of influence lines employed in the present invention;
FIG. 2 is a slow lane influence line recognized by the method of the present invention;
FIG. 3 is a graph of stress time courses for two vehicle operating states as a result of the method of the present invention;
FIG. 4 is an equivalent stress distribution obtained by the method of the present invention under four conditions;
FIG. 5 is a graph showing the cycle number distribution in four states obtained by the method of the present invention;
FIG. 6 is a comparison of the results of the analysis of vehicle fatigue of the bridge under four conditions obtained by the implementation of the method of the present invention;
fig. 7 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and an example.
The vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data is divided into four steps of fatigue strain influence line inversion, random traffic flow load simulation of each lane, virtual loading of strain influence lines and fatigue damage uncertainty evaluation, the specific implementation mode is given above, and the using method and the characteristics of the invention are explained by combining with specific examples.
In the specific numerical calculation example, firstly, loading is carried out on a bridge by adopting a loading vehicle with known axle weight and axle distance according to the loading scheme in the step 1, the real-time position of the vehicle and structural fatigue detail strain data caused by loading are synchronously recorded and obtained, and an influence line identification equation is established on the basis. And solving an equation by adopting a least square method to obtain a fatigue influence line. Taking the slow lane as an example, the identified fatigue influence line results are shown in fig. 2. Secondly, acquiring the vehicle weight, the vehicle distance and the vehicle speed data of random traffic flow of each lane under the normal operation state through the dynamic weighing data installed on the bridge, and obtaining a vehicle load sample set corresponding to two sparse operation states and a dense operation state according to the clustering analysis method in the step 2. And (4) carrying out memorability statistical analysis on the vehicle load data of each lane in different running states, and fitting to obtain an optimal distribution model. On the basis, random traffic flows under different vehicle running states are respectively generated by adopting a Monte Carlo method, and the random traffic flows are decomposed into random loading flows according to the inter-axle distance and the axle-to-weight ratio. Thirdly, synchronously applying the simulated random traffic flow corresponding to each lane to the strain influence line of the corresponding lane identified in the step 1 according to the influence line virtual loading mode shown in fig. 1, and acquiring a stress time course curve of the fatigue details of the bridge structure under the action of the random traffic flow. Stress time-course curves of the slow lane influence lines in two running states obtained after random traffic flow loading are shown in fig. 3.
Finally, in order to compare and analyze the reasonability of the method of the invention in the evaluation of the vehicle-induced fatigue compared with the existing method, the following four models are established: the model 1 is a random traffic flow only loaded with dense state simulation, the model 2 is a random traffic flow loaded with differentiated vehicle running state simulation, the model 3 is a random traffic flow loaded with indistinguishable vehicle running state simulation, and the model 4 is a random traffic flow loaded with sparse state simulation. Fatigue stress amplitude and stress cycle times of the 4 models are respectively obtained from the stress time course curve by adopting a rain flow counting method, and a daily equivalent stress and daily cycle time probability distribution model is obtained as shown in fig. 4 and 5.
And (4) according to the uncertainty evaluation method for the fatigue damage of the bridge structure introduced in the step (4), establishing a fatigue life limit state function under the loading of four random traffic flow models according to the formula (5). And (4) carrying out fatigue evaluation on the four models by adopting a Monte Carlo method according to a formula (6) to obtain the fatigue life of the bridge structure, as shown in figure 6. It can be seen that the equivalent stress magnitude and frequency of the bridge will be underestimated and the corresponding fatigue life of the structure is overestimated by about 10 years without considering the differentiation of the vehicle running states, such a fatigue analysis structure is more dangerous. The results prove the rationality of the method for evaluating the fatigue damage caused by the bridge vehicle.
Claims (1)
1. A vehicle-induced bridge fatigue damage analysis method based on vehicle dynamic weighing data is characterized by comprising the following steps:
step 1. fatigue strain influence line inversion
Adopting a two-axle or multi-axle heavy vehicle as a loading vehicle, and calibrating to obtain the axle weight and the axle distance of the loading vehicle; enabling the loading vehicle to sequentially run through each lane at a constant speed of not more than 10km/h, and synchronously recording the position of the loading vehicle and strain data of structural fatigue details caused by vehicle loading; defining a vehicle loading matrix obtained in the process that a loaded vehicle runs through the ith lane as AiThe structural fatigue detail strain vector is BiThe bridge influence coefficient vector is Ci(ii) a Establishing an equation (1) according to the definition of the strain influence line; solving an equation (1) by adopting a least square method to obtain a strain influence line of the structural fatigue detail corresponding to the ith lane; similarly, strain influence lines of all lanes are obtained through actually measured data;
AiCi＝Bi (1)
step 2, simulating random traffic load of each lane
(2.1) acquiring four vehicle load characteristic monitoring data of the vehicle load on each lane, namely the traffic flow, the vehicle weight and the vehicle speed of the vehicle load on each lane and the time interval of the two adjacent vehicles to reach the same position through a dynamic weighing system, calculating the average value of the vehicle load parameters every half hour to form a vehicle-mounted characteristic data sample set, and recording the vehicle-mounted characteristic data set on the ith lane as an expression (2);
Xi＝{x1(v1,w1,s1,d1),x2(v2,w2,s2,d2),...,xN(vN,wN,sN,dN)} (2)
wherein v, w, s and d are average values of the traffic flow, the weight, the speed and the distance between two vehicles on a certain lane on the bridge within half an hour respectively; taking the 4 parameters as clustering indexes, dividing the running state of the random traffic flow data set in one day on each lane by adopting a two-step clustering method, and determining the optimal clustering number m; finally, dividing the vehicle load data into m sample sets corresponding to different running states;
(2.2) respectively carrying out statistical analysis on vehicle load data corresponding to different running states in each lane, fitting probability distribution of vehicle distance, vehicle weight and vehicle speed by adopting a normal distribution model, a logarithmic normal distribution model, an extreme value distribution model, a Weibull distribution model, a gamma distribution model and a Gaussian mixed distribution model, and carrying out K-S test; finally, determining the optimal distribution closest to the actual data according to the difference value between the vehicle load parameter test statistic value and the assumed critical value;
(2.3) sequentially and randomly sampling the traffic flow, the vehicle type proportion, the vehicle weight, the vehicle speed and the vehicle distance by adopting a Monte Carlo random sampling method according to the optimal probability distribution model of the vehicle-mounted data obtained by fitting on each lane, and generating a random vehicle flow consisting of a random sequence of the vehicle speed, the vehicle weight and the vehicle distance on the lane;
step 3. virtual loading of strain influence line
(3.1) virtually loading the strain influence lines of all lanes obtained in the step 1 by adopting the random traffic flow obtained in the step 2; synchronously applying the simulated random traffic flow corresponding to each lane to the strain influence line of the corresponding lane, acquiring a stress time-course curve of the fatigue details of the bridge structure under the action of the random traffic flow, and further acquiring the fatigue stress amplitude and the stress cycle times by adopting a rain flow counting method;
(3.2) simultaneously adopting the simulated random traffic flow to virtually load the theoretical strain influence line of the bridge, obtaining the minimum value of the structural fatigue stress amplitude caused by the random traffic flow in an ideal state, and taking 10 percent of the minimum vehicle-caused fatigue stress amplitude as a cut-off stress sigmaL(ii) a When the fatigue stress obtained by the virtual loading of the actual measurement influence line in the step (3.1) is larger than the cut-off stress, the fatigue damage stress amplitude is considered to be effective, otherwise, the fatigue damage stress amplitude is considered to be an interference component caused by the inversion error of the actual measurement influence line and the noise in the data field acquisition process, and the contribution of the interference component to the fatigue damage is not taken into account during analysis;
step 4. fatigue damage uncertainty assessment
(4.1) calculating the equivalent fatigue stress and the stress cycle number of each day through formulas (3) and (4) according to a linear accumulated fatigue damage criterion; on the basis, a daily equivalent fatigue stress amplitude and stress cycle frequency probability distribution model is fitted;
wherein D is0Daily accumulated fatigue damage caused by random traffic loading; siAnd SjGreater and less than constant amplitude fatigue limit delta sigma in stress spectrum caused by random traffic loadingDThe stress amplitude of (a); n isiAnd njGreater and less than constant amplitude fatigue limit delta sigma in stress spectrum caused by random traffic loadingDThe number of cycles corresponding to the stress amplitude of (a); kCAnd KDStress amplitude greater than and less than delta sigma caused by random traffic loadingDFatigue strength, coefficient of
ΔσCFatigue stress amplitude corresponding to component detail type with fatigue life of two million times;
(4.2) establishing a fatigue damage limit state function of the fatigue details of the bridge structure, as shown in the formula (5); acquiring the fatigue life of the bridge structure conforming to the reliability beta by adopting a Monte Carlo method according to the established probability distribution model of each parameter in the extreme state function;
β＝Φ-1(1-Pf)＝Φ-1(Pf) (6)
wherein D isfCritical damage to the bridge member; n is the service life of the bridge; a is the annual increase coefficient of the traffic flow; pfIs the probability of fatigue failure.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036808A (en) * | 2022-01-10 | 2022-02-11 | 中铁大桥科学研究院有限公司 | Intelligent identification and monitoring method for fatigue parameters of ballastless track of high-speed rail large-span cable-stayed bridge |
| CN114117622A (en) * | 2022-01-20 | 2022-03-01 | 武汉人云智物科技有限公司 | Method and system for calculating fatigue life of bridge steel structure |
| CN114580176A (en) * | 2022-03-08 | 2022-06-03 | 哈尔滨工业大学 | Automobile load intelligent reconstruction method based on multi-dimensional feature clustering |
| CN114936398A (en) * | 2022-05-12 | 2022-08-23 | 石家庄铁道大学 | Load effect calculation method based on axle coupling and random vehicle load model |
| CN115408754A (en) * | 2022-09-05 | 2022-11-29 | 北京交通大学 | Combined beam bridge dynamic fatigue performance analysis method considering time-varying effect |
| CN117332491A (en) * | 2023-10-23 | 2024-01-02 | 江苏科技大学 | Axle coupling linear calculation method based on accurate time interval integration under random traffic flow loading |
| CN117349947A (en) * | 2023-12-04 | 2024-01-05 | 中交长大桥隧技术有限公司 | Structural safety intelligent monitoring method based on SN curve and SVM |
| CN117554006A (en) * | 2024-01-09 | 2024-02-13 | 北京云庐科技有限公司 | Bridge health assessment method and system based on dynamic strain monitoring |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004044116A (en) * | 2002-07-09 | 2004-02-12 | Mitsubishi Heavy Ind Ltd | Remaining life estimating method for bridge |
| DE102007053815A1 (en) * | 2007-11-12 | 2009-05-20 | Ford Global Technologies, LLC, Dearborn | Method for operating active control of front axis of vehicle, involves determining force requirement value for lateral force at front axis of vehicle |
| CN103279588A (en) * | 2013-04-09 | 2013-09-04 | 东南大学 | Method for calculating fatigue stress of steel bridge deck slab under combined action of vehicle load and temperature |
| CN103870705A (en) * | 2014-03-26 | 2014-06-18 | 上海同豪土木工程咨询有限公司 | Inversion method of dynamic bearing capacity of plate girder bridge structure |
| JP2014228480A (en) * | 2013-05-24 | 2014-12-08 | 国立大学法人福井大学 | Device and method for passing vehicle weight analysis processing |
| CN112347668A (en) * | 2020-09-29 | 2021-02-09 | 华东交通大学 | Steel bridge deck fatigue reliability assessment method based on probabilistic fracture mechanics |
-
2021
- 2021-10-19 CN CN202111213302.3A patent/CN113868749B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004044116A (en) * | 2002-07-09 | 2004-02-12 | Mitsubishi Heavy Ind Ltd | Remaining life estimating method for bridge |
| DE102007053815A1 (en) * | 2007-11-12 | 2009-05-20 | Ford Global Technologies, LLC, Dearborn | Method for operating active control of front axis of vehicle, involves determining force requirement value for lateral force at front axis of vehicle |
| CN103279588A (en) * | 2013-04-09 | 2013-09-04 | 东南大学 | Method for calculating fatigue stress of steel bridge deck slab under combined action of vehicle load and temperature |
| JP2014228480A (en) * | 2013-05-24 | 2014-12-08 | 国立大学法人福井大学 | Device and method for passing vehicle weight analysis processing |
| CN103870705A (en) * | 2014-03-26 | 2014-06-18 | 上海同豪土木工程咨询有限公司 | Inversion method of dynamic bearing capacity of plate girder bridge structure |
| CN112347668A (en) * | 2020-09-29 | 2021-02-09 | 华东交通大学 | Steel bridge deck fatigue reliability assessment method based on probabilistic fracture mechanics |
Non-Patent Citations (3)
| Title |
|---|
| 刘扬;鲁乃唯;邓扬;: "基于实测车流的钢桥面板疲劳可靠度评估", 中国公路学报, no. 05, 15 May 2016 (2016-05-15) * |
| 艾庆华;王东升;李宏男;孙治国;: "基于塑性铰模型的钢筋混凝土桥墩地震损伤评价", 工程力学, no. 04, 25 April 2009 (2009-04-25) * |
| 鲁乃唯;刘扬;邓扬: "随机车流作用下悬索桥钢桥面板疲劳损伤与寿命评估", 中南大学学报. 自然科学版, vol. 46, no. 11, 26 November 2015 (2015-11-26) * |
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