Bridge influence line identification method under random vehicle flow interference

Abstract

The application provides a bridge influence line identification method under random vehicle flow interference, comprising the following steps: S1, obtaining the axle load, the wheelbase of a detection vehicle, the span of a bridge to be measured, and recording the time when the vehicle goes onto the bridge and the time when the vehicle goes off the bridge; S2, detecting the vehicle passing through the bridge to be measured and recording the random interference vehicle passing condition, and obtaining an initial bridge displacement response Y(t); S3, processing the initial bridge displacement response Y(t) to obtain a bridge displacement response matrix Y1(t); S4, constructing a vehicle information matrix L according to the linear superposition principle and the vehicle information at the same time; S5, calculating a bridge displacement influence line matrix through the vehicle information matrix L and the bridge displacement response matrix Y1(t); further obtaining an initial bridge displacement influence line; S6, performing iterative optimization through the initial bridge influence line and the bridge displacement response matrix Y1(t) to obtain a vehicle weight matrix m i ; S7, repeating the steps of S5 and S6 until the optimization iteration result meets the preset relative residual requirement, and obtaining the final bridge influence line.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of bridge health monitoring, and particularly relates to a bridge influence line identification method under random vehicle flow interference. BACKGROUND

[0002] As of now, there are more than one million highway and railway bridges in China. With a large number of bridges in service, it has become an urgent and core challenge to ensure the safe operation of these bridges during their service life. During the operation of the bridge, the performance of the bridge gradually deteriorates over time, coupled with the continuous growth of traffic volume, leading to the accumulation of bridge damage and the attenuation of the reserved resistance beyond the expected speed, resulting in safety hazards under normal operating conditions of the bridge.

[0003] The dynamic and static characteristics of the bridge can be measured by periodic inspection using direct or indirect methods, and are widely used to assess the condition of the bridge. However, in recent years, scholars have made many explorations and researches on the extraction and identification of bridge influence lines. Traditionally, the identification of bridge influence lines is usually achieved through periodic static load testing, which is costly, requires traffic interruption, and takes years.

[0004] It is of great significance to identify the influence line under normal operating conditions of the bridge. According to the definition of bridge influence line, the product of the vehicle load information matrix and the bridge influence line matrix corresponds to the measured response matrix. In theory, given the vehicle load information matrix and the measured response matrix, the bridge influence line can be identified.

[0005] Over the past few decades, researchers have developed a variety of solution methods, including least squares, regularization, basis function fitting, frequency domain method, etc. However, the application of these methods is usually based on an assumption that there is only a single vehicle passing through the bridge during detection, which does not match the conditions under normal operating conditions of the bridge. When the bridge is in normal operation, traffic is not closed, and other vehicles will pass through the bridge at the same time, including small and large vehicles. Therefore, under normal operating conditions, the vehicle information matrix and the measured response matrix will be affected by interfering vehicles, and existing research has not fully considered this, resulting in poor performance of existing methods in practical applications.

[0006] In summary, traditional bridge influence line identification methods do not fully consider the impact of interfering vehicles on the identification and extraction process. In the process of identifying and extracting the bridge influence line, it is usually necessary to close the traffic when the detection vehicle passes through the bridge to avoid the influence of other vehicles. However, with the increase in traffic volume and the development of technology, there is an urgent need for a method to identify and extract the influence line under the operating conditions of the bridge, and the present application is exactly to solve this demand. SUMMARY

[0007] The main purpose of the present application is to overcome the above-mentioned defects in the prior art, and provide a method for identifying and extracting influence lines under the operation state of the bridge, which has the advantages of wide applicability, high identification accuracy and use under the operation state of the bridge, and has a wide application scenario in bridge health monitoring.

[0008] In order to solve the above technical problems, the present application provides a bridge influence line identification method under random vehicle flow interference, and the specific technical solutions are as follows:

[0009] A bridge influence line identification method under random vehicle flow interference, comprising a displacement sensor, a signal acquisition module, a weighing system, a time monitoring module, a time-frequency analysis module and a bridge influence line calculation module.

[0010] The identification method comprises the following steps:

[0011] S1, before detection, obtaining the axle load and wheelbase of the detection vehicle to construct a vehicle information matrix, and obtaining the span and lane information of the bridge to be detected; recording the time when the detection vehicle and the random interference vehicle pass through the bridge to be detected;

[0012] S2, using the detection vehicle to pass through the bridge to be detected at a low and uniform speed, and recording the passing conditions of a plurality of random interference vehicles to obtain the initial bridge displacement response Y(t);

[0013] S3, decomposing the initial bridge displacement response Y(t) based on the variational nonlinear component through the time-frequency analysis module to obtain a smooth bridge displacement response, and then obtaining a bridge displacement response matrix Y1(t);

[0014] S4, in the vehicle information matrix, setting the vehicle weight of the random interference vehicle as 0 and setting the detection vehicle as the true vehicle weight, and constructing a complete vehicle information matrix L according to the linear superposition principle and the vehicle information at the same time;

[0015] S5, using the bridge influence calculation module to calculate the bridge displacement influence line matrix by performing matrix inverse operation on the vehicle information matrix L and the bridge displacement response matrix Y1(t) by using the least square method; and performing inverse operation on the bridge displacement influence line matrix and the vehicle information matrix L to obtain the initial bridge displacement influence line;

[0016] S6, combining the obtained initial bridge influence line with the bridge displacement response matrix Y1(t) to perform iterative optimization, and performing inverse operation to obtain the vehicle weight matrix m i .

[0017] S7, repeating the steps of S5 and S6 after obtaining the vehicle weight matrix m i , until the optimization iteration result meets the preset relative residual requirement, stopping iteration, and obtaining the finally determined bridge influence line.

[0018] In a preferred embodiment, in S1, the wheelbase and axle load of the detection vehicle are obtained by the weighing system before the detection vehicle passes through the bridge to be detected, for constructing the vehicle information matrix.

[0019] In a preferred embodiment, in S1, the time monitoring module is used to detect the time of passing through the bridge and the time of leaving the bridge of the detection vehicle and the random interference vehicle when they pass through the bridge to be detected, for calculating the average speed of the detection vehicle and the random interference vehicle.

[0020] In a preferred embodiment, in S1, the average speed of the detection vehicle and the random interference vehicle is calculated according to the time of passing through the bridge and the time of leaving the bridge of the detection vehicle and the random interference vehicle and the span of the bridge to be detected;

[0021] Or in S1, the speed of the detection vehicle and the random interference vehicle is measured.

[0022] In a preferred embodiment, in S2, the displacement sensor is installed at the to-be-detected position of the main girder of the bridge to be detected, and the displacement response of the bridge to be detected generated during the driving of the detection vehicle and the interference vehicle is measured by the displacement sensor;

[0023] The displacement sensor is connected to the signal acquisition module, which receives the voltage or current signal transmitted by the displacement sensor, records and converts the signal into a digital signal, which is the displacement response signal.

[0024] In a preferred embodiment, in S3, the time-frequency analysis module receives the displacement response output by the signal acquisition module, and performs Fourier transform on the displacement response, obtains the approximate bridge fundamental frequency through peak picking, calculates the driving frequency of the loaded vehicle, and decomposes the displacement response of the bridge by using variational nonlinear component decomposition with the bridge fundamental frequency and the driving frequency of the loaded vehicle as the initial frequency, to obtain the static displacement response and the dynamic displacement response respectively.

[0025] In a preferred embodiment, in S4, it further includes the specific steps that the displacement calculation formula of a single detection vehicle at adjacent two points on the bridge to be detected is:

[0026] Wherein, P is the weight of the detection vehicle, is the influence line of the bridge to be detected at the to-be-detected position x n , and α(x) and β(x) are distribution coefficients and α(x)+β(x)=1;

[0027] The distribution coefficient is calculated as: β(vt)=1-α(vt).

[0028] In a preferred embodiment, in S4, the complete vehicle information matrix L is calculated as:

[0029]

[0030] wherein P is the weight of the detection vehicle, P i is the weight of the ith interfering vehicle, is the time at the kth moment of the ith vehicle. In a preferred embodiment, in S5, the calculation formula of the initial bridge influence line is:

[0031] L x = Y1(t)

[0032] wherein L is the vehicle information matrix, Y1(t) is the bridge displacement response matrix, and x is the bridge displacement influence line matrix.

[0033] In a preferred embodiment, in S6, the calculation formula of the vehicle weight matrix m i is:

[0034] L = m X

[0035] wherein m is the vehicle weight matrix, and X is the vehicle position matrix.

[0036] Compared with the prior art, the technical scheme of the present application has the following beneficial effects:

[0037] 1. The present application can accurately extract the bridge influence line from the detection environment of multiple vehicle interference, which is more close to the health detection of the bridge operation state, and has the advantages of wide applicability, high recognition accuracy, and use in the bridge operation state.

[0038] 2. The bridge influence line recognition method under random vehicle flow interference provided by the present application can detect the bridge to be detected by the detection vehicle without closing the traffic, which provides a reference for further development of the bridge health detection in the operation state, and has the advantages of short operation time and simple calculation.

[0039] 3. The method of the present application has good noise resistance and robustness, and can adapt to changes in various environmental conditions and traffic conditions, ensuring the reliability and stability in practical application.

[0040] 4. Through the method of the present application, the structural state of the bridge can be more accurately recognized and evaluated, thereby improving the safety management level of the bridge and ensuring public safety. Moreover, the traffic delay and closure caused by bridge detection are reduced, which helps to reduce economic losses and improve social benefits. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is the flow chart of the recognition method in the preferred embodiment of the present application;

[0042] Figure 2The iteration flow chart of the optimization algorithm in the preferred embodiment of the present application is shown in Fig. 1.

[0043] Figure 3 The cross-sectional view of the bridge to be measured in the preferred embodiment of the present application is shown in Fig. 2.

[0044] Figure 4 The side view of the bridge to be measured in the preferred embodiment of the present application is shown in Fig. 3.

[0045] Figure 5 The parameter diagram of the actual wheelbase, track and loaded vehicle of the detection vehicle in the preferred embodiment of the present application is shown in Fig. 4.

[0046] Figure 6 The driving route of the vehicle to be measured in the preferred embodiment of the present application is shown in Fig. 5.

[0047] Figure 7 The quasi-static response diagram in the dynamic response of the bridge to be measured in the preferred embodiment of the present application is shown in Fig. 6.

[0048] Figure 8 The influence line diagram obtained under the static response of the bridge to be measured in the preferred embodiment of the present application is shown in Fig. 7. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application; obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application, and all other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative labor fall within the protection scope of the present application.

[0050] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom end" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0051] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "provided with", "sleeved / connected", "connected" and the like should be understood broadly, for example, "connected" can be wall-mounted connection, can be detachable connection, or integrally connected, can be mechanical connection, can be electrical connection, can be directly connected, can be indirectly connected through an intermediate medium, can be the communication between two elements, and those of ordinary skill in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0052] Reference Figures 1-8 The embodiment provides a bridge influence line identification method under random vehicle flow interference, and comprises the following detection devices.

[0053] A displacement sensor is installed at a to-be-detected position of a main girder of a to-be-detected bridge, and is used for measuring displacement responses generated by the bridge in a driving process of a detection vehicle and interference vehicles.

[0054] A signal acquisition module receives voltage or current signals transmitted by the displacement sensor, converts the signals from analog signals to digital signals, and records the signals.

[0055] A weighing system obtains an axle distance and an axle weight of the detection vehicle before the detection vehicle passes through the to-be-detected bridge, and is used for constructing a vehicle information matrix.

[0056] A time monitoring module detects up-bridge and down-bridge times of the detection vehicle and the random interference vehicles when the detection vehicle and the random interference vehicles pass through the to-be-detected bridge, and is used for calculating average speeds of the detection vehicle and the random interference vehicles.

[0057] A time-frequency analysis module receives displacement responses output by the signal acquisition module, performs Fourier transform on the displacement responses, obtains approximate bridge fundamental frequencies through peak picking, calculates driving frequencies of the loaded vehicles, and decomposes the displacement responses of the bridge by using variational nonlinear component decomposition with the bridge fundamental frequencies and the driving frequencies of the loaded vehicles as initial frequencies, to obtain static displacement responses and dynamic displacement responses, respectively.

[0058] A bridge influence line calculation module obtains final bridge influence lines of the to-be-detected bridge by using least square inverse operation iteration optimization on the vehicle information matrix and the static displacement responses.

[0059] A bridge influence line identification method under random vehicle flow interference comprises the following steps.

[0060] S1, vehicle information such as axle weight, speed, up-bridge time and down-bridge time of a detection vehicle, speeds, up-bridge times and down-bridge times of other random up-bridge vehicles, and a span of a to-be-detected bridge are obtained in advance.

[0061] This step specifically comprises the following steps: before detection starts, the axle weight and the axle distance of the detection vehicle are measured to obtain real axle weight and axle distance of the detection vehicle. In addition, basic information such as the span and lane information of the to-be-detected bridge is obtained in advance. When the detection vehicle and the random interference vehicles pass through the to-be-detected bridge, the up-bridge time and the down-bridge time of the detection vehicle and each random interference vehicle are recorded, and the average speed of the detection vehicle and the random interference vehicles is calculated according to the up-bridge time and the down-bridge time and the span of the bridge. In the case where conditions permit, the speed of the detection vehicle and the random interference vehicles can also be directly measured.

[0062] S2, using the detection vehicle to pass the bridge to be measured at low speed and uniform speed, and using the displacement sensor to detect the initial bridge displacement response Y(t).

[0063] This step specifically includes: installing the displacement sensor at the position to be measured on the bridge, and connecting the signal acquisition module. The detection vehicle passes the bridge to be measured at low speed and uniform speed along the same lane, and the initial bridge displacement response Y(t) is measured and recorded.

[0064] S3, processing the initial bridge displacement response to obtain a smooth bridge displacement response, thereby obtaining the required bridge displacement response matrix Y1(t).

[0065] This step specifically includes: using the time-frequency analysis module to decompose the initial bridge displacement response Y(t) based on the variational nonlinear component to obtain the smooth bridge displacement response matrix Y1(t).

[0066] S4, in the vehicle information matrix, setting the vehicle weight of the random interference vehicle to 0, setting the detection vehicle as the true vehicle weight, and constructing a complete vehicle information matrix L according to the linear superposition principle and the vehicle information at the same time.

[0067] This step specifically includes: placing the vehicle information of the detection vehicle and the interference vehicle into the vehicle information matrix according to the linear superposition principle to obtain a complete vehicle information matrix.

[0068] For a single detection vehicle, the displacement at a certain point on the bridge can be represented as follows:

[0069]

[0070] where P is the weight of the detection vehicle, is the influence line of the bridge to be measured at the position x n , and α(x) and β(x) are distribution coefficients and α(x)+β(x)=1.

[0071] The distribution coefficient is calculated as:

[0072] β(vt)=1-α(vt)

[0073] Generally, the speed of the detection vehicle is lower than that of the interference vehicle, so the up-bridge and down-bridge times of the detection vehicle and the interference vehicle are monitored when the detection vehicle passes the bridge to be measured, and the average speed of each vehicle is obtained.

[0074] At this time, the true vehicle weight of the detection vehicle needs to be input, and the vehicle weights of the remaining interference vehicles are set to 0, to obtain a complete vehicle information matrix L of the detection vehicle and the interference vehicle.

[0075]

[0076] where P is the weight of the test vehicle, P i is the weight of the ith interfering vehicle, is the time of the kth moment of the ith vehicle.

[0077] S5, inverse operation is performed on the bridge displacement response matrix Y1(t) and the vehicle information matrix L to obtain the initial bridge displacement influence line.

[0078] This step specifically includes: according to the definition of the influence line, the vehicle load information matrix and the measured response matrix can be used to identify the bridge influence line.

[0079] L·x=Y1(t)

[0080] Where L is the vehicle information matrix, including the axle load and wheelbase of the heavy vehicle and the interfering vehicle; Y1(t) is the bridge displacement response matrix, including the displacement of the measuring point position during the whole bridge process of the heavy vehicle and the interfering vehicle; x is the bridge displacement influence line matrix.

[0081] The bridge displacement influence line matrix x is calculated by using the least square method to perform matrix inverse operation on the vehicle information matrix L and the bridge displacement response matrix Y1(t) to obtain the initial influence line of the bridge.

[0082] S6, the obtained initial bridge influence line is combined with the bridge displacement response matrix Y1(t) to perform iterative optimization, and inverse operation is performed to obtain the vehicle weight matrix m i .

[0083] This step specifically includes: according to the definition of the influence line, the vehicle load information matrix and the measured response matrix can be used to identify the bridge influence line.

[0084] L·x=Y1(t)

[0085] Where L is the vehicle information matrix, including the axle load and wheelbase of the heavy vehicle and the interfering vehicle; Y1(t) is the bridge displacement response matrix, including the displacement of the measuring point position during the whole bridge process of the heavy vehicle and the interfering vehicle; x is the bridge displacement influence line matrix.

[0086] L=m·X

[0087] Where m is the vehicle weight matrix, and X is the vehicle position matrix.

[0088] Then, under the condition that X, x and Y1(t) are known, the matrix inverse operation can be used to obtain m i The initial influence line of the bridge obtained in S5 is used to calculate the vehicle weight matrix m i .

[0089] S7, the vehicle weight matrix m iRepeat the steps of S5 and S6 until the optimization iteration result meets the preset relative residual error requirement, stop iteration, and obtain the final determined bridge influence line.

[0090] The step specifically comprises: obtaining the vehicle weight matrix m i Repeat the operations of S5 and S6 until the iteration result meets the relative residual error requirement, stop iteration, and obtain the final determined bridge influence line.

[0091] Referring to Figure 2 , an optimization algorithm flowchart used in a bridge influence line identification method under random vehicle flow interference. The system in this embodiment is used to execute the above-mentioned bridge influence line identification method under random vehicle flow interference. The bridge influence line identification method based on linear superposition principle under multi-vehicle interference introduced in this embodiment extracts the bridge influence line from a three-span continuous beam model. In order to further verify the feasibility of the proposed method and system, field experiments are conducted to verify the actual effect.

[0092] The bridge to be detected is a three-span continuous beam, and its cross-sectional view is shown in Figure 3 . The deflection is observed using a dynamic deflection instrument, and the deflection measuring point is arranged at the center of the beam bottom in the middle span, and the schematic view is shown in Figure 3 , 4 . The cross-sectional arrangement of the bridge to be detected is 0.5m (guardrail) + 12.25m (roadway) + 0.5m (guardrail) + 12.25m (roadway) + 0.5m (guardrail) = 26.0.

[0093] In the experiment, standard three-axle trucks are used to simulate the wheel spacing, axle weight and wheel pressure of the design standard load. These experiments will help to verify the actual feasibility of the proposed method. In order to ensure that the test load will not cause local load on the bridge structure beyond the design range, the following measures are taken. Before the test, each loading vehicle is weighed and weighed to ensure that the axle weight of the vehicle meets the test requirements and does not change significantly during the test. The vehicle used is shown in Figure 5 , where A is 3.8m, B is 1.4m, the front axle weight is 7t, the middle axle weight is 16t, and the rear axle weight is 12t. In the experiment, the vehicle is driven at a constant speed as much as possible, and the deflection acquisition instrument records the deflection information of the bridge during the experiment. The deflection acquisition instrument is an instrument used to measure the vertical displacement (i.e. deflection) of the bridge when the vehicle passes through. The deflection acquisition instrument is used as a displacement sensor, and the deflection acquisition instrument can record the dynamic response of the bridge structure under load.

[0094] In the experiment, the detection vehicle passes through route 3 at a speed of 5km / h, and random interference vehicles with different speeds and weights pass through routes 2 and 4 on both sides of the detection vehicle, and the route diagram is shown in Figure 6 .Figure 7 The actual deflection information of the three-span continuous beam is displayed. These measures help ensure the accuracy and reliability of the test.

[0095] The vehicle information of the detection vehicle and the interference vehicle is shown in Table 1. The detection vehicle and the interference vehicles A and B go on the bridge at time 0, 1, and 3 respectively. The detection vehicle and the interference vehicle are allocated to different nodes of the bridge according to the allocation method in S4.

[0096] Table 1

[0097] Vehicle Detection vehicle Jammer vehicle A Jammer vehicle B Vehicle weight (t) 35 Unknown Unknown Vehicle speed (m / s) 2 15 20 Time over bridge (s) 0 16 20

[0098] After obtaining the bridge displacement matrix and vehicle information matrix, the final bridge influence line is obtained through an iterative optimization algorithm, and the results calculated by the invention are compared with the measured benchmark bridge influence line results, such as Figure 8 shown.

[0099] The above is only a preferred specific embodiment of the present invention, but the design concept of the present invention is not limited to this. Any technician familiar with this technical field who uses this concept to make non-substantial changes to the present invention within the technical scope disclosed by the present invention shall be deemed to infringe the scope of protection of the present invention.

Claims

1. A bridge influence line identification method under random traffic flow interference, characterized by: It includes displacement sensor, signal acquisition module, weighing system, time monitoring module, time-frequency analysis module and bridge influence line calculation module; The identification method includes the following steps: S1, before testing, obtain the axle weight and wheelbase of the test vehicle to build a vehicle information matrix, and obtain the span and lane information of the bridge to be tested; when the test vehicle and the random interference vehicle pass through the bridge to be tested, record the time when the test vehicle and the random interference vehicle enter and exit the bridge; S2, use the detection vehicle to pass the bridge to be tested equipped with displacement sensors at a low and constant speed, and record the passing of several random interference vehicles at the same time to obtain the initial bridge displacement response Y(t); S3, decomposing the initial bridge displacement response Y(t) based on the variational nonlinear component through the time-frequency analysis module to obtain a smooth bridge displacement response, and then obtain the bridge displacement response matrix Y1(t); S4, in the vehicle information matrix, set the weight of the random interference vehicle to 0, set the test vehicle to the real weight, and construct a complete vehicle information matrix L based on the linear superposition principle and the vehicle information at the same time; S5, using the bridge influence calculation module to perform matrix inversion calculation using the least squares method on the vehicle information matrix L and the bridge displacement response matrix Y1(t) to obtain a bridge displacement influence line matrix; performing an inverse calculation on the bridge displacement influence line matrix and the vehicle information matrix L to obtain an initial bridge displacement influence line; S6, combine the obtained initial bridge influence line with the bridge displacement response matrix Y1(t), perform iterative optimization, and inversely calculate the vehicle weight matrix m i ; S7, get the vehicle weight matrix m i Then repeat steps S5 and S6 until the optimization iteration result meets the preset relative residual requirement, stop the iteration, and obtain the final bridge influence line.

2. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized by: In S1, the wheelbase and axle weight of the inspection vehicle are obtained through the weighing system before the inspection vehicle passes through the bridge to be inspected, which are used to construct the vehicle information matrix.

3. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized by: In S1, according to the time monitoring module, when the inspection vehicle and the random interference vehicle pass through the bridge to be inspected, the time for the inspection vehicle and the random interference vehicle to go on and off the bridge is detected, which is used to calculate the average speed of the inspection vehicle and the random interference vehicle.

4. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized by: In S1, the average speeds of the test vehicle and the random interference vehicle are calculated based on the time they go up and down the bridge and the span of the bridge to be tested; Or in S1, the speeds of the detection vehicle and the random interference vehicle are measured.

5. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized by: In S2, a displacement sensor is installed at a position to be tested on the main beam of the bridge to be tested, and the displacement response of the bridge to be tested generated during the driving of the detection vehicle and the interference vehicle is measured by the displacement sensor; The displacement sensor is connected to a signal acquisition module, which receives the voltage or current signal from the displacement sensor, records and converts the signal into a digital signal, which is a displacement response signal.

6. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized by: In S3, the displacement response output by the signal acquisition module is received through the time-frequency analysis module, and the displacement response is Fourier transformed. The approximate bridge fundamental frequency is obtained through peak picking and the loading vehicle running frequency is calculated. The bridge fundamental frequency and the loading vehicle running frequency are used as the initial frequencies, and the displacement response of the bridge is decomposed using variational nonlinear component decomposition to obtain the static displacement response and dynamic displacement response, respectively.

7. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized in that: S4 also includes the following specific steps: the displacement calculation formula of two adjacent points on the bridge to be tested by a single inspection vehicle is: Among them, P is the weight of the test vehicle, The bridge to be tested is at the position x to be tested n The influence line of , α(x) and β(x) are the distribution coefficients and α(x)+β(x)=1; The partition coefficient is calculated as: β(vt)=1-α(vt).

8. The bridge influence line identification method under random traffic flow interference according to claim 7 is characterized by: In S4, the complete vehicle information matrix L is calculated as: Where P is the weight of the inspection vehicle, P i is the weight of the i-th interfering vehicle, is the time of the i-th vehicle at time k.

9. The bridge influence line identification method under random traffic flow interference according to claim 1 is characterized by: In S5, the calculation formula of the initial bridge displacement influence line is: L·x=Y1(t) Among them, L is the vehicle information matrix, Y1(t) is the bridge displacement response matrix, and x is the bridge displacement influence line matrix.

10. The bridge influence line identification method under random traffic flow interference according to claim 9 is characterized in that: In S6, the vehicle weight matrix m i The calculation formula is: L=m·X Where m is the vehicle weight matrix and X is the vehicle position matrix.

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