Bridge load carrying capacity assessment method, apparatus, system, and storage medium

Abstract

This invention provides a method, device, system, and storage medium for assessing the load-bearing capacity of bridges, relating to the field of bridge load-bearing capacity assessment technology. The method includes: acquiring structural response data of the target bridge from the moment a valid test vehicle crosses the bridge to the moment it exits; performing signal decomposition on the structural response data to obtain the quasi-static response of the target bridge based on the decomposition results; alternately inverting the influence line of the target bridge and the axle load of the valid test vehicle based on the quasi-static response to obtain the inverted influence line and the inverted axle load; calculating the fitted deflection value and fitted strain value of the target bridge based on the inverted influence line, the inverted axle load, and a preset loading scheme in the load test; calculating the deflection verification coefficient and the strain verification coefficient based on the fitted deflection value and the fitted strain value; and obtaining the load-bearing capacity assessment result of the target bridge based on the deflection verification coefficient and the strain verification coefficient. This invention enables automated and rapid assessment of bridge load-bearing capacity.

Description

Technical Field

[0001] This invention relates to the field of bridge load-bearing capacity assessment technology, and in particular to a bridge load-bearing capacity assessment method, equipment, system and storage medium. Background Technology

[0002] During long-term operation, the load-bearing capacity of bridges gradually decreases due to factors such as material aging, overloading, and environmental erosion. Regular assessment is a key means of ensuring traffic safety.

[0003] According to relevant regulations, the load-bearing capacity of bridges is usually assessed through load tests (including static and dynamic load tests). However, this method has the following main shortcomings: First, traditional static load tests require traffic interruption, deployment of heavy-duty loading vehicles, and multi-condition loading, taking several hours to several days, which is inefficient and has a significant impact on busy traffic routes. Second, while traditional dynamic load tests can be conducted without interrupting traffic, they mainly obtain overall dynamic parameters such as frequency and impact coefficient, making it difficult to directly assess the load-bearing capacity and failing to obtain key information such as structural stiffness distribution. In addition, some assessment methods based on bridge influence lines have emerged. However, on the one hand, existing influence line-based methods are mostly limited to the single index of deflection, lacking monitoring of local stress and failing to comprehensively reflect the actual stress state of the structure, thus falling short of the multi-index comprehensive assessment required by the standards. On the other hand, influence lines calculated purely theoretically are based on design drawings and idealized assumptions, failing to reflect actual structural damage, changes in boundary conditions, and material degradation, leading to a disconnect between theoretical calculations and actual conditions. Moreover, existing methods, when calculating influence lines, require either pre-calibrating the axle load or using the axle load as a known input, due to the coupling between the axle load and the influence line. This limits the automation and universality of the methods.

[0004] Therefore, there is an urgent need for a rapid assessment method for bridge load-bearing capacity that does not require prior calibration, does not disrupt traffic, integrates multiple indicators, and directly connects with standards. Summary of the Invention

[0005] This invention provides a method, device, system, and storage medium for assessing the load-bearing capacity of bridges, in order to solve the problem that traditional methods are difficult to implement for automated and rapid assessment of bridge load-bearing capacity.

[0006] In a first aspect, embodiments of the present invention provide a method for assessing the load-bearing capacity of a bridge, comprising: Acquire the structural response data of the target bridge from the moment a valid test vehicle crosses the bridge to the moment it exits the bridge; The structural response data is decomposed into signals, and the quasi-static response of the target bridge is obtained based on the decomposition results. Based on the influence line of the quasi-static response on the target bridge and the axle load of the effective test vehicle, the inverted influence line and the inverted axle load are obtained by alternating inversion. Based on the inversion influence line, the inversion axle load, and the preset loading scheme in the load test, calculate the fitted deflection value and fitted strain value of the target bridge; The deflection verification coefficient and the strain verification coefficient are calculated based on the fitted deflection value and the fitted strain value, and the bearing capacity assessment result of the target bridge is obtained based on the deflection verification coefficient and the strain verification coefficient.

[0007] In one possible implementation, the identification process for the effective test vehicle includes: The system monitors vehicles before the entrance to the target bridge. When a vehicle enters the pre-screening area before the entrance to the target bridge, the system obtains information related to the vehicle's total weight. The total weight of the vehicle is estimated based on the total weight information to obtain the estimated total weight of the vehicle. Determine whether the estimated total weight is greater than or equal to a preset weight threshold; If the estimated total weight is greater than or equal to the preset weight threshold, the vehicle is recorded as a valid test vehicle.

[0008] In one possible implementation, the method further includes: acquiring the structural response data of the target bridge during a preset period before the time the effective test vehicle enters the bridge, and recording it as baseline response data; The structural response data is decomposed into signals, and the quasi-static response of the target bridge is obtained based on the decomposition results, including: The structural response data is decomposed into several intrinsic mode components using variational mode decomposition, and the center frequency of each intrinsic mode component is determined. The intrinsic modal components whose center frequency is less than a preset frequency threshold are selected, and the selected intrinsic modal components are reconstructed to obtain the initial quasi-static response of the target bridge. The mean of the baseline response data is calculated, and the mean is subtracted from the initial quasi-static response to obtain the quasi-static response of the target bridge.

[0009] In one possible implementation, the influence line of the target bridge and the axle load of the effective test vehicle are alternately inverted based on the quasi-static response to obtain the inverted influence line and the inverted axle load, including: Obtain the theoretical influence line of the target bridge; Based on the preset mapping relationship between static response, influence line and axle load, the theoretical influence line is used as the current known quantity in the preset mapping relationship. The axle load of the effective test vehicle is inverted according to the quasi-static response and the current known quantity to obtain the current inverted axle load. The current inverted axis load is used as the new current known quantity in the preset mapping relationship. The influence line of the target bridge is inverted based on the quasi-static response and the new current known quantity to obtain the current inverted influence line. The current inversion influence line is used as a new known quantity in the preset mapping relationship for iterative iteration, and the current inversion axis weight and current inversion influence line obtained when the iterative termination condition is met are used as the inversion influence line and inversion axis weight; Alternatively, obtain the estimated axle load of the effective test vehicle; Based on the preset mapping relationship between static response, influence line and axle load, the estimated axle load is used as the current known quantity in the preset mapping relationship. The influence line of the target bridge is inverted according to the quasi-static response and the current known quantity to obtain the current inverted influence line. The current inversion influence line is used as the new current known quantity in the preset mapping relationship. The axle load of the effective test vehicle is inverted based on the quasi-static response and the new current known quantity to obtain the current inverted axle load. The current inversion axis weight is used as a new known quantity in the preset mapping relationship for iterative iteration, and the current inversion axis weight and current inversion influence line obtained when the iterative termination condition is met are used as the inversion influence line and inversion axis weight.

[0010] In one possible implementation, the axle load of the effective test vehicle is inverted based on the quasi-static response and currently known quantities to obtain the current inverted axle load, including: Based on the quasi-static response and the current known quantities, regularized least squares is used to solve for the axle load of the effective test vehicle, and inequality constraints regarding the axle load of the effective test vehicle are introduced to obtain the current inverted axle load.

[0011] In one possible implementation, the influence line of the target bridge is inverted based on the quasi-static response and new currently known quantities to obtain the current inverted influence line, including: Based on the quasi-static response and the new known quantities, the influence line of the target bridge is inverted using regularized deconvolution, and boundary conditions for the influence line of the target bridge are introduced to obtain the current inverted influence line.

[0012] In one possible implementation, the quasi-static response includes quasi-static deflection and quasi-static strain; the influence line of the target bridge includes displacement influence line and strain influence line, and the inversion influence line includes inversion displacement influence line and inversion strain influence line; Based on the influence line of the quasi-static response on the target bridge and the axle load of the effective test vehicle, an alternating inversion is performed to obtain the inverted influence line and the inverted axle load, including: Based on the quasi-static deflection, the displacement influence line and the axle load of the effective test vehicle are alternately inverted to obtain the inverted displacement influence line and the first inverted axle load; Based on the quasi-static strain, the strain influence line and the axle load of the effective test vehicle are alternately inverted to obtain the inverted strain influence line and the second inverted axle load; Based on the inverted displacement influence line and the first inverted axis load, the inverted static deflection is obtained; Calculate the first residual between the inverted static deflection and the quasi-static deflection, and obtain the first residual variance based on the first residual; Based on the inverted strain influence line and the second inverted axis weight, the inverted static strain is obtained; Calculate the second residual between the inverted static strain and the quasi-static strain, and obtain the second residual variance based on the second residual; Based on the first residual variance and the second residual variance, calculate the first weight corresponding to the first inversion axis weight and the second weight corresponding to the second inversion axis weight; The first inversion axis weight and the second inversion axis weight are weighted and fused according to the first weight and the second weight to obtain the inversion axis weight.

[0013] In a second aspect, embodiments of the present invention provide a bridge load-bearing capacity assessment device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation of the first aspect.

[0014] Thirdly, embodiments of the present invention provide a bridge bearing capacity assessment system, including the bridge bearing capacity assessment equipment as described in the second aspect above, and further including: vehicle pre-screening equipment, vehicle location identification equipment, and synchronous acquisition equipment; The vehicle pre-screening device is used to identify whether a vehicle is a valid test vehicle; The vehicle location identification device is used to record the time when the valid test vehicle enters and exits the bridge; The synchronous acquisition device is used to synchronously acquire the structural response data of the target bridge from the moment the valid test vehicle crosses the target bridge to the moment it exits the bridge, and send the structural response data to the bridge load-bearing capacity assessment device.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0016] In this embodiment of the invention, the structural response data of the target bridge is first acquired from the moment a valid test vehicle crosses the bridge to the moment it exits. Then, the structural response data is decomposed to obtain the quasi-static response of the target bridge. Next, based on the quasi-static response, the influence line of the target bridge and the axle load of the valid test vehicle are alternately inverted to obtain the inverted influence line and the inverted axle load. Based on the inverted influence line, the inverted axle load, and the preset loading scheme in the load test, the fitted deflection value and fitted strain value of the target bridge are calculated. The deflection verification coefficient and strain verification coefficient are calculated based on the fitted deflection value and fitted strain value, and the load-bearing capacity assessment result of the target bridge is obtained based on these coefficients. Through alternating inversion, the deflection verification coefficient and strain verification coefficient can be calculated using the influence line method without prior calibration of the axle load, thus quickly and automatically obtaining the load-bearing capacity assessment result of the target bridge. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the bridge bearing capacity assessment method provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the bridge bearing capacity assessment system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the displacement influence line in which a virtual model of the effective test vehicle is arranged in the inversion influence line according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the theoretical displacement influence line and the theoretical strain influence line provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of the bridge load-bearing capacity assessment device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] See Figure 1The document illustrates a flowchart of the bridge load-bearing capacity assessment method provided in this embodiment of the invention. The executing entity of this method can be a bridge load-bearing capacity assessment device within a bridge load-bearing capacity assessment system. To enable the bridge load-bearing capacity assessment device to execute the aforementioned bridge load-bearing capacity assessment method, as shown... Figure 2 As shown, the bridge load-bearing capacity assessment system also includes vehicle pre-screening equipment 1, vehicle location identification equipment 2, and synchronous acquisition equipment 3.

[0020] Vehicle pre-screening device 1 is used to identify whether a vehicle is a valid test vehicle.

[0021] Vehicle location identification device 2 is used to record the time when a valid test vehicle enters or exits the bridge.

[0022] Synchronous acquisition device 3 is used to synchronously acquire the structural response data of the target bridge from the moment of entering the bridge to the moment of exiting the bridge when a valid test vehicle passes over the target bridge, and send the structural response data to the bridge bearing capacity assessment device.

[0023] In this embodiment, the target bridge is the bridge whose load-bearing capacity is to be assessed.

[0024] For example, the process of identifying valid test vehicles through vehicle pre-screening equipment may include: The system monitors vehicles before the entrance to the target bridge. When a vehicle enters the pre-screening area before the entrance to the target bridge, it obtains information related to the vehicle's total weight.

[0025] The total weight of the vehicle is estimated based on the relevant information on total weight, thus obtaining the estimated total weight of the vehicle.

[0026] Determine whether the estimated total weight is greater than or equal to the preset weight threshold.

[0027] If the estimated total weight is greater than or equal to the preset weight threshold, the vehicle is recorded as a valid test vehicle.

[0028] Among them, a vehicle pre-screening device 1 can be deployed upstream of the approach road of the target bridge. The vehicle pre-screening device 1 may include a high-speed camera, which can monitor vehicles before the entrance of the target bridge. When a vehicle enters the pre-screening area before the entrance of the target bridge, the high-speed camera obtains the image information of the vehicle and extracts the total weight information from the image information.

[0029] For example, computer vision algorithms can be used to accurately segment the tire profile from image information and measure deformation parameters such as its contact length with the ground and its deflection. Simultaneously, tire specifications and rated tire pressure can be obtained by recognizing characters on the tire sidewall. Combined with a pre-established tire mechanics model, the load on each tire can be calculated, and then summed to obtain the estimated total weight of the vehicle.

[0030] Alternatively, the vehicle's three-dimensional outline, number of axles, etc., can be identified from the image information. Then, the vehicle type can be identified through a deep learning model. Finally, the identified vehicle type can be matched with a publicly available database of typical vehicle models to obtain the estimated total weight of the vehicle.

[0031] For example, the preset weight threshold can be 10 tons, 15 tons, etc. Taking a preset weight threshold of 10 tons as an example, if the estimated total weight is greater than or equal to 10 tons, it can be determined as a valid test vehicle, triggering the collection of subsequent structural response data; otherwise, the vehicle is ignored and no further processing is performed.

[0032] For example, the vehicle pre-screening device 1 may also include a lidar scanner and a laser velocimeter. When a vehicle enters the pre-screening area before the entrance to the target bridge, the speed of the vehicle can be obtained through the laser velocimeter. For valid test vehicles, the lidar scanner records the precise time when each axle passes through the cross-section, and then calculates the vehicle's wheelbase by combining the speed. The calculated wheelbase is then encoded and transmitted, for example, wirelessly to the bridge load-bearing capacity assessment device for subsequent processing. For example, this allows the bridge load-bearing capacity assessment device to perform alternating inversion by combining the influence line of the calculated wheelbase on the target bridge and the axle load of the valid test vehicles.

[0033] After identifying a valid test vehicle, vehicle position identification devices 2 (such as laser-beam light curtain triggers) can be deployed at the entrance and exit approaches of the target bridge to accurately detect the moment when a valid test vehicle enters and leaves the bridge. When a vehicle blocks the light beam, the laser-beam light curtain trigger generates a pulse signal, which is recorded as the moment the vehicle enters the bridge. and the time to get off the bridge .

[0034] The synchronous acquisition device 3 may include a high-precision timing module (such as a Beidou or GPS dual-mode receiver), a synchronous controller, a displacement sensor (such as a millimeter-wave radar, used to acquire the dynamic deflection time history of the target bridge when the valid test vehicle passes over the target bridge), and a strain sensor (such as a distributed sensing fiber and a fiber demodulator, used to acquire the dynamic strain time history of the target bridge when the valid test vehicle passes over the target bridge).

[0035] Specifically, a high-precision timing module can be used to output a PPS (pulses per second) signal. The millimeter-wave radar, fiber optic demodulator, and vehicle position recognition device 2 are all connected to the same synchronization controller. The synchronization controller generates a synchronization sampling clock based on the PPS, and then uses the high-precision timing module to provide a unified time reference for all acquisition devices, ensuring that the data collected by each sensor are accurately aligned on the time axis and achieving microsecond-level synchronization.

[0036] Specifically, when vehicle location recognition device 2 detects the moment of entering the bridge... At this time, the synchronous controller can automatically broadcast a trigger command to all acquisition devices (i.e., displacement sensors and strain sensors) to start continuous acquisition; when the bridge drop moment is detected... At that time, the broadcast stops. To ensure the integrity and accuracy of data collection, an advance distance and a delay distance can be set. The vehicle position identification device 2 is then positioned at the entrance approach of the target bridge according to the advance distance and at the exit approach of the target bridge according to the delay distance, thus setting the collection window as follows: ,in and These are preset time margins (e.g., 2 seconds) corresponding to the advance distance and the delay distance, used to collect silent baseline data before the effective test vehicle enters the target bridge and recovery data after the effective test vehicle leaves the target bridge.

[0037] The collected silent baseline data can be used for subsequent baseline correction, and the collected recovered data can be used for data quality inspection. If the steady-state mean of the recovered data deviates from the silent baseline data by more than a preset deviation threshold (e.g., 5%), it can indicate that there may be residual deformation or sensor drift, and verification or calibration is required.

[0038] After data acquisition is complete, the system can automatically check whether the amplitude of the millimeter-wave radar and fiber optic signals exceeds the corresponding threshold (e.g., 2 or 3 times the environmental noise threshold). If the amplitude is below the threshold, the acquisition is deemed invalid, and the system waits for the next valid test vehicle. If the amplitude is above the threshold, the acquired structural response data (including dynamic deflection time history and dynamic strain time history) is sent to the bridge load-bearing capacity assessment equipment, so that the bridge load-bearing capacity assessment equipment can execute the bridge load-bearing capacity assessment method provided in this embodiment of the invention.

[0039] For example, bridge load-bearing capacity assessment equipment can be divided into a data processing and joint inversion module and a virtual loading and assessment module.

[0040] The data processing and joint inversion module can be an industrial control computer with built-in VMD decomposition and alternating iterative regularization joint inversion algorithms.

[0041] The virtual loading and evaluation module can be a software platform with a built-in standard load library, finite element theoretical value database, and comprehensive evaluation expert system.

[0042] The process of applying the bridge load-bearing capacity assessment method provided in this embodiment of the invention to the bridge load-bearing capacity assessment equipment is described in detail below: Step 101: Obtain the structural response data of the target bridge from the moment of entering the bridge to the moment of exiting the bridge when a valid test vehicle passes over the target bridge.

[0043] For example, the structural response data of the target bridge can be obtained by receiving the dynamic deflection time history and dynamic strain time history collected by the synchronous acquisition device 3 in the bridge bearing capacity assessment system.

[0044] Step 102: Perform signal decomposition on the structural response data and obtain the quasi-static response of the target bridge based on the decomposition results.

[0045] For example, the bridge load-bearing capacity assessment method provided in this embodiment of the invention further includes: acquiring the structural response data of the target bridge within a preset time period before the effective test vehicle enters the bridge, and recording it as baseline response data (which can be obtained by collecting silent baseline data according to the aforementioned advance distance).

[0046] Accordingly, step 102 includes: Variational mode decomposition is used to decompose the structural response data into several intrinsic mode components, and the center frequency of each intrinsic mode component is determined.

[0047] The intrinsic modal components with center frequencies lower than a preset frequency threshold are selected, and the selected intrinsic modal components are reconstructed to obtain the initial quasi-static response of the target bridge.

[0048] Calculate the mean of the baseline response data and subtract the mean from the initial quasi-static response to obtain the quasi-static response of the target bridge.

[0049] In this embodiment, the structural response data may include dynamic deflection time history and dynamic strain time history. These can be processed synchronously via dual channels, processing the dynamic deflection time history acquired by the millimeter-wave radar separately. Dynamic strain time history of distributed fiber optic acquisition The following processing is performed to obtain the aligned quasi-static deflection. and quasi-static strain : (1) Obtain the measured first-order natural frequency of the target bridge For example, the theoretical value can be obtained from the design drawings of the target bridge.

[0050] (2) Adaptive decomposition based on variational mode decomposition (VMD): Variational mode decomposition is used to decompose the original signal (i.e., dynamic deflection time history) into a variable mode decomposition model. or dynamic strain time history Decomposed into K eigenmode functions The number of decomposition layers K can be adaptively determined using the center frequency observation method: initially set K=2, gradually increasing to K=8, stopping when the center frequency of the newly added mode overlaps with an existing mode, and taking the current K as the optimal value. Penalty factor. Use 2000-5000 for convergence tolerance. .

[0051] (3) Calculate the center frequency of each intrinsic mode function (IMF): Perform Hilbert transform on each IMF, calculate its instantaneous frequency, and take the mean as the center frequency of that mode. .

[0052] (4) Separating quasi-static components based on a preset frequency threshold: preset frequency threshold The center frequency Modal reconstruction into quasi-static components Center frequency Modal reconstruction into dynamic components .

[0053] (5) Baseline correction: Take the average value of the data 1 second before the effective test vehicle goes on the bridge as the zero baseline, subtract this average value from the quasi-static component to eliminate temperature drift and zero point error.

[0054] Step 103: Based on the influence line of the quasi-static response on the target bridge and the axle load of the effective test vehicle, perform alternating inversion to obtain the inversion influence line and the inversion axle load.

[0055] In this embodiment, based on the quasi-static response and a preset mapping relationship, the influence line of the target bridge or the axle load of the effective test vehicle is used as a known quantity to alternately invert the influence line of the target bridge and the axle load of the effective test vehicle, thereby obtaining the inverted influence line and the inverted axle load. The preset mapping relationship is the mapping relationship between the static response, the influence line, and the axle load. Thus, by synchronously alternating inversion, the influence line of the target bridge and the axle load of the effective test vehicle are obtained simultaneously, avoiding the impact of pre-calibrated axle load on the automation level of the method, thereby improving the speed of bridge load-bearing capacity assessment.

[0056] For example, step 103 includes: Obtain the theoretical influence line of the target bridge.

[0057] Based on the preset mapping relationship between static response, influence line and axle load, the theoretical influence line is used as the current known quantity in the preset mapping relationship. The axle load of the effective test vehicle is inverted according to the quasi-static response and the current known quantity to obtain the current inverted axle load.

[0058] The current inverted axis load is used as a new known quantity in the preset mapping relationship. The influence line of the target bridge is inverted based on the quasi-static response and the new known quantity to obtain the current inverted influence line.

[0059] The current inversion influence line is used as a new known quantity in the preset mapping relationship for iterative iteration, and the current inversion axis weight and current inversion influence line obtained when the iterative termination condition is met are used as the inversion influence line and inversion axis weight.

[0060] Alternatively, obtain the estimated axle load of the valid test vehicle.

[0061] Based on the preset mapping relationship between static response, influence line and axle load, the estimated axle load is used as the current known quantity in the preset mapping relationship. The influence line of the target bridge is inverted according to the quasi-static response and the current known quantity to obtain the current inverted influence line.

[0062] The current inversion influence line is used as a new known quantity in the preset mapping relationship. The axle load of the effective test vehicle is inverted based on the quasi-static response and the new known quantity to obtain the current inverted axle load.

[0063] The current inversion axis weight is used as a new known quantity in the preset mapping relationship for iterative iteration, and the current inversion axis weight and current inversion influence line obtained when the iterative termination condition is met are used as the inversion influence line and inversion axis weight.

[0064] Specifically, the axle load of the effective test vehicle is inverted based on the quasi-static response and currently known quantities to obtain the current inverted axle load, including: Based on the quasi-static response and current known quantities, regularized least squares is used to solve for the axle load of the effective test vehicle, and inequality constraints regarding the axle load of the effective test vehicle are introduced to obtain the current inverted axle load.

[0065] The process involves inverting the influence line of the target bridge based on the quasi-static response and new known quantities to obtain the current inverted influence line, including: Based on the quasi-static response and the new known quantities, regularized deconvolution is used to invert the influence line of the target bridge, and boundary conditions for the influence line of the target bridge are introduced to obtain the current inverted influence line.

[0066] For example, the quasi-static response includes quasi-static deflection and quasi-static strain; the influence lines include displacement influence lines and strain influence lines, and the inversion influence lines include inversion displacement influence lines and inversion strain influence lines.

[0067] Accordingly, step 103 includes: Based on the quasi-static deflection, the displacement influence line and the axle load of the effective test vehicle are alternately inverted to obtain the inverted displacement influence line and the first inverted axle load.

[0068] Based on the quasi-static strain, the strain influence line and the axle load of the effective test vehicle are alternately inverted to obtain the inverted strain influence line and the second inverted axle load.

[0069] The inverted static deflection is obtained based on the inverted displacement influence line and the first inverted axis load.

[0070] Calculate the first residual between the inverted static deflection and the quasi-static deflection, and obtain the first residual variance based on the first residual.

[0071] The inverted static strain is obtained based on the inverted strain influence line and the second inverted axis weight.

[0072] Calculate the second residual between the inverted static strain and the quasi-static strain, and obtain the second residual variance based on the second residual.

[0073] Based on the first residual variance and the second residual variance, calculate the first weight corresponding to the first inversion axis weight and the second weight corresponding to the second inversion axis weight.

[0074] The first inversion axis weight and the second inversion axis weight are weighted and fused according to the first weight and the second weight to obtain the inversion axis weight.

[0075] In this embodiment, based on influence line theory, the quasi-static response output in step 102 can be determined. (include and () is the axle load of each axle Corresponding influence line of the target bridge The convolution result of (displacement influence lines and strain influence lines):

[0076] It is a coefficient matrix composed of influence lines and axis distances. This is a typical inverse problem with two unknowns. and Both are unknown and coupled. Therefore, an alternating iterative regularization strategy is adopted to jointly solve both problems, decoupling the coupled problem into two subproblems that are solved alternately: Sub-problem A (axle load estimation): Fix the current influence line Solve for axle load Sub-problem B (Influence Line Update): Fix the current axle weight. Update the influence line Through iterative iteration, both solutions converge to a stable solution that closely matches the measured response (or, the current axle load can be fixed first). Solve for the influence lines Then fix the current influence line. Update axle load ).

[0077] The following uses "fixed current influence line" Solve for axle load Taking "" as an example, the solution process will be explained: Algorithm initialization: Provide an initial estimate of the influence line before the first iteration. For example, a finite element model can be built based on bridge design drawings to calculate the theoretical influence line under a unit concentrated force. Alternatively, the Moses algorithm can be used to quickly extract the rough influence line from the structural response data. Or, in the absence of prior information, the influence line can be directly set as a constant vector (convergence is slower). An iteration counter t=0 and a maximum number of iterations T can be set. max =20, convergence tolerance .

[0078] Sub-problem A: Solving for axle load using a fixed influence line. Construction of the coefficient matrix: Given the current influence line estimate Construct the coefficient matrix Where K is the number of sampling points and N is the number of axles. Matrix elements For the first Axis to the first The contribution coefficient of the response at each sampling point is obtained by linear interpolation of the influence line at the corresponding position:

[0079] For the first axis in the The location of each sampling point For the first The distance between the axis and the first axis can be obtained from the axis distance calculated in the above steps. When outside the bridge's boundaries (axle outside the bridge), =0.

[0080] Regularized least squares solution: Introducing Tikhonov regularization to solve for axle load:

[0081] in: ], It is the identity matrix (constrained magnitude). It is a first-order difference operator (constraining the rate of change of adjacent axle loads). 0.2 can be taken. The regularization parameter can be adaptively optimized using the L-curve method.

[0082] Introduction of physical constraints: For example, the following inequality constraints can be introduced into the solution: Non-negative axle load:

[0083] Total weight range: ( , This can be determined when the estimated gross weight of the vehicle is obtained.

[0084] Axle load ratio constraint: For a typical truck, (As an option).

[0085] After adding constraints, the problem is transformed into a quadratic programming problem with inequality constraints, which can be solved using the effective set method.

[0086] Sub-problem B, fixed axle load update influence line: Coefficient matrix reconstruction: Axle load obtained from subproblem A Given the coefficient matrix, reconstruct it. ,in The number of discrete points affecting the line. Matrix elements. The contribution of the j-th influence line node to the response of the k-th sampling point is represented by the sum of the contributions from each axis: ,in, It is a linear interpolation basis function (usually linear interpolation is used, that is, each axis reassigns its axis to the two adjacent influence line nodes).

[0087] Regularized deconvolution: Establish a system of linear equations for solving influence lines Introducing regularization:

[0088] in: We use a second-order difference operator to constrain the smoothness of the influence line (which conforms to physical reality). The L-curve method can also be used to optimize the selection.

[0089] Boundary condition constraints: The influence line should be zero outside the bridge area; therefore, boundary constraints are applied after solving. , This refers to the length of the bridge.

[0090] Calculate the relative change of the axis weight vector between two consecutive iterations:

[0091] when or Stop iterating if t=t+1; otherwise, let t=t+1 and continue iterating.

[0092] Perform the above process on the quasi-static deflection and quasi-static strain channels respectively to obtain two sets of outputs: and .

[0093] Axle load integration: The weighted average method is used to fuse the two-channel axis weight estimates:

[0094] in, and These are the residual variances of the last iteration for the two channels (i.e., the first and second residual variances mentioned above). , , This is the residual from the last iteration of the displacement channel. , This is the residual from the last iteration of the strain channel. .

[0095] The displacement influence line and the strain influence line are two quantities with different physical meanings, so they are not merged and are output independently. .

[0096] Therefore, after the iteration converges, the output is the fused axis weight vector. and displacement influence line Strain influence lines .

[0097] In addition, consistency checks can be performed, which involves calculating the relative difference between the axle load estimates of the two channels: ,like If the value is greater than 0.15, a consistency warning will be triggered, indicating that there may be local damage or data anomalies.

[0098] Step 104: Calculate the fitted deflection and fitted strain values ​​of the target bridge based on the inversion influence line, the inversion axle load, and the preset loading scheme in the load test.

[0099] In this embodiment, the displacement influence line in the inversion influence line obtained based on the above steps In conjunction with the inversion of axle load, according to relevant load test regulations, a suitable loading scheme can be selected from the proposed load test plan. Based on the standard vehicle train arrangement in the loading scheme, the virtual model of the effective test vehicle is placed at the corresponding position on the displacement influence line, and the fitted deflection value is calculated using the superposition principle. :

[0100] Where: n is the number of valid test vehicles, and m is the number of axles per vehicle. Let i be the axle load of the j-th vehicle on the i-th axle. Displacement influence line At the horizontal axis position The vertical coordinate value at the location. For example, a layout diagram is shown below. Figure 3 As shown, the horizontal axis represents the placement location. , , The axes represent the axle loads of the j-th vehicle on the 1st, 2nd, and 3rd axles, respectively, and the vertical axis represents the value of the displacement influence line. , , The values ​​of the displacement influence lines corresponding to the positions of the first, second, and third axles of the j-th vehicle, respectively.

[0101] Similarly, based on the strain influence lines obtained from the inversion... Calculate the fitted strain value .

[0102] The aforementioned load test scheme can include multiple load modes, such as standard loads, historical heavy vehicle loads, and special vehicle loads, to achieve rapid analysis under multiple working conditions.

[0103] Step 105: Calculate the deflection verification coefficient and strain verification coefficient based on the fitted deflection value and fitted strain value, and obtain the load-bearing capacity assessment result of the target bridge based on the deflection verification coefficient and strain verification coefficient.

[0104] Based on step 104, the theoretical displacement influence line can be obtained based on the finite element theory model (e.g., Figure 4 (as shown in (b)) and the theoretical strain influence line (as shown in (b)) Figure 4 (as shown in (a)), and then calculate the theoretical deflection value under the same load based on the theoretical displacement influence line and the theoretical strain influence line. and theoretical strain value Then, the deflection verification coefficients are calculated respectively. and strain verification coefficient .

[0105] Then, based on the constant values ​​of the deflection verification coefficient and the constant values ​​of the strain verification coefficient in the relevant regulations, the following can be done: and Each judgment is made separately. For example, the comprehensive evaluation logic can be: like and If all values ​​meet the specified limits, then the bearing capacity is deemed to meet the requirements.

[0106] If both verification coefficients exceed the limits, the bearing capacity is deemed not to meet the requirements.

[0107] If the two coefficients deviate (e.g., one satisfies the condition while the other does not), the damage identification module is triggered, indicating the presence of localized defects.

[0108] In addition, the dynamic components separated in the above steps can be combined to identify the bridge's measured fundamental frequency, calculate the impact coefficient, and use it as an auxiliary evaluation index to finally output a bridge bearing capacity level evaluation report.

[0109] The invention will be described in detail below using a 30m span prestressed concrete simply supported T-beam bridge as an example: Implementation Step 1: On-site preparation and system deployment.

[0110] Vehicle pre-screening equipment is deployed 50m before the approach road to the bridge entrance, including a lidar (Ouster OS1), a high-speed camera (Basler acA1300), and a laser velocimeter (LTI 20 / 20). The system uses a deep learning model to identify vehicle types, estimate axle load, and screen vehicles with a total weight ≥10t.

[0111] Laser-type light curtain triggers (SICK WL4-3P3232) are installed 5m before the entrance guide and 5m after the exit guide, respectively. The triggers output TTL signals to the synchronous controller.

[0112] A millimeter-wave radar (IDS IBIS-FS) was installed 8m below the mid-span of the bridge, with a sampling rate of 200Hz. Distributed optical fibers (polyimide-coated single-mode optical fibers) were laid longitudinally along the bottom of the beam, bonded with structural adhesive, and connected to an optical fiber demodulator (Micron Optics sm130) with a sampling rate of 100Hz.

[0113] The synchronization controller uses an STM32 main control chip and is connected to an external Beidou / GPS dual-mode timing module (UM980, timing accuracy ±50ns). It generates a 200Hz synchronization clock and distributes it to the millimeter-wave radar and fiber optic demodulator via RS485.

[0114] Implementation Step 2: On-site Testing.

[0115] One day, a three-axle truck (estimated total weight approximately 25t) entered the pre-screening area. The system identified it as a three-axle truck, estimating axle loads of 6t, 9t, and 10t (the estimated axle loads can be used for subsequent alternating inversion), a speed of v=18km / h (i.e., 5m / s), and wheelbases calculated by laser profile scanning to be 3.5m and 1.4m. The system determined it to be a valid test vehicle and triggered data acquisition preparation.

[0116] The vehicle continued driving until it reached the light curtain at the bridgehead; the moment the vehicle's front blocked the beam of light was recorded as the moment the vehicle entered the bridge. The synchronization controller immediately broadcasts a command to start data acquisition, and each device then... Start recording data (using internal buffer). The vehicle crosses the 30m bridge at a speed of 5m / s, taking 6 seconds. The moment the front of the vehicle reaches the light curtain at the end of the bridge is recorded as the moment the vehicle exits the bridge. The synchronous controller broadcasts a stop command, which is then collected. End. Data collection took approximately 10 seconds.

[0117] Step 3: Signal decomposition.

[0118] The acquired dynamic deflection signal (i.e., dynamic deflection time history) is imported into the industrial control computer and decomposed into four modes using the VMD algorithm (the number of decomposition layers K=4 was determined by the center frequency observation method), with center frequencies of 0.3Hz, 0.9Hz, 4.2Hz, and 12.5Hz, respectively. A preset frequency threshold is used. The modes with center frequencies of 0.3 Hz and 0.9 Hz were reconstructed into quasi-static deflections. The remaining components are reconstructed as dynamic components. The strain signal (i.e., the dynamic strain time history) is processed similarly to obtain the quasi-static strain. After baseline correction, the quasi-static component waveform is smooth and the peaks are clear.

[0119] Step 4: Joint inversion of axle load and influence line.

[0120] Using the theoretical influence line (calculated based on the finite element model) as the initial value Enter alternating iteration.

[0121] First iteration: fixed Construct the coefficient matrix Tikhonov regularization (L-curve method) is used to select Solving for the axle load, we get... .

[0122] fixed Construct the coefficient matrix Second-order difference regularization (L-curve method) is used to select... Update the influence line to get .

[0123] calculate Continue iterating.

[0124] Until the 5th iteration, , Iterative convergence.

[0125] Perform the above process on the deflection and strain channels respectively to obtain... , The residual variances of the two channels are respectively , The final axis weight is obtained by weighted fusion. Consistency check <0.15, take the final influence line. and .

[0126] Implementation Step 5: Virtual Loading and Evaluation.

[0127] According to relevant regulations, a Highway-I level load is selected. Based on the standard load and loading location specified in the load test plan, the loaded vehicles are virtually positioned at the corresponding locations along the influence line. The specific plan is as follows: Two three-axle trucks (P1=60kN, P2=120kN, P3=120kN) are arranged side-by-side in the middle of the span. The corresponding positions and influence line values ​​of each axle are: Axle 1 (15m) = 0.0174mm / kN, Axle 2 (11.5m) = 0.0139mm / kN, Axle 3 (10.1m) = 0.0117mm / kN. According to the formula... Calculate the fitted deflection value Theoretical deflection value Deflection verification coefficient Similarly, the calculated fitted strain value is 65 με, the theoretical strain value is 72 με, and the strain verification coefficient is... .

[0128] According to the specifications, the constant range of the deflection verification coefficient for prestressed concrete beams is 0.70-1.00, and the constant range of the strain verification coefficient is 0.60-0.90. qualified, The reading is at the upper limit, indicating a need to pay attention to the local stress state. Meanwhile, the fundamental frequency is identified as 4.2Hz (theoretically 4.5Hz) from the dynamic components, and the impact factor is calculated to be 0.15.

[0129] Step 6: Output the conclusion.

[0130] Overall assessment: The bridge has good overall stiffness (deflection verification coefficient is qualified), but the stress level of the cross section is too high (strain verification coefficient is close to the upper limit). It is recommended to strengthen daily inspections, pay attention to the development of cracks at the bottom of the beams, and issue a formal assessment report.

[0131] Compared with the prior art, the embodiments of the present invention have the following significant advantages: 1. Achieved the collaborative inversion of axle weight and influence line: The pioneering alternating iterative regularization framework eliminates the need for pre-calibrating axle weight and jointly inverts the axle weight and dual-indicator influence line from a single passage response, solving the "double unknown" problem of traditional methods and significantly improving the degree of automation.

[0132] 2. One-time passage, multiple parameter outputs: Only one vehicle needs to pass through once to simultaneously obtain multiple standard evaluation indicators such as deflection verification coefficient, strain verification coefficient, bridge fundamental frequency, and impact coefficient, resulting in an order-of-magnitude improvement in information acquisition efficiency.

[0133] 3. Dual-indicator synergy of displacement and strain: It reflects both the overall stiffness of the structure and captures the local stress of the cross section, making the evaluation results more comprehensive and reliable, and highly consistent with the multi-indicator evaluation requirements of the standard.

[0134] 4. Directly connects to existing standards: By virtually loading and outputting verification coefficients that conform to the relevant regulations, the evaluation results can be directly used to issue legally valid bridge technical condition evaluation reports.

[0135] 5. Realistic assessment based on actual measured conditions: The extracted influence lines are a comprehensive reflection of the current structural damage, boundary conditions, and material degradation, truly realizing the assessment of the bridge's "actual bearing capacity".

[0136] 6. Extremely high testing efficiency and minimal traffic disruption: The entire process does not require traffic interruption or pre-weighing. Each test only requires the vehicle to cross the bridge for a few seconds, improving field efficiency by more than 95% compared to traditional static load tests.

[0137] 7. Fully automated process, suitable for long-term health monitoring: Through vehicle pre-screening, automatic triggering of data collection, and joint inversion processing, long-term online monitoring without human intervention can be achieved.

[0138] 8. Provide a data foundation for digital twins of bridges: The measured high-precision influence lines can be used to calibrate finite element models and realize the accurate inversion of structural stiffness and boundary conditions.

[0139] In summary, the bridge load-bearing capacity assessment method provided by this invention realizes the synergistic inversion of axle load and influence line, and can obtain multiple standard assessment indicators in a single pass. It has the advantages of high efficiency, high accuracy and high degree of automation, and can be widely used for the rapid assessment of the load-bearing capacity of in-service bridges.

[0140] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0141] Figure 5 This is a schematic diagram of the bridge load-bearing capacity assessment device provided in an embodiment of the present invention. Figure 5 As shown, the bridge load-bearing capacity assessment device 5 of this embodiment includes a processor 50 and a memory 51. The memory 51 stores a computer program 52. When the processor 50 executes the computer program 52, it implements the steps in the above-described method embodiments. Alternatively, when the processor 50 executes the computer program 52, it implements the functions of each module / unit in the above-described device embodiments.

[0142] For example, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 52 in the bridge load-bearing capacity assessment device 5.

[0143] The bridge load-bearing capacity assessment device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of the bridge load-bearing capacity assessment device 5 and does not constitute a limitation on the bridge load-bearing capacity assessment device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the bridge load-bearing capacity assessment device 5 may also include input / output devices, network access devices, buses, etc.

[0144] The processor 50 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0145] The memory 51 can be an internal storage unit of the bridge load-bearing capacity assessment device 5, such as a hard disk or RAM. The memory 51 can also be an external storage device of the bridge load-bearing capacity assessment device 5, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the bridge load-bearing capacity assessment device 5. Furthermore, the memory 51 can include both internal and external storage units of the bridge load-bearing capacity assessment device 5. The memory 51 is used to store the computer program 52 and other programs and data required by the bridge load-bearing capacity assessment device 5. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0146] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0147] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0148] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0149] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0150] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0151] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for assessing the load-bearing capacity of bridges, characterized in that, include: Acquire the structural response data of the target bridge from the moment a valid test vehicle crosses the bridge to the moment it exits the bridge; The structural response data is decomposed into signals, and the quasi-static response of the target bridge is obtained based on the decomposition results. Based on the influence line of the quasi-static response on the target bridge and the axle load of the effective test vehicle, the inverted influence line and the inverted axle load are obtained by alternating inversion. Based on the inversion influence line, the inversion axle load, and the preset loading scheme in the load test, calculate the fitted deflection value and fitted strain value of the target bridge; The deflection verification coefficient and the strain verification coefficient are calculated based on the fitted deflection value and the fitted strain value, and the bearing capacity assessment result of the target bridge is obtained based on the deflection verification coefficient and the strain verification coefficient.

2. The bridge load-bearing capacity assessment method according to claim 1, characterized in that, The process for identifying the valid test vehicle includes: The system monitors vehicles before the entrance to the target bridge. When a vehicle enters the pre-screening area before the entrance to the target bridge, the system obtains information related to the vehicle's total weight. The total weight of the vehicle is estimated based on the total weight information to obtain the estimated total weight of the vehicle. Determine whether the estimated total weight is greater than or equal to a preset weight threshold; If the estimated total weight is greater than or equal to the preset weight threshold, the vehicle is recorded as a valid test vehicle.

3. The bridge bearing capacity assessment method according to claim 1, characterized in that, Also includes: Obtain the structural response data of the target bridge within a preset time period before the effective test vehicle enters the bridge, and record it as the baseline response data; The structural response data is decomposed into signals, and the quasi-static response of the target bridge is obtained based on the decomposition results, including: The structural response data is decomposed into several intrinsic mode components using variational mode decomposition, and the center frequency of each intrinsic mode component is determined. The intrinsic modal components whose center frequency is less than a preset frequency threshold are selected, and the selected intrinsic modal components are reconstructed to obtain the initial quasi-static response of the target bridge. The mean of the baseline response data is calculated, and the mean is subtracted from the initial quasi-static response to obtain the quasi-static response of the target bridge.

4. The bridge bearing capacity assessment method according to claim 1, characterized in that, Based on the influence line of the quasi-static response on the target bridge and the axle load of the effective test vehicle, an alternating inversion is performed to obtain the inverted influence line and the inverted axle load, including: Obtain the theoretical influence line of the target bridge; Based on the preset mapping relationship between static response, influence line and axle load, the theoretical influence line is used as the current known quantity in the preset mapping relationship. The axle load of the effective test vehicle is inverted according to the quasi-static response and the current known quantity to obtain the current inverted axle load. The current inverted axis load is used as the new current known quantity in the preset mapping relationship. The influence line of the target bridge is inverted based on the quasi-static response and the new current known quantity to obtain the current inverted influence line. The current inversion influence line is used as a new known quantity in the preset mapping relationship for iterative iteration, and the current inversion axis weight and current inversion influence line obtained when the iterative termination condition is met are used as the inversion influence line and inversion axis weight; Alternatively, obtain the estimated axle load of the effective test vehicle; Based on the preset mapping relationship between static response, influence line and axle load, the estimated axle load is used as the current known quantity in the preset mapping relationship. The influence line of the target bridge is inverted according to the quasi-static response and the current known quantity to obtain the current inverted influence line. The current inversion influence line is used as the new current known quantity in the preset mapping relationship. The axle load of the effective test vehicle is inverted based on the quasi-static response and the new current known quantity to obtain the current inverted axle load. The current inversion axis weight is used as a new known quantity in the preset mapping relationship for iterative iteration, and the current inversion axis weight and current inversion influence line obtained when the iterative termination condition is met are used as the inversion influence line and inversion axis weight.

5. The bridge bearing capacity assessment method according to claim 4, characterized in that, Based on the quasi-static response and currently known quantities, the axle load of the effective test vehicle is inverted to obtain the current inverted axle load, including: Based on the quasi-static response and the current known quantities, regularized least squares is used to solve for the axle load of the effective test vehicle, and inequality constraints regarding the axle load of the effective test vehicle are introduced to obtain the current inverted axle load.

6. The bridge bearing capacity assessment method according to claim 4, characterized in that, Based on the quasi-static response and the new currently known quantities, the influence line of the target bridge is inverted to obtain the current inverted influence line, including: Based on the quasi-static response and the new known quantities, the influence line of the target bridge is inverted using regularized deconvolution, and boundary conditions for the influence line of the target bridge are introduced to obtain the current inverted influence line.

7. The bridge load-bearing capacity assessment method according to claim 1, characterized in that, The quasi-static response includes quasi-static deflection and quasi-static strain; the influence line of the target bridge includes displacement influence line and strain influence line, and the inversion influence line includes inversion displacement influence line and inversion strain influence line. Based on the influence line of the quasi-static response on the target bridge and the axle load of the effective test vehicle, an alternating inversion is performed to obtain the inverted influence line and the inverted axle load, including: Based on the quasi-static deflection, the displacement influence line and the axle load of the effective test vehicle are alternately inverted to obtain the inverted displacement influence line and the first inverted axle load; Based on the quasi-static strain, the strain influence line and the axle load of the effective test vehicle are alternately inverted to obtain the inverted strain influence line and the second inverted axle load; Based on the inverted displacement influence line and the first inverted axis load, the inverted static deflection is obtained; Calculate the first residual between the inverted static deflection and the quasi-static deflection, and obtain the first residual variance based on the first residual; Based on the inverted strain influence line and the second inverted axis weight, the inverted static strain is obtained; Calculate the second residual between the inverted static strain and the quasi-static strain, and obtain the second residual variance based on the second residual; Based on the first residual variance and the second residual variance, calculate the first weight corresponding to the first inversion axis weight and the second weight corresponding to the second inversion axis weight; The first inversion axis weight and the second inversion axis weight are weighted and fused according to the first weight and the second weight to obtain the inversion axis weight.

8. A bridge load-bearing capacity assessment device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

9. A bridge load-bearing capacity assessment system, characterized in that, The bridge load-bearing capacity assessment equipment as described in claim 8 also includes: vehicle pre-screening equipment, vehicle location identification equipment, and synchronous data acquisition equipment; The vehicle pre-screening device is used to identify whether a vehicle is a valid test vehicle; The vehicle location identification device is used to record the time when the valid test vehicle enters and exits the bridge; The synchronous acquisition device is used to synchronously acquire the structural response data of the target bridge from the moment the valid test vehicle crosses the target bridge to the moment it exits the bridge, and send the structural response data to the bridge load-bearing capacity assessment device.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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