63 results about "Influence line" patented technology

A method for testing and evaluating lateral stiffness of small box girder bridges is provided. The method includes the following steps: a deflection sensor collects a deflection value of a measuring point based on a loading process of a loading vehicle; based on the deflection value of the measuring point and an information matrix of the loading vehicle, an influence line of longitudinal bridge deflection of each measuring point is obtained; based on the influence line of longitudinal bridge deflection, the influence line of lateral bridge deflection of small box girder bridge is obtained by interpolation method; and based on the influence line of lateral deflection of small box girder bridge, a stiffness evaluation result is obtained.

The invention discloses a cable-stayed bridge tension and compression support service life evaluation method and system under random traffic flow load, and belongs to the field of bridge engineering and structure health monitoring, and the method comprises the steps: carrying out the statistical analysis of traffic load data, building a random traffic flow load model, and generating a load spectrum; correcting stay cable force and modal frequency based on bridge monitoring data, and constructing a refined multi-scale finite element model; a unit fatigue vehicle load is applied to the model, a stress influence line is obtained, a rain flow counting method is adopted to calculate stress amplitude, cycle index and average stress, the stress amplitude, the cycle index and the average stress are converted into daily average equivalent stress amplitude and equivalent daily cycle index through a fatigue damage equivalence principle, and the fatigue life of the tension-compression support is evaluated by utilizing a Miner criterion. According to the method, the problem that the influence of three-way alternating stress on bolt fatigue damage cannot be accurately reflected in the prior art is solved, the service life prediction precision of the cable-stayed bridge tension and compression support is improved, the damage of the tension and compression support can be early warned in advance, and data support is provided for safe operation and maintenance of the cable-stayed bridge.

The invention relates to the technical field of highway bridge safety monitoring, and discloses a bridge dynamic weighing algorithm based on likelihood estimation. A bridge is used as a carrier for vehicle weighing, an influence line of the bridge is obtained through a calibration test, and an influence line mean vector and an influence line covariance matrix are calculated; an influence line matrix is obtained based on the influence line mean vector, the vehicle speed and the axle distance, and an initial axle load value is calculated through a Moses algorithm; calculating a mean square error diagonal matrix of a measurement error according to the influence line matrix, the bridge load response and the axle load of the previous iteration step, and obtaining a covariance matrix of the bridge load response; calculating an axle load corresponding to the maximum likelihood probability based on the covariance matrix of the bridge load response in combination with the influence line matrix and the bridge load response; and repeating until the difference value between the axle weight updated this time and the axle weight obtained last time is smaller than a preset value, and taking the axle weight updated this time as a final result. According to the invention, the problem of low axle load identification precision of the existing bridge dynamic weighing system is solved.

The invention discloses a bridge structure monitoring method based on multi-source data and digital twinning and a related device, and relates to the technical field of bridge engineering.The method comprises the steps that an initial finite element model of a target bridge is constructed based on a design drawing and structural parameters of the target bridge, and the initial finite element model of the target bridge is constructed based on the initial finite element model and preset load conditions; calculating to obtain simulation mechanical response data of each grid node of the target bridge; based on the simulation mechanical response data and the obtained actually measured mechanical response data of each grid node of the target bridge, obtaining a corrected finite element model; inputting the obtained traffic load data passing through the target bridge into a digital twinborn calculation model constructed based on the corrected influence line data for numerical calculation to obtain an overall structure response cloud picture and a structure response-time curve of the key monitoring point, and fusing the overall structure response cloud picture and the structure response-time curve with the obtained three-dimensional visualization model to obtain a three-dimensional visualization model; and obtaining the structural state of the target bridge. According to the invention, accurate monitoring and real-time state evaluation of the bridge structure are realized.

The invention relates to the technical field of highway bridge safety monitoring, in particular to a bridge dynamic weighing algorithm based on Bayesian maximum posterior probability. Obtaining influence lines through a bridge calibration test, and calculating an influence line matrix, a mean value and a covariance; using a Moses algorithm to obtain the vehicle axle load as the axle load of the initial main cycle i = 0; initial axle load distribution is set, the covariance of load response is obtained through the influence line covariance, the measurement noise and the axle load of the main cycle, and an axle load mean vector and a stable value of the covariance are iterated together with the influence line matrix, the initial axle load distribution and the load response to serve as the axle load distribution of the main cycle; according to the axle load distribution, the influence line matrix, the load response and the covariance thereof, the posterior probability of the axle load is obtained, and the axle load when the probability is the maximum serves as the axle load corresponding to the (i + 1) th main cycle; and repeating until the axle load update difference value is smaller than the preset value, and outputting the axle load result, thereby solving the problem of low axle load identification precision of the existing bridge dynamic weighing system.

The invention discloses a method for rapidly evaluating the flexural rigidity of a bridge under vehicle-induced excitation, and belongs to the technical field of flexural rigidity evaluation of variable cross-section beams. The method comprises the following steps of: firstly, acquiring a bridge time-history response by adopting vehicle movement loading, separating a power component by utilizing a VMD method, then identifying a main frequency of an IMF component through FFT, removing the power component higher than a bridge fundamental frequency, and reconstructing a bridge actual measurement quasi-static time-history response; then, an axle effect in bridge actual measurement quasi-static time history response is eliminated, an influence line recognition model is established, and Tikhonov regularization is introduced to further solve a deflection influence line; then deriving an analytic solution of a deflection influence line of the variable cross-section continuous beam based on a virtual work principle, establishing a mathematical relationship between the flexural rigidity and the deflection influence line, and identifying the flexural rigidity of the bridge through the deflection influence line based on the mathematical relationship to obtain flexural rigidity distribution of the full bridge; finally, multiple evaluation indexes are defined and calculated, the bending rigidity distribution of the full bridge is comprehensively evaluated, and the method has the advantages of being good in engineering applicability and high in safety evaluation innovativeness.

A bridge response reconstruction method based on main contribution mode superposition comprises the following steps: firstly, testing a bridge influence line, firstly obtaining a deflection influence line of a bridge, calculating a rigidity distribution function of the bridge through the deflection influence line, and then obtaining a vibration mode matrix of the bridge through a characteristic value method by combining a quality function obtained by a bridge design drawing. And analyzing the order of the main contribution mode by using the responded singular value spectrum, obtaining the vibration mode of the main contribution mode in combination with the vibration mode matrix, and substituting the measurement point vibration mode of the main contribution mode into a measurement point bridge response calculation formula based on mode superposition to obtain modal response. And substituting the vibration mode of the unmeasured area of the main contribution mode and the modal response into a bridge response calculation formula based on modal superposition, and reconstructing the response of the unmeasured area of the bridge. In the bridge unmeasured area response reconstruction theory, the error between the modal shape obtained by the finite element model and the actual modal shape is solved, and the precision of the modal response and the reconstruction response of the unmeasured area is improved.

The invention discloses a load distribution method and system for multi-pier coordination of a scouring damaged beam bridge and a storage medium. The method comprises the steps that horizontal and vertical loads acting on a bridge superstructure are obtained; calculating a load distribution result of a vertical load acting on the upper structure of the bridge on the pier according to the counter-force influence line; calculating the horizontal load which acts on the upper structure of the bridge and is distributed to the pier by adopting an iterative method, namely calculating the pier top displacement according to the latest calculated horizontal load of the pier, calculating the equivalent stiffness according to the latest calculated pier top displacement, recalculating the horizontal load distributed to the pier according to the latest calculated equivalent stiffness, and calculating the horizontal load of the upper structure of the bridge according to the calculated equivalent stiffness. The pier top displacement is the pier top displacement of the pier-pile foundation under the conditions of scouring damage, flood load and vehicle load. According to the method, the influence of scouring damage, flood and vehicle load on load distribution is considered, and the accuracy of full-bridge bearing characteristic analysis is improved.

The invention relates to the technical field of bridge health monitoring, and discloses a stay cable damage identification method of influence line cubic spline interpolation micro separation difference, comprising the following steps: S1, constructing an elastic support continuous beam model considering shear deformation and rotational inertia, and establishing a mapping relationship between support stiffness matrix difference before and after damage and cable force influence line change, calculating the cable force influence lines before and after damage, and making a difference to obtain a cable force influence line difference curve; s2, cubic spline interpolation differential is carried out on the cable force influence line difference curve; and S3, sequentially carrying out smoothing processing and mean variance separation calculation on the curvature curve. By constructing an elastic support continuous beam model integrated with shear deformation and rotational inertia and cooperating with a force normal equation of full-bridge support interaction, a cable force influence line difference curve can be accurately calculated, compared with a traditional model, the curve is more accurate, a solid data foundation is laid for damage identification, the identification accuracy is remarkably improved, and it is ensured that damage diagnosis is more reliable.

The invention discloses a bridge bearing capacity dynamic evaluation method based on multi-source monitoring data driving, and aims to solve the problem that the bearing evaluation precision is difficult to guarantee under a sparse sensing coverage condition. According to the method, through cross-modal attention alignment guided by event anchor points, mask codes influencing line and propagation time delay constraints and geometric position coding, Green function kernels and time-varying boundary flexibility latent variables are introduced into a physical constraint neural operator model, symplectic integral propulsion is adopted, and block shape-preserving interval estimation and order-preserving calibration weighted according to working conditions are adopted. According to the method, the full-bridge space-time response and the bearing utilization coefficient are calculated, the risk level and the load limiting suggestion are output, and the technical effects of reliability improvement, physical consistency and long-time energy stability evaluation under the complex working conditions and the low signal-to-noise ratio are achieved.

The invention discloses a bridge model correction method of a frequency and deflection influence line dual-objective function, and belongs to the technical field of bridge finite element model correction. According to the method, the VMD is combined with the Tikhonov regularization influence line identification method, vehicle-induced bridge response power components and multi-axis effect interference are effectively eliminated, the actually-measured deflection time history response of the bridge is restored to the deflection influence line under the unit concentrated load, the effect of identifying the variable-cross-section steel-concrete combined continuous beam bridge influence line is good, and the applicability is wide; according to the model correction method based on combination of a PSO-BP agent model and NSGA-II dual-objective optimization, a complex implicit function relationship between a to-be-corrected parameter and an objective function can be effectively constructed, a Pareto coordinated optimal solution under the dual-objective optimization problem is obtained, then a corrected parameter is obtained, and finite element model correction is achieved.

The invention discloses an existing concrete beam bridge crack diagnosis method based on an influence line sparse coefficient, which is characterized in that force method graph multiplication and a crack nonlinear damage function are substituted into an analytical solution for solving, and finite element simulation relates to respiration crack simulation. A nonlinear spring is adopted to simulate crack opening and perform grid division and encryption processing on the tip of the crack, bridge section rigidity change caused by the breathing crack is accurately simulated by constructing a crack rigidity damage function, and the model is introduced into an influence line analytical solution to obtain a nonlinear influence line of the cracked bridge. In combination with a sparse coefficient representation method, damage position and damage degree information is extracted from influence line data, and accurate positioning and quantitative evaluation of beam bridge section cracking are achieved; the method not only improves the accuracy and efficiency of damage identification, but also provides a reliable technical means for rapid detection and state evaluation of the existing beam bridge.

The present invention discloses an arch bridge damage identification method based on the curvature of the influence line of the arch thrust with a parabolic variable cross-section, belonging to the technical field of arch bridge damage detection. Based on the force method and Ritter's formula, through the setting of the variable cross-section and the fitting of the arch axis shape, a new index - the thrust influence line difference curvature TILDC for reflecting the damage degree of the two-hinged arch structure is proposed. Through the TILDC index, the damage location of the parabolic variable cross-section two-hinged arch structure can be realized, which can be used as the design basis for the strength of the arch seat foundation under the action of moving loads and the theoretical reference for the rapid detection application of arch bridges, verifying the feasibility of using the influence line of the thrust as a damage identification method.

This invention discloses a method for assessing the load-bearing capacity of bridges under random traffic flow, belonging to the technical field of bridge load-bearing capacity assessment. This method improves the accuracy and efficiency of the assessment by extracting the bridge deflection influence line and calculating the compliance matrix through traffic flow statistics, Monte Carlo simulation, finite element analysis, and signal processing. Employing non-destructive testing technology, this method provides real-time health status of the bridge. By analyzing dynamic response data during heavy vehicle travel and combining it with the deflection influence line model, the method predicts the bridge's load-bearing limit and potential damage areas, providing a scientific basis for bridge safety assessment and maintenance. This invention is characterized by high efficiency, accuracy, and strong applicability, avoiding the problems of long testing times, traffic disruption, and large workload associated with traditional static load tests. It is suitable for the load-bearing capacity assessment and condition monitoring of small and medium-span bridges.

The present invention discloses a bridge seismic vulnerability assessment method based on an influence line correction model, belonging to the technical field of bridge seismic vulnerability assessment, and comprising the following steps: S1: finite element model correction; S2: seismic vulnerability assessment. The present invention uses the measured bridge structure strain influence line as the target parameter and utilizes an artificial neural network to correct the bridge finite element model; performs seismic vulnerability analysis on the optimized model of the corrected bridge finite element model, imports 20 seismic motion records and 1 artificial seismic wave, extracts the displacement response of the bridge structure under different peak ground accelerations, plots the structural exceedance probability, and calculates the vulnerability matrix. Furthermore, an expression for the structural seismic damage index is constructed based on the β probability distribution, and a seismic damage comparison curve is plotted using the expected value of the seismic damage index, thereby achieving an accurate evaluation of the seismic resistance of existing bridge structures.

The present application relates to the technical field of bridge health monitoring and detection, and discloses a prefabricated assembled beam bridge longitudinal damage rapid diagnosis method, which comprises the following steps: collecting actual structure parameters of a bridge, establishing a bridge finite element calculation model for calculating a theoretical strain influence line; arranging strain measuring points at the longitudinal midspan of the two outer side beams and the middle beam of the prefabricated assembled beam, and performing a pseudo-static loading test; extracting the strain of the beam bridge strain measuring points, fitting and drawing a strain influence line; calculating the total strain meters of the two outer side beam strain measuring points; calculating the total strain meter change amplitude ΔS of the two outer side beam measuring points; establishing a ΔS column chart of the two in the same coordinate axis, and comparing with a longitudinal damage qualitative diagnosis graph system to obtain a qualitative diagnosis result. The prefabricated assembled beam bridge longitudinal damage rapid diagnosis method has the advantages of short diagnosis time, no interruption of traffic, improved economy, realization of rapid comprehensive diagnosis of the prefabricated assembled beam bridge longitudinal damage, and improved diagnosis accuracy.

The invention relates to the technical field of highway bridge safety monitoring, and discloses a bridge dynamic weighing algorithm based on a regularization algorithm. According to the method, a penalty term is added into an error function to obtain an axle load calculation formula with a regularization matrix, and the regularization matrix is determined by an axle load covariance matrix and a load response covariance matrix. The method specifically comprises the steps of obtaining influence line distribution through a calibration test; giving an initial regularization matrix to obtain an initial axle weight; calculating a load response covariance matrix based on the influence line distribution, the initial axle load and the measurement noise, and iterating a stable axle load covariance matrix in combination with a preset initial axle load covariance matrix; generating a regularization matrix according to the load response covariance matrix and the updated axle load covariance matrix, and calculating a new axle load; and repeating the iteration process until the axle load update difference value is smaller than a preset value, and finally outputting an axle load result. According to the invention, the axle load identification precision of the bridge dynamic weighing system is effectively improved.

The application discloses a kind of bridge existing bearing capacity evaluation methods based on influence surface, comprising the following contents: inclination instrument is arranged in each monitoring point of the bridge surface to be measured;Distance measuring instrument is provided on loading vehicle, vehicle travels according to established route, and distance and inclination data are transmitted to control terminal by synchronous acquisition of distance measuring instrument and inclination instrument, control terminal calculates the displacement of each monitoring point, and vehicle position and bridge deflection information are displayed in real time;Position-deflection curve is determined by single vehicle driving measurement in each lane, and then the deflection influence line of each lane is calculated;Based on the deflection influence line of each lane, the deflection influence surface of the main girder of the bridge to be measured is synthesized;The load position and load value of the test condition to be measured are input into the deflection influence surface, and the deflection estimated value is obtained, which is compared with the allowable value of load specification, to evaluate the bearing capacity of the bridge.The deflection influence surface of the bridge is obtained by rapid load test, the existing bearing capacity of the bridge can be effectively judged, the test time is short, and the identification precision is high.

This application provides a bridge dynamic weighing algorithm based on maximum entropy regularization. The steps include: First, obtain the measured response M of the bridge when the vehicle crosses the bridge m and the influence line matrix IL; Second, use M m and IL to calculate the initial value A of the axle weight 0 , and define the initial value r of the regularization parameter i=1 , where i represents the number of times of the main loop; Third, enter the sub-loop, substitute r i and A 0 into the iterative formula of the non-linear conjugate gradient method for calculation until the axle weight converges, and the absolute value of the difference between the axle weights obtained from the previous and the current iterations is less than e, that is: |A i k - A i k‑1 | < e. Assign the axle weight A i k at the k-th iteration after the convergence of the sub-loop to A i ; Fourth, substitute A i into the Regińska formula to obtain the parameter V, and calculate the next regularization parameter r i+1 ; Fifth, judge whether i is greater than 10. If not, substitute r i=i+1 into step three, and repeat the calculation process of steps three to five; If so, draw the r-V curve; Sixth, observe whether there is a minimum value in the curve. If not, substitute r i=i+1 into step three, and repeat the calculation process of steps three to five; If there is, output the axle weight corresponding to the minimum value and use it as the axle weight identification result.

The present application relates to the technical field of bridge, especially to a bridge dynamic calibration coefficient calculation method, system and device based on space-time characteristics and a storage medium, the method comprises: obtaining a theoretical displacement change value caused by temperature according to main beam temperature time series data; correcting the main beam displacement time series response data by using the theoretical displacement change value to obtain the corrected main beam displacement measured response value; obtaining dynamic load time series distribution data and time-varying influence line, and obtaining a theoretical displacement value based on real operating conditions and bridge time-varying state; obtaining a time series sequence of calibration coefficients according to the corrected main beam displacement measured response value and the theoretical displacement value; dividing the time series sequence of calibration coefficients into windows and extracting statistical characteristics to obtain a representative calibration coefficient corresponding to each window, and fusing the representative calibration coefficient corresponding to each window to obtain a final comprehensive representative calibration coefficient.

This invention discloses a method, device, equipment, and storage medium for identifying abnormal states of light rail bridges. The method includes the following steps: for bridges with the same structural form and span arrangement, monitoring data of each measuring point on the bridge is acquired based on dynamic strain measuring points set at the same control section and spatial location on the bridge; based on the monitoring data, the dynamic strain response when the first train crosses the bridge is determined, and the dynamic strain influence line of the first train crossing the bridge is obtained through the dynamic strain response; based on the dynamic strain influence line, it is determined whether there is an abnormality in the light rail bridge. This application can effectively determine the abnormal state of light rail bridges when trains pass over them, solving the problem of abnormal diagnosis of light rail bridges in actual operation. At the same time, by judging the abnormal state of light rail bridges, the structural safety and health monitoring of light rail bridges is realized, ensuring the smooth operation of the line.

The present invention discloses an arch bridge damage detection method based on the curvature of the catenary hingeless arch strain influence line, belonging to the technical field of arch bridge damage detection. The present invention combines the analysis of hingeless single arch finite element examples for damage identification research of hingeless arch structures, studies the influence of the influence line measurement point position, local damage location and degree, and test noise on the identification results, and constructs influence line difference curvature amplitude curves under different damage conditions, fits the damage degree-amplitude relationship formula, and inverts the damage degree to achieve accurate quantification of damage. The method is verified by combining with an example of a steel truss railway arch bridge structure model, showing that the damage detection method can accurately locate the damage location of the arch structure, quantify the damage degree, and can warn of local damage to the structure and perform diagnosis, thus having engineering application value.

The application belongs to the technical field of engineering structure detection data analysis, and discloses a bridge modal parameter identification method and device combined with vehicle dynamic weighing. First, the influence line of the bridge is tested and obtained, and the bridge response under the vehicle excitation is tested and obtained. The weight of the vehicle is obtained by fitting the bridge response and the influence line, and then the input excitation of the bridge is obtained. Then, the frequency response function matrix of the bridge is obtained by the input excitation and the output response. The maximum singular value curve and the singular vector are obtained by singular value decomposition of the frequency response function matrix of the bridge. The frequency corresponding to the peak value of the curve is the natural frequency of the bridge, and the corresponding singular vector is the vibration mode of the bridge. The frequency corresponding to the half peak value of the curve is the half power point. The damping ratio of the bridge is obtained by bringing the half power point into the damping ratio calculation formula. In the process of bridge operating modal parameter identification, the vehicle excitation is solved for the identification of the bridge modal, and the identification accuracy of the bridge modal parameters is improved.

The invention belongs to the technical field of engineering structure detection data analysis, and discloses a bridge modal parameter identification method and device combined with vehicle dynamic weighing, and the method comprises the steps: firstly, testing to obtain an influence line of a bridge, testing to obtain a bridge response of the bridge under vehicle excitation, and obtaining the weight of a vehicle through the fitting of the bridge response and the influence line; and the input excitation of the bridge is obtained. A frequency response function matrix of the bridge is obtained through input excitation and output response, a maximum singular value curve and a singular vector are obtained through singular value decomposition of the frequency response function matrix of the bridge, the frequency corresponding to the peak value of the curve is the inherent frequency of the bridge, and the corresponding singular vector is the vibration mode of the bridge; the frequency corresponding to the half of the peak value of the curve is a half-power point, and the half-power point is substituted into a damping ratio calculation formula to obtain the damping ratio of the bridge. In the bridge operation modal parameter identification process, the problem of bridge modal identification by vehicle excitation is solved, and the bridge modal parameter identification precision is improved.

The application belongs to the field of bridge health monitoring and rapid detection, and discloses a bridge bending stiffness identification method based on multi-point deflection influence line input, and steps are as follows: firstly, a linear model for solving bending stiffness is established by using measured deflection influence lines at multiple positions of a bridge and linear elasticity theory; then, a redundant dictionary composed of multiple types of global basis functions is used for sparse representation of the bending stiffness distribution curve, and the bending stiffness reconstruction problem is converted into a sparse vector solving problem; finally, a sparse vector solution model with 1-norm regularization constraint is established, and the optimal bending stiffness distribution is obtained by solving the model. l The application can realize continuous bending stiffness curve solving by using multi-point measured deflection influence lines only, is suitable for stiffness evaluation of statically indeterminate in-service bridges such as continuous girder bridges, and has the advantages of strong applicability, high solving efficiency and strong robustness.

The present invention relates to the technical field of construction engineering, and provides an optimization method for external stay cables, internal prestressing tendons and bridge towers of a low tower cable-stayed bridge, including: S1. Regarding the cable force, prestress and tower height as variables to be optimized at the same time, deriving the formula for their collaborative optimization, and solving the optimization problem with the help of programming language; S2. Calculating the influence of the unit cable force of a single stay cable on the stresses of the upper and lower edges of the main girder, and obtaining the influence matrix of the cable force after linear superposition. Similarly, obtaining the influence matrix of the prestressed steel bars on the top and bottom plates of the main girder; S3. Taking the concrete stress of the main girder meeting the specification requirements as the constraint condition of the optimization problem, and incorporating the constraint condition into the optimization objective function by the penalty function method; S4. Establishing the functional relationship between the variables to be optimized and the cost, and obtaining the values of the optimized variables corresponding to the minimum cost of the tower, cables and tendons through the optimization algorithm; S5. Solving the optimization problem multiple times, and statistically obtaining the uniform optimization result of the multi-valued function according to the law of large numbers. The present invention can comprehensively optimize the design of a low tower cable-stayed bridge.

This invention discloses a constraint conversion device and a demonstration teaching aid for constructing the influence line of a multi-span statically determinate beam using the kinematic method. The device includes: a first connecting member for connecting and fixing a first rigid member; a second connecting member for connecting and fixing a second rigid member; several shear constraint conversion boxes of the same shape, height, and proportionally varying length and width, with adjacent shear constraint conversion boxes nested together to form a retractable shear constraint conversion box group; each shear constraint conversion box has a first pin hole, and after all shear constraint conversion boxes are nested and overlapped, all the first pin holes can be fixed by a first pin shaft; and a hinge constraint conversion track, one end of which is fixedly connected to the first connecting member, and the other end of which is hinged or fixedly connected to the shear constraint conversion box group. Advantages: This invention allows switching between shear constraints and hinge constraints; it allows students to assemble the required structural form and release the corresponding constraints, intuitively displaying the structural motion form and deepening students' understanding of the mechanism's motion mode under external forces.

The present disclosure relates to a bridge sensor optimization layout and response reconstruction method, system and medium, relating to the field of bridges. The method comprises: establishing a finite element model of the bridge; deleting measurement points in the initial sensor layout scheme; calculating load transverse influence lines using the bridge finite element model, reconstructing strain response data of the deleted transverse strain measurement points; reconstructing displacement data of the deleted displacement measurement points and / or acceleration data of the acceleration measurement points based on the relationship between displacement and acceleration; reconstructing strain response data of the deleted longitudinal strain measurement points based on the relationship between strain and displacement, using sensor data of the retained longitudinal strain measurement points and using data of the longitudinal displacement measurement points; constructing the architecture of the BiLSTM neural network model; all sensor data and all reconstructed data are input into the network model, and the network model is used to predict the data of the deleted measurement points. The present disclosure realizes the optimization of the sensor layout while improving the reconstruction accuracy.

The application discloses a kind of based on vehicle movement loading small and medium-sized bridge bearing capacity evaluation method, belong to bridge bearing capacity evaluation technical field, comprising the following steps: S1: time history response acquisition;S2: time history response preprocessing;S3: influence line identification;S4: bearing capacity evaluation.The application utilizes single heavy vehicle movement loading to obtain bridge time history response;Then the structural dynamic component in bridge time history response is stripped using variational mode decomposition, and then a bridge influence line identification mathematical model is constructed according to sampling frequency and vehicle wheelbase, so as to eliminate the vehicle multi-axle effect in bridge time history response;And the stable solution of bridge influence line is solved using Tikhonov regularization method;Then by carrying out virtual loading on bridge influence line, reconstructing bridge virtual static response, and using traditional check coefficient method to evaluate bridge bearing capacity, the bridge bearing capacity evaluation rate is effectively improved, and the bridge structure damage caused by high loading efficiency is avoided.