CN114997009B - Bridge bearing capacity rapid assessment method based on machine vision and model correction - Google Patents
Bridge bearing capacity rapid assessment method based on machine vision and model correction Download PDFInfo
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Abstract
The invention discloses a bridge bearing capacity rapid assessment method based on machine vision and model correction. The method comprises two parts, wherein the first part is bridge span cross section measuring point vibration data acquisition and bridge finite element model correction, and the second part is bridge bearing capacity assessment; collecting vibration images of bridge cross-section measuring points in the moving process of a vehicle and after the vehicle leaves a bridge by using a high-resolution high-frame rate camera, identifying and extracting deflection time course data of the bridge cross-section measuring points in the moving process of the vehicle and free vibration deflection time course data of the bridge cross-section measuring points after the vehicle leaves the bridge by using an image identification algorithm, calculating and identifying to obtain an actual measurement deflection influence line of the bridge cross-section and actual measurement frequency of the bridge, correcting and optimizing an initial finite element model by taking an actual measurement deflection value and the bridge frequency as state variables and taking parameters obtained by sensitivity analysis as design variables to obtain a bridge reference finite element model; and carrying out bridge bearing capacity assessment based on the corrected reference finite element model.
Description
Technical Field
The invention relates to a bridge bearing capacity rapid assessment method based on machine vision and model correction, and belongs to the technical field of bridge detection and assessment.
Background
The bridge evaluation is a process of evaluating the safety and reliability of the existing bridge and making engineering decisions by using static parameters such as strain, deflection and the like or dynamic parameters such as frequency, damping, vibration mode and the like. The existing common bridge bearing capacity assessment method mainly comprises an appearance detection assessment method, a load test assessment method, a design specification based assessment method, an expert system assessment method and the like, and the assessment methods have own limitations: appearance assessment methods and expert system assessment methods are simple and depend on experience of engineers and the expertise level of experts, and the assessment process comprises a plurality of uncertainty factors; traffic needs to be interrupted in the load test, and time and effort are consumed; the actual degradation condition of the bridge cannot be truly and reliably reflected based on the design specification evaluation method, and the problems of updating of the design specification, load mode change and the like need to be faced, so that the evaluation accuracy is affected to a certain extent.
The bridge detection work is developed into various nondestructive detection by manual detection and bridge inspection vehicle detection, and in recent years, along with rapid development and wide application of computer science and optics, a non-contact intelligent detection technology appears, so that a better choice is provided for bridge detection, wherein a machine vision method is a bridge detection method which is paid attention to at present. The method is based on a machine vision theory, and aims to enable a computer to replace manual work to automatically detect and identify the bridge in a long distance, high precision and low cost. The machine vision method avoids the defects of low detection speed, low efficiency, poor real-time performance and traffic influence of manual detection and bridge inspection.
The bridge field test comprises a dynamic test and a static test, and in the dynamic test, the frequency test precision of the structure is higher and the integral effect of the structure can be reflected; in the static test, the deflection test precision of the structure is higher and reflects the integral effect of the structure, and the strain test data mainly reflects the local effect. Therefore, the invention selects two indexes of deflection and frequency for measurement, and can reflect the overall static and dynamic performance of the bridge.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for rapidly evaluating the bridge bearing capacity.
The invention is realized by the following technical scheme: a bridge bearing capacity rapid assessment method based on machine vision and model correction is characterized by comprising the following steps: comprises two parts:
the first part is bridge span cross section measuring point vibration data acquisition and finite element model correction, and comprises the following steps:
step one: establishing an initial finite element model of the bridge according to a design drawing and related specifications, simulating a loading vehicle to move across the bridge deck by adopting a static force method, and calculating a theoretical deflection influence line of a bridge cross section and 2-3 th order theoretical frequency in front of the bridge;
step two: performing sensitivity analysis on bridge design parameters by using finite element software, and selecting bridge structure parameters with larger influence on deflection and frequency as parameters to be corrected;
step three: a loading vehicle slowly runs through an actual bridge at a certain speed, deflection measuring points with the same positions as the finite element model are selected from the cross-center section to serve as machine vision measuring points, a high-resolution high-frame-rate camera is used for measuring vibration images of the bridge cross-center section measuring points in the moving process of the vehicle and vibration images of the bridge cross-center section measuring points after the vehicle leaves the bridge, a dynamic image recognition program is compiled, a bilateral filtering algorithm is used for carrying out noise reduction on the images, an ACE image enhancement algorithm is used for improving image contrast, RGB three-channel images are converted into single-channel images, and image gray scale processing is achieved; identifying and extracting a deflection time course curve of a bridge cross-section measuring point in the moving process of the vehicle and a free vibration deflection time course curve of the bridge cross-section measuring point after the vehicle leaves the bridge by adopting an image identification tracking algorithm, and respectively calculating and identifying to obtain a bridge actual measurement deflection influence line and frequency;
step four: taking the parameters to be corrected of the bridge, which are obtained in the step two, as design variables, taking the actually measured deflection influence line and the actually measured frequency, which are obtained in the step three, as state variables, and constructing an objective function according to a difference value between an actually measured value and a theoretical value, wherein the objective function is as shown in the formula (1):
wherein omega is i 、Respectively a calculated value and an actual measured value of the ith-order natural vibration frequency of the bridge, u i 、/>Respectively calculating a static deflection value and an actual measurement value of an ith measuring point of the bridge; w (w) i 、w′ i Is a weight coefficient; f is the difference objective function between the actual measurement value and the theoretical value;
step five: and (3) carrying out post-processing on finite element calculation results: analyzing and finishing the bridge theoretical self-vibration frequency and mid-span deflection data obtained in the step one, comparing the bridge theoretical self-vibration frequency and mid-span deflection data with an actual measurement value of a static and dynamic load test, and substituting the actual measurement value into an objective function for calculation;
step six: judging whether the response of the objective function and the bridge structure meets the optimization precision requirement or not: if the requirements are met, the adopted calculation parameters are optimal parameter values, the model correction process is finished, and the optimized design variables are used for correcting the initial finite element model of the bridge to obtain a reference finite element model capable of representing the actual working state of the bridge; if the requirements are not met, changing the bridge design parameter value, and continuing to carry out loop iteration analysis until the accuracy requirements are met;
the second part is bridge bearing capacity evaluation, which comprises the following steps:
step one: according to the highway bridge load test procedure, a bridge static load test scheme is formulated, so that the loading efficiency is between 0.95 and 1.05, a triaxial truck can be adopted by a loading truck, and the theoretical design deflection of a bridge under test load is calculated based on an initial finite element model;
step two: according to the test load of the static load test scheme, predicting actual measured deflection of the bridge span cross section under the test load based on a reference finite element model, wherein the deflection reflects the structural deformation of an actual bridge;
step three: calculating bridge deflection check coefficient by adopting the following formulaBridge assessment using deflection verification coefficientsLoad carrying capacity of the beam:
wherein: s is S e The method is characterized in that the method is an actually measured elastic deflection or strain value of a main measuring point of a reference finite element model under the action of test load; s is S s And calculating a deflection or strain value for the theory of the main measuring point of the initial finite element model under the test load.
Static load test structure calibration coefficientIs the ratio of the measured elastic deflection of the measuring point under the action of the test load to the corresponding theoretical value. According to the rules of the highway bridge bearing capacity detection evaluation procedure->When the value is smaller than 1, the actual condition of the bridge is superior to the theoretical condition of the design, which indicates that the bearing capacity of the bridge meets the design requirement.
The invention uses a finite element model correction optimization algorithm, adopts two indexes of measured data of bridge deflection influence line and frequency measured by a machine vision technology and theoretical data calculated by a finite element model to carry out model correction, carries out bridge bearing capacity assessment based on a corrected reference finite element model, is the finite element model correction combined with static and dynamic measured data, and can increase the reliability of the corrected finite element model.
Further, the image recognition algorithm in the third step is a template matching method or an optical flow method or a feature point matching method or a shape-based matching or a deep learning-based target tracking method. The principle of the template matching algorithm for identifying and extracting bridge displacement is as follows:
a digital camera with fixed space position is used for acquiring a gray level image containing preset targets, firstly, in a first frame of image, all targets containing preset targets to be tested are extracted in a form of a template, and the template is a subset of the targets to be tested in the image. The template matching process is to search the position of the target in the image by using a preset template in the subsequent image shot by the digital camera. The search process typically scans the template from the top left corner of the image to the right, line by line, until the best match position is found. And (3) performing two-dimensional image correlation operation on the position where the template coincides with the image in the scanning process to obtain a correlation coefficient. When the correlation coefficient reaches a maximum value, it is considered that the best matching position of the object is found in the image.
As shown in fig. 1, an initial (first frame) image g is obtained from a size m×n 0 Rectangular image subset f extracted in (x, y) k (x, y) as a template for the kth object. The centrum of the kth template is marked (x) k 0 ,y k 0 ) Template f k (x, y) and g 0 The correlation coefficients of (x, y) are:
where i=0, 1,..m-1, j=0, 1,..n-1, t is the image capturing time;
assuming that the origin of coordinates of the image is at the upper left corner, the process of correlation calculation is to calculate the superposition of the template and the image from the upper left corner of the image to obtain a correlation coefficient c k (i, j) after the calculation is completed, shifting the line of the template by one pixel to the right for calculation. Moving one pixel down into the second row after reaching the rightmost edge, continuing to move from left to right until each position makes a correlation calculation. When the kth template completes all the correlation calculations on the image at the t time instant, a set C of correlation coefficients is obtained k Set C k The maximum value of the image represents the meaning that the template of the kth object is optimally matched with a subset on the image, and the position of the object is determined. The centrum of the subset of target images located at this time is marked (x t k ,y t k )。
Due to the above-mentioned correlation coefficient c k (i, j) geometric size and pixel ash for templates and imagesThe change of the degree value is sensitive, and has no influence on matching of the same template in the same image. However, when the template and the image are changed, the geometric size and the gray value are changed, so that a unified standard evaluation system needs to be established, so that all the matches are defined in the range of-1 to 1, and the matching effect can be compared with the value of the unified correlation coefficient. Therefore, no matter how the size of the image and the template and the pixel range are changed, all the matching can be evaluated by using the same standard, and the matching precision of the same target at different moments (i.e. on different frames) in the time sequence and the matching precision of different targets at the same moment (i.e. on the same frame) can be measured, and the measuring index is the normalized correlation coefficient.
Since the monitoring target point of the engineering structure will not "free" a certain area (a certain subset in the image) within a certain working range, w t (x, y), and this region is also referred to as the region of interest (ROI), by framing the ROI, the search and matching tasks are performed only in a small region of the ROI, not in the entire image, reducing unnecessary irrelevant matching processes, saving memory occupied by the computer, and reducing matching time. Here the normalized correlation coefficient is corrected as:
in the method, in the process of the invention,is a template f k Gray value mean value on all pixel points in (x, y), is +.>Is the average of gray values at all pixels in the subset of the image, i.e., the region of interest ROI.
After the template matching is completed, the position coordinates of the target on the image at time t are already determined, but the displacement of the target to be obtained needs to be converted from the change of the position of the target in the image coordinates to the change in the actual spatial scale coordinates. Here, assuming that the shooting direction of the camera is perpendicular to the two-dimensional motion plane of the target, and the distance between two points with H pixels on the image in the actual space is H, the image-space scale conversion scaling factor is defined as:
the center coordinates (x) of the subset of images when the initial coordinates of the template center of the kth object are obtained and the best match of the object on the image at time t t k ,y t k ) Thereafter, the horizontal displacement x of the kth target at time t k (t) and vertical Displacement of y k (t) is:
according to the principle, the actual measurement deflection influence line of the bridge is extracted from the deflection time course of the bridge cross section in the vehicle moving process.
Further, based on free vibration deflection time course data of bridge cross section measuring points after the vehicle drives away from the bridge, the front 2-3 order self-vibration frequency of the bridge is calculated and identified by adopting a structural modal parameter identification method, wherein the structural modal parameter identification method comprises a random subspace identification method, a least square complex frequency domain method, a frequency domain decomposition method or a peak extraction method.
The beneficial effects of the invention are as follows: for checking and calculating the bridge bearing capacity, the finite element model correction result is utilized to check and calculate the bridge bearing capacity, and the method is essentially a combination of a checking algorithm and a finite element model correction method, and the calculation parameters of the method are corrected according to the bridge field actual measurement data, so that the method has higher accuracy, better feasibility and higher reliability. For bridge load tests, the method can quickly and efficiently evaluate the bridge bearing capacity by using the check coefficient, and reduces the economic loss caused by the traditional load test. The method has the advantages of non-contact, low cost and high precision, can acquire two-dimensional and three-dimensional displacement values of the bridge structure, overcomes the defects of small static load data quantity and inaccurate dynamic load test result, requires less time in the acquisition process, can avoid the defects of large amount of manpower and material resources and traffic influence in the traditional dynamic and static load test, does not need to install a sensor on the bridge, removes the measurement error of the sensor and the error generated by contact collision between the sensor and the bridge deck, and ensures that the detection result can accurately reflect the actual working state of the bridge. According to the bridge bearing capacity rapid assessment method, traffic is not required to be interrupted for carrying out a static load test, only a short period of a few minutes of a social vehicle running gap is required, a large number of sensor installation procedures which are time-consuming and labor-consuming in the static load test and a bridge lower support erection procedure in the traditional deflection measurement are avoided, only the bridge deck is required to be utilized for temporarily carrying out a few minutes when the deflection influence line and the frequency are measured, and the time cost of the bridge load test is saved. The method has simple and quick test process, can obviously improve the efficiency of bridge detection, and has great engineering application value in road and urban bridge detection.
Drawings
FIG. 1 is a graph of image correlation operations and template matching;
FIG. 2 is a schematic diagram of a finite element model of a bridge in an embodiment of the invention;
FIG. 3 is a graph comparing deflection impact lines of optimized mid-span measurement points in an embodiment of the present invention;
FIG. 4 is a graph of theoretical deflection values in a bridge static test in accordance with an embodiment of the present invention;
FIG. 5 is a graph of measured deflection values for a bridge static test in an embodiment of the invention.
Detailed Description
The invention is further illustrated by the following non-limiting examples, in conjunction with the accompanying drawings:
the invention is described in detail below by means of a seat 3 spanning a continuous box girder bridge. The bridge is a prestressed concrete continuous box girder bridge, the total length of the bridge is 90 meters, the upper structure is a C50 prestressed concrete single box single chamber box girder with the thickness of 3 multiplied by 30m, liang 1.8.8 m, the total width of the bridge deck is 8.0m,0.5m (protective bumping bar) +7.0m (roadway) +0.5m (protective bumping bar), the support is a basin-type rubber support, the paving layer is 80mmC concrete+bridge deck waterproof layer+50 mm medium-grain asphalt concrete+40 mm asphalt horseshoe macadam mixture, the guardrail is C25 concrete, the expansion joint is 120-type comb plate expansion joint, and the bridge design automobile load grade is city-A grade.
The bridge bearing capacity evaluation steps are as follows:
(1) And establishing a bridge finite element initial model, and calculating to obtain a theoretical deflection influence line of the cross section and a theoretical frequency of the bridge under the action of a 36-ton loading vehicle as shown in fig. 2.
(2) A 36 ton loading vehicle is adopted to drive the bridge, deflection measuring points are selected in each midspan section, and a high-resolution camera is used for measuring and identifying deflection time course curves of the midspan sections of the bridge in the running process of the bridge deck; and simultaneously, measuring and identifying a free vibration deflection time course curve of the bridge midspan section after the vehicle leaves the bridge by using a high-frame rate camera. In the test, the frame rate of the video recording camera is 60 frames/second, the sampling frequency of the vision system is 60Hz, the video is led into a computer, a plurality of pictures are cut according to the number of frames, the color pictures are converted into gray images, and the sub-pixel edges of the gray images are fitted by using polynomials to obtain the positions of all points of the edge of the main beam at each moment. And (3) selecting a gray scale template matching algorithm method to perform structural deflection calculation, extracting actual measurement deflection influence line data of different measuring points of the bridge after the time course data of the vehicle driving time period on the bridge are scattered, performing bridge self-vibration frequency calculation by adopting a random subspace method, and identifying the front 2-3-order actual measurement self-vibration frequency of the bridge for the time course data of the vehicle driving away from the bridge. The motion trail of the cross-section measuring point is obtained by adopting a single-lens reflex digital camera, and the model of the camera is Nikon D3400 and is manufactured by Nikon manufacturers of Kabushiki Kaisha.
(3) The sensitivity analysis is carried out on the main design parameters by utilizing finite element software, and the sensitive design parameters with larger influence on deflection and frequency are determined, such as: and the elastic modulus of the concrete and the section moment of inertia of the bridge are used as design variables in model correction, model optimization design is carried out by combining an actually measured deflection influence line and the bridge self-vibration frequency, a reference finite element model capable of representing the actual working state of the bridge is obtained after optimization analysis, and the numerical values of the design variables after optimization are shown in table 1. And then calculating and drawing a deflection influence line of the bridge reference finite element model under the action of a 36-ton loading vehicle, wherein the optimized bridge influence line is very close to the actually measured influence line. The theoretical deflection influence line, the actual deflection influence line and the optimized deflection influence line of the second span middle section measuring point of the bridge are shown in figure 3.
Table 1 design variables before and after optimization
According to the steps (1), (2) and (3), calculating to obtain a cross-section maximum deflection value and bridge front 2-order frequency, as shown in table 2:
table 2 comparison of the model corrected before and after deflection values and frequency values
(4) And (3) designing a bridge static load test scheme, selecting a midspan section of the second midspan as a control section, controlling with the least adverse bending moment, calculating the loading efficiency to be 1.009, meeting the requirement that the loading efficiency in the specification is between 0.95 and 1.05, and calculating the maximum deflection value of the initial finite element model midspan measuring point under the static load test working condition, as shown in fig. 4.
(5) And (3) correcting the initial finite element model of the bridge by utilizing the optimized parameters obtained in the step (3), modifying the design parameters such as the section moment of inertia of the bridge, the elastic modulus of concrete and the like, and calculating the maximum deflection value of the mid-span measuring point of the reference finite element model under the static load test working condition, namely the actual measured deflection, as shown in figure 5.
(6) S calculated according to steps (4) and (5) e 6.99mm, S s 6.25mm, according to the structural check coefficient calculation method in the highway bridge bearing capacity detection evaluation procedureCalculating deflection check coefficient by a method, and according to a calculation formulaThe deflection verification coefficient of the actual bridge is calculated to be 1.1, which indicates that the actual working condition of the bridge is worse than the original design theoretical condition, and the bearing capacity of the bridge is judged to not meet the design requirement according to the highway bridge bearing capacity detection and assessment procedure.
Other parts in this embodiment are all of the prior art, and are not described herein.
Claims (3)
1. A bridge bearing capacity rapid assessment method based on machine vision and model correction is characterized by comprising the following steps: comprises two parts:
the first part is bridge span cross section measuring point vibration data acquisition and bridge finite element model correction, and comprises the following steps:
step one: establishing an initial finite element model of the bridge according to a design drawing and related specifications, and simulating a loading vehicle to drive across the bridge deck by adopting a static force method to obtain a theoretical deflection influence line of a bridge cross section and a bridge front second-order theoretical frequency;
step two: performing sensitivity analysis on bridge design parameters by using finite element software, and selecting structural parameters with larger influence on deflection and frequency as parameters to be corrected;
step three: a loading vehicle slowly runs through an actual bridge at a certain speed, deflection measuring points with the same positions as the finite element model are selected from the midspan section and serve as machine vision measuring points, and a high-resolution high-frame-rate camera is used for collecting vibration images of bridge midspan section measuring points in the moving process of the vehicle and vibration images of bridge midspan section measuring points after the vehicle leaves the bridge; compiling a dynamic image recognition program, adopting a bilateral filtering algorithm to perform noise reduction treatment on the image, utilizing an ACE image enhancement algorithm to improve the image contrast, converting an RGB three-channel image into a single-channel image, and realizing image gray scale treatment; an image recognition algorithm is adopted to recognize and extract a deflection time course curve of a bridge span middle section measuring point in the moving process of the vehicle and a free vibration deflection time course curve of the bridge span middle section measuring point after the vehicle leaves the bridge, and the deflection time course curve and the frequency are calculated and recognized respectively to obtain a bridge actual measurement deflection influence line and frequency;
step four: taking the parameters to be corrected of the bridge, which are obtained in the step two, as design variables, taking the actually measured deflection influence line and the actually measured frequency, which are obtained in the step three, as state variables, and constructing an objective function according to a difference value between an actually measured value and a theoretical value, wherein the objective function is as shown in the formula (1):
wherein omega is i 、Respectively a calculated value and an actual measured value of the ith-order natural vibration frequency of the bridge, u i 、/>Respectively calculating a static deflection value and an actual measurement value of an ith measuring point of the bridge; w (w) i 、w i ' is a weight coefficient; f is the difference objective function between the actual measurement value and the theoretical value;
step five: and (3) carrying out post-processing on finite element calculation results: analyzing and finishing the bridge theoretical self-vibration frequency and mid-span deflection data obtained in the step one, comparing the bridge theoretical self-vibration frequency and mid-span deflection data with an actual measurement value of a static and dynamic load test, and substituting the actual measurement value into an objective function for calculation;
step six: judging whether the reaction of the objective function and the bridge structure meets the requirement of optimizing precision: if the requirements are met, the adopted calculation parameters are optimal parameter values, the model correction process is finished, and the optimized design variables are used for correcting the initial finite element model of the bridge to obtain a reference finite element model capable of representing the actual working state of the bridge; if the requirements are not met, changing the bridge design parameter value, and continuing to carry out loop iteration analysis until the accuracy requirements are met;
the second part is bridge bearing capacity evaluation, which comprises the following steps:
step one: according to the highway bridge load test procedure, a bridge static load test scheme is formulated, so that the loading efficiency is between 0.95 and 1.05, a triaxial truck can be adopted by a loading truck, and the theoretical design deflection of a bridge under test load is calculated based on an initial finite element model;
step two: according to the test load of the static load test scheme, predicting actual measured deflection of the bridge span cross section under the test load based on a reference finite element model, wherein the deflection reflects the structural deformation of an actual bridge;
step three: calculating bridge deflection check coefficient by adopting the following formulaAnd evaluating the bearing capacity of the bridge by using the deflection verification coefficient:
wherein: s is S e The method is characterized in that the method is an actually measured elastic deflection or strain value of a main measuring point of a reference finite element model under the action of test load; s is S s And calculating a deflection or strain value for the theory of the main measuring point of the initial finite element model under the test load.
2. The bridge bearing capacity rapid assessment method based on machine vision and model correction according to claim 1, characterized by comprising the following steps: the image recognition algorithm in the third step is a template matching method or an optical flow method or a feature point matching method or a shape-based matching or a deep learning-based target tracking method.
3. The bridge bearing capacity rapid assessment method based on machine vision and model correction according to claim 1, characterized by comprising the following steps: based on free vibration deflection time-course data of a bridge cross-section measuring point after a vehicle drives away from a bridge, calculating and identifying the front 2-3 order self-vibration frequency of the bridge by adopting a structural modal parameter identification method, wherein the structural modal parameter identification method comprises a random subspace identification method, a least square complex frequency domain method, a frequency domain decomposition method or a peak extraction method.
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