CN111354035A - Automatic bridge dynamic load detection and inversion system and method - Google Patents

Automatic bridge dynamic load detection and inversion system and method Download PDF

Info

Publication number
CN111354035A
CN111354035A CN202010188342.6A CN202010188342A CN111354035A CN 111354035 A CN111354035 A CN 111354035A CN 202010188342 A CN202010188342 A CN 202010188342A CN 111354035 A CN111354035 A CN 111354035A
Authority
CN
China
Prior art keywords
bridge
vehicle
detected
vehicles
moving
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010188342.6A
Other languages
Chinese (zh)
Other versions
CN111354035B (en
Inventor
成高立
雷雪芹
张宏军
梁田
冉海峰
李炜
梁红涛
杨苏
刘高阳
高壮
王朵
徐娇
乔华玺
李明
贾保国
夏涛
张涛
韩晓凯
严元刚
侯群杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Baoji Branch Of Shaanxi Baohan Expressway Construction Management Co ltd
XI'AN UNIVERSITY OF FINANCE AND ECONOMICS
SHAANXI HIGH-SPEED MECHANIZATION ENGINEERING CO LTD
Original Assignee
Baoji Branch Of Shaanxi Baohan Expressway Construction Management Co ltd
XI'AN UNIVERSITY OF FINANCE AND ECONOMICS
SHAANXI HIGH-SPEED MECHANIZATION ENGINEERING CO LTD
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Baoji Branch Of Shaanxi Baohan Expressway Construction Management Co ltd, XI'AN UNIVERSITY OF FINANCE AND ECONOMICS, SHAANXI HIGH-SPEED MECHANIZATION ENGINEERING CO LTD filed Critical Baoji Branch Of Shaanxi Baohan Expressway Construction Management Co ltd
Priority to CN202010188342.6A priority Critical patent/CN111354035B/en
Publication of CN111354035A publication Critical patent/CN111354035A/en
Application granted granted Critical
Publication of CN111354035B publication Critical patent/CN111354035B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
    • G01B11/0608—Height gauges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30236—Traffic on road, railway or crossing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Geometry (AREA)
  • Computer Graphics (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

The invention discloses a system and a method for automatically detecting and inverting a dynamic load of a bridge, which comprise a three-dimensional laser scanner (11), a computer, a plurality of cameras (12) and a timer used for being matched with the three-dimensional laser scanner (11); a plurality of road rods are uniformly distributed on the same-direction lane on the bridge, a camera (12) for covering the field of view of the lane area between two adjacent road rods is erected on each road rod, the cameras synchronously pick up images, the synchronous precision is less than 10 microseconds, a three-dimensional laser scanner (11) is erected right above the same-direction double lanes at the entrance of the bridge, and the measuring range covers two lanes; the stress sensor is arranged on the bridge (21) at least at the maximum stress position of the bridge, and the stress sensor, the camera (12), the timer and the three-dimensional laser scanner (11) are all connected with the computer. The method realizes the dynamic load detection of the bridge and the accurate monitoring of the strain of each part of the bridge.

Description

Automatic bridge dynamic load detection and inversion system and method
Technical Field
The invention belongs to the technical field of bridge dynamic load detection, and particularly relates to a system and a method for automatically detecting and inverting bridge dynamic loads.
Background
Bridges are an important part of highways. Due to unreasonable bridge structure, natural disasters or overload and other reasons, bridge collapse accidents are often caused, and great property loss is brought to the nation and people. Therefore, in order to avoid the occurrence of similar accidents, many newly built bridges or bridges in use require the installation of bridge monitoring systems. At present, bridge monitoring mostly stops at regular detection of fixed-point positions of bridges, or monitoring video is adopted to monitor traffic flow of bridge decks, or a weighing sensor is buried underground to monitor load of a single section, and tracking or stress monitoring of dynamic load of the whole bridge decks is not really realized.
Meanwhile, the existing system vehicle monitoring and strain measurement are two isolated systems. Meanwhile, a full-bridge vehicle tracking system based on videos does not exist, most importantly, a plurality of videos cannot be synchronized, and the current monitoring systems are independent and have no function of distinguishing empty vehicles during heavy vehicle driving.
Disclosure of Invention
In order to solve the technical problems, the invention provides a system and a method for automatically detecting and inverting the dynamic load of a bridge, which realize the detection of the dynamic load of the bridge and the accurate monitoring of the strain of each part of the bridge.
The invention is realized by the following technical scheme:
a bridge dynamic load automatic detection and inversion system comprises a three-dimensional laser scanner, a computer, a plurality of cameras and a timer used for being matched with the three-dimensional laser scanner; a plurality of road rods are uniformly distributed on a same-direction lane on a bridge, a camera for covering a lane area view field between two adjacent road rods is erected on each road rod, the cameras synchronously pick up images, the synchronous precision is less than 10 microseconds, a three-dimensional laser scanner is erected right above the same-direction double lanes at the entrance of the bridge, and the measuring range covers the two lanes; and at least the maximum stress position of the bridge is provided with a stress sensor, and the stress sensor, the camera, the timer and the three-dimensional laser scanner are all connected with the computer.
The three-dimensional laser scanner is used for obtaining point cloud data of respective surfaces of detected vehicles entering the same-direction double lanes of the bridge and sending the obtained point cloud data to the computer;
the camera is used for shooting the continuous driving process of all moving detected vehicles passing through the lower part of the camera, and after shooting the continuous driving video of the moving detected vehicles, sending the shot continuous driving video of the moving detected vehicles to the central processing unit;
the computer is used for receiving the point cloud data sent by the three-dimensional laser scanner, obtaining three-dimensional contour maps of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction on the basis of the point cloud data, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour maps of the surfaces of all detected vehicles, and judging whether the detected vehicles are empty or not according to the height of carriages in the size data of the detected vehicles so as to obtain load judgment information of the detected vehicles; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information;
meanwhile, the computer is used for receiving the continuous driving video of the moving detected vehicle shot by the camera and processing the video into a frame of uninterrupted image; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then performing self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a target result of the plurality of moving detected vehicles after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane and obtaining the tracking frames with the sizes of the moving detected vehicles;
obtaining the centroid of each moving detected vehicle through the average value of the horizontal and vertical coordinates of the Harris characteristic angular point of each moving detected vehicle in the Harris characteristic angular points extracted through calculation, and meanwhile, connecting the centroids of the vehicles with the same labels in each frame of video image through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the positions of the vehicles so as to obtain the running tracks of the vehicles with the same labels, thereby realizing the tracking of the detected vehicles with multiple target movements and further obtaining the distribution information of the whole bridge deck moving vehicles;
calculating parameter information according to the obtained vehicle load and the obtained distribution information of the whole bridge deck moving vehicle to obtain load information of all vehicles on the bridge, and obtaining stress-strain distribution values of each point of the bridge by using a finite element model algorithm according to the obtained load information and the obtained related parameter information of the bridge during bridge design;
and correcting the obtained stress-strain distribution value of each point of the corresponding part of the bridge by polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution value of each point of the corresponding part of the bridge obtained in the step.
A bridge dynamic load automatic detection and inversion method comprises the following steps:
(1) on the same-direction double lanes of the bridge, utilizing a three-dimensional laser scanner which is erected right above the entrance of the bridge and covers two lanes in the measuring range to obtain point cloud data of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge, sending the obtained point cloud data to a computer, obtaining a three-dimensional contour map of the surfaces of all the detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction by using the point cloud data as the basis by the computer, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour map of the surface of each detected vehicle, and judging whether the detected vehicle is an empty vehicle according to the height of a compartment in the size data of the detected vehicle so as to obtain the load judgment information of the detected vehicle; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information;
(2) on the same-direction double lanes of the bridge, shooting the continuous running process of all moving detected vehicles passing through the lower part of the bridge by utilizing a camera which is erected on each uniformly distributed road rod and has a synchronous function with other cameras and covers the area view field between the two adjacent road rods, and after shooting the continuous running video of the moving detected vehicles, sending the shot continuous running video of the moving detected vehicles to a central processing unit, and processing the video into a frame of uninterrupted image by the central processing unit; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then performing self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a target result of the plurality of moving detected vehicles after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane and obtaining the tracking frames with the sizes of the moving detected vehicles;
(3) the central processing unit is used for calculating the average value of the horizontal and vertical coordinates of the Harris characteristic angular points of each moving detected vehicle in the extracted Harris characteristic angular points in the step 2) to obtain the mass center of each moving detected vehicle, and meanwhile, the mass centers of the vehicles with the same mark number in each frame of video image are connected through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the position of the vehicle, so that the running tracks of the vehicles with the same mark number are obtained, the tracking of the detected vehicles with multiple target movements is realized, and the distribution information of the whole bridge deck moving vehicle is obtained;
(4) the central processing unit is used for obtaining load information of all vehicles to the bridge according to the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicle obtained in the step 3), and obtaining stress-strain distribution values of each point of the bridge by using a finite element model algorithm according to the obtained load information and the related parameter information of the bridge during bridge design;
(5) and the central processing unit corrects the stress-strain distribution values of the points of the corresponding part of the bridge obtained in the step 4) through polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution values of the points of the corresponding part of the bridge obtained in the step 4).
The method for measuring the length of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the length of the detected vehicle passing through is measured, a timer used in cooperation with the three-dimensional laser scanner starts to time when the head of the detected vehicle just enters a detection plane formed by the three-dimensional laser scanner, the timer stops to time when the tail of the detected vehicle is driven away from the detection plane formed by the three-dimensional laser scanner, the speed of the detected vehicle passing through the three-dimensional laser scanner is v, the passing time of the detected vehicle recorded by the timer is t, and the length L of the detected vehicle body can be expressed as L ═ vt.
The method for measuring the width of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the width of a passing detected vehicle is measured, a polar coordinate system is established by taking the three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, the polar axis is along the vertical direction, when laser irradiates the leftmost position of the vehicle body of the detected vehicle, the distance measurement result is recorded as r1, the included angle between the laser line and the polar axis is a1, and the distance between the left side of the detected vehicle and the polar axis direction is r1sin a1(ii) a When the laser irradiates the rightmost position of the vehicle body of the detected vehicle, the distance measurement result is r2, the included angle between the laser line and the polar axis is a2, and the distance from the right side of the detected vehicle to the polar axis direction is r2sin a2The width w of the detected vehicle can be expressed as w ═ r2sin a2-r1sin a1。
The method for measuring the height of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the height of a detected vehicle body is measured, a polar coordinate system is established by taking a three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, a polar axis is along the horizontal direction, when laser irradiates the highest position of the detected vehicle, the distance measurement result is recorded as r, at the moment, the included angle between the laser line and the polar axis is a, and the distance from the highest position of the detected vehicle to the polar axis is r sin a; the distance from the three-dimensional laser scanner to the ground is H, and the detected vehicle height H can be expressed as H-r sina.
The step 1) is specifically as follows:
the three-dimensional laser scanner scans the top of the body of the detected vehicle and one side of the vehicle by using the scanning light curtain constructed by the three-dimensional laser scanner so as to obtain a plurality of continuous scanning cross sections of the detected vehicle, draws a three-dimensional profile map of the detected vehicle according to the plurality of continuous scanning cross sections, and distinguishes different types of the detected vehicle from the three-dimensional profile map of the detected vehicle;
for a flat truck and a truck with a dump truck, whether the truck is empty or not is judged by confirming whether the three-dimensional laser scanner is the shortest distance to the left edge or the right edge of the vehicle or not.
The principle of the improved Otsu algorithm is as follows:
1) according to the formula
Figure BDA0002414969090000061
Obtaining the distribution probability F of the image gray level1，F2，F3…Fi. Wherein N is the total number of pixels, NiThe number of pixels having a gray value i.
2) The mean gray value μ of the image is calculated.
3) Let the gray level probability of the background image be P and the average value of the gray levels of the background image be mu1. The image variance is Var, and the variance at the maximum value is maxVar. Firstly, making P be equal to 0,
μ1＝0，maxVar＝0。
4) i is iterated from 1 to 256, and the following values are calculated in sequence:
P(i)＝P(i-1)+Fi
μ1(i)＝(P(i-1)×μ1(i-1)+i×Fi)/P(i)
the between-class variance can be expressed as:
Var＝P·(μ1-μ)2+P2·(μ2-μ)2(3.16)
wherein, P2Is the gray level probability, mu, of the foreground image2Is the gray level average of the foreground image.
And because of
μ＝P·μ1+P2·μ2(3.17)
Bringing formula 3.17 into formula 3.16:
Var(i)＝P(i)×(μ-μ1(i))2/(1-P(i))
5) judging whether Var (i) is larger than maxVar, if so, making maxVar ═ Var (i), and T ═ i; if not, making i equal to i +1, and returning to the step 4.
6) And (5) finishing the iterative search, wherein T is the result.
The camera has a day-night conversion monitoring function, the effective pixels of the camera reach 300 thousands, the horizontal field angle is 80 degrees, the maximum image size reaches 1920 × 1080, the resolution of a video is 1296 (horizontal) × (vertical), the video frame rate is 50Hz:25fps, the power supply power is 7W, the power supply voltage is 12VDC, the size of the camera is 194.04 ×.85 ×.52mm, the weight of the camera is 1000g, and the synchronization time precision of the camera is not less than 10 microseconds;
the cameras are composed of a plurality of high resolutions, each camera covers 50 meters of the bridge floor, and the cameras need time accurate synchronization, so that the dynamic change of the load of each bridge span of the bridge floor at different times is obtained.
The specific algorithm for obtaining the load information of all the vehicles to the bridge through the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicles obtained in the step 3) is as follows:
the load F applied to the road surface by the whole vehicle can be divided into vehicle static load FjAnd dynamic load FdI.e. by
F＝Fj+Fd
Wherein the static load F of the vehiclejI.e. the gravitational load.
In the process of obtaining the dynamic load of the vehicle, according to the D' Alembert principle, the motion equation of the vibration system of the vehicle is obtained firstly as follows:
Figure BDA0002414969090000076
wherein: quality matrix
Figure BDA0002414969090000071
Damping matrix
Figure BDA0002414969090000072
Rigidity matrix
Figure BDA0002414969090000073
Displacement array y ═ yt,ys]T；
Excitation array
Figure BDA0002414969090000074
In the above matrix, msIs a sprung mass; m istIs an unsprung mass; c. CsDamping the suspension; c. CtDamping the tire. k is a radical of1Is the rigidity coefficient of the rear tyre of the automobile; k is a radical of2The rigidity coefficient of the rear suspension of the automobile; k is a radical oftIs the tire stiffness; y iss,ytIs the vertical absolute displacement of the sprung and unsprung masses; y isrExciting vertical displacement of the unevenness of the road surface; and deltah is the vertical deformation generated by the road surface.
The random dynamic load of the vehicle on the road surface is as follows:
Figure BDA0002414969090000075
after the load of a single vehicle to the road surface is obtained, the total load of all vehicles running on the bridge to the bridge surface can be calculated by calculating the number of the vehicles on the bridge surface;
the dynamic load on the bridge deck can be obtained through the steps, and the stress-strain distribution value of the whole bridge is calculated by adopting a finite element method according to the bridge structure.
Compared with the prior art, the invention has the following beneficial technical effects:
according to the automatic detection and inversion system and method for the dynamic load of the bridge, vehicle monitoring and strain measurement are unified, automatic detection of the dynamic load of the bridge is achieved, and synchronization of multiple videos and full-bridge vehicle tracking are achieved.
The automatic detection and inversion system and method for the dynamic load of the bridge are characterized in that a three-dimensional laser scanner is used for obtaining a point cloud picture of each moving detected vehicle entering the bridge, the type of the detected vehicle and load judgment information of the vehicle are judged by performing three-dimensional reconstruction on each section, a group of high-resolution camera assemblies which are accurately synchronous and are arranged at equal intervals and cover the whole bridge floor are used for combining, the dynamic distribution of the vehicle on each span structure of the bridge floor is obtained through vehicle segmentation and track tracking algorithms, the static load and the dynamic load of the bridge floor are obtained through calculation by combining the type information of each vehicle and the load judgment information of the vehicle, and the dynamic stress distribution of each point of the whole bridge floor is obtained through a finite element bridge solving method; a stress sensor is installed at a key part of the bridge to serve as a correction reference, the calculation result of the finite element is inverted, and accurate monitoring of the strain of each part of the bridge is finally achieved.
Drawings
FIG. 1 is a schematic diagram of a system for measuring vehicle dimension data and tracking vehicles provided by an automatic bridge dynamic load detection and inversion system according to an embodiment of the invention;
FIG. 2 is a schematic diagram of moving vehicle to bridge loads provided by an automatic bridge dynamic load detection and inversion system according to an embodiment of the invention;
FIG. 3 is a schematic view of measuring the width of a vehicle body according to one embodiment of the present invention;
FIG. 4 is a schematic view of measuring vehicle body height according to one embodiment of the present invention;
FIG. 5 is a schematic illustration of determining whether a flat truck is empty according to one embodiment of the present invention;
fig. 6 is a schematic illustration of determining whether a dump truck is empty according to an embodiment of the invention;
FIG. 7 is a schematic of inversion according to the present invention;
fig. 8 is a flow chart of the present invention.
10: a bridge dynamic load automatic detection and inversion system;
11: a three-dimensional laser scanner; 12: a camera;
20: loading the bridge;
21: a bridge; 22: a vehicle;
30: measuring the width of the vehicle body;
r 1: the distance from the three-dimensional laser scanner to the leftmost side of the vehicle; a 1: the included angle between the laser line and the polar axis; r 2: the distance from the three-dimensional laser scanner to the rightmost side of the vehicle; a 2: the included angle between the laser line and the polar axis;
40: measuring the height of the vehicle body;
r: the distance from the three-dimensional laser scanner to the highest position of the vehicle; a: the included angle between the laser line and the polar axis; h: the height from the three-dimensional laser scanner to the ground;
50: judging whether the flat truck is empty;
d: the distance from the three-dimensional laser scanner to the leftmost side of the flat plate; d: three-dimensional laser scanner to cargo distance;
60: judging whether the dump truck is an empty truck or not;
d: the distance from the three-dimensional laser scanner to the leftmost side of the tipping bucket; d: three-dimensional laser scanner to cargo distance.
Detailed Description
The present invention will now be described in further detail with reference to specific examples, which are intended to be illustrative, but not limiting, of the invention.
Referring to fig. 1 to 8, an automatic detection and inversion system for dynamic load of a bridge includes a three-dimensional laser scanner 11, a computer, a plurality of cameras 12, and a timer for cooperating with the three-dimensional laser scanner 11; a plurality of road rods are uniformly distributed on the same-direction lane on the bridge, a camera 12 for covering the field of view of the lane area between two adjacent road rods is erected on each road rod, the cameras synchronously pick up images, the synchronous precision is less than 10 microseconds, the three-dimensional laser scanner 11 is erected right above the same-direction double lanes at the entrance of the bridge, and the measuring range covers the two lanes; at least the maximum stress position of the bridge is provided with a stress sensor on the bridge 21, and the stress sensor, the camera 12, the timer and the three-dimensional laser scanner 11 are all connected with a computer.
Specifically, the three-dimensional laser scanner 11 is configured to obtain point cloud data of respective surfaces of all detected vehicles entering a bridge in a same-direction two-lane, and send the obtained point cloud data to the computer;
the camera 12 is used for shooting the continuous driving process of all moving detected vehicles passing through the lower part of the camera, and after shooting the continuous driving video of the moving detected vehicles, sending the shot continuous driving video of the moving detected vehicles to the central processing unit;
the computer is used for receiving the point cloud data sent by the three-dimensional laser scanner, obtaining three-dimensional contour maps of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction on the basis of the point cloud data, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour maps of the surfaces of all detected vehicles, and judging whether the detected vehicles are empty or not according to the height of carriages in the size data of the detected vehicles so as to obtain load judgment information of the detected vehicles; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information;
meanwhile, the computer is used for receiving the continuous driving video of the moving detected vehicle shot by the camera and processing the video into a frame of uninterrupted image; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then performing self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a target result of the plurality of moving detected vehicles after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane and obtaining the tracking frames with the sizes of the moving detected vehicles;
obtaining the centroid of each moving detected vehicle through the average value of the horizontal and vertical coordinates of the Harris characteristic angular point of each moving detected vehicle in the Harris characteristic angular points extracted through calculation, and meanwhile, connecting the centroids of the vehicles with the same labels in each frame of video image through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the positions of the vehicles so as to obtain the running tracks of the vehicles with the same labels, thereby realizing the tracking of the detected vehicles with multiple target movements and further obtaining the distribution information of the whole bridge deck moving vehicles;
calculating parameter information according to the obtained vehicle load and the obtained distribution information of the whole bridge deck moving vehicle to obtain load information of all vehicles on the bridge, and obtaining stress-strain distribution values of each point of the bridge by using a finite element model algorithm according to the obtained load information and the obtained related parameter information of the bridge during bridge design;
and correcting the obtained stress-strain distribution value of each point of the corresponding part of the bridge by polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution value of each point of the corresponding part of the bridge obtained in the step.
Referring to fig. 1 to 8, an automatic detection and inversion method for dynamic load of a bridge includes the following steps:
1) on the same-direction double lanes of the bridge, utilizing a three-dimensional laser scanner which is erected right above the entrance of the bridge and covers two lanes in the measuring range to obtain point cloud data of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge, sending the obtained point cloud data to a computer, obtaining a three-dimensional contour map of the surfaces of all the detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction by using the point cloud data as the basis by the computer, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour map of the surface of each detected vehicle, and judging whether the detected vehicle is an empty vehicle according to the height of a compartment in the size data of the detected vehicle so as to obtain the load judgment information of the detected vehicle; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information.
The step 1) is specifically as follows:
the three-dimensional laser scanner scans the top of the body of the detected vehicle and one side of the vehicle by using the scanning light curtain constructed by the three-dimensional laser scanner so as to obtain a plurality of continuous scanning cross sections of the detected vehicle, draws a three-dimensional profile map of the detected vehicle according to the plurality of continuous scanning cross sections, and distinguishes different types of the detected vehicle from the three-dimensional profile map of the detected vehicle;
for a flat truck and a truck with a dump truck, whether the truck is empty or not is judged by confirming whether the three-dimensional laser scanner is the shortest distance to the left edge or the right edge of the vehicle or not.
2) On the same-direction double lanes of the bridge, shooting the continuous running process of all moving detected vehicles passing through the lower part of the bridge by utilizing a camera which is erected on each uniformly distributed road rod and has a synchronous function with other cameras and covers the area view field between the two adjacent road rods, and after shooting the continuous running video of the moving detected vehicles, sending the shot continuous running video of the moving detected vehicles to a central processing unit, and processing the video into a frame of uninterrupted image by the central processing unit; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then performing self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a target result of the plurality of moving detected vehicles after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane and obtaining the tracking frames with the sizes of the moving detected vehicles;
3) the central processing unit is used for calculating the average value of the horizontal and vertical coordinates of the Harris characteristic angular points of each moving detected vehicle in the extracted Harris characteristic angular points in the step 2) to obtain the mass center of each moving detected vehicle, and meanwhile, the mass centers of the vehicles with the same mark number in each frame of video image are connected through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the position of the vehicle, so that the running tracks of the vehicles with the same mark number are obtained, the tracking of the detected vehicles with multiple target movements is realized, and the distribution information of the whole bridge deck moving vehicle is obtained;
4) the central processing unit is used for obtaining load information of all vehicles to the bridge according to the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicle obtained in the step 3), and obtaining stress-strain distribution values of each point of the bridge by using a finite element model algorithm according to the obtained load information and the related parameter information of the bridge during bridge design;
5) and the central processing unit corrects the stress-strain distribution values of the points of the corresponding part of the bridge obtained in the step 4) through polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution values of the points of the corresponding part of the bridge obtained in the step 4).
Specifically, the method for measuring the length of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the length of the detected vehicle passing through is measured, a timer used in cooperation with the three-dimensional laser scanner 11 starts to count time when the head of the detected vehicle just enters a detection plane formed by the three-dimensional laser scanner, and stops counting time when the tail of the detected vehicle is driven away from the detection plane formed by the three-dimensional laser scanner, the speed of the detected vehicle passing through the three-dimensional laser scanner is v, and the passing time of the detected vehicle recorded by the timer is t, so that the length L of the detected vehicle body can be expressed as L ═ vt.
It should be noted that the timer is arranged in the computer acquisition card; alternatively, the timer is arranged in parallel with the three-dimensional laser scanner.
The method for measuring the width of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the width of a passing detected vehicle is measured, a polar coordinate system is established by taking the three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, the polar axis is along the vertical direction, when laser irradiates the leftmost position of the vehicle body of the detected vehicle, the distance measurement result is recorded as r1, the included angle between the laser line and the polar axis is a1, and the distance between the left side of the detected vehicle and the polar axis direction is r1sin a1(ii) a When the laser irradiates the rightmost position of the vehicle body of the detected vehicle, the distance measurement result is r2, the included angle between the laser line and the polar axis is a2, and the distance from the right side of the detected vehicle to the polar axis direction is r2sin a2The width w of the detected vehicle can be expressed as w ═ r2sin a2-r1sin a1。
The method for measuring the height of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the height of a detected vehicle body is measured, a polar coordinate system is established by taking a three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, a polar axis is along the horizontal direction, when laser irradiates the highest position of the detected vehicle, the distance measurement result is recorded as r, at the moment, the included angle between the laser line and the polar axis is a, and the distance from the highest position of the detected vehicle to the polar axis is rsina; the distance from the three-dimensional laser scanner to the ground is H, and the detected vehicle height H can be expressed as H-r sina.
The principle of the improved Otsu algorithm is as follows:
1) according to the formula
Figure BDA0002414969090000141
Obtaining the distribution probability F of the image gray level1，F2，F3…Fi. Wherein N is the total number of pixels, NiThe number of pixels having a gray value i.
2) The mean gray value μ of the image is calculated.
3) Let the gray level probability of the background image be P and the average value of the gray levels of the background image be mu1. The image variance is Var, and the variance at the maximum value is maxVar. Firstly, making P be equal to 0,
μ1＝0，axVar＝0。
4) i is iterated from 1 to 256, and the following values are calculated in sequence:
P(i)＝P(i-1)+Fi
μ1(i)＝(P(i-1)×μ1(i-1)+i×Fi)/P(i)
the between-class variance can be expressed as:
Var＝P·(μ1-μ)2+P2·(μ2-μ)2(3.16)
wherein, P2Is the gray level probability, mu, of the foreground image2Is the gray level average of the foreground image.
And because of
μ＝P·μ1+P2·μ2(3.17)
Bringing formula 3.17 into formula 3.16:
Var(i)＝P(i)×(μ-μ1(i))2/(1-P(i))
5) judging whether Var (i) is larger than maxVar, if so, making maxVar ═ Var (i), and T ═ i; if not, making i equal to i +1, and returning to the step 4.
6) And (5) finishing the iterative search, wherein T is the result.
The camera has a day-night conversion monitoring function, the effective pixels of the camera reach 300 thousands, the horizontal field angle is 80 degrees, the maximum image size reaches 1920 × 1080, the resolution of a video is 1296, the horizontal × 732 is vertical, the video frame rate is 50Hz:25fps, the power supply power is 7W, the power supply voltage is 12VDC, the size of the camera is 194.04 × 93.85.85 93.85 × 89.52.52 mm, the weight of the camera is 1000g, and the synchronization time precision of the camera is not lower than 10 microseconds;
the cameras 12 are composed of a plurality of high resolution cameras, each camera covers the deck for 50 meters, and time precise synchronization is needed among the cameras, so that dynamic changes of loads of each bridge span of the deck at different times are obtained.
The specific algorithm for obtaining the load information of all the vehicles to the bridge through the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicles obtained in the step 3 is as follows:
the load F applied to the road surface by the whole vehicle can be divided into vehicle static load FjAnd dynamic load FdI.e. by
F＝Fj+Fd
Wherein the static load F of the vehiclejI.e. the gravitational load.
In the process of obtaining the dynamic load of the vehicle, according to the D' Alembert principle, the motion equation of the vibration system of the vehicle is obtained firstly as follows:
Figure BDA0002414969090000151
wherein: quality matrix
Figure BDA0002414969090000152
Damping matrix
Figure BDA0002414969090000153
Rigidity matrix
Figure BDA0002414969090000154
Displacement array y ═ yt,ys]T；
Excitation array
Figure BDA0002414969090000155
In the above matrix, msIs a sprung mass; m istIs a non-spring loadQuality; c. CsDamping the suspension; c. CtDamping the tire. k is a radical of1Is the rigidity coefficient of the rear tyre of the automobile; k is a radical of2The rigidity coefficient of the rear suspension of the automobile; k is a radical oftIs the tire stiffness; y iss,ytIs the vertical absolute displacement of the sprung and unsprung masses; y isrExciting vertical displacement of the unevenness of the road surface; and deltah is the vertical deformation generated by the road surface.
The random dynamic load of the vehicle on the road surface is as follows:
Figure BDA0002414969090000156
after the load of a single vehicle to the road surface is obtained, the total load of all vehicles running on the bridge to the bridge surface can be calculated by calculating the number of the vehicles on the bridge surface;
the dynamic load on the bridge deck can be obtained through the steps, and the stress-strain distribution value of the whole bridge is calculated by adopting a finite element method according to the bridge structure.
In a specific embodiment, referring to fig. 1 to 6, a method for automatically detecting and inverting a dynamic load of a bridge includes the following steps:
1) on the same-direction double lanes of the bridge, utilizing a three-dimensional laser scanner which is erected right above the entrance of the bridge and covers two lanes in the measuring range to obtain point cloud data of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge, sending the obtained point cloud data to a computer, obtaining a three-dimensional contour map of the surfaces of all the detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction by the computer on the basis of the point cloud data, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour map of the surface of each detected vehicle, wherein the precision reaches 1cm, and judging whether the detected vehicle is an empty vehicle according to the height of a compartment in the size data of the detected vehicle so as to obtain the load judgment information of the detected vehicle; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information.
The step 1) is specifically as follows:
the three-dimensional laser scanner scans the top of the body of the detected vehicle and one side of the vehicle by using the scanning light curtain constructed by the three-dimensional laser scanner so as to obtain a plurality of continuous scanning cross sections of the detected vehicle, draws a three-dimensional profile map of the detected vehicle according to the plurality of continuous scanning cross sections, and distinguishes different types of the detected vehicle from the three-dimensional profile map of the detected vehicle;
for a flat truck and a truck with a dump truck, whether the truck is empty or not is judged by confirming whether the three-dimensional laser scanner is the shortest distance to the left edge or the right edge of the vehicle or not.
Since trucks with large loads generally have large influence on roads and bridges, the system focuses on detection of the trucks. The vehicle type information is specifically: whether the vehicle is a cart or a dolly.
2) On the same-direction double lanes of the bridge, shooting the continuous running process of all moving detected vehicles passing through the lower part of the bridge by utilizing a camera which is erected on each uniformly distributed road rod and has a synchronous function with other cameras and covers the area view field between the two adjacent road rods, and after shooting the continuous running video of the moving detected vehicles, sending the shot continuous running video of the moving detected vehicles to a central processing unit, and processing the video into a frame of uninterrupted image by the central processing unit; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then carrying out self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a plurality of moving detected vehicle target results after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane. Specifically, the road rods are arranged at intervals of 50m, the cameras are Haikangwei video cameras, and the cameras are arranged at the positions 10 m high on the road rods, so that the coverage of the 50m area view field by the cameras is guaranteed.
3) The central processing unit is used for calculating the average value of the horizontal and vertical coordinates of the Harris characteristic angular points of each moving detected vehicle in the extracted Harris characteristic angular points in the step 2) to obtain the mass center of each moving detected vehicle, and meanwhile, the mass centers of the vehicles with the same mark number in each frame of video image are connected through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the position of the vehicle, so that the running tracks of the vehicles with the same mark number are obtained, the tracking of the detected vehicles with multiple target movements is realized, and the distribution information of the whole bridge deck moving vehicle is obtained;
4) and the central processing unit is used for obtaining the load information of all vehicles to the bridge according to the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicle obtained in the step 3), and obtaining the stress-strain distribution values of all points of the bridge by using a finite element model algorithm according to the obtained load information and the related parameter information of the bridge during the design of the bridge.
5) And the central processing unit corrects the stress-strain distribution values of the points corresponding to the bridge in the step 4) through polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution values of the points corresponding to the bridge in the step 4), thereby realizing inversion.
Specifically, the method for measuring the length of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the length of the detected vehicle passing through is measured, a timer used in cooperation with the three-dimensional laser scanner 11 starts to count time when the head of the detected vehicle just enters a detection plane formed by the three-dimensional laser scanner, and stops counting time when the tail of the detected vehicle is driven away from the detection plane formed by the three-dimensional laser scanner, the speed of the detected vehicle passing through the three-dimensional laser scanner is v, and the passing time of the detected vehicle recorded by the timer is t, so that the length L of the detected vehicle body can be expressed as L ═ vt.
It should be noted that the timer is arranged in the computer acquisition card; alternatively, the timer is arranged in parallel with the three-dimensional laser scanner.
The method for measuring the width of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the width of a passing detected vehicle is measured, a polar coordinate system is established by taking the three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, the polar axis is along the vertical direction, when laser irradiates the leftmost position of the vehicle body of the detected vehicle, the distance measurement result is recorded as r1, the included angle between the laser line and the polar axis is a1, and the distance between the left side of the detected vehicle and the polar axis direction is r1sin a1(ii) a When the laser irradiates the rightmost position of the vehicle body of the detected vehicle, the distance measurement result is r2, the included angle between the laser line and the polar axis is a2, and the distance from the right side of the detected vehicle to the polar axis direction is r2sin a2The width w of the detected vehicle can be expressed as w ═ r2sin a2-r1sin a1。
The method for measuring the height of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the height of a detected vehicle body is measured, a polar coordinate system is established by taking a three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, a polar axis is along the horizontal direction, when laser irradiates the highest position of the detected vehicle, the distance measurement result is recorded as r, at the moment, the included angle between the laser line and the polar axis is a, and the distance from the highest position of the detected vehicle to the polar axis is r sin a; the distance from the three-dimensional laser scanner to the ground is H, and the detected vehicle height H can be expressed as H-r sina.
According to the automatic detection and inversion system for the dynamic load of the bridge, before a vehicle is tracked, adaptive threshold binarization is carried out on moving vehicles, an improved Otsu algorithm is used, the advantage of the Otsu algorithm in the aspect of real-time performance is enhanced, and certain detected separated connected regions belonging to the same moving vehicle can be connected. The principle of the improved Otsu algorithm is as follows:
1) according to the formula
Figure BDA0002414969090000191
Obtaining the distribution probability F of the image gray level1，F2，F3…Fi. Wherein N is the total number of pixels, NiThe number of pixels having a gray value i.
2) The mean gray value μ of the image is calculated.
3) Let the gray level probability of the background image be P and the average value of the gray levels of the background image be mu1. The image variance is Var, and the variance at the maximum value is maxVar. Firstly, making P be equal to 0,
μ1＝0，maxVar＝0。
4) i is iterated from 1 to 256, and the following values are calculated in sequence:
P(i)＝P(i-1)+Fi
μ1(i)＝(P(i-1)×μ1(i-1)+i×Fi)/P(i)
the between-class variance can be expressed as:
μar＝P·(μ1-μ)2+P2·(μ2-μ)2(3.16)
wherein, P2Is the gray level probability, mu, of the foreground image2Is the gray level average of the foreground image.
And because of
μ＝P·μ1+P2·μ2(3.17)
Bringing formula 3.17 into formula 3.16:
Var(i)＝P(i)×(μ-μ1(i))2/(1-P(i))
5) judging whether Var (i) is larger than maxVar, if so, making maxVar ═ Var (i), and T ═ i; if not, making i equal to i +1, and returning to the step 4.
6) And (5) finishing the iterative search, wherein T is the result.
The Haikangwei vision novel camera has a day and night conversion monitoring function, the effective pixels of the Haikangwei vision novel camera reach 300 thousands, the horizontal field angle is 80 degrees, the maximum image size reaches 1920 × 1080, the resolution of a video is 1296 horizontal × vertical, the video frame rate is 50Hz:25fps, the power supply power is 7W, the power supply voltage is 12VDC, the size of the Haikwei vision novel camera is 6393.85 ×.52mm, the weight of the Haikwei vision novel camera is 1000g, and the synchronization time precision of the camera is not lower than 10 microseconds;
the cameras 12 are composed of a plurality of high resolution cameras, each camera covers the deck for 50 meters, and time precise synchronization is needed among the cameras, so that dynamic changes of loads of each bridge span of the deck at different times are obtained.
When a vehicle passes through the bridge, the vehicle can generate pressure on the bridge floor to deform the bridge floor due to the self weight of the vehicle and the condition of loading heavy objects; the specific algorithm for obtaining the load information of all the vehicles to the bridge through the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicles obtained in the step 3 is as follows:
the load F applied to the road surface by the whole vehicle can be divided into vehicle static load FjAnd dynamic load FdI.e. by
F＝Fj+Fd
Wherein the static load F of the vehiclejI.e. the gravitational load.
In the process of obtaining the dynamic load of the vehicle, according to the D' Alembert principle, the motion equation of the vibration system of the vehicle is obtained firstly as follows:
Figure BDA0002414969090000201
wherein: quality matrix
Figure BDA0002414969090000202
Damping matrix
Figure BDA0002414969090000203
Rigidity matrix
Figure BDA0002414969090000204
Displacement array y ═ yt,ys]T；
Excitation array
Figure BDA0002414969090000205
In the above matrix, msIs a sprung mass; m istIs an unsprung mass; c. CsDamping the suspension; c. CtDamping the tire. k is a radical of1Is the rigidity coefficient of the rear tyre of the automobile; k is a radical of2The rigidity coefficient of the rear suspension of the automobile; k is a radical oftIs the tire stiffness; y iss,ytIs the vertical absolute displacement of the sprung and unsprung masses; y isrExciting vertical displacement of the unevenness of the road surface; and deltah is the vertical deformation generated by the road surface.
The random dynamic load of the vehicle on the road surface is as follows:
Figure BDA0002414969090000211
after the load of a single vehicle to the road surface is obtained, the total load of all vehicles running on the bridge to the bridge surface can be calculated by calculating the number of the vehicles on the bridge surface;
the dynamic load on the bridge deck can be obtained through the steps, and the stress-strain distribution value of the whole bridge is calculated by adopting a finite element method according to the bridge structure.
When the vehicle runs on the bridge, due to the weight of the vehicle, especially for trucks with heavy loads, the pressure of the vehicle on the bridge floor can change the shape of the bridge floor, the load of the vehicle on the bridge can be obtained by acquiring relevant parameters of the vehicle or the bridge, and a finite element model algorithm is adopted.
Because the bridge has a plurality of forms and the difference of the vehicle types is large, the stress strain value of each point of the bridge calculated by the method and the actual detection have errors. Therefore, stress sensors are arranged at key positions of the bridge in the system, and structural parameters of the bridge are continuously corrected according to a difference value between actual detection and model calculation until errors of the two meet a certain range.
The scanning light curtain constructed by the three-dimensional laser scanner can scan the top of a vehicle body and one side of the vehicle, so that a plurality of continuous scanning cross sections are obtained, and the section data lays a good foundation for drawing the outline. After the three-dimensional outline of the vehicle is drawn, different types of vehicles such as cars, SUVs, trucks and the like can be distinguished.
For a flat truck and a truck with a dump truck, whether the truck is empty can be judged by confirming whether the distance from the three-dimensional laser scanner to the left edge or the right edge of the vehicle is the shortest.
Generally, flat trucks are loaded with objects having fixed shapes, such as containers, other vehicles, and the like. When the three-dimensional laser scanner is arranged at the upper left of the vehicle, if the flat wagon is in an empty state, the distance from the distance meter to the left edge of the flat plate of the wagon is the shortest distance from the distance meter to any point of the flat plate of the vehicle; if the flat truck is loaded with goods, the distance from the distance meter to a certain point of the goods is smaller than the distance from the distance meter to the left edge of the flat plate of the truck. Dump trucks typically carry bulk cargo, such as silt, gravel, bricks, etc., that vary in overall shape. The method of determining whether a dump truck is empty is similar to a flat truck.
A stress-strain sensor is provided at the maximum strain of each bridge span. When the strain exceeds the threshold value, the obtained stress strain value of the whole bridge is inverted, and the reliability of early warning can be improved.
The invention adopts a multi-sensor fusion technology to organically integrate vehicle type detection, vehicle distribution, vehicle tracking and strain stress monitoring into a monitoring platform with a feedback effect.
According to the automatic detection and inversion system and method for the dynamic load of the bridge, vehicle monitoring and strain measurement are unified, automatic detection of the dynamic load of the bridge is achieved, and synchronization of multiple videos and full-bridge vehicle tracking are achieved.
The automatic detection and inversion system and method for the dynamic load of the bridge are characterized in that a three-dimensional laser scanner is used for obtaining a point cloud picture of each moving detected vehicle entering the bridge, the type of the detected vehicle and load judgment information of the vehicle are judged by performing three-dimensional reconstruction on each section, a group of high-resolution camera assemblies which are accurately synchronous and are arranged at equal intervals and cover the whole bridge floor are used for combining, the dynamic distribution of the vehicle on each span structure of the bridge floor is obtained through vehicle segmentation and track tracking algorithms, the static load and the dynamic load of the bridge floor are obtained through calculation by combining the type information of each vehicle and the load judgment information of the vehicle, and the dynamic stress distribution of each point of the whole bridge floor is obtained through a finite element bridge solving method; a stress sensor is installed at a key part of the bridge to serve as a correction reference, the calculation result of the finite element is inverted, and accurate monitoring of the strain of each part of the bridge is finally achieved.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. The automatic bridge dynamic load detection and inversion system is characterized by comprising a three-dimensional laser scanner (11), a computer, a plurality of cameras (12) and a timer used for being matched with the three-dimensional laser scanner (11) for use; a plurality of road rods are uniformly distributed on the same-direction lane on the bridge, a camera (12) for covering the field of view of the lane area between two adjacent road rods is erected on each road rod, the cameras synchronously pick up images, the synchronous precision is less than 10 microseconds, a three-dimensional laser scanner (11) is erected right above the same-direction double lanes at the entrance of the bridge, and the measuring range covers two lanes; the stress sensor is arranged on the bridge (21) at least at the maximum stress position of the bridge, and the stress sensor, the camera (12), the timer and the three-dimensional laser scanner (11) are all connected with the computer.
2. The automatic bridge dynamic load detection and inversion system according to claim 1, wherein the three-dimensional laser scanner (11) is configured to obtain point cloud data of respective surfaces of all detected vehicles entering the same-direction dual lanes of the bridge, and send the obtained point cloud data to the computer;
the camera (12) is used for shooting the continuous driving process of all moving detected vehicles passing through the lower part of the camera, and sending the shot continuous driving video of the moving detected vehicles to the central processing unit after shooting the continuous driving video of the moving detected vehicles;
the computer is used for receiving the point cloud data sent by the three-dimensional laser scanner, obtaining three-dimensional contour maps of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction on the basis of the point cloud data, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour maps of the surfaces of all detected vehicles, and judging whether the detected vehicles are empty or not according to the height of carriages in the size data of the detected vehicles so as to obtain load judgment information of the detected vehicles; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information;
meanwhile, the computer is used for receiving the continuous driving video of the moving detected vehicle shot by the camera and processing the video into a frame of uninterrupted image; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then performing self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a target result of the plurality of moving detected vehicles after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane and obtaining the tracking frames with the sizes of the moving detected vehicles;
obtaining the centroid of each moving detected vehicle through the average value of the horizontal and vertical coordinates of the Harris characteristic angular point of each moving detected vehicle in the Harris characteristic angular points extracted through calculation, and meanwhile, connecting the centroids of the vehicles with the same labels in each frame of video image through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the positions of the vehicles so as to obtain the running tracks of the vehicles with the same labels, thereby realizing the tracking of the detected vehicles with multiple target movements and further obtaining the distribution information of the whole bridge deck moving vehicles;
calculating parameter information according to the obtained vehicle load and the obtained distribution information of the whole bridge deck moving vehicle to obtain load information of all vehicles on the bridge, and obtaining stress-strain distribution values of each point of the bridge by using a finite element model algorithm according to the obtained load information and the obtained related parameter information of the bridge during bridge design;
and correcting the obtained stress-strain distribution value of each point of the corresponding part of the bridge by polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution value of each point of the corresponding part of the bridge obtained in the step.
3. A bridge dynamic load automatic detection and inversion method is characterized by comprising the following steps:
(1) on the same-direction double lanes of the bridge, utilizing a three-dimensional laser scanner which is erected right above the entrance of the bridge and covers two lanes in the measuring range to obtain point cloud data of the surfaces of all detected vehicles entering the same-direction double lanes of the bridge, sending the obtained point cloud data to a computer, obtaining a three-dimensional contour map of the surfaces of all the detected vehicles entering the same-direction double lanes of the bridge through three-dimensional reconstruction by using the point cloud data as the basis by the computer, calculating the size data of the length, the width and the height of each detected vehicle according to the three-dimensional contour map of the surface of each detected vehicle, and judging whether the detected vehicle is an empty vehicle according to the height of a compartment in the size data of the detected vehicle so as to obtain the load judgment information of the detected vehicle; meanwhile, selecting a certain truck as a sample vehicle, and recording the size data of the truck in length, width and height; comparing the size data of the detected vehicle with the size data of the sample vehicle, and judging the vehicle type information of the detected vehicle; the vehicle type information and the load judgment information of the detected vehicle form vehicle load calculation parameter information;
(2) on the same-direction double lanes of the bridge, shooting the continuous running process of all moving detected vehicles passing through the lower part of the bridge by utilizing a camera which is erected on each uniformly distributed road rod and has a synchronous function with other cameras and covers the area view field between the two adjacent road rods, and after shooting the continuous running video of the moving detected vehicles, sending the shot continuous running video of the moving detected vehicles to a central processing unit, and processing the video into a frame of uninterrupted image by the central processing unit; extracting gray level images of a plurality of moving detected vehicles from the images by using a background difference method, then performing self-adaptive binarization processing on the gray level images of the plurality of moving detected vehicles by using an improved Otsu algorithm, extracting Harris characteristic angular points from a target result of the plurality of moving detected vehicles after binarization processing, creating a plurality of tracking frames with the sizes of the self-adaptive moving detected vehicles according to the extracted Harris characteristic angular points to start tracking the moving detected vehicles in a field of view, obtaining the number of the vehicles distributed in the field of view according to the number of the tracking frames, and simultaneously labeling the moving detected vehicles by using continuous driving videos of the moving detected vehicles shot by a camera arranged at an entrance of a bridge equidirectional double lane and obtaining the tracking frames with the sizes of the moving detected vehicles;
(3) the central processing unit is used for calculating the average value of the horizontal and vertical coordinates of the Harris characteristic angular points of each moving detected vehicle in the extracted Harris characteristic angular points in the step 2) to obtain the mass center of each moving detected vehicle, and meanwhile, the mass centers of the vehicles with the same mark number in each frame of video image are connected through a nearest tracking filter algorithm and an improved Kalman filtering algorithm for tracking and judging the position of the vehicle, so that the running tracks of the vehicles with the same mark number are obtained, the tracking of the detected vehicles with multiple target movements is realized, and the distribution information of the whole bridge deck moving vehicle is obtained;
(4) the central processing unit is used for obtaining load information of all vehicles to the bridge according to the vehicle load calculation parameter information obtained in the step 1) and the distribution information of the whole bridge deck moving vehicle obtained in the step 3), and obtaining stress-strain distribution values of each point of the bridge by using a finite element model algorithm according to the obtained load information and the related parameter information of the bridge during bridge design;
(5) and the central processing unit corrects the stress-strain distribution values of the points of the corresponding part of the bridge obtained in the step 4) through polynomial regression according to the actual stress-strain distribution value of the bridge measured by the stress sensor arranged at the maximum stress position of the bridge and the stress-strain distribution values of the points of the corresponding part of the bridge obtained in the step 4).
4. The automatic detection and inversion method for the dynamic load of the bridge according to claim 3, wherein the method for measuring the length of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the length of the detected vehicle passing through is measured, a timer matched with the three-dimensional laser scanner (11) starts to time when the head of the detected vehicle just enters a detection surface formed by the three-dimensional laser scanner, when the tail of the detected vehicle is driven away from a detection plane formed by the three-dimensional laser scanner, the timer stops timing, the speed of the detected vehicle passing through the three-dimensional laser scanner is v, the passing time of the detected vehicle recorded by the timer is t, and the length L of the vehicle body of the detected vehicle can be expressed as L ═ vt.
5. The automatic detection and inversion method for the dynamic load of the bridge according to claim 3, wherein the method for measuring the width of the passing detected vehicle by using the three-dimensional laser scanner comprises the following steps:
when the width of a passing detected vehicle is measured, a polar coordinate system is established by taking the three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, the polar axis is along the vertical direction, when laser irradiates the leftmost position of the vehicle body of the detected vehicle, the distance measurement result is recorded as r1, the included angle between the laser line and the polar axis is a1, and the distance between the left side of the detected vehicle and the polar axis direction is r1sina1(ii) a When the laser irradiates the rightmost position of the vehicle body of the detected vehicle, the distance measurement result is r2, the included angle between the laser line and the polar axis is a2, and the distance from the right side of the detected vehicle to the polar axis direction is r2sina2The width w of the detected vehicle can be expressed as w ═ r2sina2-r1sina1。
6. The automatic bridge dynamic load detection and inversion method according to claim 3, characterized in that the method for measuring the height of passing detected vehicles by using the three-dimensional laser scanner comprises the following steps:
when the height of a detected vehicle body is measured, a polar coordinate system is established by taking a three-dimensional laser scanner as a pole, in the polar coordinate system by taking the three-dimensional laser scanner as the pole, a polar axis is along the horizontal direction, when laser irradiates the highest position of the detected vehicle, the distance measurement result is recorded as r, at the moment, the included angle between the laser line and the polar axis is a, and the distance from the highest position of the detected vehicle to the polar axis is rsina; the distance from the three-dimensional laser scanner to the ground is H, and the detected vehicle height H can be expressed as H-r sina.
7. The automatic detection and inversion method for the dynamic load of the bridge according to claim 3, wherein the step 1) is specifically as follows:
the three-dimensional laser scanner scans the top of the body of the detected vehicle and one side of the vehicle by using the scanning light curtain constructed by the three-dimensional laser scanner so as to obtain a plurality of continuous scanning cross sections of the detected vehicle, draws a three-dimensional profile map of the detected vehicle according to the plurality of continuous scanning cross sections, and distinguishes different types of the detected vehicle from the three-dimensional profile map of the detected vehicle;
for a flat truck and a truck with a dump truck, whether the truck is empty or not is judged by confirming whether the three-dimensional laser scanner is the shortest distance to the left edge or the right edge of the vehicle or not.
8. The automatic bridge dynamic load detection and inversion method according to claim 3, wherein the principle of the improved Otsu algorithm is as follows:
1) according to the formula
Figure FDA0002414969080000051
Obtaining the distribution probability F of the image gray level1，F2，F3…Fi. Wherein N is the total number of pixels, NiThe number of pixels having a gray value i.
2) The mean gray value μ of the image is calculated.
3) Let the gray level probability of the background image be P and the average value of the gray levels of the background image be mu1. The image variance is Var, and the variance at the maximum value is maxVar. Let P equal 0, mu1＝0，maxVar＝0。
4) i is iterated from 1 to 256, and the following values are calculated in sequence:
P(i)＝P(i-1)+Fi
μ1(i)＝(P(i-1)×μ1(i-1)+i×Fi)/P(i)
the between-class variance can be expressed as:
Var＝P·(μ1-μ)2+P2·(μ2-μ)2(3.16)
wherein, P2Is the gray level probability, mu, of the foreground image2Is the gray level average of the foreground image. Because mu is P.mu1+P2·μ2(3.17)
Bringing formula 3.17 into formula 3.16:
Var(i)＝P(i)×(μ-μ1(i))2/(1-P(i))
5) judging whether Var (i) is larger than maxVar, if so, making maxVar ═ Var (i), and T ═ i; if not, making i equal to i +1, and returning to the step 4.
6) And (5) finishing the iterative search, wherein T is the result.
9. The automatic detection and inversion method for the dynamic load of the bridge according to claim 8, wherein the camera has a day-night conversion monitoring function, the effective pixels of the camera reach 300 ten thousand, the horizontal field angle is 80 degrees, the maximum image size reaches 1920 × 1080, the resolution of the video is 1296 (horizontal) × 732 (vertical), the video frame rate is 50Hz:25fps, the power supply power is 7W, the power supply voltage is 12VDC, the size of the camera is 194.04 × 93.85.85 93.85 × 89.52.52 mm, and the weight of the camera is 1000 g;
the cameras (12) are composed of a plurality of high resolution cameras, each camera covers 50 meters of the bridge floor, and the cameras need to be accurately synchronized in time, so that dynamic changes of loads of each bridge of the bridge floor in different time are obtained.
10. The automatic detection and inversion method for dynamic loads of bridges according to claim 3, wherein a specific algorithm for obtaining load information of all vehicles on the bridges through the vehicle load calculation parameter information obtained in step 1) and the distribution information of the whole bridge deck moving vehicles obtained in step 3) is as follows:
the load F applied to the road surface by the whole vehicle can be divided into vehicle static load FjAnd dynamic load FdI.e. by
F＝Fj+Fd
Wherein the static load F of the vehiclejI.e. the gravitational load.
In the process of obtaining the dynamic load of the vehicle, according to the D' Alembert principle, the motion equation of the vibration system of the vehicle is obtained firstly as follows:
Figure FDA0002414969080000071
wherein: quality matrix
Figure FDA0002414969080000072
Damping matrix
Figure FDA0002414969080000073
Rigidity matrix
Figure FDA0002414969080000074
Displacement array y ═ yt,ys]T；
Excitation array
Figure FDA0002414969080000075
In the above matrix, msIs a sprung mass; m istIs an unsprung mass; c. CsDamping the suspension; c. CtDamping the tire. k is a radical of1Is the rigidity coefficient of the rear tyre of the automobile; k is a radical of2The rigidity coefficient of the rear suspension of the automobile; k is a radical oftIs the tire stiffness; y iss,ytIs the vertical absolute displacement of the sprung and unsprung masses; y isrExciting vertical displacement of the unevenness of the road surface; and deltah is the vertical deformation generated by the road surface.
The random dynamic load of the vehicle on the road surface is as follows:
Figure FDA0002414969080000076
after the load of a single vehicle to the road surface is obtained, the total load of all vehicles running on the bridge to the bridge surface can be calculated by calculating the number of the vehicles on the bridge surface;
the dynamic load on the bridge deck can be obtained through the steps, and the stress-strain distribution value of the whole bridge is calculated by adopting a finite element method according to the bridge structure.
CN202010188342.6A 2020-03-17 2020-03-17 Automatic bridge dynamic load detection and inversion system and method Active CN111354035B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010188342.6A CN111354035B (en) 2020-03-17 2020-03-17 Automatic bridge dynamic load detection and inversion system and method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010188342.6A CN111354035B (en) 2020-03-17 2020-03-17 Automatic bridge dynamic load detection and inversion system and method

Publications (2)

Publication Number Publication Date
CN111354035A true CN111354035A (en) 2020-06-30
CN111354035B CN111354035B (en) 2023-09-05

Family

ID=71197510

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010188342.6A Active CN111354035B (en) 2020-03-17 2020-03-17 Automatic bridge dynamic load detection and inversion system and method

Country Status (1)

Country Link
CN (1) CN111354035B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112362149A (en) * 2020-09-21 2021-02-12 中铁第四勘察设计院集团有限公司 Method and system for dynamically identifying vehicle axle load based on vertical displacement influence surface loading
CN112387982A (en) * 2020-10-21 2021-02-23 上海交通大学 Laser additive process power combined regulation and control method
CN113077632A (en) * 2021-06-07 2021-07-06 四川紫荆花开智能网联汽车科技有限公司 V2X intelligent network connection side system and realizing method
CN114993544A (en) * 2022-07-18 2022-09-02 安徽建筑大学 Bridge structure stress detection device
CN115265378A (en) * 2022-09-23 2022-11-01 北京东方国信科技股份有限公司 Detection system for three-dimensional size of carriage
CN115506431A (en) * 2022-09-16 2022-12-23 福建晋工新能源科技有限公司 Automatic material shoveling system and automatic material shoveling control method of automatic material shoveling system

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002101328A1 (en) * 2001-06-12 2002-12-19 Hans Heister Method for recording the surface of a roadway
JP2011257256A (en) * 2010-06-09 2011-12-22 Fukken Co Ltd Method of measuring altitude of bridge in no-live-load state
US8306747B1 (en) * 2007-01-19 2012-11-06 Starodub, Inc. Travel way measurement system
CN103593678A (en) * 2013-10-16 2014-02-19 长安大学 Long-span bridge vehicle dynamic load distribution detection method
CN104598753A (en) * 2015-02-06 2015-05-06 华北水利水电大学 Bridge moving vehicle load recognition method based on Brakhage V method
CN106123797A (en) * 2016-08-29 2016-11-16 北京交通大学 Bridge floor deflection metrology system based on image procossing and method
CN108460231A (en) * 2018-03-23 2018-08-28 中交公路长大桥建设国家工程研究中心有限公司 A kind of bridge builds foster overall process intellectual monitoring assessment early warning decision system and method
CN109167956A (en) * 2018-05-21 2019-01-08 同济大学 The full-bridge face traveling load spatial distribution merged based on dynamic weighing and more video informations monitors system
CN109635386A (en) * 2018-11-27 2019-04-16 中电建冀交高速公路投资发展有限公司 A kind of bridge move vehicle Load Identification Methods

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002101328A1 (en) * 2001-06-12 2002-12-19 Hans Heister Method for recording the surface of a roadway
US8306747B1 (en) * 2007-01-19 2012-11-06 Starodub, Inc. Travel way measurement system
JP2011257256A (en) * 2010-06-09 2011-12-22 Fukken Co Ltd Method of measuring altitude of bridge in no-live-load state
CN103593678A (en) * 2013-10-16 2014-02-19 长安大学 Long-span bridge vehicle dynamic load distribution detection method
CN104598753A (en) * 2015-02-06 2015-05-06 华北水利水电大学 Bridge moving vehicle load recognition method based on Brakhage V method
CN106123797A (en) * 2016-08-29 2016-11-16 北京交通大学 Bridge floor deflection metrology system based on image procossing and method
CN108460231A (en) * 2018-03-23 2018-08-28 中交公路长大桥建设国家工程研究中心有限公司 A kind of bridge builds foster overall process intellectual monitoring assessment early warning decision system and method
CN109167956A (en) * 2018-05-21 2019-01-08 同济大学 The full-bridge face traveling load spatial distribution merged based on dynamic weighing and more video informations monitors system
CN109635386A (en) * 2018-11-27 2019-04-16 中电建冀交高速公路投资发展有限公司 A kind of bridge move vehicle Load Identification Methods

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
向小菊;侯旭;周志祥;楚玺;姜腾蛟;: "大跨度桥梁变形三维激光扫描结果与有限元模拟对比分析" *
周志祥;姜腾蛟;唐亮;楚玺;杨晶;: "移动式三维激光扫描***在桥面全息变形监测的应用研究" *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112362149A (en) * 2020-09-21 2021-02-12 中铁第四勘察设计院集团有限公司 Method and system for dynamically identifying vehicle axle load based on vertical displacement influence surface loading
CN112362149B (en) * 2020-09-21 2022-01-18 中铁第四勘察设计院集团有限公司 Method and system for dynamically identifying vehicle axle load based on vertical displacement influence surface loading
CN112387982A (en) * 2020-10-21 2021-02-23 上海交通大学 Laser additive process power combined regulation and control method
CN112387982B (en) * 2020-10-21 2021-10-12 上海交通大学 Laser additive process power combined regulation and control method
CN113077632A (en) * 2021-06-07 2021-07-06 四川紫荆花开智能网联汽车科技有限公司 V2X intelligent network connection side system and realizing method
CN114993544A (en) * 2022-07-18 2022-09-02 安徽建筑大学 Bridge structure stress detection device
CN115506431A (en) * 2022-09-16 2022-12-23 福建晋工新能源科技有限公司 Automatic material shoveling system and automatic material shoveling control method of automatic material shoveling system
CN115265378A (en) * 2022-09-23 2022-11-01 北京东方国信科技股份有限公司 Detection system for three-dimensional size of carriage

Also Published As

Publication number Publication date
CN111354035B (en) 2023-09-05

Similar Documents

Publication Publication Date Title
CN111354035B (en) Automatic bridge dynamic load detection and inversion system and method
CN109064495B (en) Bridge deck vehicle space-time information acquisition method based on fast R-CNN and video technology
CN103718224B (en) Three-dimensional body pick-up unit and three-dimensional body detection method
CN107463890B (en) A kind of Foregut fermenters and tracking based on monocular forward sight camera
CA2644006C (en) Systems and methods for obtaining improved accuracy measurements of moving rolling stock components
CN109359409A (en) A kind of vehicle passability detection system of view-based access control model and laser radar sensor
CN101223416A (en) Object detection device
CN107314741A (en) Measurement of cargo measuring method
CN112504414B (en) Vehicle dynamic weighing method and system based on non-contact measurement of dynamic deflection of bridge
CN112698302A (en) Sensor fusion target detection method under bumpy road condition
CN106503636A (en) A kind of road sighting distance detection method of view-based access control model image and device
CN110132220A (en) A kind of dynamic 3 D tunnel cross-section shape changing detection and analysis system, method and device
CN103938531B (en) Laser road faulting of slab ends detecting system and method
Kong et al. Non-contact vehicle weighing method based on tire-road contact model and computer vision techniques
CN111694011A (en) Road edge detection method based on data fusion of camera and three-dimensional laser radar
CN109074653A (en) The method of the object beside road for detecting motor vehicles calculates equipment, driver assistance system and motor vehicles
CN110023953A (en) Information processing equipment, imaging device, apparatus control system, moving body, information processing method and computer program product
CN114022537A (en) Vehicle loading rate and unbalance loading rate analysis method for dynamic weighing area
US9098774B2 (en) Method for detection of targets in stereoscopic images
CN116448224A (en) Bridge dynamic weighing and grading alarm method based on visual strain monitoring
CN112161685B (en) Vehicle load measuring method based on surface characteristics
CN112278891B (en) Carriage internal attitude detection method
CN116245996A (en) Point cloud rendering method and system for unstructured road in mining area
CN111023975B (en) Device and method for detecting ore volume of dump truck for strip mine
CN206905735U (en) One kind bridge joint slope sedimentation automatic detection device and vehicle

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant