Highway bridge deformation monitoring method and system based on laser measurement

Abstract

The invention relates to the technical field of bridge deformation monitoring, in particular to a highway bridge deformation monitoring method and system based on laser measurement, and the method comprises the steps: obtaining a slope difference of a fitting straight line and a curvature difference of all edge points based on x and y coordinates of all edge points between two side edge clusters; de-noising various point clouds in combination with the local change trend of the curvature of all edge points in each side cluster; fitting each plane point and all plane points in the neighborhood of the plane point, analyzing the error condition of the fitting process, and screening out a feature cluster by combining the dispersion degree of the curvature of each plane point and all plane points in the neighborhood of the plane point; all matching point pairs between the feature clusters of different feature point clouds are obtained, key indexes are determined by analyzing the distance between the matching point pairs and the difference of the concave-convex coefficients between the matching point pairs, and deformation monitoring is carried out on the highway bridge. The invention aims to improve the precision of monitoring the deformation of the highway bridge.

Description

Technical Field

[0001] This application relates to the field of bridge deformation monitoring technology, specifically to a method and system for monitoring the deformation of highway bridges based on laser measurement. Background Technology

[0002] As crucial nodes in transportation networks, highway bridges directly impact regional economic development and public safety. Over long periods of use, bridges are susceptible to structural deformation and even damage accumulation due to environmental erosion, material aging, excessive traffic loads, and natural disasters. Traditional deformation monitoring methods, such as total stations, fiber optic sensors, or GPS positioning technology, suffer from low measurement efficiency, poor environmental adaptability, and insufficient dynamic response, failing to meet the demands for high-precision, real-time bridge monitoring. In recent years, laser measurement technology, with its advantages of non-contact operation, high precision, and strong anti-interference capabilities, has gradually become a research hotspot in the field of structural health monitoring, providing a new technological approach for multi-dimensional information perception of bridge deformation.

[0003] Currently, research on bridge deformation detection using 3D laser scanners typically involves comparing the changes at a specific point on the bridge over time to determine if deformation has occurred. However, point cloud data is easily affected by external environmental factors, and existing technologies generally do not deeply analyze the impact of environmental factors and vehicles traveling on the bridge on the point cloud quality. Therefore, there can be significant errors in determining the comparison points, which reduces the accuracy of monitoring highway bridge deformation. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for monitoring the deformation of highway bridges based on laser measurement. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a method for monitoring the deformation of highway bridges based on laser measurement, the method comprising the following steps: S1: Use the point cloud acquisition module of the system to acquire various point clouds of the road surface of the bridge to be tested, including standard point clouds and ordinary point clouds. S2: Compare ordinary point clouds with standard point clouds to determine key indices, specifically: All edge points in various point clouds are acquired, and all edge points are clustered. The positive and negative differences between the x-coordinate values ​​of all edge points in each cluster are analyzed to determine the lateral confidence of each cluster, so as to select two lateral clusters from all clusters of various point clouds. Based on the slope difference of the fitted line obtained from the x and y coordinates of all edge points between the two lateral clusters and the curvature difference of all edge points, the difference index between the two lateral clusters is determined. Combined with the local change trend of the curvature of all edge points in each lateral cluster, the various point clouds are denoised. All the denoised point clouds are recorded as feature point clouds. Point cloud data excluding all edge points in various feature point clouds are denoted as planar points. A fitting process is performed on each planar point and all planar points in its neighborhood. The error in the fitting process is analyzed, and the concavity / convexity coefficient of each planar point is determined by combining the dispersion of the curvature of each planar point and all planar points in its neighborhood. All planar points in various feature point clouds are clustered. By analyzing the proportion of all planar points in each cluster to the total number of planar points in the corresponding feature point cloud and the average distribution of the concavity / convexity coefficient of all planar points in each cluster, feature clusters for various feature point clouds are selected. Obtain all matching point pairs between feature clusters of different feature point clouds, and determine the key indices between matching point pairs by analyzing the distance between each matching point pair and the difference in the concavity and convexity coefficients between each matching point pair. S3: Based on the aforementioned key indices, conduct deformation monitoring of highway bridges.

[0005] Preferably, the method for determining the lateral confidence of each cluster is as follows: The total number of edge points with negative x-coordinates and the total number of edge points with positive x-coordinates within each cluster are divided by the total number of edge points within the corresponding cluster. The results of the division are recorded as positive and negative proportions, respectively. The absolute value of the difference between the positive and negative proportions is taken as the lateral confidence of each cluster.

[0006] Preferably, the two side clusters are the clusters corresponding to the first two values ​​in the descending order of the side confidence scores of all clusters in various point clouds.

[0007] Preferably, the difference index between the two side clusters is the sum of the slope difference of the fitted line obtained from the (x, y) coordinates of all edge points between the two side clusters and the curvature difference of all edge points.

[0008] Preferably, the denoising of various point clouds includes: The curvature of all edge points within each side cluster is used as the input of the mutation point detection algorithm, and the output is the mutation curvature. The edge point corresponding to the mutation curvature is denoted as the mutation point. The curvature of each mutation point and all edge points in its neighborhood are arranged in ascending order of their respective y-coordinates. In the arrangement result, the curvature of all edge points before each mutation point is used to form the pre-curvature sequence of each mutation point, and the curvature of all edge points after each mutation point is used to form the post-curvature sequence of each mutation point. First-order difference sequences of the front and back curvature sequences are obtained respectively and denoted as front curvature change sequence and back curvature change sequence. The ratio of the number of negative elements to the total number of elements in the front curvature change sequence is denoted as the increasing proportion, and the ratio of the number of positive elements to the total number of elements in the back curvature change sequence is denoted as the decreasing proportion. The average of the increasing proportion and the decreasing proportion is used as the deformation index of each side cluster. The ratio of the difference index between two side clusters to the average deformation index of the two side clusters is used as the noise interference coefficient between the two side clusters. The noise interference coefficient between two side clusters is used as the input of an exponential function with the natural constant as the base, and the output is used as the neighborhood parameter in the filtering algorithm. The filtering algorithm is then used to denoise various point clouds.

[0009] Preferably, the method for determining the concavity / convexity coefficient of each planar point is as follows: Calculate the mean square error during the fitting process for each plane point and all plane points in its neighborhood; The product of the degree of dispersion of the curvature of each plane point and all plane points in its neighborhood with the mean square error is used as the concavity / convexity coefficient of each plane point.

[0010] Preferably, the method for obtaining the feature clusters of the various feature point clouds is as follows: In various feature point clouds, the ratio of the number of all planar points in each cluster to the total number of planar points in the corresponding feature point cloud is calculated and denoted as the proportion of each cluster. The ratio of the proportion of each cluster to the average concavity coefficient of all planar points in the corresponding cluster is denoted as the confidence ratio of each cluster. Among various feature point clouds, the cluster with the highest confidence ratio is taken as the feature cluster of each feature point cloud.

[0011] Preferably, the key index between each pair of matching points is the result of multiplying the difference in the concavity / convexity coefficient between each pair of matching points by the distance between the three-dimensional coordinates of the corresponding pair of matching points.

[0012] Preferably, the deformation monitoring of the highway bridge includes: Between ordinary point clouds and standard point clouds, the key indices between all matching point pairs are used as input to the clustering algorithm, and multiple clusters are output and denoted as cluster sets. The mean of all key indices in each cluster set is calculated and denoted as the key mean. The cluster set with the largest key mean is taken as the key cluster. The points in the ordinary point cloud that belong to the matching point pairs corresponding to all key indices in the key cluster are taken as monitoring points. If the difference between the coordinate values ​​of the monitoring point and its matching point on any coordinate axis exceeds the preset deformation threshold, the bridge is determined to be deformed; otherwise, the bridge is determined not to be deformed.

[0013] Secondly, another embodiment of this application also provides a highway bridge deformation monitoring system based on laser measurement, which implements the aforementioned highway bridge deformation monitoring method based on laser measurement. The system includes a point cloud acquisition module, a point cloud data analysis module, and a bridge deformation monitoring module. The point cloud acquisition module is used to acquire 3D point cloud data of the bridge when there are no vehicles passing by, and 3D point cloud data of the bridge road surface when there are normal vehicles passing by, and respectively denoted as standard point cloud and normal point cloud. The vehicle-mounted laser scanner travels at a constant speed along the central axis of the bridge from one end to the other. The direction of travel is regarded as the y-axis, the lateral direction of the bridge is regarded as the x-axis, and the z-axis is set in the direction perpendicular to the x-axis and y-axis. The acquired standard point cloud data and normal point cloud data are transmitted to the point cloud data analysis module. The point cloud data analysis module is used to analyze and process the data acquired by the point cloud acquisition module. The analysis and processing method adopts S2 in the laser measurement-based highway bridge deformation monitoring method. Furthermore, the processed results are input into the bridge deformation monitoring module. The bridge deformation monitoring module is used to monitor bridge deformation based on the results output by the point cloud data analysis module. The monitoring method adopts S3 in the laser-based highway bridge deformation monitoring method.

[0014] This application has at least the following beneficial effects: This application considers the impact of various environmental factors and vehicle conditions on point cloud data. By constructing a noise interference coefficient, it can dynamically assess the degree of noise impact, thereby more accurately filtering the point cloud and improving the accuracy of the point cloud data. Furthermore, by analyzing the geometric differences between the bridge road surface and other objects on the bridge, it removes these redundant points from the ordinary point cloud, obtaining feature clusters reflecting the bridge road surface, thus providing a basis for subsequent monitoring point selection. Further, by registering planar points in the feature clusters between the ordinary point cloud and the standard point cloud, it constructs a key index between matching point pairs, thereby assessing the criticality of each planar point and selecting the most critical monitoring points. This application analyzes the impact of environmental and vehicle factors on point cloud data, performing filtering and redundant point removal operations on the point cloud data, providing accurate data support for monitoring point selection. Finally, based on the impact of vehicle load on changes in the bridge point cloud, it selects the most critical monitoring points, improving the accuracy of monitoring highway bridge deformation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a laser-based highway bridge deformation monitoring method according to an embodiment of this application; Figure 2 This is a schematic diagram of a key index extraction process provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the laser-based highway bridge deformation monitoring method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the laser-based highway bridge deformation monitoring method and system provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a laser-based method for monitoring the deformation of highway bridges according to an embodiment of this application. The method includes the following steps: Step S1: Use the point cloud acquisition module of the laser-based road surface smoothness detection system to acquire various point clouds of the road surface of the bridge to be tested, including standard point clouds and ordinary point clouds.

[0021] This embodiment uses a vehicle-mounted 3D laser scanner to scan a highway bridge under conditions of no vehicle traffic and normal vehicle traffic. The scanning method is as follows: the driving direction is considered as the y-axis, the transverse direction of the bridge is the x-axis, and the direction perpendicular to the x-axis and y-axis is set as the z-axis, constructing a 3D scanning system. The vehicle carrying the scanner travels from one end of the bridge to the other at a constant speed in a straight line. The bridge is scanned under conditions of no vehicle traffic and normal vehicle traffic, respectively, obtaining the 3D point cloud of the bridge under conditions of no vehicle traffic and 3D point cloud of the bridge under conditions of vehicle traffic.

[0022] Furthermore, the Random Sampling Consensus (RANSAC) algorithm is used to automatically identify and segment multiple structural surfaces in the 3D point cloud when there are no vehicles, and multiple structural surfaces in the 3D point cloud when there are vehicles. The structural surface with the largest area among all structural surfaces in the 3D point cloud when there are no vehicles is taken as the road surface of the bridge under test when there are no vehicles, and the structural surface with the largest area among all structural surfaces in the 3D point cloud when there are vehicles is taken as the road surface of the bridge when there are vehicles. This yields the point clouds of the road surface of the bridge under test when there are no vehicles and the point clouds of the road surface when there are vehicles, denoted as the standard point cloud and the ordinary point cloud, respectively. For ease of description, the standard point cloud and the ordinary point cloud are collectively referred to as various point clouds, i.e., various point clouds include standard point clouds and ordinary point clouds. Further, the AC method in the PCL point cloud library is used to extract all edge points from the road surfaces of various point clouds, which are then used as the edge points of various point clouds.

[0023] Among them, the Random Sampling Consensus (RANSAC) algorithm and the AC method in the PCL point cloud library are well-known technologies, and their specific principles and processes will not be elaborated here.

[0024] Step S2: Compare the feature differences between ordinary point clouds and standard point clouds to determine the key indices between each matching point pair.

[0025] Factors affecting the reflectivity of bridge surfaces, such as material, roughness, and color contrast, can lead to errors in the point cloud data acquired by a 3D laser scanner. Furthermore, external environmental factors, such as temperature, disturbances from passing vehicles, and wind, can also influence the direction of laser propagation in the air, resulting in data acquisition errors. Therefore, to more accurately monitor bridge deformation and determine precise monitoring points, the acquired point cloud data needs to be filtered to reduce errors. The specific process is as follows: S201: Obtain all edge points in various point clouds, cluster the x-coordinate values ​​of all edge points, analyze the positive and negative differences between the x-coordinate values ​​of all edge points in each cluster, determine the side confidence of each cluster, and select two side clusters from all clusters; based on the local change trend of the curvature of all edge points in each side cluster, determine the deformation index of each side cluster, and combine the slope difference of the fitted line obtained by the (x, y) coordinates of all edge points between the two side clusters and the curvature difference of all edge points to denoise various point clouds, and record all denoised point clouds as feature point clouds.

[0026] Since bridge structures typically possess regular geometric shapes, such as straight lines, planes, and symmetrical structures, their point cloud data will exhibit characteristics such as smooth continuity in local areas and symmetry between the two sides. However, the presence of noise can cause the point cloud to become irregular, with uneven local areas and disrupted symmetry between the two sides. Therefore, the degree of difference between bridge point cloud data can be assessed to reflect the noise interference affecting the point cloud data. Specifically: Considering that the vehicle-mounted 3D laser scanner travels from one end of the bridge to the other, the road surface of the bridge will have four edges. Since the point cloud coordinates of the four edges of the bridge are not consistent, while the point cloud coordinates of the same edge are relatively consistent, a clustering algorithm can be used to distinguish the four edges.

[0027] All edge points in the road surface are used as input to the clustering algorithm. The Euclidean distance between the three-dimensional coordinates of the edge points is used as the distance metric in the clustering algorithm. The number of clusters is set to 4, and the final output is 4 clusters, which represent the four edges of the bridge respectively.

[0028] It should be noted that there are many commonly used clustering algorithms. In this embodiment, the k-means clustering algorithm is used to cluster the edges. In practical applications, as other implementation methods, implementers may also use the CURE hierarchical clustering algorithm or the fuzzy C-means clustering algorithm. This embodiment does not impose any special restrictions on the selection of clustering algorithms.

[0029] The calculation method of Euclidean distance and the k-means clustering algorithm are well-known techniques, and their specific clustering principles will not be elaborated here.

[0030] Furthermore, the total number of edge points with negative x-coordinates and the total number of edge points with positive x-coordinates within each cluster are divided by the total number of edge points within the corresponding cluster. The results of these divisions are recorded as positive and negative proportions, respectively. The absolute value of the difference between the positive and negative proportions is used as the lateral confidence of each cluster. The higher the lateral confidence, the greater the probability that all edge points in the cluster have positive or negative x-coordinates, and the greater the probability that the edge points within the cluster are the left and right edge points that are in the same direction as the vehicles traveling on the bridge.

[0031] The clusters corresponding to the top two values ​​of the side confidence scores of all clusters of various point clouds in descending order are used as the two side clusters of various point clouds to represent the left and right sides that are consistent with the direction of vehicle movement on the bridge.

[0032] Furthermore, since both bridge deformation and noise cause fluctuations in the curvature of edge points in the point cloud, disrupting the symmetry between the two edges, it becomes impossible to identify edge points requiring denoising and thus hinder accurate noise removal. However, because the bridge experiences force in one direction during deformation, the curvature gradually increases and then decreases from the edge of the deformation region towards the edge center and back to the edge, exhibiting a trend. Noise, on the other hand, is random and lacks this gradual trend. Therefore, based on the above analysis, the point cloud is denoised as follows: The difference between the slopes of the fitted lines obtained from the x and y coordinates of all edge points between the two side clusters and the difference between the curvatures of all edge points are added together to form the difference index between the two side clusters. This index is used to determine whether the symmetry of the bridge has been disrupted. Since the edges of the bridge are regular and the changes are relatively smooth, the fluctuations of the point clouds on both sides of the bridge should also be relatively consistent. If the difference index is larger, it indicates that the symmetry of the two sides' edges is more likely to be disrupted. Conversely, if the difference index is smaller, it indicates that the symmetry of the two sides' edges is less likely to be disrupted.

[0033] It should be understood that in this embodiment, the DTW distance of the curvature of all edge points between two side clusters is taken as the curvature difference of all edge points between two side clusters. In actual application, as other implementation methods, implementers may also use other methods such as Euclidean distance and Manhattan distance to measure the difference between data groups, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the difference between data groups.

[0034] The method for calculating the DTW distance is a well-known technique, and its specific calculation process will not be elaborated here.

[0035] It should be noted that there are many methods to measure the differences between data. In this embodiment, whenever the difference between data is calculated, the method of taking the absolute value of the difference is used.

[0036] It should be noted that there are many commonly used fitting methods. In this embodiment, the least squares fitting method is used to fit the x and y coordinates of the edge points. In practical applications, as other implementation methods, the implementer may also use other fitting methods such as linear regression. This embodiment does not impose any special restrictions on the selection of fitting methods.

[0037] The least squares fitting method and the method for obtaining the curvature of data points in point clouds are both well-known techniques, and their specific principles will not be elaborated here.

[0038] Furthermore, the curvature of all edge points within each side cluster is used as the input to the mutation point detection algorithm, and the output is the mutation curvature. The edge point corresponding to the mutation curvature is denoted as the mutation point. The curvature of each mutation point and all edge points in its neighborhood are arranged in ascending order of their respective y-coordinates. In the arrangement result, the curvature of all edge points before each mutation point is used to form the pre-curvature sequence of each mutation point, and the curvature of all edge points after each mutation point is used to form the post-curvature sequence of each mutation point.

[0039] Furthermore, the first-order difference sequences of the front and back curvature sequences are obtained separately and denoted as the front curvature change sequence and the back curvature change sequence, respectively. The ratio of the number of negative elements to the total number of elements in the front curvature change sequence is denoted as the increasing proportion, and the ratio of the number of positive elements to the total number of elements in the back curvature change sequence is denoted as the decreasing proportion. The average of the increasing proportion and the decreasing proportion is used as the deformation index of each side cluster. This index is used to characterize the possibility that the curvature of the edges in the side cluster may change abruptly due to bridge deformation in the region where the side cluster is located. The larger the deformation index, the greater the possibility of bridge deformation.

[0040] The method for obtaining the first-order difference sequence is a well-known technique, and its specific acquisition process will not be described in detail here.

[0041] Furthermore, the ratio of the difference index between the two side clusters to the average deformation index of the two side clusters is used as the noise interference coefficient between the two side clusters. This is used to determine the degree of influence of noise on the curvature of the edge points. The larger the difference index and the smaller the average deformation index, the larger the noise interference coefficient, indicating that the sudden change in the curvature of the edge points is more likely to be caused by noise rather than bridge deformation. Conversely, if the noise interference coefficient is smaller, it indicates that the sudden change in the curvature of the edge points is more likely to be caused by bridge deformation. In this case, a smaller filtering window should be set to retain the details of the bridge deformation.

[0042] Furthermore, the noise interference coefficient between the two side clusters is used as the input of an exponential function with the natural constant as the base, and the output is used as the neighborhood parameter in the filtering algorithm. The filtering algorithm is used to denoise various point clouds, and the denoised point clouds are all denoised as feature point clouds.

[0043] Thus, by analyzing the spatial distribution and curvature variation trend of the side point clouds, various point clouds were denoised, resulting in various denoised feature point clouds.

[0044] S202: Denote the point cloud data, excluding all edge points, in various feature point clouds as planar points. Fit each planar point and all planar points in its neighborhood, analyze the error in the fitting process, and determine the concavity / convexity coefficient of each planar point by combining the dispersion of the curvature of each planar point and all planar points in its neighborhood. Cluster all planar points in various feature point clouds, and select feature clusters for various feature point clouds by analyzing the proportion of the number of all planar points in each cluster to the total number of planar points in the corresponding feature point cloud and the average distribution of the concavity / convexity coefficient of all planar points in each cluster.

[0045] Furthermore, considering that scanning bridges with a 3D laser scanner inevitably includes irrelevant objects such as vehicles, pedestrians, streetlights, and surrounding vegetation, resulting in numerous redundant points, directly selecting bridge monitoring points might mistakenly select these redundant points, thus affecting the accuracy of subsequent bridge deformation monitoring. Therefore, after filtering the initial point cloud data, it is necessary to further remove redundant points to improve the accuracy of bridge monitoring point selection. The specific process is as follows: Because bridges have a consistent overall color and smooth, flat road surfaces, while redundant points such as vehicles, pedestrians, and flowers and plants around the bridge have inconsistent colors, shapes, and sizes, the bridge point cloud and redundant point cloud can be separated by analyzing the color and local differences between the point clouds.

[0046] Since the edge point cloud of the road surface has been extracted as described above, the point cloud data other than all edge points in various point clouds are denoted as planar points.

[0047] Furthermore, by analyzing the distribution characteristics of data points surrounding the planar point, the concavity / convexity coefficient of each planar point is determined, thereby judging whether the local area where the planar point is located corresponds to the bridge pavement area. Specifically: The neighborhood is divided with each plane point as the center. There are many ways to divide the neighborhood. In this embodiment, the distance between each plane point and all other plane points is calculated, and the distances are arranged in ascending order. The plane point cloud corresponding to the first preset number of distances in the arrangement result constitutes the neighborhood of each plane point.

[0048] The preset number is set manually. In this embodiment, the preset number is 15. In actual application, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0049] It should be noted that there are many commonly used methods for measuring the distance between points. In this embodiment, Euclidean distance is used to measure the distance between two points. In practical applications, as other implementation methods, implementers may also use Mahalanobis distance or Manhattan distance, etc., to measure the distance between points. This embodiment does not impose any special restrictions on the selection of methods for measuring the distance between points.

[0050] Furthermore, planar fitting is performed on each planar point cloud data and all planar points in its neighborhood, and the mean square error in the fitting process is calculated. The larger the mean square error, the greater the difference in geometric features between the planar point and other planar points in its neighborhood. The local area where the planar point is located is less likely to conform to the flat features of the bridge road surface, and the greater the possibility that it is a redundant point. Conversely, the smaller the mean square error, the smaller the difference in geometric features between the planar point and other planar points in its neighborhood, and the more likely the local area where the planar point is located is the bridge road surface.

[0051] In this embodiment, the least squares fitting method is used to fit each plane point and all plane points in its neighborhood. In addition, in practical applications, as other implementation methods, the implementer may also use other fitting methods such as polynomial fitting. This embodiment does not impose any special restrictions on the selection of fitting methods.

[0052] It should be understood that the method for calculating the mean square error is a well-known technique, and its specific calculation process will not be elaborated here.

[0053] Furthermore, considering that although the surface of a vehicle is relatively smooth, it exhibits curvature variations, i.e., bending characteristics, we analyze the dispersion of curvature of each planar point and all planar points in its neighborhood to measure whether the local area where the planar point is located has bending or irregular characteristics, thereby further excluding data points corresponding to other objects besides the bridge and road surfaces.

[0054] It should be noted that there are many methods to measure the degree of dispersion of the curvature of each plane point and all plane points in its neighborhood. In this embodiment, the variance of the curvature of each plane point and all plane points in its neighborhood is used as the degree of dispersion of the curvature of each plane point and all plane points in its neighborhood. In practical applications, as other implementation methods, the implementer may also use other methods such as the coefficient of variation to measure the degree of data dispersion. This embodiment does not impose any special restrictions on the selection of methods for measuring the degree of data dispersion.

[0055] Furthermore, the product of the dispersion of the curvature of each planar point and all planar points in its neighborhood with the mean square error is used as the concavity coefficient of each planar point. The larger the concavity coefficient, the greater the local regional fluctuation of the planar point, and the greater the possibility that the planar point cloud data does not belong to the bridge point cloud. Conversely, the smaller the concavity coefficient, the smaller the geometric feature fluctuation of the local region where the planar point is located, and the greater the possibility that the planar point belongs to the bridge point cloud.

[0056] Furthermore, in order to better distinguish the point cloud data corresponding to bridges and other objects, the RGB information of all planar points is obtained and converted into grayscale values. The normalized value of the concavity coefficient of each planar point is combined with the grayscale value to form a tuple for each planar point.

[0057] Cluster all planar points in various point clouds, where the Euclidean distance between the pairs of planar points is used as the distance metric in the clustering algorithm. The elbow method is used to determine the number of clusters, and all clusters are output.

[0058] In this embodiment, the k-means clustering algorithm is used. In practical applications, implementers may also use other clustering algorithms such as fuzzy C-means clustering; this embodiment does not impose any special restrictions. Furthermore, the elbow method for determining the number of clusters during the clustering process is a well-known technique, and its specific principles will not be elaborated further.

[0059] Furthermore, in various feature point clouds, the ratio of the number of all planar points in each cluster to the total number of planar points in the corresponding feature point cloud is calculated and denoted as the proportion of each cluster. The ratio of the proportion of each cluster to the average concavity / convexity coefficient of all planar points in the corresponding cluster is denoted as the confidence ratio of each cluster. If the confidence ratio is larger, it indicates that there are more elements in the current cluster and the concavity / convexity coefficient is lower, and the point cloud region corresponding to the current cluster is more consistent with the characteristics of a bridge road surface. Conversely, if the confidence ratio is smaller, it indicates that there are fewer elements in the current cluster and the concavity / convexity coefficient is larger, and the point cloud region corresponding to the current cluster is less consistent with the characteristics of a bridge road surface.

[0060] Finally, among various feature point clouds, the cluster with the highest confidence ratio is selected as the feature cluster for each feature point cloud.

[0061] Thus, by analyzing the geometric features of planar points, distinguishing the point clouds corresponding to the bridge road surface and other objects on the bridge, feature clusters were obtained, which enabled more accurate determination of the monitoring points for bridge deformation.

[0062] S203: Obtain all matching point pairs between feature clusters of different feature point clouds, and determine the key index between each matching point pair by analyzing the distance between each matching point pair and the difference in the concavity and convexity coefficients between each matching point pair.

[0063] Furthermore, considering that critical areas of a bridge prone to deformation under external forces such as vehicles may deviate from the standard point cloud due to vehicle load, the deviation between the normal point cloud and the standard point cloud can be used to extract critical areas of the bridge susceptible to deformation hazards and monitor them. Specifically: Since there are no vehicles driving in the standard point cloud data, there will be no phenomenon of point cloud data not being obtained due to vehicle occlusion. Therefore, after removing redundant points, the data in its standard feature clusters can contain the data in the normal feature clusters.

[0064] The algorithm takes ordinary point cloud and standard point cloud as input and outputs all matching point pairs between ordinary point cloud and standard point cloud.

[0065] It should be noted that commonly used point cloud registration algorithms include the Iterative Closest Point (ICP) algorithm and the Normal Distribution Transformation (NDT) algorithm. In this embodiment, the Iterative Closest Point (ICP) algorithm is used to match ordinary point clouds and standard point clouds. In practical applications, as other implementation methods, implementers may also use other point cloud registration algorithms such as the Normal Distribution Transformation (NDT) algorithm. This embodiment does not impose any special restrictions on the selection of point cloud registration algorithms.

[0066] Among them, the Iterative Closest Point (ICP) algorithm is a well-known technique in the field of point cloud registration, and its specific principles and processes will not be elaborated here.

[0067] Furthermore, the result of multiplying the difference in the concavity / convexity coefficients between each pair of matching points by the distance between the three-dimensional coordinates of the corresponding pair of matching points is used as the key index between each pair of matching points.

[0068] Thus, by comparing ordinary point clouds with standard point clouds, we further prepared for the selection of subsequent monitoring points and obtained the key indices between matching point pairs between ordinary point clouds and standard point clouds.

[0069] Preferably, the schematic diagram of the key index extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0070] Step S3: Based on the key index, perform deformation monitoring on the highway bridge.

[0071] Between ordinary point clouds and standard point clouds, key indices between all matching point pairs are used as input to a clustering algorithm. The absolute value of the difference in key indices between matching point pairs is used as the distance metric for the clustering algorithm. The elbow method is used to determine the number of clusters, resulting in multiple clusters, which are denoted as cluster sets. The mean of all key indices in each cluster set is calculated and denoted as the key mean. The cluster set with the largest key mean is designated as the key cluster. Points in the ordinary point cloud corresponding to all key indices within the key cluster are designated as monitoring points. These points in the key clusters are those that significantly affect bridge deformation during normal vehicle movement.

[0072] It should be noted that the k-means clustering algorithm is used to cluster the key indices in this embodiment.

[0073] If the difference in coordinate values ​​between the monitoring point and its matching point on any coordinate axis exceeds the preset deformation threshold, the bridge is determined to be deformed; otherwise, the bridge is determined not to be deformed.

[0074] It should be noted that the preset threshold value is set manually. In this embodiment, the preset threshold value is 3mm. In actual application, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0075] Thus, this application analyzes the impact of environmental factors, vehicles, and other factors on point cloud data, dynamically assesses the degree of noise impact, denoises various point clouds, and further, registers these two point clouds by comparing the differences between ordinary point clouds and standard point clouds, thereby improving the accuracy of selecting monitoring points and thus improving the accuracy of monitoring subsequent bridge deformation.

[0076] Based on the same inventive concept as the above method, this application also provides a highway bridge deformation monitoring system based on laser measurement, which implements the aforementioned highway bridge deformation monitoring method based on laser measurement. The system includes a point cloud acquisition module, a point cloud data analysis module, and a bridge deformation monitoring module. The point cloud acquisition module is used to acquire 3D point cloud data of the bridge when there are no vehicles passing by, and 3D point cloud data of the bridge road surface when there are normal vehicles passing by, and respectively denoted as standard point cloud and normal point cloud. The vehicle-mounted laser scanner travels at a constant speed along the central axis of the bridge from one end to the other. The direction of travel is regarded as the y-axis, the lateral direction of the bridge is regarded as the x-axis, and the z-axis is set in the direction perpendicular to the x-axis and y-axis. The acquired standard point cloud data and normal point cloud data are transmitted to the point cloud data analysis module. The point cloud data analysis module is used to analyze and process the data acquired by the point cloud acquisition module. The analysis and processing method adopts S2 in the laser measurement-based highway bridge deformation monitoring method. Furthermore, the processed results are input into the bridge deformation monitoring module. The bridge deformation monitoring module is used to monitor bridge deformation based on the results output by the point cloud data analysis module. The monitoring method adopts S3 in the laser-based highway bridge deformation monitoring method.

[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0079] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for monitoring the deformation of highway bridges based on laser measurement, wherein the method is implemented by a pavement smoothness detection system based on laser measurement, characterized in that, The method includes the following steps: S1: Use the point cloud acquisition module of the system to acquire various point clouds of the road surface of the bridge to be tested, including standard point clouds and ordinary point clouds. S2: Compare ordinary point clouds with standard point clouds to determine key indices, specifically: All edge points in various point clouds are acquired, and all edge points are clustered. The positive and negative differences between the x-coordinate values ​​of all edge points in each cluster are analyzed to determine the lateral confidence of each cluster, so as to select two lateral clusters from all clusters of various point clouds. Based on the slope difference of the fitted line obtained from the x and y coordinates of all edge points between the two lateral clusters and the curvature difference of all edge points, the difference index between the two lateral clusters is determined. Combined with the local change trend of the curvature of all edge points in each lateral cluster, the various point clouds are denoised. All the denoised point clouds are recorded as feature point clouds. Point cloud data excluding all edge points in various feature point clouds are denoted as planar points. A fitting process is performed on each planar point and all planar points in its neighborhood. The error in the fitting process is analyzed, and the concavity / convexity coefficient of each planar point is determined by combining the dispersion of the curvature of each planar point and all planar points in its neighborhood. All planar points in various feature point clouds are clustered. By analyzing the proportion of all planar points in each cluster to the total number of planar points in the corresponding feature point cloud and the average distribution of the concavity / convexity coefficient of all planar points in each cluster, feature clusters for various feature point clouds are selected. Obtain all matching point pairs between feature clusters of different feature point clouds, and determine the key indices between matching point pairs by analyzing the distance between each matching point pair and the difference in the concavity and convexity coefficients between each matching point pair. S3: Based on the aforementioned key indices, conduct deformation monitoring of highway bridges.

2. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The method for determining the lateral confidence of each cluster is as follows: The total number of edge points with negative x-coordinates and the total number of edge points with positive x-coordinates within each cluster are divided by the total number of edge points within the corresponding cluster. The results of the division are recorded as positive and negative proportions, respectively. The absolute value of the difference between the positive and negative proportions is taken as the lateral confidence of each cluster.

3. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The two side clusters are the clusters corresponding to the first two values ​​in the descending order of the side confidence scores of all clusters in various point clouds.

4. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The difference index between the two side clusters is the sum of the slope difference of the fitted line obtained from the (x, y) coordinates of all edge points between the two side clusters and the curvature difference of all edge points.

5. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The denoising of various point clouds includes: The curvature of all edge points within each side cluster is used as the input of the mutation point detection algorithm, and the output is the mutation curvature. The edge point corresponding to the mutation curvature is denoted as the mutation point. The curvature of each mutation point and all edge points in its neighborhood are arranged in ascending order of their respective y-coordinates. In the arrangement result, the curvature of all edge points before each mutation point is used to form the pre-curvature sequence of each mutation point, and the curvature of all edge points after each mutation point is used to form the post-curvature sequence of each mutation point. First-order difference sequences of the front and back curvature sequences are obtained respectively and denoted as front curvature change sequence and back curvature change sequence. The ratio of the number of negative elements to the total number of elements in the front curvature change sequence is denoted as the increasing proportion, and the ratio of the number of positive elements to the total number of elements in the back curvature change sequence is denoted as the decreasing proportion. The average of the increasing proportion and the decreasing proportion is used as the deformation index of each side cluster. The ratio of the difference index between two side clusters to the average deformation index of the two side clusters is used as the noise interference coefficient between the two side clusters. The noise interference coefficient between two side clusters is used as the input of an exponential function with the natural constant as the base, and the output is used as the neighborhood parameter in the filtering algorithm. The filtering algorithm is then used to denoise various point clouds.

6. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The method for determining the concavity / convexity coefficients of each plane point is as follows: Calculate the mean square error during the fitting process for each plane point and all plane points in its neighborhood; The product of the degree of dispersion of the curvature of each plane point and all plane points in its neighborhood with the mean square error is used as the concavity / convexity coefficient of each plane point.

7. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The method for obtaining the feature clusters of the various feature point clouds is as follows: In various feature point clouds, the ratio of the number of all planar points in each cluster to the total number of planar points in the corresponding feature point cloud is calculated and denoted as the proportion of each cluster. The ratio of the proportion of each cluster to the average concavity coefficient of all planar points in the corresponding cluster is denoted as the confidence ratio of each cluster. Among various feature point clouds, the cluster with the highest confidence ratio is taken as the feature cluster of each feature point cloud.

8. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The key index between each matching point pair is the result of multiplying the difference in the concavity / convexity coefficient between each matching point pair by the distance between the three-dimensional coordinates of the corresponding matching pair.

9. The method for monitoring highway bridge deformation based on laser measurement as described in claim 1, characterized in that, The deformation monitoring of highway bridges includes: Between ordinary point clouds and standard point clouds, the key indices between all matching point pairs are used as input to the clustering algorithm, and multiple clusters are output and denoted as cluster sets. The mean of all key indices in each cluster set is calculated and denoted as the key mean. The cluster set with the largest key mean is taken as the key cluster. The points in the ordinary point cloud that belong to the matching point pairs corresponding to all key indices in the key cluster are taken as monitoring points. If the difference between the coordinate values ​​of the monitoring point and its matching point on any coordinate axis exceeds the preset deformation threshold, the bridge is determined to be deformed; otherwise, the bridge is determined not to be deformed.

10. A laser-based highway bridge deformation monitoring system, implementing the laser-based highway bridge deformation monitoring method as described in any one of claims 1-9, characterized in that, The system includes a point cloud acquisition module, a point cloud data analysis module, and a bridge deformation monitoring module. The point cloud acquisition module is used to acquire 3D point cloud data of the bridge when there are no vehicles passing by, and 3D point cloud data of the bridge road surface when there are normal vehicles passing by, and respectively denoted as standard point cloud and normal point cloud. The vehicle-mounted laser scanner travels at a constant speed along the central axis of the bridge from one end to the other. The direction of travel is regarded as the y-axis, the lateral direction of the bridge is regarded as the x-axis, and the z-axis is set in the direction perpendicular to the x-axis and y-axis. The acquired standard point cloud data and normal point cloud data are transmitted to the point cloud data analysis module. The point cloud data analysis module is used to analyze and process the data acquired by the point cloud acquisition module. The analysis and processing method adopts S2 in the laser measurement-based highway bridge deformation monitoring method. Furthermore, the processed results are input into the bridge deformation monitoring module. The bridge deformation monitoring module is used to monitor bridge deformation based on the results output by the point cloud data analysis module. The monitoring method adopts S3 in the laser-based highway bridge deformation monitoring method.

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