Assessment method of concrete spalling damage of precast components based on 3D laser point cloud

Abstract

The present application relates to a method for assessing the spalling disease condition of prefabricated concrete components based on three-dimensional laser point cloud. The method includes: extracting the point cloud of the test surface from the three-dimensional laser point cloud data of the concrete component; fitting the point cloud plane equation of the test surface, obtaining the plane normal vector, and rotating the test surface; meshing the test surface and establishing a point cloud list, and storing the coordinates of the points falling within the grid in the corresponding point cloud list; traversing the grid nodes of the test surface and generating a ruler; searching for points within the neighborhood range of the ruler ε on both sides of the ruler, and screening out points whose vertical distance δ from the ruler is greater than a threshold δ t The points are stored in the list of disease points; the region growing method is used to generate the concrete spalling disease damage area; the point cloud outer contour points of the concrete spalling disease damage area are extracted, and the point cloud of the spalling disease damage area is triangulated to calculate the area of ​​the concrete spalling disease damage area and determine the comprehensive evaluation result of the spalling condition. The measurement points are evenly and fully covered, and the measurement accuracy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of three-dimensional laser point cloud measurement, and in particular to a method for assessing the condition of concrete spalling damage of prefabricated components based on three-dimensional laser point clouds. Background Art

[0002] Prefabricated buildings are a new construction technology in the construction industry. Components are prefabricated in factories and transported to the site, where they are assembled with cast-in-place components to form a whole building. Whether surface concrete spalling damage and the degree of damage occur during the demolding, transportation, and beam storage of prefabricated components also reflect the quality of component manufacturing.

[0003] At present, the quality inspection method recommended in relevant specifications is manual measurement, that is, measuring several points along a fixed direction at certain intervals. This method is inefficient and has large measurement errors. The surface to be measured cannot be evenly covered by the measuring points, which is not conducive to the quality control of the industrialized production of precast concrete components. Summary of the invention

[0004] Based on this, it is necessary to provide a method for assessing the spalling disease condition of precast concrete components based on three-dimensional laser point clouds that can solve the above-mentioned technical problems.

[0005] A method for assessing concrete spalling damage of prefabricated components based on three-dimensional laser point cloud, the method comprising:

[0006] Using a laser scanner to acquire and preprocess the three-dimensional laser point cloud data of the precast concrete component to obtain a preprocessed point cloud;

[0007] Performing plane segmentation on the preprocessed point cloud to extract the point cloud of the test surface for spalling disease condition assessment;

[0008] Fitting the plane equation of the point cloud of the test surface using the least squares method to obtain the plane normal vector of the test surface, and rotating the point cloud of the test surface in space until the plane normal vector is perpendicular to the XOY plane;

[0009] Divide the point cloud of the test surface into two-dimensional grids in the X-axis and Y-axis directions, establish a point cloud list corresponding to the grid, and store the coordinates of the points falling in the corresponding grid in the corresponding point cloud list;

[0010] Traversing the grid nodes of the test surface, so that a set of radial rulers are generated at each grid node, and the rulers are laid flat on the test surface;

[0011] Find the points in the neighborhood of the ruler ε on both sides of the ruler, and filter out the points in the preset neighborhood that are below the ruler in the Z-axis direction and whose vertical distance δ from the ruler is greater than the threshold δ t The point is found and the coordinates of the point are stored in the list of disease points;

[0012] The regional growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t is the growth rule, generating the concrete spalling disease damage area, the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t ;

[0013] The Alpha-Shape algorithm is used to extract the outer contour points of the point cloud of the concrete spalling disease damage area, the Delaunay triangulation algorithm is used to triangulate the point cloud of the concrete spalling disease damage area, and the area of ​​the concrete spalling disease damage area is calculated;

[0014] The single area, cumulative area and location distribution of all concrete spalling damage areas on all test surfaces are statistically analyzed to determine the comprehensive assessment results of the spalling condition of precast concrete components.

[0015] In one embodiment, the step of traversing the grid nodes of the test surface to generate a set of radial rulers at each grid node, and laying the rulers flat on the test surface, comprises:

[0016] Traverse the grid nodes V of the test surface uv , V uv Indicates the uth grid node in the X direction and the vth grid node in the Y direction, which is in the same grid node as the current grid node V uv List of connected mesh point clouds P ij Find the distance V uv The nearest point S uv As the starting point of the ruler, take the starting point as the center, and take the grid edge where the starting point is as the starting edge. Make a radial edge with a length of l for each rotation angle d until the radial edge covers the current grid node V. uv Connected Grid C ij , in the point cloud list P ij Find the point F closest to the end of the radial edge uvt As the end point of the ruler, connect the starting point and the end point to generate a ruler, denoted by R uvt .

[0017] In one embodiment, the region growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ. t is the growth rule, generating the concrete spalling disease damage area, the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t The steps include:

[0018] The points p in the list of diseased points L are sequentiallyi Add as the initial seed point to the corresponding seed point set T i In, i = 1, 2, ..., n, n is the total number of points stored in the defect point list L;

[0019] A single growth process includes: searching T i The k neighboring points of a seed point in , if the neighboring point is below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t , then add the neighborhood point to the corresponding seed point set T i And the corresponding possible spalling disease area point set Q i In the above example, i = 1, 2, ..., n, where n is the total number of points stored in the defect point list L. After the k neighboring points are judged, the points are selected from T i Delete the current seed point and repeat the above growth process to obtain n possible peeling disease area point set Q i ;

[0020] Statistical point set Q of possible spalling disease areas i The number of points in is greater than the point threshold N t This area is considered to be a concrete spalling damage area.

[0021] In one embodiment, the steps of extracting the outer contour points of the point cloud of the concrete spalling disease damage area by using the Alpha-Shape algorithm, triangulating the point cloud of the concrete spalling disease damage area by using the Delaunay triangulation algorithm, and calculating the area of ​​the concrete spalling disease damage area include:

[0022] The point cloud in the concrete spalling disease damage area is projected onto the XOY plane, and the Alpha-Shape algorithm is used to detect the spalling disease area point set Q corresponding to each concrete spalling disease damage area with a detection circle radius of α. i Draw two detection circles through any two points q1 and q2. If there is no third point in a detection circle, it is considered that points q1 and q2 are the outer contour points of the point cloud of the concrete spalling disease damage area.

[0023] The point cloud of the concrete spalling disease damage zone is triangulated using the Delaunay triangulation algorithm to calculate the area of ​​the concrete spalling disease damage zone.

[0024] The above-mentioned method for assessing the spalling disease condition of prefabricated concrete components based on three-dimensional laser point clouds obtains three-dimensional laser point cloud data of prefabricated concrete components by using a laser scanner and preprocesses it to obtain a preprocessed point cloud; performs plane segmentation on the preprocessed point cloud to extract the point cloud of the test surface for which the spalling disease condition assessment is to be performed; uses the least squares method to fit the plane equation of the point cloud of the test surface to obtain the plane normal vector of the test surface, and rotates the point cloud of the test surface in space until the plane normal vector is perpendicular to the XOY plane; performs two-dimensional grid division on the point cloud of the test surface in the X-axis and Y-axis directions, establishes a point cloud list of corresponding grids, and stores the coordinates of points falling in the corresponding grids in the corresponding point cloud list; traverses the grid nodes of the test surface to generate a set of radial rulers at each grid node, and the ruler is laid flat on the test surface; searches for points within the neighborhood range of the ruler ε on both sides of the ruler, and selects points within the preset neighborhood range that are below the ruler in the Z-axis direction and whose vertical distance δ from the ruler is greater than a threshold δ t The point is found and the coordinates of the point are stored in the disease point list; the regional growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t is the growth rule, generating the concrete spalling disease damage area, the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t ; Use the Alpha-Shape algorithm to extract the point cloud outer contour points of the concrete spalling disease damage area, use the Delaunay triangulation algorithm to triangulate the point cloud of the concrete spalling disease damage area, and calculate the area of ​​the concrete spalling disease damage area; statistically analyze the single area, cumulative area, and position distribution of all concrete spalling disease damage areas on all test surfaces to determine the comprehensive evaluation results of the spalling condition of precast concrete components. Achieve uniform full coverage of the measuring points on the surface of the component to be measured, and accurately calculate the area of ​​the damaged area and reflect the outer contour of the area, so as to achieve the goal of refined and automated quality inspection of precast components, and improve the cost and accuracy of measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of a flow chart of a method for assessing concrete spalling damage of precast components based on three-dimensional laser point cloud in one embodiment;

[0026] Figure 2 A schematic diagram of a two-dimensional grid division of a point cloud and a virtual ruler network of a test surface in one embodiment;

[0027] Figure 3 is a neighborhood point np in one embodiment i Schematic diagram of calculating the vertical distance δ from the ruler;

[0028] Figure 4is a schematic diagram of an ε neighborhood point of a ruler and a single point with a large vertical deviation from the ruler in one embodiment;

[0029] Figure 5 Schematic diagram of the point cloud outer contour and triangulation of a concrete spalling disease damage area in one embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] In one embodiment, Figure 1 As shown, a method for assessing the condition of concrete spalling damage of prefabricated components based on three-dimensional laser point cloud is provided, comprising the following steps:

[0032] Step S220: using a laser scanner to acquire and preprocess the three-dimensional laser point cloud data of the precast concrete component to obtain a preprocessed point cloud.

[0033] Among them, preprocessing includes point cloud registration, denoising, and downsampling. When downsampling, for key measurement surfaces with more serious spalling damage, the point spacing must be no less than 1 cm to prevent the downsampling process from causing the point cloud density to be too low and weakening the spalling damage area of ​​the point cloud.

[0034] Step S240, plane segmentation is performed on the preprocessed point cloud to extract the point cloud on the test surface for spalling disease condition assessment.

[0035] Among them, the preprocessed point cloud is subjected to plane segmentation, and the preprocessed point cloud is divided into several subsets as a whole. The points in each subset together constitute the corresponding planes, and the points in the corresponding subset of the test surface to be evaluated for the spalling disease condition are extracted.

[0036] Step S260, using the least square method to fit the plane equation of the point cloud of the test surface, obtaining the plane normal vector of the test surface, and rotating the point cloud of the test surface in space until the plane normal vector is perpendicular to the XOY plane.

[0037] Step S280, dividing the point cloud of the test surface into two-dimensional grids in the X-axis and Y-axis directions, establishing a point cloud list corresponding to the grid, and storing the coordinates of the points falling within the corresponding grid in the corresponding point cloud list.

[0038] The test surface is divided into two-dimensional grids in the X and Y axis directions, the grid side length is a, and each grid is numbered C ij , C ij Represents the i-th grid in the X direction and the j-th grid in the Y direction, and establishes the point cloud list P of the corresponding gridij , falls on grid C ij The point coordinates within are stored in the point cloud list P ij middle.

[0039] Step S300, traversing the grid nodes of the test surface, generating a set of radial rulers at each grid node, and laying the rulers flat on the test surface.

[0040] In one embodiment, the step of traversing the grid nodes of the test surface to generate a set of radial rulers at each grid node, and laying the rulers flat on the test surface, comprises: traversing the grid nodes V of the test surface uv , V uv Indicates the uth grid node in the X direction and the vth grid node in the Y direction, which is in the same grid node as the current grid node V uv List of connected mesh point clouds P ij Find the distance V uv The nearest point S uv As the starting point of the ruler, take the starting point as the center, and take the grid edge where the starting point is as the starting edge. Make a radial edge with a length of l for each rotation angle d until the radial edge covers the current grid node V. uv Connected Grid C ij , in the point cloud list P ij Find the point F closest to the end of the radial edge uvt As the end point of the ruler, connect the starting point and the end point to generate a ruler, denoted by R uvt .

[0041] A set of radial rulers is generated at each grid node to form a virtual ruler network, simulating the manual measurement method in reality. The starting point and end point of the ruler are both points in the point cloud of the test surface. A starting point is set at each grid node, and a set of rulers are arranged radially with the starting point as the center, and the angle between adjacent rulers is d.

[0042] Traverse the grid nodes V of the test surface uv , V uv Indicates the uth grid node in the X direction and the vth grid node in the Y direction. A set of radial rulers is generated at each grid node. The method for locating the starting point of the ruler is: uv List of connected mesh point clouds P ij Find the distance V uv The nearest point S uv , defined as the starting point of the ruler. The method for locating the end point of the ruler is: uv As the center, take the grid edge where the starting point is as the starting edge, and make a radial edge with a length of l for each rotation angle d until the radial edge is fully connected to the current grid node V uv Connected Grid C ij , in the point cloud list P ijFind the point F closest to the end of the radial edge uvt , t=1,2,…,360 / d, defined as the end point of the ruler. Figure 2 Schematic diagram of the two-dimensional grid division of the point cloud and the virtual ruler network of the test surface. In the schematic diagram, a=2.5m, d=30°, l=2m, where a is the grid side length, d is the angle between adjacent rulers, and l is the length of the radial side.

[0043] Step S320, search for points in the neighborhood range of the ruler ε on both sides of the ruler, and select points in the preset neighborhood range that are below the ruler in the Z direction and whose vertical distance δ from the ruler is greater than a threshold δ t The coordinates of the point are stored in the disease point list.

[0044] Among them, for each ruler R uvt , remember the starting point S uv Coordinates (x1, y1, z1), end point F uvt Coordinates (x2, y2, z2), ruler R uvt Line LN uvt The direction vector is (x1-x2, y1-y2, z1-z2), denoted by LN uvt The normal vector is (A uvt , B uvt , C uvt ), specifying that its normal vector is parallel to the xoy plane, then A uvt =1, B uvt =(x2-x1) / (y1-y2), C uvt =0, specify ruler R uvt Plane P uvt The equation is A uvt ·x+B uvt y+C uvt ·z+D uvt = 0. Plane P uvt Along the normal vector (A uvt , B uvt , C uvt ) is translated in the positive and negative directions within the range of ε to form two new planes. The points between the two new planes are called the ε neighborhood points of the ruler, which constitute the neighborhood point set NP. uvt ={np i |np i ∈NP uvt}, i = 1, 2, ..., n, n is the neighborhood point set NP uvt The total number of points in the neighborhood point set. i , find np i In plane P uvt The projection point Pro1(a1,b1,c1) on the line LN is then calculated.uvt The projection point Pro2(a2,b2,c2) on the Calculate point np i To ruler R uvt The vertical distance in Figure 3 is the neighborhood point np i Schematic diagram of calculating the vertical distance δ from the ruler. i Below the ruler in the Z-axis direction and the vertical distance δ to the ruler is greater than the threshold δ t , it is considered that the point has a large vertical deviation from the ruler, and there may be concrete spalling at this point. The coordinates of the point are stored in the list L = {p i |p i ∈L}, i=1,2,…,n, n is the total number of points stored in list L, Figure 4 Schematic diagram of the ε neighborhood points of a ruler and a single point with a large vertical deviation from the ruler.

[0045] Step S340: Using the region growing method, each point in the disease point list is used as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ. t is the growth rule, generating the concrete spalling disease damage area, the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t .

[0046] In one embodiment, the region growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ. t is the growth rule, generating the concrete spalling disease damage area, the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t The steps include: sequentially placing the points p in the list of diseased points L i Add as the initial seed point to the corresponding seed point set T i In the above example, i = 1, 2, ..., n, where n is the total number of points stored in the disease point list L. A single growth process includes: searching for T i The k neighboring points of a seed point in , if the neighboring point is below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t , then add the neighborhood point to the corresponding seed point set T i And the corresponding possible spalling disease area point set Q i In the above example, i = 1, 2, ..., n, where n is the total number of points stored in the defect point list L. After the k neighboring points are judged, the points are selected from T i Delete the current seed point and repeat the above growth process to obtain n possible peeling disease area point set Q i; Statistical point set Q of possible spalling disease areas i The number of points in is greater than the point threshold N t This area is considered to be a concrete spalling damage area.

[0047] Among them, the regional growing method is used to grow the points in the disease point list to the surrounding areas, and a certain area is generated based on the vertical distance δ as the growth rule, which is called the possible spalling disease area. The number of points contained in it is greater than the disease threshold N t , then the area is considered to be a concrete spalling damage area, and the disease threshold N t Depends on the average density of the point cloud. The points in the list of diseased points L are the initial seed points, and the neighborhood points of the seed points are located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t is the growth rule. Use the region growing method to generate a certain region, the number of points contained in the region is greater than the threshold N t , then the area is considered to be a concrete spalling disease damage area.

[0048] A point p in the list of diseased points L i As the initial seed point, the single growth process specifically includes: searching for k neighboring points of the seed point, if the neighboring point is below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t , then add the neighborhood point to the seed point set T i and the possible spalling disease area point set Q i In the above equation, i = 1, 2, ..., n, where n is the total number of points stored in the list L. After the k neighboring points are judged, the current seed point is deleted and the above growth process is repeated. According to all the points in the disease point list L, a set of n possible spalling disease area points Q is obtained. i , count the possible spalling disease area point set Q i The number of points in is greater than the point threshold N t The area is considered to be a concrete spalling damage area, and the point threshold N t Related to the point cloud density ρ.

[0049] Step S360, using the Alpha-Shape algorithm to extract the outer contour points of the point cloud of the concrete spalling disease damage area, using the Delaunay triangulation algorithm to triangulate the point cloud of the concrete spalling disease damage area, and calculating the area of ​​the concrete spalling disease damage area.

[0050] In one embodiment, the Alpha-Shape algorithm is used to extract the outer contour points of the point cloud of the concrete spalling disease damage area, and the Delaunay triangulation algorithm is used to triangulate the point cloud of the concrete spalling disease damage area. The steps of calculating the area of ​​the concrete spalling disease damage area include: projecting the point cloud in the concrete spalling disease damage area to the XOY plane, using the Alpha-Shape algorithm with a detection circle radius of α, and detecting the spalling disease area point set Q corresponding to each concrete spalling disease damage area. i Two detection circles are drawn through any two points q1 and q2. If there is no third point in a detection circle, points q1 and q2 are considered to be the outer contour points of the point cloud of the concrete spalling disease damage area. The point cloud of the concrete spalling disease damage area is triangulated using the Delaunay triangulation algorithm, and the area of ​​the concrete spalling disease damage area is calculated.

[0051] Among them, the point cloud in the concrete spalling disease damage area must first be projected to the XOY plane to achieve data dimensionality reduction. In the Alpha-Shapes algorithm, the radius of the detection circle is α. In each spalling disease area point set Q, two detection circles are drawn through any two points q1 and q2. If there is no third point in a detection circle, points q1 and q2 are considered to be the outer contour points of the area. Project the point cloud in the concrete spalling disease damage area to the XOY plane to achieve data dimensionality reduction. Use the Alpha-Shapes algorithm to extract the outer contour points of the point cloud of the concrete spalling disease damage area. Use the Delaunay triangulation algorithm to triangulate the point cloud of the concrete spalling disease damage area, and use a number of Delaunay triangles to represent the point cloud Q of the concrete spalling disease damage area. i The sum of the triangle areas is the area of ​​the concrete spalling damage zone. Figure 5 This is the point cloud outline and triangulation diagram of the concrete spalling damage area.

[0052] Step S380, statistically analyzing the single area, cumulative area, and location distribution of all concrete spalling disease damage zones on all test surfaces to determine a comprehensive assessment result of the spalling condition of the precast concrete component.

[0053] Among them, the single area, cumulative area, and location distribution of all concrete spalling damage areas on all test surfaces are counted, and a comprehensive analysis of the concrete spalling condition of the components is conducted according to the highway bridge technical condition assessment standards to determine the comprehensive assessment results of the spalling condition of precast concrete components. The comprehensive assessment results of the spalling condition include: no spalling; partial spalling - cumulative area ≤ 5% of the component area or single area ≤ 0.5m 2 ; Large-scale spalling: cumulative area > 5% and < 10% of the component area or single area > 0.5m 2 And <1.0m 2; Large-scale spalling: cumulative area ≥ 10% of the component area or single area ≥ 1.0m 2 etc.

[0054] The above-mentioned method for assessing the spalling disease condition of prefabricated concrete components based on three-dimensional laser point clouds obtains three-dimensional laser point cloud data of prefabricated concrete components by using a laser scanner and preprocesses it to obtain a preprocessed point cloud; performs plane segmentation on the preprocessed point cloud to extract the point cloud of the test surface for which the spalling disease condition assessment is to be performed; uses the least squares method to fit the plane equation of the point cloud of the test surface to obtain the plane normal vector of the test surface, and rotates the point cloud of the test surface in space until the plane normal vector is perpendicular to the XOY plane; performs two-dimensional grid division on the point cloud of the test surface in the X-axis and Y-axis directions, establishes a point cloud list of corresponding grids, and stores the coordinates of points falling in the corresponding grids in the corresponding point cloud list; traverses the grid nodes of the test surface to generate a set of radial rulers at each grid node, and the ruler is in the XOY plane; searches for points within the neighborhood range of the ruler ε on both sides of the ruler, and selects points within the preset neighborhood range that are below the ruler in the Z-axis direction and whose vertical distance δ from the ruler is greater than a threshold δ t The point is found and the coordinates of the point are stored in the disease point list; the regional growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance δ to the ruler is less than the threshold δ t is the growth rule, generating the concrete spalling disease damage area, the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t ; Use the Alpha-Shape algorithm to extract the outer contour points of the point cloud of the concrete spalling disease damage area, use the Delaunay triangulation algorithm to triangulate the point cloud of the concrete spalling disease damage area, and calculate the area of ​​the concrete spalling disease damage area; statistically analyze the single area, cumulative area, and position distribution of all concrete spalling disease damage areas on all test surfaces to determine the comprehensive evaluation results of the spalling condition of precast concrete components. Achieve uniform full coverage of the measuring points on the surface of the component to be measured, and accurately calculate the area of ​​the damaged area and reflect the outer contour of the area, so as to achieve the goal of refined and automated quality inspection of precast components, and improve the cost and accuracy of measurement.

[0055] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0056] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for assessing concrete spalling damage of precast components based on three-dimensional laser point cloud, characterized in that: The method comprises: Using a laser scanner to acquire and preprocess the three-dimensional laser point cloud data of the precast concrete component to obtain a preprocessed point cloud; Performing plane segmentation on the preprocessed point cloud to extract the point cloud of the test surface for spalling disease condition assessment; Fitting the plane equation of the point cloud of the test surface using the least squares method to obtain the plane normal vector of the test surface, and rotating the point cloud of the test surface in space until the plane normal vector is perpendicular to the XOY plane; The point cloud of the test surface is divided into two-dimensional grids in the X-axis and Y-axis directions, a point cloud list of the corresponding grid is established, and the coordinates of the points falling in the corresponding grid are stored in the corresponding point cloud list; Traversing the grid nodes of the test surface, so that a set of radial rulers are generated at each grid node, and the rulers are laid flat on the test surface; Find the points in the neighborhood of the ruler ε on both sides of the ruler, and filter out the points in the preset neighborhood that are below the ruler in the Z-axis direction and have a vertical distance from the ruler δ Greater than threshold δ t The point is found and the coordinates of the point are stored in the list of disease points; The regional growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance to the ruler δ Less than threshold δ t is the growth rule, generating the concrete spalling disease damage area, and the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t ; The Alpha-Shape algorithm is used to extract the outer contour points of the point cloud of the concrete spalling disease damage area, the Delaunay triangulation algorithm is used to triangulate the point cloud of the concrete spalling disease damage area, and the area of ​​the concrete spalling disease damage area is calculated; The single area, cumulative area and location distribution of all concrete spalling damage areas on all test surfaces are statistically analyzed to determine the comprehensive assessment results of the spalling condition of precast concrete components.

2. The method according to claim 1, characterized in that The step of traversing the grid nodes of the test surface to generate a set of radial rulers at each grid node, and laying the rulers flat on the test surface, comprises: Traverse the grid nodes V of the test surface uv , V uv Indicates the uth grid node in the X direction and the vth grid node in the Y direction, which is in the same grid node as the current grid node V uv List of connected mesh point clouds P ij Find the distance V uv The nearest point S uv As the starting point of the ruler, take the starting point as the center, take the grid edge where the starting point is as the starting edge, and make a line with a length of l until the radial edges are fully connected to the current grid node V uv Connected Grid C ij , in the point cloud list P ij Find the point F closest to the end of the radial edge uvt As the end point of the ruler, connect the starting point and the end point to generate a ruler, denoted by R uvt .

3. The method according to claim 1, characterized in that The regional growing method is used to take each point in the disease point list as the initial seed point, and the point is located below the ruler in the Z-axis direction and the vertical distance to the ruler δ Less than threshold δ t is the growth rule, generating the concrete spalling disease damage area, and the number of points contained in the concrete spalling disease damage area is greater than the point threshold N t The steps include: The points in the list of diseased points L are sequentially p i Add as the initial seed point to the corresponding seed point set T i In, i=1,2,…,n, n is the total number of points stored in the defect point list L; A single growth process includes: searching T i The k neighboring points of a seed point in , if the neighboring point is below the ruler in the Z-axis direction and the vertical distance to the ruler δ Less than threshold δ t , then add the neighborhood point to the corresponding seed point set T i And the corresponding possible spalling disease area point set Q i In the above example, i=1,2,…,n, n is the total number of points stored in the defect point list L. After the k neighboring points are judged, the points are selected from T i Delete the current seed point and repeat the above growth process to obtain n possible peeling disease area point set Q i ; Statistical point set Q of possible spalling disease areas i The number of points in is greater than the point threshold N t This area is considered to be a concrete spalling damage area.

4. The method according to claim 1, characterized in that: The steps of extracting the outer contour points of the point cloud of the concrete spalling disease damage area by using the Alpha-Shape algorithm, triangulating the point cloud of the concrete spalling disease damage area by using the Delaunay triangulation algorithm, and calculating the area of ​​the concrete spalling disease damage area include: The point cloud in the concrete spalling disease damage area is projected onto the XOY plane, and the Alpha-Shape algorithm is used to detect the spalling disease area point set Q corresponding to each concrete spalling disease damage area with a detection circle radius of α. i Draw two detection circles through any two points q1 and q2. If there is no third point in a detection circle, it is considered that points q1 and q2 are the outer contour points of the point cloud of the concrete spalling disease damage area. The point cloud of the concrete spalling disease damage zone is triangulated using the Delaunay triangulation algorithm to calculate the area of ​​the concrete spalling disease damage zone.

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