CN115560690B - Structure integral deformation analysis method based on three-dimensional laser scanning technology - Google Patents
Structure integral deformation analysis method based on three-dimensional laser scanning technology Download PDFInfo
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Abstract
The invention discloses a structural object integral deformation analysis method based on a three-dimensional laser scanning technology, which comprises the steps of periodically obtaining high-precision laser scanning data of the outline of a building or a structural object by adopting a three-dimensional laser scanner, processing the collected initial data into analyzable point cloud data through splicing, denoising, positioning and precision checking operations, further creating a gridding model, comparing the data collected in different periods for two or more times, generating a model comparison graph and a comparison report, and analyzing a deformation result. The method has the advantages that the method automatically and rapidly analyzes local and whole deformation, settlement, inclination, deviation and the like of the structural objects by acquiring the complete high-precision three-dimensional structure point cloud model, has the characteristics of convenience in operation, high precision, high efficiency and the like, helps engineering projects acquire information in the fastest time and makes decisions, improves construction efficiency and ensures construction safety.
Description
Technical Field
The invention relates to the field of civil engineering, in particular to a structural object integral deformation analysis method based on a three-dimensional laser scanning technology.
Background
Along with the high-speed development of urban rail systems in China, subway construction projects are increased increasingly, and in the subway station construction process, deep foundation pit excavation often causes deformation, sedimentation, inclination or displacement and the like to a certain extent on buildings or other structures nearby the deep foundation pit, and serious consequences can be caused if the deep foundation pit is improperly controlled. Therefore, structural deformation monitoring is an important point of attention in subway station deep foundation pit excavation engineering.
At present, the main mode of monitoring the deformation of a structure is to adopt a total station or a GPS (global positioning system) measuring instrument to periodically measure the point position coordinates of the control points at each stage before, during and after the excavation of a foundation pit by arranging a plurality of fixed control points and analyze the deformation result by comparing the coordinate changes at different stages. However, the method can only analyze the displacement of a single point according to the control points, can not reflect the deformation of the whole structure, has no clear cognition on some local details and the change of a special structure, has certain professional requirements on a user of the instrument, has low measurement efficiency under the condition of more control points, and has certain limitation.
Therefore, the existing deformation monitoring mode cannot meet the quality and efficiency requirements of the current engineering, and the use of a more efficient and accurate method is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a structural object integral deformation analysis method based on a three-dimensional laser scanning technology, which aims to solve the problems that only single-point displacement can be analyzed, professional requirements on measuring staff are high, measuring efficiency is low and the like in the traditional measuring method, help engineering projects acquire information in the fastest time and make decisions, improve construction efficiency and ensure construction safety.
The technical scheme for achieving the purpose is as follows:
a structural object integral deformation analysis method based on a three-dimensional laser scanning technology comprises the following steps:
step S1: a three-dimensional laser scanner is adopted to periodically acquire high-precision laser scanning data of the outline of a building or a structure;
step S2: processing the collected initial data into analyzable point cloud data through splicing, denoising, positioning and precision checking operations;
step S3: creating a gridding model, comparing data acquired in different periods twice or more, generating a model comparison graph and a comparison report, and analyzing a deformation result.
Preferably, the step S1 includes:
step S11: actual investigation, namely, surveying the environmental conditions around the foundation pit, and determining whether the building or the structure is provided with the three-dimensional laser scanning terrain conditions or not;
step S12: setting a scanning route, determining the positions of measuring stations according to the conditions of the site environment and the topography, keeping the distances among the measuring stations equal, and enabling the measuring stations to form a closed-loop route by the station spacing = route perimeter/station number;
step S13: laying control points, selecting a marked point position on site as a control point, marking the position of the control point, and measuring three-dimensional coordinates of the control point by using a total station or a GPS measuring instrument as a positioning control point during data processing;
step S14: laying target balls, placing two or more target balls with fixed positions and fixed diameters between every two adjacent measuring stations, and splicing measured data of the two stations through the target balls;
step S15: generating measurement parameters, and automatically generating acquisition point density, scanning maximum distance and color parameters of three-dimensional laser scanning according to the on-site environment conditions, control points, target ball setting points and data acquisition efficiency requirements;
step S16: carrying out laser scanning, and sequentially carrying out three-dimensional laser scanning on each site according to a scanning route established in advance on the premise of ensuring static surrounding environment, fixed target ball position and reasonable parameters to obtain point cloud data of the appearance of the structure;
step S17: and (3) data export, namely exporting sweep data and acquiring original model data.
Preferably, the step S2 includes:
step S21: converting scanning data, namely converting initial data acquired by scanning by a scanner into computer visual point clouds through space positioning and attribute assignment, generating 360-degree circular views scanned by each station, and checking whether the station position is correct or not and whether the scanning data of each station is complete or not;
step S22: denoising data, namely denoising data which influences the precision of a later model and the quality of analysis in initial data, and extracting main structure data;
step S23: colorization processing, namely endowing each point in the point cloud data with the stored RGB color information, and positioning according to the stored coordinate position information to realize colorization display of the point cloud;
step S24: the data are spliced, a 2D-3D point cloud splicing method is adopted to realize multi-station data splicing, a three-dimensional spline difference algorithm is adopted to generate a two-dimensional image, SIFT algorithm is adopted to match the two-dimensional homonymous feature points, a least square method is adopted to fit the three-dimensional feature points, and refined three-dimensional homonymous feature points are adopted to realize multi-station splicing;
step S25: data optimization, namely performing mass coincidence on data swept by two adjacent measuring stations, performing thinning on point cloud data by adopting a downsampling method, removing repeated redundant data, and optimizing data storage;
step S26: coordinate registration, namely identifying control points marked in advance in a scene, giving measurement coordinates to the control points, and repositioning a model according to the control point coordinates when the number of the control points is more than or equal to 3 and is not on the same straight line, so that scan data of each time are unified under the same coordinate system;
step S27: and outputting the point cloud, and exporting the data which is finished in the final processing and meets the requirements into a point cloud format which can be analyzed.
Preferably, the least square fitting function of the least square method is:
wherein,the form function, m is the order of the base function;
preferably, the step S3 includes:
step S31: generating a gridding model, connecting nearby points in the point cloud model into a TIN triangular network according to Delaunay triangles, and performing smoothing treatment by adopting a Taubin smoothing method to generate a lighter gridding model with high rendering degree and faceted characteristics;
step S32: the point positions of the models are matched, the two models realize one-to-one correspondence of the same positions through the matching of key points in the models, the comparison of the same point positions is ensured, and the data of the two control points are accurately registered by adopting an iterative closest point method;
step S33: setting a tolerance for helping the model to distinguish a boundary value n of the deformation degree, wherein a part of the model with the deformation less than n is regarded as a controllable range, and a part with the deformation exceeding n is regarded as an overrun range;
step S34: single point comparison, namely selecting a single point position coordinate in the model, and comparing three-dimensional coordinates of the point position scanned before and after twice under a unified coordinate system to obtain displacement change and three-dimensional Euclidean distance change of the point position in X, Y, Z directions;
step S35: overall comparison, namely selecting one section to be analyzed in the model, and judging the change of the whole section in all directions, wherein the change comprises verticality change, curvature change and maximum offset point;
step S36: generating a color block diagram, distinguishing deformation degrees of deformation and gradual change color areas in different directions by utilizing different color blocks, distinguishing the deformation degrees by using the depth of the color, and intuitively reflecting the overall change of the structure;
step S37: generating a structural deformation report, and outputting a specific analysis report according to a comparison result;
step S38: and analyzing the deformation result, guiding the operation of the actual engineering according to the color block diagram and the analysis report result, and efficiently making a targeted response scheme to ensure the site construction safety.
Preferably, the iterative closest point method function is:
wherein R and t are the variation errors between the nearest points, and x and p are the points in the two sets of point clouds.
Preferably, the displacement change of the point location in three directions of X, Y, Z and the three-dimensional Euclidean distance change formula are as follows:
preferably, the verticality change calculation method comprises the following steps:
wherein Deltaeta is the change value of the perpendicularity of the data at two times, and x 1 ，y 1 And x 2 ，y 2 Plane coordinates, z, of the bottom and top of the structure, respectively 1 And z 2 Is a vertical coordinate;
the curvature change is calculated using a curvature change rate:
wherein ΔK is curvature change rate, R is curve radius, l 0 Is a moderation curve length;
the maximum offset point is:
(x，y，z) max ＝max(ρ n )。
preferably, the analysis report includes: displacement of key points, deformation of key sections, sedimentation in the vertical direction, maximum and minimum deviation values and model accuracy.
The beneficial effects of the invention are as follows: the method has the advantages that the method automatically and rapidly analyzes local and whole deformation, settlement, inclination, deviation and the like of the structural objects by acquiring the complete high-precision three-dimensional structure point cloud model, has the characteristics of convenience in operation, high precision, high efficiency and the like, helps engineering projects acquire information in the fastest time and makes decisions, improves construction efficiency and ensures construction safety.
Drawings
FIG. 1 is a flow chart of a method for analyzing deformation of a structure based on three-dimensional laser scanning according to the present invention;
FIG. 2 is a flow chart of step S1 in the three-dimensional laser scanning-based structure deformation analysis method of the present invention;
FIG. 3 is a flow chart of step S2 in the three-dimensional laser scanning-based structure deformation analysis method of the present invention;
FIG. 4 is a flowchart of step S3 in the three-dimensional laser scanning-based structure deformation analysis method of the present invention;
fig. 5 is a simulated scan roadmap of a method of analyzing deformation of a structure based on three-dimensional laser scanning of the invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Referring to fig. 1-4, the method for analyzing deformation of a structure based on three-dimensional laser scanning of the present invention includes the following steps:
and S1, data acquisition, namely periodically acquiring high-precision laser scanning data of the outline of the building or the structure by adopting a three-dimensional laser scanner. The three-dimensional laser scanning utilizes the principle of laser ranging, and can quickly reconstruct three-dimensional models of the measured object and various drawing data such as lines, planes, bodies and the like by recording the information such as three-dimensional coordinates, reflectivity, textures and the like of a large number of dense points on the surface of the measured object, so that the obtained dense point data set becomes point cloud data. To monitor deformation and settlement of a building or structure, it is necessary to perform periodic static scanning to obtain models at different times, including:
and S11, performing field investigation, investigating the environmental conditions around the foundation pit, and determining whether the vicinity of the building or the structure has the three-dimensional laser scanning terrain conditions.
And step S12, a scanning route is established, the positions of the measuring stations are determined according to the conditions of the field environment and the terrain, the distances among the measuring stations are kept equal, and the station distance = route perimeter/station number enables the measuring stations to form a closed-loop route.
And S13, laying control points, selecting a marked point location on site as the control point, marking the position of the control point, and measuring three-dimensional coordinates of the control point by using a total station or a GPS (global positioning system) measuring instrument as a positioning control point during data processing.
And S14, laying target balls, wherein two or more target balls with fixed positions and fixed diameters are required to be placed between every two adjacent measuring stations, and splicing the measured data of the two stations is realized through the target balls.
And S15, generating measurement parameters, and automatically generating acquisition point density, scanning maximum distance and color parameters of three-dimensional laser scanning according to the on-site environment condition, control points, target ball setting points and data acquisition efficiency requirements.
And S16, performing laser scanning, namely sequentially performing three-dimensional laser scanning on each site according to a preset scanning route on the premise of ensuring static surrounding environment, fixed target ball position and reasonable parameters so as to acquire point cloud data of the appearance of the structure.
And S17, data export, namely exporting sweep data and acquiring original model data.
Step S2, data processing, namely, preprocessing the original data after scanning the object through a three-dimensional laser scanner, namely, reprocessing the original data, such as eliminating clutter data, supplementing missing data, smoothing noise data and the like, so that the point cloud data is more complete and accurate, the quality of the original scanned data is determined by the instrument and machine attributes of the scanner, the scanning environment, the scanning mode and a plurality of uncertainty factors in all aspects, various errors inevitably exist in the data acquisition process, the quality of actual measured data is reduced, a great number of noise points and background object clutter are mixed in the massive point cloud data, and the noise points can lead the data expressing a three-dimensional model to be omitted or repeated, therefore, the original data is not suitable for direct modeling or analysis, the preprocessing such as point cloud splicing, data denoising, coordinate positioning, accuracy checking and the like is required to be carried out on the original data in order to restore the reality of a target object to the maximum extent, and the requirements of modeling and analysis are met, and the method specifically comprises:
and S21, converting the scanning data, converting the initial data obtained by scanning by the scanner into a computer visual point cloud through space positioning and attribute assignment, generating a 360-degree look-around chart scanned by each station, and checking whether the station position is correct or not and whether the scanning data of each station is complete or not.
In step S22, the data is denoised, and the initial data contains a large amount of background, environment, noise points and a large amount of unnecessary data, which affect the accuracy of the later model and have a certain influence on the analysis quality, so that the data needs to be denoised to extract the main structure data.
In step S23, the colorization process assigns RGB (red (R), green (G), blue (B)) color information stored in each point in the point cloud data, and then locates according to the coordinate position information stored in the point cloud data, thereby realizing colorization display of the point cloud.
Step S24, data are spliced, a 2D-3D point cloud splicing method is adopted to realize multi-station data splicing, a three-dimensional spline difference algorithm is adopted to generate a two-dimensional image, a SIFT algorithm (Scale Invariant Feature Transform, scale-invariant feature transform matching) is adopted to obtain two-dimensional homonymous feature points, a least square method is adopted to obtain three-dimensional feature points, and refined three-dimensional homonymous feature points are adopted to realize multi-station splicing;
the least square fitting function is as follows:
wherein,is a shape function, m is the order of the basis function.
And S25, data optimization, namely, overlapping a large amount of data scanned by two adjacent measuring stations, thinning the point cloud data by adopting a downsampling method, removing repeated redundant data, and optimizing data storage.
And S26, coordinate registration, namely identifying control points marked in advance in a scene and giving measurement coordinates to the control points, and when the number of the control points is more than or equal to 3 and is not on the same straight line, repositioning the model according to the control point coordinates, and unifying scan data each time under the same coordinate system.
And step S27, outputting the point cloud, and exporting the data which is finished in the final processing and meets the requirements into a point cloud format which can be analyzed.
Step S3, data analysis, namely, high data precision and large data volume of a point cloud model, further creating a lighter gridding model by utilizing the point cloud, comparing data acquired in different periods for two or more times more rapidly and accurately, comparing the whole structural change of the model, the deviation of a single structural surface, the sedimentation of the height and the detail change of the single point and the point according to requirements, generating a model comparison graph and a comparison report, deeply analyzing the structural deformation result, and taking countermeasures, wherein the method specifically comprises the following steps:
step S31, generating a gridding model, connecting nearby points in the point cloud model into a TIN (Triangulated Irregular Network, irregular triangular net) triangular net according to Delaunay triangle (meeting the so-called maximum-minimum angle optimization criterion, namely the sum of all minimum internal angles is maximum), adopting a Taubin smoothing method (adopting positive factor lambda (0-1), adopting negative factor mu (-1-0) in the other process, and repeating the two processes in each iteration, thereby inhibiting high-frequency components in the model and simultaneously keeping low-frequency components which are not enhanced), and generating the gridding model which is lighter in weight, high in rendering degree and has the characteristics of surface patch.
Step S32, model point location matching, deformation of a structure can cause offset of positions of two models, the two models realize one-to-one correspondence of the same positions through matching of key points in the models, comparison of the same points is ensured, analysis accuracy is improved, and an iterative closest point method (ICP algorithm) is adopted to accurately register data of two control points:
wherein R and t are the variation errors between the nearest points, and x and p are the points in the two sets of point clouds.
In step S33, a tolerance is set, where the tolerance is used to help the model distinguish between the boundary values n of the deformation degrees, then a portion of the model with a deformation less than n is regarded as a controllable range, and a portion with a deformation exceeding n is regarded as an overrun range.
Step S34, single point comparison is carried out, single point coordinates in the model are selected, three-dimensional coordinates of the point are compared in a unified coordinate system, and displacement changes and three-dimensional Euclidean distance changes of the point in the X, Y, Z directions are obtained:
step S35, integrally comparing, namely selecting one section to be analyzed in the model, and judging the change of the whole section in each direction, wherein the change comprises verticality change, curvature change and maximum offset point;
the perpendicularity change calculation method comprises the following steps:
wherein Deltaeta is the change value of the perpendicularity of the data at two times, and x 1 ，y 1 And x 2 ，y 2 Plane coordinates, z, of the bottom and top of the structure, respectively 1 And z 2 Is a vertical coordinate;
the curvature change is calculated by using curvature change rate:
wherein ΔK is curvature change rate, R is curve radius, l 0 Is a moderation curve length;
the maximum offset point is:
(x，y，z) max ＝max(ρ n )。
in step S36, a color block diagram is generated, the deformation in different directions is distinguished by using different color blocks, the deformation degree is distinguished by using gradual change, for example, red is used for representing positive change, blue is used for representing negative change, and the deformation degree is distinguished by using the darkness and the darkness of the color, so that the overall change of the structure is intuitively embodied.
And S37, generating a structural deformation report, and outputting a specific analysis report according to a comparison result, wherein the specific analysis report comprises information such as displacement of key points, deformation of key cross sections, sedimentation in the vertical direction, maximum and minimum deviation values, model precision and the like.
And S38, analyzing the deformation result, guiding the operation of the actual engineering according to the color block diagram and the analysis report result, and efficiently making a targeted response scheme to ensure the safety of site construction.
Referring to fig. 5, a three-dimensional laser scanning simulation scan route is shown, the positions of measurement stations are determined according to the situations of the field environment and the topography, the distances between the measurement stations are kept equal, the station distance=the route perimeter/the station number, the measurement stations form a closed loop route, two or more fixed-position and fixed-diameter target balls are required to be placed between every two adjacent measurement stations, the data measured by the two stations are spliced through the target balls, and the three-dimensional laser scanning is sequentially carried out on each station according to the scan route on the premise of ensuring static surrounding environment, fixed positions of the target balls and reasonable parameters, so that the point cloud data of the appearance of a detected object is obtained.
The above embodiments are provided for illustrating the present invention and not for limiting the present invention, and various changes and modifications may be made by one skilled in the relevant art without departing from the spirit and scope of the present invention, and thus all equivalent technical solutions should be defined by the claims.
Claims (2)
1. The method for analyzing the integral deformation of the structure based on the three-dimensional laser scanning technology is characterized by comprising the following steps of:
step S1: a three-dimensional laser scanner is adopted to periodically acquire high-precision laser scanning data of the outline of the structure;
step S2: processing the collected initial data into analyzable point cloud data through splicing, denoising, positioning and precision checking operations;
step S3: creating a gridding model, comparing data acquired in different periods for more than two times, generating a model comparison graph and a comparison report, and analyzing a deformation result;
the step S1 includes:
step S11: actual investigation is carried out, the environmental conditions around the foundation pit are investigated, and whether the vicinity of the structure has the terrain condition of three-dimensional laser scanning or not is determined;
step S12: setting a scanning route, determining the positions of measuring stations according to the conditions of the site environment and the topography, keeping the distances among the measuring stations equal, and enabling the measuring stations to form a closed-loop route by the station spacing = route perimeter/station number;
step S13: laying control points, selecting a marked point position on site as a control point, marking the position of the control point, and measuring three-dimensional coordinates of the control point by using a total station or a GPS measuring instrument as a positioning control point during data processing;
step S14: laying target balls, placing more than two target balls with fixed positions and fixed diameters between every two adjacent measuring stations, and splicing measured data of the two stations through the target balls;
step S15: generating measurement parameters, and automatically generating acquisition point density, scanning maximum distance and color parameters of three-dimensional laser scanning according to the on-site environment conditions, control points, target ball setting points and data acquisition efficiency requirements;
step S16: carrying out laser scanning, and sequentially carrying out three-dimensional laser scanning on each site according to a scanning route established in advance on the premise of ensuring static surrounding environment, fixed target ball position and reasonable parameters to obtain point cloud data of the appearance of the structure;
step S17: data export, namely exporting sweep data to obtain original model data;
the step S2 includes:
step S21: converting scanning data, namely converting initial data acquired by scanning by a scanner into computer visual point clouds through space positioning and attribute assignment, generating 360-degree circular views scanned by each station, and checking whether the station position is correct or not and whether the scanning data of each station is complete or not;
step S22: denoising data, namely denoising data which influences the precision of a later model and the quality of analysis in initial data, and extracting main structure data;
step S23: colorization processing, namely endowing each point in the point cloud data with the stored RGB color information, and positioning according to the stored coordinate position information to realize colorization display of the point cloud;
step S24: the data are spliced, a 2D-3D point cloud splicing method is adopted to realize multi-station data splicing, a three-dimensional spline difference algorithm is adopted to generate a two-dimensional image, SIFT algorithm is adopted to match the two-dimensional homonymous feature points, a least square method is adopted to fit the three-dimensional feature points, and refined three-dimensional homonymous feature points are adopted to realize multi-station splicing;
step S25: data optimization, namely performing mass coincidence on data swept by two adjacent measuring stations, performing thinning on point cloud data by adopting a downsampling method, removing repeated redundant data, and optimizing data storage;
step S26: coordinate registration, namely identifying control points marked in advance in a scene, giving measurement coordinates to the control points, and repositioning a model according to the control point coordinates when the number of the control points is more than or equal to 3 and is not on the same straight line, so that scan data of each time are unified under the same coordinate system;
step S27: outputting point cloud, and exporting the data which is processed finally and meets the requirements into a point cloud format which can be analyzed;
the step S3 includes:
step S31: generating a gridding model, connecting nearby points in the point cloud model into a TIN triangular network according to Delaunay triangles, and performing smoothing treatment by adopting a Taubin smoothing method to generate a lighter gridding model with high rendering degree and faceted characteristics;
step S32: the point positions of the models are matched, the two models realize one-to-one correspondence of the same positions through the matching of key points in the models, the comparison of the same point positions is ensured, and the data of the two control points are accurately registered by adopting an iterative closest point method;
step S33: setting a tolerance for helping the model to distinguish a boundary value n of the deformation degree, wherein a part of the model with the deformation less than n is regarded as a controllable range, and a part with the deformation exceeding n is regarded as an overrun range;
step S34: single point comparison, namely selecting a single point position coordinate in the model, and comparing three-dimensional coordinates of the point position scanned before and after twice under a unified coordinate system to obtain displacement change and three-dimensional Euclidean distance change of the point position in X, Y, Z directions;
step S35: overall comparison, namely selecting one section to be analyzed in the model, and judging the change of the whole section in all directions, wherein the change comprises verticality change, curvature change and maximum offset point;
step S36: generating a color block diagram, distinguishing deformation degrees of deformation and gradual change color areas in different directions by utilizing different color blocks, distinguishing the deformation degrees by using the depth of the color, and intuitively reflecting the overall change of the structure;
step S37: generating a structural deformation report, and outputting a specific analysis report according to a comparison result;
step S38: and analyzing the deformation result, and guiding the operation of the actual engineering according to the color block diagram and the analysis report result, so as to efficiently formulate a targeted response scheme.
2. The method for analyzing the integral deformation of a structure based on the three-dimensional laser scanning technique according to claim 1, wherein the analysis report comprises: displacement of key points, deformation of key sections, sedimentation in the vertical direction, maximum and minimum deviation values and model accuracy.
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