Steel bar quality inspection method based on three-dimensional laser scanning

Abstract

The invention discloses a steel bar quality inspection method based on three-dimensional laser scanning, and the method mainly comprises the following steps: 1, planning an observation station: based on a BIM design model, planning the position of the observation station by means of a sight line detection algorithm; 2, on-site scanning, wherein a ground three-dimensional laser scanner is used for scanning a steel bar construction site, and point cloud data of all observation stations are obtained; 3, coordinate conversion and splicing are conducted, wherein coordinate conversion is conducted on the data obtained by all the observation stations, and a complete reinforcing mesh is spliced; step 4, dividing measuring areas: dividing the complete reinforcing mesh into a plurality of measuring areas according to the effective data range; compared with other detection technologies, the method has the advantages that beforehand control can be achieved, sampling detection is not limited any more, the detection range is more comprehensive, unreasonable places in the reinforcing steel bar installation process can be found and comprehensively analyzed in time, adjustment can be made, and meanwhile a more accurate analysis result can be provided.

Description

Technical Field

[0001] The invention relates to a steel bar quality inspection method, and in particular to a steel bar quality inspection method based on three-dimensional laser scanning. Background Art

[0002] 3D laser scanning technology is a non-destructive detection method used for the evaluation and detection of the quality of steel bar construction. This method uses a laser scanner to perform 3D imaging and measurement of steel bars. Through a variety of methods of high-speed 3D laser technology scanning image measurement, it can quickly and accurately obtain the spatial 3D coordinate image data of the steel bar surface over a large area, and actively collect a large amount of spatial three-dimensional point coordinate information of the measured space, so as to quickly and accurately obtain the spatial three-dimensional point coordinates of each different sampling point on the surface of the steel bar object. The principle of 3D laser scanning technology is to use a laser emission pulse inside the equipment as a transmitter to emit a beam of 3D laser pulses to the steel bar. Through the rotation of the reflector, the laser emission pulse emitted by the transmitter automatically scans the relevant target. The receiver of the signal source automatically receives the laser emission pulse sent back by the laser reflector and automatically records the relevant pulse data in real time. These data include the time distance from the target emission point to the surface of the detected object through the time required for the laser emission pulse of each point to return to the scanning device. The laser scanning pulse control processing module automatically controls a horizontal horizontal scanning angle of α and a vertical horizontal scanning angle of θ for each cloud point laser scanning pulse. The cloud points are automatically analyzed and calculated by the background processing software to obtain the relative three-dimensional spatial coordinates of the scanned target, that is, a cloud point is expressed as the absolute coordinates or cloud point model of its position in three-dimensional space in the absolute three-dimensional coordinate system after multiple conversions. The application of this method is not limited to steel bar detection, but also includes the quality inspection of large steel structure components, such as the inspection of the main structure of the second phase of the National Convention Center, demonstrating its important role in the construction field. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a steel bar quality inspection method based on three-dimensional laser scanning.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] The present invention provides a steel bar quality inspection method based on three-dimensional laser scanning, which mainly comprises the following steps:

[0006] Step 1: Plan the measuring station: Based on the BIM design model, use the sight line detection algorithm to plan the measuring station location;

[0007] Step 2: On-site scanning: Use a ground-based 3D laser scanner to scan the steel bar construction site and obtain point cloud data of each measuring station;

[0008] Step 3: Coordinate conversion and splicing: The data obtained from each measuring station are converted into coordinates and spliced ​​into a complete steel mesh;

[0009] Step 4: Divide the measurement area: Divide the complete steel mesh into multiple measurement areas according to the valid data range;

[0010] Step 5: Analyze the measurement area data separately: Analyze the steel bar point cloud data of each measurement area separately, and reconstruct the steel bar model of each measurement area according to the diameter, position, spacing, and cover thickness information;

[0011] Step 6: Integrate the steel bar model: splice and integrate the steel bar models of each measurement area into a complete steel mesh model;

[0012] Step 7: Comparison and deviation adjustment: Compare the real steel bar model with the steel bar BIM design model to find out the installation deviation. According to the comparison results, make timely adjustments to the deviation to achieve effective pre-control before concrete pouring.

[0013] As a preferred technical solution of the present invention, in step 1, the BIM design model is imported into professional 3D modeling software, the model is cleaned and optimized, unnecessary elements are removed, the accuracy and completeness of the model are ensured, the scanning target area is determined, and according to the needs of steel bar quality inspection, the steel bar distribution area that needs to be scanned in the BIM model is clearly defined, the line of sight detection algorithm is set, a suitable line of sight detection algorithm is selected, and relevant parameters are set, such as the angle range and distance limit of the line of sight, the initial station position is set, and several initial station positions are selected around the target area, and the line of sight scanning is simulated. Based on the set station position and the line of sight detection algorithm, the line of sight coverage of the laser scanner is simulated, the coverage integrity is evaluated, and the line of sight is checked to see whether it can completely cover the target steel bar area, including various parts and angles of the steel bars, and the station position is optimized. If there is a blind spot or insufficient coverage area, the station position is adjusted, and the simulation is performed again to generate multiple station position schemes, and their line of sight coverage effects, scanning efficiency and operational convenience are compared.

[0014] As a preferred technical solution of the present invention, in step 2, the ground three-dimensional laser scanner is placed at the planned measuring station position, and accurate horizontal and vertical calibration is performed, the scanner is started, and the scanning operation is performed according to the set parameters. The scanner will emit a laser beam and measure the time from the laser emission to the reflection back, so as to obtain the point cloud data of the object surface. After the scanning is completed at each measuring station, the position and posture information of the scanner is recorded for subsequent coordinate conversion and splicing. The above steps are repeated to complete the scanning work at all planned measuring station positions.

[0015] As a preferred technical solution of the present invention, in step 3, the point cloud data obtained at each measuring station are preprocessed, including removing invalid points and abnormal points, converting the point cloud data into a unified coordinate system according to the position and posture information of the scanner at each measuring station, using feature matching or target matching methods to find the common part between the cloud data of adjacent measuring stations, and seamlessly splicing the point cloud data of each measuring station into a complete steel mesh point cloud model through an iterative closest point (ICP) algorithm and an accurate splicing algorithm.

[0016] As a preferred technical solution of the present invention, in step 4, a reasonable measurement area segmentation scheme is determined according to the effective range of the acquired complete point cloud data and the distribution of steel bars. The measurement area can be divided according to the partition of the building structure, the type of steel bars or the stage of construction factors to ensure that each measurement area has clear boundaries and identifications for subsequent separate analysis and processing.

[0017] As a preferred technical solution of the present invention, in step 5, detailed feature extraction and analysis are performed on the steel bar point cloud data of each measuring area. For the diameter measurement of the steel bar, point cloud sampling can be performed through a section perpendicular to the axis of the steel bar, and then the distribution range of the sampling points is calculated to estimate the diameter. Using the RANSAC algorithm, a group of points are randomly selected from the point cloud data to assume that they are the center of the steel bar. Through continuous iteration and verification, the center coordinates that best match the distribution of most points are found. Based on the obtained center coordinates, the axis of the steel bar is fitted using the least squares method to determine the position and direction of the steel bar, calculate the point cloud distance between adjacent steel bars, determine the spacing of the steel bars, measure the point cloud distance from the steel bar surface to the concrete surface, and evaluate the thickness of the protective layer. For the noise in the spliced ​​three-dimensional point cloud data, Gaussian filtering and other methods are used to automatically denoise the noise to improve the quality and accuracy of the data.

[0018] As a preferred technical solution of the present invention, in step 6, the reconstructed steel bar models after analysis of each measurement area are spliced ​​according to their positions in the overall structure, and the continuity and consistency of the splicing are checked to ensure the integrity and accuracy of the entire steel mesh model.

[0019] As a preferred technical solution of the present invention, the reconstructed real steel bar model and the BIM design model are imported into the same analysis software, and the key parameters such as the steel bar diameter, position, spacing, protective layer thickness, etc. in the two models are compared one by one. For the parts with deviations, detailed marking and recording are carried out. According to the size of the deviation and the degree of influence on the structural safety, corresponding adjustment plans are formulated. Before concrete pouring, the installation of the steel bars is adjusted in time to ensure that it meets the design requirements and construction standards.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1: The present invention overcomes the shortcomings of low manual measurement efficiency and small number of test samples, reduces the time for field data collection, improves work efficiency, and realizes digital steel bar detection.

[0022] 2: The present invention divides the steel mesh into individual measurement areas for separate analysis and then splicing, which can reduce splicing errors and ensure the estimation accuracy of the steel bar diameter.

[0023] 3: The present invention can quickly construct the true form of an object, truly, accurately and clearly reflect various aspects of steel bar information, and by comparing with the BIM design model, a comprehensive quality inspection report can be obtained to make timely adjustments and better guide construction site operations.

[0024] 4: The present invention can quickly construct the true form of an object, truly, accurately and clearly reflect various aspects of steel bar information, and by comparing with the BIM design model, a comprehensive quality inspection report can be obtained to make timely adjustments and better guide construction site operations.

[0025] 5: The present invention extracts the linear planarity features of steel bars and mixed pixels based on a two-stage algorithm and adopts linear planarity analysis to quickly and automatically remove mixed pixels and retain the steel bar point cloud data.

[0026] 6: Compared with some other detection technologies, the present invention can not only achieve pre-control, but also is no longer limited to sampling detection. The detection range is more comprehensive. It can timely discover and comprehensively analyze the unreasonable places in the steel bar installation process and make adjustments, and at the same time provide more accurate analysis results. DETAILED DESCRIPTION

[0027] The preferred embodiments of the present invention are described below. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0028] Example 1

[0029] The present invention provides a steel bar quality inspection method based on three-dimensional laser scanning, which mainly includes the following steps:

[0030] Step 1: Plan the measuring station: Based on the BIM design model, use the sight line detection algorithm to plan the measuring station location;

[0031] Step 2: On-site scanning: Use a ground-based 3D laser scanner to scan the steel bar construction site and obtain point cloud data of each measuring station;

[0032] Step 3: Coordinate conversion and splicing: The data obtained from each measuring station are converted into coordinates and spliced ​​into a complete steel mesh;

[0033] Step 4: Divide the measurement area: Divide the complete steel mesh into multiple measurement areas according to the valid data range;

[0034] Step 5: Analyze the measurement area data separately: Analyze the steel bar point cloud data of each measurement area separately, and reconstruct the steel bar model of each measurement area according to the diameter, position, spacing, and cover thickness information;

[0035] Step 6: Integrate the steel bar model: splice and integrate the steel bar models of each measurement area into a complete steel mesh model;

[0036] Step 7: Comparison and deviation adjustment: Compare the real steel bar model with the steel bar BIM design model to find out the installation deviation. According to the comparison results, make timely adjustments to the deviation to achieve effective pre-control before concrete pouring.

[0037] In step 1, the BIM design model is imported into professional 3D modeling software, the model is cleaned and optimized, unnecessary elements are removed, the accuracy and completeness of the model are ensured, the target scanning area is determined, and according to the requirements of steel bar quality inspection, the steel bar distribution area that needs to be scanned in the BIM model is clearly defined, the line of sight detection algorithm is set, a suitable line of sight detection algorithm is selected, and relevant parameters are set, such as the angle range and distance limit of the line of sight, the initial station position is set, and several initial station positions are selected around the target area, and the line of sight scanning is simulated. Based on the set station position and line of sight detection algorithm, the line of sight coverage of the laser scanner is simulated, and the coverage integrity is evaluated. Check whether the line of sight can completely cover the target steel bar area, including all parts and angles of the steel bar, optimize the station position, if there is a line of sight blind area or insufficient coverage area, adjust the station position, re-simulate, generate multiple station position schemes, and compare their line of sight coverage effect, scanning efficiency and operation convenience;

[0038] The BIM (Building Information Model) design model contains the detailed geometric information of the building, the structural layout, the distribution of steel bars, etc. It provides a basic reference framework for planning the survey station, allowing us to understand the overall structure of the building and the location and direction of the steel bars in a virtual environment.

[0039] For example, if it is a complex frame structure building, the BIM model can clearly show the layout of steel bars on different floors and in different parts, helping us determine which areas need to be scanned intensively and which areas can appropriately reduce the scanning density;

[0040] The core of the line-of-sight detection algorithm is to simulate the line of sight of the scanner to determine where to set the measurement station to maximize coverage of the area to be scanned while avoiding line-of-sight obstructions and blind spots.

[0041] The workflow usually includes the following steps:

[0042] Determine the scanner parameters, such as scanning angle, maximum scanning distance, etc.

[0043] Starting from the possible measurement station positions, the scanner's line of sight is simulated through an algorithm.

[0044] Assess the coverage of rebar within the field of view, including whether rebar in key locations can be fully scanned.

[0045] For example, in an area with a large number of beam-column nodes, the line-of-sight detection algorithm will continuously calculate and adjust the position of the measuring station to ensure that the crossing and connection of the steel bars at the nodes can be clearly scanned;

[0046] Complexity of steel bar distribution: Areas with dense steel bars and many cross-sections require more measuring stations to ensure scanning accuracy.

[0047] Structural features of buildings: such as tall columns and special-shaped structures, may affect the line of sight and require special consideration of the location of the measuring station.

[0048] Restrictions on the construction environment: Factors such as obstacles and narrow passages on site may limit the optional locations of the measuring stations.

[0049] For example, in a building with a cantilever structure, in order to scan the steel bars of the cantilevered part, it may be necessary to set up a measuring station at a farther location, or use multi-angle scanning to compensate for the lack of line of sight.

[0050] In short, planning the measurement station location based on the BIM design model with the help of the line of sight detection algorithm is an important preliminary preparation for achieving efficient and accurate 3D laser scanning steel bar quality inspection. It is necessary to comprehensively consider multiple factors to ensure that comprehensive and accurate scanning data is obtained.

[0051] In step 2, the ground 3D laser scanner is placed at the planned measuring station position, and accurate horizontal and vertical calibration is performed. The scanner is started and scanning is performed according to the set parameters. The scanner emits a laser beam and measures the time from the laser emission to the reflection back, thereby obtaining the point cloud data of the object surface. After scanning at each measuring station, the position and posture information of the scanner is recorded for subsequent coordinate conversion and splicing. The above steps are repeated to complete the scanning work at all planned measuring station positions.

[0052] Get a detailed BIM design model:

[0053] In this step, it is necessary to ensure that the BIM design model obtained is up-to-date and accurate. It should not only include the layout, size and location information of the steel bars, but also the relevant data of the concrete structure and other building components. For example, for the steel bar quality inspection of a large commercial complex, the BIM model may show the steel bar layout of different floors and different functional areas in detail, including the beam-column joints, the steel bar distribution in the shear wall, etc.

[0054] Import the gaze detection algorithm and take into account the scanner performance parameters:

[0055] The line of sight detection algorithm needs to be imported to match the model and specifications of the scanner used. For example, a scanner has a scanning angle range of 360 degrees horizontally and 270 degrees vertically, and a maximum effective scanning distance of 100 meters. These parameters must be set accurately in the algorithm to ensure that the simulated line of sight meets the capabilities of the actual scanner. Assuming that in a bridge project, the effective distance of the scanner is limited, it is necessary to be more cautious when planning the measuring station to avoid inaccurate data due to the long distance.

[0056] Simulate sight lines based on building geometry and rebar distribution:

[0057] The geometry of a building can be very complex, with special-shaped structures, curved surfaces, etc. The distribution of steel bars can also be uneven, with dense and sparse areas. These factors should be fully considered when simulating sight lines through algorithms. For example, for a stadium with a curved appearance, it is necessary to ensure that the sight lines from different potential locations can adapt to the curved shape of the building and fully cover the steel bars.

[0058] Evaluate the line-of-sight coverage area for potential station locations:

[0059] During the evaluation process, professional software tools can be used to intuitively display the line of sight coverage of each potential measuring station location. Key locations, such as the supports of large-span beams and the steel bar nodes of the transition layer, should be inspected in detail. For example, during the construction of a high-rise residential building, if a potential measuring station location is found to be unable to cover the complex steel bar nodes at the junction of the top shear wall and the frame column, the measuring station location needs to be adjusted or the number of measuring stations needs to be increased.

[0060] Comprehensively consider the construction environment factors to determine the optimal measurement station location:

[0061] Obstacles in the construction environment may include temporary scaffolding, piled up construction materials, etc. Accessibility involves whether personnel and equipment can reach the measuring station safely and conveniently. The accessibility of the scanner should consider the space limitations on site, the feasibility of high-altitude operations, etc. For example, in a hospital project that is undergoing interior decoration, the originally planned measuring station location may be inaccessible due to the corridor being filled with decoration materials, and a more suitable location needs to be re-selected.

[0062] In short, planning a measuring station is a complex process that requires comprehensive consideration of many factors. Only through meticulous operation and accurate evaluation can we ensure that 3D laser scanning can complete the steel bar quality inspection task efficiently and accurately.

[0063] In step 3, the point cloud data obtained at each measuring station are preprocessed, including removing invalid points and abnormal points. According to the position and posture information of the scanner at each measuring station, the point cloud data are converted into a unified coordinate system. The feature matching or target matching method is used to find the common part between the point cloud data of adjacent measuring stations. The point cloud data of each measuring station are seamlessly spliced ​​into a complete steel mesh point cloud model through the iterative closest point (ICP) algorithm and precise splicing algorithm.

[0064] In step 4, a reasonable measurement area segmentation scheme is determined based on the effective range of the acquired complete point cloud data and the distribution of the steel bars. The measurement area can be divided according to the partition of the building structure, the type of steel bars or the stage of construction, ensuring that each measurement area has clear boundaries and identification for subsequent separate analysis and processing;

[0065] Analyze the effective scope of the complete point cloud data: Determine the spatial scope covered by the point cloud data and understand its distribution in the building.

[0066] Consider the zoning of the building structure: divide it according to different functional areas, floors, rooms, etc. For example, different floors or rooms can be divided into different measurement areas. In this way, the analysis and processing can be carried out according to the actual structure of the building, which is convenient for corresponding to the building design or construction plan.

[0067] Classification by rebar type: If the rebars have different specifications, diameters or uses, the same type of rebar can be grouped into the same measurement area. This helps to conduct specialized analysis on the distribution and characteristics of a specific type of rebar.

[0068] According to the construction stage factors: According to the different stages of construction, such as the foundation construction stage, the main structure construction stage, etc., the survey area is divided. In this way, targeted processing and analysis can be carried out according to the characteristics and needs of each construction stage.

[0069] Check the continuity and independence of the steel bar distribution: Ensure that the steel bar distribution in each measurement area has a certain continuity to facilitate effective analysis. At the same time, the steel bar distribution between measurement areas should be relatively independent to avoid excessive overlap or confusion.

[0070] Set clear boundaries and identification: Define clear boundaries for each survey area, using coordinate ranges, geometric shapes, or other clear identification methods. This allows each survey area to be accurately identified and manipulated in subsequent analysis and processing.

[0071] Evaluate the size of the survey area and the amount of data: Make the size of the survey area moderate, neither too large to make processing difficult, nor too small to make the analysis meaningless. At the same time, consider the amount of data in each survey area to ensure that it is within the range of processing capacity and efficiency.

[0072] Communicate and confirm with relevant parties: Communicate with architectural designers, construction teams and other relevant parties to ensure that the survey area segmentation plan meets their needs and expectations and can provide valuable information for subsequent work.

[0073] Flexibility and adjustability: Although the survey area segmentation scheme is determined in the initial stage, it may need to be appropriately adjusted according to new findings or needs during the actual processing. Therefore, the scheme should have a certain degree of flexibility so that it can be modified when necessary.

[0074] Record and document: The determined survey area division plan should be recorded and documented in detail, including the basis for division of the survey area, boundary definition, identification method, etc. This will help other personnel understand and use the plan, and also facilitate subsequent review and reference.

[0075] For example, for a multi-story building, it can be divided into multiple measurement areas according to the floors, and each floor can be further subdivided into measurement areas according to different rooms or steel bar types; if there are obvious stages in the construction process, such as foundation, frame, wall, etc., the measurement areas can be divided according to these stages; or if the steel bars in certain areas have special specifications or uses, these areas can also be divided into separate measurement areas. When dividing the measurement areas, ensure that each measurement area can be clearly identified and processed, and is consistent with the actual building structure, construction conditions, and analysis purposes.

[0076] In step 5, detailed feature extraction and analysis are performed on the steel bar point cloud data of each measurement area. For the diameter measurement of the steel bar, point cloud sampling can be performed through the section perpendicular to the axis of the steel bar, and then the distribution range of the sampling points is calculated to estimate the diameter. The RANSAC algorithm is used to randomly extract a group of points from the point cloud data to assume that the center of the steel bar is the center of the circle. Through continuous iteration and verification, the center coordinates that best match the distribution of most points are found. Based on the obtained center coordinates, the least squares method is used to fit the axis of the steel bar to determine the position and direction of the steel bar, calculate the point cloud distance between adjacent steel bars, determine the spacing of steel bars, measure the point cloud distance from the steel bar surface to the concrete surface, and evaluate the thickness of the protective layer. For the noise in the spliced ​​three-dimensional point cloud data, Gaussian filtering and other methods are used for automatic denoising to improve the quality and accuracy of the data;

[0077] RANSAC algorithm extracts the center coordinates of steel bars

[0078] The RANSAC algorithm is an iterative method for estimating model parameters from data containing outliers. When extracting the center coordinates of the steel bar, the steps are as follows:

[0079] A set of points are randomly selected as candidate circle centers.

[0080] Calculate the distance from other points to the center of this candidate circle.

[0081] According to the set threshold, determine which points belong to the circle corresponding to the candidate center.

[0082] Repeat the above steps multiple times and select the candidate center that contains the most inner points (i.e., points considered to belong to the circle) as the final center estimate.

[0083] For example, suppose we have a series of point cloud data on the cross section of a steel bar. Through multiple random sampling and verification of the RANSAC algorithm, we can finally find the coordinates that best represent the center of the cross section of the steel bar.

[0084] The axis of the steel bar is obtained based on the least squares method

[0085] After obtaining the coordinates of the center of the steel bar cross section, the least squares method is used to fit the axis of the steel bar.

[0086] The basic idea of ​​the least squares method is to minimize the sum of squares of the errors between the observed value and the fitted value. For the fitting of the steel bar axis, the steps are as follows:

[0087] It is assumed that the axis of the steel bar can be expressed by a straight line equation (such as y=ax+b) or other appropriate mathematical models.

[0088] Substitute the coordinates of the center points of multiple cross sections into the model.

[0089] By minimizing the sum of squared errors, the parameters in the model (such as a and b) are solved.

[0090] For example, there are a series of cross-section center coordinates (x1, y1), (x2, y2), ..., (xn, yn), and we need to find a straight line y = ax + b to fit these points. By calculating the error E = Σ(yi-(axi+b))^2 and taking the partial derivative of a and b to make them 0, we can solve the optimal a and b values ​​and get the fitting axis equation.

[0091] The advantage of this method is that it can effectively process data with noise and outliers, and can provide relatively accurate and stable fitting results. In practical applications, through multiple tests and parameter adjustments, the accuracy of center extraction and axis fitting can be improved, providing a reliable basis for steel bar quality inspection.

[0092] In step 6, the reconstructed steel bar models of each measurement area are spliced ​​according to their positions in the overall structure, and the continuity and consistency of the splicing are checked to ensure the integrity and accuracy of the entire steel mesh model.

[0093] The reconstructed real steel bar model and the BIM design model are imported into the same analysis software, and the key parameters such as steel bar diameter, position, spacing, protective layer thickness, etc. in the two models are compared one by one. Detailed annotation and records are made for the parts with deviations. According to the size of the deviation and the degree of impact on the structural safety, corresponding adjustment plans are formulated. Before concrete pouring, the installation of steel bars is adjusted in time to ensure that it meets the design requirements and construction standards.

[0094] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A steel bar quality inspection method based on three-dimensional laser scanning, characterized in that: The main steps include: Step 1: Plan the measuring station: Based on the BIM design model, use the sight line detection algorithm to plan the measuring station location; Step 2: On-site scanning: Use a ground-based 3D laser scanner to scan the steel bar construction site and obtain point cloud data of each measuring station; Step 3: Coordinate conversion and splicing: The data obtained from each measuring station are converted into coordinates and spliced ​​into a complete steel mesh; Step 4: Divide the measurement area: Divide the complete steel mesh into multiple measurement areas according to the valid data range; Step 5: Analyze the measurement area data separately: Analyze the steel bar point cloud data of each measurement area separately, and reconstruct the steel bar model of each measurement area according to the diameter, position, spacing, and cover thickness information; Step 6: Integrate the steel bar model: splice and integrate the steel bar models of each measurement area into a complete steel mesh model; Step 7: Comparison and deviation adjustment: Compare the real steel bar model with the steel bar BIM design model to find out the installation deviation. According to the comparison results, make timely adjustments to the deviation to achieve effective pre-control before concrete pouring.

2. A steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: In step 1, import the BIM design model into professional 3D modeling software, clean and optimize the model, remove unnecessary elements, ensure the accuracy and completeness of the model, determine the scanning target area, and clarify the steel bar distribution area that needs to be scanned in the BIM model according to the needs of steel bar quality inspection. Set the line of sight detection algorithm, select a suitable line of sight detection algorithm, and set relevant parameters, such as the angle range and distance limit of the line of sight. Set the initial station position, select several initial station positions around the target area, simulate the line of sight scanning, and simulate the line of sight coverage of the laser scanner based on the set station position and line of sight detection algorithm. Evaluate the coverage integrity and check whether the line of sight can completely cover the target steel bar area, including all parts and angles of the steel bars. Optimize the station position. If there is a blind spot or insufficient coverage area, adjust the station position, re-simulate, generate multiple station position schemes, and compare their line of sight coverage effect, scanning efficiency and ease of operation.

3. A steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: In step 2, place the terrestrial 3D laser scanner at the planned measuring station location, perform precise horizontal and vertical calibration, start the scanner, and perform scanning operations according to the set parameters. The scanner will emit a laser beam and measure the time from the laser emission to the reflection back, thereby obtaining the point cloud data of the object surface. After completing the scan at each measuring station, record the position and attitude information of the scanner for subsequent coordinate conversion and stitching. Repeat the above steps to complete the scanning work at all planned measuring station locations.

4. A steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: In step 3, the point cloud data obtained at each measuring station are preprocessed, including removing invalid points and abnormal points. According to the position and posture information of the scanner at each measuring station, the point cloud data are converted into a unified coordinate system. The feature matching or target matching method is used to find the common part between the point cloud data of adjacent measuring stations. The point cloud data of each measuring station are seamlessly spliced ​​into a complete steel mesh point cloud model through the iterative closest point (ICP) algorithm and precise splicing algorithm.

5. The steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: In step 4, a reasonable measurement area segmentation scheme is determined based on the effective range of the acquired complete point cloud data and the distribution of steel bars. The measurement area can be divided according to the partition of the building structure, the type of steel bars or the stage of construction factors to ensure that each measurement area has clear boundaries and identification for subsequent separate analysis and processing.

6. A steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: In step 5, detailed feature extraction and analysis are performed on the steel bar point cloud data of each measurement area. For the diameter measurement of the steel bar, point cloud sampling can be performed through the section perpendicular to the axis of the steel bar, and then the distribution range of the sampling points is calculated to estimate the diameter. Using the RANSAC algorithm, a group of points are randomly selected from the point cloud data to assume that the center of the steel bar is the center of the circle. Through continuous iteration and verification, the center coordinates that best match the distribution of most points are found. Based on the obtained center coordinates, the least squares method is used to fit the axis of the steel bar to determine the position and direction of the steel bar, calculate the point cloud distance between adjacent steel bars, determine the spacing of the steel bars, measure the point cloud distance from the steel bar surface to the concrete surface, and evaluate the thickness of the protective layer. For the noise in the spliced ​​three-dimensional point cloud data, Gaussian filtering and other methods are used for automatic denoising to improve the quality and accuracy of the data.

7. A steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: In step 6, the reconstructed steel bar models of each measurement area are spliced ​​according to their positions in the overall structure, and the continuity and consistency of the splicing are checked to ensure the integrity and accuracy of the entire steel mesh model.

8. The steel bar quality inspection method based on three-dimensional laser scanning according to claim 1, characterized in that: The reconstructed real steel bar model and the BIM design model are imported into the same analysis software, and the key parameters such as steel bar diameter, position, spacing, protective layer thickness, etc. in the two models are compared one by one. Detailed annotation and records are made for the parts with deviations. According to the size of the deviation and the degree of impact on the structural safety, corresponding adjustment plans are formulated. Before concrete pouring, the installation of steel bars is adjusted in time to ensure that it meets the design requirements and construction standards.