A virtual acceptance method and device for a steel pipe arch rib

Abstract

The application provides a kind of virtual acceptance method and device of steel pipe arch rib, it is related to bridge engineering construction technical field;The method comprises, by being installed in the three-dimensional laser scanner of site in advance to the prefabricated steel pipe arch rib is scanned in multiple directions and obtains multi-direction arch rib point cloud data;Accordingly, BIM real scene model and point cloud real scene model are constructed, while drawing data is imported to construct BIM design model and point cloud design model.In model construction, both can highly restore the real state of arch rib, and can realize the complete mapping of design and actual model in geometric shape and information level.When acceptance analysis, the first shape difference result is obtained by qualitative analysis of BIM real scene and design model, the second shape difference result is obtained by quantitative analysis of point cloud real scene and design model, qualitative and quantitative combination, in-depth evaluation of arch rib, so that the acceptance result is scientific and reliable, overcome the shortcomings of traditional method such as large labor cost and large error, improve the acceptance efficiency and accuracy, and guarantee the engineering quality.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the technical field of bridge engineering construction, and particularly relates to a virtual acceptance method and device for a steel pipe arch rib. BACKGROUND

[0002] The steel pipe concrete arch bridge is a kind of steel-concrete composite structure, which fills the steel pipe with concrete, limits the expansion of the compressed concrete by the radial restraint of the steel pipe, and makes the concrete in a three-way compression state, thereby significantly improving the compressive strength of the concrete. The steel pipe concrete arch bridge often adopts a circular steel pipe section because the circular section can better exert the hoop effect of the steel pipe. When the span of the arch bridge is large, a truss section is generally used to enhance the carrying capacity of the arch bridge.

[0003] The manufacturing and acceptance of the steel pipe of the steel pipe concrete arch bridge is a complex and rigorous process, which needs to strictly follow relevant standards and specifications to ensure the quality and safety of the steel pipe.

[0004] The axis of the steel pipe is generally designed as a catenary or parabolic line, and the current processing technology is to fit the curved steel pipe by using multiple straight steel pipes. When the steel pipe is manufactured, the cutting should be accurate, and the diameter error of the finished pipe should be within the specified range. The arch rib splicing should be performed on a sample table, and the quality should be strictly guaranteed during welding.

[0005] In the traditional steel pipe acceptance process, a large number of feature points are often marked on the surface of the steel pipe, and then a total station instrument is used for measurement, and the size and line type of the steel pipe are accepted by observing the coordinates of the feature points. This method has the following disadvantages: large labor consumption, the traditional acceptance method needs to manually mark a large number of feature points and manually operate the total station instrument for measurement, which is time-consuming and labor-intensive; limited data, the feature point data measured by the total station instrument can only reflect a small amount of feature point information on the surface of the steel pipe, and cannot comprehensively reflect the overall size and shape of the steel pipe; large measurement error, since the measurement process relies on manual operation, human error is easily generated, especially in the process of marking feature points, setting the total station instrument and reading data, the error accumulates seriously; low efficiency and inaccuracy, since the number of feature points is limited and the distance between each measurement point is far, the measurement accuracy and precision are low, and the overall geometric shape of the steel pipe cannot be effectively reflected.

[0006] With the development of three-dimensional laser scanner, unmanned aerial vehicle oblique photography and radar scanning technology, algorithm improvement and computing power improvement, point cloud models can be efficiently generated, and the measurement accuracy is also increasingly improved. The three-dimensional laser scanner scanning measurement can improve the accuracy to the mm level, which can meet the accuracy requirements of steel pipe acceptance.

[0007] Therefore, it is urgent to develop a virtual acceptance method and device based on a steel pipe arch rib. SUMMARY

[0008] The technical problem solved by the present application is to provide a virtual acceptance method and device for steel pipe arch ribs in view of the deficiencies of the prior art.

[0009] The technical solution for solving the above technical problem is as follows: A virtual acceptance method for steel pipe arch ribs comprises the following steps:

[0010] S1, multi-directional scanning of a prefabricated steel pipe arch rib is performed by a three-dimensional laser scanner pre-installed in a site to obtain multi-directional arch rib point cloud data;

[0011] S2, a BIM real scene model and a point cloud real scene model are constructed according to the multi-directional arch rib point cloud data, and a BIM design model and a point cloud design model are constructed according to prefabricated steel pipe arch rib drawing data, and the BIM real scene model and the BIM design model are merged into the same file, and the point cloud real scene model and the point cloud design model are merged into the same file;

[0012] S3, qualitative analysis of the BIM real scene model and the BIM design model in the same file is performed to obtain a first shape difference result, and quantitative analysis of the point cloud real scene model and the point cloud design model in the same file is performed to obtain a second shape difference result, thereby completing virtual acceptance of the steel pipe arch rib.

[0013] Another technical solution for solving the above technical problem is as follows: A virtual acceptance device for steel pipe arch ribs comprises:

[0014] A data import module is configured to import multi-directional arch rib point cloud data, which is obtained by multi-directional scanning of a prefabricated steel pipe arch rib by a three-dimensional laser scanner pre-installed in a site;

[0015] A model construction module is configured to construct a BIM real scene model and a point cloud real scene model according to the multi-directional arch rib point cloud data, and construct a BIM design model and a point cloud design model according to prefabricated steel pipe arch rib drawing data, and merge the BIM real scene model and the BIM design model into the same file, and merge the point cloud real scene model and the point cloud design model into the same file;

[0016] A virtual acceptance module is configured to perform qualitative analysis of the BIM real scene model and the BIM design model in the same file to obtain a first shape difference result, and perform quantitative analysis of the point cloud real scene model and the point cloud design model in the same file to obtain a second shape difference result, thereby completing virtual acceptance of the steel pipe arch rib.

[0017] Another technical solution of the present application to solve the above technical problems is as follows: a virtual acceptance device for a steel pipe arch rib, characterized in that it comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the virtual acceptance method for the steel pipe arch rib as described above when executing the computer program.

[0018] The present application has the following beneficial effects: in terms of model construction, the BIM real scene model and the point cloud real scene model are constructed according to the multi-directional arch rib point cloud data, which can highly restore the real state of the prefabricated steel pipe arch rib. At the same time, the BIM design model and the point cloud design model are constructed by importing the prefabricated steel pipe arch rib drawing data, so that the actual constructed model has a direct comparison basis with the design model. The model constructed in this way is not only accurate in geometric shape, but also can realize complete mapping from design to actuality in the information level, which is helpful for comprehensive evaluation of the prefabricated steel pipe arch rib at each stage; in terms of acceptance analysis, the qualitative and quantitative analysis are combined to realize comprehensive and in-depth evaluation of the prefabricated steel pipe arch rib, so that the acceptance result is more scientific and reliable. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A schematic flow chart of the virtual acceptance method for the steel pipe arch rib provided by the embodiment of the present application is provided;

[0020] Figure 2 A schematic connection diagram of each functional module of the virtual acceptance device for the steel pipe arch rib provided by the embodiment of the present application is provided;

[0021] Figure 3 A schematic diagram of the BIM real scene model provided by the embodiment of the present application is provided;

[0022] Figure 4 A schematic diagram of the BIM design model provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0023] The principles and characteristics of the present application are described below in combination with the drawings, and the examples are only used to explain the present application and not to limit the scope of the present application.

[0024] The traditional total station measurement method is labor-consuming, limited in data, large in measurement error, low in efficiency, and inaccurate. The present method is based on three-dimensional laser scanning and BIM technology, has high automation, and reduces the error caused by manual operation. The multi-directional point cloud data acquisition and accurate model construction and analysis can more quickly and accurately complete the acceptance work, greatly improve the acceptance efficiency, and also improve the accuracy of the acceptance result, which is helpful for timely finding and solving the problems in the prefabricated steel pipe arch rib manufacturing process and ensuring the engineering quality. The present application is described in detail through multiple embodiments as follows.

[0025] The experiment in the embodiment is based on a bridge reconstruction project in a certain province. The bridge adopts a bottom-supported steel tube concrete tied arch bridge with a main span of 191m, a calculated span of 180m, a bridge deck width of 51.1m, a calculated rise of 40m, and a rise-span ratio of 1 / 4.5. The arch axis adopts a catenary, and the arch axis coefficient m=1.3. The arch rib is a steel tube concrete dumbbell structure. A single span bridge has two arch ribs, and the arch ribs are spaced 18.9m apart in the transverse direction of the bridge. The single arch rib adopts a dumbbell-shaped cross-section with a dumbbell height of 3.2m. The upper and lower chord diameters are 1300mm and the wall thickness is 24-28mm.

[0026] Example 1: Figure 1 As shown, an embodiment of the present invention provides a virtual acceptance method for steel pipe arch ribs, comprising the following steps:

[0027] S1. Use a 3D laser scanner pre-installed on site to perform multi-directional scanning on the prefabricated steel tube arch ribs to obtain multi-directional arch rib point cloud data;

[0028] S2. Constructing a BIM real-scene model and a point cloud real-scene model based on the multi-directional arch rib point cloud data, and constructing a BIM design model and a point cloud design model based on the prefabricated steel pipe arch rib drawing data, and merging the BIM real-scene model and the BIM design model into the same file, and merging the point cloud real-scene model and the point cloud design model into the same file;

[0029] S3. Perform a qualitative analysis on the BIM real-scene model and the BIM design model in the same file to obtain a first appearance difference result, and perform a quantitative analysis on the point cloud real-scene model and the point cloud design model in the same file to obtain a second appearance difference result, thereby completing the virtual acceptance of the steel tube arch rib.

[0030] Specifically, S1, the prefabricated steel tube arch ribs are scanned by a 3D laser scanner pre-installed in multiple locations on the site to obtain multi-directional arch rib point cloud data, including:

[0031] 3D laser scanning technology is used to obtain point cloud data of prefabricated steel tube arch ribs in the prefabrication factory site. A multi-scale point cloud data collection solution is adopted: a 3D laser scanner is used to scan the prefabricated steel tube arch ribs of the steel tube arch bridge placed in the prefabrication factory site. Data is collected from multiple angles, including the top of the component, the cross-sections at both ends, the inner arc and the outer arc, to obtain a large amount of point cloud data containing 3D coordinate points, reflection intensity, color and other additional information.

[0032] The advantages of Example 1 are as follows: In terms of model construction, a BIM reality model and a point cloud reality model are constructed based on multi-dimensional arch rib point cloud data, which can highly restore the actual state of the prefabricated steel tube arch ribs. Furthermore, the import of prefabricated steel tube arch rib drawing data to construct a BIM design model and a point cloud design model allows for direct comparison between the actual constructed model and the design model. This constructed model is not only geometrically accurate but also achieves a complete mapping from design to actual at the information level, facilitating a comprehensive assessment of the prefabricated steel tube arch ribs at all stages. In terms of acceptance analysis, the combination of qualitative and quantitative analysis enables a comprehensive and in-depth assessment of the prefabricated steel tube arch ribs, making the acceptance results more scientific and reliable.

[0033] Example 2: Figure 3 、 Figure 4 As shown, in S2, a BIM real scene model and a point cloud real scene model are constructed based on the multi-directional arch rib point cloud data, and a BIM design model and a point cloud design model are constructed based on the prefabricated steel pipe arch rib drawing data, and the BIM real scene model and the BIM design model are merged into the same file, and the point cloud real scene model and the point cloud design model are merged into the same file, including:

[0034] S2.1. Importing the multi-directional arch rib point cloud data into a point cloud analysis tool for data splicing to obtain point cloud models of multiple prefabricated arch rib segments, virtually assembling the point cloud models of the multiple prefabricated arch rib segments to obtain a point cloud reality model of the prefabricated steel tube arch ribs, and converting the point cloud reality model into a BIM reality model using a BIM design tool;

[0035] S2.2. Importing prefabricated steel tube arch rib drawing data, inputting the prefabricated steel tube arch rib drawing data into a BIM design tool to generate a BIM design model, and converting the BIM design model into a point cloud design model using the point cloud analysis tool;

[0036] S2.3. Import the BIM real-scene model and the BIM design model into a BIM design tool as the same BIM file to merge them into the same file; and import the point cloud real-scene model and the point cloud design model into the same file.

[0037] The advantage of Example 2 is that the multi-directional arch rib point cloud data is imported into the point cloud analysis tool for data splicing and virtual assembly to obtain a point cloud real-life model of the prefabricated steel tube arch rib, which is then converted into a BIM real-life model. This process can accurately restore the actual shape of the arch rib and ensure a high degree of consistency between the real-life model and the actual structure.

[0038] By inputting the drawing data of the prefabricated steel pipe arch rib into the BIM design tool to generate a BIM design model, and then converting it into a point cloud design model, the rapid conversion from design drawings to models is realized, and the design model and the real scene model are constructed based on the same data source (point cloud data or drawing data), which facilitates subsequent comparative analysis and improves the efficiency and accuracy of model construction, laying a foundation for the smooth progress of virtual acceptance.

[0039] In embodiment 3, the BIM real scene model and the BIM design model in the same file are subjected to qualitative analysis to obtain a first shape difference result, including:

[0040] S3.1, the BIM real scene model and the BIM design model in the same file are superimposed to obtain a common spatial coordinate system;

[0041] S3.2, based on the spatial coordinate system, the bottom plane of the arch rib is selected in the BIM real scene model and the BIM design model respectively, the bottom plane of the arch rib includes two circular upper and lower parts and a middle linking part between the two circular parts, and the BIM real scene model and the BIM design model are constructed by taking the center of the upper circular part as the center and defining the three side lengths as constant values.

[0042] S3.3, the three corners of the characteristic triangle of the BIM real scene model and the characteristic triangle of the BIM design model are taken as three feature point pairs, and based on the principle of determining the position of a plane in space by three points not on the same straight line, the positions of the end planes of the BIM real scene model and the BIM design model in space are determined and superimposed.

[0043] S3.4, the difference set of the superimposed BIM real scene model and the BIM design model is calculated by using the Boolean operation method, and the first shape difference result is obtained according to the difference set.

[0044] Specifically, the real scene BIM model and the design BIM model are matched in the spatial coordinate system of the BIM file, so that the coordinates of the feature points of the real scene BIM model and the design BIM model in the spatial coordinate system of the BIM file are completely coincident. The matching scheme of the real scene BIM model and the design BIM model is as follows: a triangle with a fixed side length is drafted as a feature triangle, on the plane at one end of the real scene BIM model and the design BIM model (that is, the bottom plane of the arch rib, which is dumbbell-shaped and consists of an upper circle, a lower circle and a linking portion therebetween), the center of the upper circle is taken as the centroid of the feature triangle, and the same feature triangle is drawn, and the vertices of each corner of the two feature triangles are taken as a pair of feature points (which can be understood as specific positions marked on the surface of the model), based on the principle that the position of a plane in space is determined by three points not on the same straight line, three pairs of feature points can determine the positions of the end planes of the real scene model and the design BIM model in space. The positions of the three pairs of feature points in space are completely coincident, so the positions of the end planes of the real scene model and the design BIM model in space are completely coincident, and then the positions of the real scene model and the design BIM model in space are completely coincident.

[0045] When the real scene model and the design BIM model are completely coincident, the difference set of the two models is calculated by using Boolean operation to judge the difference in appearance between the design model and the actual model, that is, to qualitatively analyze the differences in shape, size, etc. between the two.

[0046] The advantage of embodiment 3 is that by importing the BIM real scene model and the BIM design model into the same BIM file, constructing feature triangles based on the feature of the bottom plane of the arch rib, and matching based on the feature points, the two models are accurately matched in the spatial coordinate system, ensuring the consistency of the models in space, and providing a prerequisite for subsequent accurate analysis of the shape difference.

[0047] By calculating the difference set of the coincident model by using Boolean operation, the differences in shape, size, etc. between the design model and the actual model can be clearly identified, so that the first shape difference result is obtained intuitively, which provides an effective qualitative basis for evaluating the quality of the prefabricated steel pipe arch rib, and helps to quickly judge whether the arch rib meets the design requirements, and can effectively qualitatively analyze the differences.

[0048] Embodiment 4: in S3, the point cloud real scene model and the point cloud design model in the same file are subjected to quantitative analysis to obtain a second shape difference result, comprising:

[0049] S3.5, selecting a plurality of point cloud pairs corresponding to the same position from the point cloud real scene model and the point cloud design model in the same file, and performing preliminary registration processing on the selected plurality of point cloud pairs by using the RANSAC random sample consensus algorithm;

[0050] S3.6, performing accurate registration processing on the preliminary registered point cloud real scene model and the point cloud design model by using an ICP (Iterative Closest Point) algorithm;

[0051] S3.7, performing pose difference detection on the accurately registered point cloud real scene model and the point cloud design model by using a KNN (K-Nearest Neighbor) algorithm to obtain a second shape difference result.

[0052] It should be understood that the registration of the point cloud real scene model and the point cloud design model is divided into two steps of coarse registration and accurate registration.

[0053] Since the point cloud real scene model and the point cloud design model have low correlation in space, the same position feature points are selected on the real scene point cloud model and the design point cloud model (in this step, it is not necessary to accurately select the same position points, but it is only necessary to roughly correspond), and a RANSAC (Random Sample Consensus) algorithm is used for coarse registration to make them roughly coincide; in the second step, accurate registration: an ICP (Iterative Closest Point) algorithm is used to accurately register the real scene point cloud model and the design point cloud model. The point cloud model is essentially a set of spatial data points obtained by a three-dimensional scanning device, and the role of the ICP algorithm is to continuously optimize the alignment between the two point cloud models to make the design model and the actual model completely coincide in space. In this way, the position and pose of the two point cloud models in space can be ensured to be very accurate.

[0054] The advantage of embodiment 4 is that the registration of the point cloud real scene model and the point cloud design model is divided into two steps of coarse registration and accurate registration, the RANSAC algorithm is used for preliminary registration, and then the ICP algorithm is used for accurate registration. This strategy fully considers the low correlation of the initial state of the model, gradually optimizes the alignment of the model, and ensures that the position and pose of the two point cloud models in space are very accurate, which lays a foundation for accurate pose difference detection and quantitative analysis.

[0055] The pose difference detection on the accurately registered model by using the KNN (K-Nearest Neighbor) algorithm can comprehensively obtain the second shape difference result between the models, including the spatial position deviation between each point cloud pair, and realizes the quantitative analysis of the shape difference of the prefabricated steel pipe arch rib, which provides detailed data support for more accurate evaluation of the manufacturing quality of the arch rib.

[0056] Embodiment 5: in S3.5, a plurality of point cloud pairs corresponding to the same position are selected from the point cloud real scene model and the point cloud design model, and a RANSAC (Random Sample Consensus) algorithm is used for preliminary registration processing on the selected plurality of point cloud pairs, including:

[0057] S3.5.1, taking each point cloud in the point cloud design model as a source point cloud, collecting all the source point clouds to obtain a source point cloud set P, taking each point cloud in the point cloud real scene model as a target point cloud, collecting all the target point clouds to obtain a target point cloud set Q, selecting a plurality of source point clouds and target point clouds corresponding to the same position to obtain a plurality of point cloud pairs;

[0058] S3.5.2, calculating the normal vector of the point clouds in the plurality of point cloud pairs to obtain the characteristic value of each point cloud pair;

[0059] S3.5.3, randomly selecting a point cloud pair from the plurality of point cloud pairs, denoted as wherein, p i is a source point cloud, p i ∈P, q i is a target point cloud, q i ∈Q, and K is the number of point cloud pairs;

[0060] S3.5.4, constructing a rigid transformation matrix T according to the characteristic value corresponding to the selected point cloud pair, the rigid transformation matrix T including a rotation matrix R and a translation vector t, so that:

[0061] T(P i )=R·p i +t≈q i ;

[0062] S3.5.5, applying the rigid transformation matrix T to all source point clouds in the source point cloud set P for transformation processing to obtain a transformed source point cloud set P', the source point cloud set P' consisting of transformed source point clouds, and calculating the distance difference between each transformed source point cloud in the source point cloud set P' and the target point cloud belonging to the same point cloud pair in the target point cloud set Q, comparing each distance difference with a preset difference value to obtain the point cloud pair corresponding to the distance difference less than the preset difference value, counting the number of point cloud pairs obtained by comparison, and updating the rigid transformation matrix T according to the number of point cloud pairs obtained by comparison;

[0063] S3.5.6, repeating S3.5.3 to S3.5.5 according to a preset number of iterations until the preset number of iterations is reached, thereby obtaining an updated rigid transformation matrix T;

[0064] S3.5.7, applying the updated rigid transformation matrix T to all source point clouds in the source point cloud set P for transformation processing to complete the preliminary registration processing of the point cloud real scene model and the point cloud design model.

[0065] For example, in S3.5.1, some point cloud pairs are selected at key positions of the arch rib, such as the arch top, arch foot and the like. These point cloud pairs appear to be at similar positions in the two models, but there may be a certain deviation.

[0066] For example, in S3.5.2, the normal vectors of the point clouds in the selected multiple point clouds are calculated, and the eigenvalues ​​of each point cloud pair are calculated using a suitable algorithm (such as estimation based on the local geometric shape of the point cloud). Assume that the normal vectors of one of the point cloud pairs (p1, q1) are and The eigenvalue f1=0.99 of this set of point cloud pairs is obtained by an eigenvalue calculation method (such as the cosine value of the normal vector angle, etc.).

[0067] For example, in S3.5.3, a point cloud pair is randomly selected from multiple point cloud pairs. Assume that (p3, q3) is selected, and its eigenvalue is f3 = 0.98. The rigid transformation matrix T is constructed based on the eigenvalues ​​corresponding to the selected point cloud pair, where the rotation matrix R and the translation vector t are calculated using the relevant formula,

[0068]

[0069] So that q3=R·p3+t holds within a certain error range (the error range here can be set according to actual needs, such as the eigenvalue error is less than 0.01, etc.).

[0070] For example, in S3.5.4, the rigid transformation matrix T is applied to all source point clouds in the source point cloud set P to perform transformation processing, and the transformed source point cloud set P' is obtained. For each transformed source point cloud P' in P' i , calculate the target point cloud q that belongs to the same point cloud pair with the target point cloud set Q i The distance difference Δd between i =‖P′ i -q i ‖. For example, for P′4 and q4, Δd is calculated i =0.03m.

[0071] For example, in S3.5.5, each distance difference is compared with a preset difference (assuming the preset difference is 0.05m) to obtain the number of point cloud pairs corresponding to distance differences less than the preset difference. Assume that after comparison, there are 30 groups of point cloud pairs with distance differences less than 0.05m. Based on the information of these 30 groups of point cloud pairs, the rigid transformation matrix is ​​updated (the update method can adopt a strategy such as weighted averaging, assigning different weights to adjust R and t according to the degree of matching of the point cloud pairs).

[0072] For example, in S3.5.6, the steps of randomly selecting point cloud pairs, constructing the rigid transformation matrix, transforming the source point cloud, calculating the distance difference, and updating the matrix are repeated according to a preset number of iterations (assuming 10). In each iteration, the rigid transformation matrix is ​​continuously adjusted, so that the matching degree between the point cloud real scene model and the point cloud design model is gradually improved.

[0073] For example, in S3.5.7, the updated rigid transformation matrix T is obtained after reaching the preset iteration number of 10 times. The updated rigid transformation matrix T is applied to all source point clouds in the source point cloud set P for final transformation processing, at which time the point cloud real scene model and the point cloud design model are approximately coincident, and the preliminary registration of the point cloud real scene model and the point cloud design model is completed, providing a better basis for subsequent accurate registration.

[0074] The advantage of embodiment 5 is that by reasonably selecting the source point cloud and target point cloud set, and based on a series of steps such as randomly selecting point cloud pairs, calculating eigenvalues, constructing a rigid transformation matrix, applying transformation, calculating distance difference, updating the matrix and repeating iteration, the best transformation matrix for preliminary registration can be effectively found in the case of low correlation of point cloud models, so that the point cloud real scene model and the point cloud design model are approximately coincident, providing a good initial condition for subsequent accurate registration, and improving the accuracy and stability of the entire registration process.

[0075] Embodiment 6: In S3.6, the point cloud real scene model and the point cloud design model after preliminary registration are accurately registered by the ICP iterative closest point algorithm, including:

[0076] S3.6.1, selecting point cloud pairs containing eigenvalues c from all point cloud pairs of the point cloud real scene model and the point cloud design model after preliminary registration, constructing a feature data set by the eigenvalues corresponding to each point cloud in the selected point cloud pairs, the feature data set including x p ,c∈x p and x q ,c∈x q , wherein x p ,c and x q ,c are matching feature point pairs;

[0077] S3.6.2, according to the error function, the error of the eigenvalues of the source point cloud and the eigenvalues of the target point cloud in the feature data set is represented, and the error function is;

[0078]

[0079] wherein R is the rotation matrix of the source point cloud when performing transformation, t is the translation vector of the source point cloud when performing transformation, x p,h and x q,h are the hth eigenvalues in the matching feature point pairs, and a is the number of matching feature point pairs,

[0080] Solve the error function by matrix singular value decomposition method to obtain rotation matrix R and translation vector t to minimize the error function; solve the error function by matrix singular value decomposition method to obtain rotation matrix R and translation vector t to minimize the error function. In actual calculation, the data in the feature data set is substituted into the error function, and the matrix singular value decomposition algorithm is used to perform complex mathematical calculation, and the values of R and t are adjusted step by step until the R and t combination that minimizes the error function is found.

[0081] S3.6.3, transform the feature values of the source point cloud based on the obtained rotation matrix R and translation vector t p After transformation, the feature values x p After updating the feature values x

[0082] S3.6.4, based on the updated feature values x p , calculate the average distance between all matched feature point pairs;

[0083] Transform the feature values of the source point cloud based on the obtained rotation matrix R and translation vector t. For the feature values of the source point cloud in the feature data set, the updated feature values xp,c' are calculated by ||xp,c'=Rpc+t|| (where xp,c is the coordinates of the source point cloud in the dimension related to feature value c). Then, the average distance between all matched feature point pairs is calculated. The specific calculation method is to calculate the distance (such as Euclidean distance ||xp,c'-xp,c||) between each matched feature point pair (xp,c', xp,c), then sum all these distances and divide by the number of matched feature point pairs a to obtain the average distance

[0084] S3.6.5, if the average distance is less than a set threshold, the optimization target is completed, and the calculation process stops; otherwise, return to S3.6.1 to continue iterative calculation until convergence, and output the optimal transformation matrix T best , so as to complete the accurate registration processing of the point cloud real scene model and the point cloud design model.

[0085] Set a threshold (for example, 0.05 meters), and compare the calculated average distance with the threshold. If the average distance is less than the set threshold, it means that the optimization target has been completed, and the transformation matrix composed of the rotation matrix R and the translation vector t at this time has been able to make the two point cloud models achieve high registration accuracy, and the calculation process stops. If the average distance is not less than the threshold, return to the step of screening the feature point cloud pairs to construct the data set, continue iterative calculation, re-solve the error function, update the feature values of the source point cloud, and calculate the average distance, until the average distance is less than the threshold, and output the optimal transformation matrix T bestThus, the accurate registration of the point cloud models of the stone arch bridges in two different periods is completed, the coincidence accuracy of the two models in space reaches a high level, and an accurate basis is provided for subsequent posture difference detection.

[0086] The advantages of embodiment 6 are that: by screening feature points to construct a feature dataset, using an error function to represent the error of feature values of the source point cloud and the target point cloud, and using matrix singular value decomposition method to solve the rotation matrix and the translation vector to minimize the error, the average distance is continuously iteratively calculated and compared with the set threshold, and finally the accurate registration of the point cloud real scene model and the point cloud design model is realized, the accurate alignment of the models in space is ensured, a high-precision model basis is provided for subsequent posture difference detection, and the accuracy of virtual acceptance is effectively improved.

[0087] Embodiment 7: in S3.7, the posture difference detection is performed on the accurately registered point cloud real scene model and the point cloud design model by KNN neighbor algorithm, and a second shape difference result is obtained, including:

[0088] S3.7.1, normal vector calculation is performed on all source point clouds and all target point clouds in the accurately registered point cloud real scene model and the point cloud design model, and the calculated normal vectors are taken as the feature descriptors of the point clouds; for example, for the source point cloud p1 in the point cloud real scene model, the normal vector is for the target point cloud q1 in the point cloud design model, the normal vector is

[0089] S3.7.2, for the feature descriptor of each source point cloud, the Euclidean distance formula is used to calculate the distance between the feature descriptor of each source point cloud and the feature descriptors of all points in the target point cloud, and the difference value between the feature descriptors is obtained:

[0090] d(p i ,q j )=||f(p i )-f(q j )||,

[0091] wherein d(p i ,q j ) is the difference value, f(p i ) and f(q j ) are the feature descriptors of the source point cloud p i ∈P and the target point cloud q j ∈Q respectively,

[0092] the nearest K target points q j are selected, that is, the K nearest target points q iThe K most similar points; for example, select the K target points with the smallest difference value, assuming K = 3 here, select target points q1, q2, q3 as the most similar points to source point cloud p1

[0093] S3.7.3, for each source point cloud p i and its corresponding K target points q j , calculate the straight-line distance between the source point cloud p i and the K target points q j in space by the Euclidean distance formula:

[0094] δ(p i -q j ) = ‖p i -q j ‖,

[0095] where δ(p i -q j ) is the straight-line distance;

[0096] S3.7.4, calculate the mean square error value and the average deviation value of the straight-line distance between each source point cloud p i and the K target points q j by the mean square error algorithm, respectively, to obtain the second shape difference result. For example, the straight-line distances between source point cloud p1 and target points q1, q2, q3 are calculated as d1 = 0.05 m, d2 = 0.06 m, and d3 = 0.04 m, respectively, and the mean square error value is: The average deviation value is (d1 + d2 + d3) / 3.

[0097] Through the above process, the shape difference information between the models can be comprehensively and meticulously obtained, providing rich data for accurate evaluation of the manufacturing precision of the prefabricated steel pipe arch rib, and helping to more accurately judge whether the arch rib meets the design and construction requirements. At the same time, difference detection can also be performed through methods such as calculating the steel pipe diameter deviation and fitting the arch axis function curve for comparison, further enhancing the comprehensiveness and accuracy of model difference evaluation. For example, feature points representing the diameter of the steel pipe are selected from the point cloud, and the diameter deviation of the steel pipe in the point cloud real scene model and the point cloud design model is calculated; or the vertical coordinate data of the center points of each cross section under the same horizontal coordinate in the real scene point cloud model and the design point cloud model is extracted, and the arch axis function curve is fitted to compare the two curves to judge the difference in overall shape between the design model and the actual model.

[0098] The advantages of Example 7 are: by calculating the normal vectors of all source and target point clouds in the precisely registered model as feature descriptors, using Euclidean distance to calculate the difference between feature descriptors and the straight-line distance between the source and target point clouds, and further calculating the mean square error and mean deviation, the model's posture differences are detected from multiple dimensions. This allows for comprehensive and detailed information on the appearance differences between the models, providing rich data for accurately assessing the fabrication accuracy of prefabricated steel tube arch ribs and helping to more accurately determine whether the arch ribs meet design and construction requirements. Furthermore, this method is not limited to the KNN neighbor algorithm and can also perform difference detection through methods such as calculating steel tube diameter deviation and comparing fitted arch axis function curves, providing multiple analysis angles and further enhancing the comprehensiveness and accuracy of model difference assessment.

[0099] This embodiment is not limited to detecting the posture difference between the point cloud real scene model and the point cloud design model after accurate registration by the KNN neighbor algorithm. The difference detection can also be performed by the following methods:

[0100] For example, to further analyze discrepancies between models, characteristic points that can be used to calculate the diameter of a steel pipe are selected from the point cloud. These points can be used to calculate the diameter deviation between the steel pipe in the point cloud reality model and the point cloud design model. This process allows for the assessment of specific dimensional differences between the design and the actual model, particularly for structural components like steel pipes, which require high precision.

[0101] For another example, a more detailed comparison can be made by extracting the vertical coordinate data of the centroid points of each cross section of the actual point cloud model and the design point cloud model, along the same horizontal coordinates. These centroid vertical coordinate data are then fitted using the least squares method to obtain the arch axis function curve for each model. These two fitted arch axis function curves can be used for comparative acceptance to determine the overall shape differences between the design model and the actual model. The least squares method is a commonly used mathematical method that minimizes the error between the fitted curve and the data points to obtain the curve that best fits the data trend.

[0102] Example 8: Figure 2 As shown, an embodiment of the present invention further provides a virtual acceptance device for steel pipe arch ribs, comprising:

[0103] A data import module is used to import multi-directional arch rib point cloud data, wherein the multi-directional arch rib point cloud data is obtained by performing multi-directional scanning of prefabricated steel pipe arch ribs using a three-dimensional laser scanner pre-installed in the site;

[0104] a model construction module, configured to construct a BIM real scene model and a point cloud real scene model according to the multi-orientation arch rib point cloud data, construct a BIM design model and a point cloud design model according to prefabricated steel pipe arch rib drawing data, and merge the BIM real scene model and the BIM design model into the same file and merge the point cloud real scene model and the point cloud design model into the same file;

[0105] a virtual acceptance module, configured to perform qualitative analysis on the BIM real scene model and the BIM design model in the same file to obtain a first shape difference result, and perform quantitative analysis on the point cloud real scene model and the point cloud design model in the same file to obtain a second shape difference result, so as to complete virtual acceptance of the steel pipe arch rib.

[0106] Embodiment 9: in the virtual acceptance module, the qualitative analysis on the BIM real scene model and the BIM design model in the same file to obtain the first shape difference result comprises:

[0107] the BIM real scene model and the BIM design model in the same file are imported into a BIM design tool as a same BIM file, and the BIM real scene model and the BIM design model are superimposed to obtain a common spatial coordinate system;

[0108] an arch rib bottom plane is selected in the BIM real scene model and the BIM design model based on the spatial coordinate system respectively, the arch rib bottom plane comprises an upper circular part, a lower circular part and a linking part between the two circular parts, a feature triangle corresponding to the BIM real scene model and a feature triangle corresponding to the BIM design model are constructed with the center of the upper circular part as a center and the lengths of three sides as constant values;

[0109] three feature points of the feature triangle corresponding to the BIM real scene model and the feature triangle corresponding to the BIM design model are taken as three feature point pairs, and the positions of end planes of the BIM real scene model and the BIM design model in space are determined based on the principle of determining the position of a plane in space based on three points not on the same straight line and superimposed;

[0110] the difference set of the superimposed BIM real scene model and the BIM design model is calculated by using a Boolean operation method, and the first shape difference result is obtained according to the difference set.

[0111] Embodiment 10: the embodiment of the present application further provides a virtual acceptance device for a steel pipe arch rib, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the virtual acceptance method for the steel pipe arch rib.

[0112] The advantages of the present application are: 1) in the aspect of model construction, the BIM real scene model and the point cloud real scene model are constructed according to the multi-azimuth arch rib point cloud data, which can highly restore the real state of the prefabricated steel pipe arch rib. At the same time, the BIM design model and the point cloud design model are constructed by importing the prefabricated steel pipe arch rib drawing data, so that the actual constructed model and the design model have a direct comparison basis. The model constructed in this way is not only accurate in geometric shape, but also can realize complete mapping from design to actuality in the information level, which is helpful for comprehensive evaluation of the prefabricated steel pipe arch rib at each stage;

[0113] 2) in the aspect of acceptance analysis, qualitative and quantitative analysis are combined, the BIM real scene model and the BIM design model are qualitatively analyzed, the first shape difference result is obtained by calculating the difference set of the two through Boolean operation, the approximate difference in shape, size and the like between the design model and the actual model can be quickly and intuitively judged, such as whether there is obvious omission, deformation and the like, so that the appearance quality of the prefabricated steel pipe arch rib is grasped as a whole;

[0114] The point cloud real scene model and the point cloud design model are quantitatively analyzed, the accurate deviation between the models is calculated by using RANSAC, ICP and KNN algorithms, including the spatial position deviation between the point cloud pairs, the steel pipe diameter deviation and the arch axis function curve comparison, so as to obtain the second shape difference result. This quantitative analysis can provide specific numerical value and accurately evaluate the manufacturing precision of the prefabricated steel pipe arch rib, such as the deviation size at each position, so as to provide detailed data support for quality control. The combination of qualitative and quantitative analysis realizes comprehensive and in-depth evaluation of the prefabricated steel pipe arch rib, so that the acceptance result is more scientific and reliable.

[0115] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0117] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the embodiments of the apparatus described above are merely schematic, and the division of units is merely logical function division, and there can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In this way, the actual division of units can be different from the division in the embodiment.

[0118] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0119] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0120] The above description is merely preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A virtual acceptance method for steel tube arch ribs, characterized in that: The steps include: S1. Use a 3D laser scanner pre-installed on site to perform multi-directional scanning on the prefabricated steel tube arch ribs to obtain multi-directional arch rib point cloud data; S2. Constructing a BIM real-scene model and a point cloud real-scene model based on the multi-directional arch rib point cloud data, and constructing a BIM design model and a point cloud design model based on the prefabricated steel pipe arch rib drawing data, and merging the BIM real-scene model and the BIM design model into the same file, and merging the point cloud real-scene model and the point cloud design model into the same file; S3. Performing a qualitative analysis on the BIM real-scene model and the BIM design model in the same file to obtain a first shape difference result, and performing a quantitative analysis on the point cloud real-scene model and the point cloud design model in the same file to obtain a second shape difference result, thereby completing the virtual acceptance of the steel tube arch rib; The qualitative analysis of the BIM real-scene model and the BIM design model in the same file to obtain a first appearance difference result includes: S3.

1. Overlap the BIM real-scene model and the BIM design model in the same file to obtain a common spatial coordinate system; S3.

2. Select an arch rib bottom plane in each of the BIM real-life model and the BIM design model based on the spatial coordinate system. The arch rib bottom plane includes two upper and lower circles and a connecting portion between the two circles. Construct a characteristic triangle corresponding to the BIM real-life model and a characteristic triangle corresponding to the BIM design model using the center of the upper circle in the arch rib bottom plane as the centroid and the lengths of the three sides as fixed values. S3.

3. Using the characteristic triangle corresponding to the BIM real-life model and the three corners of the characteristic triangle corresponding to the BIM design model as three characteristic point pairs, and based on the principle that three non-collinear points determine the position of a plane in space, determine the positions of the end planes of the BIM real-life model and the BIM design model in space and make them coincide with each other using the three characteristic point pairs; S3.

4. Calculate the difference between the overlapped BIM real-scene model and the BIM design model using a Boolean operation method, and obtain a first appearance difference result based on the difference.

2. The virtual acceptance method according to claim 1, characterized in that: In S2, constructing a BIM real-scene model and a point cloud real-scene model based on the multi-directional arch rib point cloud data, constructing a BIM design model and a point cloud design model based on the prefabricated steel pipe arch rib drawing data, and merging the BIM real-scene model and the BIM design model into the same file, and merging the point cloud real-scene model and the point cloud design model into the same file, including: S2.

1. Importing the multi-directional arch rib point cloud data into a point cloud analysis tool for data splicing to obtain point cloud models of multiple prefabricated arch rib segments, virtually assembling the point cloud models of the multiple prefabricated arch rib segments to obtain a point cloud reality model of the prefabricated steel tube arch ribs, and converting the point cloud reality model into a BIM reality model using a BIM design tool; S2.

2. Importing prefabricated steel tube arch rib drawing data, inputting the prefabricated steel tube arch rib drawing data into a BIM design tool to generate a BIM design model, and converting the BIM design model into a point cloud design model using the point cloud analysis tool; S2.

3. Import the BIM real-scene model and the BIM design model into a BIM design tool as the same BIM file to merge them into the same file; import the point cloud real-scene model and the point cloud design model into a point cloud analysis tool as the same point cloud file to merge them into the same file.

3. The virtual acceptance method according to claim 1, characterized in that: In S3, the point cloud reality model and the point cloud design model in the same file are quantitatively analyzed to obtain a second appearance difference result, including: S3.

5. Select multiple point cloud pairs corresponding to the same position in the point cloud reality model and the point cloud design model in the same file, and perform preliminary registration processing on the selected multiple point cloud pairs using the RANSAC random sampling consensus algorithm; S3.

6. Use the ICP iterative closest point algorithm to accurately align the initially aligned point cloud real scene model and the point cloud design model. S3.

7. Use the KNN neighbor algorithm to detect the posture difference between the accurately aligned point cloud real scene model and the point cloud design model to obtain the second appearance difference result.

4. The virtual acceptance method according to claim 3, characterized in that: In S3.5, a plurality of point cloud pairs corresponding to the same position are selected from the point cloud reality model and the point cloud design model, and a preliminary registration process is performed on the selected plurality of point cloud pairs using a RANSAC random sampling consensus algorithm, including: S3.5.

1. Assemble all source point clouds from the point cloud design model as source point clouds to obtain a source point cloud set P. Assemble all target point clouds from the point cloud reality model as target point clouds to obtain a target point cloud set Q. Select multiple source point clouds and target point clouds corresponding to the same location to obtain multiple point cloud pairs. S3.5.

2. Calculate normal vectors for the point clouds in the multiple point cloud groups to obtain eigenvalues ​​for each point cloud pair; S3.5.

3. Randomly select a point cloud pair from multiple point cloud pairs, denoted as Among them, p i is the source point cloud, p i ∈P,q i is the target point cloud, q i ∈Q, K is the number of point cloud pairs; S3.5.

4. Construct a rigid transformation matrix T based on the eigenvalues ​​corresponding to the selected point cloud pair. The rigid transformation matrix T includes a rotation matrix R and a translation vector t, such that: T(P i )=R·p i +t≈q i ; S3.5.

5. Apply a rigid transformation matrix T to all source point clouds in the source point cloud set P to perform transformation processing to obtain a transformed source point cloud set P', wherein the source point cloud set P' is composed of the transformed source point clouds, and calculate the distance difference between each transformed source point cloud in the source point cloud set P' and a target point cloud belonging to the same point cloud pair in the target point cloud set Q, compare each distance difference with a preset difference, obtain point cloud pairs corresponding to distance differences less than the preset difference, count the number of point cloud pairs obtained by comparison, and update the rigid transformation matrix T based on the number of point cloud pairs obtained by comparison; S3.5.

6. Repeat S3.5.3 to S3.5.5 according to a preset number of iterations until the preset number of iterations is reached, thereby obtaining an updated rigid transformation matrix T; S3.5.

7. Apply the updated rigid transformation matrix T to all source point clouds in the source point cloud set P for transformation processing, thereby completing the preliminary registration processing of the point cloud real scene model and the point cloud design model.

5. The virtual acceptance method according to claim 4, characterized in that: In S3.6, the point cloud real scene model and the point cloud design model after preliminary registration are accurately registered using the ICP iterative closest point algorithm, including: S3.6.

1. After preliminary registration, select point cloud pairs containing eigenvalues ​​c from all point cloud pairs of the point cloud reality model and the point cloud design model. Construct a feature dataset using the eigenvalues ​​corresponding to each point cloud in the selected point cloud pairs. The feature dataset includes x p ,c∈x p and x q ,c∈x q , where x p ,c and x q ,c is the matching feature point pair; S3.6.

2. Express the error between the source point cloud eigenvalue and the target point cloud eigenvalue in the feature data set according to an error function, wherein the error function is: Among them, R is the rotation matrix of the source point cloud during transformation, t is the translation vector of the source point cloud during transformation, and x p,h and x q,h are the hth eigenvalues ​​in the matching feature point pairs, a is the number of matching feature point pairs, Solving the error function by a matrix singular value decomposition method to obtain a rotation matrix R and a translation vector t to minimize the error function; S3.6.

3. Based on the obtained rotation matrix R and translation vector t, the eigenvalue x of the source point cloud p After transformation, we get x p ,h is the eigenvalue after update; S3.6.4, based on x p ,h updated feature value, calculate the average distance between all matching feature point pairs; S3.6.

5. If the average distance is less than the set threshold, the optimization goal has been achieved and the calculation process stops; otherwise, return to S3.6.1 to continue the iterative calculation until convergence, and output the optimal transformation matrix, thereby completing the precise registration processing of the point cloud real scene model and the point cloud design model.

6. The virtual acceptance method according to claim 5, characterized in that: In S3.7, the pose difference detection is performed on the accurately registered point cloud real scene model and the point cloud design model using the KNN neighbor algorithm to obtain a second appearance difference result, including: S3.7.

1. Calculate normal vectors for all source point clouds and all target point clouds in the accurately registered point cloud reality model and the point cloud design model, and use the calculated normal vectors as feature descriptors for each point cloud; S3.7.

2. For each feature descriptor of the source point cloud, calculate the distance between the feature descriptor of each source point cloud and the feature descriptors of all points in the target point cloud using the Euclidean distance formula to obtain the difference between the feature descriptors: d(p i ,q j )=||f(p i )-f(q j )||, Among them, d(p i ,q j ) is the difference value, f(p i ) and f(q j ) are the source point clouds p i ∈P and target point cloud q j ∈Q feature descriptor, Select the nearest K target points q j , that is, select the i The most similar K points; S3.7.

3. For each source point cloud p i and its corresponding K target points q j , calculate the source point cloud p by the Euclidean distance formula i and K target points q j Straight-line distance in space: δ(p i -q j )=||p i -q j ||, Among them, δ(p i -q j ) is the straight-line distance; S3.7.

4. Calculate each source point cloud p separately using the mean square error algorithm i and K target points q j The mean square error value and the average deviation value of the straight-line distance are calculated to obtain the second appearance difference result.

7. A virtual acceptance device for steel tube arch ribs, characterized in that: include: A data import module is used to import multi-directional arch rib point cloud data, wherein the multi-directional arch rib point cloud data is obtained by performing multi-directional scanning of prefabricated steel pipe arch ribs using a three-dimensional laser scanner pre-installed in the site; a model construction module, configured to construct a BIM real-scene model and a point cloud real-scene model based on the multi-directional arch rib point cloud data, and to construct a BIM design model and a point cloud design model based on the prefabricated steel pipe arch rib drawing data, and to merge the BIM real-scene model and the BIM design model into the same file, and to merge the point cloud real-scene model and the point cloud design model into the same file; A virtual acceptance module is used to perform a qualitative analysis on the BIM real-scene model and the BIM design model in the same file to obtain a first shape difference result, and to perform a quantitative analysis on the point cloud real-scene model and the point cloud design model in the same file to obtain a second shape difference result, thereby completing the virtual acceptance of the steel tube arch rib; The qualitative analysis of the BIM real-scene model and the BIM design model in the same file to obtain a first appearance difference result includes: Importing the BIM real-scene model and the BIM design model in the same file into a BIM design tool as the same BIM file, and overlapping the BIM real-scene model and the BIM design model to obtain a common spatial coordinate system; Based on the spatial coordinate system, an arch rib bottom plane is selected in the BIM real scene model and the BIM design model respectively, wherein the arch rib bottom plane includes two upper and lower circles and a middle connecting portion of the two circles, and a characteristic triangle corresponding to the BIM real scene model and the characteristic triangle corresponding to the BIM design model are constructed with the center of the upper circle in the arch rib bottom plane as the centroid and the lengths of the three sides as fixed values; The characteristic triangle corresponding to the BIM real scene model and the three corners of the characteristic triangle corresponding to the BIM design model are used as three characteristic point pairs. Based on the principle that three points that are not in the same straight line determine the position of a plane in space, the positions of the end planes of the BIM real scene model and the BIM design model in space are determined and overlapped by the three characteristic point pairs. A Boolean operation method is used to calculate a difference set between the overlapped BIM real-scene model and the BIM design model, and a first appearance difference result is obtained according to the difference set.

8. A virtual acceptance device for steel tube arch ribs, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for virtual acceptance of the steel tube arch rib according to any one of claims 1 to 6 is implemented.

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