Masonry arch bridge deformation detection method and device

Abstract

The invention provides a masonry arch bridge deformation detection method and device, and relates to the technical field of bridge engineering construction. The method comprises the following steps of: scanning a masonry arch bridge by using a three-dimensional laser scanner according to a set period to obtain point cloud data, and constructing a point cloud model and a BIM three-dimensional real scene model according to the point cloud data; main arch ring deformation qualitative analysis is carried out through a BIM model to obtain qualitative data, quantitative analysis is carried out through a point cloud model to obtain quantitative data, deformation is evaluated according to bridge design specifications and safety standards, and a report is generated. The complete process from data acquisition, model construction, difference analysis to final deformation evaluation is covered, the deformation condition of the masonry arch bridge can be comprehensively and deeply evaluated, and a scientific basis is provided for maintenance and management of the bridge; compared with a traditional detection method, the detection method based on the three-dimensional laser scanning technology and the BIM model has higher precision and efficiency, the deformation part and degree can be more accurately positioned, and making of more targeted maintenance and repair strategies is facilitated.

Description

Technical Field

[0001] The invention mainly relates to the technical field of bridge engineering construction, and in particular to a deformation detection method and device for a masonry arch bridge. Background Art

[0002] As a typical representative of traditional bridge types in my country, masonry arch bridges carry rich historical and cultural values. They have the advantages of solid structure and beautiful appearance. They have played an important role in transportation construction in the past and have high bearing potential. However, the design and construction technology level of early masonry arch bridges was relatively limited. Most of them used expanded foundations. Some bridges were easily affected by uneven foundation settlement and base scouring, resulting in deformation of the main arch ring, which seriously affected the safety of the bridge.

[0003] At present, the monitoring methods for foundation settlement and arch deformation are relatively simple and not accurate enough. For example, the commonly used total station or tunnel profiler detection methods can only measure limited points at the bottom of the arch, while the surface of the main arch is uneven. This local point detection method has a large error and is difficult to fully and accurately reflect the deformation of the entire arch axis.

[0004] The inability to accurately grasp the deformation of masonry arch bridges, especially the deformation of the arch axis and the uneven settlement of the foundation, has brought great difficulties to the safety assessment and maintenance of masonry arch bridges. If potential deformation problems cannot be discovered and dealt with in a timely manner, the structural safety of masonry arch bridges will be seriously threatened as time goes by and traffic loads increase, resulting in a decrease in the bridge's bearing capacity and even causing safety accidents such as bridge collapse.

[0005] Therefore, there is an urgent need for a method that can accurately and comprehensively detect the deformation of masonry arch bridges to ensure the safe operation of masonry arch bridges and the long-term preservation of their historical and cultural values. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a deformation detection method and device for a masonry arch bridge in view of the deficiencies in the prior art.

[0007] The technical solution of the present invention to solve the above technical problem is as follows: A deformation detection method for a masonry arch bridge comprises the following steps:

[0008] S1. Scanning the masonry arch bridge to be inspected by a three-dimensional laser scanner pre-installed at a scanning site according to a set scanning period to obtain point cloud data of the masonry arch bridge corresponding to multiple different periods;

[0009] S2. Constructing a point cloud model and a BIM three-dimensional real-scene model of the masonry arch bridge in the corresponding period based on the point cloud data of each masonry arch bridge;

[0010] S3. Based on the BIM three-dimensional real-scene models corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is qualitatively analyzed to obtain qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge, and based on the point cloud models of the masonry arch bridge corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is quantitatively analyzed to obtain quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge;

[0011] S4. Conducting a safety assessment on the masonry arch bridge based on the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge and the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge.

[0012] Another technical solution of the present invention to solve the above technical problem is as follows: A deformation detection device for a masonry arch bridge, comprising:

[0013] The scanning module is used to scan the masonry arch bridge to be inspected by a three-dimensional laser scanner pre-installed at the scanning site according to the set scanning period and set temperature requirements, so as to obtain point cloud data of the masonry arch bridge corresponding to multiple different periods;

[0014] Model building module, used to build the point cloud model and BIM 3D real scene model of the masonry arch bridge of the corresponding period based on the point cloud data of each masonry arch bridge;

[0015] An analysis module is used to perform a qualitative data analysis on the deformation of the main arch ring of the masonry arch bridge based on the BIM three-dimensional real-scene model corresponding to different periods, and obtain the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge; and to perform a quantitative data analysis on the deformation of the main arch ring of the masonry arch bridge based on the point cloud model of the masonry arch bridge corresponding to different periods, and obtain the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge;

[0016] The evaluation module is used to perform deformation evaluation on the deformation qualitative analysis data of the main arch ring of the masonry arch bridge and the deformation quantitative analysis data of the main arch ring of the masonry arch bridge based on bridge design specifications and safety standards, and generate a deformation evaluation report of the masonry arch bridge.

[0017] Another technical solution of the present invention to solve the above-mentioned technical problem is as follows: a deformation detection device for a masonry arch bridge, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the deformation detection method for a masonry arch bridge as described above is implemented.

[0018] Another technical solution of the present invention to solve the above technical problem is as follows: a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the deformation detection method of the masonry arch bridge as described above is implemented.

[0019] The beneficial effects of the present invention are as follows: the detection method covers the complete process from data collection, model construction, difference analysis to final deformation assessment. Each step is closely linked and logically clear, forming a systematic deformation detection system for masonry arch bridges, which can comprehensively and deeply assess the deformation of masonry arch bridges and provide a scientific basis for the maintenance and management of bridges; the detection method based on three-dimensional laser scanning technology and BIM model has higher accuracy and efficiency than traditional detection methods, can more accurately locate the deformation site and degree, and is helpful to formulate more targeted maintenance and repair strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a deformation detection method for a masonry arch bridge provided in an embodiment of the present invention;

[0021] Figure 2 A schematic connection diagram of the functional modules of the deformation detection device for a masonry arch bridge provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0023] The construction of a masonry arch bridge started in 1985 and was completed on June 28, 1989. The total length of the bridge is 290.57 meters, the total width of the bridge deck is 12.54 meters, and it is a mortar-bowl-section masonry slab arch bridge. The upper structure is a 6×40.00m variable-section mortar-laid masonry slab arch, with a clear rise of 5.00m, a clear rise-span ratio of 1 / 8, and an arch axis coefficient of 3.500. The 0# abutment in the lower structure is a combined abutment, with a bored pile foundation in front of the abutment and an expanded foundation behind the abutment; the 6# abutment is a gravity abutment with an open-cut expanded foundation; the piers are gravity piers with an open-cut expanded foundation.

[0024] The Dongmen Bridge received a score of 59.4 points in the regular inspection of its overall technical condition in 2016, which is classified as a Class 4 bridge (dangerous bridge), which is a poor grade. The technical condition is described as major defects in the main components, which seriously affect the bridge's use function or the bridge's bearing capacity, and the bridge cannot be guaranteed to be used normally. Therefore, signs such as traffic restrictions, height restrictions, speed restrictions, and load restrictions are set on the bridge or at the bridgehead. In 2018, a reinforced concrete arch bridge scheme and a prestressed concrete continuous beam bridge scheme were respectively made.

[0025] The bridge was modeled using a 3D laser scanner in 2016. In 2023, it was decided to reinforce the bridge.

[0026] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0027] Example 1: Figure 1 As shown, an embodiment of the present invention provides a deformation detection method for a masonry arch bridge, comprising the following steps:

[0028] S1. Scanning the masonry arch bridge to be inspected by a three-dimensional laser scanner pre-installed at a scanning site according to a set scanning period to obtain point cloud data of the masonry arch bridge corresponding to multiple different periods;

[0029] S2. Constructing a point cloud model and a BIM three-dimensional real-scene model of the masonry arch bridge in the corresponding period based on the point cloud data of each masonry arch bridge;

[0030] S3. Based on the BIM three-dimensional real-scene models corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is qualitatively analyzed to obtain the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge, and based on the point cloud models of the masonry arch bridge corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is quantitatively analyzed to obtain the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge;

[0031] S4. Perform deformation assessment on the masonry arch bridge based on the qualitative analysis data of deformation of the main arch ring of the masonry arch bridge and the quantitative analysis data of deformation of the main arch ring of the masonry arch bridge.

[0032] Specifically, the set temperature requirement is: the difference in ambient temperature between two consecutive scans of the masonry arch bridge to be inspected does not exceed 3°C, and the ambient temperature change does not exceed 1°C during each inspection. Under the influence of ambient temperature, the main arch ring of the masonry arch bridge itself undergoes significant deformation, so the ambient temperature change is limited.

[0033] Specifically, the principle of setting temperature requirements is that the arch bridge will produce a large deformation under the action of temperature changes. If the temperature difference is large when the masonry arch bridge to be inspected is scanned twice by a 3D laser scanner, the deformation caused by temperature will be greater than the permanent deformation of the arch bridge, so strict requirements are required for the temperature during inspection. Generally, the inspection time is set in the same season, from 0 to 4 o'clock at night on a clear day.

[0034] Step S1 is specifically as follows:

[0035] S1.1. Use a 3D laser scanner to set up scanning stations upstream and downstream of the arch bridge to ensure that the bridge structure information is captured in all directions and without blind spots, and obtain a large amount of point cloud data containing 3D coordinate points, reflection intensity, color and other additional information. It is recommended to conduct a comprehensive 3D laser scanning inspection at least once every two years to monitor the long-term deformation of the bridge.

[0036] S1.2. To avoid the influence of temperature changes on the scanning results, the scanning operation is carried out during the period from 10 pm to 4 am when the temperature is relatively stable.

[0037] Step S4 is specifically as follows:

[0038] Based on the model comparison results, the deformation of each key part is calculated, including the vertical displacement and horizontal displacement of the arch axis; Safety assessment: Combined with the bridge design specifications and safety standards, the deformation is assessed to determine whether maintenance or reinforcement measures are needed.

[0039] For example, the deformation of key parts is calculated as:

[0040] Calculation of vertical displacement of arch axis: After qualitative and quantitative data analysis based on the BIM 3D real-life model and masonry arch bridge point cloud model corresponding to different periods, the vertical coordinate changes of the arch axis in the models of different periods are accurately measured for the key part. The vertical displacement is calculated by comparing the vertical coordinates of the key points of the arch axis (such as the arch crown, arch foot, etc.) in the early scanning model (such as the initial construction period model as a benchmark) and the subsequent monitoring period model. For example, if the vertical coordinate of the arch crown in the initial model is Z1, and the vertical coordinate becomes Z2 in the monitoring model after a certain period of time, the vertical displacement of the arch crown ΔZ=Z2-Z1. The same calculation is performed on multiple key nodes on the arch axis, so as to fully grasp the vertical displacement of the arch axis and evaluate its settlement or uplift under the influence of gravity, traffic load and other factors.

[0041] Calculation of horizontal displacement of arch axis: In addition to vertical displacement, horizontal displacement of arch axis is also an important deformation indicator. Using the three-dimensional coordinate information in the model, measure the position changes of key nodes in the horizontal direction (X-axis and Y-axis direction) of the arch axis. For example, for a point at a specific position on the arch ring, the horizontal coordinates in the early model are (X1, Y1), and later become (X2, Y2). The horizontal displacement of the point is ΔX = X2-X1 in the X-axis direction and ΔY = Y2-Y1 in the Y-axis direction. By calculating and analyzing the horizontal displacement of multiple key nodes, we can understand the movement trend of the arch axis in the horizontal plane and determine whether there is abnormal deformation such as lateral offset, which is crucial for evaluating the overall stability of masonry arch bridges.

[0042] The deformation calculation of other key parts is as follows: In addition to the arch axis, the piers, abutments and other parts of the masonry arch bridge are also the focus of attention. For the piers, it is necessary to calculate the position changes of their top and bottom in the model at different times, covering the vertical and horizontal displacements, and evaluate the settlement and inclination of the piers. The deformation calculation of the abutment includes factors such as the displacement of the abutment body and the relative deformation of the connection with the arch ring. For example, the vertical displacement of the top of the pier may affect the force distribution of the superstructure. By accurately measuring and calculating the deformation of these key parts, the structural state changes of each part of the masonry arch bridge can be fully grasped.

[0043] For example, safety assessment: When abnormal deformation or structural cracks occur in masonry structures, special inspections and assessments should be carried out and timely treatment should be carried out.

[0044] Data processing process description: The data processing process is described in detail, starting from the preprocessing of the original point cloud data (such as noise removal, point cloud filtering, etc.), to the specific methods and steps for qualitative and quantitative data analysis based on models from different periods. In the qualitative analysis, it explains how to import BIM three-dimensional real-life models from different periods into the same file and match the spatial coordinate system, as well as the process and significance of using Boolean operations to calculate the model difference to obtain the qualitative analysis data of the main arch ring deformation. In terms of quantitative analysis, it describes in detail the application of the RANSAC random sampling consistency algorithm, the ICP iterative closest point algorithm, and the KNN neighbor algorithm in point cloud model alignment and posture difference detection, including the algorithm parameter selection, iterative process, and how to finally obtain the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge (such as the deviation value, mean square error value, and average deviation value between point cloud pairs).

[0045] Presentation of deformation analysis results: The deformation analysis results are clearly presented in a combination of charts, graphs and text. The deformation trend diagrams of key parts of masonry arch bridges (such as arch axis, bridge piers, abutments, etc.) at different times are drawn to intuitively show the changes in deformation over time. For example, a line graph of the vertical displacement of the arch over time is drawn, with the horizontal axis being time (in years or the number of tests) and the vertical axis being the vertical displacement. The settlement or uplift trend of the arch can be clearly seen through the trend of the line. At the same time, the deformation values ​​of each key part are listed in detail, compared with the allowable values ​​of the specification one by one, and the parts that exceed the safety range and the parts that are within the safety range but need to continue to pay attention to their changing trends are clearly marked. In addition, combined with the safety assessment results, a comprehensive evaluation of the overall structural health of the masonry arch bridge is conducted to determine whether it can continue to be used normally or whether corresponding maintenance, reinforcement or traffic restrictions are needed.

[0046] Recommended measures: Based on the deformation analysis results and safety assessment conclusions, targeted recommended measures are proposed. If the deformation of the masonry arch bridge is small and within the safety range, but there is a trend of gradual development, it is recommended to shorten the inspection cycle and increase the monitoring frequency so as to timely discover possible problems. For parts where the deformation is close to or exceeds the allowable value, a detailed repair or reinforcement plan is formulated, such as repairing arch ring cracks, strengthening bridge pier foundations, etc., and the project cost and construction period are accurately estimated. At the same time, according to the current status of the masonry arch bridge, reasonable traffic restriction measures are formulated to restrict the passage of overweight vehicles and ensure the safe use of the bridge before repair or reinforcement. At the end of the report, general recommendations can also be made for the future maintenance and management of masonry arch bridges, emphasizing the importance of regular inspection and maintenance, as well as the necessity of establishing a long-term bridge health monitoring system, to provide guidance for the sustainable operation of masonry arch bridges.

[0047] The advantages of Example 1 are: this detection method covers the complete process from data collection, model construction, difference analysis to final deformation assessment. Each step is closely linked and logically clear, forming a systematic deformation detection system for masonry arch bridges, which can comprehensively and deeply evaluate the deformation of masonry arch bridges and provide a scientific basis for the maintenance and management of bridges; the detection method based on three-dimensional laser scanning technology and BIM model has higher accuracy and efficiency than traditional detection methods, can more accurately locate the deformation site and degree, and help to formulate more targeted maintenance and repair strategies.

[0048] Embodiment 2: Preferably, the method further comprises step S5:

[0049] Visualization processing is performed based on the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge to display the deformation detection result of the masonry arch bridge.

[0050] Specifically, the calculated matching point pairs and their deviations are visualized. Usually, the source point cloud and the target point cloud are displayed overlapping, and the matching point pairs are connected with lines. At the same time, different colors can be set according to the size of the deviation to intuitively display the point cloud deviation. In the representation of the lines connecting the matching point pairs, different attributes can be set according to the size of the deviation. For example, the lines connecting the point pairs with smaller deviations can be set to be thinner and lighter in color, such as light blue; while the lines connecting the point pairs with larger deviations can be set to be thicker and darker in color, such as red. In this way, the difference in the degree of deformation of the masonry arch bridge in different parts can be clearly seen through the changes in the thickness and color of the lines. At the same time, the software can also provide interactive functions. Users can rotate, zoom, and translate the three-dimensional model through mouse operations to observe the deformation of the masonry arch bridge from different angles, so as to have a more comprehensive understanding of its structural changes.

[0051] The advantages of Example 2 are: it can intuitively present the deformation detection results. By visualizing the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge, the deformation detection results of the masonry arch bridge can be displayed in an intuitive and easy-to-understand manner. For example, the source point cloud and the target point cloud are displayed overlapping, and the matching point pairs are connected with lines. At the same time, different colors are set according to the size of the deviation, so that the deformation situation is clear at a glance. This visualization method makes it easy for non-professionals (such as bridge managers, etc.) to quickly understand the deformation state of the masonry arch bridge, which helps to improve the efficiency and accuracy of decision-making.

[0052] Embodiment 3: Preferably, in S2, based on the point cloud data of each masonry arch bridge, a point cloud model of the masonry arch bridge and a BIM three-dimensional real scene model of the corresponding period are constructed, including:

[0053] Import the point cloud data of each masonry arch bridge into the point cloud analysis component in sequence according to the order of scanning periods, and generate a point cloud model of the masonry arch bridge corresponding to the period based on the order of scanning periods;

[0054] The point cloud models of masonry arch bridges corresponding to each period are imported into the BIM analysis component for format conversion, and the BIM three-dimensional real-scene models corresponding to each masonry arch bridge point cloud model are obtained.

[0055] The advantages of Example 3 are: it can efficiently build a model, import point cloud data in the order of scanning periods to build a point cloud model of a masonry arch bridge, and the operation process is clear and orderly, which is convenient for managing and tracing data from different periods. This method can effectively utilize point cloud analysis components, improve the efficiency and accuracy of model construction, ensure that point cloud data of each period can be accurately converted into a corresponding point cloud model, and provide a reliable basis for subsequent analysis.

[0056] Embodiment 4: Preferably, in S3, a qualitative data analysis is performed on the deformation of the main arch ring of the masonry arch bridge based on the BIM three-dimensional real-scene model corresponding to different periods, and the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge at different periods are obtained, including:

[0057] S3.1. Import the BIM 3D real scene models corresponding to different periods into the same BIM file, match the various BIM 3D real scene models in the spatial coordinate system of the same BIM file, and make the coordinates of the feature points of the 3D real scene models of the masonry arch bridges in different periods coincide with each other in the spatial coordinate system of the BIM file;

[0058] S3.2. Use the Boolean operation method to calculate the difference sets of the BIM three-dimensional real-scene models in two different periods, so as to obtain each difference set, which is the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge in different periods.

[0059] The advantages of Example 4 are: it can effectively compare and analyze the deformation of the main arch ring, import the BIM three-dimensional real-scene models of different periods into the same file and match them in the spatial coordinate system, so that the coordinates of the characteristic points of the models of each period coincide, laying the foundation for accurately comparing the deformation of the main arch ring of the masonry arch bridge in different periods. This method can accurately locate and analyze the deformation changes of the main arch ring of the masonry arch bridge at different time points, which helps to discover subtle deformation and damage, and provides a basis for taking timely measures.

[0060] The ability to use Boolean operations to obtain qualitative analysis data and use Boolean operations to calculate model differences as qualitative analysis data for the deformation of the main arch ring is an innovative and effective data processing method. In this way, the difference between models in different periods can be directly obtained, which can intuitively reflect the increase or decrease or change in the deformation of the main arch ring of the masonry arch bridge, provide a quantitative basis for the qualitative evaluation of the deformation of the masonry arch bridge, and facilitate the rapid judgment of the overall change trend of the deformation of the main arch ring of the masonry arch bridge.

[0061] Embodiment 5: Preferably, in S3.1, the BIM three-dimensional real scene models corresponding to each period are imported into the same BIM file, and each BIM three-dimensional real scene model is matched in the spatial coordinate system of the same BIM file, so that the coordinates of the feature points of the three-dimensional real scene models of the masonry arch bridge in each period coincide in the spatial coordinate system of the BIM file to which they belong, including:

[0062] In the BIM three-dimensional real-scene model, a triangle with three sides of fixed length is proposed as a feature triangle, and a feature plane of the three-dimensional real-scene models of masonry arch bridges in two different periods is designated (that is, a plane on which the three-dimensional real-scene models of masonry arch bridges in two different periods theoretically completely overlap), and the center of the feature plane is used as the centroid of the feature triangle to make the same feature triangle, and the vertices of each angle of the two feature triangles are used 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 three points that are not in the same straight line determine the position of a plane in space, three pairs of feature points determine the positions of the feature planes of the three-dimensional real-scene models of masonry arch bridges in two different periods in space, and the positions of the three pairs of feature points in space are overlapped, so that the coordinates of the feature points of the three-dimensional real-scene models of masonry arch bridges in two different periods overlap in the spatial coordinate system of the BIM file to which they belong, that is, the positions of the feature planes of the three-dimensional real-scene models of masonry arch bridges in two different periods in space completely overlap, thereby achieving complete overlap of the positions of the three-dimensional real-scene models of masonry arch bridges in space in two different periods. In this way, the coordinates of the feature points of the three-dimensional real-scene models of masonry arch bridges in different periods are made to coincide with each other in the spatial coordinate system of the corresponding BIM file.

[0063] The advantages of Example 5 are: it can accurately match the model feature points, by drafting a feature triangle in the BIM three-dimensional real-scene model, and making the same feature triangle with the center of the feature plane as the centroid, using three pairs of feature points to determine the position of the model feature plane and make them coincide, thereby achieving accurate matching of the model feature points of each period in the spatial coordinate system. This method is based on geometric principles, has high accuracy and reliability, can effectively solve the problem of aligning the spatial positions of models in different periods, and provides an accurate basis for subsequent analysis.

[0064] The method can improve the operability of model matching. It is relatively simple to operate and has clear rules. It does not require complex equipment or a large amount of computing resources. In practical applications, engineers can easily determine feature points and feature triangles on the BIM model according to this method, achieve accurate matching of models at different times, and improve the operability and efficiency of deformation detection of masonry arch bridges.

[0065] Embodiment 6: Preferably, in S3, based on the point cloud models of the masonry arch bridge corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is quantitatively analyzed to obtain the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge, including:

[0066] S3.3, importing the corresponding masonry arch bridge point cloud models of different periods into the same point cloud file;

[0067] S3.4, selecting two masonry arch bridge point cloud models of different periods to be compared, selecting multiple point cloud pairs corresponding to the same position in the two masonry arch bridge point cloud models of different periods, and performing preliminary registration processing on the selected multiple point cloud pairs through the RANSAC random sampling consistency algorithm;

[0068] S3.5, using the ICP iterative closest point algorithm to accurately register the point cloud models of the masonry arch bridges in two different periods after preliminary registration;

[0069] S3.6, using the KNN neighbor algorithm to detect the posture difference of the point cloud models of the masonry arch bridge in two different periods after precise registration;

[0070] S3.7. Repeat steps S3.4 to S3.6 until the posture difference detection of the point cloud model of the masonry arch bridge at different periods is completed, and the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge is obtained.

[0071] It should be understood that in the above embodiment, the registration of the point cloud models of masonry arch bridges in different periods is divided into two steps: coarse registration and precise registration.

[0072] The first step is coarse registration: Since the point cloud models of masonry arch bridges in different periods have low correlation in space, we first manually select feature points with the same position on the point cloud models of masonry arch bridges in different periods (in this step, it is not necessary to select points with the same position accurately, as long as they are roughly corresponding), and use the Random Sample Consensus (RANSAC) algorithm for coarse registration to make the two models roughly overlap; the second step is precise registration: the point cloud models of masonry arch bridges in different periods are precisely registered using the Iterative Closest Point (ICP) algorithm. The point cloud model of masonry arch bridges is essentially a set of spatial data points acquired by a 3D scanning device, and the role of the ICP algorithm is to optimize the alignment between the two point cloud models of masonry arch bridges through continuous iteration, so that the point cloud models of masonry arch bridges in different periods completely overlap in space. In this way, the position and posture of the two point cloud models in space can be ensured to be very accurate.

[0073] The Random Sample Consensus (RANSAC) algorithm is an iterative algorithm that is widely used in point cloud data processing, especially in the presence of a lot of noise or outliers, to estimate the parameters of mathematical models. For example, in point cloud segmentation and registration tasks, the RANSAC algorithm iteratively searches for multiple models in the data, effectively removes mismatched points, and solves the affine transformation matrix. It is often used in data fitting and registration problems, especially in point cloud registration, computer vision, and robotics.

[0074] The advantage of Example 6 is that it can comprehensively and systematically analyze the process of quantitative analysis, covering the complete process from point cloud model import, point cloud object selection, preliminary registration, precise registration to posture difference detection, forming a systematic quantitative analysis system for the deformation of the main arch ring of the masonry arch bridge. Each step is closely connected and progressive, which can comprehensively and deeply evaluate the quantitative changes of the deformation of the main arch ring of the masonry arch bridge at different periods, and provide a scientific and reliable method for accurately grasping the deformation of the masonry arch bridge.

[0075] It can improve the accuracy by using multiple algorithms in a coordinated manner, and comprehensively use multiple advanced algorithms such as RANSAC random sampling consistency algorithm, ICP iterative closest point algorithm and KNN neighbor algorithm to give full play to the advantages of each algorithm. The RANSAC algorithm performs preliminary registration, quickly removes most of the erroneous matching points, and provides good initial conditions for subsequent precise registration; the ICP algorithm further accurately registers the model, so that the model can achieve higher precision overlap in space; the KNN neighbor algorithm detects posture differences and accurately obtains the main arch ring deformation deviation between models. The synergistic effect of multiple algorithms has significantly improved the accuracy and reliability of the quantitative analysis of the deformation of the main arch ring of the masonry arch bridge.

[0076] Embodiment 7: Preferably, in S3.4, multiple point cloud pairs corresponding to the same position are selected from the point cloud models of the masonry arch bridge in two different periods, and a preliminary registration process is performed on the selected multiple point cloud pairs using the RANSAC random sampling consensus algorithm, including:

[0077] S3.4.1. Taking each point cloud of the masonry arch bridge point cloud model in the earlier time period in two different periods as the source point cloud, collecting all the source point clouds to obtain the source point cloud set P, taking each point cloud of the masonry arch bridge point cloud model in the later time period as the target point cloud, collecting all the target point clouds to obtain the target point cloud set Q, selecting multiple source point clouds and target point clouds corresponding to the same position, and obtaining multiple groups of point cloud pairs;

[0078] S3.4.2, calculating normal vectors of point clouds in multiple groups of point clouds to obtain eigenvalues ​​of each group of point cloud pairs;

[0079] S3.4.3. Randomly select a set of point cloud pairs from multiple sets of 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;

[0080] S3.4.4. Construct a rigid transformation matrix T according to the eigenvalues ​​corresponding to the selected point cloud pair, wherein the rigid transformation matrix T includes a rotation matrix R and a translation vector t, such that:

[0081] T(P i )=R·p i +t≈q i ;

[0082] S3.4.5, based on the rigid transformation matrix T applied to all source point clouds in the source point cloud set P for transformation processing, a transformed source point cloud set P' is obtained, wherein the source point cloud set P' is composed of the transformed source point clouds, and 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 is calculated, each distance difference is compared with a preset difference, and point cloud pairs corresponding to distance differences less than the preset difference are obtained, and the number of point cloud pairs obtained by comparison is counted, and the rigid transformation matrix T is updated according to the number of point cloud pairs obtained by comparison;

[0083] S3.4.6, repeat S3.4.3 to S3.4.5 according to a preset number of iterations until the preset number of iterations is reached, thereby obtaining an updated rigid transformation matrix T;

[0084] S3.4.7. Apply the updated rigid transformation matrix T to all the source point clouds in the source point cloud set P for transformation processing to complete the preliminary registration of the point cloud models of the masonry arch bridges in two different periods.

[0085] Here is a specific example:

[0086] 1) Division of source point cloud and target point cloud sets

[0087] For the point cloud models of the masonry arch bridge in two different periods, each point cloud of the point cloud model of the masonry arch bridge in the earlier period is determined as the source point cloud. For example, for the point cloud model of the masonry arch bridge scanned in 2014 (set as period A) and the point cloud model of the masonry arch bridge scanned in 2023 (set as period B), the point cloud of period A is the source point cloud. All source point clouds are collected together to form a source point cloud set P. Similarly, each point cloud of the point cloud model of the masonry arch bridge in the later period (period B) is taken as the target point cloud, and all target point clouds are collected to obtain the target point cloud set Q.

[0088] 2) Point cloud pair selection and normal vector calculation

[0089] In the source point cloud set P and the target point cloud set Q, multiple source point clouds and target point clouds corresponding to the same position (roughly the same position is sufficient) are selected to obtain multiple sets of point cloud pairs. For example, corresponding point clouds are selected at key locations such as arch rings and bridge piers. For each set of point cloud pairs, the normal vectors of the point clouds are calculated to obtain the eigenvalues ​​of each set of point cloud pairs. These eigenvalues ​​will be used to construct the subsequent rigid transformation matrix.

[0090] 3) Randomly select point cloud pairs and construct rigid transformation matrix

[0091] A set of point cloud pairs is randomly selected from multiple sets of point cloud pairs, denoted as where p i is the source point cloud and p i ∈P,q i is the target point cloud and q i ∈Q, K is the number of point cloud pairs. According to the eigenvalues ​​corresponding to the selected set of point cloud pairs, a rigid transformation matrix T is constructed. The rigid transformation matrix T includes the rotation matrix R and the translation vector t, so that T(P i )=R·p i +t≈q i For example, the rotation angle and translation amount are calculated through the eigenvalues, and then the values ​​of R and t are determined.

[0092] 4) Source point cloud transformation and distance difference calculation based on rigid transformation matrix

[0093] The constructed rigid transformation matrix T is applied to all source point clouds in the source point cloud set P for transformation processing to obtain the transformed source point cloud set P'. For each transformed source point cloud in the set P', the distance difference between it and the target point cloud belonging to the same point cloud pair in the target point cloud set Q is calculated. For example, the transformed source point cloud p' is calculated. i And the corresponding target point cloud q i The Euclidean distance between i -q i ‖.

[0094] 5) Point cloud pair statistics and rigid transformation matrix update

[0095] Each distance difference is compared with the preset difference to obtain the point cloud pairs corresponding to the distance difference less than the preset difference. The number of these point cloud pairs that meet the conditions is counted, and the rigid transformation matrix T is updated according to the number of point cloud pairs obtained by comparison. For example, if the number of point cloud pairs that meet the conditions obtained in this iteration is greater than that in the previous iteration, it means that the effect of matrix T is better, and T is optimized to make it closer to the optimal solution.

[0096] 6) Iteration process and preliminary registration completed

[0097] According to the preset number of iterations (such as 100 times), the above steps 3 to 5 are repeated until the preset number of iterations is reached. After multiple iterations, the updated rigid transformation matrix T is obtained. Finally, the updated rigid transformation matrix T is applied to all source point clouds in the source point cloud set P for final transformation processing, completing the preliminary registration processing of the point cloud models of the masonry arch bridge in two different periods, so that the two models roughly overlap in space, laying the foundation for subsequent precise registration.

[0098] The advantages of Example 7 are: efficient preliminary registration processing, by reasonably dividing the source point cloud and the target point cloud set, randomly selecting point cloud pairs to construct a rigid transformation matrix, and then updating the matrix according to the distance difference of the point cloud pairs, after multiple iterations, an optimized rigid transformation matrix is ​​obtained, and the preliminary registration of the point cloud models of the masonry arch bridges in two different periods is realized. This method can quickly process a large amount of point cloud data while ensuring a certain registration accuracy, improve the efficiency of preliminary registration, and lay a good foundation for subsequent precise registration.

[0099] It can effectively handle the initial matching of point cloud models. Considering the low spatial correlation between the point cloud real-scene model and the design model, the use of the RANSAC algorithm can find relatively reliable matching point pairs through multiple random sampling and evaluations when the point cloud pair selection does not need to be precisely corresponding, so that the models roughly overlap. This process effectively solves the problem of initial matching of the masonry arch bridge point cloud model, improves the robustness of the entire registration process, and reduces the risk of large subsequent analysis errors due to inaccurate initial matching.

[0100] Embodiment 8: Preferably, in S3.5, the point cloud models of two masonry arch bridges in different periods after preliminary registration are accurately registered by using an ICP iterative closest point algorithm, including:

[0101] S3.5.1. After preliminary registration, select point cloud pairs containing eigenvalue c from all point cloud pairs of the masonry arch bridge point cloud models at two different times, and construct a feature data set based on the eigenvalues ​​corresponding to each point cloud in the selected point cloud pairs. The feature data set 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; for example, for the arch ring part, the feature value c can be set to be related to the curvature of the arch ring. Through screening, the point cloud pairs containing the feature value are selected, and then the feature data set is constructed using the feature values ​​corresponding to each point cloud in these screened point cloud pairs.

[0102] S3.5.2. The error between the source point cloud feature value and the target point cloud feature value in the feature data set is represented by an error function, wherein the error function is:

[0103]

[0104] 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,

[0105] The error function is solved by the matrix singular value decomposition method to obtain the rotation matrix R and the translation vector t to minimize the error function; the error function is solved by the matrix singular value decomposition method to obtain the rotation matrix R and the translation vector t that minimize the error function. In actual calculations, the data in the feature data set are substituted into the error function, and the matrix singular value decomposition algorithm is used to perform complex mathematical calculations, and the values ​​of R and t are gradually adjusted until the combination of R and t that minimizes the error function is found.

[0106] S3.5.3. Based on the obtained rotation matrix R and translation vector t, the eigenvalue x of the source point cloud p After the transformation, the updated eigenvalues ​​of xp,h are obtained;

[0107] S3.5.4, based on the updated feature values ​​of xp,h, calculate the average distance between all matching feature point pairs;

[0108] The eigenvalues ​​of the source point cloud are transformed based on the solved rotation matrix R and translation vector t. For the eigenvalues ​​of the source point cloud in the feature data set, the updated eigenvalue xp,c' is calculated by ‖xp,c'=Rpc+t‖ (wherein it is the coordinate of the source point cloud in the dimension related to the eigenvalue c). Then, the average distance between all matching feature point pairs is calculated. The specific calculation method is to calculate the distance between each pair of matching feature point pairs (xp,c', xp, c) (such as the Euclidean distance ‖xp, c′-xp, c‖), and then sum all these distances and divide them by the number of matching feature point pairs a to obtain the average distance.

[0109] S3.5.5, if the average distance is less than the set threshold, the optimization goal has been achieved and the calculation process stops; otherwise, return to S3.5.1 to continue iterative calculation until convergence, and output the optimal transformation matrix T best , thus completing the precise registration processing of the point cloud models of masonry arch bridges in two different periods.

[0110] Set a threshold (determined according to the accuracy requirements of masonry arch bridge deformation detection, such as 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 goal has been achieved. At this time, the transformation matrix composed of the rotation matrix R and the translation vector t can already make the two point cloud models achieve a high registration accuracy, and the calculation process stops. If the average distance is not less than the threshold, return to the above step of screening feature point clouds to construct a data set, continue iterative calculations, re-solve the error function, update the source point cloud eigenvalues ​​and calculate the average distance until the average distance is less than the threshold, and then output the optimal transformation matrix T best , thereby completing the precise registration processing of the point cloud models of the masonry arch bridges in two different periods, so that the overlap accuracy of the two models in space reaches a high level, providing an accurate basis for the subsequent posture difference detection.

[0111] It should be understood that the iterative closest point (ICP) algorithm is an algorithm used to minimize the difference between two point clouds. The basic idea of ​​the ICP algorithm is to find the best corresponding point match through iteration, and calculate the rigid body transformation (including rotation and translation), gradually approaching the optimal solution, and achieving precise alignment between the two point clouds.

[0112] The advantages of Example 8 are: accurate model registration optimization, constructing a feature data set by screening point cloud pairs containing eigenvalues, solving the rotation matrix and translation vector using the error function and matrix singular value decomposition method, and iterative calculation with the goal of minimizing the error function, and finally achieving accurate registration of point cloud models of masonry arch bridges in two different periods. This registration process based on eigenvalues ​​and mathematical optimization methods can further improve the registration accuracy of the model on the basis of preliminary registration, so that the design model and the actual model are more accurately overlapped in space, providing a guarantee for accurately analyzing the deformation of the main arch ring of the masonry arch bridge.

[0113] It can perform adaptive iterative calculations. During the iterative calculation process, the comparison result between the average distance between the matching feature point pairs and the set threshold determines whether to continue the iteration, thus realizing an adaptive calculation process. When the average distance is less than the threshold, it indicates that the registration has achieved a high accuracy, and the calculation stops, thus avoiding unnecessary waste of computing resources; otherwise, it continues to iterate and optimize, ensuring that the optimal transformation matrix is ​​finally obtained, thus improving the accuracy and efficiency of the registration.

[0114] Embodiment 9: Preferably, in S3.6, the posture difference detection is performed on the point cloud models of the masonry arch bridges of two different periods after precise registration by using the KNN neighbor algorithm, including:

[0115] S3.6.1. Calculate the normal vectors of all source point clouds and all target point clouds in the point cloud models of masonry arch bridges in two different periods after precise registration, and use the calculated normal vectors as the feature descriptors of each point cloud; for example, for a masonry arch bridge model containing thousands of point clouds, for each point cloud, calculate the normal vector of the point cloud based on the spatial distribution of the point clouds around it. These normal vectors will be used as feature descriptors of each point cloud for subsequent difference analysis. For example, in the point cloud of the arch ring, the normal vector can reflect the geometric direction characteristics of the arch ring surface; in the point cloud of the pier, the normal vector can reflect the vertical or inclined direction characteristics of the pier.

[0116] S3.6.2. For each feature descriptor of the source point cloud, the distance between the feature descriptor of each source point cloud and the feature descriptors of all points in the target point cloud is calculated using the Euclidean distance formula to obtain the difference between the feature descriptors:

[0117] d(p i ,q j )=||f(p i )-f(q j )||,

[0118] 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,

[0119] Select the nearest K target points q j , that is, select i The most similar K points (K value can be set according to the actual situation, such as K = 5), that is, the K points that are most similar to the source point cloud are selected. For example, after calculating the difference values ​​of the feature descriptors of all points in a source point cloud and a target point cloud, these difference values ​​are sorted from small to large, and the first K corresponding target points are selected.

[0120] S3.6.3. 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 With K target points q j Straight-line distance in space:

[0121] δ(p i -q j )=‖p i -q j ‖,

[0122] Among them, δ(pi -q j ) is the straight-line distance. This step can obtain the actual spatial distance between each source point cloud and its most similar K target points, thereby reflecting the degree of deviation of the point cloud models in the two periods at these positions. For example, for a source point cloud on the arch, calculate the straight-line distance from it to the five nearest neighboring target points. These distance values ​​can intuitively show the position change of the point in the model at different periods.

[0123] S3.6.4. Calculate each source point cloud p by using the mean square error algorithm i With K target points q j The mean square error value and the average deviation value of the straight-line distance are calculated to complete the posture difference detection of the point cloud models of the masonry arch bridge in two different periods.

[0124] Each source point cloud p is calculated by the mean square error algorithm i With K target points q j The mean square error of the straight-line distance is:

[0125]

[0126] in, is the average straight-line distance.

[0127] The average deviation is:

[0128]

[0129] The mean square error value can measure the degree of discreteness of the distance between the source point cloud and the target point cloud, and the average deviation value can reflect the overall deviation direction and size. By calculating these indicators, the posture difference detection of the point cloud model of the masonry arch bridge in two different periods is completed, so as to comprehensively and accurately evaluate the deformation of the main arch ring of the masonry arch bridge in different periods, and provide detailed data support for judging the structural health status of the masonry arch bridge. For example, when analyzing the deformation of a masonry arch bridge over the years, by calculating the mean square error value and average deviation value of the point cloud of different parts, it is possible to determine which areas have larger deformations, as well as the trend and degree of deformation.

[0130] The advantages of Example 9 are: it can detect detailed posture differences, by calculating the normal vector as the point cloud feature descriptor, and using the Euclidean distance formula to calculate the difference between the feature descriptors, selecting the nearest K target points, and further calculating the straight-line distance between the source point cloud and these target points and their mean square error value and average deviation value, to achieve a comprehensive detection of the posture differences of the point cloud models of the masonry arch bridges in two different periods. This method can carefully analyze the relationship between each point in the model and its neighboring points, thereby accurately identifying the posture differences between models in different periods, and providing detailed data support for evaluating the deformation changes of the main arch ring of the appearance of the masonry arch bridge.

[0131] It can evaluate the deformation change of the main arch ring in multiple dimensions. It can not only calculate the deviation in spatial position between each pair of feature points in the point cloud model, but also evaluate the deformation change degree of the main arch ring of the masonry arch bridge from multiple dimensions through indicators such as mean square error value and average deviation value. These indicators can more comprehensively reflect the deformation characteristics of the masonry arch bridge at different times, help engineers to have a deeper understanding of the structural changes of the masonry arch bridge, and provide richer information for judging the health status of the masonry arch bridge.

[0132] Example 10: Figure 2 As shown, an embodiment of the present invention further provides a deformation detection device for a masonry arch bridge, comprising:

[0133] A scanning module is used to scan the masonry arch bridge to be inspected by using a three-dimensional laser scanner pre-installed at a scanning site according to a set scanning period, so as to obtain point cloud data of the masonry arch bridge corresponding to multiple different periods;

[0134] Model building module, used to build the point cloud model and BIM 3D real scene model of the masonry arch bridge of the corresponding period based on the point cloud data of each masonry arch bridge;

[0135] The detection module is used to perform data qualitative analysis on the deformation of the main arch ring of the masonry arch bridge based on the BIM three-dimensional real-scene models corresponding to different periods, and obtain qualitative analysis data on the deformation of the main arch ring of the masonry arch bridge, and to perform data quantitative analysis on the deformation of the main arch ring of the masonry arch bridge based on the point cloud models of the masonry arch bridge corresponding to different periods, and obtain quantitative analysis data on the deformation of the main arch ring of the masonry arch bridge;

[0136] The evaluation module is used to perform deformation evaluation based on the deformation qualitative analysis data of the main arch ring of the masonry arch bridge and the deformation quantitative analysis data of the main arch ring of the masonry arch bridge, and generate a deformation evaluation report of the masonry arch bridge.

[0137] Example 11: An embodiment of the present invention also provides a deformation detection device for a masonry arch bridge, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the deformation detection method for a masonry arch bridge as described above is implemented.

[0138] Embodiment 12: The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the deformation detection method of the masonry arch bridge as described above is implemented.

[0139] It should be noted that, in this article, relational terms such as first and second, etc. 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 these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0143] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A deformation detection method for a masonry arch bridge, characterized in that: The steps include: S1. Scanning the masonry arch bridge to be inspected by a three-dimensional laser scanner pre-installed at a scanning site according to the set scanning period and set temperature requirements to obtain point cloud data of the masonry arch bridge corresponding to multiple different periods; S2. Constructing a point cloud model and a BIM three-dimensional real-scene model of the masonry arch bridge in the corresponding period based on the point cloud data of each masonry arch bridge; S3. Based on the BIM three-dimensional real-scene models corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is qualitatively analyzed to obtain the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge, and based on the point cloud models of the masonry arch bridge corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is quantitatively analyzed to obtain the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge; S4. Perform deformation assessment based on the deformation qualitative analysis data of the main arch ring of the masonry arch bridge and the deformation quantitative analysis data of the main arch ring of the masonry arch bridge, and generate a deformation assessment report of the masonry arch bridge.

2. The deformation detection method of a masonry arch bridge according to claim 1, characterized in that: The set temperature requirement is that the difference in ambient temperature between two adjacent scans of the masonry arch bridge to be inspected shall not exceed 3°C, and the ambient temperature shall not change by more than 1°C during each inspection.

3. The deformation detection method of a masonry arch bridge according to claim 1, characterized in that: In S2, a point cloud model of a masonry arch bridge and a BIM three-dimensional real scene model of the corresponding period are constructed based on the point cloud data of each masonry arch bridge, including: Import the point cloud data of each masonry arch bridge into the point cloud analysis component in sequence according to the order of scanning periods, and generate a point cloud model of the masonry arch bridge corresponding to the period based on the order of scanning periods; The point cloud models of masonry arch bridges corresponding to each period are imported into the BIM analysis component for format conversion, and the BIM three-dimensional real-scene models corresponding to each masonry arch bridge point cloud model are obtained.

4. The deformation detection method of a masonry arch bridge according to claim 1, characterized in that: In S3, based on the BIM three-dimensional real-scene models corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is qualitatively analyzed to obtain qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge at different periods, including: S3.

1. Import the BIM 3D real scene models corresponding to different periods into the same BIM file, match each BIM 3D real scene model in the spatial coordinate system of the same BIM file, so that the characteristic points of the 3D real scene models of masonry arch bridges in different periods belong to each other; S3.

2. Use the Boolean operation method to calculate the difference sets of the BIM three-dimensional real-scene models in two different periods, so as to obtain each difference set, which is the qualitative analysis data of the deformation of the main arch ring of the masonry arch bridge in different periods.

5. The deformation detection method of a masonry arch bridge according to claim 4, characterized in that: In the above S3.1, the BIM three-dimensional real scene models corresponding to each period are imported into the same BIM file, and each BIM three-dimensional real scene model is matched in the spatial coordinate system of the same BIM file, so that the coordinates of the feature points of the three-dimensional real scene models of the masonry arch bridge in each period coincide in the spatial coordinate system of the corresponding BIM file, including: In the BIM three-dimensional real-scene model, a triangle with three sides of fixed length is proposed as a feature triangle, and on the feature planes of the three-dimensional real-scene models of masonry arch bridges in two different periods, the center of the feature plane is designated as the centroid of the feature triangle to make the same feature triangle, and the vertex of each angle of the two feature triangles is used as a pair of feature points. Based on the principle that three points that are not in the same straight line determine the position of a plane in space, three pairs of feature points determine the positions of the feature planes of the three-dimensional real-scene models of masonry arch bridges in two different periods in space, and the positions of the three pairs of feature points in space are overlapped, so that the coordinates of the feature points of the three-dimensional real-scene models of masonry arch bridges in two different periods in the spatial coordinate system of the BIM file overlap, and in this way, the coordinates of the feature points of the three-dimensional real-scene models of masonry arch bridges in each period in the spatial coordinate system of the BIM file to which they belong overlap.

6. The deformation detection method of a masonry arch bridge according to claim 1, characterized in that: In S3, based on the point cloud models of the masonry arch bridge corresponding to different periods, the deformation of the main arch ring of the masonry arch bridge is quantitatively analyzed to obtain the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge, including: S3.3, importing the corresponding masonry arch bridge point cloud models of different periods into the same point cloud file; S3.4, selecting two masonry arch bridge point cloud models of different periods to be compared, selecting multiple point cloud pairs corresponding to the same position in the two masonry arch bridge point cloud models of different periods, and performing preliminary registration processing on the selected multiple point cloud pairs through the RANSAC random sampling consistency algorithm; S3.5, using the ICP iterative closest point algorithm to accurately register the point cloud models of the masonry arch bridges in two different periods after preliminary registration; S3.6, using the KNN neighbor algorithm to detect the posture difference of the point cloud models of the masonry arch bridge in two different periods after precise registration; S3.

7. Repeat steps S3.4 to S3.6 until the posture difference detection of the point cloud model of the masonry arch bridge at different periods is completed, and the quantitative analysis data of the deformation of the main arch ring of the masonry arch bridge is obtained.

7. The deformation detection method of a masonry arch bridge according to claim 6, characterized in that: In S3.4, multiple point cloud pairs corresponding to the same position are selected from the point cloud models of the masonry arch bridge in two different periods, and a preliminary registration process is performed on the selected multiple point cloud pairs using the RANSAC random sampling consensus algorithm, including: S3.4.

1. Taking each point cloud of the masonry arch bridge point cloud model in the earlier time period in two different periods as the source point cloud, collecting all the source point clouds to obtain the source point cloud set P, taking each point cloud of the masonry arch bridge point cloud model in the later time period as the target point cloud, collecting all the target point clouds to obtain the target point cloud set Q, selecting multiple source point clouds and target point clouds corresponding to the same position, and obtaining multiple groups of point cloud pairs; S3.4.2, calculating normal vectors of point clouds in multiple groups of point clouds to obtain eigenvalues ​​of each group of point cloud pairs; S3.4.

3. Randomly select a set of point cloud pairs from multiple sets of 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; S3.4.

4. Construct a rigid transformation matrix T according to the eigenvalues ​​corresponding to the selected point cloud pair, wherein 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 ; S3.4.5, based on the rigid transformation matrix T applied to all source point clouds in the source point cloud set P for transformation processing, a transformed source point cloud set P' is obtained, wherein the source point cloud set P' is composed of the transformed source point clouds, and 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 is calculated, each distance difference is compared with a preset difference, and point cloud pairs corresponding to distance differences less than the preset difference are obtained, and the number of point cloud pairs obtained by comparison is counted, and the rigid transformation matrix T is updated according to the number of point cloud pairs obtained by comparison; S3.4.6, repeat S3.4.3 to S3.4.5 according to a preset number of iterations until the preset number of iterations is reached, thereby obtaining an updated rigid transformation matrix T; S3.4.

7. Apply the updated rigid transformation matrix T to all source point clouds in the source point cloud set P for transformation processing, and complete the preliminary registration processing of the point cloud models of the masonry arch bridges in two different periods.

8. The deformation detection method of a masonry arch bridge according to claim 6, characterized in that: In S3.5, the point cloud models of the masonry arch bridges of two different periods after preliminary registration are accurately registered by using the ICP iterative closest point algorithm, including: S3.5.

1. After preliminary registration, select point cloud pairs containing eigenvalue c from all point cloud pairs of the masonry arch bridge point cloud models at two different times, and construct a feature data set based on the eigenvalues ​​corresponding to each point cloud in the selected point cloud pairs. The feature data set 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; S3.5.

2. The error between the source point cloud feature value and the target point cloud feature value in the feature data set is represented by 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 matrix singular value decomposition method to obtain rotation matrix R and translation vector t, so as to minimize the error function; S3.5.

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 updated eigenvalue; S3.5.4, based on x p ,h updated feature value, calculate the average distance between all matching feature point pairs; S3.5.5, if the average distance is less than the set threshold, the optimization goal has been achieved and the calculation process stops; otherwise, return to S3.5.1 to continue iterative calculation until convergence, and output the optimal transformation matrix T best , thus completing the precise registration processing of the point cloud models of masonry arch bridges in two different periods.

9. The deformation detection method of a masonry arch bridge according to claim 6, characterized in that: In S3.6, the KNN neighbor algorithm is used to detect the posture difference of the point cloud models of the masonry arch bridges in two different periods after precise registration, including: S3.6.

1. Calculate normal vectors for all source point clouds and all target point clouds in the point cloud models of the masonry arch bridge in two different periods after precise registration, and use the calculated normal vectors as feature descriptors of each point cloud; S3.6.

2. For each feature descriptor of the source point cloud, the distance between the feature descriptor of each source point cloud and the feature descriptors of all points in the target point cloud is calculated 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 i The most similar K points; S3.6.

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 With 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; S3.6.

4. Calculate each source point cloud p by using the mean square error algorithm i With K target points q j The mean square error value and the average deviation value of the straight-line distance are calculated to complete the posture difference detection of the point cloud models of the masonry arch bridge in two different periods.

10. A deformation detection device for a masonry arch bridge, characterized in that: include: A scanning module is used to scan the masonry arch bridge to be inspected by using a three-dimensional laser scanner pre-installed at a scanning site according to a set scanning period, so as to obtain point cloud data of the masonry arch bridge corresponding to multiple different periods; Model building module, used to build the point cloud model and BIM 3D real scene model of the masonry arch bridge of the corresponding period based on the point cloud data of each masonry arch bridge; The detection module is used to perform data qualitative analysis on the deformation of the main arch ring of the masonry arch bridge based on the BIM three-dimensional real-scene models corresponding to different periods, and obtain qualitative analysis data on the deformation of the main arch ring of the masonry arch bridge, and to perform data quantitative analysis on the deformation of the main arch ring of the masonry arch bridge based on the point cloud models of the masonry arch bridge corresponding to different periods, and obtain quantitative analysis data on the deformation of the main arch ring of the masonry arch bridge; The evaluation module is used to perform deformation evaluation based on the deformation qualitative analysis data of the main arch ring of the masonry arch bridge and the deformation quantitative analysis data of the main arch ring of the masonry arch bridge, and generate a deformation evaluation report of the masonry arch bridge.

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