Method for levelling a support plate

Abstract

This invention discloses a leveling method for bridge bearing plates, relating to the field of leveling technology for bridge bearing plates. The method includes: pre-embedding the bearing plate at the beam end using adjustable bolts; acquiring initial three-dimensional point cloud data of the bearing plate using a 3D laser scanner at a resolution of 1–5 mm to establish a digital elevation model; calculating the elevation deviation based on the point cloud data, simultaneously rotating the adjusting nut for coarse adjustment, and then re-measuring with laser to ensure corner deviation ≤ ±5 mm; generating a high-resolution digital elevation model through dense scanning at a resolution of 1–2 mm, and making minor adjustments in different areas to ensure levelness ≤ 1 mm / m; tightening the locking nut in stages, and allowing it to stand for 12–24 hours before laser scanning to verify stability. This invention, through the deep integration of laser scanning technology and adjustable bolt leveling technology, achieves rapid measurement, real-time feedback, and precise control of the bearing plate's levelness, improving leveling efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of leveling technology for bridge bearing plates, and particularly to a leveling method for bearing plates. Background Technology

[0002] In recent years, the scale of bridge construction has continued to expand, and prefabrication technology has been widely used in the construction of precast box girders due to its advantages such as high construction efficiency and strong quality control. As a key component connecting the beam and the pier, the levelness of the bearing plate directly affects the uniformity of stress distribution and traffic safety stability of the beam. Therefore, leveling the bearing plate has become a core procedure in bridge construction.

[0003] With the development of measurement technology, precision methods such as laser scanning have been gradually introduced into this field, but practical applications still face many challenges. Currently, leveling of bearing plates mainly relies on traditional manual measurement methods. Among these, the single-point measurement method using a level or total station requires operators to set up the instrument point by point and manually read the data. This process is cumbersome and time-consuming, and a single operation can only obtain data from a limited number of discrete points, making it difficult to comprehensively capture the overall flatness characteristics of the bearing plate surface. Especially for large bearing plates, insufficient measurement point density can easily lead to the omission of local uneven areas. At the same time, manual readings are significantly affected by ambient light, operator experience, and instrument parallax. Data repeatability is poor when different operators perform the operation, and measurement accuracy fluctuates greatly, making it difficult to meet the requirements of high-precision construction. The shim leveling method involves inserting steel shims of different thicknesses between the bearing plate and the bearing pad. However, this method relies on the operator's subjective judgment, cannot quantify the adjustment amount, and is prone to over-adjustment or under-compensation. Some projects have attempted to use laser levels for assisted measurement; however, these only provide a rough level reference, and the measurement data is not organically integrated with the adjustment process. They are only used in the static inspection phase and cannot provide real-time feedback on elevation changes during adjustment, resulting in a lack of dynamic monitoring mechanisms for the leveling operation. While existing laser scanning technology can acquire surface point cloud data, the scanning and adjustment processes are disconnected, and point cloud processing is time-consuming. This prevents the achievement of closed-loop control for measurement-adjustment-verification, making the leveling process reliant on experience, inefficient, and difficult to maintain consistently high accuracy. These shortcomings collectively result in large fluctuations in the leveling quality of the bearing plates, a high rework rate, and severely restrict the bridge construction progress and structural safety. Summary of the Invention

[0004] The main objective of this invention is to propose a leveling method for support plates, which aims to achieve rapid measurement, real-time feedback, and precise control of the levelness of the support plates, thereby solving the technical problems of low measurement efficiency, unstable accuracy, and lack of dynamic monitoring in existing technologies.

[0005] To achieve the above objectives, the present invention proposes a leveling method for a support plate, the leveling method for the support plate comprising: Before the concrete of the beam is poured, the support plate is pre-embedded in the designed position at the beam end by adjustable bolts; wherein, the adjustable bolts include pre-embedded screws, adjusting nuts and locking nuts, and the lower end of the pre-embedded screws is fixedly connected to the beam reinforcement skeleton; A three-dimensional laser scanner is arranged above the support plate, with a scanning resolution of 1 to 5 mm and a scanning angle covering the entire surface of the support plate. The scanner acquires three-dimensional point cloud data of the support plate in its initial state. The first digital elevation model of the support plate surface is established through processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks to form elevation data. Based on the elevation data, the deviation of each corner point of the support plate from the design elevation is calculated. The support plate is then coarsely adjusted using the adjustable bolts. After the coarse adjustment, the elevation deviation of each corner point is re-measured by laser scanning to ensure that it is controlled within ±5mm. The three-dimensional laser scanner is used to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface. The levelness deviation of each measuring point is calculated. The adjustable bolts at the corresponding positions are independently rotated according to the deviation value for fine adjustment so that the levelness of the support plate is ≤1 mm / m. Tighten the locking nut, and retest after 12 to 24 hours to verify the stability of the levelness, and generate a leveling acceptance report containing the three-dimensional point cloud data.

[0006] In one embodiment, a three-dimensional laser scanner is arranged above the support plate, with a scanning resolution of 1-5 mm and a scanning angle covering the entire surface of the support plate to acquire three-dimensional point cloud data of the support plate in its initial state. A first digital elevation model of the support plate surface is then established using processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks. The steps to form elevation data include: The three-dimensional laser scanner is installed at a height of 2-5m above the support plate. The number of three-dimensional laser scanners is determined according to the size of the support plate, so that the coverage area of ​​a single three-dimensional laser scanner is ≥2m*2m. For large support plates, multiple three-dimensional laser scanners are arranged around the support plate, and the overlap area between two adjacent three-dimensional laser scanners is ≥20%. Reflective targets are attached around the support plate and at its center. The number of targets is ≥4. The absolute coordinates of the targets are determined by a total station or GPS and used as control points for the stitching and coordinate transformation of the three-dimensional point cloud data. The target positioning accuracy is ≤1mm. The horizontal angular resolution of the 3D laser scanner is set to 0.01°~0.05°, the vertical angular resolution is set to 0.01°~0.05°, the scanning time is ≥2 minutes, and the acquired point cloud density is ≥1000 points / m². 2 This ensures that the surface details of the support plate are completely captured.

[0007] In one embodiment, the steps of calculating the deviation of each corner point of the support plate from the design elevation based on the elevation data, coarsely adjusting the support plate using the adjustable bolts, and then re-measuring using laser scanning to control the elevation deviation of each corner point within ±5mm include: The three-dimensional point cloud data is filtered and denoised to remove background point clouds other than the support plate. A plane fitting algorithm is used to extract the point cloud on the upper surface of the support plate, identify the four corners and edge feature points of the support plate, and calculate the average elevation of each feature point. Compare the elevation of each feature point with the design elevation, calculate the elevation deviation value, formulate an adjustment strategy based on the magnitude and distribution pattern of the deviation value, and determine the rotation direction and rotation angle of each adjustment nut; After each round of adjustment, the resolution is reduced to 5-10mm using the three-dimensional laser scanner for remeasurement. The elevation deviation after adjustment is calculated. When the deviation of each corner point is ≤±5mm and the diagonal height difference is ≤10mm, the coarse adjustment is completed.

[0008] In one embodiment, the steps of using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions according to the deviation values ​​for fine adjustment to make the levelness of the support plate ≤1 mm / m include: The aforementioned 3D laser scanner is used to perform dense scanning on the upper surface of the support plate at a resolution of 1-2 mm, with a scanning angle step of ≤0.02°, to acquire high-density point cloud data with a point cloud density of ≥5000 points / m. 2 This ensures that local unevenness and minor defects can be identified; The dense point cloud data is processed into a grid to establish a second digital elevation model with a grid size of 2mm*2mm. The elevation value of each grid is calculated by an interpolation algorithm to generate the elevation contour map and three-dimensional visualization model of the support plate surface. Based on the elevation distribution analysis of the three-dimensional visualization model, the surface of the support plate is divided into several adjustment areas, each area corresponding to one or more adjustment nuts. The average elevation and target adjustment amount of each area are calculated. The adjustment nuts are rotated independently to make micro-adjustments, with a single adjustment amount ≤0.5mm. After adjustment, the levelness is re-measured until it is ≤1mm / m.

[0009] In one embodiment, the step of retesting after an interval of 12 to 24 hours to verify the stability of the levelness and generating a leveling acceptance report containing the three-dimensional point cloud data includes: Tighten the locking nut in 2-3 stages diagonally using a torque wrench. Apply 50% of the design torque for the first stage, 75% for the second stage, and 100% for the third stage. After locking, let it stand for 12 to 24 hours to allow the support plate to complete its initial settlement under its own weight and preload, and then re-measure it to generate a digital elevation model for the re-measurement and calculate the change in levelness. By comparing the second digital elevation model of the support plate surface with the remeasured digital elevation model, if the change in levelness is ≤0.3mm / m, the leveling is deemed qualified, and a complete archive containing initial three-dimensional point cloud data, adjustment process data, and final acceptance three-dimensional point cloud data is generated.

[0010] In one embodiment, the step of using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolt at the corresponding position according to the deviation value for fine adjustment to make the levelness of the support plate ≤1 mm / m further includes: The three-dimensional laser scanner is used to perform periodic rapid scanning at intervals of 5 to 10 seconds to obtain the elevation changes of the support plate surface in real time. The scanning resolution is set to 3 to 5 mm to realize dynamic monitoring of the adjustment process. The point cloud data acquired in real time is compared with the target elevation model to calculate the current elevation deviation, and the remaining adjustment amount of each adjustment point is displayed in real time through a visualization interface. When the elevation of a certain area is detected to have reached the target value in real time, the operation of the corresponding adjusting nut in that area is immediately stopped, and the adjustment is switched to the adjacent area.

[0011] In one embodiment, after using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions according to the deviation values ​​for fine adjustment to make the levelness of the support plate ≤ 1 mm / m, the leveling method for the support plate further includes: No fewer than 10 of the aforementioned feature points are selected on the upper surface of the support plate for contact measurement to obtain the elevation value of each feature point, and then compared with the point cloud elevation of the same location obtained by the three-dimensional laser scanner. Calculate the elevation difference between the 3D laser scanner and the contact measurement. If the difference is ≤0.5mm, the accuracy of the 3D laser scanner is determined to meet the requirements. If the difference is >0.5mm, the 3D laser scanner is calibrated. Record and compare the measurement results and calibration data, and generate a laser scanning accuracy verification report as an attachment to the leveling and acceptance report.

[0012] In one embodiment, a three-dimensional laser scanner is arranged above the support plate, with a scanning resolution of 1-5 mm and a scanning angle covering the entire surface of the support plate to acquire three-dimensional point cloud data of the support plate in its initial state. A first digital elevation model of the support plate surface is then established using processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks. Before the step of forming elevation data, the leveling method for the support plate further includes: When the ambient light intensity is greater than 10,000 lux, a narrow-band filter is installed in front of the lens of the 3D laser scanner to filter out stray light that is inconsistent with the laser wavelength, or scanning is carried out under low light conditions such as night or cloudy days to improve the signal-to-noise ratio of point clouds. When the dust concentration at the construction site is high, a pulsed laser scanner is used to distinguish the echo signal using the time-of-flight method and filter out the interference signal from dust scattering. Alternatively, a mist cannon can be used to reduce dust before scanning to reduce the impact of dust on laser scattering. When the ambient temperature is >35℃, heat dissipation treatment should be performed on the 3D laser scanner, or a high-temperature resistant scanner should be used, and temperature compensation calibration should be performed before and after scanning to eliminate instrument drift caused by temperature.

[0013] In one embodiment, the step of pre-embedding the support plate at the designed position at the beam end using adjustable bolts before pouring the beam concrete includes: The outline of the support plate and the center line of the bolts are projected onto the steel reinforcement skeleton of the beam using a laser line projector or a laser theodolite, and the position of each of the pre-embedded bolts is marked. The layout accuracy is ≤2mm. After the pre-embedded screw is installed, use a laser plumb bob or laser vertical gauge to calibrate the verticality of the screw to ensure that the verticality deviation of the screw is ≤1°. If the deviation exceeds the limit, it can be corrected by adjusting the fastener. Before concrete pouring, the three-dimensional coordinates of each pre-embedded screw are remeasured using laser scanning or a total station, and the initial position deviation is recorded as a correction parameter for later leveling calculations.

[0014] In one embodiment, the steps of tightening the locking nut, retesting after an interval of 12 to 24 hours to verify the levelness stability, and generating a leveling acceptance report containing the three-dimensional point cloud data further include: The laser scanning point cloud data from the initial state, after coarse adjustment, after fine adjustment, and after locking and retesting are registered and superimposed to generate a three-dimensional animation of the leveling process, which intuitively shows the leveling process of the support plate. Based on the digital elevation model of the final acceptance, a heat map of elevation deviation on the surface of the bearing plate is generated, with different colors used to mark the areas of elevation deviation: red indicates too high, blue indicates too low, and green indicates that the standard is met, serving as a visual basis for quality acceptance. The laser scanning point cloud after leveling is integrated with the BIM model of the bearing plate design to construct a digital twin model of the bearing plate, and the leveling parameters and measured data are recorded as a benchmark archive for bridge operation and maintenance.

[0015] The technical solution of this invention involves deploying a 3D laser scanner above the bearing plate to acquire 3D point cloud data of the bearing plate surface at millimeter-level resolution, establishing a digital elevation model as a leveling benchmark. In the coarse adjustment stage, laser scanning is used for rapid re-measurement to verify the adjustment effect. In the fine adjustment stage, high-density scanning is used to generate a digital elevation model with a resolution of 2mm*2mm to guide minor adjustments in different areas. In the locking stage, laser scanning after a period of settling is used to verify the stability of the levelness. Periodic rapid scanning enables real-time monitoring and closed-loop control of the adjustment process. Finally, through multi-period point cloud comparison and digital twin model construction, a complete digital quality archive is formed. Compared to traditional single-point measurement with a leveling instrument, laser scanning can acquire tens of thousands of measurement points across the entire surface at once, increasing measurement efficiency by more than 10 times. The digital elevation model can intuitively display the elevation distribution on the bearing plate surface, avoiding errors from manual readings and improving measurement accuracy to within 0.5mm. Real-time linkage monitoring avoids over-adjustment, reduces the number of repeated adjustments, and improves construction efficiency by more than 30%. Digital archiving provides an accurate benchmark for bridge operation monitoring, achieving full life-cycle quality management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of an embodiment of the leveling method for a support plate provided by the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0021] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0022] Existing methods for leveling bearing plates mainly rely on single-point measurements using levels or total stations, which are inefficient, have a limited number of measuring points, and cannot comprehensively reflect the overall flatness of the bearing plates. Manual readings are prone to parallax errors, and measurement accuracy is greatly affected by the operator's experience. Existing laser measurement methods are only used for static detection and cannot be linked with the adjustment process in real time, making it impossible to achieve dynamic monitoring and closed-loop control of the leveling process.

[0023] To address this technical problem, the present invention proposes a leveling method for support plates.

[0024] Please see Figure 1 In one embodiment of the present invention, the leveling method for the support plate includes: Step S10: Before the concrete is poured, the support plate is pre-embedded at the designed position at the beam end using adjustable bolts; wherein, the adjustable bolts include pre-embedded screws, adjusting nuts and locking nuts, and the lower end of the pre-embedded screws is fixedly connected to the beam reinforcement skeleton; Step S20: Arrange a three-dimensional laser scanner above the support plate, set the scanning resolution to 1-5mm, and cover the entire surface of the support plate with the scanning angle to obtain the three-dimensional point cloud data of the support plate in its initial state. Then, establish the first digital elevation model of the support plate surface through processing software, and extract the elevation values ​​of each feature point as the leveling benchmark to form elevation data. Step S30: Based on the elevation data, calculate the deviation of each corner point of the support plate from the design elevation, and perform coarse adjustment of the support plate using the adjustable bolts. After coarse adjustment, re-measure by laser scanning to ensure that the elevation deviation of each corner point is controlled within ±5mm. Step S40: The three-dimensional laser scanner is used to densely scan the upper surface of the support plate at a resolution of 2-3mm to generate a second digital elevation model of the support plate surface. The levelness deviation of each measuring point is calculated. The adjustable bolts at the corresponding positions are independently rotated according to the deviation value for fine adjustment so that the levelness of the support plate is ≤1mm / m. Step S50: Tighten the locking nut, and retest after an interval of 12 to 24 hours to verify the stability of the levelness and generate a leveling acceptance report containing the three-dimensional point cloud data.

[0025] For ease of understanding, the following explains some key terms in this embodiment: Bearing plates, generally speaking, are components in bridge structures used to support the superstructure and transfer loads to the substructure. The flatness and levelness of their surfaces have a significant impact on the load-bearing performance and service life of the bridge.

[0026] Adjustable bolts are connectors used to achieve fine adjustments to the elevation and level of structural members. They typically consist of a pre-embedded bolt, an adjusting nut, and a locking nut. The pre-embedded bolt is used for fixed connection to the foundation structure, the adjusting nut provides the lifting and lowering adjustment function, and the locking nut secures the adjusted position after adjustment, preventing loosening.

[0027] A 3D laser scanner is a non-contact measurement device that rapidly and densely acquires three-dimensional spatial coordinate data of an object's surface by emitting a laser beam and receiving reflected signals, forming a point cloud. This device can obtain geometric information of complex surface structures and is widely used in fields such as engineering surveying and reverse engineering.

[0028] 3D point cloud data is a set of discrete points with X, Y, and Z coordinate information acquired by a 3D laser scanner. These points together constitute the three-dimensional geometric shape of an object's surface. By processing point cloud data, a 3D model of the object can be reconstructed.

[0029] A digital elevation model (DEM) is a digital representation of surface elevation information, typically stored in the form of a regular grid or an irregular triangular network. In this embodiment, it refers to the surface elevation model of the bearing plate established by processing three-dimensional point cloud data, used to reflect the undulations and unevenness of the bearing plate surface.

[0030] Elevation data refers to the elevation values ​​of specific points or areas extracted from a digital elevation model, which serve as a benchmark or reference during the leveling process.

[0031] Levelness refers to the degree of inclination of an object's surface relative to a horizontal plane, usually expressed as the height difference per unit length. In this embodiment, the levelness of the support plate is an important indicator for measuring its leveling quality.

[0032] This embodiment provides a leveling method for a support plate, the specific implementation process of which is as follows: First, pre-embedded installation is performed. Before the beam concrete is poured, the support plate is pre-embedded at the designed position at the beam end using adjustable bolts. These adjustable bolts include an embedded threaded rod, an adjusting nut, and a locking nut. The lower end of the embedded threaded rod is fixedly connected to the beam's reinforcing steel skeleton. Specifically, after the beam's reinforcing steel skeleton is tied, traditional measuring tools, such as a steel tape measure and a chalk line, can be used to mark the outline of the support plate and the center position of the embedded threaded rod on the reinforcing steel skeleton. Subsequently, the embedded threaded rod is fixedly connected to the beam's reinforcing steel skeleton by welding or binding. During the fixing process, a plumb line or a simple level can be used to initially check the verticality of the embedded threaded rod, ensuring it is approximately perpendicular to the design plane. The support plate is placed above the embedded threaded rod and initially fixed using the adjusting nut.

[0033] Secondly, a laser scan is performed to establish a reference. A 3D laser scanner is positioned above the support plate, with a scanning resolution of 1-5 mm and a scanning angle covering the entire surface of the support plate. This acquires the initial 3D point cloud data of the support plate, and the software is used to create a first digital elevation model of the support plate surface. The elevation values ​​of each feature point are extracted as a leveling reference, forming elevation data. Specifically, a 3D laser scanner can be mounted above the support plate, ensuring its field of view covers the entire surface. Simple reflective stickers can be manually affixed to the edges and center of the support plate as auxiliary positioning points. The 3D laser scanner is then activated to acquire the raw 3D point cloud data of the support plate surface. This point cloud data is imported into point cloud processing software, where it undergoes preliminary processing, such as removing obvious noise points. Subsequently, based on the processed point cloud data, a first digital elevation model of the support plate surface can be generated. From this model, feature points at the four corners and other key locations of the support plate can be manually selected, and their Z-coordinate values ​​extracted as elevation data for subsequent leveling references.

[0034] Next, a coarse adjustment is performed. Based on the elevation data, the deviation of each corner point of the support plate from the design elevation is calculated. The support plate is then coarsely adjusted using the adjustable bolts. After the coarse adjustment, a laser scan is used for re-measurement to ensure that the elevation deviation of each corner point is controlled within ±5mm. Specifically, the elevation values ​​of each feature point obtained in the laser scanning benchmark establishment step are compared with the design elevation to calculate the elevation deviation of each feature point. Based on these deviation values, the operator can roughly determine the tilt direction and degree of the support plate. Subsequently, the support plate is initially adjusted by manually rotating the corresponding adjusting nuts. For example, for areas with excessively high elevations, the adjusting nuts can be rotated counterclockwise to lower them; for areas with excessively low elevations, the adjusting nuts can be rotated clockwise to raise them. After completing one round of coarse adjustment, the support plate is scanned again using a 3D laser scanner to obtain new point cloud data and calculate the elevation deviation of each corner point. This re-measurement process can use the same scanning parameters as the benchmark establishment step. Repeat the coarse adjustment and re-measurement process until the elevation deviation of each corner point of the support plate is controlled within the allowable range of ±5mm.

[0035] Subsequently, fine-tuning is performed. The 3D laser scanner is used to densely scan the upper surface of the support plate at a resolution of 2–3 mm, generating a second digital elevation model of the support plate surface. The levelness deviation of each measuring point is calculated, and the adjustable bolts at the corresponding positions are independently rotated based on the deviation values ​​for fine-tuning, ensuring that the levelness of the support plate is ≤1 mm / m. Specifically, after coarse adjustment, the scanning resolution of the 3D laser scanner is adjusted to 2–3 mm, and a more dense scan is performed on the upper surface of the support plate to obtain more detailed surface point cloud data. This high-density point cloud data is imported into processing software to generate a second digital elevation model of the support plate surface. This model can more precisely reflect the local undulations of the support plate surface. By analyzing this digital elevation model, the levelness deviation of each measuring point on the support plate surface can be calculated. Based on these deviation values, the operator can identify areas requiring further fine-tuning and the corresponding adjusting bolts. Subsequently, each adjusting bolt requiring adjustment is rotated slightly to achieve minor adjustments to the local elevation of the support plate. The fine-tuning process is iterative, with each adjustment followed by a retest, until the overall levelness of the support plate meets or exceeds the design requirement of 1 mm / m.

[0036] Finally, locking and re-inspection are performed. After fine-tuning, the locking nut is tightened, and a re-measurement is conducted after 12-24 hours to verify the stability of the levelness and generate a leveling acceptance report containing the 3D point cloud data. Specifically, after all the fine-tuning work on the support plate is completed, the operator tightens the locking nut on each adjustable bolt to fix the position of the adjusting nut and prevent the support plate from shifting or settling during subsequent construction or use. After locking, the support plate is left to stand for a period of time, such as 12 to 24 hours, to allow the support plate to complete the initial settlement or stress release under its own weight. After the standing period, the surface of the support plate is re-measured using a 3D laser scanner to obtain the final 3D point cloud data. By comparing this re-measured data with the data after fine-tuning, the stability of the support plate's levelness can be verified. Finally, the 3D point cloud data, elevation data, and leveling results obtained throughout the leveling process are compiled to generate a complete leveling acceptance report, which serves as the basis for project quality acceptance.

[0037] This leveling method for bearing plates achieves precise control over the elevation and levelness of the bearing plates by pre-embedding adjustable bolts before the concrete pouring of the beam and combining it with three-dimensional laser scanning technology. This method overcomes the limitations of traditional manual measurement, which suffers from low efficiency and unstable accuracy. It can comprehensively acquire three-dimensional point cloud data of the bearing plate surface, establish a digital elevation model, and perform coarse and fine adjustments based on the elevation data. As a result, the overall flatness of the bearing plates is effectively improved, ensuring uniform stress on the bridge superstructure and traffic safety, and enhancing construction quality and efficiency.

[0038] In an embodiment of the present invention, a three-dimensional laser scanner is arranged above the support plate, with a scanning resolution of 1-5 mm and a scanning angle covering the entire surface of the support plate, to acquire three-dimensional point cloud data of the support plate in its initial state. A first digital elevation model of the support plate surface is then established using processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks. The steps for forming elevation data include: Step S21: Install the 3D laser scanner at a height of 2-5m above the support plate. The number of 3D laser scanners is determined according to the size of the support plate, so that the coverage area of ​​a single 3D laser scanner is ≥2m*2m. For large support plates, multiple 3D laser scanners are arranged around the support plate, and the overlap area between two adjacent 3D laser scanners is ≥20%. Step S22: Attach reflective targets around the support plate and at its center. The number of targets is ≥4. Measure the absolute coordinates of the targets using a total station or GPS. These coordinates will serve as control points for the stitching and coordinate transformation of the three-dimensional point cloud data. The target positioning accuracy is ≤1mm. Step S23: Set the horizontal angular resolution of the 3D laser scanner to 0.01°~0.05°, the vertical angular resolution to 0.01°~0.05°, the scanning time to ≥2 minutes, and the acquired point cloud density to ≥1000 points / m². 2 This ensures that the surface details of the support plate are completely captured.

[0039] Specifically, in the scanner deployment step, a 3D laser scanner is erected at a height of 2–5m above the support plate. This height range aims to balance the scanning coverage area and the accuracy of point cloud details. The number of 3D laser scanners is determined according to the size of the support plate, ensuring that the coverage area of ​​a single 3D laser scanner is ≥2m*2m. For large support plates, multiple 3D laser scanners are deployed around the plate to ensure complete coverage. The overlap area between two adjacent 3D laser scanners is ≥20%, which is crucial for subsequent point cloud data stitching and registration, effectively eliminating stitching errors and ensuring the integrity and consistency of the overall data.

[0040] In the target positioning step, reflective targets are affixed to the perimeter and center of the support plate, with at least four targets serving as high-precision control points. The absolute coordinates of these targets are determined using a total station or GPS and used as control points for 3D point cloud data stitching and coordinate transformation, with a positioning accuracy of ≤1mm. These absolute coordinates are crucial for the subsequent accurate stitching of point cloud data acquired from different scanners and their transformation to a unified coordinate system, thus ensuring the spatial accuracy of the entire support plate point cloud data.

[0041] In the point cloud acquisition step, the horizontal angular resolution of the 3D laser scanner was set to 0.01°~0.05°, and the vertical angular resolution was also set to 0.01°~0.05°. These precise resolution settings ensured that the laser beam was sufficiently dense within the scanning area. The scanning time was ≥2 minutes to ensure that the laser scanner had enough time to complete a comprehensive scan of the target area at the set resolution. Through these parameter settings, the acquired point cloud density was ≥1000 points / m², thus ensuring the complete acquisition of details on the support plate surface and laying the foundation for the subsequent establishment of an accurate first digital elevation model.

[0042] Through the aforementioned technical solutions, the establishment of a leveling benchmark via laser scanning effectively overcomes the problems of incomplete and inaccurate initial data acquisition and large data stitching errors by carefully deploying the scanner, using high-precision target positioning, and optimizing point cloud acquisition parameters. The rational deployment of the scanner and the design of overlapping areas ensure comprehensive coverage and data integrity of the large support plate; the precise coordinate positioning of the target provides a reliable basis for seamless stitching and unified coordinate transformation of multi-station scanning data, significantly improving the global accuracy of the point cloud data; and the high-resolution and high-density point cloud acquisition guarantees the complete reproduction of the surface details of the support plate. These measures work together to ensure that the established first digital elevation model has extremely high accuracy and reliability, providing a solid and accurate leveling benchmark for subsequent coarse and fine adjustment steps, thereby significantly improving the efficiency and final leveling accuracy of the entire leveling method.

[0043] In an embodiment of the present invention, the steps of calculating the deviation of each corner point of the support plate from the design elevation based on the elevation data, coarsely adjusting the support plate using the adjustable bolts, and then re-measuring using laser scanning to control the elevation deviation of each corner point within ±5mm include: Step S31: Filter and denoise the three-dimensional point cloud data, remove the background point cloud other than the support plate, extract the point cloud on the upper surface of the support plate using a plane fitting algorithm, identify the four corners and edge feature points of the support plate, and calculate the average elevation of each feature point. Step S32: Compare the elevation of each feature point with the design elevation, calculate the elevation deviation value, formulate an adjustment strategy based on the magnitude and distribution pattern of the deviation value, and determine the rotation direction and rotation angle of each adjustment nut; Step S33: After each round of adjustment, the three-dimensional laser scanner is used to re-measure with the resolution reduced to 5-10mm. The elevation deviation after adjustment is calculated. When the deviation of each corner point is ≤±5mm and the diagonal height difference is ≤10mm, the coarse adjustment is completed.

[0044] The point cloud segmentation and feature extraction steps aim to accurately separate the surface of the support plate from the original 3D point cloud data and locate its key feature points, providing an accurate data foundation for subsequent elevation calculations. Specifically, the acquired 3D point cloud data is first filtered and denoised to eliminate outliers and noise generated during the measurement process. Common methods include statistical filtering, radius filtering, or pass-through filtering. Then, background point clouds other than the support plate are removed using algorithms such as region growing, cluster analysis, or algorithms based on geometric constraints (e.g., height thresholding, shape matching), retaining only the 3D point cloud data of the support plate itself. Based on this, a plane fitting algorithm, such as RANSAC (Random Sample Consensus) or least squares, is used to fit the plane of the upper surface of the support plate from the point cloud, thereby extracting the upper surface point cloud of the support plate more accurately. Next, boundary detection algorithms (e.g., convex hull algorithm, edge extraction algorithm) or geometric shape analysis are used to identify the four corners and edge feature points of the support plate. Finally, the elevation average of each identified feature point set is calculated to reduce local measurement errors and obtain more stable and reliable average elevation values ​​of the feature points.

[0045] The purpose of the deviation calculation and adjustment strategy formulation steps is to accurately compare the extracted feature point elevations with the design elevation, quantify the elevation deviation, and formulate a specific adjustment plan accordingly. Specifically, the average elevation of each feature point is compared with the preset design elevation to calculate the elevation deviation value for each feature point. Based on these deviation values ​​(including positive and negative values ​​and magnitudes) and their distribution pattern on the support plate surface, the overall tilt direction and degree of the support plate can be determined, and a corresponding adjustment strategy can be formulated. For example, if the elevation of a certain corner point is too high, the corresponding adjusting nut needs to be rotated counterclockwise; if it is too low, it needs to be rotated clockwise. During this process, the rotation direction and angle of each adjusting nut need to be accurately determined. The amount of rise or fall produced by each 1° rotation of the adjusting nut is a key parameter, typically ranging from 0.007 to 0.014 mm. This parameter depends on the thread lead and pitch, and is obtained through pre-calibration or by consulting a technical manual. Based on this amount of rise or fall, combined with the calculated elevation deviation value, the specific rotation angle required for each adjusting nut can be accurately calculated.

[0046] The rapid verification step for coarse adjustment aims to quickly and efficiently evaluate the adjustment effect during the coarse adjustment process, avoiding over-adjustment or under-adjustment, and determining whether the coarse adjustment has met the preset requirements. Specifically, after each round of adjustment, a re-measurement is performed using the aforementioned 3D laser scanner at a reduced resolution of 5-10 mm. Reducing the scanning resolution significantly shortens the scanning time and improves verification efficiency because the accuracy requirements for local details are relatively low during the coarse adjustment stage. After the re-measurement, point cloud processing is performed again to calculate the elevation deviation of each feature point after adjustment. When the elevation deviation of all corner points is controlled within ±5 mm, and the diagonal height difference also meets the requirement of ≤10 mm, the coarse adjustment can be considered complete. This rapid verification mechanism ensures the efficiency of the coarse adjustment process and lays a good foundation for subsequent fine adjustment.

[0047] The above technical solution enables precise separation of the support plate surface from the original 3D point cloud data, identifying its key corner and edge feature points. This avoids the errors and inefficiencies that may arise from manual feature point selection, providing a reliable data foundation for subsequent deviation calculations. In the deviation calculation and adjustment strategy formulation steps, the elevations of these precisely extracted feature points are compared with the design elevation. Combined with the lifting characteristics of the adjusting nuts, the required rotation direction and angle for each adjusting nut can be systematically calculated, making the coarse adjustment process data-driven and significantly improving the accuracy and efficiency of the adjustment. Furthermore, the rapid verification step for the coarse adjustment effect, through retesting at reduced scanning resolution, enables rapid evaluation of the coarse adjustment effect. This ensures that, while meeting the coarse adjustment accuracy requirements (corner point deviation ≤ ±5mm and diagonal height difference ≤ 10mm), unnecessary repeated adjustments are avoided, effectively shortening the coarse adjustment cycle and laying a solid foundation for subsequent fine adjustment. Overall, the introduction of these steps makes the coarse adjustment process of the support plate more intelligent, efficient, and precise, significantly improving the quality and efficiency of the leveling operation.

[0048] In an embodiment of the present invention, the steps of using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolt at the corresponding position according to the deviation value for fine adjustment to make the levelness of the support plate ≤1 mm / m include: Step S41: The three-dimensional laser scanner is used to perform a dense scan on the upper surface of the support plate at a resolution of 1-2 mm, with a scanning angle step of ≤0.02°, to acquire high-density point cloud data with a point cloud density of ≥5000 points / m. 2 This ensures that local unevenness and minor defects can be identified; Step S42: The dense point cloud data is processed into a grid to establish a second digital elevation model with a grid size of 2mm*2mm. The elevation value of each grid is calculated by an interpolation algorithm to generate the elevation contour map and three-dimensional visualization model of the support plate surface. Step S43: Analyze the elevation distribution based on the three-dimensional visualization model, divide the surface of the support plate into several adjustment areas, each area corresponding to one or more adjustment nuts, calculate the average elevation and target adjustment amount of each area, independently rotate the corresponding adjustment nut to make a micro-adjustment, with a single adjustment amount ≤0.5mm, and re-measure after adjustment until the levelness is ≤1mm / m.

[0049] To obtain more detailed geometric information of the upper surface of the support plate, this application employs the aforementioned 3D laser scanner to perform dense scanning of the upper surface of the support plate at a resolution of 1–2 mm. This scanning process ensures that the laser beam forms an extremely high sampling density on the support plate surface by setting the scanning angle step to less than or equal to 0.02°, thereby acquiring high-density point cloud data with a point cloud density greater than or equal to 5000 points / m². This high-density point cloud data can capture extremely minute undulations, local unevenness, and potential minor defects on the support plate surface, providing a sufficient data foundation for subsequent fine leveling. In practical operation, high-resolution and high-density point cloud acquisition can be achieved by adjusting the scanning mode, scanning speed, and distance from the support plate of the 3D laser scanner. For example, a line scan mode or multi-angle repeated scanning can be used to increase data coverage and density.

[0050] After acquiring high-density point cloud data, it needs to be meshed to construct a second digital elevation model of the bearing plate surface. Specifically, the point cloud data is projected onto a two-dimensional plane and divided into 2mm*2mm grids. For each grid, its elevation value is calculated using an interpolation algorithm. Commonly used interpolation algorithms include inverse distance weighted interpolation, Kriging interpolation, natural neighbor interpolation, or triangular mesh interpolation. These algorithms can estimate the elevation of unknown grid points based on the elevation information of surrounding known points, thereby generating a continuous surface model. In this way, an elevation contour map of the bearing plate surface can be generated, intuitively displaying the elevation change trend, and further constructing a three-dimensional visualization model, allowing operators to comprehensively observe the elevation distribution and uneven areas of the bearing plate surface from different angles. For example, elevation differences can be represented by color coding, or local slopes can be analyzed through profiles.

[0051] Based on the generated second digital elevation model and the three-dimensional visualization model, the elevation distribution on the surface of the support plate is analyzed in detail. According to the analysis results, the surface of the support plate is divided into several independent adjustment zones. Each adjustment zone corresponds to one or more adjustment nuts, which are responsible for the local elevation adjustment of that zone. For example, for a rectangular support plate with four adjustable nuts, its surface can be divided into four quadrants, each quadrant controlled by one nut for adjustment. For each zone, the deviation between its average elevation value and the design target elevation is calculated, and the corresponding target adjustment amount is determined. Subsequently, the operator independently rotates the adjustment nut corresponding to the zone for fine-tuning, with each adjustment controlled to be less than or equal to 0.5 mm to avoid over-adjustment. After adjustment, a laser scan is performed again for re-measurement until the overall levelness of the support plate reaches an accuracy requirement of less than or equal to 1 mm / m.

[0052] Through the above technical solution, this application can effectively solve the problem of potential local unevenness and slight tilting on the surface of the support plate after coarse adjustment. High-resolution dense point cloud acquisition ensures that all details of the support plate surface, including minute defects and local undulations, can be accurately captured. Based on this, the constructed second digital elevation model and the three-dimensional visualization model provide a comprehensive and intuitive view of the elevation distribution on the support plate surface, enabling operators to clearly identify and quantify the deviations of each region. By dividing the support plate surface into multiple adjustment regions and independently and micro-adjusting the adjustable nuts corresponding to each region, precise control of the local elevation of the support plate can be achieved, avoiding the drawbacks of "paying attention to one aspect but losing attention to another" in traditional coarse adjustment methods. This refined regional micro-adjustment strategy, combined with high-precision data support, ultimately ensures that the overall levelness of the support plate meets extremely high requirements (less than or equal to 1 mm / m), significantly improving the accuracy and reliability of leveling and providing a solid foundation for the safety and stability of the superstructure.

[0053] In an embodiment of the present invention, the step of retesting after an interval of 12 to 24 hours to verify the stability of the levelness and generating a leveling acceptance report containing the three-dimensional point cloud data includes: Step S51: Use a torque wrench to tighten the locking nut in 2-3 stages diagonally. Apply 50% of the design torque for the first stage, 75% of the design torque for the second stage, and 100% of the design torque for the third stage. Step S52: After locking, let it stand for 12 to 24 hours to allow the support plate to complete the initial settlement under its own weight and preload, and then re-measure it to generate a re-measured digital elevation model and calculate the change in levelness. Step S53: Compare the second digital elevation model of the support plate surface with the remeasured digital elevation model. If the change in levelness is ≤0.3mm / m, the leveling is deemed qualified, and a complete archive containing initial three-dimensional point cloud data, adjustment process data, and final acceptance three-dimensional point cloud data is generated.

[0054] In the graded locking process, after fine-tuning, a torque wrench is used to tighten the locking nut in 2-3 stages diagonally. The first stage applies 50% of the design torque, the second stage applies 75% of the design torque, and the third stage applies 100% of the design torque. The design torque is determined based on the bolt diameter; for example, 150-200 N·m for an M20 bolt and 250-320 N·m for an M24 bolt. Tightening the locking nut is a crucial step in fixing the position of the support plate. The graded locking and diagonal sequence avoids applying excessive torque at once, which could lead to uneven stress on the support plate and cause new deformation or warping. Applying torque in stages allows the support plate to slowly settle and adapt under gradually increasing preload, reducing stress concentration and ensuring a smooth locking process. The use of a torque wrench ensures the accuracy of torque application, while determining the design torque based on the bolt diameter ensures the rationality of the bolt preload, effectively fixing the support plate while avoiding bolt overload or underload.

[0055] Following this, the aging retesting step begins. After locking, the support plate is left to stand for 12–24 hours to allow it to settle initially under its own weight and preload. After this, a retest is performed using the same laser scanning parameters as in the fine-tuning step, generating a retested digital elevation model and calculating the change in levelness. After locking the nuts, the support plate is subjected to the combined effects of bolt preload, its own weight, and potential structural stresses. These effects may cause slight settlement or deformation, known as "initial settlement" or "stress release." The 12–24 hour standing period allows sufficient time for the support plate to complete this initial settlement process and stabilize. After this, a retest using the same laser scanning parameters as in the fine-tuning step acquires accurate three-dimensional point cloud data of the support plate in a stable state, generating a retested digital elevation model. This model is then compared with the fine-tuned model to quantify the change in levelness.

[0056] Finally, perform the stability determination and acceptance steps. Compare the second digital elevation model of the bearing plate surface with the复测 digital elevation model. If the change in levelness ≤ 0.3 mm / m and the elevation deviation of each measuring point is still within the allowable range, it is determined that the leveling is qualified, and a complete file containing the initial three-dimensional point cloud data, adjustment process data, and final acceptance three-dimensional point cloud data is generated. This step is the final link in the leveling quality control. By comparing the second digital elevation model after fine adjustment with the复测 digital elevation model after aging复测, the levelness stability of the bearing plate after locking and static placement can be accurately evaluated. Setting the change in levelness ≤ 0.3 mm / m as the qualified standard is a strict and quantifiable indicator, ensuring the reliability of the bearing plate during long-term use. At the same time, requiring the elevation deviation of each measuring point to still be within the allowable range further guarantees the overall flatness. Finally, generating a complete file containing the initial, adjustment process, and final acceptance three-dimensional point cloud data not only provides a traceable basis for the project quality but also provides basic data for subsequent monitoring and maintenance.

[0057] Through the above technical solution, after the fine adjustment of the bearing plate is completed, the hierarchical locking step is adopted. The locking nuts are tightened in 2 - 3 levels in the diagonal order by a torque wrench, and the precise designed torque is applied according to the bolt diameter, effectively avoiding the deformation or warping of the bearing plate caused by applying excessive torque at one time or uneven force, ensuring the smoothness of the locking process. Subsequently, through the aging复测 step, the bearing plate is given a static placement time of 12 - 24 hours to complete the initial settlement under its own weight and pre-tightening force, so that the state of the bearing plate tends to be stable. On this basis, the复测 is carried out using the same laser scanning parameters as the fine adjustment step to obtain the precise three-dimensional point cloud data of the bearing plate in the stable state and generate the复测 digital elevation model. Finally, through the stability determination and acceptance steps, the复测 digital elevation model is compared with the second digital elevation model after fine adjustment, with the change in levelness ≤ 0.3 mm / m as the strict qualified standard, and requiring the elevation deviation of each measuring point to still be within the allowable range, so as to accurately evaluate the levelness stability of the bearing plate after locking and static placement, effectively solving the problem that it is difficult to guarantee the long-term stability of the bearing plate after fine adjustment. This not only improves the leveling quality and reliability of the bearing plate but also provides a traceable digital basis for the project quality, ensuring the safety and service life of the bridge structure.

[0058] In the embodiment of the present invention, the step of densely scanning the upper surface of the bearing plate with the three-dimensional laser scanner at a resolution of 2 - 3 mm to generate the second digital elevation model of the bearing plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions for fine adjustment according to the deviation value to make the levelness of the bearing plate ≤ 1 mm / m further includes: Step S410: The three-dimensional laser scanner is used to perform periodic rapid scanning at intervals of 5 to 10 seconds to obtain the elevation changes of the support plate surface in real time. The scanning resolution is set to 3 to 5 mm to realize dynamic monitoring of the adjustment process. Step S420: Compare the point cloud data obtained by real-time scanning with the target elevation model, calculate the current elevation deviation, and display the remaining adjustment amount of each adjustment point in real time through a visualization interface; Step S430: When the elevation of a certain area reaches the target value in real time, immediately stop the operation of the corresponding adjusting nut in that area and switch to the adjustment of the adjacent area.

[0059] The real-time scanning and monitoring step refers to the use of the 3D laser scanner to perform periodic, rapid scans at 5-10 second intervals during the fine-tuning process. This acquires real-time elevation changes on the support plate surface, with a scan resolution of 3-5 mm, enabling dynamic monitoring of the adjustment process. This step aims to provide continuous, near-real-time data on the support plate surface condition. By setting a shorter scan interval, such as 5 or 10 seconds, the system can quickly capture elevation changes on the support plate surface after each fine-tuning operation. Setting the scan resolution to 3-5 mm, compared to the dense scanning (1-2 mm resolution) in the fine-tuning stage, significantly shortens the scanning time while maintaining sufficient accuracy, thus meeting real-time requirements. This dynamic monitoring mechanism allows operators to instantly understand the impact of each adjustment operation on the support plate elevation, providing a data foundation for subsequent precise adjustments.

[0060] The real-time deviation feedback step involves comparing the point cloud data acquired through real-time scanning with the target elevation model, calculating the current elevation deviation, and displaying the remaining adjustment amount for each adjustment point in real-time through a visual interface, guiding operators to precisely control the adjustment range. In this step, the processing software receives point cloud data from the real-time scanning monitoring step and quickly compares it with a preset target elevation model representing the ideal level state. The algorithm calculates the current elevation deviation for each area on the support plate surface or the corresponding position of each adjustable bolt, and further calculates the remaining adjustment amount required to achieve the target levelness. This information is presented to the operator through an intuitive visual interface; for example, areas with high or low elevation can be displayed on the screen using color coding, or the specific amount of upward or downward rotation required for each adjustable bolt can be directly displayed. This real-time, quantitative feedback greatly reduces the difficulty of judgment for operators, enabling them to accurately grasp the force and direction of each adjustment.

[0061] The closed-loop control adjustment step refers to immediately stopping the operation of the corresponding adjusting nut in a certain area when the elevation of that area is detected to have reached the target value, and then switching to the adjustment of the adjacent area to avoid over-adjustment and achieve closed-loop control of the leveling process. This step is the key to achieving intelligence and efficiency in the entire fine-tuning process. Based on the data provided by the real-time deviation feedback step, the system can continuously judge the leveling status of each area of ​​the support plate. Once the elevation deviation of a certain area meets the preset accuracy requirements (i.e., reaches the target value), the system will immediately issue an instruction or prompt, instructing the operator to stop operating the corresponding adjusting nut in that area. Subsequently, the focus of operation will automatically or manually shift to the adjacent area that has not yet reached the target value. This mechanism effectively avoids over-adjustment caused by operator error or lack of experience, ensuring that each area can accurately reach the target elevation, thereby improving the overall leveling accuracy and efficiency.

[0062] Through the above technical solution, real-time, dynamic monitoring of the leveling status can be achieved during the fine-tuning of the bearing plate, providing operators with precise feedback on the adjustment amount. This real-time feedback mechanism, combined with a closed-loop control strategy, allows operators to more accurately control the rotation of each adjustable bolt, avoiding the problems of repeated trial and error and over-adjustment in traditional methods. This not only significantly improves the efficiency and accuracy of fine-tuning, ensuring that the final levelness of the bearing plate meets stringent requirements, but also reduces reliance on operator experience, making the leveling process more standardized and controllable. Ultimately, this solution effectively improves the overall quality and construction efficiency of bearing plate leveling.

[0063] In an embodiment of the present invention, after using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions according to the deviation values ​​for fine adjustment to make the levelness of the support plate ≤ 1 mm / m, the leveling method for the support plate further includes: Step S501: Select no less than 10 feature points on the upper surface of the support plate, perform contact measurement, obtain the elevation value of each feature point, and compare it with the point cloud elevation of the same location obtained by the three-dimensional laser scanner. Step S502: Calculate the elevation difference between the 3D laser scanner and the contact measurement. If the difference is ≤0.5mm, the accuracy of the 3D laser scanner is determined to meet the requirements. If the difference is >0.5mm, the 3D laser scanner is calibrated. Step S503: Record the comparison measurement results and calibration data, and generate a laser scanning accuracy verification report as an attachment to the leveling acceptance report.

[0064] The point cloud and measured comparison step aims to verify the accuracy of the laser scanning data and identify potential systematic errors. Specifically, at least 10 feature points are selected on the upper surface of the support plate. These feature points should be evenly distributed at key locations such as the four corners, center, and edges of the support plate to ensure representativeness of the entire measurement area. Then, using a high-precision contact measuring tool, such as a digital level, precision altimeter, or total station with a prism, the elevation of these selected feature points is measured to obtain their precise absolute elevation values. Simultaneously, the point cloud elevation values ​​corresponding to the locations of these feature points are extracted from the point cloud data acquired by the 3D laser scanner. Finally, the elevation values ​​obtained from the contact measurement are compared point-by-point with the point cloud elevations at the same locations obtained from the laser scanning, and the differences between the two are calculated.

[0065] The systematic error calibration step is used to eliminate or reduce the systematic errors of the 3D laser scanner and ensure measurement accuracy. After the comparative measurement is completed, the elevation difference calculated in the point cloud and actual measurement comparison step is used to determine whether the accuracy of the 3D laser scanner meets the requirements. A precision threshold is usually set, such as 0.5 mm. If the elevation difference of all or most feature points is less than or equal to 0.5 mm, the accuracy of the 3D laser scanner is considered to meet the requirements, and subsequent operations can continue. If the difference is greater than 0.5 mm, it indicates that the 3D laser scanner may have systematic errors and needs to be calibrated. Calibration methods may include adjusting internal instrument parameters, such as zero-point offset and scaling factor, to correct the overall measurement deviation; or, if the error is large or cannot be resolved by parameter adjustment, the 3D laser scanner needs to be recalibrated. This usually involves scanning using a standard target range or an object of known size, and using specialized software to calculate and correct the scanner's internal geometric parameters and distance measurement parameters to eliminate systematic errors. During the calibration process, the influence of environmental factors such as temperature, humidity, and atmospheric pressure on laser ranging must also be considered and compensated accordingly.

[0066] The point cloud accuracy report generation step aims to provide objective proof of the accuracy of laser scanning data, enhancing the reliability of the leveling acceptance report. This step requires detailed recording of the contact measurement elevation, laser scanning elevation, elevation difference, and statistical data such as average difference, maximum difference, and standard deviation for all feature points in the point cloud and measured comparison steps. Simultaneously, if systematic error calibration was performed, information such as parameter adjustments and calibration results during the calibration process must also be recorded. This data will be compiled into a laser scanning accuracy verification report according to a standardized document format. This report should include key information such as project name, date, scanner model, calibration personnel, calibration method, comparison results, calibration results, and conclusions. Finally, this laser scanning accuracy verification report will serve as an attachment to the leveling acceptance report, providing traceability and verification of the accuracy of the leveling results.

[0067] By introducing independent, high-precision contact measurements as a reference to cross-validate the laser scanning data through the above technical solution, potential systematic errors in the fine-tuning of the 3D laser scanner can be detected in a timely manner, avoiding leveling deviations caused by inaccurate measurement data. The systematic error calibration step ensures the reliability of the scanner's data in subsequent leveling operations, enabling the fine-tuning process to be based on high-precision measurement results, thereby significantly improving the final accuracy and stability of the support plate leveling. The point cloud accuracy report generation step provides objective quality control evidence for the entire leveling process, enhancing the credibility of the leveling acceptance report and ensuring that the support plate's levelness truly meets design requirements.

[0068] In an embodiment of the present invention, a three-dimensional laser scanner is arranged above the support plate, with a scanning resolution of 1-5 mm and a scanning angle covering the entire surface of the support plate to acquire three-dimensional point cloud data of the support plate in its initial state. Before the step of establishing a first digital elevation model of the support plate surface using processing software and extracting the elevation values ​​of each feature point as a leveling benchmark to form elevation data, the leveling method for the support plate further includes: Step S101: When the ambient light intensity is >10000 lux, a narrow-band filter is installed in front of the lens of the three-dimensional laser scanner to filter out stray light that is inconsistent with the laser wavelength, or scanning is performed under low light conditions such as night or cloudy days to improve the signal-to-noise ratio of point clouds. Step S102: When the dust concentration at the construction site is high, a pulsed laser scanner is used to distinguish the echo signal using the time-of-flight method and filter out the interference signal of dust scattering, or a fog cannon is used to reduce dust before scanning to reduce the influence of dust on laser scattering. Step S103: When the ambient temperature is >35℃, perform heat dissipation treatment on the three-dimensional laser scanner, or use a high-temperature resistant scanner, and perform temperature compensation calibration before and after scanning to eliminate instrument drift caused by temperature.

[0069] The strong light environment adaptation steps aim to address the interference of high-intensity ambient light on the measurement accuracy of 3D laser scanners. When the ambient light intensity exceeds 10,000 lux, such as during a sunny day or under strong lighting conditions, excessive stray light enters the receiver of the 3D laser scanner, interfering with the signal light emitted by the laser emitter, reducing the signal-to-noise ratio, and causing noise or measurement errors in the point cloud data. One solution is to install a narrowband filter in front of the lens of the 3D laser scanner. This filter is designed to allow only light of the same wavelength as the laser to pass through, effectively filtering out stray light of other wavelengths and ensuring that the received signal primarily originates from the laser reflected from the target. Another solution is to perform scanning during periods of lower ambient light intensity, such as at night, on cloudy days, or in the early morning / evening, to naturally reduce the impact of stray light, thereby improving the signal-to-noise ratio and measurement accuracy of the point cloud data.

[0070] The dusty environment adaptation steps aim to address the impact of construction site dust on the measurement results of 3D laser scanners. In environments with high dust concentrations, the laser beam is scattered by dust particles during propagation, resulting in partial laser energy loss and generating false echo signals, thus affecting the integrity and accuracy of point cloud data. One solution is to use a pulsed laser scanner. Pulsed laser scanners utilize the time-of-flight principle for measurement, determining distance by precisely measuring the time difference between laser pulse emission and reception. When the laser encounters dust particles and is scattered, the echo time of these scattered signals differs from the echo time of the main signal reflected from the support plate surface. Pulsed laser scanners can distinguish and filter out these interference signals generated by dust scattering. Another solution is to take physical dust reduction measures before scanning, such as using a mist cannon to spray water mist into the air, causing dust particles to absorb the water mist and settle, thereby reducing the dust concentration in the air and minimizing laser scattering.

[0071] The high-temperature environment adaptation steps aim to address the impact of high-temperature environments on the performance and measurement stability of 3D laser scanners. When the ambient temperature exceeds 35°C, the electronic components inside the 3D laser scanner may overheat, leading to performance degradation, measurement drift, or even equipment malfunction. One approach is to implement heat dissipation measures for the 3D laser scanner, such as by adding active cooling devices (e.g., fans, heat sinks) or using a liquid cooling system to maintain the equipment within a suitable operating temperature range. Another approach is to directly use a high-temperature resistant scanner, which is designed and uses materials with stable operation in high-temperature environments in mind. Furthermore, regardless of the scanner used, temperature compensation calibration is required before and after scanning in high-temperature environments. This involves measuring the ambient temperature and the instrument's internal temperature, and correcting the measurement data according to a preset temperature-error model to eliminate measurement errors caused by deformation of the instrument's internal structure or drift in electronic characteristics due to temperature changes.

[0072] Through the above technical solutions, this application effectively solves the problem of data acquisition by 3D laser scanners being easily interfered with and experiencing a decrease in accuracy under harsh construction environments such as strong light, dust, and high temperatures. The strong light environment adaptation step significantly improves the signal-to-noise ratio of the laser signal by using narrow-band filters or selecting scanning periods with low light, ensuring clear and accurate point cloud data can still be acquired under complex lighting conditions. The dusty environment adaptation step effectively suppresses the interference of dust scattering on laser measurements by using the time-of-flight method of pulsed laser scanners or dust suppression with fog cannons, ensuring the integrity and reliability of point cloud data. The high temperature environment adaptation step maintains the stable operating performance of the 3D laser scanner at high temperatures through heat dissipation treatment, the use of high-temperature resistant scanners, and temperature compensation calibration, eliminating measurement errors caused by temperature drift. These environmental adaptation measures ensure the accuracy and reliability of the initial 3D point cloud data and the first digital elevation model used for bearing plate leveling under various harsh environments, providing a solid data foundation for subsequent coarse and fine adjustments. This significantly improves the accuracy, stability, and applicability of the entire bearing plate leveling method, making the leveling results more in line with design requirements and reducing the risk of rework due to environmental factors.

[0073] In an embodiment of the present invention, the step of pre-embedding the support plate at the designed position at the beam end using adjustable bolts before pouring the beam concrete includes: Step S11: Use a laser line projector or laser theodolite to project the outline of the support plate and the center line of the bolts onto the steel reinforcement skeleton of the beam, and mark the position of each of the pre-embedded bolts. The layout accuracy is ≤2mm. Step S12: After the pre-embedded screw is installed, use a laser plumb bob or laser vertical gauge to calibrate the verticality of the screw to ensure that the verticality deviation of the screw is ≤1°. If the deviation exceeds the limit, it can be corrected by adjusting the fastener. Step S13: Before pouring concrete, use laser scanning or a total station to remeasure the three-dimensional coordinates of each of the pre-embedded screws and record the initial position deviation as a correction parameter for later leveling calculations.

[0074] Specifically, in the laser layout step, a laser line projector or laser theodolite is used to project the outline of the support plate and the center line of the bolts onto the steel reinforcement cage of the beam, and to mark the positions of each embedded bolt, ensuring a layout accuracy of ≤2mm. This step aims to directly project the precise positional information from the design drawings onto the steel reinforcement cage on the construction site using high-precision laser projection technology, providing an accurate positioning benchmark for the subsequent installation of embedded bolts. The laser line projector or laser theodolite can provide clear and stable laser lines or points, allowing operators to accurately place the embedded bolts based on these laser marks, effectively avoiding human error and cumulative error that may be introduced by traditional measurement methods, and ensuring the initial accuracy of the embedded positions.

[0075] In the verticality laser calibration step, after the pre-embedded screw is installed, a laser plumb bob or laser vertical gauge is used to calibrate the screw's verticality, ensuring that the verticality deviation is ≤1°. If the deviation exceeds the limit, it is corrected by adjusting the fixing components. The purpose of this step is to ensure that the pre-embedded screw is in the vertical position required by the design before concrete pouring. The laser plumb bob or laser vertical gauge can provide a high-precision vertical reference line, allowing operators to visually compare the actual verticality of the pre-embedded screw with the laser reference line and make timely fine adjustments to the fixing components to eliminate or reduce the screw's tilt. Ensuring the screw's verticality is crucial for the smooth rotation of the subsequent adjusting nut, preventing the screw from jamming or being unbalanced under load, thus ensuring the stability of the leveling process and the reliability of the final leveling effect.

[0076] In the location re-measurement step, before concrete pouring, the three-dimensional coordinates of each pre-embedded screw are re-measured using laser scanning or a total station, and the initial position deviation is recorded as a correction parameter for subsequent leveling calculations. This step aims to perform precise "as-built" measurements of the final installation position of the pre-embedded screws, obtaining their actual coordinates in three-dimensional space. Using high-precision measuring equipment such as laser scanning or a total station, the X, Y, and Z coordinate data of each pre-embedded screw can be comprehensively and accurately obtained. These measured data are compared with the design coordinates, and the calculated initial position deviation is recorded and used as input parameters for subsequent leveling algorithms. This means that during coarse and fine adjustments, the system can intelligently correct based on these initial deviation data, rather than simply using the design position as the sole benchmark, thus making the leveling process more accurate and efficient.

[0077] By employing refined pre-embedded installation methods such as laser layout, laser verticality calibration, and position re-measurement before the concrete pouring of the beam, this application significantly improves the initial installation accuracy and verticality of the pre-embedded bolts in the support plate. Laser layout ensures the accurate projection of the support plate outline and bolt centerline, controlling the initial positioning deviation within a minimal range. Laser verticality calibration effectively prevents bolt tilting, ensuring smoothness and uniform stress distribution during subsequent adjustments. Furthermore, precise position re-measurement before concrete pouring provides accurate initial deviation data for subsequent leveling calculations, enabling more precise corrections to the leveling algorithm. These measures work together to significantly reduce the initial elevation and levelness deviations of the support plate, thereby reducing the workload of subsequent coarse and fine adjustments, improving leveling efficiency, and ultimately ensuring the overall accuracy and stability of the support plate leveling, laying a solid foundation for the successful implementation of the entire leveling method.

[0078] In an embodiment of the present invention, the steps of tightening the locking nut, retesting after an interval of 12 to 24 hours to verify the stability of the levelness, and generating a leveling acceptance report containing the three-dimensional point cloud data further include: Step S510: Register and overlay the laser scanning point cloud data from the initial state, after coarse adjustment, after fine adjustment, and after locking and retesting to generate a three-dimensional animation of the leveling process, which intuitively shows the leveling process of the support plate. Step S520: Based on the final acceptance digital elevation model, generate a heat map of elevation deviation on the surface of the bearing plate, and use different colors to mark the elevation deviation area: red indicates too high, blue indicates too low, and green indicates that it meets the standard, as a visual basis for quality acceptance. Step S530: The laser scan point cloud after leveling is integrated with the BIM model of the bearing plate design to construct a digital twin model of the bearing plate, and the leveling parameters and measured data are recorded as a benchmark archive for bridge operation monitoring and maintenance.

[0079] The multi-stage point cloud comparison step aims to register and overlay multiple stages of laser scanning point cloud data—initial state, after coarse adjustment, after fine adjustment, and after locking and re-measurement—to generate a 3D animation of the leveling process, intuitively demonstrating the leveling process of the support plate. Specifically, at each key stage of support plate leveling—initial state, after coarse adjustment, after fine adjustment, and after locking and re-measurement—a 3D laser scanner is used to scan the upper surface of the support plate to obtain corresponding 3D point cloud data. These multi-stage point cloud data are aligned in a unified coordinate system using a high-precision registration algorithm (e.g., the Iterative Closest Point (ICP) algorithm or a common control point-based conversion method). Subsequently, using professional point cloud processing software or 3D modeling software, the registered multi-stage point cloud data are overlaid and rendered in chronological order to generate a continuous 3D animation. This animation dynamically displays the evolution of the support plate surface elevation from uneven to gradually becoming flat. Users can observe the geometric changes of the support plate from different perspectives and at adjustable speeds, thus intuitively understanding the dynamic changes and effects of the entire leveling process.

[0080] The deviation heatmap generation step aims to generate a heatmap of elevation deviation on the bearing plate surface based on the final acceptance digital elevation model. Different colors are used to identify areas of elevation deviation: red indicates too high, blue indicates too low, and green indicates compliance, serving as a visual basis for quality acceptance. Specifically, during the final acceptance phase, a digital elevation model (DEM) of the bearing plate surface is obtained using a 3D laser scanner. This model contains the elevation values ​​of each measuring point or grid on the bearing plate surface. Each elevation value in the DEM is precisely compared with the design elevation or target horizontal plane to calculate the actual elevation deviation at each point. Based on these deviation values, a color mapping rule is established: for example, positive deviation (indicating too high) is mapped to red, negative deviation (indicating too low) to blue, and near-zero deviation (indicating compliance) to green. The larger the deviation, the higher the color saturation or brightness can be. Finally, these color maps are applied to the 2D planar projection or 3D model of the bearing plate to generate an intuitive elevation deviation heatmap. This heat map can clearly show which areas on the bearing plate surface have elevation deviations that are too high, too low, or within the standard, as well as the degree of deviation, providing a direct and quantitative visual basis for rapid assessment and acceptance of leveling quality.

[0081] The digital twin model construction process aims to integrate the laser-scanned point cloud data obtained after leveling with the BIM model of the bearing plate design, constructing a digital twin model of the bearing plate and recording leveling parameters and measured data as a benchmark archive for bridge operation monitoring and maintenance. Specifically, high-precision laser-scanned point cloud data representing the final state of the bearing plate, acquired after leveling, is deeply integrated with the pre-established bearing plate design BIM (Building Information Modeling). The integration process is achieved through precise coordinate transformation and model alignment technology, ensuring that the point cloud data and the BIM model are in a unified spatial reference system. The integrated data jointly construct the digital twin model of the bearing plate. This model not only contains the precise geometric information of the bearing plate but also integrates non-geometric information related to the leveling process, such as the final adjustment amount, locking torque, final levelness, elevation deviation heatmap, leveling date, operator information, and other key parameters and measured data for each adjustable bolt. This digital twin model has functions such as information query, visualization, and data update. Users can query the measured elevation and deviation values ​​of any area through the model or view relevant adjustment records. This model serves as a benchmark for bridge operation monitoring and maintenance, providing accurate and comprehensive data support for subsequent periodic monitoring, performance evaluation, and maintenance decisions.

[0082] Through the above technical solution, in the locking and re-inspection steps of the bearing plate leveling, not only is a leveling acceptance report containing 3D point cloud data generated, but further, through a multi-stage point cloud comparison step, the laser scanning point cloud data of the bearing plate at different leveling stages (initial, after coarse adjustment, after fine adjustment, and after locking and re-inspection) are registered and superimposed to generate a 3D change animation. This allows for a direct and dynamic display of the entire leveling process of the bearing plate from unevenness to final compliance, making the quality control and effect evaluation of the leveling process more transparent and comprehensive. Simultaneously, through the deviation heatmap generation step, based on the final acceptance digital elevation model, different colors are used to clearly indicate the elevation deviation of each area on the bearing plate surface: red indicates too high, blue indicates too low, and green indicates compliance. This provides an intuitive and quantitative visual basis for the acceptance of leveling quality, facilitating the rapid identification and location of potential local unevenness problems. Furthermore, through the digital twin model construction process, the laser-scanned point cloud after leveling is deeply integrated with the BIM model of the bearing plate design, constructing a digital twin model of the bearing plate containing leveling parameters and measured data. This not only provides accurate benchmark archives for monitoring and maintenance during the bridge's operational period but also greatly improves the integration of leveling results with the engineering design model, laying the foundation for full life-cycle management. These measures collectively address the shortcomings of traditional acceptance reports in terms of process visualization, deviation intuitiveness, and integration with the engineering model, significantly enhancing the refined management level and long-term value of the bearing plate leveling method.

[0083] The following example will provide a more detailed explanation of the above technical solution: At the installation site of a precast box girder bridge, the bearing plate located at the beam end needs to be leveled with high precision. The bearing plate measures 2 meters by 3 meters, and its levelness directly affects the safety and service life of the superstructure.

[0084] First, pre-installation is carried out before the concrete pouring of the beam. Construction workers use a laser line projector to precisely project the outline of the support plate and the center line of the adjustable bolts onto the beam's reinforcing steel skeleton, marking the positions of the pre-embedded bolts to ensure a layout accuracy within 2mm. Subsequently, the support plate is pre-embedded in the designed position at the beam end using the adjustable bolts. These adjustable bolts include pre-embedded bolts, adjusting nuts, and locking nuts, with the lower end of the pre-embedded bolts securely connected to the beam's reinforcing steel skeleton. After the pre-embedded bolts are installed, a laser plumb line is used to calibrate their verticality, ensuring the verticality deviation is controlled within 1°. Before concrete pouring, a laser scanner is used to re-measure the three-dimensional coordinates of each pre-embedded bolt, recording the initial positional deviation as a correction parameter for subsequent leveling calculations.

[0085] After the concrete pouring and curing are completed, the support plate is in its initial state. At this point, the laser scanning step to establish the baseline begins. Construction workers set up a 3D laser scanner approximately 3 meters above the support plate. Due to the large size of the support plate, the scanner's placement was optimized to ensure comprehensive coverage, with a coverage area greater than 2m x 2m. At least four reflective targets were affixed around the support plate and at its center, and their absolute coordinates were determined using a total station. These coordinates served as control points for subsequent point cloud data stitching and coordinate transformation, with the target positioning accuracy controlled within 1mm. The scanner's horizontal and vertical angular resolutions were both set to 0.02°, and the scanning time was set to 3 minutes to obtain high-density 3D point cloud data, achieving a point cloud density of 1500 points / m. 2 To ensure complete acquisition of surface details of the bearing plate, a narrow-band filter is added to the scanner lens if the ambient light intensity is high to improve the signal-to-noise ratio. The acquired initial 3D point cloud data is filtered and denoised using processing software to remove background point clouds. A plane fitting algorithm is then used to extract the point cloud on the upper surface of the bearing plate, identifying the four corners and edge feature points of the bearing plate and calculating the average elevation of each feature point. These elevation values ​​are extracted as leveling benchmarks, forming elevation data and establishing the first digital elevation model of the bearing plate surface.

[0086] The next step is the coarse adjustment. Based on the elevation data, the deviation of each corner point of the support plate from the design elevation is calculated. For example, if one corner point is 8mm higher than the design elevation, and another corner point is 6mm lower, an adjustment strategy is formulated based on these deviation values ​​and distribution patterns, determining the rotation direction and angle of each adjusting nut. For example, each 1° rotation of the adjusting nut produces approximately 0.01mm of elevation adjustment. The operator performs coarse adjustments to the support plate using the adjustable bolts. After each round of adjustment, a 3D laser scanner is used for rapid remeasurement at a resolution of 5mm to calculate the adjusted elevation deviation. When the elevation deviation of each corner point is controlled within ±5mm, and the diagonal height difference is less than 10mm, the coarse adjustment is complete.

[0087] The fine-tuning process then commenced. A 3D laser scanner was used to perform a dense scan of the upper surface of the support plate at a resolution of 1.5mm, with a scanning angle step set to 0.01°, achieving a point cloud density of 6000 points / m. 2High-density point cloud data is used to ensure the identification of local unevenness and minor defects. The dense point cloud data is meshed to create a second digital elevation model with a grid size of 2mm*2mm. An interpolation algorithm is used to calculate the elevation value of each grid cell, generating a contour map and a 3D visualization model of the support plate surface. Based on the elevation distribution analysis of the 3D visualization model, the support plate surface is divided into several adjustment zones, each corresponding to one or more adjustment nuts. The average elevation and target adjustment amount for each zone are calculated. The corresponding adjustment nuts are rotated independently for fine-tuning, with each adjustment controlled within 0.3mm. During fine-tuning, a 3D laser scanner performs periodic rapid scans at 10-second intervals with a scanning resolution of 4mm, acquiring real-time elevation changes on the support plate surface to achieve dynamic monitoring of the adjustment process. The real-time point cloud data is compared with the target elevation model to calculate the current elevation deviation. The remaining adjustment amount for each adjustment point is displayed in real-time through a visualization interface, guiding operators to precisely control the adjustment range. When the elevation of a certain area reaches the target value, the operation of the corresponding adjusting nut in that area is immediately stopped, and the adjustment is switched to the adjacent area to avoid over-adjustment and achieve closed-loop control of the leveling process. Through this fine adjustment, the levelness of the support plate is finally achieved to 1 mm / m. After fine adjustment, 10 feature points are selected on the upper surface of the support plate for contact measurement to obtain the elevation value of each feature point, which is then compared with the point cloud elevation of the same location obtained by the 3D laser scanner. If the difference is greater than 0.5 mm, the 3D laser scanner is calibrated, the instrument parameters are adjusted, and system errors are eliminated.

[0088] Finally, the locking and verification steps are performed. After fine-tuning, the locking nut is tightened in three stages diagonally using a torque wrench. The first stage applies 50% of the design torque, the second stage applies 75% of the design torque, and the third stage applies 100% of the design torque. For example, if the bolt is M24 and the design torque is 300 N·m, then torques of 150 N·m, 225 N·m, and 300 N·m are applied in stages. After locking, the plate is left to stand for 18 hours to allow the support plate to complete its initial settlement under its own weight and preload. Subsequently, a re-measurement is performed using the same laser scanning parameters as in the fine-tuning steps, generating a re-measured digital elevation model and calculating the change in levelness. By comparing the second digital elevation model of the support plate surface with the re-measured digital elevation model, if the change in levelness is less than 0.3 mm / m and the elevation deviation of each measuring point is still within the allowable range, the leveling is deemed qualified. Finally, a complete leveling acceptance report is generated, containing initial 3D point cloud data, adjustment process data, and final acceptance 3D point cloud data. This report also includes a laser scanning accuracy verification report as an attachment. Simultaneously, multiple phases of laser scanning point cloud data—initial state, after coarse adjustment, after fine adjustment, and after locked re-measurement—are registered and overlaid to generate a 3D animation of the leveling process, visually demonstrating the leveling history of the bearing plate. Based on the final acceptance digital elevation model, a heat map of elevation deviation on the bearing plate surface is generated, with different colors used to mark areas of elevation deviation, serving as a visual basis for quality acceptance. After leveling is completed, the laser scanning point cloud is integrated with the bearing plate design BIM model to construct a digital twin model of the bearing plate, recording leveling parameters and measured data as a benchmark archive for bridge operation monitoring and maintenance.

[0089] Compared to traditional single-point leveling, this method acquires high-density point cloud data of the entire surface of the support plate through three-dimensional laser scanning, solving the problem of limited measurement points and difficulty in comprehensively reflecting the overall flatness of traditional methods. Simultaneously, laser scanning avoids parallax errors caused by manual readings, improving measurement accuracy and reducing reliance on operator experience. More importantly, this method integrates laser scanning with the coarse-fine-tuning-locking process of adjustable bolts. In particular, the introduction of real-time scanning monitoring and closed-loop control adjustment during the fine-tuning process enables dynamic monitoring and accuracy closed-loop control of the leveling process. This effectively solves the problem that existing laser measurement methods are only used for static detection and fail to be linked with the adjustment process in real time, significantly improving the efficiency and accuracy of support plate leveling.

[0090] The above description is merely an exemplary embodiment of the present invention and does not limit the scope of protection of the present invention. Any equivalent structural transformations made based on the technical concept of the present invention and the contents of the specification and drawings of the present invention, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A leveling method for a support plate, characterized in that, The leveling method for the support plate includes: Before the concrete of the beam is poured, the support plate is pre-embedded in the designed position at the beam end by adjustable bolts; wherein, the adjustable bolts include pre-embedded screws, adjusting nuts and locking nuts, and the lower end of the pre-embedded screws is fixedly connected to the beam reinforcement skeleton; A three-dimensional laser scanner is arranged above the support plate, with a scanning resolution of 1 to 5 mm and a scanning angle covering the entire surface of the support plate. The scanner acquires three-dimensional point cloud data of the support plate in its initial state. The first digital elevation model of the support plate surface is established through processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks to form elevation data. Based on the elevation data, the deviation of each corner point of the support plate from the design elevation is calculated. The support plate is then coarsely adjusted using the adjustable bolts. After the coarse adjustment, the elevation deviation of each corner point is re-measured by laser scanning to ensure that it is controlled within ±5mm. The three-dimensional laser scanner is used to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface. The levelness deviation of each measuring point is calculated. The adjustable bolts at the corresponding positions are independently rotated according to the deviation value for fine adjustment so that the levelness of the support plate is ≤1 mm / m. Tighten the locking nut, and retest after 12 to 24 hours to verify the stability of the levelness, and generate a leveling acceptance report containing the three-dimensional point cloud data.

2. The leveling method for a support plate as described in claim 1, characterized in that, A three-dimensional laser scanner is positioned above the support plate, with a scanning resolution of 1–5 mm and a scanning angle covering the entire surface of the support plate. This acquires three-dimensional point cloud data of the support plate in its initial state. A first digital elevation model of the support plate surface is then established using processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks. The steps for generating elevation data include: The three-dimensional laser scanner is installed at a height of 2-5m above the support plate. The number of three-dimensional laser scanners is determined according to the size of the support plate, so that the coverage area of ​​a single three-dimensional laser scanner is ≥2m*2m. For large support plates, multiple three-dimensional laser scanners are arranged around the support plate, and the overlap area between two adjacent three-dimensional laser scanners is ≥20%. Reflective targets are attached around the support plate and at its center. The number of targets is ≥4. The absolute coordinates of the targets are determined by a total station or GPS and used as control points for the stitching and coordinate transformation of the three-dimensional point cloud data. The target positioning accuracy is ≤1mm. The horizontal angular resolution of the 3D laser scanner is set to 0.01°~0.05°, the vertical angular resolution is set to 0.01°~0.05°, the scanning time is ≥2 minutes, and the acquired point cloud density is ≥1000 points / m². 2 This ensures that the surface details of the support plate are completely captured.

3. The leveling method for a support plate as described in claim 2, characterized in that, Based on the elevation data, the steps of calculating the deviation of each corner point of the support plate from the design elevation, coarsely adjusting the support plate using the adjustable bolts, and then re-measuring using laser scanning to ensure that the elevation deviation of each corner point is controlled within ±5mm include: The three-dimensional point cloud data is filtered and denoised to remove background point clouds other than the support plate. A plane fitting algorithm is used to extract the point cloud on the upper surface of the support plate, identify the four corners and edge feature points of the support plate, and calculate the average elevation of each feature point. Compare the elevation of each feature point with the design elevation, calculate the elevation deviation value, formulate an adjustment strategy based on the magnitude and distribution pattern of the deviation value, and determine the rotation direction and rotation angle of each adjustment nut; After each round of adjustment, the resolution is reduced to 5-10mm using the three-dimensional laser scanner for remeasurement. The elevation deviation after adjustment is calculated. When the deviation of each corner point is ≤±5mm and the diagonal height difference is ≤10mm, the coarse adjustment is completed.

4. The leveling method for a support plate as described in claim 3, characterized in that, The steps of using the aforementioned 3D laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions according to the deviation values ​​for fine adjustment to ensure that the levelness of the support plate is ≤1mm / m include: The aforementioned 3D laser scanner is used to perform dense scanning on the upper surface of the support plate at a resolution of 1-2 mm, with a scanning angle step of ≤0.02°, to acquire high-density point cloud data with a point cloud density of ≥5000 points / m. 2 This ensures that local unevenness and minor defects can be identified; The dense point cloud data is processed into a grid to establish a second digital elevation model with a grid size of 2mm*2mm. The elevation value of each grid is calculated by an interpolation algorithm to generate the elevation contour map and three-dimensional visualization model of the support plate surface. Based on the elevation distribution analysis of the three-dimensional visualization model, the surface of the support plate is divided into several adjustment areas, each area corresponding to one or more adjustment nuts. The average elevation and target adjustment amount of each area are calculated. The adjustment nuts are rotated independently to make micro-adjustments, with a single adjustment amount ≤0.5mm. After adjustment, the levelness is re-measured until it is ≤1mm / m.

5. The leveling method for a support plate as described in claim 4, characterized in that, The steps for retesting after 12 to 24 hours to verify the stability of the levelness and generating a leveling acceptance report containing the 3D point cloud data include: Tighten the locking nut in 2-3 stages diagonally using a torque wrench. Apply 50% of the design torque for the first stage, 75% for the second stage, and 100% for the third stage. After locking, let it stand for 12 to 24 hours to allow the support plate to complete its initial settlement under its own weight and preload, and then re-measure it to generate a digital elevation model for the re-measurement and calculate the change in levelness. By comparing the second digital elevation model of the support plate surface with the remeasured digital elevation model, if the change in levelness is ≤0.3mm / m, the leveling is deemed qualified, and a complete archive containing initial three-dimensional point cloud data, adjustment process data, and final acceptance three-dimensional point cloud data is generated.

6. The leveling method for a support plate as described in any one of claims 1 to 5, characterized in that, The steps of using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions according to the deviation values ​​for fine adjustment to make the levelness of the support plate ≤1 mm / m further include: The three-dimensional laser scanner is used to perform periodic rapid scanning at intervals of 5 to 10 seconds to obtain the elevation changes of the support plate surface in real time. The scanning resolution is set to 3 to 5 mm to realize dynamic monitoring of the adjustment process. The point cloud data acquired in real time is compared with the target elevation model to calculate the current elevation deviation, and the remaining adjustment amount of each adjustment point is displayed in real time through a visualization interface. When the elevation of a certain area is detected to have reached the target value in real time, the operation of the corresponding adjusting nut in that area is immediately stopped, and the adjustment is switched to the adjacent area.

7. The leveling method for a support plate as described in any one of claims 1 to 5, characterized in that, After using the three-dimensional laser scanner to densely scan the upper surface of the support plate at a resolution of 2-3 mm to generate a second digital elevation model of the support plate surface, calculating the levelness deviation of each measuring point, and independently rotating the adjustable bolts at the corresponding positions according to the deviation values ​​for fine adjustment to make the levelness of the support plate ≤ 1 mm / m, the leveling method for the support plate further includes: No fewer than 10 of the aforementioned feature points are selected on the upper surface of the support plate for contact measurement to obtain the elevation value of each feature point, and then compared with the point cloud elevation of the same location obtained by the three-dimensional laser scanner. Calculate the elevation difference between the 3D laser scanner and the contact measurement. If the difference is ≤0.5mm, the accuracy of the 3D laser scanner is determined to meet the requirements. If the difference is >0.5mm, the 3D laser scanner is calibrated. Record and compare the measurement results and calibration data, and generate a laser scanning accuracy verification report as an attachment to the leveling and acceptance report.

8. The leveling method for a support plate as described in any one of claims 1 to 5, characterized in that, A three-dimensional laser scanner is positioned above the support plate, with a scanning resolution of 1–5 mm and a scanning angle covering the entire surface of the support plate. This acquires three-dimensional point cloud data of the support plate in its initial state. A first digital elevation model of the support plate surface is then established using processing software, and the elevation values ​​of each feature point are extracted as leveling benchmarks. Before the step of forming elevation data, the leveling method for the support plate further includes: When the ambient light intensity is greater than 10,000 lux, a narrow-band filter is installed in front of the lens of the 3D laser scanner to filter out stray light that is inconsistent with the laser wavelength, or scanning is carried out under low light conditions such as night or cloudy days to improve the signal-to-noise ratio of point clouds. When the dust concentration at the construction site is high, a pulsed laser scanner is used to distinguish the echo signal using the time-of-flight method and filter out the interference signal from dust scattering. Alternatively, a mist cannon can be used to reduce dust before scanning to reduce the impact of dust on laser scattering. When the ambient temperature is >35℃, heat dissipation treatment should be performed on the 3D laser scanner, or a high-temperature resistant scanner should be used, and temperature compensation calibration should be performed before and after scanning to eliminate instrument drift caused by temperature.

9. The leveling method for a support plate as described in any one of claims 1 to 5, characterized in that, The steps of pre-embedding the support plate at the designed position at the beam end using adjustable bolts before pouring the beam concrete include: The outline of the support plate and the center line of the bolts are projected onto the steel reinforcement skeleton of the beam using a laser line projector or a laser theodolite, and the positions of each of the pre-embedded bolts are marked. The layout accuracy is ≤2mm. After the pre-embedded screw is installed, use a laser plumb bob or laser vertical gauge to calibrate the verticality of the screw to ensure that the verticality deviation of the screw is ≤1°. If the deviation exceeds the limit, it can be corrected by adjusting the fastener. Before concrete pouring, the three-dimensional coordinates of each pre-embedded screw are remeasured using laser scanning or a total station, and the initial position deviation is recorded as a correction parameter for later leveling calculations.

10. The leveling method for a support plate as described in any one of claims 1 to 5, characterized in that, The steps of tightening the locking nut, retesting after 12-24 hours to verify the levelness stability, and generating a leveling acceptance report containing the 3D point cloud data also include: The laser scanning point cloud data from the initial state, after coarse adjustment, after fine adjustment, and after locking and retesting are registered and superimposed to generate a three-dimensional animation of the leveling process, which intuitively shows the leveling process of the support plate. Based on the digital elevation model of the final acceptance, a heat map of elevation deviation on the surface of the bearing plate is generated, with different colors used to mark the areas of elevation deviation: red indicates too high, blue indicates too low, and green indicates that the standard is met, serving as a visual basis for quality acceptance. The laser scanning point cloud after leveling is integrated with the BIM model of the bearing plate design to construct a digital twin model of the bearing plate, and the leveling parameters and measured data are recorded as a benchmark archive for bridge operation and maintenance.

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