Tunnel lining wall roughness calculation method based on 3D scanning technology

Abstract

The application discloses a tunnel lining wall roughness calculation method based on a 3D scanning technology and belongs to the field of concrete material surface roughness measurement. The method comprises the following steps: 3D scanning equipment selection, scanning sub-surface calibration, rough wall scanning, three-dimensional digital model construction, point cloud data processing, numerical model reconstruction and tunnel lining wall roughness calculation. The tunnel lining concrete wall roughness calculation method based on the 3D scanning technology is suitable for actual engineering environment, has the advantages of fast measurement speed and high precision, the calculation result can accurately represent the roughness of the tunnel lining wall, meanwhile, the method standardizes the operations such as digital model construction, image processing, point cloud data processing, interpolation method selection, sampling interval determination and roughness calculation formula selection, and has good application prospect and popularization value.

Description

Technical Field

[0001] This invention belongs to the field of concrete material surface roughness measurement technology, specifically a method for calculating the roughness of tunnel lining wall based on 3D scanning technology. Background Technology

[0002] Tunnels are permanent support structures constructed around the tunnel walls using reinforced concrete and other materials to prevent deformation or collapse of the surrounding rock. The surface roughness of the tunnel lining is a crucial parameter for calculating tunnel wall roughness and for measuring the strength and sealing of the bond between the lining and the surrounding soil or secondary lining concrete. Researching high-precision and efficient methods for measuring and calculating tunnel lining surface roughness has significant engineering value and theoretical implications.

[0003] Methods for measuring the surface roughness of concrete can be categorized into contact and non-contact types. Contact measurement methods directly measure surface roughness and include stylus methods, sand-spreading methods, comparative methods, and impression methods. These methods are simple and intuitive to operate, but they can cause some damage to the surface being tested and are also labor-intensive and time-consuming. Non-contact measurement methods mostly involve optical instruments, such as laser scanning, optical sectioning, and holography. These methods offer high accuracy and speed without damaging the surface being measured, but the equipment is expensive and the operation is complex. Currently, they are mainly used in laboratory research or for large-scale, important engineering projects, and their widespread application in small and medium-sized projects faces certain difficulties.

[0004] With the continuous development of engineering informatization, 3D scanning technology has been gradually applied to the field of engineering surveying. Based on the principles of structured light scanning, laser scanning, and coordinate measuring machines (CMMs), this technology collects on-site data through practical measurement methods. It features real-time operation, high precision, high speed, visualization, digitization, non-destructive processing, high resolution, and non-contact operation. It can quickly and precisely collect the three-dimensional coordinates, reflectivity, and texture information of physical surfaces, completely storing various complex, precise, and irregular surface features into a computer, establishing a digital model of the scanned structural surface, and realizing the three-dimensional digital reconstruction of the measured structural surface.

[0005] Currently, 3D scanning technology has been introduced into the measurement of concrete surface roughness. However, in actual engineering, 3D scanning technology is greatly affected by environmental factors, and on-site measurement is difficult. In addition, the amount of point cloud data obtained by 3D scanning is huge, the post-processing process is cumbersome, and there is a lack of unified and standardized methods, which affects the efficiency of structural surface roughness calculation.

[0006] The closest prior art to this invention is Chinese Patent Application No. CN202111564471.1, entitled "A Method for Measuring the Surface Roughness of Tunnel Lining Concrete Segments." This patent discloses a method for measuring the surface roughness of tunnel lining concrete segments, comprising the following steps: Step 1, calibrating the measurement sub-surface: the measurement sub-surface includes a calculation sub-surface and a feature sub-surface; Step 2, acquiring images: the illumination during photography is controlled between 250 and 400 lux; the same photographing equipment is used for the same tunnel lining concrete segment surface; the distance between the lens of the photographing equipment and the surface of the tunnel lining concrete segment is controlled between 30 cm and 50 cm; Step 3, cropping and preprocessing the images; Step 4, measuring the actual roughness; Step 5, calculating the image feature parameters; Step 6, selecting a general formula for roughness calculation; Step 7, calculating the roughness value of the measurement sub-surface; Step 8, determining the error correction coefficient; Step 9, calculating the surface roughness of the lining concrete segment.

[0007] The difference between this invention and existing technologies lies in that, based on the characteristics of the concrete wall surface and the tunnel measurement conditions, this invention employs 3D scanning technology to collect three-dimensional coordinate point cloud information of the wall surface. The point cloud data is preprocessed using GEOMAGIC STUDIO software to construct a three-dimensional digital model. Point cloud data processing is then implemented using SUFER software, and the reliability of the point cloud data processing and filtering is verified through numerical model reconstruction. Finally, the surface roughness coefficient JRC is calculated to quantitatively characterize the roughness, thus obtaining the roughness value of the tunnel lining wall surface. This invention uses preprocessing operations such as point cloud data coordinate alignment and noise point filtering to remove unreasonable points in the point cloud data. Point cloud data with large holes on the surface of the three-dimensional digital model is eliminated to ensure the accuracy of the collected data. Kriging interpolation is used to filter the massive point cloud data, improving data processing efficiency and saving time costs while ensuring that important features of the lining wall surface are not ignored. Summary of the Invention

[0008] The technical problem this invention aims to solve is to address the shortcomings of existing technologies by providing a method for calculating the roughness of tunnel lining concrete walls based on 3D scanning technology. This method, based on the characteristics of the concrete wall itself and the tunnel measurement conditions, uses 3D scanning technology to collect three-dimensional coordinate point cloud information of the wall. The SUFER software is used to interpolate the three-dimensional coordinate point cloud data to obtain a matrix sequence, and the roughness coefficient JRC of the scanned sub-surface is calculated to quantitatively characterize its roughness, thereby obtaining the roughness value of the tunnel lining wall. Compared with traditional contact roughness measurement methods, this invention offers high accuracy and speed, does not damage the wall surface being measured, and overcomes the shortcomings of limited measurement range, the need for readjustment during mobile equipment installation, and unsuitability for on-site measurement by deploying the 3D scanning equipment on a drone or a prefabricated directional sliding track.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0010] A method for calculating the surface roughness of tunnel lining walls based on 3D scanning technology, specifically including the following steps:

[0011] Step 1: Select a 3D scanning device, mark the scanning sub-faces on the concrete wall of the tunnel lining to be tested, mark and number the scanning sub-faces, and determine the number and number of the scanning sub-faces.

[0012] Step 2: Collect topographic feature data of the scanned sub-surface;

[0013] Step 3: Preprocess the point cloud data using GEOMAGIC STUDIO software, including data reading, coordinate alignment, and noise point filtering. After preprocessing, construct a 3D digital model.

[0014] Step 3-1, Data Reading: Transmit the point cloud data collected by the 3D scanning device to the computer, and then read the data using GEOMAGIC STUDIO software;

[0015] Step 3-2, Coordinate Alignment: GEOMAGIC STUDIO software uses the neutral surface calibration method to perform coordinate alignment processing on the point cloud data; Step 3-3, Noise Removal: First, isolated point cloud data is manually deleted, and then the software's ReduceNoise command automatically filters out noise points exceeding the set threshold.

[0016] Steps 3-4: Construct a three-dimensional digital model using polygon meshing, observe the generated three-dimensional digital model, and remove data with holes on the model surface;

[0017] Step 4: Implement point cloud data processing based on SUFER software, and verify the reliability of point cloud data processing and filtering through numerical model reconstruction;

[0018] Step 4-1, Determining the interpolation method: Based on the principle of minimizing the error after processing point cloud data, the Kriging interpolation method is selected to further process the preprocessed point cloud data;

[0019] Step 4-2, Determining the sampling interval: The sampling interval selected is the same as the sampling interval used in the empirical formula for roughness calculation;

[0020] Step 4-3, Numerical Model Reconstruction: Reconstruct the processed point cloud data and compare it with the three-dimensional digital model generated in Step 3-4. Observe whether the surface undulation of the model is consistent. If it is inconsistent, the point cloud data should be reprocessed.

[0021] Step 5: Calculation of tunnel lining wall roughness: Based on statistical methods, three-dimensional morphological feature parameters are used in conjunction with the PYTHON program to characterize and calculate the roughness of the lining wall.

[0022] Step 5-1: Select an empirical formula for roughness calculation and combine it with the statistical parameter method to calculate the surface average gradient modulus Z of 10 standard profile curves. 2S Regression analysis was used to analyze Z. 2S Fit the relationship with JRC;

[0023] Step 5-2: Run the program to calculate the average gradient modulus Z of each scanned sub-surface. 2S The roughness calculation value JRC is obtained; where the average gradient modulus of the i-th scanned sub-surface is Z. 2Si The roughness calculation value is JRC. i ;

[0024] Step 5-3: Calculate the average roughness of the lining wall surface The formula is:

[0025]

[0026] In the formula: denoted as the average roughness of the lining wall surface; i represents the scanned sub-face number; and n represents the number of scanned sub-faces.

[0027] More preferably, in step 1, the 3D scanning equipment is selected according to the illuminance of the light source inside the cave, the angle of illumination, and the size of the cave. This includes active 3D and passive 3D scanning equipment. If passive 3D scanning equipment is used, an additional artificial light source needs to be arranged for supplementary lighting.

[0028] More preferably, in step 1, the specific steps for calibrating and scanning the sub-surface of the tunnel lining wall to be tested are as follows:

[0029] Step 1-1: Determine the size A of the scanned sub-surface based on the single-sided measurement range of the selected 3D scanning equipment;

[0030] Steps 1-2: Determine the number of scanned sub-faces n based on the overall area S of the concrete lining wall of the tunnel to be tested.

[0031] When S≤10m 2 At that time, the number of sub-faces scanned, n, is not less than And n≥5;

[0032] When S > 10m 2 At that time, the number of sub-faces scanned, n, is not less than

[0033] Steps 1-3: Starting from the lower left corner of the wall to be tested, mark and number the scanned sub-surfaces in the order from left to right and from bottom to top. The numbers are 1 to n, where n is the number of scanned sub-surfaces and i represents the scanned sub-surface number.

[0034] More preferably, in step 2, the scanning sub-surface morphology feature data of the tunnel lining wall are collected, and the specific steps are as follows:

[0035] Step 2-1: Set up the 3D scanning equipment according to the lens selected by the 3D scanning equipment and the distance between the equipment and the surface of the tunnel lining wall. The 3D scanning equipment should be placed on a drone or a slide rail with lifting and horizontal movement functions.

[0036] Step 2-2: Adjust the brightness, lens focus, and spatial calibration of the 3D scanning equipment;

[0037] Steps 2-3: After the equipment is debugged, data is collected sequentially on the scanned sub-surface. During scanning, the instrument's projection light source should be kept perpendicular to the wall surface. If a passive 3D scanning device is used, the artificial light source illuminance should be controlled between 250 and 400 lux, and the illumination angle should be controlled at 90 ± 20° relative to the lining concrete wall surface.

[0038] More preferably, in step 3, the specific steps for constructing the three-dimensional digital model are as follows:

[0039] Step 3-4-1: Transfer the collected point cloud data to the computer, and then read the data using the open command in the GEOMAGIC STUDIO software. Data types include TXT and STL formats.

[0040] Step 3-4-2: Use the Shade Points command to perform coordinate alignment processing on the point cloud data;

[0041] Step 3-4-3: Use the Reduce Noise command to automatically filter out noise points that exceed the set threshold;

[0042] Step 3-4-4: By clicking the Point, Wrap, and surface commands in sequence, construct a three-dimensional digital model using polygon meshing.

[0043] More preferably, in step 4, the point cloud data is processed and the numerical model is reconstructed based on SUFER software. The three-dimensional optical scanning results are saved as an ASC file and imported into SUFER software. In SUFER software, the Kriging interpolation method is selected and the sampling interval is set to 1.27 mm. After clicking start, the numerical model reconstruction is completed. It is observed whether the surface undulation of the reconstructed model is consistent with that of the three-dimensional digital model in steps 3-4. If they are inconsistent, the point cloud data should be reprocessed.

[0044] More preferably, in step 5, the specific steps for calculating the surface roughness of the tunnel lining wall based on PYTHON software are as follows:

[0045] Step 5-1: Select an empirical formula for roughness calculation. The specific formula is as follows:

[0046] JRC = 32.2 + 32.47lgZ 2S

[0047] In the formula: Z 2S The average gradient modulus of the surface is represented by the following formula:

[0048]

[0049] Where: N x N y Δx and Δy represent the number of sampling points along the X and Y axes, respectively; Δx and Δy represent the sampling intervals along the X and Y axes, respectively; Z i Z i+1 , i and j are the Z-axis coordinates of the i-th and i+1-th discrete points of the fracture, respectively, and i and j are the indices of the mesoscopic planes along the X-axis and Y-axis, respectively;

[0050] Step 5-2: Input the formula into a Python program, specify the working path to retrieve point cloud data, and calculate the average gradient magnitude and Z-axis of each scanned sub-surface. 2S The roughness calculation value JRC, where the average gradient modulus of the i-th scanned sub-surface is Z. 2Si The roughness calculation value is JRC. i , 1≤i≤n;

[0051] Step 5-3: Calculate the average roughness of the lining wall surface The specific calculation formula is as follows:

[0052]

[0053] In the formula: denoted as the average roughness of the lining wall surface; i represents the scanned sub-face number; and n represents the number of scanned sub-faces.

[0054] The present invention has the following beneficial effects:

[0055] First, the present invention provides a method for calculating the roughness of lining wall based on 3D scanning technology. Compared with the traditional contact roughness measurement method, this technology has higher accuracy and faster speed, and does not damage the wall surface to be measured. At the same time, by arranging the 3D scanning equipment on a drone or a prefabricated directional sliding track, it overcomes the shortcomings of the equipment, such as small measurement range, need for re-adjustment in mobile equipment, and unsuitability for on-site measurement.

[0056] Secondly, this invention employs preprocessing operations such as point cloud data coordinate alignment and noise point filtering to remove unreasonable points in the point cloud data. By eliminating point cloud data with large holes on the surface of the 3D digital model, the accuracy of the collected data is ensured. Kriging interpolation is used to filter the massive point cloud data, improving data processing efficiency and saving time costs while ensuring that important features of the lining wall are not overlooked.

[0057] Third, this invention addresses the lack of a unified method in the post-processing of point cloud data by standardizing the operational steps of digital model construction, image processing, point cloud data processing, interpolation method selection, sampling interval determination, and roughness calculation formula selection. This provides scientific guidance for the operation, allows for repeated implementation, and yields accurate results. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the principle of the present invention.

[0059] Figure 2 This is a flowchart of the method for calculating the roughness of tunnel lining walls according to the present invention.

[0060] Figure 3 This is a comparison image of the point cloud data coordinates before and after alignment according to the present invention.

[0061] Figure 4 This is a comparison chart of noise data processing before and after the present invention.

[0062] Figure 5 This is a schematic diagram of the three-dimensional digital model constructed in this invention.

[0063] Figure 6 This is a schematic diagram of the reconstruction model of the present invention. Detailed Implementation

[0064] In the description of this invention, it should be understood that the terms "left side," "right side," "upper part," "lower part," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. "First," "second," etc., do not indicate the importance of the components, and therefore should not be construed as a limitation of this invention. The specific dimensions used in this embodiment are only for illustrating the technical solution and do not limit the scope of protection of this invention.

[0065] like Figure 1 As shown, a method for calculating the roughness of tunnel lining wall based on 3D scanning technology includes the following steps: 3D scanning equipment selection, scanning sub-surface calibration, rough wall scanning, three-dimensional digital model construction, point cloud data processing and numerical model reconstruction, and tunnel lining wall roughness calculation.

[0066] The present invention will now be described in further detail with reference to the accompanying drawings and specific preferred embodiments.

[0067] like Figure 2 As shown, the method for calculating the surface roughness of tunnel lining walls specifically includes the following steps:

[0068] Step 1: Select a 3D scanning device:

[0069] Mark the scanning sub-faces on the concrete lining wall of the tunnel to be tested, mark and number the scanning sub-faces, and determine the number and number of scanning sub-faces.

[0070] Considering that tunnel environments in actual engineering are mostly dark and narrow, lacking suitable natural light sources and with limited space, passive 3D scanning equipment cannot project its own light source, requiring additional artificial light sources for supplementary lighting. This not only reduces the portability of the equipment, but also the illuminance and angle of the artificial light source affect the data acquisition results, causing errors and impacting scanning accuracy. Therefore, this embodiment selects an active 3D scanning device. This type of device is based on the principle of optical triangulation and consists of an optical projector, a camera, and a computer computing system. Its principle is as follows: the device projects a pattern encoded according to certain rules and patterns onto the surface of the object being measured. The encoded pattern is deformed by the shape of the object's surface. The deformed structured light is captured by a camera at another location. The three-dimensional shape of the object can be determined by the positional relationship between the camera and the projection light source and the degree of deformation of the structured light. Depending on the type of beam projected by the projector, it can be classified as point, line, grating, or surface structured light. These structured light 3D scanning devices have their own projection light source, and the illuminance of the light source is adjustable. The built-in light source and the camera are on the same plane. These structural designs effectively solve the problems of strictly controlling the distance between the light source and the camera when artificial external light sources are added, as well as the impact of light source illuminance and light source angle on the detection results.

[0071] Based on the above analysis, in this embodiment, the Wiiboox Reeyee 3M active 3D scanning device was selected to collect morphological data of the tunnel lining wall. The device parameters are shown in Table 1.

[0072]

[0073] Table 1 Wiiboox Reeyee 3M Parameters

[0074] The scanning sub-surface of the tunnel lining wall to be tested:

[0075] Step 1-1: Determine the size of the scanning sub-face: Determine the size A of the scanning sub-face based on the single-sided measurement range of the selected 3D scanning equipment. To ensure that the scanning sub-face is not larger than the maximum single measurement range of the scanning equipment itself, the size A of the scanning sub-face should be consistent with the single-sided measurement range of the selected 3D scanning equipment itself.

[0076] Step 1-2: Determine the number of scanned sub-faces: Determine the number of scanned sub-faces n based on the overall area S of the concrete wall lining of the tunnel to be tested.

[0077] When S≤10m 2 To ensure that the data represents the morphological features of the entire lining wall surface under test, the number of scanned sub-surfaces shall not be less than [number missing]. And n≥5;

[0078] When S > 10m 2 To improve scanning efficiency and save time, the number of sub-faces scanned, n, should not be less than [a certain value].

[0079] In this embodiment, the size of the scanned sub-surface is consistent with the maximum single-surface measurement range of the Wiiboox Reeyee 3M 3D scanning device used; therefore, the size A of the scanned sub-surface is 400×300mm. 2 The tunnel lining wall to be tested is 4m high, 6m long, and has an area S of 24m². 2 Therefore, it is determined that the number of scanned subfaces should be no less than 20, and 20 will be selected in this case.

[0080] Steps 1-3: Scanning Sub-face Calibration: To ensure that the scanning sub-faces can uniformly cover the lining wall to be tested and reduce the dispersion of scanning data, the scanning sub-faces need to be reasonably arranged first. The number of scanning sub-faces in the length and height directions and the interval between adjacent scanning sub-faces should be determined. After the arrangement is completed, a marker pen and ruler are used to mark and number the scanning sub-faces from the lower left corner of the lining wall to be tested, in the order from left to right and from bottom to top. The numbers are 1 to n, where n is the number of scanning sub-faces and i represents the scanning sub-face number, to facilitate subsequent scanning.

[0081] In this embodiment, 5 scanning sub-faces along the length of the lining wall to be tested and 4 scanning sub-faces along the height are determined, for a total of 20 scanning sub-faces. Using a white sampling pen and a ruler, starting from the lower left corner of the lining wall to be tested, the scanning sub-faces are marked in a left-to-right, bottom-to-top order. The interval between adjacent scanning sub-faces along the length is 1m, and the interval along the height is 0.93m. The sub-faces are numbered sequentially from 1 to 20.

[0082] Step 2: Collect the topographic feature data of the scanned sub-face. The specific steps are as follows:

[0083] Step 2-1, Instrument Setup: The distance between the 3D scanning equipment and the lining wall to be scanned should be determined according to the requirements of the selected equipment. When the 3D scanning equipment is too far from the lining wall, the industrial-grade camera cannot completely capture the returned grating fringes, which may cause missing point cloud data and affect scanning accuracy. When the 3D scanning equipment is too close to the lining wall, the industrial-grade camera cannot focus and cannot complete the scan. To improve scanning efficiency, save time and costs, and avoid equipment damage during frequent movement, the 3D scanning equipment should be placed on a drone or a sliding rail with lifting and left / right translation functions.

[0084] In this embodiment, the Wiiboox Reeyee 3M 3D scanning device has a lens distance of 30-50cm from the object being scanned. Therefore, the slide rail with lifting and left-right translation functions is arranged at a vertical distance of 40cm from the front of the lining wall to be measured. After installing and connecting all the components of the instrument, it is placed on the slide rail to complete the equipment setup.

[0085] Step 2-2, Instrument Debugging: In order to improve scanning accuracy and reduce errors, the 3D scanning equipment needs to be debugged before scanning, including but not limited to brightness adjustment, lens focal length adjustment and spatial calibration debugging.

[0086] Brightness Adjustment: Adjust the light source brightness by adjusting the knobs on the two eyepieces of the device. Observe the number of red dots on the calibration board displayed on the computer screen to determine if the light source brightness is appropriate. If many red dots are displayed on the calibration board, the light source of the machine head is too bright; if no red dots are displayed, the light source of the machine head is too dim. Readjust the knobs until a small number of red dots are displayed on the calibration board.

[0087] Lens focusing: Two knobs on the two eyepieces of the lens can control the rotation of the eyepieces in the horizontal and vertical directions. By adjusting the eyepiece knobs, the calibration plate is placed between the two eyepieces, and the focus is adjusted so that the white dot on the calibration plate appears in the clearest state on the computer screen.

[0088] Spatial calibration: The main function of spatial calibration is to define a three-dimensional space. During scanning, the object to be scanned is placed within the calibrated three-dimensional space, thus obtaining the relative coordinates of each point on the surface of the object. To obtain the relative coordinates of each point on the scanned object, it is necessary to ensure that the entire scanned object is within the calibration space. Therefore, the calibration space must be larger than the volume of the scanned object. Spatial calibration requires eight data acquisitions, including four horizontal acquisitions and four acquisitions with the calibration plate raised on all four sides. During data acquisition, it is important to ensure that the midpoint of the calibration plate coincides with the intersection of the crosshairs of the lens, and that the four large circles on the calibration plate are displayed within the lens. After eight successful data acquisitions, spatial calibration is complete, and the lining wall scanning can begin.

[0089] Steps 2-3: Data Acquisition: After the equipment is debugged, data is acquired sequentially for the scanned sub-surface. During scanning, the instrument's projection light source should be kept perpendicular to the wall surface. If a passive 3D scanning device is used, the artificial light source illuminance should be controlled between 250 and 400 lux, and the illumination angle should be controlled at 90 ± 20° relative to the lining concrete wall surface.

[0090] Step 3: Construction of the 3D Digital Model

[0091] Point cloud data preprocessing is performed using GEOMAGIC STUDIO software, including data reading, coordinate alignment, and noise point filtering. A 3D digital model is then constructed after preprocessing. Currently, there are numerous point cloud data processing software options available, including Surfacerl 0.0 and TRACE. Among them, GEOMAGIC STUDIO, based on advanced mathematical models and surface construction theory, utilizes polygonal meshing to rapidly construct surfaces, resulting in high data processing efficiency and powerful functionality. It is widely used for digital model creation, modification, and output. Preprocessing operations include data reading, coordinate alignment, and noise point filtering.

[0092] Step 3-1, Data Reading: Currently, GEOMAGIC STUDIO software supports loading point cloud data in various formats. It can transfer point cloud data collected by 3D scanning equipment to a computer, and then read the data through the open command in GEOMAGIC STUDIO software.

[0093] In this embodiment, the point cloud data acquired by the Wiiboox Reeyee 3M 3D scanning device is in STL format, which can be directly imported into the GEOMAGIC STUDIO software for operation.

[0094] Step 3-2, Coordinate Alignment: To regularize the point cloud data while increasing its intuitiveness, it is necessary to unify the point cloud data to a reference spatial coordinate system. GEOMAGIC STUDIO software uses the neutral plane calibration method to perform coordinate alignment processing on the point cloud data. First, a median reference plane is established using the least squares method, and then the established reference plane is aligned with the xy plane of the reference spatial coordinate system. At this point, the projection of the lining wall falls on the xy plane, with the undulation direction of the crack surface as the z-coordinate direction.

[0095] In this embodiment, the Shade Points command is used to perform coordinate alignment processing on the point cloud data. The comparison before and after coordinate alignment is as follows: Figure 3 As shown.

[0096] Step 3-3: Filter out noise points: Due to image processing algorithms, the presence of other objects during the scanning process, and other reasons, there is a lot of noisy data in the point cloud data. To avoid its impact on the calculation results, noise points need to be filtered out.

[0097] In this embodiment, large areas of isolated point cloud data are first manually deleted, and then the Reduce Noise command in the software is used to automatically filter out noise points exceeding a set threshold. A comparison of the noise data before and after processing is shown below. Figure 4 As shown.

[0098] Steps 3-4: 3D Digital Model Construction: During actual scanning, poor surface reflectivity and low camera resolution can lead to missing point cloud data, resulting in holes and affecting subsequent calculation accuracy. To ensure the reliability of the point cloud data, the generated 3D digital model needs to be observed, and data sets with large holes on the model surface should be discarded.

[0099] The specific steps for constructing the 3D digital model in this embodiment are as follows:

[0100] Step 3-4-1: Transfer the collected point cloud data to the computer, and then read the data using the open command in the GEOMAGIC STUDIO software. Data types include, but are not limited to, TXT and STL formats.

[0101] Step 3-4-2: Use the Shade Points command to perform coordinate alignment processing on the point cloud data;

[0102] Step 3-4-3: Use the Reduce Noise command to automatically filter out noise points that exceed the set threshold;

[0103] Step 3-4-4: By clicking the Point, Wrap, and surface commands in sequence, construct a three-dimensional digital model using polygon meshing.

[0104] The constructed three-dimensional digital model, such as Figure 5 Show.

[0105] Step 4: Point cloud data processing and numerical model reconstruction:

[0106] Because 3D scanning equipment has high measurement accuracy, and the scanning range is limited in a single scan, it uses automatic stitching technology to combine the results of multiple scans to obtain all the data of the scanned object's surface. This results in densely packed coordinates in the stitched area and a large overall data volume. Therefore, further processing and filtering of the collected point cloud data are necessary. Currently, interpolation is a common method for processing point cloud data, but different interpolation methods vary in terms of data processing effectiveness and time. Therefore, it is necessary to compare and select from multiple interpolation methods.

[0107] This embodiment uses SUFER software to process point cloud data and verifies the reliability of point cloud data processing and filtering through numerical model reconstruction.

[0108] Step 4-1: Determining the Interpolation Method: SURFER software includes several built-in interpolation methods, such as inverse distance weighted interpolation, Kriging interpolation, and the modified Shepard method. The advantages and disadvantages of each interpolation method are evaluated by calculating the average height error of the centerline of the structural surface before and after point cloud data processing. The study found that the average height error of the centerline before and after cloud data processing using Kriging interpolation is only about 1%, and the data processing time is the shortest compared to other methods. Based on the principle of minimizing the error after point cloud data processing, Kriging interpolation is selected for further processing of the preprocessed point cloud data.

[0109] Step 4-2, Sampling Interval Determination: Besides the interpolation method, the sampling interval is also an important parameter affecting data processing results and time. If the sampling interval is too large, some important features on the lining wall may be ignored, leading to significant errors in the calculation results. If the sampling interval is too small, the amount of point cloud data to be processed will be enormous, resulting in excessively long processing time.

[0110] Research revealed an inverse relationship between rock mass surface roughness and sampling point spacing. Furthermore, when using statistical parametric methods to characterize rock mass roughness, even for the same structural surface, roughness calculation results differed under different sampling point spacings. Therefore, to maintain consistency in point cloud data processing and filtering, the sampling point spacing was made consistent with the spacing used in the selected empirical roughness calculation formula.

[0111] In this embodiment, the sampling interval is consistent with the sampling point spacing taken by the selected roughness calculation empirical formula, which is 1.27 mm.

[0112] Step 4-3, Numerical Model Reconstruction: To verify the reliability of point cloud data processing and screening, the model can be reconstructed based on the processed point cloud data. The reconstructed model is then compared with the 3D digital model in Step 3-4 to observe whether the surface undulation of the model remains consistent. If they are inconsistent, the point cloud data should be reprocessed.

[0113] In this embodiment, the specific operation process for numerical model reconstruction is as follows: The three-dimensional optical scanning results are saved as an ASCII file and imported into SUFER software; in SUFER software, Kriging interpolation is selected and the sampling interval is set to 1.27 mm. Clicking "Start" causes SUFER software to discretize the point cloud data into a square grid with side lengths consistent with the sampling interval, thus reconstructing the numerical model of the lining wall. The reconstructed model is as follows: Figure 6 As shown; observe whether the surface undulation of the reconstructed model is consistent with that of the 3D digital model in steps 3-4. If they are inconsistent, the point cloud data should be reprocessed.

[0114] Step 5: Calculation of tunnel lining wall roughness:

[0115] Based on statistical methods, the surface roughness of the lining wall was characterized and calculated using three-dimensional morphological feature parameters combined with the PYTHON program:

[0116] Step 5-1: Select an empirical formula for roughness calculation: Currently, there are numerous methods for calculating the roughness coefficient of rock mass structural surfaces. Among them, R.Tse et al. combined the statistical parameter method to calculate the surface average gradient modulus Z of 10 standard profile curves. 2S Regression analysis was used to analyze Z. 2S Fitting the relationship between JRC and the formula shows a good correlation, making this formula the most widely used currently. This invention selects this formula to calculate the surface roughness of the lining wall, as shown in the following expression:

[0117] JRC = 32.2 + 32.47lgZ 2S

[0118] In the formula: Z 2S The average gradient modulus of the surface is represented by the following formula:

[0119]

[0120] Where: N x N y Δx and Δy represent the number of sampling points along the X and Y axes, respectively; Δx and Δy represent the sampling intervals along the X and Y axes, respectively; Z i Z i+1 , i and j are the Z-axis coordinates of the i-th and i+1-th discrete points of the fracture, respectively, and i and j are the indices of the mesoscopic planes along the X-axis and Y-axis, respectively.

[0121] Step 5-2: Calculate the roughness value of the scanned sub-surface: Input the above formula into a Python program, and run the program to calculate the average gradient modulus Z of each scanned sub-surface. 2S The roughness calculation value JRC is obtained; where the average gradient modulus of the i-th scanned sub-surface is Z. 2Si The roughness calculation value is JRC. i , 1≤i≤n.

[0122] The calculation results in this embodiment are shown in Table 2:

[0123]

[0124] Table 2 JRC Calculation Values ​​for Scanned Subfaces

[0125] Step 5-3: Calculate the average roughness of the lining wall surface The formula is:

[0126]

[0127] In the formula: denoted as the average roughness of the lining wall surface; i represents the scanned sub-face number; and n represents the number of scanned sub-faces.

[0128] In this embodiment, the average roughness of the lining wall is calculated using the formula for the average roughness of the lining wall. The value is 14.5691.

[0129] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for calculating the surface roughness of tunnel lining walls based on 3D scanning technology, characterized in that: Specifically, the following steps are included: Step 1: Select a 3D scanning device, mark the scanning sub-faces on the concrete wall of the tunnel lining to be tested, mark and number the scanning sub-faces, and determine the number and number of the scanning sub-faces. Step 2: Collect topographic feature data of the scanned sub-surface; Step 3: Preprocess the point cloud data using GEOMAGIC STUDIO software, including data reading, coordinate alignment, and noise point filtering. After preprocessing, construct a 3D digital model. Step 3-1, Data Reading: Transmit the point cloud data collected by the 3D scanning device to the computer, and then read the data using GEOMAGICSTUDIO software; Step 3-2, Coordinate Alignment: GEOMAGIC STUDIO software uses the neutral surface calibration method to perform coordinate alignment processing on the point cloud data; Step 3-3, Noise Removal: First, isolated point cloud data is manually deleted, and then the software's ReduceNoise command automatically filters out noise points exceeding the set threshold. Steps 3-4: Construct a three-dimensional digital model using polygon meshing, observe the generated three-dimensional digital model, and remove data with holes on the model surface; Step 4: Implement point cloud data processing based on SUFER software, and verify the reliability of point cloud data processing and filtering through numerical model reconstruction; Step 4-1, Determining the interpolation method: Based on the principle of minimizing the error after processing point cloud data, the Kriging interpolation method is selected to further process the preprocessed point cloud data; Step 4-2, Determining the sampling interval: The sampling interval selected is the same as the sampling interval used in the empirical formula for roughness calculation; Step 4-3, Numerical Model Reconstruction: Reconstruct the processed point cloud data and compare it with the three-dimensional digital model generated in Step 3-4. Observe whether the surface undulation of the model is consistent. If it is inconsistent, the point cloud data should be reprocessed. Step 5: Calculation of tunnel lining wall roughness: Based on statistical methods, three-dimensional morphological feature parameters are used in conjunction with the PYTHON program to characterize and calculate the roughness of the lining wall. Step 5-1: Select an empirical formula for roughness calculation and combine it with the statistical parameter method to calculate the surface average gradient modulus Z of 10 standard profile curves. 2S Regression analysis was used to analyze Z. 2S Fit the relationship with JRC; Step 5-2: Run the program to calculate the average gradient modulus Z of each scanned sub-surface. 2S The roughness calculation value JRC is obtained; where the average gradient modulus of the i-th scanned sub-surface is Z. 2Si The roughness calculation value is JRC. i ; Step 5-3: Calculate the average roughness of the lining wall surface The formula is: In the formula: denoted as the average roughness of the lining wall surface; i represents the scanned sub-face number; and n represents the number of scanned sub-faces.

2. The method for calculating the roughness of tunnel lining walls based on 3D scanning technology according to claim 1, characterized in that: In step 1, 3D scanning equipment is selected based on the illuminance, illumination angle, and size of the cave, including active 3D and passive 3D scanning equipment. If passive 3D scanning equipment is used, additional artificial light sources need to be arranged for supplementary lighting.

3. The method for calculating the roughness of tunnel lining walls based on 3D scanning technology according to claim 1, characterized in that: In step 1, the specific steps for calibrating and scanning the sub-surface of the tunnel lining wall to be tested are as follows: Step 1-1: Determine the size A of the scanned sub-surface based on the single-sided measurement range of the selected 3D scanning equipment; Steps 1-2: Determine the number of scanned sub-faces n based on the overall area S of the concrete lining wall of the tunnel to be tested. When S≤10m 2 At that time, the number of sub-faces scanned, n, is not less than And n≥5; When S > 10m 2 At that time, the number of sub-faces scanned, n, is not less than Steps 1-3: Starting from the lower left corner of the wall to be tested, mark and number the scanned sub-surfaces in the order from left to right and from bottom to top. The numbers are 1 to n, where n is the number of scanned sub-surfaces and i represents the scanned sub-surface number.

4. The method for calculating the roughness of tunnel lining walls based on 3D scanning technology according to claim 1, characterized in that: In step 2, the scanning sub-surface topographic feature data of the tunnel lining wall are collected. The specific steps are as follows: Step 2-1: Set up the 3D scanning equipment according to the lens selected by the 3D scanning equipment and the distance between the equipment and the surface of the tunnel lining wall. The 3D scanning equipment should be placed on a drone or a slide rail with lifting and horizontal movement functions. Step 2-2: Adjust the brightness, lens focus, and spatial calibration of the 3D scanning equipment; Steps 2-3: After the equipment is debugged, data is collected sequentially on the scanned sub-surface. During scanning, the instrument's projection light source should be kept perpendicular to the wall surface. If a passive 3D scanning device is used, the artificial light source illuminance should be controlled between 250 and 400 lux, and the illumination angle should be controlled at 90 ± 20° relative to the lining concrete wall surface.

5. The method for calculating the roughness of tunnel lining wall based on 3D scanning technology according to claim 1, characterized in that: Step 3, the specific steps for constructing the 3D digital model are as follows: Step 3-4-1: Transfer the collected point cloud data to the computer, and then read the data using the open command in the GEOMAGIC STUDIO software. Data types include TXT and STL formats. Step 3-4-2: Use the Shade Points command to perform coordinate alignment processing on the point cloud data; Step 3-4-3: Use the Reduce Noise command to automatically filter out noise points that exceed the set threshold; Step 3-4-4: By clicking the Point, Wrap, and surface commands in sequence, construct a three-dimensional digital model using polygon meshing.

6. The method for calculating the roughness of tunnel lining wall based on 3D scanning technology according to claim 1, characterized in that: In step 4, the point cloud data is processed and the numerical model is reconstructed using SUFER software. The 3D optical scanning results are saved as an ASC file and imported into SUFER software. In SUFER software, Kriging interpolation is selected and the sampling interval is set to 1.27mm. After clicking start, the numerical model reconstruction is completed. It is observed whether the surface undulation of the reconstructed model is consistent with that of the 3D digital model in steps 3-4. If they are inconsistent, the point cloud data should be reprocessed.

7. The method for calculating the roughness of tunnel lining wall based on 3D scanning technology according to claim 1, characterized in that: In step 5, the specific steps for calculating the surface roughness of the tunnel lining wall using PYTHON software are as follows: Step 5-1: Select an empirical formula for roughness calculation. The specific formula is as follows: JRC=32.2+32.47lgZ 2S In the formula: Z 2S The average gradient modulus of the surface is represented by the following formula: Where: N x N y Δx and Δy represent the number of sampling points along the X and Y axes, respectively; Δx and Δy represent the sampling intervals along the X and Y axes, respectively; Z i Z i+1 , i and j are the Z-axis coordinates of the i-th and i+1-th discrete points of the fracture, respectively, and i and j are the indices of the mesoscopic planes along the X-axis and Y-axis, respectively; Step 5-2: Input the formula into a Python program, specify the working path to retrieve point cloud data, and calculate the average gradient magnitude and Z-axis of each scanned sub-surface. 2S The roughness calculation value JRC, where the average gradient modulus of the i-th scanned sub-surface is Z. 2Si The roughness calculation value is JRC. i , 1≤i≤n; Step 5-3: Calculate the average roughness of the lining wall surface The specific calculation formula is as follows: In the formula: JRC is the average roughness of the lining wall surface; i is the scanned sub-face number; n is the number of scanned sub-faces.

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