Multi-parameter joint characterization method for flatness of single-layer lining of tunnel

Abstract

The invention discloses a tunnel single-layer lining flatness multi-parameter joint characterization method, and relates to the technical field of tunnel single-layer lining flatness characterization, the flatness of the surface of a tunnel single-layer lining is calculated and jointly characterized by the following steps: using a three-dimensional laser scanner to collect point cloud data of the surface of the tunnel lining; and preprocessing the point cloud data to generate the point cloud data of the tunnel contour surface. According to the invention, through the three-dimensional laser scanning technology and multi-parameter conjoint analysis, accurate measurement of the tunnel lining flatness is realized, and the evaluation precision and reliability are improved. Compared with a traditional method, three-dimensional scanning comprehensively captures geometric changes of the lining surface, comprehensive quality evaluation is provided in combination with indexes such as the fractal dimension, the Z2 value and the JRC value, construction is helped to be monitored in real time, uneven areas are reduced, problems can be recognized in advance, repeated repair is avoided, construction efficiency is improved, cost is reduced, and economic benefits of a tunnel project are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel single - lining flatness characterization, and particularly to a multi - parameter joint characterization method for tunnel single - lining flatness. Background Art

[0002] With the rapid development of tunnel construction in China, especially its wide application in highway, railway and underground traffic systems, significant progress has been gradually made in the support technology of tunnel structures. The composite lining support structure (primary support of shotcrete + waterproofing membrane + secondary lining) is one of the most widely used support forms in current tunnel construction in China. This structure gives full play to the supporting role of the primary support, improves the sealing effect of the waterproof layer and the stability of the secondary lining. However, although a large amount of practical experience has been accumulated in the technology of the composite lining structure, problems such as the difficulty in ensuring the close adhesion between the surrounding rock and the primary support, and between the primary support and the secondary lining also exist, which easily lead to uneven stress of the lining and the gradual deterioration of the support structure. In addition, the construction process of the composite lining is cumbersome, the construction period is long, the material consumption is large, and the economic benefit is low. This makes this structure expose its limitations when facing the increasing construction requirements and economic pressure.

[0003] Compared with the composite lining, the single - lining has more obvious economic benefits in some tunnel constructions because it simplifies the construction process, reduces material consumption, and has a relatively short construction period. The single - lining forms a support system by canceling the waterproof board and relying on the strong bonding force and shear resistance between the shotcrete layers. Ensuring sufficient bonding strength between the primary sprayed layer and the secondary sprayed layer is the key to ensuring the safety of the single - lining. However, after the shotcrete construction, the formed tunnel contour often differs from the theoretical contour required by the design. The geometric shape of the primary sprayed layer directly affects the bonding performance and mechanical transfer between the layers. Therefore, quantifying the flatness of the shotcrete layer is crucial for ensuring the structural safety of the single - lining. Although the commonly used chord - height ratio method can be used as an evaluation index for flatness, it relies on manual measurement, has poor detection accuracy, and cannot effectively reflect the concave - convex characteristics of the three - dimensional surface irregularities. This makes this method have significant limitations in the application of tunnel single - lining and cannot meet the accurate detection requirements in actual projects.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a multi-parameter joint characterization method for the flatness of a single-layer tunnel lining. By using 3D laser scanning technology and multi-parameter joint analysis method, the precise measurement of the flatness of the tunnel lining surface is realized, significantly improving the accuracy and reliability of the evaluation. Compared with traditional methods, 3D scanning can comprehensively capture the minute geometric changes on the lining surface and, combined with multi-dimensional indexes such as fractal dimension, Z2 value and JRC value, provide a more comprehensive quality assessment, helping construction personnel to monitor the quality in real time and reducing the occurrence of uneven areas. This technology can identify problems in advance, avoid repeated repairs in traditional inspection methods, improve construction efficiency, reduce costs, and enhance the overall economic benefits of tunnel projects, so as to solve the problems in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: A multi-parameter joint characterization method for the flatness of a single-layer tunnel lining, which calculates and jointly characterizes the flatness of the single-layer tunnel lining surface through the following steps. The specific steps are as follows:

[0007] Use a 3D laser scanner to collect the point cloud data of the tunnel lining surface;

[0008] Use CloudCompare software to preprocess the point cloud data, including operations such as rotation correction and cropping and denoising, to generate the point cloud data of the tunnel contour surface;

[0009] In CloudCompare software, use the Cross Section function to extract two-dimensional contour lines from the tunnel center line at a set interval;

[0010] Import the extracted contour line data into the CAD system and cut it at a set interval to form multiple contour segments;

[0011] Import the contour segment data into MATLAB for processing, calculate the fractal dimension D and the root mean square of the first derivative Z2 value of each contour segment, and then calculate the two-dimensional JRC value;

[0012] Convert the obtained two-dimensional JRC value into a three-dimensional JRC value as a measure of the flatness of the tunnel lining surface.

[0013] Preferably, the flatness calculation method includes the following steps:

[0014] When calculating the fractal dimension D, the following processing is performed on the contour line through a MATLAB program:

[0015] Perform binary processing on the image of the contour line;

[0016] Cover the image with boxes of different sizes, count the number of pixels of the contour line in each box, and obtain the box size r and the corresponding box count N;

[0017] Take the logarithm of the box counting N and the box size r, and use the logarithmic difference method to calculate the local fractal dimension;

[0018] Select several middle terms of the local fractal dimension and calculate the average value D of the global fractal dimension;

[0019] Use the fractal dimension D as a description parameter for the geometric characteristics of the tunnel lining surface, and further use it to calculate the JRC value.

[0020] Preferably, calculate the power parameters of the root mean square Z2 of the first derivative, and the specific steps are as follows:

[0021] Convert the contour line in the image into a black and white image and extract the curve boundary;

[0022] Use the find function in MATLAB to obtain the coordinates of the curve and convert them into standardized actual coordinates;

[0023] Process the scatter data of the curve, remove the abnormal points, and obtain a new scatter data set through mean processing;

[0024] Use the spline interpolation method to fit the curve data, calculate the Z2 values at different intervals (Δ = 0.5mm, 1mm, 2mm), and perform power function fitting to obtain the power function parameters a and b;

[0025] Obtain the power function parameters a and b of Z2 through power function fitting for subsequent calculation of the JRC value.

[0026] Preferably, the JRC value calculation formula is as follows:

[0027]

[0028] , where D is the fractal dimension, a and b are the power parameters of the root mean square Z2 of the first derivative, is the two-dimensional roughness coefficient, R 2 The goodness of fit is 0.998, indicating that the formula has high accuracy and reliability.

[0029] Preferably, adjust the obtained two-dimensional JRC value for size effect to convert it into a three-dimensional JRC value, and the conversion expression is:

[0030]

[0031] , where, is the two-dimensional roughness coefficient of the actual contour segment, L0 is 0.1 meter, L n is the length of the actual contour segment, and formula (2) takes into account the size effect of the tunnel contour segment.

[0032] Preferably, the three-dimensional JRC value is calculated by the weighted average method, and the calculation formula is:

[0033]

[0034] , where is the three-dimensional roughness coefficient of the actual contour segment, and n represents the number of contour segments intercepted on a tunnel contour line.

[0035] Preferably, by jointly using the fractal dimension D, the power parameter of the root mean square of the first derivative Z2, and the three-dimensional JRC value, and comprehensively considering the geometric characteristics, sampling spacing, and size effect of the tunnel surface, a more accurate flatness evaluation result can be obtained.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] By adopting the three-dimensional laser scanning technology and high-precision point cloud data processing, the present invention realizes the accurate measurement of the flatness of the tunnel lining surface. Compared with the traditional manual measurement or two-dimensional method, the three-dimensional scanning can comprehensively collect the minute geometric changes on the tunnel surface. Especially in the complex internal environment of the tunnel, it can accurately identify the uneven areas on the lining surface. With the help of the advanced calculation methods of the fractal dimension and the Z2 value, the geometric characteristics of the tunnel lining can be analyzed in detail, providing more comprehensive evaluation data. This method reduces the human error in practical applications, improves the automation degree of data processing, thus ensuring the reliability of the evaluation results and meeting the requirements of high-precision monitoring and quality control.

[0038] The multi-parameter joint characterization method of the present invention combines various characterization indexes such as the fractal dimension, the Z2 value, and the JRC value, and can comprehensively evaluate the flatness of the tunnel lining from different dimensions. These indexes can effectively reflect the minute fluctuations on the lining surface, covering aspects such as surface roughness, size effect, and the geometric shape of the lining. Through this method, tunnel construction personnel can monitor the quality of the lining in real time during the construction process, timely discover uneven or non-compliant areas, and avoid long-term maintenance and repair problems caused by lining defects. This all-round quality control scheme can greatly improve the construction accuracy and quality, and reduce the cost and difficulty of later construction maintenance.

[0039] The present invention adopts a three-dimensional laser scanning and multi-parameter joint analysis method, which can accurately identify and solve the flatness problem at the early stage of tunnel construction, avoiding the repeated labor and large-scale repair projects caused by untimely inspection in the traditional method. This preventive quality monitoring not only reduces the rework and repair requirements in the project, but also speeds up the construction progress and improves the efficiency of the entire construction process. At the same time, after accurately evaluating the flatness of the lining surface by using the present invention, it is possible to reduce resource waste, optimize the construction process, and reduce the overall cost of the tunnel project. Through this refined and real-time quality management, tunnel construction can not only improve the technical level, but also bring significant economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a method flow chart of a multi-parameter joint characterization method for the flatness of a single-layer lining in a tunnel according to the present invention.

[0042] Figure 2 It is a processing diagram of the three-dimensional laser scanning point cloud of the tunnel according to the present invention.

[0043] Figure 3 It is a schematic diagram of the tunnel contour line cutting according to the present invention.

[0044] Figure 4 For the initial and corrected two-dimensional value calculation result diagram of the present invention.

[0045] Figure 5 For the three-dimensional value calculation result diagram at different positions of the tunnel according to the present invention.

[0046] Figure 6 It is a diagram of ten JRC standard profile lines according to the present invention.

[0047] Figure 7 It is a relationship diagram between Z2 and the sampling interval according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art.

[0049] The present invention provides asFigures 1 to 7 A multi-parameter joint characterization method for the flatness of a single-layer tunnel lining is shown. The flatness of the single-layer tunnel lining surface is calculated and jointly characterized through the following steps. The specific steps are as follows:

[0050] Use a 3D laser scanner to collect the point cloud data of the tunnel lining surface;

[0051] Adopt CloudCompare software to preprocess the point cloud data, including operations such as rotation correction, cropping and denoising, to generate the point cloud data of the tunnel contour surface;

[0052] In the CloudCompare software, use the Cross Section function to extract two-dimensional contour lines from the tunnel center line at a set interval;

[0053] Import the extracted contour line data into the CAD system and cut it at a set interval to form multiple contour segments;

[0054] Import the contour segment data into MATLAB for processing, calculate the fractal dimension D and the root mean square of the first derivative Z2 value of each contour segment, and then calculate the two-dimensional JRC value;

[0055] Convert the obtained two-dimensional JRC value into a three-dimensional JRC value as a measure of the flatness of the tunnel lining surface.

[0056] Specific implementation method 1: Tunnel flatness evaluation based on 3D laser scanning and MATLAB processing;

[0057] Implementation step 1: Tunnel 3D laser scanning and data collection;

[0058] In this implementation method, first use a high-precision 3D laser scanner to comprehensively scan the tunnel lining to generate the point cloud data of the tunnel lining surface. To ensure the accuracy of the scanning, the scanner will collect the point cloud data evenly distributed along the length and width of the tunnel, and adjust the resolution and sampling frequency of the scanner according to the specific on-site situation. During the scanning process, considering the complexity of the tunnel, especially the curves in the tunnel and different lining surface states, the scanner needs to be able to capture the tiny geometric changes on the tunnel surface. The point cloud data obtained through scanning can accurately reconstruct the 3D model of the tunnel, providing detailed original data for subsequent flatness analysis.

[0059] Implementation step 2: Preprocessing of point cloud data;

[0060] After the point cloud data is collected, the obtained point cloud is preprocessed using CloudCompare software. First, denoising processing of the point cloud is carried out. Since the point cloud data obtained by 3D laser scanning may contain errors and noise, it is necessary to use the filtering function in CloudCompare to remove abnormal data points and excessive noise points. Then, rotational correction of the point cloud data is performed to ensure that the scanned data is aligned with the actual coordinate system of the tunnel. At this stage, the software will crop the point cloud to remove irrelevant areas and ensure that only the data on the tunnel lining surface is retained. The preprocessed point cloud data is accurately converted into a three-dimensional surface model of the tunnel lining and used in subsequent calculations.

[0061] Implementation step three: Extraction and cutting of contour lines;

[0062] After completing the preprocessing of the point cloud, use the Cross Section function of CloudCompare to segment the tunnel surface and extract the two-dimensional contour lines along the tunnel alignment. To more accurately describe the geometric features of the tunnel, a cross-section contour line is extracted every 0.5 meters along the tunnel centerline, which can ensure the uniformity and representativeness of each contour segment. The two-dimensional contour line file extracted by this method can be saved in DXF format and imported into CAD software for subsequent cutting and analysis processing.

[0063] Implementation step four: Calculation of fractal dimension and Z2 value in MATLAB;

[0064] In MATLAB, first digitize the exported contour line data, convert each contour line into a binary image for subsequent fractal dimension calculation. Through image processing technology, set the pixel values of the contour line part to 1 and the background to 0 to ensure the clarity and distinguishability of the contour line. Then, use the classical box-counting method to calculate the fractal dimension D of each contour line. The specific approach is to select different box sizes and count the number of contour line pixels in each box. By taking the logarithm of the relationship between the box count N and the box size r and using the logarithmic difference method, the fractal dimension D of each contour line is calculated.

[0065] Meanwhile, calculate the root mean square of the first derivative Z2 value. Through the differential processing of the contour line, calculate the first derivative of each contour segment, and then obtain the Z2 value. Calculate the corresponding Z2 values according to different spacings Δ (such as 0.5 mm, 1 mm, and 2 mm), and fit the Z2 values and the spacing Δ with different intervals through the power function model (Z 2 = aΔ b ) to obtain the power parameters a and b.

[0066] Implementation step five: Calculate two-dimensional JRC value and three-dimensional JRC value;

[0067] After calculating the fractal dimension D and the power parameter of Z2, substitute these parameters into the formula to calculate the two-dimensional JRC value of each contour segment. The formula provides the relationship between the fractal dimension, the Z2 power parameter, and the JRC value. To consider the size effect, use to adjust the JRC value for size, and obtain the three-dimensional JRC value of the actual contour segment. At this time, the three-dimensional JRC value is calculated by weighted averaging the values of multiple contour segments, so as to obtain the flatness index of the entire tunnel lining.

[0068] Implementation step six: Flatness evaluation and optimization;

[0069] Through the calculated three-dimensional JRC value, the flatness of the tunnel lining surface can be accurately evaluated. This method can comprehensively reflect the geometric characteristics of the tunnel surface. Especially under complex geological conditions, it can effectively overcome the limitations of traditional methods. Through multiple scans and analyses, combined with the standards of tunnel construction quality, engineers can timely detect the unevenness problems on the lining surface during the construction process and take effective remedial measures, thereby improving the construction quality of the tunnel.

[0070] Specific implementation method two: Tunnel lining contour line extraction and JRC value calculation;

[0071] Implementation step one: Point cloud data acquisition and preprocessing;

[0072] In this implementation method, first obtain the point cloud data of the tunnel lining surface through a three-dimensional laser scanner. During this process, the scanner is set to the high-precision mode to ensure that it can capture the subtle geometric changes on the tunnel surface. The scanner scans step by step along the tunnel center line in the tunnel, and collects data at regular intervals to form a complete point cloud set. After completing the data acquisition, use CloudCompare software to denoise the point cloud data. Remove the noise points and error data through filters to ensure obtaining a clean point cloud data set, and thus avoid the influence of errors on subsequent calculations.

[0073] Implementation step two: Contour line extraction and segmentation;

[0074] After the point cloud data processing is completed, use the Cross Section tool in CloudCompare to extract 2D contour lines from the tunnel surface. To accurately represent the geometric features of the tunnel lining surface, start from the midpoint of the tunnel centerline and extract contour lines at fixed intervals (e.g., 1 meter). The acquisition interval for each contour line is kept consistent to ensure that the extracted contour lines are well representative. The extracted contour lines can be saved in DXF format and imported into the CAD system for further processing. To analyze the flatness of the tunnel lining in detail, each contour line is cut into multiple small segments, and each segment of data is used for individual analysis.

[0075] Implementation step three: Fractal dimension calculation and Z2 value fitting;

[0076] In MATLAB, first digitize the extracted contour lines and convert them into binary images. Extract the boundaries of the contour lines through image processing techniques and calculate the fractal dimension D of each contour segment. Use the box-counting method to calculate the fractal dimension. The specific approach is to cover the contour lines with boxes of different sizes and count the number of contour pixels contained in each box. By taking the logarithm of the relationship between the box count N and the box size r, the value of the fractal dimension D is obtained. Then, use the root mean square of the first derivative Z2 calculation formula to obtain the root mean square value of the first derivative of each contour segment. Through fitting the Z2 values at different spacings Δ, the power function parameters a and b are obtained, thus establishing the relationship between Z2 and the spacing Δ.

[0077] Implementation step four: Calculate the JRC value and size effect adjustment;

[0078] Use the formula to calculate the 2D JRC value of each contour segment. The JRC value can comprehensively reflect the surface roughness of the tunnel lining and is an important indicator for evaluating the flatness of the tunnel. To correct the influence of the size effect on the JRC value, use the formula to adjust the JRC value of each contour segment to ensure that the evaluation results are consistent with the actual situation. By performing a weighted average of the JRC values of all contour segments, the 3D JRC value of the tunnel lining is obtained. The weighted average method takes into account the different contributions of each 2D contour line to the flatness of the 3D contour surface. With the idea that rougher contour lines contribute more to the overall flatness and smoother contour lines contribute less as the weight, the specific formula is: This value can more accurately reflect the flatness of the entire tunnel lining.

[0079] Implementation step five: Final evaluation and quality monitoring;

[0080] With the three-dimensional JRC values obtained by this method, tunnel engineers can monitor the flatness of tunnel linings in real time to ensure that it meets the design standards. During the construction process, engineers can adjust the construction plan in a timely manner according to the measurement results to avoid excessive unevenness on the lining surface. The accuracy and reliability of this method make it an effective tool in tunnel construction quality management, which can greatly improve the project quality and reduce the cost of later maintenance.

[0081] Specific implementation method three: Joint characterization of the flatness of the tunnel lining surface with multiple parameters;

[0082] Implementation step one: Three-dimensional laser scanning and data preprocessing;

[0083] In this implementation method, a three-dimensional laser scanner is first used to comprehensively scan the tunnel lining to collect complete point cloud data. During the scanning process, a high-precision scanner is used to ensure that small geometric changes on the tunnel lining surface can be captured. Data is collected at regular intervals (for example, every 50 cm). After the data collected by the scanner is preliminarily processed, a complete point cloud set is formed. The CloudCompare software is used to denoise, rotate and correct, and crop the scanned data to ensure the accuracy of the data. Through these steps, an accurate three-dimensional model of the tunnel lining can be obtained.

[0084] Implementation step two: Contour line extraction and cutting;

[0085] After the processing of the point cloud data is completed, the Cross Section function in the CloudCompare software is used to extract contour lines from the tunnel at regular intervals (for example, every 1 meter). The contour lines extracted in this way can accurately reflect the geometric characteristics of the tunnel surface, and each contour segment is representative. The CAD system is used to cut the extracted contour data, and each contour line is divided into multiple small segments, and each segment of data is used for separate analysis. This operation helps to improve the control of the details of the tunnel surface.

[0086] Implementation step three: Joint calculation of the fractal dimension and Z2 value;

[0087] In MATLAB, the extracted contour lines are first digitized. Through image processing technology, the contour lines are converted into binary images for subsequent calculations. The fractal dimension D of each contour segment is calculated, and the box counting method is used to calculate the fractal dimension. The number of contour pixels contained in different box sizes is counted. By taking the logarithm of the relationship between the box counting and the box size, the fractal dimension value D of each contour segment is obtained. Then, the root mean square Z2 value of the first derivative is calculated to obtain the derivative change trend of each contour. Through the fitting of the Z2 values at multiple intervals, the parameters a and b of the power function are obtained.

[0088] Implementation Step 4: JRC Value Calculation and Size Effect Adjustment;

[0089] Calculate the two-dimensional JRC value of each contour segment through the formula and adjust the size effect according to the formula Perform a weighted average on the obtained adjusted JRC values to obtain the three-dimensional JRC value of the entire tunnel lining. This value can accurately reflect the flatness of the tunnel surface and provide a scientific basis for the construction quality control of the tunnel.

[0090] Implementation Step 5: Flatness Evaluation and Real-time Monitoring;

[0091] Through the joint characterization of multiple parameters, the surface flatness of the tunnel lining can be accurately evaluated. Using these technologies, engineers can conduct real-time monitoring of the tunnel construction process to ensure that the quality of each stage meets the standards. Through regular measurement and analysis, uneven areas can be detected during the tunnel construction process, and the construction method can be adjusted in a timely manner to ensure that the flatness of the tunnel meets the design requirements and reduce the later maintenance cost.

[0092] The rock joint roughness coefficient (JRC) is an important mechanical parameter in rock mechanics used to describe the influence of surface roughness and undulation on joint shear strength. In 1977, Barton et al. proposed ten typical joint profiles through shear experiments on 136 rock specimens to evaluate the JRC value, and the value ranges from 0 to 20. The ten standard profile lines proposed by Barton are as Figure 5 shown. However, relying solely on visual comparison with these ten standard profile lines when determining the JRC value is highly subjective, and thus a variety of characterization parameters and their estimation methods have emerged. These parameters can be roughly divided into two categories: one is based on spatial geometric features and uses statistical parameter methods to study surface roughness, mainly including parameters such as undulation amplitude, undulation angle, and trace length. However, these parameters are greatly affected by the sampling interval, and the fitting relationship between JRC and statistical parameters is also different under different sampling intervals; the other is to abstract a characteristic parameter, namely the fractal dimension, from the spatial geometric appearance to distinguish different geometric bodies. The fractal dimension can accurately describe the secondary fine rough structure of a rough surface, but its ability to describe the first-level undulation structure is limited. Therefore, a single type of parameter is difficult to comprehensively reflect the rough characteristics of the surface topography.

[0093] Fractal Dimension:

[0094] Estimate the fractal dimension of the contour line using the box dimension method. This method is to cover the image with boxes of different sizes r, count the number of contour line pixels contained in the boxes, and then calculate the fractal dimension according to the following formula. In the formula, D is the fractal dimension, r is the step size, and N is the number of segments of the two-dimensional contour line at the step size r.

[0095] Statistical parameters:

[0096] The formula for calculating the root mean square of the first derivative Z2 is as follows: In the formula, N is the number of discrete points on the contour line, Δx is the sampling interval, and yi is the height coordinate of the i-th point. The calculation results of statistical parameters are usually significantly affected by the sampling interval. For example, as the sampling interval of the contour line increases, the Z2 value shows a decreasing trend, and there is a power function relationship between the two (see Figure 7 ). Therefore, two power function parameters independent of the sampling interval obtained by fitting are used to characterize JRC to solve the problem of inconsistent statistical parameters caused by different sampling intervals.

[0097] Based on this, the present invention jointly characterizes JRC by combining the two power function parameters corresponding to Z2 with the fractal dimension D. This method not only overcomes the limitations of a single parameter in comprehensively characterizing complex topography features, but also solves the problem of inconsistent statistical parameters caused by sampling at different intervals, thus significantly improving the accuracy of the estimation results.

[0098] By adopting three-dimensional laser scanning technology and high-precision point cloud data processing, the present invention can accurately measure the flatness of the tunnel lining surface. Compared with traditional manual measurement or two-dimensional methods, three-dimensional scanning can comprehensively collect the minute geometric changes on the tunnel surface. Especially in the complex internal environment of the tunnel, it can accurately identify the uneven areas on the lining surface. With the aid of advanced fractal dimension and Z2 value calculation methods, it can meticulously analyze the geometric features of the tunnel lining and provide more comprehensive evaluation data. This method reduces human errors in practical applications, improves the automation degree of data processing, thus ensuring the reliability of the evaluation results and meeting the requirements of high-precision monitoring and quality control.

[0099] The multi-parameter joint characterization method of the present invention combines various characterization indexes such as fractal dimension, Z2 value, and JRC value, and can comprehensively evaluate the flatness of the tunnel lining from different dimensions. These indexes can effectively reflect the minute fluctuations on the lining surface, covering aspects such as surface roughness, size effect, and the geometric shape of the lining. Through this method, tunnel construction personnel can monitor the quality of the lining in real time during the construction process, promptly discover uneven or non-compliant areas, and avoid long-term maintenance and repair problems caused by lining defects. This all-round quality control scheme can greatly improve the construction accuracy and quality, and reduce the cost and difficulty of subsequent construction maintenance.

[0100] The present invention adopts a three-dimensional laser scanning and multi-parameter joint analysis method, which can accurately identify and solve the flatness problem at the early stage of tunnel construction, avoiding the repeated labor and large-scale repair projects caused by untimely inspection in the traditional method. This preventive quality monitoring not only reduces the rework and repair requirements in the project, but also speeds up the construction progress and improves the efficiency of the whole construction process. At the same time, after accurately evaluating the flatness of the lining surface by using the present invention, it is possible to reduce resource waste, optimize the construction process and reduce the overall cost of the tunnel project. Through this refined and real-time quality management, the tunnel construction can not only improve the technical level, but also bring significant economic benefits.

[0101] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of the claims of the present invention.

[0102] It should be noted that in this text, if there are relational terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0103] It should be understood that in various embodiments of the present application, the magnitude of the serial numbers of the above processes does not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0104] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0105] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0106] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0109] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0110] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A multi-parameter joint characterization method for the flatness of a single-layer tunnel lining, characterized in that: The flatness of the single-layer lining surface of the tunnel is calculated and jointly characterized by the following steps: Use 3D laser scanner to collect point cloud data of tunnel lining surface; Preprocess the point cloud data to generate point cloud data of the tunnel contour surface; Extract 2D contour lines from the tunnel centerline at set intervals; Import the extracted contour line data into the CAD system and cut it according to the set intervals to form multiple contour segments; The contour segment data were imported into MATLAB for processing, and the fractal dimension D and the first-order derivative root mean square Z2 value of each contour segment were calculated, and then the two-dimensional JRC value was calculated; The obtained two-dimensional JRC value is converted into a three-dimensional JRC value as a measure of the surface smoothness of the tunnel lining.

2. A multi-parameter joint characterization method for the flatness of a single-layer tunnel lining according to claim 1, characterized in that: The flatness calculation method includes the following steps: When calculating the fractal dimension D, the contour lines are processed as follows by the MATLAB program: Binarize the image of the contour line; Use boxes of different sizes to cover the image, count the number of pixels of the contour line in each box, and get the box size r and the corresponding box count N; Take the logarithm of the box count N and the box size r, and use the logarithmic difference method to calculate the local fractal dimension; Select several middle items of the local fractal dimension and calculate the average value D of the global fractal dimension; The fractal dimension D is used as a descriptive parameter of the geometric characteristics of the tunnel lining surface to calculate the JRC value.

3. The multi-parameter joint characterization method for the flatness of a single-layer tunnel lining according to claim 1 is characterized in that: Calculate the power parameter of the first-order derivative root mean square Z2. The specific steps are as follows: Convert the contour lines in the image into black and white images and extract the curve boundaries; Use the find function in MATLAB to obtain the coordinates of the curve and convert them into standardized real coordinates; Process the scattered data of the curve, remove the outliers, and obtain a new scattered data set through mean processing; The curve data is fitted using the spline interpolation method, and the Z2 values ​​at different intervals are calculated. The power function is then fitted to obtain the power function parameters a and b. The power parameters a and b of Z2 are obtained by power function fitting and used for subsequent calculation of JRC values.

4. A multi-parameter joint characterization method for the flatness of a single-layer tunnel lining according to claim 3, characterized in that: The JRC value calculation formula is as follows: Where D is the fractal dimension, a and b are the power parameters of the first-order derivative root mean square Z2, is the two-dimensional roughness coefficient, R 2 The goodness of fit is 0.

998.

5. The multi-parameter joint characterization method for the flatness of a single-layer tunnel lining according to claim 1 is characterized in that: The obtained two-dimensional JRC value is adjusted for size effect and converted into a three-dimensional JRC value. The conversion expression is: in, is the two-dimensional roughness coefficient of the actual contour segment, L0 is 0.1 m, L n is the actual contour segment length.

6. A multi-parameter joint characterization method for the flatness of a single-layer tunnel lining according to claim 5, characterized in that: The three-dimensional JRC value is calculated by weighted average method, and the calculation expression is: in, is the three-dimensional roughness coefficient of the actual contour segment, and n represents the number of contour segments intercepted on a tunnel contour line.

7. The multi-parameter joint characterization method for the flatness of a single-layer tunnel lining according to claim 1 is characterized in that: By combining the fractal dimension D, the power parameter of the first-order derivative root mean square Z2, and the three-dimensional JRC value, and comprehensively considering the geometric characteristics of the tunnel surface, sampling spacing, and size effect, a more accurate flatness evaluation result can be obtained.

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