A method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete

Abstract

The present invention relates to the technical field of construction quality inspection, and provides a method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete. First, a three-dimensional laser scanner is used to measure the flow surface to be evaluated, and three-dimensional point cloud data is collected; the processed three-dimensional point cloud data is fitted with plane / curved surface features, and the distance and average distance between each point of the three-dimensional point cloud data and the best fitted plane / curved surface are calculated, and then the overall standard deviation, overall flatness and equivalent roughness are calculated. Finally, the equivalent roughness is brought into the roughness calculation formula according to the applicable conditions to calculate the roughness, and compared with the design parameter index to evaluate and judge the flow capacity of the flow surface concrete. The present invention collects complete point cloud data of the flow surface concrete by using three-dimensional laser scanning technology, detects and evaluates the overall flatness and flow capacity of the flow surface concrete, thereby solving the problems of strong subjectivity, large interference from human factors, limited range and limited accuracy existing in traditional methods.

Description

Technical Field

[0001] The present invention relates to the technical field of construction quality detection, and in particular to a method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete based on three-dimensional laser scanning. Background Art

[0002] Controlling the construction quality of the flow surface is a very important process in the construction process, among which the flatness of the flow surface is an extremely important quality control indicator. Flow surface concrete is widely used in various types of hydraulic structures. Its flatness is directly related to the smoothness and uniformity of water flow. If the flow surface is uneven, undulating or uneven, it will cause problems such as water flow obstruction, increased turbulence, and local scouring, which will affect the safety and stability of the project. Compared with an uneven surface, a flat flow surface can reduce the turbulence and friction of the water flow, reduce energy loss, and save engineering consumption. Therefore, it is of great significance to the stability and safety of the project by accurately evaluating the flatness and taking corresponding measures to improve it in a timely manner.

[0003] At present, domestic and foreign engineering and technical personnel mainly use the ruler method, total station method and image processing method to measure the flatness of the concrete on the flow surface. The ruler method places a ruler on the flow surface and observes the contact between the ruler and the flow surface to judge the flatness of the flow surface. This method is simple and easy to operate, but it is affected by the operator's subjective judgment and measurement skills, and its accuracy and repeatability are poor; the total station method is a more accurate method for measuring flatness. Using the total station measuring equipment, multiple measurement points are set on the flow surface to obtain the three-dimensional coordinate information of the points, and data processing and analysis are performed to evaluate the flatness. Although it has high accuracy, it requires professional equipment and operating skills, and has certain limitations in the selection of measurement points. When the detection range is wide, the total station needs to observe a large number of detection points one by one, and the operation efficiency is very low; the image processing method uses a camera to take images or videos of the flow surface, and uses image processing algorithms to perform image analysis and extract feature information to evaluate the flatness. This method requires appropriate image processing technology and algorithms, and can achieve fast non-contact measurement, but for flow surfaces with more complex surface textures, the accuracy may be affected to a certain extent. The general method for evaluating flow capacity is to calculate it through hydraulic calculation methods, or to determine it through model tests.

[0004] At present, some people also use three-dimensional laser scanning to evaluate the flatness of structures, but it does not involve the flatness evaluation of the flow surface and the flow capacity analysis. The laser scanning method uses a three-dimensional laser scanner for non-contact scanning, and evaluates the flatness by obtaining a large amount of point cloud data on the surface of the flow surface. The laser scanning method can provide high-precision and comprehensive data, reduce human errors, and can perform data processing and visualization at the same time, providing more comprehensive and accurate flatness evaluation results.

[0005] Disadvantages of traditional methods:

[0006] 1) Disadvantages of the ruler method:

[0007] The evaluation results of the ruler method are affected by the operator's subjective judgment and measurement skills, and lack objectivity and consistency; the flatness is evaluated only by the contact conditions of a few measuring points, which cannot fully reflect the conditions of the entire flow surface.

[0008] 2) Disadvantages of the total station method: The total station method requires professional equipment and operating skills, is complex to operate, and has high requirements for the operator; due to certain limitations in the selection and setting of measurement points, it may not be possible to cover all areas of the flow surface, resulting in local evaluation results.

[0009] 3) Disadvantages of image processing method: For flow surfaces with more complex surface textures, the image processing algorithm may be disturbed by the texture, resulting in reduced accuracy; the accuracy and feasibility of the image processing method are limited by the selection and application of image processing technology and algorithms.

[0010] Therefore, traditional methods have the problems of strong subjectivity, lack of representativeness, complex operation, low execution efficiency and limited accuracy. Therefore, it is necessary to seek more advanced technical methods to improve the accuracy and efficiency of evaluation. Summary of the invention

[0011] In view of the above problems, the purpose of the present invention is to provide a method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete based on three-dimensional laser scanning, which has the advantages of comprehensive evaluation, high-precision evaluation, visual presentation and data processing, and provides a more accurate, comprehensive and reliable method for evaluating flow surface concrete. The technical solution is as follows:

[0012] A method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete, comprising the following steps:

[0013] Step 1: Use a 3D laser scanner to measure the flow surface to be evaluated, set up identification target balls, perform multi-station measurement, and collect 3D point cloud data;

[0014] Step 2: The data measured at each measuring station are adjusted to a unified coordinate system by identifying the target sphere, and the three-dimensional point cloud data in the unified coordinate system is obtained;

[0015] Step 3: Perform algorithm preprocessing on the spliced ​​point cloud data. After noise removal, external isolated point removal, hole filling and other point cloud processing methods, a high-precision three-dimensional point cloud data model of the flow surface to be tested is obtained to reduce the impact of scanning errors on subsequent evaluation results.

[0016] Step 4: Perform plane / surface feature fitting on the pre-processed 3D point cloud data. Plane feature fitting adopts the best fitting method (such as least squares method, SVD method, Ransac algorithm, ICP algorithm, etc.) according to the point cloud data, and surface feature fitting fits the best surface according to the design parameters;

[0017] Step 5: Based on the fitting results, calculate the distance x between each point of the 3D point cloud data and the best plane / curved surface fitted. i , output the distance cloud diagram between the 3D point cloud data and the fitted plane / surface, and visualize the distance data;

[0018] Step 6: Calculate the average distance from each point in the 3D point cloud data to the best fitted plane / surface , x i and Substitute into the following formula (1) to calculate the overall standard deviation σ of the distance from the 3D point cloud data to the best fitting plane / curved surface;

[0019]

[0020] Where: σ is the overall standard deviation of the distance from the 3D point cloud data to the best fitting plane / surface; x i Fit the best plane / surface distance for each point of the 3D point cloud data; is the average distance between each point of the 3D point cloud data and the best fitted plane / curved surface; N is the number of 3D point cloud data points;

[0021] Step 7: Flatness evaluation

[0022] Step 7.1: Substitute the overall standard deviation σ of the distance from the 3D point cloud data to the best fitting plane / curved surface into the following formula to calculate the overall flatness S of the plane / curved surface;

[0023]

[0024] Where: σ is the overall standard deviation of the distance from the 3D point cloud data to the best fitting plane / surface;

[0025] Step 7.2: Calculate the overall flatness evaluation index S 标

[0026] Take n areas, perform manual sampling measurements according to the specifications, and calculate the overall deviation value σ of a single area based on the manual measurement data n , and then according to the overall deviation value σ of a single region n Calculate the flatness S of a single area n , determine whether a single measurement area is qualified according to the specification requirements; if qualified, calculate the average overall flatness of the qualified area , and the average overall flatness of the qualified area The overall flatness evaluation index S is obtained without rounding and retaining the first decimal place. 标 ;

[0027] Step 7.3: Flatness evaluation

[0028] If the overall flatness S calculated based on the 3D point cloud data is greater than the overall flatness evaluation index S 标 , then the flatness is qualified, otherwise it is unqualified;

[0029] Step 8: Current Capacity Evaluation

[0030] Calculate the equivalent roughness K of a flat / curved surface s :

[0031]

[0032] The equivalent roughness K s Substitute the following roughness calculation formula into the applicable conditions to calculate the roughness, and compare it with the design parameter index to evaluate and judge the flow capacity of the flow surface concrete;

[0033] 1) When the model test is not carried out, the tunnel flow roughness n is calculated by the following formula:

[0034]

[0035] Where: g is the acceleration due to gravity;

[0036] 2) Calculate the roughness n' of the rough area by the open channel hydraulic formula:

[0037]

[0038] Where: R is the hydraulic radius; K n is the formula coefficient.

[0039] The beneficial effects of the present invention are:

[0040] 1) Non-contact evaluation: The traditional ruler method requires manual contact with the concrete of the flow surface for measurement, while the total station method requires point measurement at different locations. In contrast, the present invention uses 3D laser scanning, which is a non-contact measurement technology that does not require physical contact, thus avoiding possible human errors and measurement inconveniences.

[0041] 2) Comprehensiveness and high precision: The present invention uses three-dimensional laser scanning to generate high-density point cloud data, providing a comprehensive description of the concrete surface of the flow surface, capable of capturing subtle surface changes and details, and providing more accurate flatness and flow capacity information; in contrast, traditional methods may not be able to capture local unevenness and detail information.

[0042] 3) Efficiency and speed: The present invention uses three-dimensional laser scanning to obtain a large amount of point cloud data in a short time and quickly generate a surface model; compared with the total station method, it can greatly improve measurement efficiency and reduce manpower and time costs.

[0043] 4) Data visualization and analysis: The present invention can perform three-dimensional visualization and detailed data analysis by processing and analyzing point cloud data, which makes it possible to better understand the morphological characteristics of the flow surface concrete, discover problem areas, and support more in-depth evaluation and decision-making.

[0044] 5) Automation and intelligence: The present invention adopts a method based on three-dimensional laser scanning combined with automated and intelligent data processing and analysis technology, which makes it possible to automatically extract key information and indicators, reduce the influence of subjective factors, and improve the consistency and reliability of evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is the overall effect diagram after splicing the three-dimensional point cloud data of the flow surface in a certain embodiment.

[0046] Figure 2 For Figure 1 The enlarged local rendering of the three-dimensional point cloud of the flow surface.

[0047] Figure 3 For Figure 2 The enlarged local rendering of the three-dimensional point cloud of the flow surface.

[0048] Figure 4 This is the process diagram for denoising and removing isolated points of spliced ​​3D point cloud data.

[0049] Figure 5 This is the result of denoising and removing isolated points of the spliced ​​3D point cloud data.

[0050] Figure 6 This is the local result image after denoising and removing isolated points of the spliced ​​3D point cloud data.

[0051] Figure 7 For Figure 4 The effect of hole filling based on noise reduction and isolated point removal.

[0052] Figure 8 For Figure 4 The local effect of hole filling based on noise reduction and isolated point elimination.

[0053] Fig. 9 This is the effect diagram of the fitted plane after plane fitting based on the preprocessed 3D point cloud.

[0054] Fig.10This is a cloud diagram of the distance between the output points and the fitting plane after plane fitting based on the preprocessed 3D point cloud. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] The present invention provides a method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete based on three-dimensional laser scanning, comprising the following steps:

[0057] S1. Use a 3D laser scanner to measure the flow surface to be evaluated and collect 3D point cloud data. If the scanning range is large, in order to ensure accuracy, it is necessary to deploy identification target balls and perform multi-station measurements.

[0058] S2. The data measured at each measuring station are adjusted to a unified coordinate system by identifying the target sphere, and three-dimensional point cloud data in the unified coordinate system is obtained.

[0059] Different 3D laser scanners have different scanning software for data processing. In actual operation, you should choose the appropriate stitching software according to the 3D laser scanner. This embodiment uses a 3D laser scanner produced by FARO, and the stitching processing software is SENCE. The overall effect of the 3D point cloud data after stitching is as follows Figure 1 As shown, the local effect diagram is as follows Figure 2 and Figure 3 shown.

[0060] S3. Perform algorithm preprocessing on the spliced ​​point cloud data.

[0061] After noise removal, external isolated point removal, hole filling and other point cloud processing methods, a high-precision 3D point cloud data model of the flow surface to be tested is obtained to reduce the impact of scanning errors on subsequent evaluation results. Figure 4 The processing results are shown as Figure 5 As shown, the local effect diagram is as follows Figure 6 The effect of hole filling based on noise reduction and isolated point removal is shown in Figure 7 As shown, the local effect diagram is as follows Figure 8 shown.

[0062] S4. Fit the plane or surface features of the pre-processed 3D point cloud data. The plane fitting adopts the best fitting method according to the point cloud data, such as the least squares method, SVD method, Ransac algorithm, ICP algorithm, etc. The surface fitting is to fit the best surface according to the design parameters; the fitting plane effect diagram after plane fitting based on the pre-processed 3D point cloud is as follows Fig. 9 shown.

[0063] S5. According to the fitting results, calculate the distance x between each point of the 3D point cloud data and the best plane (curve) fitted i , output the distance cloud map between the 3D point cloud data and the fitted plane (curve), visualize the distance data, and output the distance cloud map as follows Fig.10 shown.

[0064] S6. Calculate the average distance from each point of the 3D point cloud data to the best fitted plane (curve) x i and Substitute into the following formula (1) to calculate the overall standard deviation σ of the distance from the 3D point cloud data to the best fitting plane (curve);

[0065]

[0066] Where:

[0067] σ is the overall standard deviation of the distance from the 3D point cloud data to the best fitting plane (curve), in mm;

[0068] x i The distance between each point of the 3D point cloud data and the best fitted plane (curve), in mm;

[0069] The average distance between each point of the 3D point cloud data and the best fitted plane (curve), in mm;

[0070] N is the number of 3D point cloud data points, in units of pieces.

[0071] In this embodiment, the three-dimensional laser point cloud is processed and calculated to obtain an overall standard deviation σ=0.623787.

[0072] S7. Perform a flatness evaluation, and substitute the overall standard deviation σ of the distance from the three-dimensional point cloud data calculated in S6 to the best fitting plane (curved) surface into the following formula (2) to calculate the overall flatness S of the plane (curved) surface.

[0073]

[0074] Where: σ is the overall standard deviation of the distance from the 3D point cloud data to the best fitting plane (curve), in mm.

[0075] Then, according to the specification, the selected area is sampled and measured manually to calculate the area flatness S n , according to the specification requirements, determine whether the manually measured area is qualified. If qualified, calculate the average overall flatness of the qualified area Because the 3D laser scanning point cloud data is huge and the number of points is large, the average overall flatness of the qualified area is taken as Do not round off and keep the first decimal place to get S标 As an overall flatness evaluation index, flatness evaluation is then carried out.

[0076] If the overall flatness S calculated based on the 3D point cloud data is greater than the average overall flatness S of the qualified area 标 , then the flatness is qualified, otherwise it is unqualified.

[0077] Table 1 The measured values ​​of the manually selected area and the corresponding calculated values ​​of flatness

[0078]

[0079] Among them, the area flatness S n According to the specification, n areas are selected for sampling and measurement manually, and then the overall deviation value σ of a single area is calculated according to formula (1): n , and then according to the overall deviation value σ of a single region n , calculate the single area flatness S by the same method as formula (2) n , then check the specification judgment S n Is it qualified? If qualified, calculate the S of all qualified individual areas n The average overall flatness is obtained by averaging

[0080] The measured data and calculated values ​​of the manually selected areas are shown in Table 1. The average overall flatness of the qualified areas is calculated. Then the overall flatness evaluation index is rounded down to S 标 =0.6.

[0081] The overall flatness of the three-dimensional point cloud data calculated according to formula (2) is S=0.616.

[0082] By comparison, S = 0.616 > S 标 =0.6, it can be seen that the overall flatness of the flow surface is qualified.

[0083] S8. Current capacity evaluation

[0084] x calculated from S5 and S6 i and Enter the following formula (3) to calculate the equivalent roughness K of the plane (curved) surface: s ;

[0085]

[0086] Where:

[0087] K s is the equivalent roughness of the plane (curved) surface, in m;

[0088] x iThe distance between each point of the 3D point cloud data and the best fitted plane (curve), in meters;

[0089] The average distance between each point of the 3D point cloud data and the best fitted plane (curve), in meters;

[0090] N is the number of 3D point cloud data points, in units of pieces.

[0091] In this embodiment, after calculating the three-dimensional point cloud data, K s =0.623787mm.

[0092] Substitute the equivalent roughness obtained above into the following roughness calculation formula according to applicable conditions to calculate the roughness, and compare it with the design parameter index to evaluate and judge the flow capacity of the flow surface concrete.

[0093] (1) When model tests are not carried out, the tunnel flow roughness n can be calculated according to the following formula (4):

[0094]

[0095] Where:

[0096] K s is the equivalent roughness of the flat (curved) surface, in mm;

[0097] g is the acceleration due to gravity.

[0098] (2) Open channel hydraulic formula, which is applicable to the calculation of the roughness n' of the rough area:

[0099]

[0100] Where:

[0101] K s is the equivalent roughness of the plane (curved) surface, in m;

[0102] g is the acceleration due to gravity;

[0103] R is the hydraulic radius, in m;

[0104] K n It is the formula coefficient, and its value is selected according to the specific engineering conditions.

[0105] The present invention is applied to open channel engineering and uses formula (5) for calculation. According to the above steps, it can be known that the plane equivalent roughness K s =0.623787mm=0.000623787m, gravitational acceleration g is 9.81m / s 2 , hydraulic radius R = 4.87m, formula coefficient K nTaking the value as 1, substituting it into formula (5) to calculate, the roughness of the rough area of ​​the tunnel is n′=0.01478<0.015, which meets the requirements of the specifications and design documents and has good flow capacity.

[0106] In summary, the method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete based on three-dimensional laser scanning of the present invention has the following characteristics:

[0107] 1) Non-contact evaluation: Compared with the traditional method based on ruler and total station, 3D laser scanning provides a non-contact evaluation method. This method can quickly and accurately obtain the geometric information of the concrete flow surface, avoiding the human errors and measurement inconveniences that may exist in traditional methods, reducing the interference and errors that may be introduced during the measurement process, and improving the measurement efficiency and safety.

[0108] 2) Comprehensive and accurate detection and evaluation: 3D laser scanning can generate high-density point cloud data, providing a large amount of point cloud data on the concrete surface of the flow surface; through the processing and analysis of the point cloud data, a more accurate and comprehensive detection and evaluation of the flatness and flow capacity can be obtained, realizing the overall evaluation of the surface area, rather than the local evaluation of the traditional method.

[0109] 3) Comprehensive evaluation of flatness and flow capacity: The method of the present invention realizes a comprehensive evaluation of the overall flatness and flow capacity of the flow surface concrete by processing and analyzing the three-dimensional point cloud data in combination with corresponding algorithms and evaluation calculations. This can obtain more comprehensive and objective evaluation results and better understand the performance and quality of the project.

[0110] 4) Data visualization and analysis: The point cloud data generated by 3D laser scanning can be visualized and analyzed in 3D, providing intuitive surface display and detail analysis, which enables a better understanding of the morphological characteristics and problem areas of the flow surface concrete, supporting more in-depth evaluation and decision-making.

Claims

1. A method for detecting and evaluating the overall flatness and flow capacity of flow surface concrete, characterized in that: The steps include: Step 1: Use a 3D laser scanner to measure the flow surface to be evaluated, set up identification target balls, perform multi-station measurement, and collect 3D point cloud data; Step 2: The data measured at each measuring station are adjusted to a unified coordinate system by identifying the target sphere, and the three-dimensional point cloud data in the unified coordinate system is obtained; Step 3: Perform algorithm preprocessing on the spliced ​​point cloud data; Step 4: Perform plane / surface feature fitting on the pre-processed 3D point cloud data. The plane feature fitting adopts the best fitting method according to the point cloud data, and the surface feature fitting fits the best surface according to the design parameters. Step 5: Based on the fitting results, calculate the distance x between each point of the 3D point cloud data and the best plane / curved surface fitted. i , output the distance cloud diagram between the 3D point cloud data and the fitted plane / surface, and visualize the distance data; Step 6: Calculate the average distance from each point in the 3D point cloud data to the best fitted plane / surface x i and Substitute into the following formula (1) to calculate the overall standard deviation σ of the distance from the 3D point cloud data to the best fitting plane / curved surface; Where: σ is the overall standard deviation of the distance from the 3D point cloud data to the best fitting plane / surface; x i Fit the best plane / surface distance for each point of the 3D point cloud data; is the average distance between each point of the 3D point cloud data and the best fitted plane / curved surface; N is the number of 3D point cloud data points; Step 7: Flatness evaluation Step 7.1: Substitute the overall standard deviation σ of the distance from the 3D point cloud data to the best fitting plane / curved surface into the following formula to calculate the overall flatness S of the plane / curved surface; Where: σ is the overall standard deviation of the distance from the 3D point cloud data to the best fitting plane / surface; Step 7.2: Calculate the overall flatness evaluation index S 标 Take n areas, perform manual sampling measurements according to the specifications, and calculate the overall deviation value σ of a single area based on the manual measurement data n , and then according to the overall deviation value σ of a single region n Calculate the flatness S of a single area n , determine whether a single measurement area is qualified according to the specification requirements; if qualified, calculate the average overall flatness of the qualified area The average overall flatness of the qualified area The overall flatness evaluation index S is obtained without rounding and retaining the first decimal place. 标 ; Step 7.3: Flatness evaluation If the overall flatness S calculated based on the 3D point cloud data is greater than the overall flatness evaluation index S 标 , then the flatness is qualified, otherwise it is unqualified; Step 8: Current Capacity Evaluation Calculate the equivalent roughness K of a flat / curved surface s : The equivalent roughness K s Substitute the following roughness calculation formula into the applicable conditions to calculate the roughness, and compare it with the design parameter index to evaluate and judge the flow capacity of the concrete on the flow surface; 1) When the model test is not carried out, the tunnel flow roughness n is calculated by the following formula: Where: g is the acceleration due to gravity; 2) Calculate the roughness n' of the rough area by the open channel hydraulic formula: Where: R is the hydraulic radius; K n is the formula coefficient.

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