Seabed foundation covering interface sample representative sampling method based on global search method

Abstract

The invention discloses a representative sampling method for a seabed foundation-covering interface sample based on a global search method. The method comprises the following steps: (1) performing contact measurement on occurrence and mechanical parameters of a field seabed foundation-covering interface; (2) carrying out three-dimensional laser scanning fine measurement on the seabed base covering interface; (3) processing and analyzing point cloud data of the seabed base covering interface; and (4) determining representative samples of various sizes of the substrate covering interface. According to the method, the shear strength of the base-covering interface and the strength change rule of the base-covering interface along with the size can be accurately obtained.

Description

Technical Field

[0001] The present invention belongs to the field of engineering technology and relates to a method for determining representative samples of underlying bedrock within a certain scale range in a seabed base-cover interface in a marine environment. In particular, the present invention starts from the complexity and randomness of the base-cover interface morphology, considers the characteristics of the base-cover interface such as non-uniformity, anisotropy, heterogeneity and size effect, and combines mathematical statistical methods to propose a representative sampling method for seabed base-cover interface samples based on a global search method. Background Art

[0002] The seabed base-cover interface is a clear interface formed by the combined effects of geological forces and ocean currents. The overall stability of the seabed and the structures on it depends to a large extent on the shear strength of the base-cover interface. Due to the long and complex geological effects, the distribution and morphology of the base-cover interface are complex and random. Accurately obtaining the shear strength of the base-cover interface is an important basis for the safety and stability of seabed geotechnical engineering. Indoor direct shear test is a common method for obtaining the shear strength of the base-cover interface. However, the surface morphology of the base-cover interface is quite random, which leads to a very large discreteness in the shear strength obtained when the same size of specimens is used for shear test. At the same time, as the size of the specimen increases, the shear strength of specimens of different sizes obtained through shear test has obvious size effect. The discreteness and size effect of the shear strength of the base-cover interface obtained through indoor direct shear test have formed a huge obstacle to the accurate evaluation of its stability. Summary of the invention

[0003] In order to overcome the discreteness of the existing shear strength of the base-cover interface and the lack of accuracy in the stability evaluation due to the size effect, the present invention proposes a representative sampling method for seabed base-cover interface samples based on a global search method, which adopts three-dimensional laser scanning technology to obtain the morphological data of the underlying bedrock of the base-cover interface, adopts a progressive coverage global search method to obtain the surface morphology of different areas on the base-cover interface, and adopts a simple straight-edge method to calculate the roughness coefficient values ​​in different directions on each study area and calculates its statistical mean and standard deviation, and compares them to obtain representative samples of different sizes.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A representative sampling method for seabed base-cover interface samples based on a global search method, the method comprising the following steps:

[0006] (1) On-site contact measurement of the occurrence and mechanical parameters of the seabed base-cover interface;

[0007] (2) 3D laser scanning and precise measurement of the seabed base-cover interface;

[0008] (3) Processing and analysis of seabed base-cover interface point cloud data;

[0009] (4) Determination of representative samples of various sizes at the base-cover interface, the process is as follows:

[0010] 4.1. Based on the processed point cloud data, a series of standard-sized sample point clouds are intercepted from the point cloud processing software, and contour curves in different directions are extracted for each size sample at set angle intervals and set spacings;

[0011] 4.2. Sample statistical analysis: extract structural surface profile curve analysis samples of different sizes, calculate the structural surface roughness coefficient JRC value and perform statistical analysis, and use the simple straight edge method to calculate the roughness coefficient JRC;

[0012] 4.3. Determination of representative samples. Based on the statistical analysis results, determine the polar coordinate diagram of the anisotropy statistical results. Determine the polar coordinate diagram of the anisotropy results of each sample. Compare the anisotropy statistical results of the base-cover interface roughness with the measured results of samples of various sizes to obtain representative samples.

[0013] Further, the process of step (2) is as follows:

[0014] 2.1. According to the working area of ​​the construction site, select a suitable data collection area on the exposed base-cover interface as the measurement location and mark it;

[0015] 2.2. Set up the 3D laser scanner according to the size and spatial orientation of the acquisition area, and set the resolution and scanning speed parameters of the 3D laser scanner;

[0016] 2.3. Use the same resolution to precisely scan the seabed base-cover interface morphology surface to obtain point cloud data;

[0017] 2.4. Check the point cloud data. First, check whether the point cloud data is complete, and perform additional scanning on the areas that have not been scanned. Secondly, check the uniformity of the point cloud data, and perform additional processing on the sparsely scanned areas. Finally, delete and fill in the noise points and holes generated during the scanning process.

[0018] Furthermore, the process of step (3) is as follows:

[0019] 3.1. Use the built-in noise removal function of the point cloud processing software to identify and delete noise points in the point cloud data;

[0020] 3.2. Find the marked position from the point cloud data and crop the scanned data to obtain the area for research and analysis;

[0021] 3.3. According to the obtained seabed basement interface occurrence, determine the approximate slip direction of the overlying layer and define it as the positive direction of the X-axis;

[0022] 3.4. Level the point cloud data of the study area. First, use the following formula to calculate the coefficients of the plane equation of the least squares fitting of the point cloud:

[0023]

[0024] Z=a0X+a1Y+a2

[0025] In the formula, (x i ,y i ,z i ) is the spatial coordinate of the i-th point cloud, n is the number of all point clouds, a0, a1, a2 are the coefficients of the least squares fitting plane of the point cloud;

[0026] 3.5. Let the point cloud normal vector be n, the horizontal plane normal vector be m, calculate the angle θ between the two normal vectors, cross-multiply the normal vector n with the horizontal plane normal vector m to get the rotation axis vector l, rotate the point cloud in the opposite direction of the overall tilt direction to make the normal vector n coincide with the horizontal plane normal vector m, and get the base-cover interface point cloud data after leveling. The rotation equation is as follows:

[0027]

[0028] In the formula, (x old ,y old ,z old ) and (x new ,y new ,z new ) are the spatial coordinates of the base-covering interface point cloud data before and after leveling, and a, b and c are the normalized vector coordinates of the rotation axis.

[0029] Preferably, in 4.1, point cloud processing software is used to extract sample point clouds of a series of standard sizes of 10 cm, 20 cm, 30 cm, 40 cm ... 1000, and the number of samples of the series of standard sizes is set to 100; contour curves in different directions are extracted for samples of each size at intervals of 1° and at a spacing of 0.5 mm.

[0030] In 4.2, the formula of roughness coefficient JRC is as follows:

[0031]

[0032] In the formula, JRC n is the roughness coefficient JRC value of the nth profile curve, L n A is the sampling length of the sample, max is the maximum fluctuation amplitude of the contour curve. When calculating, draw a straight line between the two local maxima of the contour line. The maximum vertical distance from a point on the contour line to this straight line is the required A.max .

[0033] In 4.3, by comparing the similarity between the JRC statistical results and the statistical average values ​​in all directions of all samples, the sample number whose JRC all-directional statistical results are most similar to the JRC all-directional statistical average value is determined and defined as a representative sample. The calculation expression is as follows:

[0034] S R =min{∑S 2 (i,j)}

[0035] In the formula, S R is the representative sample calculated, ∑S 2 (i, j) is the sum of the squares of the JRC statistics of the specimen with position number (i, j) and the average JRC statistics of all specimens.

[0036] The beneficial effects of the present invention are mainly manifested in that the shear strength of the base-covering interface and the law of its strength variation with size can be accurately obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of point cloud rotation.

[0038] Figure 2 It is a schematic diagram of section extraction.

[0039] Figure 3 This is a schematic diagram for calculating the maximum fluctuation amplitude.

[0040] Figure 4 This is a comparison chart of JRC anisotropy results at the base-cover interface. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Reference Figure 1 to Figure 4 Taking a cross-sea bridge between cities on the eastern coast as an example, a representative sampling method for seabed base-cover interface samples based on a global search method is proposed, including on-site base-cover interface occurrence measurement, three-dimensional laser scanning fine measurement of the base-cover interface, base-cover interface point cloud processing, and determination of representative samples of various sizes of the base-cover interface. The specific contents of each part are as follows:

[0043] (1) On-site contact measurement of the substrate-cover interface occurrence, the process is as follows:

[0044] 1.1. Observe the exposed seabed base-cover interface in the construction area to determine the overall characteristics of the base-cover interface development within the research scope;

[0045] 1.2. Based on the observed overall characteristics of the basement-cover interface, use a geological compass to measure typical areas of the basement-cover interface as a reference for the occurrence of the basement-cover interface, and take photos to record the location and other information of the corresponding areas;

[0046] 1.3: Select one or several typical areas on the base-cover interface as data collection points, make corresponding marks, and record the corresponding information;

[0047] (2) Three-dimensional laser scanning fine measurement of the substrate-covering interface, the process is as follows:

[0048] 2.1. Set up the laser scanner according to the position of the marking point and calibrate the laser scanner to reduce the measurement error;

[0049] 2.2. Set scanning parameters and scan the topography data of the base-covering interface surface with equal resolution;

[0050] 2.3. Check the point cloud data. First, check whether the point cloud data is complete, and perform additional scanning on the areas that have not been scanned. Secondly, check the uniformity of the point cloud data, perform additional processing on the sparsely scanned areas, and finally delete and supplement the noise points and holes generated during the scanning process.

[0051] (3) Point cloud processing of base-cover interface: the process is as follows:

[0052] 3.1. Use the built-in noise removal function of the point cloud processing software to identify and delete noise points in the point cloud data;

[0053] 3.2. Find the marked position from the point cloud data and crop the scanned data to obtain the area for research and analysis;

[0054] 3.3. According to the obtained seabed basement interface occurrence, determine the approximate slip direction of the overlying layer and define it as the positive direction of the X-axis;

[0055] 3.4. Level the point cloud data of the study area. First, use the following formula to calculate the coefficients of the least squares fitting plane equation of the point cloud:

[0056]

[0057] Z=a0X+a1Y+a2

[0058] In the above formula, (x i ,y i ,z i ) is the spatial coordinate of the i-th point cloud, n is the number of all point clouds, a0, a1, a2 are the coefficients of the least squares fitting plane of the point cloud;

[0059] 3.5. Let the point cloud normal vector be n, the horizontal plane normal vector be m, calculate the angle θ between the two normal vectors, cross-multiply the normal vector n with the horizontal plane normal vector m to get the rotation axis vector l, rotate the point cloud in the opposite direction of the overall tilt direction so that the normal vector n coincides with the horizontal plane normal vector m, and get the base-cover interface point cloud data after leveling. The rotation equation is as follows:

[0060]

[0061] Where (x old ,y old ,z old ) and (x new ,y new ,z new ) are the spatial coordinates of the base-covering interface point cloud data before and after leveling, a, b and c are the vector coordinates of the normalized rotation axis l, and the rotation diagram is shown in the attached figure. Figure 1 As shown; according to the attached Figure 1 The angle is 16.3°.

[0062] (4) Determination of representative samples of various sizes at the base-cover interface, the process is as follows:

[0063] 4.1. Based on the processed point cloud data, the point cloud processing software is used to extract the point clouds of the samples of a series of standard sizes such as 10cm, 20cm, 30cm, 40cm, etc. The number of samples of the series of standard sizes is set to 100; the contour curves of different directions are extracted for the samples of each size at an interval of 1° and a spacing of 0.5mm, as shown in the attached figure. Figure 2 As shown;

[0064] 4.2. Sample statistical analysis: extract structural surface profile curve analysis samples of different sizes, calculate the structural surface roughness coefficient JRC value and perform statistical analysis. The roughness coefficient JRC is calculated using the simple straight edge method. The formula is as follows:

[0065]

[0066] In the formula, JRC n is the roughness coefficient JRC value of the nth profile curve, L n A is the sampling length of the specimen, max is the maximum fluctuation amplitude of the contour curve, and its calculation principle is as shown in the attached Figure 3 As shown, when calculating, draw a straight line between the two local maxima of the contour line. The maximum vertical distance from a point on the contour line to this straight line is the required A. max .

[0067] 4.3. Determination of representative samples: Based on the statistical analysis results, determine the polar coordinate diagram of the anisotropy statistical results, determine the polar coordinate diagram of the anisotropy results of each sample, compare the anisotropy statistical results of the base-cover interface roughness with the measured results of samples of various sizes, and obtain representative samples. The calculation principle is as shown in the attached Figure 4 As shown, by comparing the similarity between the JRC statistical results and the statistical average values ​​in all directions of all samples, the sample number whose JRC all-directional statistical results are most similar to the JRC all-directional statistical average value is determined and defined as a representative sample. The calculation expression is as follows:

[0068] S R =min{∑S 2 (i,j)}

[0069] In the formula, S R is the representative sample calculated, ∑S 2 (i, j) is the sum of the squares of the JRC statistics of the sample with position number (i, j) and the average JRC statistics of all samples. Figure 4 As shown, taking the 10 cm sample as an example, sample No. 56 is a representative sample of this size.

[0070] The contents described in the embodiments of this specification are merely enumerations of implementation forms of the inventive concept and are for illustrative purposes only. The protection scope of the present invention should not be considered to be limited to the specific forms described in this embodiment, and the protection scope of the present invention also extends to equivalent technical means that can be thought of by ordinary technicians in this field based on the inventive concept.

Claims

1. A representative sampling method for seabed base-cover interface samples based on a global search method, characterized in that: The method comprises the following steps: (1) On-site contact measurement of the occurrence and mechanical parameters of the seabed base-cover interface; (2) 3D laser scanning and precise measurement of the seabed base-cover interface; (3) Processing and analysis of seabed base-cover interface point cloud data; (4) Determination of representative samples of various sizes at the base-cover interface, the process is as follows: 4.

1. Based on the processed point cloud data, a series of standard-sized sample point clouds are intercepted from the point cloud processing software, and contour curves in different directions are extracted for each size sample at set angle intervals and set spacings; 4.

2. Sample statistical analysis: extract structural surface profile curve analysis samples of different sizes, calculate the structural surface roughness coefficient JRC value and perform statistical analysis, and use the simple straight edge method to calculate the roughness coefficient JRC; 4.

3. Determination of representative samples. Based on the statistical analysis results, determine the polar coordinate diagram of the anisotropy statistical results. Determine the polar coordinate diagram of the anisotropy results of each sample. Compare the anisotropy statistical results of the base-cover interface roughness with the measured results of samples of various sizes to obtain representative samples.

2. The representative sampling method for seabed basement interface samples based on the global search method according to claim 1 is characterized in that: The process of step (2) is as follows: 2.

1. According to the working area of ​​the construction site, select a suitable data collection area on the exposed base-cover interface as the measurement location and mark it; 2.

2. Set up the 3D laser scanner according to the size and spatial orientation of the acquisition area, and set the resolution and scanning speed parameters of the 3D laser scanner; 2.

3. Use the same resolution to precisely scan the seabed base-cover interface morphology surface to obtain point cloud data; 2.

4. Check the point cloud data. First, check whether the point cloud data is complete, and perform additional scanning on the areas that have not been scanned. Secondly, check the uniformity of the point cloud data, supplement the local sparse locations, and finally delete and supplement the noise points and holes generated during the scanning process.

3. The representative sampling method for seabed base-cover interface samples based on the global search method according to claim 1 or 2, characterized in that the process of step (3) is as follows: 3.

1. Use the built-in noise removal function of the point cloud processing software to identify and delete noise points in the point cloud data; 3.

2. Find the marked position from the point cloud data and crop the scanned data to obtain the area for research and analysis; 3.

3. According to the obtained seabed basement interface occurrence, determine the approximate slip direction of the overlying layer and define it as the positive direction of the X-axis; 3.

4. Level the point cloud data of the study area. First, use the following formula to calculate the coefficients of the plane equation of the least squares fitting of the point cloud: Z=a0X+a1Y+a2 In the formula, (x i ,y i ,z i ) is the spatial coordinate of the i-th point cloud, n is the number of all point clouds, a0, a1, a2 are the coefficients of the least squares fitting plane of the point cloud; 3.

5. Let the point cloud normal vector be n, the horizontal plane normal vector be m, calculate the angle θ between the two normal vectors, cross-multiply the normal vector n with the horizontal plane normal vector m to get the rotation axis vector l, rotate the point cloud in the opposite direction of the overall tilt direction to make the normal vector n coincide with the horizontal plane normal vector m, and get the base-cover interface point cloud data after leveling. The rotation equation is as follows: In the formula, (x old ,y old ,z old ) and (x new ,y new ,z new ) are the spatial coordinates of the base-covering interface point cloud data before and after leveling, and a, b and c are the normalized vector coordinates of the rotation axis.

4. The representative sampling method for seabed base-cover interface samples based on the global search method according to claim 1 or 2, characterized in that: In 4.1, point cloud processing software is used to extract point clouds of samples of a series of standard sizes of 10 cm, 20 cm, 30 cm, 40 cm ... 1000, and the number of samples of the series of standard sizes is set to 100; contour curves in different directions are extracted for samples of each size at intervals of 1° and at a spacing of 0.5 mm.

5. The representative sampling method for seabed base-cover interface samples based on the global search method according to claim 4 is characterized in that: In 4.2, the formula of roughness coefficient JRC is as follows: In the formula, JRC n is the roughness coefficient JRC value of the nth profile curve, L n A is the sampling length of the specimen, max is the maximum fluctuation amplitude of the contour curve. When calculating, draw a straight line between the two local maxima of the contour line. The maximum vertical distance from a point on the contour line to this straight line is the required A. max .

6. The representative sampling method for seabed base-cover interface samples based on the global search method according to claim 5 is characterized in that: In 4.3, by comparing the similarity between the JRC statistical results and the statistical average values ​​in all directions of all samples, the sample number whose JRC all-directional statistical results are most similar to the JRC all-directional statistical average value is determined and defined as a representative sample. The calculation expression is as follows: S R =min{∑S 2 (i,j)} In the formula, S R is the representative sample calculated, ∑S 2 (i, j) is the sum of the squares of the JRC statistics of the specimen with position number (i, j) and the average JRC statistics of all specimens.

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