Subsea base-cover interface sample representative sampling method based on global search method

Abstract

The application discloses a seabed base-cover interface sample representative sampling method based on a global search method, and the method comprises the following steps: (1) contact measurement of seabed base-cover interface occurrence and mechanical parameters; (2) fine measurement of seabed base-cover interface three-dimensional laser scanning; (3) seabed base-cover interface point cloud data processing and analysis; and (4) determination of base-cover interface each size representative sample. The application can accurately obtain the shear strength of the base-cover interface and the strength variation law with the size.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of engineering technology, and relates to a method for determining representative samples of underlying bedrock in a certain scale range in a seabed foundation-cover interface under a marine environment, in particular, the present application starts from the complexity and randomness of the seabed foundation-cover interface topography, considers the non-uniformity, anisotropy, each quality anisotropy and size effect of the seabed foundation-cover interface, and combines a mathematical statistical method to propose a representative sampling method of the seabed foundation-cover interface sample based on a global search method. BACKGROUND

[0002] The seabed foundation-cover interface is a clear interface formed under the joint action of geological forces and ocean current movements, and the overall stability of the seabed and the structures thereon depends on the shear strength of the seabed foundation-cover interface to a great extent. The seabed foundation-cover interface is subjected to long and complex geological action, and the distribution and morphology thereof have complexity and randomness, and therefore, accurate acquisition of the shear strength of the seabed foundation-cover interface is an important basis for the safety and stability of seabed geotechnical engineering. The indoor direct shear test is a common method for acquiring the shear strength of the seabed foundation-cover interface, however, the surface topography of the seabed foundation-cover interface has considerable randomness, and therefore, the shear strength obtained by using the samples of the same size in the shear test has very large discreteness, and meanwhile, with the increase of the size of the sample, the shear strength of the samples of different sizes obtained by the shear test has obvious size effect. The discreteness and size effect of the shear strength of the seabed foundation-cover interface obtained by the indoor direct shear test form a great obstacle to the accurate evaluation of the stability thereof. SUMMARY

[0003] In order to overcome the deficiency of the discreteness and size effect of the shear strength of the seabed foundation-cover interface in the accurate evaluation of the stability thereof, the present application proposes a representative sampling method of the seabed foundation-cover interface sample based on a global search method, three-dimensional laser scanning technology is used to acquire the topography data of the underlying bedrock of the seabed foundation-cover interface, a global search method of gradual coverage is used to acquire the surface topography of different regions on the seabed foundation-cover interface, a simple straight edge method is used to calculate the roughness coefficient values in different directions on each research region and calculate the statistical average value and standard deviation thereof, and the representative samples under different sizes are obtained by comparison.

[0004] The technical scheme adopted by the present application to solve the technical problem is as follows:

[0005] A representative sampling method of the seabed foundation-cover interface sample based on a global search method, the method comprises the following steps:

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

[0007] (2) three-dimensional laser scanning fine measurement of the seabed foundation-cover interface;

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

[0009] (4) The base-cover interface each size representative sample determination process is as follows:

[0010] 4.1, based on the processed point cloud data, a series of standard size sample point clouds are intercepted from the point cloud processing software, and the contour curves of different directions are extracted at a set angle interval and a set pitch for each size sample;

[0011] 4.2, sample statistical analysis, extract different size structure surface contour curve analysis sample, calculate structure surface roughness coefficient JRC value and carry out statistical analysis, adopt simple straight edge method to calculate roughness coefficient JRC;

[0012] 4.3, determination of representative sample, based on the statistical analysis result, determine the polar coordinate graph of anisotropy statistical result, determine the polar coordinate graph of anisotropy result of each sample, compare the base-cover interface roughness anisotropy statistical result with the measured result of each size sample, and obtain the representative sample.

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

[0014] 2.1, according to the working area where the construction site is located, select appropriate data collection area as the measurement position on the exposed base-cover interface and mark well;

[0015] 2.2, according to the area size and space orientation of the collection area, erect a three-dimensional laser scanner, set the resolution and scanning speed parameters of the three-dimensional laser scanner;

[0016] 2.3, the same resolution and precision are used to scan the seabed base-cover interface surface to obtain point cloud data;

[0017] 2.4, check the point cloud data, first check whether the point cloud data is complete, supplement the scanning process for the area that cannot be scanned, secondly check the uniformity of the point cloud data, supplement the local sparse position, and finally delete and supplement the noise and holes generated in the scanning process.

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

[0019] 3.1, identify and delete the noise in the point cloud data through the noise removal function built in the point cloud processing software;

[0020] 3.2, find the marked position from the point cloud data, and cut the scanned data to obtain the research and analysis area;

[0021] 3.3, according to the occurrence of the seabed base-cover interface, determine the approximate sliding direction of the overburden layer and define it as the positive direction of X axis;

[0022] 3.4. The point cloud data of the study area is leveled. First, the coefficients of the least squares fitted plane equation of the point cloud are calculated using the following formula.

[0023] ;

[0024] ;

[0025] In the formula, For the first i Spatial coordinates of a point cloud n For the total number of point clouds, The coefficients of the least squares fitting plane of the point cloud;

[0026] 3.5. Let the normal vector of the point cloud be... n The horizontal plane normal vector is m The angle between the two normal vectors is calculated. θ , normal vector n Normal vector to the horizontal plane m Cross product yields the rotation axis vector l Rotate the point cloud in the opposite direction of the overall tilt by an angle, so that the normal vector n Normal vector to the horizontal plane m By overlapping, the leveled base-cover interface point cloud data is obtained, and the rotation equation is as follows.

[0027] ;

[0028] In the formula, and These are the spatial coordinates of the point cloud data of the base cover interface before and after leveling. a , b and c These are the vector coordinates of the rotation axis after normalization.

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

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

[0031] ;

[0032] In the formula, Let JRC be the roughness coefficient value of the nth profile curve. The sampling length of the sample is denoted as . The maximum fluctuation amplitude of the profile curve is calculated by drawing a straight line between two local maximum values of the profile curve, and the maximum vertical distance between a point on the profile curve and the straight line is the maximum fluctuation amplitude. .

[0033] In 4.3, by comparing the similarity relationship between the JRC statistical results of all samples in each direction and the statistical average value, the sample number with the most similar JRC statistical results in each direction and the JRC statistical average value is determined, which is defined as the representative sample, and the calculation expression is as follows:

[0034]

[0035] In the formula, is the representative sample calculated, is the square sum of the JRC statistical value of the sample in each direction with position number and the JRC statistical average value of all samples in each direction.

[0036] The beneficial effects of the present application mainly lie in: the shear strength of the base-cover interface and the strength variation law with size can be accurately obtained. BRIEF DESCRIPTION OF DRAWINGS

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

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

[0039] Figure 3 is a maximum fluctuation amplitude calculation schematic diagram.

[0040] Figure 4 is a base-cover interface JRC anisotropy result comparison diagram. DETAILED DESCRIPTION

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

[0042] Referring to Figures 1-4 Taking a cross-sea bridge between some cities in the eastern coastal area as an example, a seabed base-cover interface sample representative sampling method based on a global search method includes four aspects of contents of field base-cover interface occurrence measurement, base-cover interface three-dimensional laser scanning fine measurement, base-cover interface point cloud processing and base-cover interface sample size representative determination, and each part of the content is specifically introduced as follows:

[0043] (1) The base-cover interface occurrence is measured by contact measurement on site, and the process is as follows:

[0044] 1.1, by observing the exposed seabed base-cover interface in the construction area, the overall characteristics of the base-cover interface development in the research scope are determined;

[0045] 1.2, according to the observed overall characteristics of the base-cover interface, use the geological compass to measure the typical area in the base-cover interface as the reference base-cover interface, and take pictures to record the location information of the corresponding area;

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

[0047] (2) Fine measurement of base-cover interface three-dimensional laser scanning, the process is as follows:

[0048] 2.1, set up the laser scanner according to the position of the marker point, and calibrate the laser scanner to reduce measurement error;

[0049] 2.2, set the scanning parameters, and use equal resolution to scan the topographic data of the base-cover interface surface;

[0050] 2.3, check the point cloud data, first check whether the point cloud data is complete, supplement the scanning process for the area that cannot be scanned, secondly check the uniformity of the point cloud data, supplement the local sparse position, finally delete the noise and holes generated in the scanning process;

[0051] (3) Base-cover interface point cloud processing, the process is as follows:

[0052] 3.1, identify and delete the noise in the point cloud data through the noise removal function built in the point cloud processing software;

[0053] 3.2, find the marker position from the point cloud data, and cut the scanned data to obtain the research and analysis area;

[0054] 3.3, determine the approximate sliding direction of the overburden layer on the base-cover interface according to the obtained base-cover interface occurrence, and define it as the positive direction of X axis;

[0055] 3.4, level the point cloud data of the research area, first calculate the coefficients of the least square fitting plane equation of the point cloud by the following formula

[0056] ;

[0057] ;

[0058] In the above formula, is the spatial coordinates of the i th point cloud, n is the number of all point clouds, is the coefficient of the least square fitting plane of the point cloud;

[0059] 3.5, set the point cloud normal vector as n, the horizontal plane normal vector is m , the included angle of the two normal vectors is calculated θ , the normal vector n is crossed with the horizontal plane normal vector m to obtain the rotation axis vector l , the point cloud is rotated along the inverse rotation angle of the overall tilt direction, so that the normal vector n is coincident with the horizontal plane normal vector m , to obtain the leveled base-cover interface point cloud data, and the rotation equation is as follows

[0060] ;

[0061] wherein and are the spatial coordinates of the base-cover interface point cloud data before and after leveling, a , b and c are the vector coordinates of the rotation axis l after unitization, and the rotation schematic diagram is shown in the accompanying Figure 1 ; the included angle is 16.3° according to the accompanying Figure 1 ;

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

[0063] 4.1, based on the processed point cloud data, 10 cm, 20 cm, 30 cm, 40 cm…etc. Standard size sample point cloud is intercepted from the point cloud processing software, and the number of series standard size samples is set to 100; the contour curves of different directions are extracted at an interval of 1° and a pitch of 0.5 mm for each size sample, as shown in the accompanying Figure 2 ;

[0064] 4.2, sample statistical analysis, extract the structural surface contour curve analysis sample of different sizes, calculate the structural surface roughness coefficient JRC value and conduct statistical analysis, and the simple straight edge method is used to calculate the roughness coefficient JRC, and the formula is as follows:

[0065] ;

[0066] wherein, is the roughness coefficient JRC value of the nth contour curve, is the sampling length of the sample, is the maximum fluctuation amplitude of the contour curve, and the calculation principle is shown in the accompanying Figure 3 , when calculating, a straight line is drawn between the two local maximum values of the contour line, and the maximum vertical distance of a point on the contour line to the straight line is the maximum fluctuation amplitude .

[0067] 4.3, the representative sample is determined based on the statistical analysis results, the polar coordinate diagram of the anisotropy statistical results is determined, the polar coordinate diagram of the anisotropy results of each sample is determined, the anisotropy statistical results of the base-cover interface roughness are compared with the measured results of each size sample, and the representative sample is obtained. The calculation principle is shown in the attached Figure 4 By comparing the similarity of the JRC statistical results of all samples in each direction with the statistical average value, the sample number with the most similar JRC anisotropy statistical results and JRC anisotropy statistical average value is determined, which is defined as the representative sample, and the calculation expression is as follows:

[0068] ;

[0069] In the formula, is the representative sample calculated, is the sum of squares of the anisotropy JRC statistical value of the sample with the position number and the anisotropy JRC statistical average value of all samples. In this example, as shown in the attached Figure 4 , taking a sample with a size of 10 cm as an example, the 56th sample is the representative sample of this size.

[0070] The content described in the embodiments of the present specification is only a list of implementation forms of the inventive concept, and is only for the purpose of description. The protection scope of the present application should not be regarded as being limited to the specific forms described in the present embodiments, and the protection scope of the present application also includes equivalent technical means that can be thought of by those skilled in the art according to the inventive concept.

Claims

1. A method for representative sampling of a subsea base-cover interface test sample based on a global search method, characterized in that, The method comprises the following steps: (1) contact measurement of seabed base-cover interface occurrence and mechanical parameters; (2) fine measurement of seabed base-cover interface three-dimensional laser scanning; (3) seabed base-cover interface point cloud data processing and analysis; (4) determination of base-cover interface representative sample of each size, the process is as follows: 4.1, based on the processed point cloud data, a series of standard size sample point clouds are cut from the point cloud processing software, and the profile curves in different directions are extracted at a set angle interval and a set pitch for each size sample; 4.2, sample statistical analysis, extract different size structural surface profile curve analysis sample, calculate the structural surface roughness coefficient JRC value and carry out statistical analysis, and calculate the roughness coefficient JRC by using the simple straight edge method; 4.3, determination of representative sample, based on the statistical analysis result, the polar coordinate graph of anisotropy statistical result is determined, the polar coordinate graph of anisotropy result of each sample is determined, the base-cover interface roughness anisotropy statistical result is compared with the measured result of each size sample, and the representative sample is obtained.

2. The seabed base-interface sample representative sampling method based on a global search method according to claim 1, characterized by, The process of step (2) is as follows: 2.1, according to the working area where the construction site is located, select appropriate data acquisition area as the measurement position on the exposed base-cover interface and mark it; 2.2, according to the area size and spatial orientation of the acquisition area, set up a three-dimensional laser scanner, and set the resolution and scanning speed parameters of the three-dimensional laser scanner; 2.3, the same resolution is adopted to scan the seabed base-cover interface surface to obtain point cloud data; 2.4, check the point cloud data, first check whether the point cloud data is complete, supplement the scanning process for the area that cannot be scanned, secondly check the uniformity of the point cloud data, supplement the local sparse position, and finally delete and supplement the noise and holes generated in the scanning process.

3. The seabed base-cover interface sample representative sampling method based on the global search method according to claim 1 or 2, wherein the process of step (3) is as follows: 3.1, identify and delete the noise in the point cloud data by using the noise removal function built in the point cloud processing software; 3.2, find the marked position from the point cloud data, and cut the scanned data to obtain the research and analysis area; 3.3, determine the approximate sliding direction of the overburden layer on the seabed base-cover interface according to the seabed base-cover interface occurrence, and define it as the positive direction of X axis; 3.4, level the point cloud data of the research area, first calculate the coefficients of the least square fitting plane equation of the point cloud by using the following formula, ； ； wherein is the spatial coordinate of the i-th point cloud, i is the number of point clouds, n is the number of point clouds, is the coefficient of the least square fitted plane of the point cloud. 3.5, set the point cloud normal vector as n , the horizontal plane normal vector as m , the included angle between the two normal vectors is calculated θ , the normal vector n is crossed with the horizontal plane normal vector m to obtain the rotation axis vector l , the point cloud is rotated along the inverse direction of the overall tilt direction by an angle, so that the normal vector n is coincident with the horizontal plane normal vector m , to obtain the leveled base cover interface point cloud data, and the rotation equation is as follows, ； In the formula, and are the spatial coordinates of the point cloud data of the base-terrain interface before and after leveling, respectively, a , b and c are the vector coordinates of the rotation axis after unitization.

4. The seabed boundary interface sample representative sampling method based on a global search method according to claim 1 or 2, characterized by, In 4.1, a series of standard size sample point clouds of 10 cm, 20 cm, 30 cm, 40 cm…1000 cm are cut from the point cloud processing software, and the number of series of standard size samples is set to 100; the profile curves in different directions are extracted at an interval of 1° and a pitch of 0.5 mm for each size sample.

5. The seabed base-interface sample representative sampling method based on a global search method according to claim 4, characterized by, In 4.2, the formula of the roughness coefficient JRC is as follows: ； wherein JRC is the roughness coefficient of the n contour curve, L is the sampling length of the specimen, Rmax is the maximum height of the profile curve, calculated as the absolute value of the difference between the highest and lowest points of the profile curve, i.e. the maximum vertical distance between the profile line and the mean line. .

6. The seabed base-interface sample representative sampling method based on a global search method according to claim 5, characterized by, In the 4.3, by comparing the JRC statistical results of all samples in each direction with the similar relationship of statistical average value, the sample number with the most similar JRC statistical results in each direction and the JRC statistical average value is determined, which is defined as the representative sample, and the calculation expression is as follows: ； wherein are the calculated representative samples, are the position numbers of the samples are the sums of squares of the JRC statistics of each orientation of the samples with position numbers

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