CN109859301A - A kind of rock structural face roughness value fining characterizing method - Google Patents
A kind of rock structural face roughness value fining characterizing method Download PDFInfo
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Abstract
The present invention relates to rock engineering technologies, it is desirable to provide a kind of rock structural face roughness value fining characterizing method.It include: the topographic data for obtaining rock structural face；Three dimensional point cloud is pre-processed；The processing of grid thinization is carried out to point cloud data；Canonical sorting operation is carried out to coordinate data；Calculate the JRC value of all structural plane contour lines in point cloud data；Draw JRC magnitude frequency distribution histogram.The present invention obtains the three dimensional point cloud for reflecting entire rock structural face shape characteristic by 3-D scanning technology, and gained JRC calculated result is more comprehensive, objective, accurate.It can get mean value, variance, the coefficient of variation and the anisotropic rule of JRC data, provide technical support for comprehensive, accurate evaluation JRC value and Shear Strength along Rock Stratum Plane.It can realize that accurately and rapidly batch is calculated in conjunction with programming, can be widely applied to rock texture surface roughness involved in all kinds of rock engineering projects and shear strength parameter evaluation.
Description
Technical field
The present invention relates to rock engineering technology, in particular to a kind of rock texture surface roughness based on three dimensional point cloud
The acquisition methods of coefficient (JRC), the fining characterization of the rock structural face JRC data suitable for various forms.
Background technique
With the fast development of Chinese national economy, the great infrastructure construction such as highway, high-speed rail, water conservancy and hydropower is related to
The rock engineerings such as more and more side slopes, tunnel, underground chamber, the dam foundation.Rock stability by its structure space of planes occurrence and
Mechanical characteristic control, is related to engineering construction safety and cost.Wherein, structural face shear strength is evaluation rock engineering stability
Key mechanics parameter.Research shows that the degree of roughness of structural plane is to influence the principal element of its shearing strength.Barton etc. is learned
Person on the basis of a large number of experiments, propose rock structural face roughness value (Joint Roughness Coefficient,
JRC concept), establish JRC-JCS model to estimate Shear Strength along Rock Stratum Plane, and summary and induction rock structural face is thick
Ten bar normal structure facial contour lines of roughness coefficient (JRC) value between 0~20.This method is by by practical structures facial contour
Line and standard contour line, which compare, can substantially obtain rock structural face JRC estimated value, can estimate rock knot further according to JRC value
Structure face shearing strength.This method is widely applied due to simple, intuitive in engineering.But there is also artificial subjective judgements
The problem for causing JRC valuation inaccurate.For this purpose, domestic and foreign scholars have carried out a large amount of research work around JRC quantification characterization, mention
Formula and method that a series of quantification calculate JRC are gone out, these methods are all based on greatly the single structural plane profile line number of acquisition
According to being evaluated.Since rock structural face has natural scrambling and Spatial Variability, single contour line measurement data is past
It is larger toward error, it is difficult to accurately reflect the global feature of rock texture surface roughness and its variability rule.With science and technology
Development, noncontact measurement provides technological means for the accurate rock structural face shape characteristic that obtains, for example, by using three-dimensional
Scanning technique can be convenient, fast, accurately obtain the three dimensional point cloud of structural plane, establish a kind of rock structural face accordingly
JRC data refine characterizing method, can provide reference for Overall Acquisition and accurate evaluation Shear Strength along Rock Stratum Plane.
Summary of the invention
The technical problem to be solved by the present invention is to overcome deficiency in the prior art, it is coarse to provide a kind of rock structural face
It spends coefficient and refines characterizing method, realize the acquisition comprehensive, accurately and fast of rock structural face roughness value.
In order to solve the technical problem, the technical scheme adopted by the invention is that:
A kind of rock structural face roughness value fining characterizing method is provided, is included the following steps:
(1) topographic data of rock structural face is obtained
On the basis of the analysis of engineering rock texture, scanning obtains the topographic data of quasi- measurement rock structural face, and stores
For the three dimensional point cloud file of stl format；
(2) three dimensional point cloud is pre-processed
Reserved positioning target spot or mark determine analyzed area when in conjunction with scanning, delete the redundant data outside analyzed area；
Three dimensional point cloud file coordinate system is adjusted, keeps the rock structural face of analyzed area parallel with horizontal plane (X-Y plane), after processing
Data save as the point cloud data file of dxf format；
(3) processing of grid thinization is carried out to point cloud data
Point cloud data interval is determined according to required precision；Based on sparse point cloud data density principle, in original point cloud data
The data point of position needed for middle reservation and the data point for deleting extra position；Reconstruct meets the point cloud data of space requirement, and will
Its coordinate data file for saving as dat format is exported；
(4) canonical sorting operation is carried out to coordinate data:
The empty matrix of n × 3 is constructed first, and wherein n is total points；Every a line represents the coordinate of a point in matrix
Data, first row represent x coordinate, and secondary series represents y-coordinate, and third column represent z coordinate；Search for the minimum value y of y-coordinate0, enable y0
The element of the first row secondary series as matrix is y from y-coordinateoAll the points in select the smallest value x of x coordinate0, enable x0As
The first column element of the first row is x with season x coordinate0, y-coordinate y0The z coordinate z of the corresponding point0It is arranged as the first row third
Element；Enable y0As second the second column element of row, y-coordinate y0All the points in select the small value x of x coordinate second1As second
The first column element of row, enabling x coordinate is x1, y-coordinate y0The z coordinate z of corresponding points1As the second row third column element；With such
It pushes away, until y-coordinate is y0All the points coordinate all have been placed in matrix, then change the second column element of next line into all y-coordinates
In sub-minimum y1, similarly until all point coordinates are put into the matrix of the n × 3, complete canonical sequence processing；Obtained seat
Mark data are used to calculate structural plane contour line JRC value along the x-axis direction；It such as will be to structural plane contour line JRC value along the y-axis direction
Calculating analysis is carried out, then first carries out similar canonical sorting operation；
(5) the JRC value of all structural plane contour lines in point cloud data is calculated
First calculate the characteristic parameter Z of all structural plane contour lines2, Z2For first derivative root mean square；It is then based on Z2By estimating
Formula calculates the JRC value of every structural plane contour line；Wherein,
The different point of x coordinate identical for y-coordinate constitutes the structural plane contour line on the direction x, calculates according to the following formula
Z2With JRC value:
JRC=65.18tan (Z2)-3.88
In formula, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
The different point of y-coordinate identical for x coordinate constitutes the structural plane contour line on the direction y, calculates according to the following formula
Z2With JRC value:
JRC=65.18tan (Z2)-3.88
Wherein, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
By above-mentioned calculating, the JRC value of thousands of different location contour lines on entire rock structural face is obtained；
(6) JRC magnitude frequency distribution histogram is drawn
By for statistical analysis to thousands of JRC values, mean value, variance and the coefficient of variation are calculated；Draw JRC numerical value frequency
Rate distribution histogram, and realize that the fining of entire rock structural face JRC value characterizes with this.
It is using three-dimensional laser scanner or three-dimensional structure photoscanner to rock knot in the step (1) in the present invention
Structure face is scanned, and obtains topographic data to be measured.
In the present invention, in the step (1), when carrying out the processing of grid thinization, in order to ensure the point cloud data essence after reconstruct
Degree, Ying Cong little sampling interval carries out sparse processing to big spacing, and makes the integral multiple of sampling interval difference minimum interval.
Compared with prior art, the beneficial effects of the present invention are:
1, the present invention obtains the three dimensional point cloud for reflecting entire rock structural face shape characteristic by 3-D scanning technology,
The method for characterizing structural plane JRC data compared to one or several contour line is only measured in the past, the metrical information base of this method
In the 3-D scanning point cloud data of entire rock structural face, gained JRC calculated result is more comprehensive, objective, accurate.
2, pass through three dimensional point cloud pretreatment, coordinate system adjustment, data file transition, grid LS-SVM sparseness, canonical
The sequence of operations such as sequence processing can pass through programming on this basis and realize JRC so that original point cloud data is accurate, orderly
The fining of data calculates, and by statistical analysis, can get mean value, variance, the coefficient of variation and the anisotropic rule of JRC data
Rule provides technical support for comprehensive, accurate evaluation JRC value and Shear Strength along Rock Stratum Plane.
3, method of the invention more focuses on comprehensive investigation to rock texture surface roughness and shearing strength, can combine
Programming realizes that accurately and rapidly batch calculates, and the measurement for rock structural face JRC data provides more comprehensive and accurate acquisition
Method.
4, rock texture surface roughness and shearing resistance involved in all kinds of rock engineering projects be the composite can be widely applied to
Intensive parameter evaluation, provides accurate, reliable basic data for rock engineering estimation of stability and minute design.
Detailed description of the invention
Fig. 1 is grid rarefaction schematic diagram；
Fig. 2 is canonical sequenceization schematic diagram；
Fig. 3 is rock structural face assay maps and 3D illustraton of model in example；
Fig. 4 is the resulting histogram frequency distribution diagram of example rock structural face JRC Mathematical Statistics Analysis.
Specific embodiment
With reference to the accompanying drawing, implementation of the invention is described in detail.
Rock structural face roughness value refines characterizing method, includes the following steps:
1, it on the basis of engineering rock texture is analyzed, is obtained using three-dimensional laser scanner or three-dimensional structure photoscanner
The topographic data for the rock structural face of being measured, and it is stored as three dimensional point cloud file (stl format)；
2, the three dimensional point cloud file of acquired rock structural face pattern is pre-processed.It is reserved when in conjunction with scanning
Positioning target spot or mark, confirm suitable analyzed area, delete the redundant data outside analyzed area.Adjust three dimensional point cloud
File coordinate system keeps the rock structural face of analyzed area parallel with horizontal plane (X-Y plane), and finally by treated, data are saved as
The point cloud data file of dxf format；
3, what the file of dxf format stored is the point cloud coordinate data of random sequence, and in the friendship of not same period scanning area
It will overlap a little at boundary.For the batch processing convenient for realizing point cloud data subsequently through programming, net need to be carried out to point cloud data
The processing of lattice thinization and canonical sorting operation.Point cloud grid LS-SVM sparseness can determine point cloud data interval according to required precision, base
In sparse point cloud data density principle, the data point of position needed for retaining in original point cloud data and the number for deleting extra position
Strong point, reconstruct meets the point cloud data of space requirement accordingly.For example, sparse 50% operation is carried out to the point cloud number of interval 0.1mm,
I.e. every two data point chooses one and deletes one, the point cloud data of 0.2mm is divided between can obtaining, as shown in Figure 1.It is worth note
Meaning should follow small sampling interval to big spacing and carry out sparse processing, and its in order to ensure the point cloud data precision after reconstruct
Sampling interval difference is minimum interval integral multiple.Grid LS-SVM sparseness solves the problems, such as point cloud data overlapping simultaneously, is obtaining
Simultaneously, eliminate point cloud data overlapping bring influences the point cloud data of spacing needed for obtaining.By the point cloud number after grid rarefaction
It is exported according to the coordinate data file for saving as dat.
4, the dat file obtained after grid LS-SVM sparseness is coordinate data line by line.Three data difference of every row
Indicate the x coordinate, y-coordinate and z coordinate of a point, total line number of file is the sum of data point.Adjacent rows are not necessarily sky
Between upper adjacent point.For this purpose, need to the data of these fall into disarray be carried out with canonical sequence processing, it is convenient to be realized subsequently through programming
The data of mass are analyzed.The specific method is as follows for canonical sequence processing: constructing empty matrix (the wherein n of n × 3 first
Always to count), the every a line of matrix represents the coordinate data of a point, and first row represents x coordinate, and secondary series represents y-coordinate, third
Column represent z coordinate.Search for the minimum value y of y-coordinate0, enable y0The element of the first row secondary series as matrix is y from y-coordinateo's
The smallest value x of x coordinate is selected in all the points0, enable x0It is x with season x coordinate as the first column element of the first row0, y-coordinate y0
The z coordinate z of the corresponding point0As the first row third column element.Enable y0 as second the second column element of row, y-coordinate y0Institute
The small value x of x coordinate second is selected in a little1As second the first column element of row, enabling x coordinate is x1, y-coordinate y0The z of corresponding points
Coordinate z1As the second row third column element.And so on, until y-coordinate is y0All the points coordinate all have been placed in matrix, then
The second column element of next line is changed into the sub-minimum y in all y-coordinates1, similarly until all point coordinates are put into n × 3
Matrix just completes canonical sequence processing, as shown in Figure 2.Above-mentioned sort algorithm can be by programming realization, can be very convenient right
Structural plane contour line JRC value along the x-axis direction is calculated and is statisticallyd analyze.To structural plane contour line along the y-axis direction
JRC value carries out calculating analysis, can carry out similar canonical sorting operation；
5, on the basis of above-mentioned point cloud data, the characteristic parameter Z of all structural plane contour lines in point cloud data is calculated2(Z2For
First derivative root mean square).Based on Z2The JRC value of every structural plane contour line can be calculated according to existing estimation formula.Entirely
In the point cloud data of structural plane, the different point of the identical x coordinate of y-coordinate constitutes the structural plane contour line on the direction x, by data point x
Coordinate and z coordinate can calculate its JRC value.Along the direction y, the JRC value of the corresponding every structural plane contour line of different x coordinates is calculated
When, using following formula:
JRC=65.18tan (Z2)-3.88
In formula, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
Similarly, in the point cloud data in total face, the different point of the identical y-coordinate of x coordinate constitutes the structural plane in the direction y
Contour line can calculate its JRC value by data point y-coordinate and z coordinate.Along the direction x, calculates corresponding every of different y-coordinates and tie
When the JRC value of structure facial contour line, using following formula:
JRC=65.18tan (Z2)-3.88
Wherein, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
Above-mentioned calculating can complete automatic calculate by computer by programming.
6, on the basis of above-mentioned calculating, the JRC value of thousands of different location contour lines on entire rock structural face is obtained,
By for statistical analysis to its, mean value, variance, the coefficient of variation can be calculated, draws JRC magnitude frequency distribution histogram, from
And realize the fining characterization of entire rock structural face JRC value.Accordingly, rock structural face JRC number can very easily be analyzed
According to variability, the changing rules such as anisotropy.For finally accurately, reasonably evaluation Shear Strength along Rock Stratum Plane parameter provides
Comprehensive, reliable JRC data.
Specific embodiment:
The granite rock sample for choosing the acquisition of certain mountain area, carries out the rock structural face JRC number based on three dimensional point cloud to it
According to acquisition and evaluation, comprising the following steps:
Step 1: the granite structure facial plane chosen is having a size of 317mm × 289mm.It is obtained using 3 D laser scanning
The three dimensional point cloud of rock structural face pattern, and stl formatted file is saved as, (in figure, upper left is rock knot as shown in Figure 3
Structure face photo, upper right are that point cloud data is selected and cut, and lower-left is the rock structural face (top view) by Surface Reconstruction from Data Cloud, right
Lower is the rock structural face (shaft side figure) by Surface Reconstruction from Data Cloud；
Step 2: using software (such as AutoCAD, Geomagic) to the stl lattice of acquired rock structural face pattern
Formula file is pre-processed.Reserved positioning target spot or mark, choose the analyzed area of rock sample, it is extra to delete when in conjunction with scanning
Point cloud data.Structural plane and non-level when due to scanning, scans the datum level of obtained rock texture surface model often
It is inclination, needs to adjust by coordinate position, keep the rock structural face of analyzed area parallel with X-Y plane.Finally by resulting number
According to the data file for saving as dxf format；
Step 3: the point cloud data to dxf format carries out grid LS-SVM sparseness.Point cloud grid LS-SVM sparseness can basis
Required precision determines point cloud data interval, is based on sparse point cloud data density principle, position needed for retaining in original point cloud data
The data point set and the data point for deleting extra position, reconstruct meets the point cloud data of space requirement accordingly.What this example used
The precision of spatial digitizer is 0.1mm, in order to obtain higher required precision, carries out sparse grid processing to former point cloud data
The spacing of selection is also 0.1mm, i.e., carrys out selected point at interval of 0.1mm, deletes the point of extra position, is divided between can obtaining
The point cloud data of 0.1mm.After grid LS-SVM sparseness, X-direction just obtains 3171 points, and Y-direction just obtains 2891 points.This
Spacing in example is only for example for 0.1mm, and can according to need the smaller spacing of selection also can choose bigger spacing.It will place
The file obtained after reason saves as dat format and exports.The point cloud coordinate data of institute's study of rocks structural plane is just obtained in this way
File, the coordinate datas comprising 3171 × 7891 total 9167361 points in file.By grid LS-SVM sparseness, delete
Extra point cloud data forms the point cloud data of the grid type of rule, eliminates the point cloud data overlapping of scanning overlapping region
Influence；
Step 4: opening dat file obtained in the previous step, 9167361 row data are obtained, three data of every row are respectively one
X coordinate, y-coordinate and the z coordinate of a point.Adjacent rows are not necessarily spatially adjacent point.For this purpose, need to be to these fall into disarray
Data carry out canonical sequence processing, convenient to realize that the data of mass are analyzed subsequently through programming.Canonical sequence handles specific
Method are as follows: construct one 9167361 × 3 empty matrix first.The minimum value y of y-coordinate is searched out using the method for programming0,
Enable y0The element of the first row secondary series as matrix is y from y-coordinate0All the points in select the smallest value x of x coordinate0, enable x0
It is x with season x coordinate as the first column element of the first row0, y-coordinate y0The z coordinate z of the corresponding point0As the first row
Three column elements.Enable y0As second the second column element of row, y-coordinate y0All the points in select the small value x of x coordinate second1As
Second the first column element of row, enabling x coordinate is x1, y-coordinate y0The z coordinate z of corresponding points1As the second row third column element.With this
Analogize, until y-coordinate is y0All the points coordinate all have been placed in matrix, then change the second column element of next line into all y and sit
Sub-minimum y in mark1, similarly until all point coordinates are put into 9167361 × 3 matrix, just complete at canonical sequence
Reason.The above are the canonical sort method for statistical analysis of the lines to x-axis direction, it is suitable for determining rock structural face x-axis side
To JRC data when use, this example only carries out calculating analysis to x-axis direction；
Step 5: using the characteristic parameter Z for constituting all structural plane contour lines in MATLAB program calculation point cloud data2,
And the corresponding JRC value of different location structural plane contour line is calculated according to this feature parameter.This example only analyzes structural plane x-axis side
To JRC value.3171 o'clock in x-axis direction can be calculated to the Z of the line segment as a line segment2Value and JRC value.Along y
Axis direction amounts to the structural plane contour line in 2891 x-axis directions on entire rock structural face, and every contour line is by 3171 points
Control its shape characteristic.Go out the Z of 2891 contour lines using MATLAB program calculation2Value and JRC value；
JRC=65.18tan (Z2)-3.88
Wherein, Z2For first derivative root mean square, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
Step 6:, meter for statistical analysis to the JRC value of 2891 contour lines of x-axis direction on entire rock structural face
The JRC average value for calculating entire rock structural face in x-axis direction is 12.87, median 12.75, variance 1.03, standard deviation
It is 1.02, the coefficient of variation 0.079 draws the histogram frequency distribution diagram of JRC data, as shown in Figure 4.
Finally it should be noted that the above enumerated are only specific embodiments of the present invention.It is clear that the invention is not restricted to
Above embodiments can also have many variations.Those skilled in the art can directly lead from present disclosure
Out or all deformations for associating, it is considered as protection scope of the present invention.
Claims (3)
1. a kind of rock structural face roughness value refines characterizing method, which is characterized in that include the following steps:
(1) topographic data of rock structural face is obtained
On the basis of the analysis of engineering rock texture, scanning obtains the topographic data of quasi- measurement rock structural face, and is stored as st1
The three dimensional point cloud file of format；
(2) three dimensional point cloud is pre-processed
Reserved positioning target spot or mark determine analyzed area when in conjunction with scanning, delete the redundant data outside analyzed area；Adjustment
Three dimensional point cloud file coordinate system, is parallel to the horizontal plane the rock structural face of analyzed area, and by treated, data are saved
For the point cloud data file of dxf format；
(3) processing of grid thinization is carried out to point cloud data
Point cloud data interval is determined according to required precision；Based on sparse point cloud data density principle, protected in original point cloud data
It stays the data point of required position and deletes the data point of extra position；Reconstruct meets the point cloud data of space requirement, and is protected
The coordinate data file for saving as dat format is exported；
(4) canonical sorting operation is carried out to coordinate data:
The empty matrix of n × 3 is constructed first, and wherein n is total points；Every a line represents the number of coordinates of a point in matrix
According to first row represents x coordinate, and secondary series represents y-coordinate, and third column represent z coordinate；Search for the minimum value y of y-coordinate0, enable y0Make
It is y from y-coordinate for the element of the first row secondary series of matrix0All the points in select the smallest value x of x coordinate0, enable x0As
The first column element of a line is x with season x coordinate0, y-coordinate y0The z coordinate z of the corresponding point0Member is arranged as the first row third
Element；Enable y0As second the second column element of row, y-coordinate y0All the points in select the small value x of x coordinate second1As the second row
First column element, enabling x coordinate is x1, y-coordinate y0The z coordinate z of corresponding points1As the second row third column element；And so on,
Until y-coordinate is y0All the points coordinate all have been placed in matrix, then the second column element of next line is changed into all y-coordinates
Sub-minimum y1, similarly until all point coordinates are put into the matrix of the n × 3, complete canonical sequence processing；Obtained number of coordinates
According to for calculating structural plane contour line JRC value along the x-axis direction；Such as structural plane contour line JRC value along the y-axis direction is carried out
Analysis is calculated, then first carries out similar canonical sorting operation；
(5) the JRC value of all structural plane contour lines in point cloud data is calculated
First calculate the characteristic parameter Z of all structural plane contour lines2, Z2For first derivative root mean square；It is then based on Z2By estimation formula
Calculate the JRC value of every structural plane contour line；Wherein,
The different point of x coordinate identical for y-coordinate constitutes the structural plane contour line on the direction x, calculates Z according to the following formula2With
JRC value:
JRC=65.18tan (Z2)-3.88
In formula, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
The different point of y-coordinate identical for x coordinate constitutes the structural plane contour line on the direction y, calculates Z according to the following formula2With
JRC value:
JRC=65.18tan (Z2)-3.88
Wherein, L is that curve projection is long, and N is the point number on line segment, and i is natural number；
By above-mentioned calculating, the JRC value of thousands of different location contour lines on entire rock structural face is obtained；
(6) JRC magnitude frequency distribution histogram is drawn
By for statistical analysis to thousands of JRC values, mean value, variance and the coefficient of variation are calculated；Draw JRC magnitude frequency point
Cloth histogram, and realize that the fining of entire rock structural face JRC value characterizes with this.
2. the method according to claim 1, wherein in the step (1), be using three-dimensional laser scanner or
Three-dimensional structure photoscanner is scanned rock structural face, obtains topographic data to be measured.
3. the method according to claim 1, wherein when carrying out the processing of grid thinization, being in the step (1)
Point cloud data precision after ensuring to reconstruct, Ying Cong little sampling interval carries out sparse processing to big spacing, and keeps the sampling interval poor
Value is the integral multiple of minimum interval.
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Application publication date: 20190607 |