A three-dimensional roughness evaluation method for rock mass structural surfaces

Abstract

This invention belongs to the field of data processing, specifically relating to a three-dimensional roughness assessment method for rock mass structural surfaces. The method includes: using two-dimensional continuous wavelet transform to perform multi-scale decomposition of point cloud data, and introducing scale confidence to correct the unreliability of small-scale analysis; automatically identifying valley scales in the double logarithmic corrected scale roughness spectrum to separate macroscopic undulation and micro-roughness representing roughness types; and establishing a dual-threshold classification system by combining total roughness and wave-roughness energy ratio to classify rock mass structural surfaces into four types, thereby providing more accurate and physically meaningful mechanical response predictions and parameter basis for stability analysis and support design in geotechnical engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing. More specifically, the present application relates to a method for evaluating three-dimensional roughness of rock mass structure surface. BACKGROUND

[0002] In the field of mining and geotechnical engineering, the roughness of rock mass structure surface is a key physical property that determines its shear strength and stability. In order to evaluate this property, the existing technology usually obtains three-dimensional point cloud data of the rock mass structure surface by means of three-dimensional laser scanning, and calculates a global statistical parameter to represent the overall roughness based on the data. However, this single parameter evaluation method has the inherent defect of scale confusion: the true topography of the rock mass structure surface is a superposition of features of different scales, including large-scale gentle undulations and small-scale sharp rough bodies. The undulations and the rough bodies dominate different mechanical responses such as dilatancy effect and initial engagement strength in the process of rock shear. The traditional global parameter confuses these two physically different features, resulting in a serious disconnection between the evaluation result and the true mechanical behavior of the rock mass structure surface, and thus cannot provide accurate and reliable basis for engineering design.

[0003] In order to solve the above-mentioned scale confusion problem, the academic and engineering circles have begun to try to introduce multi-scale analysis methods. Among them, two-dimensional continuous wavelet transform, as an effective signal processing tool, can decompose the topography of the rock mass structure surface into a series of different physical scales for analysis. However, when applying wavelet transform to the discrete point cloud data obtained by three-dimensional scanning, due to the limited sampling density of data acquisition, interpolation processing is needed when generating the two-dimensional height field for analysis. This process inevitably introduces false, high-frequency interpolation noise, which mainly affects the accuracy of small-scale analysis results. Therefore, the small-scale components obtained by directly applying wavelet transform have low credibility and cannot fully and truly reflect the actual morphology of the micro rough bodies, constituting a technical obstacle for accurate evaluation. SUMMARY

[0004] In order to solve the above-mentioned problems of scale confusion and inaccurate evaluation of the roughness of the rock mass structure surface caused by interpolation noise, the present application provides a method for evaluating the three-dimensional roughness of the rock mass structure surface, comprising:

[0005] The three-dimensional point cloud data of the rock mass structure surface is acquired, and a two-dimensional height field matrix is generated; the two-dimensional height field matrix is subjected to two-dimensional continuous wavelet transform, and a series of wavelet coefficient matrices at different scales are obtained; the energy sum of the wavelet coefficient matrices at different scales is calculated, and scale roughness at different scales is obtained; scale confidence at different scales is obtained based on the ratio of the size of each scale to the average point spacing of the two-dimensional height field matrix; the scale roughness at different scales is corrected based on the scale confidence at different scales, and the corrected scale roughness at different scales is obtained, and the corrected scale roughness at all scales is used to form a corrected scale roughness spectrum; in a double logarithmic coordinate system, the size of the maximum point of the negative value of the second derivative of the corrected scale roughness spectrum is taken as the valley point scale; the corrected scale roughness spectrum is divided into a micro-roughness energy band and a macro-wavy energy band according to the valley point scale; the ratio of the integral of the corrected scale roughness in the macro-wavy energy band to the integral of the corrected scale roughness in the micro-roughness energy band is taken as the wave-roughness energy ratio; the sum of the corrected scale roughness at all scales is taken as the total roughness, and the rock mass structure surface is classified and evaluated according to the total roughness and the wave-roughness energy ratio.

[0006] Firstly, the application converts the three-dimensional point cloud data into a two-dimensional height field matrix, and adopts two-dimensional continuous wavelet transform for multi-scale decomposition, so that the morphological features at different scales are separated in the corresponding scale channels, avoiding the problem of multi-scale feature aliasing in the traditional method. Subsequently, considering that the point cloud data sampling density is limited, false high-frequency noise is easily introduced in small-scale analysis due to interpolation, the application introduces scale confidence to correct the roughness at each scale. The confidence is constructed based on the ratio of the analysis scale to the average point spacing of the point cloud, which can adaptively reduce the weight of unreliable small-scale components, thereby improving the authenticity of the full-scale roughness spectrum, especially the credibility of the micro-roughness feature. On this basis, the application automatically identifies the valley point scale by analyzing the second derivative of the corrected scale roughness spectrum in the double logarithmic coordinate system. The valley point scale objectively reflects the transition position between micro-roughness and macro-wavy, providing a stable and repeatable basis for the energy division of the two types of morphological features. Further, the application integrates the corrected roughness in the macro and micro energy bands respectively, and calculates the wave-roughness energy ratio to quantify the relative contribution of large-scale undulation and small-scale concave-convex. At the same time, the sum of the corrected roughness at all scales is summed to obtain the total roughness, forming two evaluation indexes with physical meaning. Finally, the application combines the total roughness and the wave-roughness energy ratio, and uses an adaptive threshold method to divide the classification boundary in the two-dimensional feature space, dividing the rock mass structure surface into four types. The classification not only reflects the degree of roughness, but also embodies the nature of the dominant morphology, which helps to more accurately predict the mechanical response of the rock mass in the shearing process, such as the initial bite strength or the shear dilation trend.

[0007] Preferably, the scale confidence satisfies the expression: ; wherein, is the scale confidence of the rock mass structural plane at the scale ; is the scale size, is the average point distance of the original three-dimensional point cloud on the reference surface; is the confidence adjustment coefficient, is a natural exponential function.

[0008] The present application improves the reliability of each scale component in multi-scale roughness analysis by introducing scale confidence. The scale confidence is based on the ratio of the analysis scale to the average point distance of the original three-dimensional point cloud on the reference surface, adopts a Gaussian-type attenuation form, and makes the confidence decrease smoothly as the scale decreases, avoiding abrupt truncation of small-scale information. When the analysis scale is much larger than the average point distance, the confidence tends to the maximum value, indicating that the roughness calculation result at this scale has high reliability. When the scale is close to or smaller than the average point distance, the confidence decreases rapidly, effectively suppressing the interference of high-frequency noise introduced by interpolation on small-scale roughness. The confidence adjustment coefficient of the scale confidence provides the implementer with the freedom to adjust the confidence decay rate, enabling a reasonable balance between preserving true microscopic features and suppressing false noise, improving the robustness of small-scale analysis, and providing a more reliable data basis.

[0009] Preferably, the valley point scale satisfies the expression: ; wherein, is the scale size, is the identified valley point scale; is an operator that finds the parameter that maximizes the function value in the parentheses; is a natural logarithm operation; is the modified scale roughness of the rock mass structural plane at the scale .

[0010] The present application realizes the objective division of the energy boundary between macro-waviness and micro-roughness by adopting a valley point scale identification method based on the curvature characteristics of the modified scale roughness spectrum. The second derivative of the modified scale roughness spectrum is calculated in the double logarithmic coordinate system, and the scale corresponding to the maximum point of the negative value of the derivative is taken as the valley point scale, thereby automatically positioning the transition position between the micro and macro components in the energy spectrum. Since the double logarithmic coordinate can magnify the details of the spectrum, and the second derivative can accurately describe the concave and convex changes of the curve, this strategy reduces the subjectivity and inconsistency caused by manually setting the division scale, improves the scientificity of the energy band division, and provides a unified and physically meaningful scale reference for subsequent wave roughness energy ratio calculation and structural plane classification, enhancing the automation level and engineering applicability of the entire evaluation process.

[0011] Preferably, the generating the two-dimensional height field matrix comprises: performing plane fitting on the three-dimensional point cloud data by using a least square method to obtain a best-fitted reference surface representing a macroscopic average trend of the rock mass structural surface; projecting all the three-dimensional point cloud data vertically onto the reference surface to obtain height data of the projected points; constructing a two-dimensional grid on the reference surface, and performing interpolation on the two-dimensional grid based on the height data of the projected points to generate a two-dimensional height field matrix.

[0012] Preferably, the two-dimensional continuous wavelet transform adopts a two-dimensional Mexican hat mother wavelet function.

[0013] The present application adopts a two-dimensional Mexican hat mother wavelet function, because the rough and undulating morphological characteristics on the rock mass structural surface generally do not have obvious directionality, and are isotropic, the two-dimensional Mexican hat wavelet is sensitive to the isotropic characteristic response, and can effectively identify irregularly distributed protrusions and depressions; at the same time, the two-dimensional Mexican hat wavelet has good localization characteristics, which ensures that not only the morphological characteristics of different scales can be identified in the decomposition process, but also the spatial position and undulation strength on the structural surface can be accurately corresponded, so that the wavelet coefficient matrix under each scale can more truly reflect the real morphology of the rock mass structural surface under each scale, and provide a reliable data basis for subsequent calculation.

[0014] Preferably, the wave roughness energy ratio satisfies the expression: ; wherein, is the wave roughness energy ratio of the rock mass structural surface, is the scale size, is the modified scale roughness, is the valley point scale, is the total scale range of analysis, is the minimum scale of analysis, is the maximum scale of analysis.

[0015] The present application realizes the evaluation of the relative development degree of the macroscopic undulation and the microscopic roughness of the rock mass structural surface by introducing the wave roughness energy ratio, the ratio takes the valley point scale as the boundary, respectively integrates the modified scale roughness of the macroscopic scale segment and the microscopic scale segment, and takes the ratio, so as to objectively reflect the energy proportion of the large-scale undulation and the small-scale concave-convex in the overall morphology. The index can effectively distinguish different types of structural surfaces dominated by undulating or fine roughness, and provides a key criterion for subsequent classification and evaluation. When used in combination with the total roughness, the wave roughness energy ratio further enriches the description dimension of the geometric characteristics of the rock mass structural surface, so that the evaluation result is closer to the actual situation, and helps to improve the pertinence and reliability of the rock mass stability analysis.

[0016] Preferably, the classification evaluation of the rock mass structural plane according to the total roughness and the wave roughness energy ratio comprises: taking the total roughness and the wave roughness energy ratio of each sample as a point in a two-dimensional feature space, segmenting the two-dimensional feature space by using a two-dimensional adaptive threshold algorithm to obtain a total roughness threshold and a wave roughness energy ratio threshold ; and classifying the rock mass structural plane according to the comparison result of the total roughness, the wave roughness energy ratio and the threshold , .

[0017] Preferably, the classification of the rock mass structural plane comprises: determining a low roughness, rough body dominant type in response to the total roughness being less than or equal to and the wave roughness energy ratio being less than or equal to ; determining a low roughness, waviness dominant type in response to the total roughness being less than or equal to and the wave roughness energy ratio being greater than ; determining a high roughness, rough body dominant type in response to the total roughness being greater than and the wave roughness energy ratio being less than or equal to ; and determining a high roughness, waviness dominant type in response to the total roughness being greater than and the wave roughness energy ratio being greater than .

[0018] Preferably, the confidence adjustment coefficient is set to 2.

[0019] Preferably, the three-dimensional point cloud data of the rock mass structural plane is obtained by using a three-dimensional laser scanner.

[0020] The present application has the beneficial effects that: the present application effectively separates the contribution of macroscopic waviness and microscopic roughness in the rock mass structural plane by two-dimensional continuous wavelet transform and scale confidence correction, overcomes the problem of ambiguous mechanical meaning caused by scale confusion of the traditional single roughness parameter; the present application introduces the scale confidence related to the average point distance of the point cloud to adaptively suppress the high-frequency noise introduced by interpolation, so that the corrected scale roughness spectrum is closer to the real topography, and the reliability of small-scale features is improved; the present application constructs a two-parameter classification framework based on the total roughness and the wave roughness energy ratio, and uses adaptive threshold segmentation, which avoids subjective experience setting, so that the classification of the rock mass structural plane has more physical basis and engineering interpretability; the classification result of the present application can provide more targeted basis for stability evaluation, support design and the like in geotechnical engineering. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a flowchart schematically showing a three-dimensional roughness evaluation method of a rock mass structural plane in the present application;

[0022] Figure 2 is a flow chart schematically showing S2 in the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0024] The specific embodiments of the present application will be described in detail below with reference to the drawings.

[0025] The embodiments of the present application disclose a three-dimensional roughness evaluation method of a rock mass structural surface, refer to Figure 1 , comprising steps S1 to S5:

[0026] S1, acquiring three-dimensional point cloud data of the rock mass structural surface, and generating a two-dimensional height field matrix, performing two-dimensional continuous wavelet transform on the two-dimensional height field matrix to obtain a series of wavelet coefficient matrices at different scales.

[0027] It should be noted that, in order to evaluate the three-dimensional roughness of the rock mass structural surface, the present application converts the acquired original three-dimensional point cloud data into a two-dimensional height field matrix convenient for multi-scale analysis, and decomposes the topographic features of the rock mass structural surface at different scales through two-dimensional continuous wavelet transform.

[0028] Specifically, a three-dimensional laser scanner or a high-precision digital photogrammetry system is used to acquire high-density three-dimensional point cloud data of the rock mass structural surface to be measured. In order to establish a unified analysis reference, the least square method is used to perform plane fitting on the point cloud data to obtain a best-fitted reference surface representing the macroscopic average trend of the rock mass structural surface. The three-dimensional point cloud data is vertically projected onto the reference surface to obtain height data of the projected points; based on the height data of the projected points, interpolation is performed on a two-dimensional grid whose relative size of the grid cell size to the average point spacing of the original three-dimensional point cloud data is within a certain range to generate a two-dimensional height field matrix , wherein the value of each element in the two-dimensional height field matrix represents the deviation of the actual height of the rock mass structural surface relative to the best-fitted reference surface at the grid coordinates , and a positive value indicates a protrusion and a negative value indicates a depression. In this embodiment, the bilinear interpolation method is used to interpolate the two-dimensional grid, and in other embodiments, the implementer can select an interpolation algorithm according to actual conditions.

[0029] More specifically, the grid cell size of the two-dimensional grid should ensure that the two-dimensional height field can both sufficiently preserve the spatial details of the original point cloud and avoid introducing too much interpolation noise due to over-dense grid or losing valid information due to under-dense grid. The grid cell size is set to 0.8-1.2 times of the average point spacing of the original three-dimensional point cloud on the reference surface: when the grid size is less than 0.8 times of the average point spacing, most grid cells lack direct observation points, and the interpolation result is easily affected by algorithm assumptions, resulting in false high-frequency components; when the grid size is greater than 1.2 times of the average point spacing, it may not be able to effectively distinguish the real existing small undulations, resulting in the loss of topographic details. Therefore, by controlling the grid cell size to be between 0.8-1.2 times of the average point spacing, a reasonable balance can be achieved between preserving the real geometric features of the structural surface and suppressing the uncertainty of interpolation, providing a reliable height field input for subsequent multi-scale analysis. In this embodiment, the grid cell size is set to 1.1 times of the average point spacing of the original three-dimensional point cloud on the reference surface, and in other embodiments, the implementer can set the grid cell size according to the actual situation.

[0030] Further, a series of preset physical scale sizes, such as, from to are taken in an exponential sequence . The two-dimensional continuous wavelet transform (2D-CWT) is used to process the two-dimensional height field matrix: for each scale size, the mother wavelet function of the two-dimensional continuous wavelet transform is convolved with the two-dimensional height field matrix to obtain the wavelet coefficient matrix at each scale. The numerical distribution in the wavelet coefficient matrix reflects the strength and position of the undulation features in the original rock mass structural surface topography that match the current scale.

[0031] It should be noted that the two-dimensional Mexican Hat wavelet is selected as the mother wavelet function in this embodiment because the undulation features of the rock mass structural surface are usually isotropic, i.e., they have similar statistical properties in different directions. The two-dimensional Mexican Hat wavelet has good localization properties and is sensitive to isotropic morphological features, and can effectively capture these irregularly distributed but not strongly directional protrusions and depressions, thereby accurately identifying and quantifying rough bodies and undulations of different sizes. In other embodiments, the implementer can select the mother wavelet function of the two-dimensional continuous wavelet transform according to the actual implementation situation.

[0032] S2, calculate the scale roughness of each scale, obtain the scale confidence of each scale, modify the scale roughness of each scale based on the scale confidence of each scale, and construct the modified scale roughness spectrum by using the modified scale roughness of all scales.

[0033] It should be noted that after obtaining the wavelet coefficient matrix of each scale, in order to quantitatively characterize the roughness of the rock mass structure surface at different scales from the multi-scale decomposition result, and overcome the distortion problem of small-scale analysis caused by the limited sampling density of the point cloud, the present application evaluates the contribution of each scale to the total roughness, and adaptively corrects the calculation error introduced at the small-scale end due to insufficient data sampling density.

[0034] Specifically, the flowchart of step S2 refers to Figure 2 , including steps S201 to S203:

[0035] S201, calculate the energy sum of the wavelet coefficient matrix of each scale to obtain the scale roughness of each scale.

[0036] It should be noted that since the energy of the wavelet coefficient can directly reflect the fluctuation intensity of the rock mass structure surface at the corresponding scale, in order to evaluate the fluctuation intensity of the rock mass structure surface at a specific scale, the present application obtains the scale roughness of the rock mass structure surface at each scale according to the wavelet coefficient energy at each scale.

[0037] Specifically, the scale roughness of the rock mass structure surface at each scale satisfies the expression:

[0038]

[0039] wherein, is the scale roughness of the rock mass structure surface at the scale size ; is the scale size; is the grid coordinate of the measurement plane area; is the wavelet coefficient value at the scale size , coordinate , The integral region of covers the entire measurement plane area. The greater the value of , the more significant the corresponding topographic feature of the rock mass structure surface at the scale , and the more intense the fluctuation.

[0040] S202, obtain the scale confidence of each scale based on the ratio of the size of each scale to the average point distance of the two-dimensional height field matrix.

[0041] It should be noted that the scale roughness can evaluate the roughness of the rock mass structure surface at each scale from a macroscopic perspective, but the average point distance of the original point cloud data limits the minimum topographic size that can be reliably analyzed. When the scale size is close to or smaller than the average point distance, false high-frequency noise will be introduced during interpolation, resulting in overestimation of the roughness and reduction of the credibility. Therefore, the present application corrects the roughness of each scale based on the scale roughness combined with the scale confidence to obtain the corrected scale roughness.

[0042] Specifically, the scale confidence satisfies the expression:

[0043]

[0044] wherein, is the scale confidence of the rock mass structural plane at the scale ; is the scale size; is the average point distance of the original three-dimensional point cloud on the reference surface, which can be directly calculated by vertically projecting the three-dimensional point cloud data onto the reference surface; is the coefficient for adjusting the influence of the average point distance, is a natural exponential function.

[0045] In the embodiment, the preferred range of the coefficient for adjusting the influence of the average point distance is to When the value of is less than , the confidence function has too strong attenuation effect, which may excessively suppress part of the effective small-scale information; when the value of is greater than , the suppression effect on the interpolation noise is not obvious, and in the embodiment, the value of is set to , which can achieve a good balance between effectively suppressing noise and retaining real features, and in other embodiments, the value of may be selected according to actual conditions.

[0046] When the scale size is much greater than the average point distance, the ratio is very large, the exponential term tends to , and tends to , indicating that the analysis result of the large-scale feature has high confidence. Conversely, when is close to or less than , the ratio is close to , the exponential term tends to , and tends to , indicating that the analysis result at this time is mainly dominated by interpolation noise, and the confidence is very low.

[0047] S203, based on the scale confidence of each scale, the scale roughness of each scale is corrected to obtain the corrected scale roughness of each scale, and the corrected scale roughness of all scales is composed of a corrected scale roughness spectrum.

[0048] The scale roughness of each scale is multiplied by the scale confidence of each scale, and the modified scale roughness of the rock mass structure surface at each scale satisfies the expression:

[0049]

[0050] wherein, is the scale size, is the modified scale roughness of the rock mass structure surface at the scale size . is the scale roughness of the rock mass structure surface at the scale size . is the scale confidence of the rock mass structure surface at the scale size .

[0051] Through the modification, the unreliable energy value at the small scale end is effectively lowered, while the reliable energy value at the large scale end is retained, and the modified scale roughness of each scale forms a modified scale roughness spectrum that is more in line with physical reality.

[0052] S3, in a double logarithmic coordinate system, the scale size corresponding to the maximum point of the negative value of the second derivative of the modified scale roughness spectrum is taken as the valley point scale; and the modified scale roughness spectrum is divided into a micro-roughness energy band and a macro-waviness energy band according to the valley point scale.

[0053] It should be noted that for the three-dimensional roughness evaluation of the rock mass structure surface, not only the roughness of each scale needs to be evaluated, but also the roughness type of the rock mass structure surface needs to be evaluated, because the roughness contains characteristics of different scales: such as macro-waviness, which refers to the undulating morphology of the rock mass structure surface in a larger range, affecting the overall sliding trend and stability; such as micro-roughness, which refers to the small concave-convex unevenness on the surface of the rock mass structure surface, mainly affecting the local contact and friction performance.

[0054] It should be further noted that the modified scale roughness spectrum of the natural rock mass structure surface usually presents a double-peak morphology, with energy concentrated in the small scale region representing micro-roughness and the large scale region representing macro-waviness, and there is a valley point scale with relatively low energy between the two, which can be used as an objective basis for dividing the macro-waviness energy band and the micro-roughness energy band. The valley point scale of the modified scale roughness spectrum corresponds to the position where the curve bends most sharply in mathematics, i.e., the point with the maximum curvature. The present application can realize automatic identification of the valley point scale by finding the extreme value of the second derivative.

[0055] Specifically, to accurately identify the valley point scale, the modified scale roughness spectrum is plotted in a double logarithmic coordinate system, with the horizontal axis as and the vertical axis as .

[0056] Further, the valley point scale satisfies the expression:

[0057]

[0058] wherein, is the scale size, is the identified valley point scale; is an operator for finding the argument that makes the function value in the bracket reach the maximum; is a natural logarithm operation; is the modified scale roughness of the rock mass structural plane at the scale .

[0059] When the second derivative is positive, the curve is concave, that is, the curve is curved upward, and when the second derivative is negative, the curve is convex, that is, the curve is curved downward, and the valley point scale is just the point with the strongest concavity. By calculating the negative value of the second derivative, the problem of finding the maximum concavity is converted into a standard problem of finding the maximum value of a function. The operator automatically locates the scale corresponding to the maximum value point, that is, the valley point scale .

[0060] Therefore, the entire scale range is objectively divided into a micro-roughness energy band and a macro-waviness energy band .

[0061] S4, the ratio of the integral of the modified scale roughness in the macro-waviness energy band to the integral of the modified scale roughness in the micro-roughness energy band is taken as the wave-roughness energy ratio.

[0062] It should be noted that after completing the energy band segmentation, the present application extracts a parameter for evaluating the relative development degree of the macro-waviness and the micro-roughness of the two scale topographies, which is denoted as the wave-roughness energy ratio.

[0063] Specifically, the wave-roughness energy ratio of the rock mass structural plane satisfies the expression:

[0064]

[0065] wherein, is the wave-roughness energy ratio of the rock mass structural plane, is the scale size, is the modified scale roughness, is the valley point scale, is the total scale range of analysis, is the minimum scale of analysis, is the maximum scale of analysis. ​

[0066] The wave-roughness energy ratio reflects the energy contrast of two forms: when , the macro-wave energy is greater than the micro-roughness energy, indicating that the surface morphology of the rock mass structure is mainly large-scale and gentle undulation; when , it indicates that the morphology is mainly small-scale and sharp roughness. The size of the wave-roughness energy ratio evaluates the relative dominance of the macro-wave energy and the micro-roughness energy.

[0067] S5, the sum of the modified scale roughness of all scales is taken as the total roughness, and the rock mass structure is classified according to the total roughness and the wave-roughness energy ratio.

[0068] It should be noted that in geotechnical engineering practice, the mechanical behavior of the rock mass structure is not only affected by the overall roughness, but also closely related to the dominant type of the surface morphology: the structure surface mainly with large-scale undulation is prone to produce shear dilation effect in the shear process, and the structure surface mainly with small-scale roughness mainly provides initial occlusion resistance. If only a single index is used to describe the roughness, it is difficult to distinguish the two types of morphology with different engineering responses. Therefore, the present application constructs a two-dimensional classification system composed of total roughness and wave-roughness energy ratio, and automatically determines the classification boundary according to the distribution characteristics of the actual sample data by using the self-adaptive threshold method. Because the rock mass of different engineering sites has different origins, weathering states and tectonic histories, the distribution range and concentration trend of the roughness and the wave-roughness energy ratio are different, and it is difficult to be universal with fixed threshold. The present application dynamically determines the segmentation threshold, so that the classification result can accurately reflect the roughness of the structure surface, and effectively identify the dominant type of the surface morphology, so as to realize the fine description of the mechanical properties of the rock mass structure.

[0069] Specifically, the sum of the modified scale roughness of all scales of the rock mass structure is taken as the total roughness of the rock mass structure.

[0070] Further, the total roughness of each rock mass structure sample is taken as the horizontal axis, and the wave-roughness energy ratio of each rock mass structure sample is taken as the vertical axis, a two-dimensional feature space is constructed, and each rock mass structure sample is represented as a point in the two-dimensional feature space, whose coordinates are , ). By analyzing a large number of sample points, two optimal segmentation thresholds are automatically determined by using a two-dimensional adaptive threshold algorithm: a total roughness threshold and a wave-roughness energy ratio threshold , which divides the two-dimensional feature space into four quadrants, corresponding to four typical types of rock mass structure. In this embodiment, the maximum inter-class variance method is used as the two-dimensional adaptive threshold algorithm, and the two-dimensional adaptive threshold algorithm can be selected according to the actual situation.

[0071] In response to the total roughness being less than or equal to and the wave roughness energy ratio is less than or equal to , the rock mass structure surface is judged as low roughness, micro-dominant type, the rock mass structure surface is almost smooth, only with weak and small rough texture; in response to the total roughness less than or equal to and the wave roughness energy ratio is greater than , it is determined as low roughness, wave-dominant type, the rock mass structure surface is judged as low roughness, macro-dominant type, the rock mass structure surface is macroscopically gentle fluctuation, but the fluctuation range is not large, the whole is relatively smooth; in response to the total roughness greater than and the wave roughness energy ratio is less than or equal to , the rock mass structure surface is judged as high roughness, micro-dominant type, the rock mass structure surface is like coarse sandpaper, densely covered with a large number of small size protrusions; in response to the total roughness greater than and the wave roughness energy ratio is greater than , the rock mass structure surface is judged as high roughness, macro-dominant type, the rock mass structure surface exists significant large-scale wave fluctuation, and may be superimposed with moderate roughness.

[0072] So far, the three-dimensional roughness evaluation of the rock mass structure surface is realized.

Claims

1. A method for evaluating three-dimensional roughness of a rock mass structural plane, characterized by, The method comprises the following steps: acquiring three-dimensional point cloud data of a rock mass structure surface and generating a two-dimensional height field matrix; performing two-dimensional continuous wavelet transform on the two-dimensional height field matrix to obtain a series of wavelet coefficient matrices at different scales; calculating the energy sum of the wavelet coefficient matrices at different scales to obtain scale roughness at different scales; and obtaining scale confidence at different scales based on the ratio of the size of each scale to the average point spacing of the two-dimensional height field matrix; based on the scale confidence at different scales, the scale roughness at different scales is corrected to obtain corrected scale roughness at different scales, and the corrected scale roughness at all scales constitutes a corrected scale roughness spectrum; in a double logarithmic coordinate system, the size of the scale corresponding to the maximum point of the negative value of the second derivative of the corrected scale roughness spectrum is taken as the valley point scale; according to the valley point scale, the corrected scale roughness spectrum is divided into a micro-roughness energy band and a macro-wavy roughness energy band; the ratio of the integral of the corrected scale roughness in the macro-wavy roughness energy band to the integral of the corrected scale roughness in the micro-roughness energy band is taken as the wave roughness energy ratio; the sum of the corrected scale roughness at all scales is taken as the total roughness, and the rock mass structure surface is classified and evaluated according to the total roughness and the wave roughness energy ratio.

2. The method according to claim 1, wherein, The scale confidence satisfies the expression: ； wherein, is the scale confidence of the rock mass structural plane at scale ; is the scale size; is the average point distance of the original 3D point cloud on the reference surface; is the confidence adjustment coefficient; is the natural exponential function.

3. The method of claim 1, wherein the method further comprises: The valley point scale satisfies the expression: ； in, It refers to scale or size. It is the scale of the identified valley points; It is a parameter that maximizes the value of the function within the parentheses. Operators; For natural logarithm operations; It is the rock mass structural plane at the scale The corrected scale roughness.

4. The method of claim 1, wherein the method further comprises: The generation of the two-dimensional height field matrix comprises: a least square method is used to fit the three-dimensional point cloud data to obtain a best fitting reference surface representing the macroscopic average trend of the rock mass structure surface; the height data of the projected points is obtained by vertically projecting all three-dimensional point cloud data onto the reference surface; a two-dimensional grid is constructed on the reference surface, and interpolation is performed on the two-dimensional grid based on the height data of the existing projected points to generate a two-dimensional height field matrix.

5. The method of claim 1, wherein the method further comprises: The two-dimensional continuous wavelet transform uses a two-dimensional Mexican hat mother wavelet function.

6. The method of claim 1, wherein the method further comprises: The wave roughness energy ratio satisfies the expression: ； wherein, is the wave energy ratio for the rock mass discontinuity, is the size scale, is the modified size roughness, is the valley point scale, is the total size range of the analysis, is the minimum size of the analysis, is the maximum size of the analysis.

7. The method of claim 1, wherein the method further comprises: determining a three-dimensional roughness of the rock structure surface. The classification and evaluation of the rock mass structure surface comprises: The total roughness and the wave roughness energy ratio of each sample are taken as points in a two-dimensional feature space, the two-dimensional feature space is segmented by using a two-dimensional adaptive threshold algorithm to obtain a threshold value of the total roughness and a threshold value of the wave roughness energy ratio ; and the rock mass structural plane is classified and evaluated through a comparison result of the total roughness, the wave roughness energy ratio and the threshold value 、 .

8. The method of claim 7, wherein the method further comprises: The classification and evaluation of the rock mass structure surface comprises: in response to the total roughness being less than or equal to 0.5 μm and the wave roughness energy ratio being less than or equal to 0.5 , the low roughness, roughness body dominant type is determined; in response to the total roughness being less than or equal to 0.5 μm and the wave roughness energy ratio being greater than 0.5 , the low roughness, waviness dominant type is determined; in response to the total roughness being greater than 0.5 μm and the wave roughness energy ratio being less than or equal to 0.5 , the high roughness, roughness body dominant type is determined; in response to the total roughness being greater than 0.5 μm and the wave roughness energy ratio being greater than 0.5 , the high roughness, waviness dominant type is determined.

9. The method of claim 2, wherein the method further comprises: determining the three-dimensional roughness of the rock structure surface based on the three-dimensional surface profile data. The confidence adjustment coefficient is set to 2.

10. The method of claim 1, wherein the method further comprises: determining a three-dimensional roughness of the rock structure surface. The acquisition of the three-dimensional point cloud data of the rock mass structure surface comprises: a three-dimensional laser scanner is used to acquire the three-dimensional point cloud data of the rock mass structure surface.

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