CN116858302A - Visualization method for data fusion of deep surrounding rock - Google Patents
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- 239000011435 rock Substances 0.000 title claims abstract description 124
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Abstract
The invention discloses a visualization method for multi-data fusion of deep surrounding rock, which is applied to the technical field of deep surrounding rock monitoring and comprises the following steps: measuring displacement deformation characteristics of surrounding rock, and generating a surrounding rock displacement field; measuring regional ground stress distribution, inverting the stress field, and simulating the regional ground stress field; measuring to obtain a surrounding rock disturbance zone and evolution characteristics of the surrounding rock disturbance zone, and determining a surrounding rock damage zone; and carrying out data fusion on the surrounding rock displacement field, the regional ground stress field and the surrounding rock damage area, and realizing deep surrounding rock visualization. According to the invention, 3D modeling work is performed based on a displacement field, a stress field and a damaged area, multi-data fusion is realized, so that visualization is realized for deep tunnel engineering, more visual stability analysis of a tunnel model is obtained, and the method has important significance for smooth development of work such as support and protection engineering installation and the like.
Description
Technical Field
The invention relates to the technical field of monitoring of deep surrounding rocks, in particular to a visualization method for multi-data fusion of the deep surrounding rocks.
Background
Under the current social development background, the development of the ground space and the shallow earth surface part gradually becomes saturated, and under the demands of civil traffic, resource development, military protection engineering and the like, the deep development of the earth has become the problem which must be faced by the current times of development. Deep rock masses are generally subjected to the construction forces of dead weight stress and geological movement that cover the rock mass, and have a more complex stress field environment than the above-ground space and shallow earth mass. One of the greatest differences between deep underground engineering and surface engineering is that the architecture of the whole engineering cannot be intuitively reflected, so that more measurement data analysis is needed, the analysis of relevant parameters of surrounding rocks is a comprehensive index reflecting the influence of various factors, and certain difficulty still exists in the visualization of the deep surrounding rocks at present, and if the real-time monitoring of the deep surrounding rocks can be realized, more reasonable and economical protective measures can be arranged.
Therefore, how to provide a visualization method for realizing real-time monitoring of deep surrounding rock and data fusion of deep surrounding rock is a problem to be solved by those skilled in the art.
Disclosure of Invention
The theoretical core of the tunnel engineering data visualization is application of an inversion analysis method, and the method comprises establishment of a numerical model of a rock mass and a tunnel structure, monitoring of data information such as displacement, stress and strain in the tunnel construction process, feedback analysis and the like. Inversion analysis is carried out on rock deformation parameters by monitoring tunnel displacement, a numerical model is required to be established firstly, then inverse analysis calculation is carried out on rock-soil parameters by using monitored displacement data, deformation of the tunnel is predicted by using updated rock-soil parameters, and accordingly protection work is timely carried out according to a predicted result.
In view of the above, the invention provides a visualization method for multi-data fusion of deep surrounding rock.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a visualization method for deep surrounding rock multi-data fusion, comprising:
and measuring displacement deformation characteristics of the surrounding rock, and generating a surrounding rock displacement field.
And measuring the regional ground stress distribution, inverting the stress field, and simulating the regional ground stress field.
And measuring to obtain a surrounding rock disturbance zone and evolution characteristics of the surrounding rock disturbance zone, and determining a surrounding rock damage zone.
And carrying out data fusion on the surrounding rock displacement field, the regional ground stress field and the surrounding rock damage area.
Optionally, an unmanned aerial vehicle oblique photography technology and a laser scanning technology are adopted to measure displacement deformation characteristics of the surrounding rock.
Alternatively, the stress distribution of the zone is measured using a hydraulic fracturing method.
Optionally, an ultrasonic detection device is used for measuring and obtaining the surrounding rock disturbance area and the evolution characteristics of the surrounding rock disturbance area.
Optionally, stress field inversion is specifically:
and carrying out stress field inversion by a regressive side pressure coefficient based on regional ground stress distribution, and calculating to obtain the maximum horizontal main stress and the minimum horizontal main stress to simulate the regional ground stress field.
Optionally, after performing stress field inversion and calculating to obtain the maximum horizontal main stress and the minimum horizontal main stress, three-dimensional conversion is performed on the maximum horizontal main stress and the minimum horizontal main stress, where the calculation formula is as follows:
σ 1 ＝max(σ n ,σ l ,σ v )；
σ 1 ＝min(σ n ,σ l ,σ v )；
σ θ ＝3σ 1 -σ 3 ；
wherein S is H Is the maximum horizontal principal stress, S h For the minimum horizontal main stress, alpha is the included angle between the tunnel axis direction and the maximum horizontal main stress direction, sigma n To act on the horizontal positive stress of the tunnel cross section, sigma l Is the horizontal normal stress perpendicular to the tunnel axis, sigma 1 Is the maximum initial stress of the cross section of the tunnel, sigma 3 Sigma is the minimum initial stress of the cross section of the tunnel θ Is the maximum tangential stress.
Optionally, the specific steps for measuring and obtaining the evolution characteristics of the surrounding rock disturbance area are as follows: the high-frequency elastic pulse wave is transmitted into the rock mass medium by the ultrasonic pulse transmitting source, so that the high-frequency elastic pulse wave propagates in the rock mass medium, and the fluctuation characteristic of the wave propagating in the rock mass is recorded by the receiving system.
Optionally, the specific steps for determining the surrounding rock damage area are as follows:
and solving a space change function of the wave velocity of the disturbance zone by utilizing a wave equation of the elastic wave in the rock mass medium.
And combining the quantitative expression of the H-B criterion parameter with the spatial variation function of the wave speed of the disturbance zone to establish the spatial variation function of the H-B criterion parameter.
And determining the spatial variation rule of rock mechanical parameters of the disturbance zone by using the measured surrounding rock disturbance zone and evolution characteristics of the surrounding rock disturbance zone, determining the surrounding rock damage condition and determining the surrounding rock damage zone.
Optionally, the spatial variation function of the wave velocity in the disturbance zone is:
the wave equation of elastic waves in rock mass media is expressed as:
wherein u, v, w are displacements in the x, y, z directions of the coordinate axes,for the laplace operator, G is the shear modulus, λ is the pull Mei Jishu, e is the sign of the volume strain of the unit cell, e=ε x +∈ y +∈ z ；
And (5) solving an equation of the longitudinal wave velocity:
solving an equation of transverse wave velocity:
wherein V is P And V S The wave velocity of longitudinal waves and the wave velocity of transverse waves are respectively, E is an elastomer, the elastic modulus of the rock mass medium to be measured, ρ is the density of the rock mass medium, and γ is the Poisson's ratio of the rock mass medium.
Optionally, the data fusion is 3D modeling based on the surrounding rock displacement field, the regional ground stress field, and the surrounding rock damage region.
Compared with the prior art, the invention provides a visualization method for multi-data fusion of deep surrounding rock. The displacement deformation monitoring is carried out on the surrounding rock of the grotto excavated in the deep part by adopting the unmanned aerial vehicle, the displacement field of the surrounding rock deformation is obtained through analysis, the range of the surrounding rock disturbance area is detected by adopting the ultrasonic detection device, so that a related damage model is determined, the stress of a monitoring point is obtained through a hydraulic fracturing method, the stress field is obtained through stress inversion work, finally, the 3D modeling work is carried out by adopting BIM software based on the displacement field, the stress field and the damage area, the multi-data fusion is realized, the visualization of the deep tunnel engineering is realized, the stability analysis of the tunnel model is relatively visual, and the method has important significance for the smooth development of the work such as supporting and protection engineering installation and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of the overall structure of the present invention.
Fig. 3 is a schematic structural diagram of the unmanned aerial vehicle system of the present invention.
Fig. 4 is a schematic structural diagram of a hydraulic fracturing device according to the present invention.
Fig. 5 is a schematic structural view of an ultrasonic probe according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
the embodiment 1 of the invention discloses a visualization method for multi-data fusion of deep surrounding rock, which is shown in fig. 1 and comprises the following steps:
and measuring displacement deformation characteristics of the surrounding rock by adopting an unmanned aerial vehicle oblique photography technology and a laser scanning technology. The existing displacement monitoring technology for the surrounding rock of the deep burial chamber generally needs to set a large number of datum points and monitoring points for periodic manual monitoring, is time-consuming and labor-consuming, has low efficiency, has large potential safety hazard, and cannot well meet the monitoring requirement;
in order to solve the problems, the invention provides a displacement field for establishing a cavity surrounding rock based on a mode of combining unmanned aerial vehicle oblique photography and three-dimensional laser scanning. Firstly, an integral cavity structure model is established by adopting unmanned aerial vehicle oblique photography technology, and then the positions of a plurality of monitoring points are determined through the model.
The unmanned aerial vehicle adopting the battery power system can obtain better imaging effect and operational convenience; the fixed wing aircraft body has the capability of more stable displacement monitoring, the multi-rotor wing aircraft body has the capability of more stable flying, and proper models are selected according to actual needs. The multi-view three-dimensional live-action view model can be formed by related image data processing software, and specifically comprises the following steps:
the unmanned aerial vehicle system shown in fig. 3 mainly comprises a flight platform, a sensor, a data processing system and the like, and the shooting equipment mounted on the unmanned aerial vehicle system can shoot in the vertical direction and 4 lateral directions. The unmanned aerial vehicle flies into the tunnel, collects photos, records shooting height and plane coordinates, ensures that a certain degree of overlap exists between adjacent photos, and the shot video and influence serve as a data source for three-dimensional modeling.
According to the camera parameters, the photos, the image POS data, the image control point information and other data, the tunnel surrounding rock displacement information can be obtained, and the three-dimensional live-action modeling can be completed in Context Capture software, and the specific flow is as follows:
(1) Firstly, building engineering, importing an original photo and a corresponding POS into a Context Capture according to camera grouping, then importing an image control point, after finishing the measured result into a format, selecting a corresponding coordinate system to import the Context Capture, and marking the acquired image control point on each photo in sequence.
(2) And after the image control point marking is finished, performing automatic aerial triangulation operation with control points again, and converting the photographing coordinate system into an object coordinate system.
(3) After the automatic air triangulation is completed, the Context Capture can automatically match feature points, construct point clouds, automatically generate a three-dimensional model, map textures and finally obtain the three-dimensional model of the real-scene tunnel.
After the three-dimensional model is obtained, a cavity space coordinate system is established, the space coordinates of each detection point are calibrated, and data is imported to the unmanned aerial vehicle system, so that the position of the monitoring point can be better positioned in a assisted mode, and errors are reduced when displacement monitoring is carried out on specific monitoring points in the follow-up mode. Manually controlling the monitoring points on site, performing distance measurement, running the laser scanning function of the unmanned aerial vehicle, and recording the first elevation, depth and other data of each monitoring point. The work is circulated, the change condition of the related data is recorded every day, the data processing is carried out in an electronic computer, the displacement change curve of each monitoring point is obtained, the displacement condition of each monitoring point is synthesized, and the basic distribution condition of the displacement field of the surrounding rock of the grotto can be obtained.
The method comprises the steps of measuring regional ground stress distribution by adopting a hydraulic fracturing method, inverting stress fields, and simulating regional ground stress fields, wherein the specific steps are as follows:
basic principle of stress measurement by a hydraulic fracturing method: the hydraulic fracturing device shown in fig. 4 comprises the following components: the hydraulic fracturing ground stress testing system comprises a hydraulic fracturing ground stress testing system, a pressure source, pipelines and control, a sensor, a data acquisition instrument and acquisition software, a special testing drill rod, an in-hole orientation instrument and a matched tool. The specific operation steps are as follows:
(1) And taking a point of the tunnel, drilling at a position where stress is to be measured, and sealing the section to be pressurized by using a packer.
(2) The isolation section between the two packers is injected with high-pressure water, the water pressure is continuously increased, the hole wall is cracked, and the initial cracking pressure Pi is obtained. The application of water pressure is continued and the high pressure water system is shut down as the fracture expands to a depth of three diameters, the stress at which it is called the closure stress Ps.
(3) During the whole pressurizing process, the pressure-time and flow-time relationship curves are recorded, and the pressure-time curves are followed
The initial cracking pressure Pi and the closing pressure Ps can be obtained, and the depth of crack extension can be determined from the flow-time curve.
(4) High pressure water is re-injected into the closed segment to re-open the fracture and the pressure Pr and subsequent constant closing pressure Ps are recorded as the fracture reopens.
(5) Repeating the processes (2) - (4) for 2-3 times to improve the accuracy of the measured data.
(6) The packer was completely depressurized and all equipment was removed from the borehole. And finally, determining the natural joint, the fracture position, the fracture direction and the size of the hydraulic fracturing fracture and the drilling test section.
Based on regional ground stress distribution, stress field inversion is performed through regressive side pressure coefficients, maximum horizontal main stress and minimum horizontal main stress are obtained through calculation, ground stress statistical analysis results of all drilling holes can be used for obtaining a ground stress inversion calculation formula of surrounding rock of a tested section, and vertical ground stress is deduced by the weight of an overburden stratum. The main stress direction of the initial ground stress field is inconsistent with the axial direction of the tunnel, a certain included angle exists between the main stress direction and the axial direction of the tunnel, the initial stress parameter of the cross section of the tunnel can be obtained by three-dimensional conversion calculation, and the three-dimensional conversion calculation formula is as follows:
σ 1 ＝max(σ n ,σ l ,σ v )；
σ 1 ＝min(σ n ,σ l ,σ v )；
σ θ ＝3σ 1 -σ 3 ；
wherein S is H Is the maximum horizontal principal stress, S h For the minimum horizontal main stress, alpha is the included angle between the tunnel axis direction and the maximum horizontal main stress direction, sigma n To act on the horizontal positive stress of the tunnel cross section, sigma l Is the horizontal normal stress perpendicular to the tunnel axis, sigma 1 Is the maximum initial stress of the cross section of the tunnel, sigma 3 Sigma is the minimum initial stress of the cross section of the tunnel θ Is the maximum tangential stress.
By FLAC 3D The finite difference software can well simulate the stress field inversion condition simulation area ground stress field.
The surrounding rock disturbance area and the evolution characteristics of the surrounding rock disturbance area are measured by an ultrasonic detection device shown in fig. 5, specifically:
the high-frequency elastic pulse wave is transmitted into the rock mass medium by the ultrasonic pulse transmitting source, so that the high-frequency elastic pulse wave propagates in the rock mass medium, and the fluctuation characteristic of the wave propagating in the rock mass is recorded by the receiving system.
The specific steps for determining the surrounding rock damage area are as follows:
the space change function of the wave velocity of the disturbance zone is solved by utilizing the wave equation of the elastic wave in the rock mass medium, and the method specifically comprises the following steps:
the spatial variation function of the wave velocity of the disturbance zone is as follows:
the wave equation of elastic waves in rock mass media is expressed as:
wherein u, v, w are displacements in the x, y, z directions of the coordinate axes,for the laplace operator, G is the shear modulus, λ is the pull Mei Jishu, e is the sign of the volume strain of the unit cell, e=ε x +∈ y +∈ z ；
And (5) solving an equation of the longitudinal wave velocity:
solving an equation of transverse wave velocity:
wherein V is P And V S The wave velocity of longitudinal waves and the wave velocity of transverse waves are respectively, E is an elastomer, the elastic modulus of the rock mass medium to be measured, ρ is the density of the rock mass medium, and γ is the Poisson's ratio of the rock mass medium.
The equation of the longitudinal wave velocity and the transverse wave velocity is the space change function of the wave velocity of the disturbance area.
And combining the quantitative expression of the H-B criterion parameter with the spatial variation function of the wave speed of the disturbance zone to establish the spatial variation function of the H-B criterion parameter. And determining the spatial variation rule of rock mechanical parameters of the disturbance zone by using the measured surrounding rock disturbance zone and evolution characteristics of the surrounding rock disturbance zone, determining the surrounding rock damage condition and determining the surrounding rock damage zone. Substituting the change rule expression of the H-B criterion parameter into a related derivation formula to respectively obtain the change rule of rock mass mechanical parameters such as compressive strength, tensile strength, deformation modulus, cohesive force, internal friction angle and the like in the disturbance zone, determining the surrounding rock damage condition, and determining the range of the damage zone.
Based on the surrounding rock displacement field, the regional ground stress field and the surrounding rock damage region, 3D modeling is performed through BIM software such as Revit, multi-data fusion is achieved, visualization is achieved for deep tunnel engineering, relatively visual tunnel model stability analysis is obtained, and smooth development of work such as support and protection engineering installation is facilitated.
Fig. 2 is a schematic diagram of the overall structure of the unmanned aerial vehicle system, the hydraulic fracturing device and the ultrasonic detection device.
The embodiment of the invention discloses a visualization method for multi-data fusion of deep surrounding rock. The displacement deformation monitoring is carried out on the surrounding rock of the grotto excavated in the deep part by adopting the unmanned aerial vehicle, the displacement field of the surrounding rock deformation is obtained through analysis, the range of the surrounding rock disturbance area is detected by adopting the ultrasonic detection device, so that a related damage model is determined, the stress of a monitoring point is obtained through a hydraulic fracturing method, the stress field is obtained through stress inversion work, finally, the 3D modeling work is carried out by adopting BIM software based on the displacement field, the stress field and the damage area, the multi-data fusion is realized, the visualization of the deep tunnel engineering is realized, the stability analysis of the tunnel model is relatively visual, and the method has important significance for the smooth development of the work such as supporting and protection engineering installation and the like.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A visualization method for data fusion of deep surrounding rock, which is characterized by comprising the following steps:
measuring displacement deformation characteristics of surrounding rock, and generating a surrounding rock displacement field;
measuring regional ground stress distribution, inverting the stress field, and simulating the regional ground stress field;
measuring to obtain a surrounding rock disturbance zone and evolution characteristics of the surrounding rock disturbance zone, and determining a surrounding rock damage zone;
and carrying out data fusion on the surrounding rock displacement field, the regional ground stress field and the surrounding rock damage area.
2. The visualization method for multiple data fusion of deep surrounding rock according to claim 1, wherein the displacement deformation characteristics of the surrounding rock are measured by adopting an unmanned aerial vehicle oblique photography technology and a laser scanning technology.
3. The visualization method for multiple data fusion of deep surrounding rock according to claim 1, wherein the regional ground stress distribution is measured by a hydraulic fracturing method.
4. The visualization method for deep surrounding rock multi-data fusion according to claim 1, wherein the surrounding rock disturbance zone and the evolution characteristics of the surrounding rock disturbance zone are measured by an ultrasonic detection device.
5. The visualization method for deep surrounding rock multi-data fusion according to claim 1, wherein the stress field inversion is specifically as follows:
and carrying out stress field inversion by means of regressive side pressure coefficients based on the regional ground stress distribution, calculating to obtain maximum horizontal main stress and minimum horizontal main stress, and simulating the regional ground stress field.
6. The visualization method for multiple data fusion of deep surrounding rock according to claim 5, wherein after performing stress field inversion and calculating to obtain a maximum horizontal principal stress and a minimum horizontal principal stress, the method further comprises performing three-dimensional conversion on the maximum horizontal principal stress and the minimum horizontal principal stress, wherein the calculation formula is as follows:
σ 1 ＝max(σ n ,σ l ,σ v )；
σ 1 ＝min(σ n ,σ l ,σ v )；
σ θ ＝3σ 1 -σ 3 ；
wherein S is H Is the maximum horizontal principal stress, S h For the minimum horizontal main stress, alpha is the included angle between the tunnel axis direction and the maximum horizontal main stress direction, sigma n To act on the horizontal positive stress of the tunnel cross section, sigma l Is the horizontal normal stress perpendicular to the tunnel axis, sigma 1 Is the maximum initial stress of the cross section of the tunnel, sigma 3 Sigma is the minimum initial stress of the cross section of the tunnel θ Is the maximum tangential stress.
7. The visualization method for deep surrounding rock multi-data fusion according to claim 1, wherein the specific steps of measuring and obtaining surrounding rock disturbance areas and evolution features of the surrounding rock disturbance areas are as follows: and transmitting high-frequency elastic pulse waves into the rock mass medium by an ultrasonic pulse transmitting source, so that the high-frequency elastic pulse waves propagate in the rock mass medium, and recording the fluctuation characteristics of the waves propagating in the rock mass by a receiving system.
8. The visualization method for multiple data fusion of deep surrounding rock according to claim 1, wherein the specific steps of determining the damaged surrounding rock area are as follows:
solving a space change function of the wave speed of the disturbance zone by utilizing a wave equation of elastic waves in a rock medium;
combining the quantitative expression of the H-B criterion parameter with the spatial variation function of the wave speed of the disturbance zone to establish the spatial variation function of the H-B criterion parameter;
and determining the spatial variation rule of rock mechanical parameters of the disturbance area by using the measured surrounding rock disturbance area and the evolution characteristics of the surrounding rock disturbance area, determining the surrounding rock damage condition and determining the surrounding rock damage area.
9. The visualization method for deep surrounding rock multi-data fusion according to claim 8, wherein the spatial variation function of the wave velocity of the disturbance zone is:
the wave equation of elastic waves in rock mass media is expressed as:
wherein u, v, w are displacements in the x, y, z directions of the coordinate axes,for the laplace operator, G is the shear modulus, λ is the pull Mei Jishu, e is the sign of the volume strain of the unit cell, e=ε x +∈ y +∈ z ；
And (5) solving an equation of the longitudinal wave velocity:
solving an equation of transverse wave velocity:
wherein V is P And V S The wave velocity of longitudinal waves and the wave velocity of transverse waves are respectively, E is an elastomer, the elastic modulus of the rock mass medium to be measured, ρ is the density of the rock mass medium, and γ is the Poisson's ratio of the rock mass medium.
10. The visualization method of claim 1, wherein the data fusion is 3D modeling based on the surrounding rock displacement field, the regional ground stress field, and the surrounding rock damage region.
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