CN117078878A - Method and system for establishing three-dimensional tunnel rock mass structure fine model - Google Patents
Method and system for establishing three-dimensional tunnel rock mass structure fine model Download PDFInfo
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Abstract
The invention discloses a method and a system for establishing a three-dimensional tunnel rock mass structure fine model, wherein the method comprises the following steps: a three-dimensional laser scanner is utilized to initially generate a three-dimensional point cloud model of the tunnel rock mass, noise and a separated rock mass structural surface are removed through preprocessing of the three-dimensional point cloud model of the tunnel rock mass by a point cloud processing model, and then external information of the three-dimensional point cloud model of the tunnel rock mass is identified by a machine vision model; and then, acquiring rock mass information in the tunnel rock mass through the in-hole detection model, combining the rock mass information into the three-dimensional point cloud model of the tunnel rock mass, and finally, realizing the fine establishment of the tunnel rock mass structural model through the fine treatment of the fine treatment model. The invention can display the structural detail information of the outside and the inside of the tunnel rock mass, realize the transparency of geological modeling and adapt to the high-precision requirement in tunnel construction.
Description
Technical Field
The invention belongs to the field of tunnel engineering, and particularly relates to a method and a system for establishing a three-dimensional tunnel rock mass structure fine model.
Background
Along with the gradual development of the digitizing technology, three-dimensional visual display aspects of tunnels, bridges and the like are affected, however, in engineering, including design and geological information of various buildings, most of the three-dimensional visual display aspects exist in a two-dimensional mode, the problems of inaccurate data and insufficient refinement are inevitably generated in the data acquisition process, and engineering loss and resource waste are extremely easy to cause. Compared with a two-dimensional image, the three-dimensional display can intuitively express the space state of the structure, particularly in a complex environment such as a tunnel, the three-dimensional geological model can omnidirectionally display the position relationship between rock bodies, and the three-dimensional display has important guiding significance for subsequent blasting design or excavation construction.
At present, a plurality of specific methods can obtain a three-dimensional structure model, but accuracy and convincing performance are generally lacking, and guidance on construction by means of scientific and technical means is a great trend in future tunnel engineering. In tunnel construction, uncontrollable factors are more, and the internal information of the rock mass in the tunnel is not fully known, so that the damage to engineering caused by the potential safety hazard of the rock mass is eliminated by a technical means. In recent years, laser technology has rapidly developed, wherein the three-dimensional laser scanning technology is gradually applied to underground engineering, but generated model data is too single, the outer part of a rock body is blurred, surface cracks are not obvious, the information in the rock body is not known from the past, and complex rock body conditions cannot be accurately displayed in one step. The existing method for establishing the refined model is less, has deviation, is easy to cause problems of misjudgment and the like in engineering guidance, increases construction difficulty, and is extremely easy to cause potential safety hazard.
In summary, a method or system for creating a fine model is proposed, which may have important value for construction.
Disclosure of Invention
The invention provides a method and a system for establishing a three-dimensional tunnel rock mass structure fine model, and aims to solve the problems that the existing method for establishing the fine model is less, has deviation and is easy to cause judgment errors in engineering guidance.
In order to achieve the above purpose, the specific technical scheme is as follows:
the invention provides a method for establishing a three-dimensional tunnel rock mass structure fine model, which comprises the following steps:
step one, obtaining a tunnel face point cloud image through a three-dimensional laser scanner, and preliminarily generating a three-dimensional point cloud model;
inputting the three-dimensional point cloud model obtained in the step (I) into a point cloud processing model, and converting point coordinate data of the three-dimensional point cloud model into a geodetic coordinate system by the point cloud processing model; removing noise in the three-dimensional point cloud model by using a neighborhood averaging method; performing dimension reduction treatment on the point cloud model, and rapidly separating the tunnel face from the structural face of the tunnel wall by using a DBSCAN algorithm;
inputting the structural surface of the tunnel face in the step (II) into a machine vision model, and identifying the structural surface of the tunnel face in a three-dimensional point cloud model by the machine vision model through a graph roll-up neural network GCN, and displaying an identification result in the three-dimensional point cloud model through a Revit platform to obtain a three-dimensional tunnel rock mass structural model for realizing filling of external information of a tunnel;
drilling by using drilling equipment through an in-hole detection model, observing the rule of the drilling process and drilling parameters, and obtaining lithology information in the rock mass to obtain the uniaxial compressive strength sigma of the rock; the coordinate information of the undetected area in the drilling process is expressed through an improved inverse distance weighted average interpolation method IDW, the uniaxial compressive strength sigma of the rock and the coordinate information of the undetected area are input into a Revit platform, and a three-dimensional tunnel rock mass structure model which utilizes colors to represent different rock mass strength information is obtained;
Obtaining a hole inner wall panoramic image and hole rock coordinate information through a hole imaging technology of a hole detection model;
step six, using a PBR technology to render the hole inner wall panoramic image and the rock mass information obtained in the step five into the three-dimensional tunnel rock mass structure model obtained in the step four, reflecting the internal layering interface of the rock mass according to the hole inner wall panoramic image, reflecting the change trend of the internal layering interface of the rock mass by combining the rock mass information, keeping the interface smooth, realizing the filling of the internal information of the tunnel, realizing the acquisition of the crack information in the hole by using an Ostu algorithm, realizing the filling of the internal information of the tunnel, and further refining the three-dimensional tunnel rock mass structure model;
inputting the three-dimensional tunnel rock mass structure model processed in the step (three) and the step (six) into a refinement processing model, and performing refinement reconstruction of the three-dimensional tunnel rock mass structure model by using an improved Delaunay triangulation algorithm to generate a three-dimensional tunnel rock mass structure refinement model containing information of the outside and the inside of the rock mass in the tunnel.
Further, the step of the DBSCAN algorithm in the step (two) is as follows:
(1) Firstly, dividing a three-dimensional point cloud model into cube grid areas, wherein each cube grid comprises at least 50 point cloud data;
(2) Generating a small cube with one half side length of the cube grid in the cube grid area, moving the small cube from one corner in the cube grid area to traverse the cube grid area along the side at a fixed distance, and recording the quantity of point cloud data in the small cube each time until the whole cube grid area is traversed;
(3) Marking the position of the area with the largest amount of point cloud data in the small cube, amplifying the small cube but keeping the size of the points unchanged until the small cube is amplified to the same size as the cube grid area and replaces the cube grid area to serve as a new cube grid area; repeating the steps twice to four times, reducing the dimension of the three-dimensional point cloud model and reserving the characteristics as far as possible;
(4) In a three-dimensional point cloud model, selecting random scattered points between a tunnel face and a tunnel wall, including a closest point and a farthest point, solving a distance L between the selected random scattered points, and solving an average value as a field radius eps, wherein the specific formula is as follows:
wherein: l is the distance between random scattered points; x, y, z, x 0 、y 0 、z 0 Coordinates of two random scattered points; the field radius eps is denoted by R; l (L) 1 …L n Distance between different random scattered points; n is the number;
(5) Randomly selecting point cloud data in the new cube grid area in the step (3), wherein the average number of the point cloud data is used as the minimum point density minPts;
(6) The three-dimensional point cloud model is set to be 2 types, wherein one type is a tunnel face, and the other type is a tunnel wall; the point cloud data contained in the neighborhood radius eps of each point cloud data cannot be less than the minimum point density minPts, otherwise, the point cloud data are divided into another type, and therefore separation of the tunnel face and the structural face of the tunnel wall is achieved.
Further, the expression formula of the improved inverse distance weighted average interpolation method IDW in the step (four) is as follows:
wherein: g is the element estimation value of the point to be interpolated; d, d i The Euclidean distance between the point to be interpolated and the ith sample space position; n is the number of control points in the estimation and lambda is the distance parameter.
Further, the modified Delaunay triangulation algorithm in step (seven) comprises the following steps:
(1) Dividing the point cloud data into rectangular blocks with a plurality of sizes according to the areas of the tunnel face and the structural face of the tunnel wall obtained in the step (two);
(2) Selecting two point cloud data from part of edges in a point cloud data set in a rectangular block area, connecting to form an initial side length, taking the center of the initial side length as a circle center, and searching a point which is closest to the side length of the circle center in a radius and is perpendicular to the side length of the circle center as a third point to construct an initial triangle;
(3) Searching a point with the side length of the initial triangle as the side to be expanded and the closest vertical distance to the side length of the center of a circle in the radius by taking the side length of the initial triangle as the side to be expanded, generating the initial expanded triangle, always positioning the searching point at the outer side of the initial expanded triangle, and subsequently selecting the positions of the scattered points at the outer side of the initial expanded triangle to form the expanded triangle; the normal vector included angle of the extended triangle and the initial extended triangle is calculated, and the extended triangle with the largest included angle is selected as the final extended triangle;
(4) Continuously generating n extension triangles through the steps, and completing grid division in the area; and communicating the divided areas, so as to obtain the complete three-dimensional point cloud model.
Further, the divided area in the step (4) includes a boundary area, and the method for generating the smooth triangular mesh curved surface by the boundary area includes: selecting two points closest to the edge connected with the initial area, selecting two points closest to the edge connected with the initial area in the area connected with the initial area, generating a sphere through the selected four points, marking a center point, taking the intersection point of two connecting lines of the two points which are farthest away from each other in the four points as the center point, making vertical lines perpendicular to the two connecting lines from the center point to two ends, firstly contacting the points of the sphere as connecting points, connecting the two points selected in the area, generating a triangle, and finally connecting all the connecting points to generate a smooth triangular grid curved surface.
The invention further provides a system for establishing a three-dimensional tunnel rock mass structure fine model, which comprises a three-dimensional laser scanner, a point cloud processing model, a machine vision model, an in-hole detection model and a fine processing model, wherein the three-dimensional laser scanner, the point cloud processing model and the machine vision model are sequentially connected, and the machine vision model and the in-hole detection model are both connected with the fine processing model;
the three-dimensional laser scanner is used for acquiring point cloud data of the tunnel face and the tunnel wall;
the point cloud processing model is used for removing noise of point cloud data of the tunnel face and the tunnel wall and separating the tunnel face and the tunnel wall by using a DBSCAN algorithm;
the machine vision model is used for acquiring external information of the tunnel rock mass;
the in-hole detection model is used for acquiring information in the tunnel rock mass;
the refinement processing model is used for further refinement reconstruction of the three-dimensional tunnel rock mass structure model.
The invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the method for establishing the three-dimensional tunnel rock mass structure fine model when executing the computer program.
The invention also proposes a computer-readable storage medium, on which a computer program is stored, which when being executed by a processor implements a method for building a fine model of a three-dimensional tunnel rock mass structure.
The invention has the beneficial effects that:
by establishing a three-dimensional tunnel rock mass structure fine model, a large amount of practical reference information can be provided, the relation of the rock mass structure is displayed in an all-round manner, and the method has important guiding significance for follow-up construction such as blasting;
the rock mass information is obtained by three-dimensional laser scanning instead of the traditional shooting mode, so that the defects of unclear imaging, low measurement efficiency and precision, poor measurement safety and the like caused by darker environment are avoided;
the method has the advantages that the method can provide guidance for subsequent field blasting or technical schemes to a great extent for the establishment of the tunnel three-dimensional model, avoids influence caused by larger errors, and protects life and property losses;
the method has the advantages that the detailed information of the inside and the outside of the tunnel rock body can be displayed by combining the three-dimensional laser scanner, the point cloud processing model, the machine vision model, the in-hole detection model and the refinement processing model, so that the safety in tunnel construction and the control on the construction process are improved.
Drawings
FIG. 1 is a general flow chart for establishing a fine model of a three-dimensional tunnel rock mass structure;
FIG. 2 is a flow chart of a process model using a three-dimensional laser scanner and a point cloud;
FIG. 3 is a flow chart of machine vision model steps;
FIG. 4 is a flow chart of the use of drilling equipment in an in-hole detection model;
FIG. 5 is a flow chart of an in-bore imaging technique used in an in-bore detection model;
FIG. 6 is a representation of an in-hole image;
FIG. 7 is a flowchart of the steps of refining a process model;
FIG. 8 is a graph of the effect of in-hole image fracture treatment;
the drawing is marked:
1. the three-dimensional laser scanner comprises a three-dimensional laser scanner body, a point cloud processing model, a machine vision model, an in-hole detection model and a fine processing model.
Detailed Description
The present invention will be further described in detail with reference to the drawings and the detailed description, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
The invention relates to a system for establishing a fine model of a three-dimensional tunnel rock mass structure, which is shown in fig. 1, and specifically comprises a three-dimensional laser scanner 1, a point cloud processing model 2, a machine vision model 3, an in-hole detection model 4 and a fine processing model 5, wherein the three-dimensional laser scanner 1, the point cloud processing model 2 and the machine vision model 3 are sequentially connected, and the machine vision model 3 and the in-hole detection model 4 are both connected with the fine processing model 5; based on a three-dimensional point cloud model acquired by a three-dimensional laser scanner 1, optimizing the three-dimensional point cloud model through a point cloud processing model 2, adding data or images acquired by a machine vision model 3 and an in-hole detection model 4 into the three-dimensional point cloud model, finally carrying out final processing on the three-dimensional point cloud model by utilizing a refinement processing model 5, and realizing refinement construction of a three-dimensional tunnel rock mass structure model through the three-dimensional laser scanner 1, the point cloud processing model 2, the machine vision model 3, the in-hole detection model 4 and the refinement processing model 5 by means of the combined action of a Revit platform and a PBR technology.
The three-dimensional laser scanner 1 is used for acquiring point cloud data, the three-dimensional laser scanner 1 is existing equipment, a specific principle part comprises a transmitting diode and a receiving diode, a time difference t between laser emission and laser receiving is calculated, the distance L between a scanning head and a measured object can be calculated by utilizing a light speed c according to a calculation formula, and meanwhile, a precise clock control encoder synchronously measures a transverse scanning angle observation value a and a longitudinal scanning angle observation value b of each laser pulse;
the point cloud processing model 2 is used for preprocessing a three-dimensional point cloud image, removing noise of point cloud data of a tunnel face and a tunnel wall and separating the tunnel face and the tunnel wall by using a DBSCAN algorithm. The noise points which are irrelevant with dust emission and mechanical equipment and the structural plane are unavoidably existed in the obtained initial point cloud data, the analysis and the processing of the point cloud data are not facilitated, the noise of the point cloud is removed by using a neighborhood averaging method, according to the characteristics of image noise, namely that the gray value of the image noise is obviously different from the gray value of the adjacent pixels, the gray value of the noise point is approximate to the adjacent elements by using a neighborhood averaging method, and the purpose of noise filtering is achieved. And then, performing dimension reduction treatment on the point cloud, and then, rapidly separating the tunnel face from the structural face of the tunnel wall by using a DBSCAN algorithm.
The machine vision model 3 acquires external information of a tunnel rock body, identifies tunnel face images through a graph convolution neural network, classifies the tunnel face images according to the integrity, cracks and defects of the rock body, and provides effective references for subsequent blasting by utilizing color distinction. Graph convolutional neural networks (GCNs) can implement multidimensional data processing, whose processing objects are graph data, as compared to conventional convolutional neural networks.
The Revit platform is software for realizing BIM technology, can create a parameterized three-dimensional model, is used for three-dimensional design and can generate drawings, and contains various data and construction information; the Revit platform is provided with rich model output interfaces, lossless interaction with other software can be realized after the model is built, a refined model is built by combining on-site investigation drilling data through a space model modeling method carried by the Revit platform, and drilling data nodes are input into the Revit platform based on Dynamo plugins, so that batch automatic processing is realized.
The in-hole detection model 4 is used for acquiring information inside the tunnel rock mass; the method comprises the steps of obtaining a three-dimensional tunnel rock mass structure model which represents different rock mass intensity information by colors by using drilling equipment, and obtaining a hole inner wall panoramic image and hole inner rock mass coordinate information by using a hole inner shooting method. The drilling equipment specifically comprises a drill bit, a drill rod, a sensor, an engine and the like, and lithology information in a rock body is obtained and uploaded to a computer (a Revit platform) by observing the rule of a drilling process and drilling parameters. The information in the rock mass, such as the rock mass type, the rock mass grade and the like, is determined by acquiring data of the pressure, the rotation speed and the like of the drill bit by installing a sensor on the equipment. The sensor comprises a vibration sensor, a current sensor and a displacement sensor, and the types of the rocks are analyzed by vibration signals, current signals and rock strength generated during coring of different rocks.
The in-hole imaging part in the in-hole detection model 4 is realized by an in-hole imaging technology, the borehole imaging technology comprises a computer, a panoramic imaging probe, a wire, a push rod, a bracket and the like, the computer mainly comprises a software part for detecting in-hole images in real time, displaying specific images and data through a display screen, storing the images and the data through the computer, externally connecting storage equipment to derive data, after leaving the scene, deriving video recording through the software part, and operating in a player in a single frame or continuous play mode, wherein the acquired in-hole image data can be divided into a vertical view angle, a horizontal view angle and an in-hole view angle, and also displaying all parts of an in-hole panoramic expansion image through a segmentation length, such as 3 m of each section and multi-section display; the panoramic camera probe is a key part of an in-hole imaging technology, determines imaging definition and depth information, mainly comprises a searchlight, a miniature camera, a compass and the like, wherein the searchlight mainly illuminates the inside of a hole, improves imaging definition, can acquire a 360-degree panoramic image in the hole, can modify a specific area for acquiring the image and is realized through a software part; the compass mainly provides specific pointer directions and drilling depths, and acquired data are checked through a software part. When in use, the surface of the panoramic camera is required to be cleaned. The panoramic image of the inner wall of the hole can be formed through an in-hole imaging technology, the drilling depth, the fracture trend, the dip angle and the like are observed, the fracture information in the panoramic image of the inner wall of the hole is obtained through an Ostu algorithm, and the geological structure inside the rock mass can be analyzed.
The PBR technology is a rendering technology based on physical rules, a realistic shadow illumination model and model parameter values are used for completing the real simulation of real things, the core of the PBR technology is natural and real, and the PBR technology has the advantages that the control of model materials and illumination can be completely adjusted according to reference standards by oneself, and various comprehensive-expression gloss effects of mapping can be generated by formulating types of the materials; the PBR-based mapping can be conveniently and quickly manufactured through substance painter texture mapping software. The three-dimensional model detail is finer by leading in the panoramic image in the hole and setting parameters such as material mapping precision and the like.
The refinement processing model 5 is used for further refinement reconstruction of a three-dimensional tunnel rock mass structure model, a three-dimensional point cloud triangular mesh reconstruction technology is adopted, a complete mesh model is reconstructed through point cloud data, the surface micro defects can be reconstructed, an improved Delaunay triangulation algorithm is utilized to display the surface details in the model, the Delaunay triangulation algorithm is a typical TIN algorithm and can be divided into a divide-and-congregation method, a point-by-point insertion method and a triangular mesh growth method, the divide-and-congregation method divides the point cloud data into a plurality of sub-data, the sub-data are subjected to independent triangulation respectively, and then the combination processing is carried out; the dividing and combining method shortens the time for generating the triangular network and reduces the operation complexity through the point cloud data dividing and processing, and improves the construction efficiency of the triangular network; the point-by-point insertion method is a process of generating and optimizing a triangle net by taking an insertion point as a center according to the position of a new insertion point in the field range and continuously expanding the scale of the triangle net. And (3) carrying out fine reconstruction on the three-dimensional point cloud model by combining the processing result of the machine vision model 3.
As shown in FIG. 1, the method for establishing the fine model of the three-dimensional tunnel rock mass structure comprises the following working procedures:
step one, obtaining a tunnel face point cloud image through a three-dimensional laser scanner 1, and preliminarily generating a three-dimensional point cloud model;
by using the three-dimensional laser scanner 1, the three-dimensional coordinate of the target object can be determined from one scan by means of the emitted laser without other light sources, the processing time is shorter than that of other measuring tools, and the three-dimensional point cloud data can be directly derived without additional algorithms. The scanner is enabled to acquire complete tunnel face point cloud images as much as possible, and incomplete areas are supplemented through an algorithm subsequently. When a tunnel is scanned to obtain a point cloud image, a tunnel face area, a tunnel supporting structure and the like are generally included, and the two parts are used as the basis of subsequent data analysis.
Step two, processing the three-dimensional point cloud model through a point cloud processing model 2, preliminarily generating a three-dimensional point cloud model through a three-dimensional laser scanner 1 by using the tunnel face point cloud image obtained in the step one, and converting the obtained three-dimensional point cloud model point coordinate data into a geodetic coordinate system; removing noise in the three-dimensional point cloud model by using a neighborhood averaging method; then, performing dimension reduction treatment on the point cloud, and then rapidly separating the tunnel face from the structural face of the tunnel wall by using a DBSCAN algorithm;
As shown in fig. 2, the coordinates of the scanning point can be automatically determined by three-dimensional laser scanning, the scanning center is taken as the origin of coordinates, the x coordinate is on the transverse scanning plane, the y coordinate is also on the transverse scanning plane and perpendicular to the x axis, the z coordinate is in the vertical direction, and the calculation formula is as follows:
wherein: l is the distance between the scanning head and the object to be measured, c is the speed of light, and t is the time difference between laser emission and laser reception.
Wherein: x, y and z are coordinates, L is the distance between the scanning head and the object to be measured, a is the observation value of the transverse scanning angle, and b is the observation value of the longitudinal scanning angle.
The coordinate data of the target point is converted into a geodetic coordinate system, and the following steps are:
wherein: x is x n 、y n 、z n Coordinate of the geodetic coordinate system after conversion of each point, x 0 、y 0 、z 0 The coordinates of the center point in the geodetic coordinate system are scanned.
The method is characterized in that a neighborhood averaging method is used for removing noise of point cloud, according to the characteristics of image noise, namely that the gray level value of the image noise is obviously different from the gray level value of the adjacent pixels, the gray level value of a noise point is approximate to the adjacent elements through the neighborhood averaging method, so that the purpose of noise filtering is achieved, and the specific formula is as follows:
wherein: i, j are pixel points of the image, and M is the number of adjacent pixels.
The method comprises the steps of converting a plurality of point clouds into the same space coordinate system by adopting point cloud registration, splicing the point clouds, and using an iterative closest point method, in particular to a matching algorithm, mainly converting two local point clouds into the same coordinate system by line segment transformation such as rotation and translation, firstly finding the closest point from each point in one point cloud to the other point cloud, then calculating translation and rotation matrixes with minimum average cost required by converting the points into the closest point, performing linear transformation to obtain new point clouds, continuously repeating the steps until the average distance between the closest points between the point clouds meets the minimum error, determining the size of the error according to specific engineering requirements, and realizing by using geomic studio point cloud processing software.
The three-dimensional point cloud model is required to be classified into the tunnel face and the tunnel wall, so that subsequent point cloud processing is facilitated, after dimension reduction processing is performed on the three-dimensional point cloud model to reduce characteristic dimensions, a DBSCAN algorithm is used for separating the tunnel face from the structural face of the tunnel wall, and the dimension reduction processing is performed to enable the DBSCAN algorithm to be higher and accurate in classification efficiency. The DBSCAN algorithm is a clustering method based on density. The DBSCAN is a clustering method based on density with noise, is mainly used for clustering out denser points, is suitable for non-planar distribution and complex fracture distribution of an excavation surface, is not easily affected by noise by utilizing the distribution difference clustering extraction of a structural surface and a non-structural surface in a local target domain, can find out a set of various shapes in two-dimensional or three-dimensional point cloud data, and is suitable for classifying tunnel face and tunnel wall, and the method comprises the following specific steps:
(1) Firstly, dividing a three-dimensional point cloud model into cube grid areas, wherein each cube grid comprises at least 50 point cloud data;
(2) Generating a small cube with one half of the cube grid in the cube grid area, moving from one corner in the cube grid to traverse the cube grid along the edge at a fixed distance, and recording the number of point cloud data in the small cube each time until the whole cube grid is traversed;
(3) Marking the position of the area with the largest amount of point cloud data in the small cube, amplifying the small cube but keeping the size of the point unchanged until the small cube is amplified to the same size as the cube grid where the small cube is positioned and replacing the small cube as a new cube grid; repeating the steps to reduce the dimension of the three-dimensional point cloud model and keep the characteristics as much as possible;
(4) In a three-dimensional point cloud model, selecting random scattered points between a tunnel face and a tunnel wall, including a closest point and a farthest point, calculating the distance between the points by the following formula, and calculating an average value as a field radius eps, wherein the specific formula is as follows:
wherein: l is the distance between random scattered points; x, y and z are coordinates of points; the radius of the field (eps) is denoted by R.
(5) By randomly selecting the point cloud data in the plurality of new cube grid areas, the average number of the point cloud data is taken as the minimum point density minPts.
(6) The classification is set as 2 types, one type is a tunnel face, and the other type is a tunnel wall; for each point cloud data in one class, the point cloud data contained in the field radius eps cannot be less than the minimum point density minPts, otherwise, the point cloud data are divided into another class, so that the separation of the tunnel face and the structural face of the tunnel wall is realized.
Specifically, a point p is selected from the three-dimensional point cloud model i I epsilon 1-n, giving the neighborhood radius eps and the minimum point density minPts obtained in the steps, and if the point meets the condition that the neighborhood radius eps has the minimum number of adjacent points of the minimum point density minPts, moving the circle center to the next point p 1 If the point p 1 If the above condition is not satisfied, the point p is reselected 2 Reclustering according to the set neighborhood radius eps and the minimum point density minPts, and traversing the points p in all the three-dimensional point cloud models n And the three-dimensional point cloud model is divided into two types of tunnel face and tunnel wall, so that the separation of the tunnel face and the structural face of the tunnel wall is completed.
Inputting the structural surface of the tunnel face in the step (two) to a machine vision model 3, wherein the machine vision model 3 utilizes a graph convolutional neural network GCN to identify the structural surface of the tunnel face in the three-dimensional point cloud model, such as a tunnel face crack, a rock mass structural surface and the like; the Revit platform displays the identification result in the three-dimensional point cloud model to obtain a three-dimensional tunnel rock mass structure model for realizing filling of external information of the tunnel;
Compared with a common convolutional neural network, the GCN can realize multidimensional data processing, and the processing object is image data; displaying the identification result on the three-dimensional model;
as shown in fig. 3, through a detection network based on a PointNet network model of a graph roll-up neural network GCN for a three-dimensional point cloud model, according to the requirements of the PointNet network model on a training sample data set, storing data into txt files in a format of (x, y, z, r, g, b) according to categories, wherein x, y, z are three-dimensional coordinates of tunnel point cloud, and r, g, b are color information of sample point cloud data; the PointNet network model is developed based on Microsoft Visual Vtudio 2022, and is based on a pytorch framework by adopting the Python language;
the specific flow steps of the PointNet network model are as follows:
(1) Firstly, inputting a frame of point cloud set, representing as an n multiplied by 3 two-dimensional tensor, wherein n represents the number of the point cloud, and 3 is three coordinate axes of x, y and z;
(2) Spatial alignment: realizing space alignment through a 3X 3 space conversion network T-Net, and rotating an angle more favorable for classification and segmentation;
(3) Feature alignment: after feature extraction is carried out on cloud data of each point through multiple times of MLP, the features are aligned by using T-Net;
(4) Performing a max pooling operation on each dimension of the feature to obtain a final global feature;
(5) Predicting the final classification score of the global feature through the MLP, connecting the global feature with the local feature of each point cloud learned before in series, and obtaining the classification result of each data point through the MLP.
In order to solve the problem of unbalanced sample class distribution in the process of training the PointNet network model, 3 times of sampling are firstly carried out, and the sampling ratios are respectively 0.5, 0.25 and 0.125. The 3 sampled data and the non-sampled data are respectively used as training data sets and applied to PointNet tunnel element segmentation training, training results are evaluated according to F1 values, wherein the F1 values are harmonic weighted averages of accuracy rate and recall rate, and the specific formulas are as follows:
wherein: p is the accuracy rate, which indicates the ratio of the number of correctly classified samples of a certain class to the total number of samples classified into the class; r is recall, which is the ratio of the number of correctly classified samples to the actual number of such samples.
Presenting the result of the PointNet network model identifying the tunnel face part in the three-dimensional point cloud model on the three-dimensional point cloud model to realize the filling of external information of the tunnel, such as tunnel face cracks, rock mass structural faces and the like;
drilling by using drilling equipment through the in-hole detection model 4, observing the rule of the drilling process and drilling parameters, and obtaining lithology information in the rock mass to obtain the uniaxial compressive strength sigma of the rock; the coordinate information of the undetected area in the drilling process is expressed through an improved inverse distance weighted average interpolation method IDW, the uniaxial compressive strength sigma of the rock and the coordinate information of the undetected area are input into a Revit platform, and a three-dimensional tunnel rock mass structure model which utilizes colors to represent different rock mass strength information is obtained;
The drilling equipment is existing equipment and at least comprises a drill bit, a drill rod, a sensor, an engine and the like, and lithology information in a rock body is obtained by observing the rule of a drilling process and drilling parameters. Converting the drilling data into a three-dimensional model by using modeling software, and refining the model according to a spatial interpolation method;
as shown in fig. 4, the specific steps are as follows:
(1) Firstly, connecting all parts of an instrument, selecting proper drill bits according to different lithology, configuring fuel required by an engine according to a certain proportion, and keeping a pressure water bucket or a water pump to keep water flow out all the time in the drilling process according to different environments so as to cool the drill bits and wash dust and broken stone in the holes;
(2) Marking drilling points on the tunnel face and the tunnel wall according to preset point positions, marking by using cross symbols, and preventing the deviation of the drilling direction caused by too large angle change during drilling, so that the direction can be corrected in time by observing the relative positions of the cross symbols; the point location is determined by a field blasting scheme;
(3) When the rock body is inclined small pits, drilling equipment is perpendicular to cross symbols to drill holes, the driller controls the direction, the speed is kept at a constant speed, and meanwhile, quantitative water flow is kept to cool the drill bit;
(4) As the drill bit advances, information such as bit pressure, rotational speed, etc. is acquired by vibration sensors, current sensors, etc., and the relationship of the above parameters to the rock mass is analyzed.
(5) Establishing a rock mass type dividing standard, and using data such as drill bit pressure, rotation speed, drill rod torque, drilling speed and the like to obtain the following specific formulas:
wherein: sigma is the uniaxial compressive strength of the rock and MPa; f is the axial pressure on the drill bit, N; v is the rotation speed, r/min; d is the diameter of the drill bit, m; alpha and beta are influence coefficients.
Carrying out internal geologic modeling through the obtained rock uniaxial compressive strength, and displaying the internal geology according to different rock uniaxial compressive strengths;
(6) According to the obtained rock uniaxial compressive strength of different strata, carrying out internal geologic modeling of a three-dimensional tunnel rock mass structure model by using a Revit platform, and marking a space point set through different colors according to rock mass types divided by the rock mass strength;
extracting the spatial information of the parameter information of the uniaxial compressive strength of the rock obtained in the step (four), such as the set drilling number, the x and y coordinates of the drilling when the uniaxial compressive strength of the rock changes, and the buried depth z of the top surface of the stratum, establishing the spatial coordinates, and editing the spatial coordinates into a form of a table; inputting data of a space coordinate table into Dynamo, and corresponding x and y data of the coordinate table to coordinate z data of each stratum to create a space point set; marking a space point set by different colors according to rock types divided by rock strength;
(7) The rock mass intensity information of the area which is not involved in the drilling process in the step (four) is represented by colors through an improved inverse distance weighted average interpolation method (IDW);
the theoretical assumption of spatial interpolation is that the similarity of the spatial position distance determination point values is that as the distance increases, the probability of similarity of the estimated values is smaller, the inverse distance weighted square Interpolation (IDW) has good universality and smaller errors, and when calculating the value of an interpolation point, the value of the estimated point is fitted by using the linear weighting of a plurality of adjacent points according to the principle that the weight value is larger when the distance is closer, wherein the specific formula is as follows:
wherein: g is the element estimation value of the point to be interpolated; g i An estimated value for control point i; d, d i The Euclidean distance between the point to be interpolated and the ith sample space position; p is the power of the distance, typically 2; n is the number of control points in the estimation.
In most cases, the drilling distribution is uneven, and at the moment, the accuracy of the inverse distance weighted square Interpolation (IDW) is difficult to ensure, so that the distance parameter lambda is introduced, the adaptability of the inverse distance weighted square Interpolation (IDW) is improved, and when the distance is too small or 0, a certain value of the distance parameter lambda is given, so that the situation of infinity of an estimated value is prevented; when the distance among a plurality of points is overlarge, and the distance parameter lambda is given to be negative to adjust the size of the estimated value, so that the presentation transition of the drilling information in the model is smooth, and the specific formula is as follows:
Wherein: g is the element estimation value of the point to be interpolated; d, d i The Euclidean distance between the point to be interpolated and the ith sample space position; n is the number of control points in the estimation and lambda is the distance parameter.
According to the calculated estimated value and the similarity of the coordinate information of the points in the space, adding the coordinate information of a new point between the coordinate information of adjacent points by using the estimated value, and expressing the undetected area in the internal geology by using the coordinate information of the new point so as to achieve the effect of refinement;
inputting three-dimensional coordinates of x, y and z and three-dimensional coordinates obtained by an improved inverse distance weighted square interpolation method into an Excel table by using a tool for creating a topographic surface of the Revit, converting the table into a csv format, importing the csv format into the Revit, generating a stratigraphic curved surface, and representing the rock type by colors.
Step five, obtaining a panoramic image of the inner wall of the hole and the coordinate information of the rock mass in the hole through an in-hole imaging technology of the in-hole detection model 4; drilling in the step (four), forming an in-hole image by using equipment through an in-hole imaging technology, photographing the drilled hole, observing Kong Naqing conditions in real time, acquiring drilling data such as spatial positions (x, y and z) of the hole, drilling depth and the like in the process, and preparing for the step (six);
The drilling imaging technology is the prior art, equipment is needed to be used and comprises a computer, a panoramic camera probe, a wire, a push rod, a support and the like, the equipment is the prior equipment, wherein the computer mainly comprises a software part for detecting images in holes in real time, specific images and data are displayed through a display screen, the images and the data are stored through the computer, data can be exported by external storage equipment, after leaving a site, video recording can be exported through the software part, and single-frame or continuous playing can be realized in a player.
As shown in fig. 5, a panoramic image of the inner wall of the hole can be formed by an in-hole imaging technology, and further the drilling depth, the fracture trend, the dip angle and the like are observed, and the geological structure in the rock mass can be analyzed, wherein the specific operation steps are as follows:
(1) Firstly, connecting all equipment through a wire, fixing the connecting part through a buckle, then connecting a push rod with a certain length to a panoramic camera probe according to the detected depth in a hole, keeping the surface of the panoramic camera probe clean, and coating a waterproof reagent on the connecting part of the panoramic camera probe and the push rod;
(2) Turning on a computer, entering a software part, checking whether each part is normal, inputting each parameter into the software by drawing up an experimental scheme, adjusting the brightness of a searchlight in a panoramic camera probe, checking the range and definition of an acquired image, and improving the effect by adjusting the brightness if the display effect is poor;
(3) Impurities in the cleaning hole are kept clean through continuous water injection in the hole, so that the acquired image is clearer;
(4) The panoramic camera probe is pushed into the hole at a constant speed through the push rod, the speed cannot be too high, the imaging is kept clear and continuous, pulleys can be arranged in front of and behind the panoramic camera probe, and the panoramic camera probe is prevented from touching the rock body in the hole to generate scratches;
(5) Along with the gradual penetration of the panoramic camera probe, the computer processes the acquired images in real time, maintains the continuity of the images, clicks on the computer to stop after detecting a certain depth, and finishes the once complete in-hole detection process;
(6) The images in other holes are detected for many times through the steps, data are recorded into a computer, the image definition is continuously ensured in the process, as shown in fig. 6, after the detection is finished, each device is restored to the initial state, and the instrument is kept clean.
In combination with the rock mass information acquired by the drilling device, the rock mass information includes: the drilling depth, the fracture trend and the inclination angle enrich the internal information display of the three-dimensional tunnel rock mass structural model, and the internal information of the rock mass can be clearly received through the model by applying the intra-hole image to the three-dimensional tunnel rock mass structural model, so as to guide the blasting scheme, control the blasting explosive dosage and the like.
Step six, rendering the hole inner wall panoramic image and the rock mass information obtained in the step five into the three-dimensional tunnel rock mass structure model obtained in the step four by using a PBR technology, reflecting a rock mass internal layering interface according to the hole inner wall panoramic image, reflecting the change trend of the rock mass internal layering interface by combining the rock mass information, keeping the interface smooth, realizing the acquisition of hole inner crack information in the hole inner wall panoramic image by using an Otsu algorithm, realizing the filling of the tunnel inner information, and further refining the three-dimensional tunnel rock mass structure model;
according to the display of the hole images, the layered structure of the interior of the rock mass can be seen, the construction of the layered interface of the interior of the rock mass is completed by adopting a regional method, the part with the same lithology in the hole images is extracted, the coordinates of the target region are extracted, the layered interface of the interior of the rock mass is generated by utilizing the coordinates, the layered interface is displayed on a three-dimensional tunnel rock mass structural model in a section mode, the generated interface is optimally adjusted by combining rock mass information obtained by drilling equipment, the construction of the interface is completed by adopting methods of adding auxiliary points, auxiliary line generation surfaces and the like, auxiliary line generation auxiliary surfaces are sequentially selected according to a certain direction by utilizing the principle of a plurality of line generation surfaces, the auxiliary surfaces primarily reflect the change trend of the layered interface of the interior of the rock mass, the relations among the rock masses in all the hole images are compared, the appropriate coordinate generation structural surfaces are selected, fine adjustment or cutting is carried out without conforming to the actual conditions, and the smoothness and the attractiveness of the interface are maintained;
Image segmentation of an intra-hole image crack part in a hole inner wall panoramic image is realized through an Ostu algorithm, the crack length and width can be calculated by utilizing an orthogonal skeleton line method, the Otsu algorithm is also called a maximum inter-class variance method, the background segmentation is mainly carried out by adjusting a threshold value, the method for determining the optimal threshold value is to make the inter-class variance maximum, and the image is divided into a background part and a foreground part according to the gray level characteristic of the image; the orthogonal skeleton line method mainly determines the width of the fracture by calculating the skeleton line of the fracture, and can calculate the width by means of the medial axis transformation and kd-tree algorithm, and the specific effect is shown in fig. 8: fig. 8 (1) is an original view of the panoramic image of the inner wall of the hole, fig. 8 (2) is an effect view after the Otsu algorithm processing, and fig. 8 (3) is an effect view after the orthogonal skeleton line method processing.
Therefore, the filling of the information in the tunnel is realized, and the rock mass structure model of the three-dimensional tunnel is further refined.
Inputting the three-dimensional tunnel rock mass structure model processed in the step (three) and the step (six) into a refinement processing model 5, and performing refinement reconstruction of the three-dimensional tunnel rock mass structure model by using an improved Delaunay triangulation algorithm to generate a three-dimensional tunnel rock mass structure refinement model containing information of the outside and the inside of the rock mass in the tunnel;
The refinement processing model 5 utilizes a three-dimensional point cloud triangular mesh reconstruction technology, in particular an improved Delaunay triangulation algorithm to realize the refinement reconstruction of the three-dimensional point cloud model, extracts the advantages of a divide-and-congregation method, a point-by-point insertion method and a triangular mesh growth method, divides regions according to the shape of a rock mass structural plane, selects points at the edge of the regions to construct triangles through an algorithm, and smoothens a triangular mesh curved surface through a sphere constructing method, so that compared with a conventional method, the efficiency is improved, the precision is increased, and the method is suitable for the background of the invention, and the specific steps are as follows, as shown in fig. 7:
(1) Firstly, identifying information such as tunnel face cracks and rock mass structural surfaces obtained by a three-dimensional point cloud model according to a machine vision model 3, dividing a point cloud data area, and dividing the point cloud data into rectangular blocks with a plurality of sizes according to areas of the tunnel face cracks and the rock mass structural surfaces;
(2) Selecting two point cloud data from part of edges of a point cloud data set in the area, connecting to form an initial side length, taking the center of the initial side length as a circle center, and searching a point with the radius closest to the vertical distance of the side length of the circle center as a third point to construct an initial triangle;
(3) Searching a point with the side length of the initial triangle as the side to be expanded and the closest vertical distance to the side length of the center of a circle in the radius by taking the side length of the initial triangle as the side to be expanded, generating the initial expanded triangle, always positioning the searching point at the outer side of the initial expanded triangle, and subsequently selecting the positions of the scattered points at the outer side of the initial expanded triangle to form the expanded triangle; the normal vector included angle of the extended triangle and the initial extended triangle is calculated, and the extended triangle with the largest included angle is selected as the final extended triangle;
(4) Continuously generating an expansion triangle through the steps, and completing grid division in the area; the divided areas are communicated, so that a complete three-dimensional point cloud model is obtained, the situation that triangular grids are discontinuous can occur to boundary areas, therefore, in order to improve the smoothness of the three-dimensional point cloud model when the areas are connected, two points closest to the edge connected with the initial area are selected, two points closest to the edge connected with the initial area in the area connected with the initial area are selected, a sphere is generated through the selected four points, then a central point is marked, the intersection point of connecting lines of two points with the two points farthest from each other in the four points is taken as the central point, vertical lines are perpendicular to the two connecting lines from the central point to two ends, the points firstly contacting the sphere are taken as connecting points, the two points selected in the connecting area are generated, triangles are finally connected, and a smooth triangular grid curved surface is generated.
Under the combined action of the comprehensive three-dimensional laser scanner 1, the point cloud processing model 2, the machine vision model 3, the in-hole detection model 4 and the refinement processing model 5, a three-dimensional tunnel rock mass structure refinement model containing external and internal information of the rock mass in the tunnel is generated.
In blasting engineering, the model can be utilized to clearly reveal the contact relation between rock masses, further identify various potential hidden hazards deeply, provide critical technical guidance for engineering construction, obviously discover the geological change conditions of the deep part of the tunnel rock mass, such as cracks, holes and the like, easily cause safety threat by observing the internal information of the tunnel rock mass, integrate the rock mass type in the tunnel and the structural information outside the tunnel, adjust the blasting technical scheme, select the scheme of the most suitable site and achieve the optimal blasting effect. By establishing the three-dimensional tunnel rock mass structure refined model, the high-precision requirement in the construction process can be made up, the tunnel exploration work becomes transparent, and the geological complexity of the exterior and the interior of the tunnel rock mass is clearly shown. The model is utilized to convert the unknowns in the tunnel engineering into controllable, visible and transparent, and the model can accurately control and ensure the safety in the construction process, thereby having important significance for the development of the tunnel engineering.
An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of a method for establishing a fine model of a three-dimensional tunnel rock mass structure when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of fine model building of a three-dimensional tunnel rock mass structure.
Claims (8)
1. A method for establishing a three-dimensional tunnel rock mass structure fine model is characterized by comprising the following steps: the method comprises the following steps:
step one, acquiring a tunnel face point cloud image through a three-dimensional laser scanner (1), and preliminarily generating a three-dimensional point cloud model;
inputting the three-dimensional point cloud model obtained in the step (one) into a point cloud processing model (2), and converting point coordinate data of the three-dimensional point cloud model into a geodetic coordinate system by the point cloud processing model (2); removing noise in the three-dimensional point cloud model by using a neighborhood averaging method; performing dimension reduction treatment on the point cloud model, and rapidly separating the tunnel face from the structural face of the tunnel wall by using a DBSCAN algorithm;
inputting the structural surface of the tunnel face in the step (two) into a machine vision model (3), and identifying the structural surface of the tunnel face in the three-dimensional point cloud model by the machine vision model (3) through a graph roll-up neural network (GCN), and displaying an identification result in the three-dimensional point cloud model through a Revit platform to obtain a three-dimensional tunnel rock mass structural model for realizing filling of external information of a tunnel;
Drilling by using drilling equipment through an in-hole detection model (4), observing the rule of the drilling process and drilling parameters, and obtaining lithology information in the rock mass to obtain the uniaxial compressive strength sigma of the rock; the coordinate information of the undetected area in the drilling process is expressed through an improved inverse distance weighted average interpolation method IDW, the uniaxial compressive strength sigma of the rock and the coordinate information of the undetected area are input into a Revit platform, and a three-dimensional tunnel rock mass structure model which utilizes colors to represent different rock mass strength information is obtained;
step five, obtaining a panoramic image of the inner wall of the hole and the coordinate information of the rock mass in the hole through an in-hole imaging technology of an in-hole detection model (4);
step six, rendering the hole inner wall panoramic image and the rock mass information obtained in the step five into the three-dimensional tunnel rock mass structure model obtained in the step four by using a PBR technology, reflecting the internal layering interface of the rock mass according to the hole inner wall panoramic image, reflecting the change trend of the internal layering interface of the rock mass by combining the rock mass information, keeping the interface smooth, obtaining the hole crack information by using an Ostu algorithm, filling the tunnel inner information, and further refining the three-dimensional tunnel rock mass structure model;
Inputting the three-dimensional tunnel rock mass structure model processed in the step (three) and the step (six) into a refinement processing model (5), and performing refinement reconstruction of the three-dimensional tunnel rock mass structure model by using an improved Delaunay triangulation algorithm to generate a three-dimensional tunnel rock mass structure refinement model containing information of the outside and the inside of the rock mass in the tunnel.
2. The method for building the fine model of the three-dimensional tunnel rock mass structure according to claim 1, wherein the method comprises the following steps: the DBSCAN algorithm in the step (II) comprises the following steps:
(1) Firstly, dividing a three-dimensional point cloud model into cube grid areas, wherein each cube grid comprises at least 50 point cloud data;
(2) Generating a small cube with one half side length of the cube grid in the cube grid area, moving the small cube from one corner in the cube grid area to traverse the cube grid area along the side at a fixed distance, and recording the quantity of point cloud data in the small cube each time until the whole cube grid area is traversed;
(3) Marking the position of the area with the largest amount of point cloud data in the small cube, amplifying the small cube but keeping the size of the points unchanged until the small cube is amplified to the same size as the cube grid area and replaces the cube grid area to serve as a new cube grid area; repeating the steps twice to four times, reducing the dimension of the three-dimensional point cloud model and reserving the characteristics as far as possible;
(4) In a three-dimensional point cloud model, selecting random scattered points between a tunnel face and a tunnel wall, including a closest point and a farthest point, solving a distance L between the selected random scattered points, and solving an average value as a field radius eps, wherein the specific formula is as follows:
wherein: l is the distance between random scattered points; x, y, z, x 0 、y 0 、z 0 Coordinates of two random scattered points; the field radius eps is denoted by R; l (L) 1 …L n Distance between different random scattered points; n is the number;
(5) Randomly selecting point cloud data in the new cube grid area in the step (3), wherein the average number of the point cloud data is used as the minimum point density minPts;
(6) The three-dimensional point cloud model is set to be 2 types, wherein one type is a tunnel face, and the other type is a tunnel wall; the point cloud data contained in the neighborhood radius eps of each point cloud data cannot be less than the minimum point density minPts, otherwise, the point cloud data are divided into another type, and therefore separation of the tunnel face and the structural face of the tunnel wall is achieved.
3. The method for building the fine model of the three-dimensional tunnel rock mass structure according to claim 1, wherein the method comprises the following steps: the expression formula of the improved inverse distance weighted average interpolation method IDW in the step (four) is as follows:
wherein: g is the element estimation value of the point to be interpolated; d, d i The Euclidean distance between the point to be interpolated and the ith sample space position; n is the number of control points in the estimation and lambda is the distance parameter.
4. The method for building the fine model of the three-dimensional tunnel rock mass structure according to claim 1, wherein the method comprises the following steps: the modified Delaunay triangulation algorithm of step (seven) comprises the following steps:
(1) Dividing the point cloud data into rectangular blocks with a plurality of sizes according to the areas of the tunnel face and the structural face of the tunnel wall obtained in the step (two);
(2) Selecting two point cloud data from part of edges in a point cloud data set in a rectangular block area, connecting to form an initial side length, taking the center of the initial side length as a circle center, and searching a point which is closest to the side length of the circle center in a radius and is perpendicular to the side length of the circle center as a third point to construct an initial triangle;
(3) Searching a point with the side length of the initial triangle as the side to be expanded and the closest vertical distance to the side length of the center of a circle in the radius by taking the side length of the initial triangle as the side to be expanded, generating the initial expanded triangle, always positioning the searching point at the outer side of the initial expanded triangle, and subsequently selecting the positions of the scattered points at the outer side of the initial expanded triangle to form the expanded triangle; the normal vector included angle of the extended triangle and the initial extended triangle is calculated, and the extended triangle with the largest included angle is selected as the final extended triangle;
(4) Continuously generating n extension triangles through the steps, and completing grid division in the area; and communicating the divided areas, so as to obtain the complete three-dimensional point cloud model.
5. The method for building the fine model of the three-dimensional tunnel rock mass structure according to claim 4, wherein the method comprises the following steps: the divided areas in the step (4) comprise boundary areas, and the method for generating the smooth triangular mesh curved surface by the boundary areas comprises the following steps: selecting two points closest to the edge connected with the initial area, selecting two points closest to the edge connected with the initial area in the area connected with the initial area, generating a sphere through the selected four points, marking a center point, taking the intersection point of two connecting lines of the two points which are farthest away from each other in the four points as the center point, making vertical lines perpendicular to the two connecting lines from the center point to two ends, firstly contacting the points of the sphere as connecting points, connecting the two points selected in the area, generating a triangle, and finally connecting all the connecting points to generate a smooth triangular grid curved surface.
6. A system for a method of fine modeling a three-dimensional tunnel rock mass structure as defined in any one of claims 1 to 5, wherein:
The system comprises a three-dimensional laser scanner (1), a point cloud processing model (2), a machine vision model (3), an in-hole detection model (4) and a refinement processing model (5), wherein the three-dimensional laser scanner (1), the point cloud processing model (2) and the machine vision model (3) are sequentially connected, and the machine vision model (3) and the in-hole detection model (4) are both connected with the refinement processing model (5);
the three-dimensional laser scanner (1) is used for acquiring point cloud data of the tunnel face and the tunnel wall of the tunnel;
the point cloud processing model (2) is used for removing noise of point cloud data of the tunnel face and the tunnel wall and separating the tunnel face and the tunnel wall by using a DBSCAN algorithm;
the machine vision model (3) is used for acquiring external information of the tunnel rock mass;
the in-hole detection model (4) is used for acquiring information inside the tunnel rock mass;
the refinement processing model (5) is used for further refining and reconstructing the three-dimensional tunnel rock mass structure model.
7. An electronic device, characterized in that: the method comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the method for establishing the fine model of the three-dimensional tunnel rock mass structure according to any one of claims 1-5 when executing the computer program.
8. A computer readable storage medium having stored thereon a computer program characterized by: the computer program, when executed by a processor, implements the method for building a fine model of a three-dimensional tunnel rock mass structure as claimed in any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117803386A (en) * | 2023-12-25 | 2024-04-02 | 海南大学 | Physical and mechanical parameter three-dimensional space reconstruction method and device |
| CN118037982A (en) * | 2024-04-12 | 2024-05-14 | 泰山学院 | Three-dimensional visual modeling method for geological structure of rock mass and related equipment thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117803386A (en) * | 2023-12-25 | 2024-04-02 | 海南大学 | Physical and mechanical parameter three-dimensional space reconstruction method and device |
| CN118037982A (en) * | 2024-04-12 | 2024-05-14 | 泰山学院 | Three-dimensional visual modeling method for geological structure of rock mass and related equipment thereof |
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