A self-growing modeling method for tunnel joint surfaces based on ant body tracing technology

Abstract

The present invention provides a self-growing modeling method for tunnel joint surfaces based on the ant-like body tracing technology. Based on the principle that the ant-like body always selects the shortest path during crawling in the ant-like body tracing technology, and the finally selected shortest path is most likely to pass through the tunnel joint surface, the ant-like body is scattered in the discrete data jointly composed of the geophysical exploration data containing rich geological structure information and the three-dimensional point cloud data containing the trace line information of the tunnel excavation surface obtained. Through the crawling of the ant-like body and the release of pheromones, the self-growing modeling of the tunnel joint surface based on the ant-like body tracing technology is realized. The tunnel joint surface model established based on the above bionic method that mimics the behavior of ants in nature makes full use of and expresses multi-source geological information, can be used for subsequent numerical analysis, and overcomes the problems of incomplete expression of geological information, large influence of human subjective factors, and low reliability of subsequent analysis in the traditional structural surface model.

Description

Technical Field

[0001] The present invention relates to the technical field of mountain tunnel engineering, and more particularly to a self-growing modeling method for tunnel joint surfaces based on ant body tracing technology. Background Art

[0002] Most surrounding rocks of rock tunnels have complex and changeable geological conditions, with discontinuity, inhomogeneity and anisotropy, making tunnel construction difficult and the risk of engineering geological disasters high. The distribution pattern, physical and mechanical strength and combination of tunnel joint surfaces control the mechanical behavior and stability of surrounding rocks. Accurately establishing a joint surface model is a key issue for tunnel safety design and construction. To establish a tunnel joint surface model, many joint surface models and modeling methods have been proposed by experts in the tunnel field at home and abroad. However, most of the geological information considered in the current joint surface models during the modeling process is data on the excavation surface, that is, the established models do not fully consider the existing geological data and are greatly affected by subjective factors of people, resulting in a low reliability of subsequent model analysis.

[0003] In view of this, it is necessary to improve the modeling method in the prior art to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to disclose a self-growing modeling method for tunnel joint surfaces based on ant body tracing technology, which solves the problems of incomplete expression of geological information, great influence of subjective factors of people and low reliability of subsequent analysis in the existing structural surface model.

[0005] To achieve the above purpose, the present invention provides a self-growing modeling method for tunnel joint surfaces based on ant body tracing technology. Based on the principle that the ant body always selects the shortest path when crawling in the ant body tracing technology, and the finally selected shortest path is most likely to pass through the tunnel joint surface, the ant body is scattered in the discrete data composed of geophysical exploration data containing rich geological structure information, 3D point cloud data containing tunnel excavation surface trace line information, and borehole position information obtained by advanced geological drilling. Through the crawling of the ant body and the release of pheromone, a self-growing modeling method for tunnel joint surfaces based on ant body tracing technology is realized;

[0006] Specifically, it includes the following steps:

[0007] S1, initialize the ant body, and set ant body parameters, including 5 parameters: the number of ant bodies N, the maximum number of iterations T max , the pheromone intensity factor q, the pheromone evaporation factor ρ, and the heuristic factor β.

[0008] S2. Initialize the pheromone matrix of the ant-like bodies such that the initial pheromone concentrations at each path and each node are equal. The pheromone matrix contains geological structure information, joint plane trace information, and borehole position information obtained from advanced geological boreholes.

[0009] S3. The ant-like bodies start crawling and calculate the relative undulation between any two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in the crawling path. The calculation formula is as follows:

[0010]

[0011] Judge whether all paths at this node meet the preset joint plane condition, that is, the absolute value of the relative undulation is less than the established threshold. The ant-like body releases pheromone to mark on this path that meets the preset joint plane condition and continues to crawl, and identifies and integrates all paths that meet the preset joint plane condition into the whole shortest path, and records the first tracing result.

[0012] S4. Update the pheromone matrix, start iteration, and trace and identify the shortest path again. Judge whether the maximum number of iterations is reached. When the maximum number of iterations is reached, organize and analyze the shortest paths recorded after each iteration, and fit these shortest paths to grow the final tunnel joint plane model.

[0013] As a further improvement of the present invention, in step S4, when the maximum number of iterations is not reached, the pheromone matrix is updated again and iterated again.

[0014] As a further improvement of the present invention, in step S1, the cross-comparison method is used to determine the relevant parameters of the ant-like body tracing technology. First, the three parameters of the pheromone intensity factor q, the pheromone evaporation factor ρ, and the heuristic factor β are combined and adjusted one by one to select the optimal interval, and then the two parameters of the number of ant-like bodies N and the maximum number of iterations Tmax, which have obvious influence on the result, are adjusted one by one for selection.

[0015] As a further improvement of the present invention, in step S3, the preset joint plane condition is realized by controlling the relative undulation threshold.

[0016] As a further improvement of the present invention, in step S4, the maximum number of iterations is determined by the scale of the discrete data body.

[0017] As a further improvement of the present invention, in step S3, the probability calculation formula for the pheromone to determine the path selection during the tracing process of the ant-like body is as follows:

[0018]

[0019] In the formula, τ ij represents the amount of pheromone between any two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in the pheromone matrix, P ij represents the transition probability of the ant-like body at the node, and n is the number of all nodes that the ant-like body needs to crawl.

[0020] As a further improvement of the present invention, in the step S4, the pheromone matrix update rule is as follows:

[0021] τ ij (t + 1) = (1 - ρ)τ ij (t) + Δτ ij

[0022]

[0023] In the formula, Δτ ij represents the increment of pheromone, and Δτ ij k represents the amount of pheromone released by the kth ant-like body between two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in a certain cycle, and m is the number of all ant-like bodies.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] A self-growing modeling method for tunnel joint surfaces based on ant-like body tracing technology makes full use of and expresses multi-source geological information. The established structural plane model has high accuracy, greatly reducing the difference between the joint surface model and the real joint surface, thus overcoming the problems of incomplete expression of geological information, large influence of subjective factors, and low reliability of subsequent analysis in traditional structural plane models. Compared with traditional joint surface modeling methods, the method comprehensively uses geological structure information, joint surface trace information, and drilling point information obtained from advanced geological drilling to establish a discrete database for ant-like body tracing technology. The established joint surface model more comprehensively reflects the influence of various geological factors; defines the relative undulation degree, presets joint surface conditions using the inherent properties of the joint surface, and controls the transfer probability of ant-like bodies at nodes. The ant-like bodies are scattered in the discrete data jointly composed of geophysical exploration data containing rich geological structure information and 3D point cloud data containing tunnel excavation surface trace information obtained. Through the crawling of ant-like bodies and the release of pheromones, self-growing modeling of the joint surface is achieved, reducing the influence of subjective factors on the modeling process. The reliability of the model established by this method is higher than that of traditional methods and can be further used for subsequent numerical analysis. Brief Description of the Drawings

[0026] Figure 1 FIG. is a flowchart of a self-growing modeling method for tunnel joint surfaces based on ant-like body tracing technology according to the present invention;

[0027] Figure 2 FIG. is a schematic diagram of a self-growing model of a tunnel joint surface in a self-growing modeling method for tunnel joint surfaces based on ant-like body tracing technology according to the present invention.

[0028] Among them, Figure 2 In the figure: 1 represents the tunnel joint surface model fitted by all the shortest paths; 2 represents the discrete data nodes; 3 represents the ant-like bodies; 10 represents the shortest path found by the ant-like bodies. Detailed Embodiments

[0029] The present invention will be described in detail below with reference to the embodiments shown in the drawings. However, it should be noted that these embodiments are not limitations on the present invention, and any equivalent transformation or substitution in function, method, or structure made by those of ordinary skill in the art according to these embodiments shall fall within the protection scope of the present invention.

[0030] Please refer to Figures 1 to 2 A specific embodiment of a self-growing modeling method for tunnel joint surfaces based on ant-like body tracing technology according to the present invention shown.

[0031] Step 1: Initialize the ant-like bodies and determine the relevant parameters of the ant-like body tracing technology

[0032] In the discrete data volume jointly composed of geophysical exploration data containing rich geological structure information and 3D point cloud data containing tunnel excavation face trace information, a certain number of ant-like bodies are scattered and the ant-like bodies are initialized. The parameters that need to be set for initializing the ant-like bodies include 5 parameters: the number of ant-like bodies N, the maximum number of iterations Tmax, the pheromone intensity factor q, the pheromone evaporation factor ρ, and the heuristic factor β.

[0033] The 5 parameters in the ant-like body tracing technology are related to the scale and complexity of the data volume, and appropriate parameters need to be selected according to the actual situation to ensure the universality and robustness of the ant-like body tracing technology. The cross-comparison method is used to select the most suitable parameters of the ant-like body tracing technology. First, the three parameters of the pheromone intensity factor q, the pheromone evaporation factor ρ, and the heuristic factor β are combined and adjusted one by one to select the optimal interval, and then the ant number N and the maximum number of iterations T max Two parameters that have an obvious impact on the results are adjusted one by one for selection.

[0034] Step 2: Initialize the pheromone matrix of the ant-like body

[0035] The established pheromone matrix contains not only geological structure information and joint surface trace information, but also some borehole location information obtained from advanced geological boreholes. Initialize the pheromone matrix so that the initial pheromone concentrations at each path and each node are equal.

[0036] Step 3: The ant-like body starts to crawl, and calculates the relative undulation degree between any two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in the crawling path. The calculation formula is as follows:

[0037]

[0038] Judge whether all paths at this node meet the preset joint surface condition, that is, the absolute value of the relative undulation degree is less than the established threshold. The ant-like body releases pheromone on this path that meets the preset joint surface condition for marking, and continues to crawl, and identifies and integrates all paths that meet the preset joint surface condition into the whole shortest path, and records the first tracing result; the established threshold of the relative undulation degree is determined by the size of the structural plane.

[0039] In the ant-like body tracing technology, the ant-like body always selects the shortest path when crawling, crawls along the discrete data volume, and the finally selected shortest path will most likely pass through the tunnel joint surface. When the ant-like body finds the shortest path of a suspected joint surface during the tracing process, the ant-like body will release a specific pheromone and substitute it into the formula

[0040]

[0041] Other ant-like bodies are summoned here by changing the transition probability. When other ant-like bodies recognize this pheromone, they will arrive nearby and continue to trace. They keep crawling to find more places that meet the preset joint plane conditions until the tracing and recognition process of the whole shortest path is completed.

[0042] Step 4: Establishment of the joint plane model

[0043] After the ant-like body completes one tracing and recognition of the joint plane, record the tracing result and according to the formula

[0044] τ ij (t + 1) = (1 - ρ)τ ij (t) + Δτ ij

[0045]

[0046] Update the pheromone matrix and start the iteration again until the maximum number of iterations is reached. The maximum number of iterations is determined by the scale of the discrete data body. After meeting the iteration termination condition, the ant-like body no longer traces. Organize and analyze the shortest paths recorded after each iteration, and fit these paths that most likely pass through the tunnel joint plane to grow the final tunnel joint plane model.

[0047] In addition, it should be understood that although this specification is described according to the embodiments, not each embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A self-growing modeling method for tunnel joint surfaces based on ant-like body tracing technology, characterized in that, Based on the principle that the ant-like body always selects the shortest path during crawling in the ant-like body tracing technology, and the finally selected shortest path is most likely to pass through the tunnel joint surface, the ant-like body is scattered in the discrete data composed of geophysical exploration data containing rich geological structure information, 3D point cloud data containing tunnel excavation surface trace line information, and borehole position information obtained from advanced geological boreholes. Through the crawling of the ant-like body and the release of pheromones, a self-growing modeling of the tunnel joint surface based on the ant-like body tracing technology is realized; Specifically, it includes the following steps: S1. Initialize the ant-like body and set the parameters of the ant-like body, including the number N of ant-like bodies, the maximum number of iterations T max , five parameters in total: the pheromone intensity factor q, the pheromone evaporation factor ρ, and the heuristic factor β; S2, Initialize the pheromone matrix of the ant-like body so that the initial pheromone concentration at each path and each node is equal. The pheromone matrix contains geological structure information, joint surface trace line information, and borehole position information obtained from advanced geological boreholes; S3, the ant-like body starts to crawl and calculates the relative undulation between any two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in the crawling path. The calculation formula is as follows: Judge whether all paths at this node meet the preset joint surface condition, that is, the absolute value of the relative undulation is less than the established threshold. The ant-like body releases pheromones on this path that meets the preset joint surface condition for marking, and continues to crawl. Identify and integrate all paths that meet the preset joint surface condition into the whole shortest path, and record the first tracing result; S4, Update the pheromone matrix, start iteration, trace and identify the shortest path again, and judge whether the maximum iteration number is reached. When the maximum iteration number is reached, organize and analyze the shortest paths recorded after each iteration, and fit these shortest paths to grow the final tunnel joint surface model.

2. The self-growing modeling method of tunnel joint surface based on ant-like body tracing technology according to claim 1, characterized in that In step S4, when the maximum iteration number is not reached, re-update the pheromone matrix and iterate again.

3. A self-growing modeling method for tunnel joint surfaces based on ant body tracing technology according to claim 1, characterized in that In the step S1, the relevant parameters of the ant-like body tracing technology are determined by using the cross-comparison method. First, the three parameters of the pheromone intensity factor q, the pheromone evaporation factor ρ, and the heuristic factor β are combined and adjusted one by one to select the optimal interval, and then the number N of ant-like bodies and the maximum number of iterations T are added. max The two parameters that have obvious influence on the results are adjusted one by one for selection.

4. A self-growing modeling method for tunnel joint surfaces based on ant-like tracing technology according to claim 1, characterized in that In step S3, the preset joint surface condition is realized by controlling the relative undulation threshold.

5. A self-growing modeling method for tunnel joint surfaces based on ant-like tracing technology according to claim 1, characterized in that, In step S4, the maximum iteration number is determined by the scale of the discrete data body.

6. The self-growing modeling method for tunnel joint surfaces based on ant body tracing technology according to claim 1, characterized in that In step S3, the probability calculation formula for the pheromone to determine the path selection during the tracing process of the ant-like body is as follows: Where τ ij represents the amount of pheromone between any two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in the pheromone matrix, P ij represents the conversion probability of the ant-like body at the node, and n is the total number of nodes that the ant-like body needs to crawl through.

7. A self-growing modeling method for tunnel joint surfaces based on ant body tracing technology according to claim 1 or 2, characterized in that In step S4, the pheromone matrix update rule is as follows: τ ij (t + 1) = (1 - ρ)τ ij (t) + Δτ ij where Δτ ij represents the increment of pheromone, and Δτ ij k represents the amount of pheromone released between two adjacent discrete data nodes Qi(x i , y i , z i ) and Qj(x j , y j , z j ) in a certain cycle of the k-th ant-like body, and m is the number of all ant-like bodies.

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