Method for predicting potential cracks of slope sliding body based on vector direction

Abstract

The application provides a method for predicting potential cracks of a slope sliding body based on vector direction, that is, a plurality of monitoring points are selected on the slope sliding body, coordinate values of each monitoring point and X, Y and Z three-direction displacement vectors of each monitoring point are input, vector distances between adjacent monitoring points are calculated, a slope sliding body displacement vector similarity matrix is established, the slope sliding body displacement vector field is divided based on the vector direction, different regional displacement vector groups are formed, main displacement direction angles of each displacement vector group are obtained, main displacement direction angles of each displacement vector group at different time points are obtained, and a junction of two displacement vector groups with a main displacement direction angle difference greater than or equal to 90 degrees is found, which is a potential crack position. The application can predict the position of the potential crack of the slope sliding body in advance, and provide a strong scientific reference for slope deformation and damage research and engineering design.

Description

Technical Field

[0001] This invention relates to a method for predicting potential cracks in slope landslides, specifically, a method for predicting potential cracks in slope landslides based on vector direction. This invention belongs to the technical field of landslide deformation and failure characteristics research. Background Technology

[0002] For a long time, engineering failure research has primarily relied on observing the development of cracks on the engineering surface to determine whether failure has occurred. For brittle materials such as soil, rock, and concrete, cracks develop rapidly under external loads, leading to sudden failure. Currently, the main method for identifying failure in these brittle materials is surface strain measurement. This involves placing several strain gauges on the surface of the soil, rock, or concrete structure and measuring the surface displacement / strain in real time. Sudden changes in strain values ​​can then be used to predict engineering failure in advance.

[0003] Currently, the drawback of this method of judging engineering failure based on changes in the strain field of the engineering surface is its poor timeliness. It is difficult to predict the occurrence of potential cracks in the engineering in advance, and they are often only discovered when the cracks are already very obvious or the engineering is about to fail. The reason for this drawback is that this surface strain measurement method mainly judges the occurrence of potential cracks and predicts engineering failure by monitoring whether the strain value at local points in the engineering exceeds the strain threshold. However, the engineering failure has often already occurred. Summary of the Invention

[0004] For the reasons mentioned above, the purpose of this invention is to provide a method for predicting potential cracks in slope landslides based on vector direction.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for predicting potential cracks in slope landslides based on vector direction, characterized in that it includes the following steps:

[0006] S1. Select several monitoring points inside and outside the slope sliding body, and input the coordinate value of each monitoring point and the X, Y, and Z displacement vectors of each monitoring point;

[0007] S2. Calculate the vector distance between adjacent monitoring points and establish the slope landslide displacement vector similarity matrix W;

[0008] S3. Based on the vector direction, the displacement vector field of the slope sliding body is divided to form displacement vector groups in different regions;

[0009] Based on the vector distance between two monitoring points in the displacement vector similarity matrix W, the monitoring points are clustered. Within the same displacement vector group, the displacement vectors of monitoring points have similar displacement vector directions. The specific method is as follows:

[0010] S3.1 Calculate the local density at each monitoring point;

[0011] S3.2. Points with a local density of 80% of the maximum local density are taken as cluster centers, and the vector distance between different cluster centers is greater than 50% of the maximum vector distance in the displacement vector similarity matrix W.

[0012] S3.3, Set the distance d between all vectors in the displacement vector similarity matrix W. i,i (i = 1, 2, ..., n) 10% of the maximum value is the cutoff distance;

[0013] S3.4 Extract the vector distance between each cluster center point and all other monitoring points in the displacement vector similarity matrix W;

[0014] S3.5. Determine the magnitude of all vector distances and cutoff distances extracted in S3.4. The monitoring points whose vector distance to the cluster center is less than the cutoff distance are assigned to the vector group to which the cluster center belongs.

[0015] S4. Obtain the main displacement direction angles of each displacement vector group;

[0016] S5. Obtain the main displacement direction angles of each displacement vector group at different times;

[0017] S6. Find the boundary between two sets of displacement vectors whose main displacement direction angles differ by more than or equal to 90°. This boundary is the potential crack location. Attached Figure Description

[0018] Figure 1 This is a diagram of the original displacement vector field of the slope sliding body according to an embodiment of the present invention;

[0019] Figure 2 This is a vector grouping diagram of the slope sliding displacement vector field selected from two cluster centers in this embodiment of the invention;

[0020] Figure 3 This is a vector grouping diagram of the slope sliding displacement vector field selected from three cluster centers in this embodiment of the invention;

[0021] Figure 4 This is a bar chart showing the vector direction angles of each displacement vector group of the slope sliding body at two cluster centers, as selected in this embodiment of the invention.

[0022] Figure 5 This is a bar chart showing the vector direction angles of each displacement vector group of the slope sliding body at three cluster centers, as selected in this embodiment of the invention.

[0023] Figure 6 This is a vector grouping diagram of the slope sliding displacement vector field at time t0, selected from three cluster centers in this embodiment of the invention;

[0024] Figure 7 This is a bar chart showing the vector direction angles of each displacement vector group of the slope sliding body at time t0, selected from three cluster centers in this embodiment of the invention.

[0025] Figure 8 This is a vector grouping diagram of the slope sliding displacement vector field at time t1, selected from three cluster centers according to an embodiment of the present invention.

[0026] Figure 9 This is a bar chart showing the vector direction angles of each displacement vector group of the slope sliding body at time t1, selected from three cluster centers in this embodiment of the invention.

[0027] Figure 10 This is a vector grouping diagram of the slope sliding displacement vector field at time t2, selected from three cluster centers according to an embodiment of the present invention.

[0028] Figure 11 This is a bar chart showing the vector direction angles of each displacement vector group of the slope sliding body at time t2, selected from three cluster centers in this embodiment of the invention. Detailed Implementation

[0029] The structure and features of the present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that various modifications can be made to the embodiments disclosed herein; therefore, the embodiments disclosed in this specification should not be considered as limitations on the present invention, but merely as examples to make the features of the present invention readily apparent.

[0030] The applicant of this invention discovered that before the formation of cracks in a slope landslide, the displacement vector field along both sides of the crack gradually deflects, eventually causing inconsistent deformation of the soil and rock on both sides of the potential crack, resulting in significant differences and a substantial increase in local shear strain or tensile strain, thus forming a crack. Based on this discovery, this invention proposes a method for predicting potential cracks in slope landslides based on vector direction, namely:

[0031] S1. Select several monitoring points inside and outside the slope sliding body, and input the coordinate value of each monitoring point and the X, Y, and Z displacement vectors of each monitoring point.

[0032] Several monitoring points a1, a2, ..., a3 were selected at different depths and locations on the surface and inside the slope landslide body. n Input the three-dimensional coordinates of each monitoring point, where the X, Y, and Z coordinates of each monitoring point are (x1, y1, z1), (x2, y2, z2), ..., (x... n ,y n ,z n The displacement vectors of each monitoring point in the X, Y, and Z directions are represented as (u1, v1, w1), (u2, v2, w2), ..., (u...). n ,v n ,w n).

[0033] S2. Calculate the vector distance between adjacent monitoring points and establish the slope landslide displacement vector similarity matrix.

[0034] Based on each monitoring point a1, a2, ..., a n Given the coordinates and displacement vectors, establish a displacement vector similarity matrix W based on the displacement vector direction and vector distance.

[0035] Taking adjacent monitoring points a1 and a2 as an example, their three-dimensional coordinates are (x1, y1, z1) and (x2, y2, z2), and their displacement vectors are (u1, v1, w1) and (u2, v2, w2), respectively. The vector distance d between a1 and a2 is... 1,2 for

[0036]

[0037]

[0038] d 1,2 =d 2,1 (3)

[0039] In the formula, θ is the angle between the two displacement vectors of monitoring points a1 and a2; wq is the weighting coefficient, which is used to measure the relative weight between the vector direction angle and the straight-line distance between each monitoring point. The value of this parameter ranges from 1 to 4.

[0040] For all monitoring points, the vector distance between any two adjacent monitoring points is calculated using the formulas (1) to (3) above, and the slope sliding displacement vector similarity matrix W is established:

[0041]

[0042] S3. Based on the vector direction, the displacement vector field of the slope sliding body is divided to form displacement vector groups in different regions.

[0043] After obtaining the displacement vector similarity matrix W, different clustering methods (e.g., the common K-means method, density clustering method, spectral clustering method, etc.) can be used to cluster the monitoring points based on the vector distance between two monitoring points in the displacement vector similarity matrix W (the smaller the vector distance, the closer the straight-line distance and the corresponding displacement vector direction between the two monitoring points are, and they can be classified into the same vector group). This divides the slope landslide displacement vector field into different regions of displacement vector groups. The displacement vectors of monitoring points within the same region / group have similar displacement vector directions. The number of regions / groups into which a complete displacement vector field is divided depends on the size of the displacement vector field, and is usually 2-10.

[0044] Taking a conventional density clustering algorithm as an example, the clustering algorithm selects cluster centers according to the following principles: First, the local density of the cluster center is 80% of the maximum local density; second, the vector distance between different cluster centers is greater than 50% of the maximum vector distance in the similarity matrix W.

[0045] The specific method for dividing the displacement vector field to form displacement vector groups in different regions is as follows:

[0046] S3.1 Calculate the local density at each monitoring point;

[0047] S3.2. Points with a local density of 80% of the maximum local density are taken as cluster centers, and the vector distance between different cluster centers is greater than 50% of the maximum vector distance in the displacement vector similarity matrix W.

[0048] S3.3, Set the distance d between all vectors in the displacement vector similarity matrix W. i,i (i = 1, 2, ..., n) 10% of the maximum value is the cutoff distance;

[0049] S3.4 Extract the vector distance between each cluster center point and all other monitoring points in the displacement vector similarity matrix W;

[0050] S3.5. Determine the magnitude of all vector distances and cutoff distances extracted in S3.4. Monitoring points whose vector distance to the cluster center is less than the cutoff distance can be assigned to the vector group to which the cluster center belongs.

[0051] like Figure 2 , Figure 3 As shown, Figure 2 This invention provides a vector grouping diagram of the slope sliding body displacement field after selecting two cluster centers for clustering. Figure 3 The diagram shows the vector grouping of the slope sliding displacement field after selecting three cluster centers for this invention.

[0052] S4. Obtain the main displacement direction angles of each displacement vector group.

[0053] The vector direction of each monitoring point is represented by the displacement vector angle α. Taking the surface of the slope sliding body as the XY plane and the vertical direction as the Z-axis, the displacement vector angle α of each monitoring point is the angle between the displacement vector of that monitoring point and the positive X-axis (counterclockwise around the positive Z-axis). The calculation formula is as follows:

[0054]

[0055] A bar chart is used to summarize the vector directions of each displacement vector group divided in step S3, as shown below. Figure 4 and Figure 5As shown, by summarizing the displacement vector angle distribution of the vector group, the main displacement direction angle of the displacement vector group is determined.

[0056] For the case where the cluster center is 2 Figure 4 This indicates that the main displacement directions of the two vector groups are 70° and 140°, respectively. Since the angle range of vector group 1 is relatively wide, the clustering results can be further refined, such as... Figure 3 As shown. After selecting three cluster centers, by Figure 5 It can be seen that after further refinement of clustering, vector group 3 with the main displacement direction angle of 95 to 100° appears, the angle range of vector group 1 is narrowed, and the displacement vector field vector grouping results are further refined.

[0057] S5. Obtain the main displacement direction angles of each displacement vector group at different times.

[0058] Repeat steps S1-S4 to obtain data on the displacement vector groups at different times during the loading process of the same analysis object, i.e., the slope sliding body. In particular, the main displacement direction angles of each displacement vector group are recorded. Figures 6-11 As shown.

[0059] Depend on Figure 7 It can be seen that at time t0 Figure 6 The principal displacement direction angles of displacement vector groups 1 to 3 are 215°, 250°, and 285°, respectively, with the maximum difference in principal displacement direction angles between adjacent groups being only 35°. As loading progresses, the slope sliding mass displacement vector field changes, such as... Figure 8 and Figure 9 As shown, from time t0 to time t1, the main displacement directions of displacement vector groups 1 and 3 are concentrated at 200° and 275°, respectively, while the displacement vector directions of displacement vector group 2 are not concentrated at any one main angle; at time t2 before the crack occurs, as... Figure 10 and Figure 11 As shown, the main displacement directions of displacement vector groups 1 and 2 are 170° and 260° respectively, with an angle difference of 90°. Displacement vector group 3 is located between displacement vector groups 1 and 2, and its displacement vector direction is close to the boundary line between displacement vector groups 1 and 2.

[0060] S6. Find the boundary between two sets of displacement vectors whose main displacement direction angles differ by more than or equal to 90°. This boundary is the potential crack location.

[0061] After obtaining the main displacement direction angles of each displacement vector group at different times through step S5, the risk of potential cracks in the slope landslide and the location of cracks can be determined based on the changing trend of the difference in the main displacement direction angles between two adjacent displacement vector groups. As the angle difference gradually increases, the relative displacement of the displacement vector field regions where two adjacent displacement vector groups are located gradually becomes more obvious. When the difference in the main displacement direction angles between two adjacent displacement vector groups is greater than or equal to 90°, the boundary between the two displacement vector groups is the cracking trajectory of the potential crack, the risk of slope landslide cracking increases, and the cracking mode gradually changes from tensile to shear cracking.

[0062] like Figure 10 , Figure 11 As shown, the main displacement direction angles of displacement vector groups 1 and 2 are 170° and 260° respectively. The difference in the main displacement direction angles between the two groups of displacement vectors is 90°. It can be determined that cracks will form at the junction of displacement vector groups 1 and 2. The relative displacement of the areas on both sides of the boundary line of displacement vector groups 1 and 2 is large, and the deformation is not coordinated, which leads to the slope sliding body cracking along the boundary line.

[0063] This invention calculates the vector displacement of each monitoring point in the displacement vector field of a slope landslide, clusters and divides the displacement vector field of the slope landslide, calculates the main displacement direction angles of different regional displacement vector groups at different times, accurately and quantitatively describes the vector characteristics of the displacement vector field, and determines the boundary lines of different regions of the displacement vector field, thereby predicting potential cracks in the slope landslide. This invention is highly timely and can predict the location of potential cracks in the slope landslide in advance, providing a strong scientific reference for slope landslide failure research and engineering design.

[0064] This invention can satisfy the needs of indoor material mechanics tests, similar material model tests, and displacement vector field analysis of actual large-scale slope engineering. It can also be widely applied to simulation displacement vector field analysis of conventional finite element mesh models, granular flow discrete element models, and finite difference mesh models, and has a wide range of applications.

[0065] Finally, it should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting potential cracks of a slope sliding body based on vector direction, characterized in that: It includes the following steps: S1. Select several monitoring points inside and outside the slope sliding body, and input the coordinate value of each monitoring point and the X, Y, and Z displacement vectors of each monitoring point; S2. Calculate the vector distance between adjacent monitoring points and establish the slope landslide displacement vector similarity matrix W; S3. Based on the vector direction, the displacement vector field of the slope sliding body is divided to form displacement vector groups in different regions; Based on the vector distance between two monitoring points in the displacement vector similarity matrix W, the monitoring points are clustered. Within the same displacement vector group, the displacement vectors of monitoring points have similar displacement vector directions. The specific method is as follows: S3.1 Calculate the local density at each monitoring point; S3.

2. Points with a local density of 80% of the maximum local density are taken as cluster centers, and the vector distance between different cluster centers is greater than 50% of the maximum vector distance in the displacement vector similarity matrix W. S3.3, Set all vector distances in displacement vector similarity matrix W d i,i 10% of the maximum value is the cutoff distance, where, i = 1, 2,..., n; S3.4 Extract the vector distance between each cluster center point and all other monitoring points in the displacement vector similarity matrix W; S3.

5. Determine the magnitude of all vector distances and cutoff distances extracted in S3.4, and classify the monitoring points whose vector distance to the cluster center is less than the cutoff distance into the vector group to which the cluster center belongs; S4. Obtain the main displacement direction angles of each displacement vector group; S5. Obtain the main displacement direction angles of each displacement vector group at different times; S6. Find the boundary between two sets of displacement vectors whose main displacement direction angles differ by more than or equal to 90°. This boundary is the potential crack location.

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