Dangerous slip crack surface prediction method based on slope safety coefficient

Abstract

The invention provides a dangerous slip crack surface prediction method based on a slope safety coefficient, and relates to the technical field of slope safety, and the method specifically comprises the steps: collecting soil layer parameter data of a certain slope; constructing a slope stability analysis and calculation model and an objective function based on the Bisphout method and the arc method; introducing a golden litsea optimization algorithm to obtain a minimum safety coefficient corresponding to the slope and a corresponding arc center position and radius; constructing the most dangerous slip crack surface of the side slope; according to the method, a brand-new litsea rotundifolia optimization algorithm method is introduced, and on the basis of keeping the original optimization ability, a hybrid strategy is proposed to improve the method, that is, a population is initialized by using Tent-Logistic-Cosine chaotic mapping, and the position of the litsea rotundifolia is disturbed by using a periodic variation strategy; and a Cauchy inverse cumulative distribution function and a tangent flight operator are introduced in a search and attack stage to update individual positions, so that the stability, the search efficiency and the global optimization capability of the algorithm are greatly enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of slope safety, and in particular to a method for predicting dangerous sliding surfaces based on slope safety factors. Background Art

[0002] Most slope stability analyses are based on safety factors. Common methods for solving the minimum safety factor include the gradient method, simplex method, simulated annealing method, neural network method, genetic algorithm, particle swarm optimization algorithm, chaos optimization algorithm, etc. These methods have their own advantages, but they also have their own shortcomings. For example, the gradient method requires that the objective function is continuous and differentiable; the simplex method tends to converge to the local optimal solution; the genetic algorithm is prone to premature convergence, and the performance of the algorithm is highly dependent on the selection of parameters; the particle swarm optimization algorithm is prone to fall into the local optimum.

[0003] The existing design of the improved golden jackal optimization algorithm introduces the Fuch mapping theory and the elite population strategy in the population initialization stage to optimize the initialization of the golden jackal population. In the iterative output stage of the population, the adaptive inertia weight ω and the explorer position update strategy in the improved sparrow algorithm are introduced to update the solution of the improved sparrow algorithm again. Although the performance of the golden jackal optimization algorithm has been improved to a certain extent in the above-mentioned improved method, it still faces the problems of slow convergence in the late iteration and insufficient global search ability when dealing with multi-peak engineering problems, and is easy to fall into local optimality. Therefore, a dangerous sliding surface prediction method based on slope safety factor is proposed to solve the above problems. Summary of the invention

[0004] The problem to be solved by the present invention is that the existing improved golden jackal optimization algorithm still faces the problem of slow convergence speed in the late iteration and insufficient global search capability when dealing with multi-peak engineering problems, and is prone to falling into local optimality.

[0005] In order to solve the above technical problems, the present invention provides a method for predicting dangerous sliding surfaces based on slope safety factor. The specific steps of the prediction method are as follows:

[0006] S1: Collect soil parameter data of a slope;

[0007] S2: Construct the slope stability analysis calculation model and objective function based on Bishop method and circular arc method;

[0008] S3: Introduce the golden jackal optimization algorithm, use the hybrid strategy to improve the golden jackal optimization algorithm, and optimize the slope safety factor calculation objective function to obtain the minimum safety factor corresponding to the slope and the corresponding arc center position and radius;

[0009] S4: Based on the calculated center position and radius of the circle corresponding to the minimum safety factor of the slope, the most dangerous sliding surface of the slope is constructed;

[0010] Preferably, the parameter data in step S1 include soil bulk density, internal friction angle, cohesion and corresponding soil layer height information.

[0011] Preferably, the specific steps of the golden jackal optimization algorithm in step S3 are:

[0012] S301: Input target parameters;

[0013] S302: Initialize target parameters;

[0014] S303: Initialize the population position using Tent-Logistic-Cosine chaotic mapping;

[0015] S304: Calculate the fitness value of the prey and allocate male and female golden jackals;

[0016] S305: Calculate the prey escape energy E;

[0017] S306: When |E|≥1, if it reaches the nfrth iteration, use Y t+1 Update the individual position of the male jackal, otherwise introduce the Cauchy inverse cumulative distribution function to update the position of the male jackal; when |E| < 1, if it reaches the nfrth iteration, use Y t+1 Update the individual position of male jackals, otherwise introduce tangent flight to update the position of male jackals;

[0018] S307: Determine whether the algorithm is finished. If the current number of iterations is less than T, jump to step S304 for execution; if the current number of iterations is equal to T, output the position of the golden jackal as the optimal solution.

[0019] Preferably, the target parameters in step S301 include the maximum algorithm iteration number T, population size N, dimension D, boundaries lb and ub.

[0020] Preferably, in step S306, Y t+1 The specific formula is:

[0021]

[0022] Where: Y t+1 The updated location of the prey.

[0023] Preferably, the Y t+1 Y in the formula Mt and Y FMt To update the positions of male and female jackals before.

[0024] Preferably, the Y t+1Am in the formula is a custom amplitude factor, and rand is a random value between 0 and 1.

[0025] Technical effects and advantages of the present invention:

[0026] 1. The present invention introduces a new golden jackal optimization algorithm method. On the basis of retaining its original optimization ability, a hybrid strategy is proposed to improve it, that is, the population is initialized using the Tent-Logistic-Cosine chaotic map, the position of the golden jackal is disturbed using the periodic mutation strategy, and the Cauchy inverse cumulative distribution function and the tangent flight operator are introduced in the search and attack stages to update the individual position, which greatly enhances the stability, search efficiency and global optimization ability of the algorithm.

[0027] 2. By comparing with other algorithms, the present invention aims at the existing algorithms, and improves the convergence speed and optimization accuracy of the golden jackal optimization algorithm by introducing Tent-Logistic-Cosine chaotic mapping, periodic variation strategy, Cauchy inverse cumulative distribution function and tangent flight strategy when dealing with high-dimensional complex problems. The improved golden jackal optimization algorithm has obvious advantages in both optimization accuracy and speed, which is conducive to improving the accuracy of prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0029] Figure 2 It is the function graph of the test function in the embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention provides a method for predicting dangerous sliding surfaces based on slope safety factor. Figure 1 As shown, the specific steps of the prediction method are:

[0031] S1: Collect soil parameter data of a slope, including soil bulk density, internal friction angle, cohesion and corresponding soil layer height information;

[0032] S2: Construct the slope stability analysis calculation model and objective function based on Bishop method and circular arc method;

[0033] S3: Introduce the golden jackal optimization algorithm, use the hybrid strategy to improve the golden jackal optimization algorithm, and optimize the slope safety factor calculation objective function to obtain the minimum safety factor corresponding to the slope and the corresponding arc center position and radius;

[0034] Furthermore, the specific steps of the golden jackal optimization algorithm in step S3 are:

[0035] S301: Input target parameters, including the maximum number of algorithm iterations T, population size N, dimension D, boundaries lb and ub;

[0036] S302: Initialize target parameters;

[0037] S303: Initialize the population position using Tent-Logistic-Cosine chaotic mapping;

[0038] S304: Calculate the fitness value of the prey and allocate male and female golden jackals;

[0039] S305: Calculate the prey escape energy E;

[0040] S306: When |E|≥1, if it reaches the nfrth iteration, use Y t+1 Update the individual position of the male jackal, otherwise introduce the Cauchy inverse cumulative distribution function to update the position of the male jackal; when |E| < 1, if it reaches the nfrth iteration, use Y t+1 Update the individual position of male jackals, otherwise introduce tangent flight to update the position of male jackals;

[0041] S307: Determine whether the algorithm is finished. If the current number of iterations is less than T, jump to step S304 for execution; if the current number of iterations is equal to T, output the position of the golden jackal as the optimal solution.

[0042] Furthermore, in step S306, Y t+1 The specific formula is:

[0043]

[0044] Where: Y t+1 is the updated position of the prey, Y t+1 Y in the formula Mt and Y FMt is the position of the male and female jackals before updating, Y t+1 In the formula, Am is a custom amplitude factor, and rand is a random value between 0 and 1.

[0045] S4: Based on the calculated center position and radius corresponding to the minimum safety factor of the slope, the most dangerous sliding surface of the slope is constructed.

[0046] Furthermore, by introducing a new golden jackal optimization algorithm, a hybrid strategy is proposed to improve it on the basis of retaining its original optimization ability, that is, using the Tent-Logistic-Cosine chaotic map to initialize the population, using the periodic mutation strategy to perturb the position of the golden jackal, and introducing the Cauchy inverse cumulative distribution function and tangent flight operator to update the individual position in the search and attack stages, which greatly enhances the algorithm's stability, search efficiency and global optimization ability.

[0047] Test Data

[0048] Furthermore, the specific test function is:

[0049]

[0050] Furthermore, according to the test function, the function graph obtained is specifically: Figure 2 .

[0051] Furthermore, by comparing with other classic optimization algorithms, the present invention aims at the existing golden jackal optimization algorithm. When dealing with high-dimensional complex problems, the convergence speed and optimization accuracy of the golden jackal optimization algorithm are improved by introducing Tent-Logistic-Cosine chaotic mapping, periodic variation strategy, Cauchy inverse cumulative distribution function and tangent flight strategy. The improved golden jackal optimization algorithm has obvious advantages in both optimization accuracy and speed.

[0052] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for predicting dangerous sliding surfaces based on slope safety factor, characterized by: The specific steps of the prediction method are: S1: Collect soil parameter data of a slope; S2: Construct the slope stability analysis calculation model and objective function based on Bishop method and circular arc method; S3: Introduce the golden jackal optimization algorithm, use the hybrid strategy to improve the golden jackal optimization algorithm, and optimize the slope safety factor calculation objective function to obtain the minimum safety factor corresponding to the slope and the corresponding arc center position and radius; S4: Based on the calculated center position and radius corresponding to the minimum safety factor of the slope, the most dangerous sliding surface of the slope is constructed.

2. The method for predicting dangerous sliding surfaces based on slope safety factor according to claim 1 is characterized by: The parameter data in step S1 include soil bulk density, internal friction angle, cohesion and corresponding soil layer height information.

3. The method for predicting dangerous sliding surfaces based on slope safety factor according to claim 1 is characterized by: The specific steps of the golden jackal optimization algorithm in step S3 are: S301: Input target parameters; S302: Initialize target parameters; S303: Initialize the population position using Tent-Logistic-Cosine chaotic mapping; S304: Calculate the fitness value of the prey and allocate male and female golden jackals; S305: Calculate the prey escape energy E; S306: When |E|≥1, if it reaches the nfrth iteration, use Y t+1 Update the individual position of the male jackal, otherwise introduce the Cauchy inverse cumulative distribution function to update the position of the male jackal; when |E| < 1, if it reaches the nfrth iteration, use Y t+1 Update the individual position of male jackals, otherwise introduce tangent flight to update the position of male jackals; S307: Determine whether the algorithm is finished. If the current number of iterations is less than T, jump to step S304 for execution; if the current number of iterations is equal to T, output the position of the golden jackal as the optimal solution.

4. The method for predicting dangerous sliding surfaces based on slope safety factor according to claim 3 is characterized by: The target parameters in step S301 include the maximum number of algorithm iterations T, population size N, dimension D, boundaries lb and ub.

5. The method for predicting dangerous sliding surfaces based on slope safety factor according to claim 3 is characterized by: In step S306, Y t+1 The specific formula is: Where: Y t+1 The updated location of the prey.

6. The method for predicting dangerous sliding surfaces based on slope safety factor according to claim 5 is characterized by: The Y t+1 Y in the formula Mt and Y FMt To update the positions of male and female jackals before.

7. The method for predicting dangerous sliding surfaces based on slope safety factor according to claim 5 is characterized by: The Y t+1 Am in the formula is a custom amplitude factor, and rand is a random value between 0 and 1.

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