CN117973229A - Slope three-dimensional most dangerous slip surface searching method, equipment and storage medium - Google Patents

Abstract

The present invention provides a method, device and storage medium for searching the three-dimensional most dangerous sliding surface of a slope, including the construction of a three-dimensional power function sliding surface, the construction of a three-dimensional power function sliding surface equation according to the slope size, and the construction of a three-dimensional sliding surface according to the control parameters (x ₀ , z ₀ , R , n , θ ) in the constructed three-dimensional power function sliding surface equation; the search of a two-dimensional arc main sliding surface, the determination of the independent variables of the objective function ( x ₀ , z ₀ , R ), the search through a genetic algorithm to obtain the minimum value of the objective function, that is, the two-dimensional main sliding surface of the slope; the search of a three-dimensional most dangerous power function sliding surface, the determination of the independent variables of the objective function ( n , θ ), the search through a genetic algorithm to obtain the minimum value of the objective function, that is, the three-dimensional most dangerous sliding surface of the slope. After adopting the above method, the method for searching the three-dimensional most dangerous sliding surface of the slope proposed by the present invention can realize the prediction of slope stability, which has important theoretical and practical significance for reducing landslide disasters.

Description

Method, device and storage medium for searching the most dangerous three-dimensional sliding surface of slope

Technical Field

The invention relates to the technical field of slope engineering such as civil engineering, water conservancy, mining, and highway, and in particular to a method, device, and storage medium for searching a three-dimensional most dangerous sliding surface of a slope.

Background technique

The search for the most dangerous sliding surface is one of the hot research topics in the field of geotechnical engineering. A large number of complex slope engineering problems continue to emerge in the construction of related highway, railway, water conservancy, port, civil engineering and other industries. However, the search for the most dangerous sliding surface is one of the problems that need to be solved in slope engineering, and it is also one of the most basic problems in slope engineering. Therefore, in the industry background of civil engineering, water conservancy engineering and transportation engineering, the research on the search for the most dangerous sliding surface has far-reaching theoretical significance and broad prospects for engineering application.

At present, in the search for the most dangerous three-dimensional sliding surface, researchers have continuously improved the efficiency and accuracy of sliding surface search through optimization algorithms, artificial intelligence algorithms and other means. For example, the Chinese patent with publication number CN105787277B discloses a method for searching the three-dimensional critical sliding surface of a slope. According to the constructed three-dimensional ellipsoid sliding surface, the safety factor of the slope is obtained according to the minimum potential energy principle, and the critical sliding surface is searched by genetic algorithm. The sliding surface searched by this method is only the ellipsoid surface, which is not general and has a limited scope of application; the safety factor calculation method adopts the minimum potential energy principle, and the limit equilibrium method is widely used in engineering, which also limits the application of this method. Although the three-dimensional sliding surface search of slopes has made certain progress, there are still some challenges and problems that need further research. For example, the solution method of the three-dimensional safety factor is complex and often does not converge, which makes it impossible to solve the optimization function, and thus it is impossible to realize the search for the most dangerous sliding surface; the three-dimensional most dangerous sliding surface assumption shape is too simple and not universal; the complex sliding surface shape has too many control parameters and is not easy to realize or fall into the local optimum.

Summary of the invention

The present invention provides a method, device and storage medium for searching the three-dimensional most dangerous sliding surface of a slope, which can at least solve one of the technical problems in the background technology.

To achieve the above object, the present invention adopts the following technical solutions:

A method for searching the most dangerous sliding surface of a slope in three dimensions comprises the following steps:

Step S101: constructing a three-dimensional power function sliding surface, constructing a three-dimensional power function sliding surface equation according to the slope size, and constructing a three-dimensional sliding surface according to the control parameters ( x0 _, z0 _, R , n , θ ) in the constructed three-dimensional power function sliding surface equation;

Step S102: Searching for a two-dimensional arc main sliding surface, determining the independent variables of the objective function ( x ₀ , z ₀ , R ), and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the two-dimensional main sliding surface of the slope;

Step S103: Searching for the three-dimensional most dangerous power function sliding surface, determining the independent variables ( n , θ ) of the objective function, and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the three-dimensional most dangerous sliding surface of the slope.

Furthermore, the step S101 includes the following steps:

Step S1011: determine the size of the slope, set the coordinate system for the slope, and measure the slope height, slope rate, and slope material parameters such as cohesion c , internal friction angle φ , and gravity γ ;

Step S1012: Assuming that the main sliding surface center coordinates of the power function sliding surface are ( x0 _, 0, z0 ), the sliding radius is R, based on the main sliding surface, three-dimensional expansion is performed in the direction of another coordinate axis y, and a cluster of three-dimensional power function sliding surface equations z=f(x0, _z0 , R, n) is obtained; at the same time, in order to reflect the generalization of the power function sliding surface, it is set that the power function sliding surface has an angle with the horizontal plane, the size is θ , and the general equation of the three-dimensional power function sliding surface of the slope is: z = f ( x0 _, z0 _, R , n , θ ). As shown in Figure 2, the equation under the established coordinate system is expressed as:

(1)

Where n is a positive real number, R y is the semi-axis length of the slip surface in the y direction;

Step S1013: Determine the control parameters. According to the exact values or ranges of the control parameters in the three-dimensional power function sliding surface equation, a series of three-dimensional power function sliding surfaces can be determined, thereby realizing the construction of the three-dimensional sliding surface.

Further, the step S102 includes the following steps:

Step S1021: Search for the two-dimensional arc main sliding surface. According to the Chinese Computer Software Copyright Registration No. 2017SR508712, a sliding surface normal stress correction slope stability calculation software based on genetic algorithm is disclosed. The center coordinates x0 , z0 of _the arc and the arc radius R are used as independent variables of the objective function. The safety factor of the slope is obtained according to the two-dimensional limit equilibrium method _of constructing the sliding surface normal stress distribution, that is, the objective function is determined. The minimum value of the objective function is obtained by searching through the genetic algorithm, that is, the corresponding x0 , z0 _, R are obtained, _and the construction of the two-dimensional arc main sliding surface is realized.

Furthermore, the step S103 includes the following steps:

Step S1031: Calculate the safety factor. According to the Chinese Computer Software Copyright with Registration No. 2018SR695247, a three-dimensional slope stability calculation software based on the correction of the normal stress of the sliding surface is disclosed. The three-dimensional power function sliding surface equation Z = S determined by step S101 and its slope surface equation Z = G ; the slope soil material parameters cohesion c , internal friction angle φ and gravity γ are substituted into the equation group obtained by three force balances and a vertical direction ( y direction) moment balance. The safety factor is solved according to the three-dimensional limit equilibrium method based on the correction of the normal stress of the sliding surface (Chinese Computer Software Copyright with Registration No. 2018SR695247), that is, the search target function is determined.

Step S1032: using the control parameters n and θ as independent variables of the objective function, and using a genetic algorithm to search for the minimum value of the objective function, that is, obtaining the corresponding n and θ , to achieve the search for the most dangerous three-dimensional sliding surface.

On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

As can be seen from the above technical solution, the present invention discloses a method for searching the three-dimensional most dangerous sliding surface of a slope, including step S101: constructing a three-dimensional power function sliding surface, constructing a three-dimensional power function sliding surface equation according to the slope size, and constructing a three-dimensional sliding surface according to the control parameters ( x ₀ , z ₀ , R , n , θ ) in the constructed three-dimensional power function sliding surface equation; step S102: searching for a two-dimensional arc main sliding surface, determining the independent variables of the objective function ( x ₀ , z ₀ , R ), and searching through a genetic algorithm to obtain the minimum value of the objective function, that is, obtaining the two-dimensional main sliding surface of the slope. Step S103: searching for the three-dimensional most dangerous power function sliding surface, determining the independent variables of the objective function ( n , θ ), and searching through a genetic algorithm to obtain the minimum value of the objective function, that is, obtaining the three-dimensional most dangerous sliding surface of the slope. After adopting the above method, the method for searching the three-dimensional most dangerous sliding surface of the slope proposed by the present invention can realize the prediction of slope stability, which has important theoretical and practical significance for reducing landslide disasters.

The method for searching the three-dimensional most dangerous sliding surface of a slope of the present invention is adopted to simplify the sliding surface of the slope instability and destruction into a three-dimensional power function sliding surface. The construction process is simple and clear and easy to implement. The shape of the constructed sliding surface is general and closer to the actual sliding surface, which is suitable for soil slopes. The calculation of the safety factor adopts the three-dimensional limit equilibrium method based on the correction of the normal stress of the sliding surface (China Computer Software Registration No. 2018SR695247), and the calculation result is an explicit solution without convergence problems. There are only five optimization control parameters ( x0 _, z0 _, R , n , θ ), and the control parameters are moderate and easy to be implemented by genetic algorithms. The search efficiency is high and it is not easy to fall into the local optimum.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a three-dimensional slope according to the present invention;

FIG2 is a schematic diagram of a three-dimensional power function sliding surface of the present invention;

FIG3 is a schematic diagram of a two-dimensional slope of the present invention;

FIG4 is a schematic diagram of a two-dimensional arc main sliding surface of the present invention;

5 is a flowchart of a genetic algorithm search for the most dangerous arc main sliding surface in two dimensions according to the present invention;

FIG6 is a schematic diagram of the three-dimensional most dangerous sliding surface of the present invention;

FIG. 7 is a flowchart of a genetic algorithm search for the three-dimensional most dangerous power function sliding surface according to the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

As shown in FIG1 , the method for searching the three-dimensional most dangerous sliding surface of a slope described in this embodiment includes the following steps:

Step S101: constructing a three-dimensional power function sliding surface, constructing a three-dimensional power function sliding surface equation according to the slope size, and constructing a three-dimensional sliding surface according to the control parameters ( x0 _, z0 _, R, n , θ ) in the constructed three-dimensional power function sliding surface equation;

Step S102: Searching for a two-dimensional arc main sliding surface, determining the independent variables of the objective function ( x ₀ , z ₀ , R ), and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the two-dimensional main sliding surface of the slope;

Step S103: Searching for the three-dimensional most dangerous power function sliding surface, determining the independent variables ( n , θ ) of the objective function, and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the three-dimensional most dangerous sliding surface of the slope.

The following are detailed descriptions:

Step S101: constructing a three-dimensional power function sliding surface, constructing a three-dimensional power function sliding surface equation according to the slope size, and constructing a three-dimensional sliding surface according to the control parameters ( x0 _, z0 _, R, n , θ ) in the constructed three-dimensional power function sliding surface equation; specifically including:

Step S1011: Determine the slope size. As shown in Figure 1, after setting the coordinate system, it can be measured that the slope height is 12m and the slope is 0.6. The soil density γ = 18kN/ ^m3 , the cohesion c = 25kPa, and the internal friction angle φ = 22°.

Step S1012: Assuming that the main sliding surface center coordinates of the power function sliding surface are ( x0 _, 0, z0 ), the sliding radius is R, and based on the main sliding surface, three -dimensional expansion is performed in the direction of another coordinate axis y , and a cluster of three-dimensional power function sliding surface equations z = f ( x0 _{, z0 ,} R, n ) is obtained; at the same time, in order to reflect the generalization of the power function sliding surface, it is set that the power function sliding surface has an angle with the horizontal plane, the size is θ , and the general equation of the three-dimensional power function sliding surface of the slope is: z = f ( x0 _, z0 _, R, n , θ ). As shown in Figure 2, the equation under the established coordinate system is expressed as:

(1)

In formula (1), n is a positive real number, and R _y is the semi-axis length of the sliding surface in the y direction. Assuming that the maximum length of the sliding body is 2 L , then , D is the shortest distance from the rotation axis to the slope, and Figure 3 is a two-dimensional sliding surface schematic diagram of this sliding surface.

Step S1013: Determine control parameters. According to the value ranges of the control parameters x ₀ , z ₀ , R , n , θ in the three-dimensional power function sliding surface equation, a series of three-dimensional power function sliding surfaces can be determined, and then the three-dimensional sliding surface can be constructed.

Step S102: Search for the two-dimensional arc main sliding surface, determine the independent variables _of the objective function ( x0 _, z0 , R ), and obtain the minimum value of the objective function through genetic algorithm search, that is, obtain the two-dimensional main sliding surface of the slope. The specific search process includes the following steps:

Step S1021: Search for the main sliding surface of a two-dimensional arc. According to the Chinese Computer Software Copyright Registration No. 2017SR508712, a sliding surface normal stress correction slope stability calculation software based on genetic algorithm is disclosed. According to the force balance ( x direction, y direction) and moment balance of the sliding body, a set of equations containing the objective function is established.

Step S1022: Encode and decode the center coordinates x ₀ and z ₀ of the arc and the radius R of the arc as independent variables of the objective function to form an initial population, and calculate the fitness values of the individuals in the population;

Step S1023: inserting new individuals formed by crossover and mutation operations into the originally selected individuals to form a new population, and calculating the fitness values of the individuals in the new population;

Step S1024: Determine whether the individuals of this generation meet the target convergence conditions. If so, output the individuals of this generation. If not, repeat the above steps until the convergence conditions are met. The optimal solution of this example is x ₀ =8.82, z ₀ =13.46, R=16.66m, and the corresponding two-dimensional main sliding surface is shown in Figure 4. The genetic algorithm search process of the two-dimensional most dangerous arc main sliding surface is shown in Figure 5.

The three-dimensional power function sliding surface equation at this time can be expressed as:

,or

(2)

Step S103: Searching for the three-dimensional most dangerous power function sliding surface, determining the independent variables of the objective function ( n , θ ), and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the three-dimensional most dangerous sliding surface of the slope. The specific search process includes the following steps:

Step S1031: Calculate the safety factor. According to the Chinese Computer Software Copyright Registration No. 2018SR695247, a three-dimensional slope stability calculation software based on the correction of the normal stress of the sliding surface is disclosed. The three-dimensional power function sliding surface equation Z = S determined by step S101 and its slope surface equation Z = G ; the slope soil material parameters cohesion c , internal friction angle φ and gravity γ are substituted into the equation group obtained by three force balances and a vertical direction ( y direction) moment balance, and the safety factor is obtained by solving it, that is, the search target function is determined.

Step S1032: Encode and decode the individuals consisting of n and θ as control parameters to form an initial population, and calculate the fitness values of the individuals in the population;

Step S1033: inserting new individuals formed by crossover and mutation operations into the originally selected individuals to form a new population, and calculating the fitness values of the individuals in the new population;

Step S1034: Determine whether the individuals of this generation meet the target convergence condition. If so, output the individuals of this generation. If not, repeat the above steps until the convergence condition is met. The optimal solution of this example is n = 15, θ = 12°, and the safety factor is 1.9514. The corresponding three-dimensional most dangerous sliding surface is shown in Figure 6.

The genetic algorithm is used for calculation, and the whole operation process is concise and efficient. The flow chart is shown in Figure 7.

On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the method for searching the three-dimensional most dangerous sliding surface of any slope in the above-mentioned embodiments.

It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

Memory, used to store computer programs;

The processor is used to implement the above-mentioned three-dimensional most dangerous sliding surface search method of the slope when executing the program stored in the memory.

The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

The communication interface is used for communication between the above electronic device and other devices.

The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A method for searching the most dangerous three-dimensional sliding surface of a slope, characterized by comprising the following steps: Step S101: constructing a three-dimensional power function sliding surface, constructing a three-dimensional power function sliding surface equation according to the slope size, and constructing a three-dimensional sliding surface according to the control parameters ( x0 _, z0 _, R , n , θ ) in the constructed three-dimensional power function sliding surface equation; Step S102: Searching for a two-dimensional arc main sliding surface, determining the independent variables of the objective function ( x ₀ , z ₀ , R ), and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the two-dimensional main sliding surface of the slope; Step S103: Searching for the three-dimensional most dangerous power function sliding surface, determining the independent variables ( n , θ ) of the objective function, and obtaining the minimum value of the objective function through a genetic algorithm search, that is, obtaining the three-dimensional most dangerous sliding surface of the slope. 2. The method for searching the three-dimensional most dangerous sliding surface of a slope according to claim 1 is characterized in that: The step S101 includes the following steps: Step S1011: determine the size of the slope, set the coordinate system for the slope, and measure the slope height, slope rate, and slope material parameters such as cohesion c , internal friction angle φ , and gravity γ ; Step S1012: Assuming that the center coordinates of the main sliding surface of the power function sliding surface are ( x0 _, 0 _, z0 ), and the sliding radius is R , based on the main sliding surface, three-dimensional expansion is performed in the direction of another coordinate axis y to obtain a cluster of three-dimensional power function sliding surface equations z = f ( x0 _, z0 _, R , n ); At the same time, in order to reflect the generalization of the power function sliding surface, it is assumed that the power function sliding surface has an angle with the horizontal plane, the size of which is θ . The general equation of the three-dimensional power function sliding surface of the slope is: z = f ( x0 _, z0 _, R , n , θ ); The equation under the established coordinate system is expressed as: (1) Where n is a positive real number, R _y is the semi-axis length of the slip surface in the y direction; Step S1013: Determine the control parameters, and determine a series of three-dimensional power function sliding surfaces according to the exact values or ranges of the control parameters in the three-dimensional power function sliding surface equation, and then realize the construction of the three-dimensional sliding surface. 3. The method for searching the three-dimensional most dangerous sliding surface of a slope according to claim 1 is characterized in that: The step S102 includes the following steps: Step S1021: Search for the two-dimensional arc main sliding surface. Take the arc center coordinates x ₀ , z ₀ and the arc radius R as the objective function independent variables, and obtain the minimum value of the objective function through genetic algorithm search, that is, obtain the corresponding x ₀ , z ₀ , R to realize the construction of the two-dimensional arc main sliding surface. 4. The method for searching the three-dimensional most dangerous sliding surface of a slope according to claim 1, characterized in that: the step S103 comprises the following steps: Step S1031: Calculate the safety factor, substitute the three-dimensional power function sliding surface equation Z = S determined in step S101 and its slope surface equation Z = G ; slope soil material parameters cohesion c , internal friction angle φ and gravity γ into the equation system obtained by three force balances and a vertical direction, i.e., y- direction moment balance, and solve to obtain the safety factor, that is, determine the search target function; Step S1032: using the control parameters n and θ as independent variables of the objective function, and using a genetic algorithm to search for the minimum value of the objective function, that is, obtaining the corresponding n and θ , to achieve the search for the most dangerous three-dimensional sliding surface. 5. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4. 6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 4.