A method, device and equipment for determining a supporting axial force of a foundation pit and a storage medium

Abstract

The application discloses a kind of foundation pit support axial force determination method, device, equipment and storage medium.The method comprises: determining the adjustable support quantity of support axial force in foundation pit and the objective function of the foundation pit, wherein the objective function is used to determine the lateral horizontal displacement of the enclosure structure of the foundation pit;According to the adjustable support quantity, the position of feasible particle is determined, and the position of the feasible particle is iterated using a preset particle swarm algorithm multiple times, to obtain particle position;For each iteration, the fitness is determined according to the particle position obtained by the current iteration and the objective function, and the target support axial force is determined according to the size of the fitness.The technical scheme of the embodiment of the application can accurately determine the optimal support axial force required by the foundation pit by using the preset particle swarm algorithm, solves the problem of low control precision of traditional control method, realizes fine control of the support axial force of the foundation pit, and avoids the deformation of the foundation pit.

Description

Technical Field

[0001] This invention relates to the field of building construction, and in particular to a method, apparatus, equipment and storage medium for determining the axial force of foundation pit support. Background Technology

[0002] In soft soil strata, foundation pit construction often causes strong environmental effects, such as uneven settlement or even cracking of nearby buildings and structures, affecting their normal use. With social development, traditional soft soil foundation pit construction techniques are increasingly unable to meet more stringent environmental protection requirements.

[0003] The load difference between the inside and outside of the pit acting on the retaining structure is one of the main causes of lateral deformation of the retaining structure. Based on the different ways in which the loads inside and outside the pit act on the retaining structure, construction control methods can be divided into two main categories: passive control technology and active control technology. Passive control technology refers to the retaining structure, support system, and soil inside the pit passively bearing the load difference between the inside and outside of the pit, and suppressing lateral deformation of the retaining structure by changing the mechanical properties of the structure during construction. Active control technology refers to the technology of reducing or even eliminating lateral deformation of the retaining structure by actively applying adjustable support axial forces to the retaining structure in real time.

[0004] While active control techniques are far less costly than passive control techniques for achieving satisfactory deformation control in soft soil foundation pits, traditional active control methods often suffer from low precision and are ineffective in addressing foundation pit deformation issues. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for determining the axial force of foundation pit supports, in order to solve the problem of low control accuracy of the axial force of foundation pit supports.

[0006] In a first aspect, the present invention provides a method for determining the axial force of a foundation pit support, comprising:

[0007] The number of adjustable supports for supporting axial force in the foundation pit and the objective function of the foundation pit are determined, wherein the objective function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit;

[0008] The positions of feasible particles are determined based on the number of adjustable supports, and the positions of the feasible particles are iteratively processed multiple times using a preset particle swarm algorithm to obtain the particle positions.

[0009] For each iteration, the fitness is determined based on the particle position obtained from the current iteration and the objective function, and the target support axial force is determined based on the magnitude of the fitness.

[0010] Secondly, the present invention provides a device for determining the axial force of a foundation pit support, comprising:

[0011] The quantity and function determination module is used to determine the number of adjustable supports with axial force in the foundation pit and the target function of the foundation pit, wherein the target function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit;

[0012] The particle position determination module is used to determine the position of feasible particles based on the number of adjustable supports, and to perform multiple iterations on the position of feasible particles using a preset particle swarm algorithm to obtain the particle position.

[0013] The target axial force determination module is used to determine the fitness based on the particle position obtained from the current iteration and the objective function for each iteration, and to determine the target support axial force based on the magnitude of the fitness.

[0014] Thirdly, the present invention provides an electronic device comprising:

[0015] At least one processor;

[0016] and memory that is communicatively connected to at least one processor;

[0017] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the method for determining the axial force of the foundation pit support described in the first aspect.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the method for determining the axial force of the foundation pit support described in the first aspect.

[0019] The present invention provides a scheme for determining the axial force of foundation pit support. This scheme determines the number of adjustable supports and the objective function of the foundation pit. The objective function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit. Based on the number of adjustable supports, the positions of feasible particles are determined. A preset particle swarm optimization algorithm is used to iteratively process the positions of these feasible particles multiple times to obtain particle positions. For each iteration, a fitness is determined based on the particle position obtained in the current iteration and the objective function. The target support axial force is then determined based on the magnitude of the fitness. By adopting the above technical solution and using a preset particle swarm optimization algorithm to iteratively process the positions of feasible particles, the optimal support axial force required by the foundation pit is accurately selected based on the fitness of the processing results (particle positions). This solves the problem of low control accuracy in traditional control methods, achieves refined control of the support axial force of the foundation pit, and avoids deformation of the foundation pit.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining the axial force of a foundation pit support according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining the axial force of a foundation pit support according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a device for determining the axial force of a foundation pit support according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0028] Example 1

[0029] Figure 1 The flowchart of a method for determining the axial force of a foundation pit support is provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of determining the axial force of a foundation pit support. The method can be executed by a device for determining the axial force of the foundation pit support. The device for determining the axial force of the foundation pit support can be implemented in hardware and / or software. The device for determining the axial force of the foundation pit support can be configured in an electronic device. The electronic device can be composed of two or more physical entities, or it can be composed of a single physical entity.

[0030] like Figure 1 As shown, the method for determining the axial force of a foundation pit support provided in Embodiment 1 of the present invention specifically includes the following steps:

[0031] S101. Determine the number of adjustable supports for the axial force of the foundation pit and the objective function of the foundation pit, wherein the objective function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit.

[0032] In this embodiment, when there is a load difference between the inside and outside of the foundation pit's retaining structure, it will cause lateral deformation of the retaining structure, resulting in displacement in the lateral horizontal direction. Therefore, it is necessary to reasonably and accurately determine the magnitude of the support axial force to avoid damage to the retaining structure. The number of adjustable supports can be preset, and the objective function can be determined using a preset method, such as simulating the magnitude of the horizontal displacement using relevant simulation software. The calculation method used in this simulation is the objective function. The support axial force in the foundation pit can be provided by a preset material (such as steel), and the number of adjustable supports for the support axial force can be understood as the number of objects (such as steel pipes) providing the support axial force.

[0033] S102. Determine the position of the feasible particle based on the number of adjustable supports, and use a preset particle swarm algorithm to iterate the position of the feasible particle multiple times to obtain the particle position.

[0034] In this embodiment, the particle swarm optimization (PSO) algorithm, developed by J. Kennedy and R.C. Eberhart et al. in 1995, is an evolutionary computation technique. It is a random search algorithm based on group cooperation, developed by simulating the foraging behavior of bird flocks, and is generally considered a type of swarm intelligence (SI). The positions of feasible particles can be determined based on the adjustable number of supports. For example, the number of feasible particles can be determined as twice the number of adjustable supports, and then a value is randomly selected within a preset range as the position of the feasible particle, thus obtaining multiple positions of feasible particles. By iteratively processing the positions of feasible particles using a preset PSO algorithm, multiple particle positions can be obtained; that is, multiple support axial forces can be determined through multiple iterations. Each iteration yields one position of a feasible particle. Due to multiple iterations, each feasible particle can correspond to multiple positions. The particle position is equivalent to the support axial force, and the spatial dimension corresponding to the preset PSO algorithm is equivalent to the number of adjustable supports.

[0035] S103. For each iteration, determine the fitness based on the particle position obtained from the current iteration and the objective function, and determine the target support axial force based on the magnitude of the fitness.

[0036] In this embodiment, a corresponding particle position is obtained after each iteration, meaning that new positions are generated during the continuous iteration process. After each iteration, the supporting axial force corresponding to the currently obtained particle position can be used as a known quantity of the objective function. Using this known quantity, the value of the objective function can be calculated, which is the fitness. Since the fitness value is equivalent to the magnitude of the lateral horizontal displacement of the enclosure structure, the fitness values ​​of the example positions obtained after each iteration can be compared, and the supporting axial force represented by the particle position with the smaller fitness value can be used as the target supporting axial force.

[0037] The method for determining the axial force of foundation pit support provided in this invention involves determining the number of adjustable supports and the objective function of the foundation pit. The objective function determines the lateral horizontal displacement of the retaining structure of the foundation pit. The method determines the positions of feasible particles based on the number of adjustable supports and iteratively processes these positions using a preset particle swarm optimization algorithm. For each iteration, a fitness is determined based on the particle position and the objective function, and the target axial force is determined based on the fitness. This invention utilizes a preset particle swarm optimization algorithm to iteratively process the positions of feasible particles. Based on the fitness of the processed particle positions, the optimal axial force required by the foundation pit is accurately selected. This solves the problem of low control accuracy in traditional control methods, achieving refined control of the foundation pit's axial force and preventing deformation of the foundation pit.

[0038] Example 2

[0039] Figure 2 This is a flowchart of a method for determining the axial force of a foundation pit support provided in Embodiment 2 of the present invention. The technical solution of the present invention is further optimized based on the above optional technical solutions, and provides a specific method for determining the axial force of the foundation pit support.

[0040] Optionally, before determining the position of feasible particles based on the number of adjustable supports, the method further includes: determining the maximum and minimum bearing capacity of the support axial force, and setting the maximum bearing capacity as the upper limit of the particle feasible region in a preset particle swarm optimization algorithm, and setting the minimum bearing capacity as the lower limit of the particle feasible region; determining the first limit information of the retaining structure and the second limit information of the concrete in the foundation pit, and determining the constraint conditions in the preset particle swarm optimization algorithm based on the first limit information and the second limit information, wherein the first limit information includes the ultimate bending moment and ultimate shear force of the retaining structure, and the second limit information includes the ultimate compressive value and ultimate tensile value of the concrete in the foundation pit; wherein determining the position of feasible particles based on the number of adjustable supports includes: determining the position of feasible particles based on the particle feasible region, the constraint conditions, and the number of adjustable supports. The advantage of this setting is that it accurately and reasonably limits the range of particle positions, ensures the accuracy and precision of the determined support axial force, and ensures that the determined support axial force can meet the requirements of risk management at the foundation pit construction site.

[0041] Optionally, the step of iteratively processing the positions of feasible particles using the preset particle swarm optimization algorithm to obtain particle positions includes: determining an initial particle velocity within a preset velocity range, wherein the upper and lower limits of the preset velocity range are determined based on the maximum bearing capacity of the supporting axis force; iteratively processing the initial particle velocity, the positions of feasible particles, the inertia weight, and the optimal particle position using the preset particle swarm optimization algorithm to obtain particle velocity; and determining the particle position based on a preset time step and the particle velocity, wherein the optimal particle position is determined based on both individual optimal position and global optimal position, and is determined based on the objective function and the constraints in the preset particle swarm optimization algorithm; the inertia weight is determined based on a preset maximum number of iterations. The advantage of this setup is that by iteratively processing the particle position and particle velocity using the preset particle swarm optimization algorithm, precise control of the supporting axis force is ensured.

[0042] Optionally, for each iteration, determining the fitness based on the particle position obtained in the current iteration and the objective function, and determining the target support axial force based on the fitness, includes: for each particle position obtained after each iteration, using the objective function to determine the fitness of the current particle position, and determining the retaining structure information and foundation pit concrete information corresponding to the current particle position, wherein the retaining structure information includes the bending moment and shear force of the retaining structure, and the foundation pit concrete information includes the compressive and tensile values ​​of the concrete in the foundation pit; after each iteration, determining whether the current retaining structure information and the current foundation pit concrete information both meet the constraints in the preset particle swarm optimization algorithm and whether the current fitness is less than a preset fitness threshold; if so, determining the particle position corresponding to the current fitness as the particle position to be determined, wherein the constraints include bending moment constraints, shear force constraints, compressive constraints, and tensile constraints; determining the minimum value among the fitness values ​​corresponding to the particle position to be determined as the target fitness, and determining the support axial force corresponding to the target fitness as the target support axial force. The advantage of this setup is that by verifying the particle position using constraints and objective functions after each iteration, it is possible to achieve precise optimization of the support axial force under different working conditions with high efficiency.

[0043] like Figure 2 As shown in Embodiment 2 of the present invention, a method for determining the axial force of a foundation pit support specifically includes the following steps:

[0044] S201. Determine the number of adjustable supports for the axial force of the foundation pit and the objective function of the foundation pit.

[0045] S202. Determine the maximum and minimum bearing capacity of the supporting axial force, and set the maximum bearing capacity as the upper limit of the particle feasible region in the preset particle swarm algorithm, and set the minimum bearing capacity as the lower limit of the particle feasible region.

[0046] Specifically, the maximum and minimum bearing capacity of the supporting axial force can be determined using a first preset method, such as a table lookup, based on the size and material of the object providing the supporting axial force. For example, if the maximum bearing capacity of a steel pipe with an outer diameter of 609 mm is 718 kN per meter and the minimum bearing capacity is 0, then the feasible region of the particle is greater than 0 and less than 718 kN.

[0047] S203. Determine the first limit information of the retaining structure and the second limit information of the concrete in the foundation pit, and determine the constraint conditions in the preset particle swarm algorithm based on the first limit information and the second limit information.

[0048] The first limit information includes the ultimate bending moment and ultimate shear force of the retaining structure, and the second limit information includes the ultimate compressive value and ultimate tensile value of the concrete in the foundation pit.

[0049] Specifically, by using methods such as looking up tables, the ultimate shear force of the retaining structure can be determined based on its material and dimensions, for example, an ultimate shear force of 3000 kN. The ultimate bending moment of the retaining structure can also be determined, including the ultimate bending moment on the soil-facing side and the ultimate bending moment on the soil-repelling side, for example, an ultimate bending moment on the soil-facing side of 2600 kN per meter and an ultimate bending moment on the soil-repelling side of 3700 kN per meter. Further, by using methods such as looking up tables, the ultimate compressive and ultimate tensile values ​​of the concrete can be determined based on its location and dimensions. For example, the ultimate compressive value of the first concrete support in the foundation pit is 6000 kN, and the ultimate tensile value is 2000 kN; the ultimate tensile value of the fourth concrete support is 7700 kN, and the ultimate tensile value is 3500 kN. The constraints can be: the bending moment of the retaining structure corresponding to the axial force of the support represented by the particle position is less than the ultimate bending moment; the shear force of the retaining structure corresponding to the axial force of the support represented by the particle position is less than the ultimate compressive value of the concrete; and the tensile value of the concrete corresponding to the axial force of the support represented by the particle position is less than the ultimate tensile value. The bending moment and shear force of the retaining structure corresponding to the axial force of the support, as well as the compressive and tensile values ​​of the concrete corresponding to the axial force of the support, can be simulated using simulation software.

[0050] S204. Determine the position of the feasible particle based on the particle feasible region, the constraint conditions, and the adjustable number of supports.

[0051] Specifically, the positions of particles that satisfy the constraints can be selected from the feasible region of the particles. These positions are the positions of feasible particles, and the number of feasible particles can be the same as the number of adjustable supports.

[0052] Optionally, determining the position of feasible particles based on the particle feasible region, the constraints, and the number of adjustable supports includes: determining a first initial position of a first initial particle within the particle feasible region, and determining the difference between the sum of the upper and lower limits of the particle feasible region and the first initial position as the second initial position of a second initial particle; determining an initial number of feasible particles based on the number of adjustable supports, and determining the positions of the initial number of feasible particles from the particle feasible region based on the constraints, the objective function, and the initial positions, wherein the initial positions include the first initial position and the second initial position. The advantage of this configuration is that it accurately determines the positions of feasible particles from the particle feasible region using the particle feasible region and the number of adjustable supports.

[0053] Specifically, the process of determining the position of a feasible particle can be as follows:

[0054] 1) First, randomly select a value in the feasible region of the particle as the first initial position of the first initial particle.

[0055] 2) Represent the second initial position x using the following method. OP :

[0056] x OP =x upper +x lower -x P

[0057] Where, x upper x is the upper limit of the feasible region of the particle. lower x is the lower bound of the feasible region of the particle. P If the number of adjustable supports is m, then the initial number of feasible particles can be determined as km, such as 3m.

[0058] 3) Test x OP and x P Check whether the constraints are met and calculate their fitness using the objective function. Identify the particles corresponding to the positions with smaller fitness (i.e. smaller lateral horizontal displacement of the enclosure structure) as feasible particles. If only one position meets the constraints, then that position is identified as a feasible particle. If neither of the two positions meets the constraints or the number of feasible particles is less than the initial number, then repeat steps 1) to 3) above.

[0059] S205. Determine the initial particle velocity within the preset velocity range.

[0060] The upper and lower limits of the preset speed range are determined based on the maximum bearing capacity of the supporting axial force.

[0061] Specifically, the maximum bearing capacity of the supporting axial force can be used as the upper limit of the preset speed range, and the opposite of the maximum bearing capacity can be used as the lower limit of the preset speed range. A value can be randomly selected from the preset speed range as the initial particle velocity.

[0062] S206. Using the preset particle swarm algorithm, iteratively process the initial particle velocity, the position of the feasible particle, the inertia weight, and the optimal position of the particle to obtain the particle velocity, and determine the particle position according to the preset time step and the particle velocity.

[0063] The optimal particle position is determined based on both the individual optimal position and the global optimal position, according to the objective function and the constraints in the preset particle swarm algorithm, and the inertia weight is determined based on a preset maximum number of iterations.

[0064] For example, the particle velocity after each iteration can be determined using the following method:

[0065]

[0066] in, This represents the particle velocity of the i-th particle obtained after the current (k+1) iteration. Let r1 and r2 be the particle velocity of the i-th particle obtained after the previous (k-th) iteration, where r1 and r2 are random numbers greater than or equal to 0 and less than or equal to 1. This represents the position of the i-th particle obtained after the previous (k-th) iteration. Let be the optimal position of the i-th particle obtained after the 1st to kth iterations. It represents the position with the minimum fitness among the multiple positions corresponding to the i-th particle obtained after the 1st to kth iterations, satisfying the constraints. The global optimal position is obtained after the 1st to kth iterations, representing the position with the minimum fitness among all the multiple positions corresponding to all particles obtained after the 1st to kth iterations, satisfying the constraints. ω k+1 This is the inertial weight corresponding to the current (k+1) iteration, which can be used to control the detection capability of the particle swarm. Each particle velocity contains multiple iterative velocity values ​​for feasible particles. These iterative velocity values ​​are obtained after each iteration. The inertial weight can be determined based on a preset maximum number of iterations; for example, the quotient of the preset value and the maximum number of iterations can be used as the inertial weight.

[0067] Optionally, determining the particle position based on the preset time step and the particle velocity includes: for each iteration, determining the product of the current particle velocity and the preset time step, and calculating the sum of the product and the historical particle positions obtained in the previous iteration to obtain the particle position corresponding to the current iteration. The advantage of this setting is that it ensures the accuracy of the particle positions obtained after each iteration.

[0068] For example, the particle position corresponding to the current iteration. It can be determined in the following way:

[0069]

[0070] in, This represents the position of the i-th particle after the current (k+1)th iteration, where Δt is the preset time step, which can be set to 1 second. This represents the position of the i-th particle after the previous (k-th) iteration.

[0071] Optionally, the determination of the inertia weight includes: determining a preset maximum inertia weight, a preset minimum inertia weight, and a preset maximum number of iterations; determining a first weight difference between the preset maximum inertia weight and the preset minimum inertia weight, and determining the quotient of the first weight difference and the preset maximum number of iterations; for each iteration, determining the product of the quotient and the number of iterations for the current iteration, and determining a second weight difference between the preset maximum inertia weight and the product, thus obtaining the inertia weight. The advantage of this setting is that by utilizing the preset maximum and minimum inertia weights, the inertia weight of the particle velocity is accurately determined, further ensuring the accuracy of the supporting axial force.

[0072] For example, the inertia weight ω can be determined in the following manner. k+1 :

[0073]

[0074] Where, ω max The preset maximum inertia weight can be 0.9, ω min The preset minimum inertia weight can be 0.4, and Tmax is the preset maximum number of iterations. Tmax can be determined based on the number of adjustable supports; for example, if the number of adjustable supports is m, then Tmax can be 6m.

[0075] S207. For the particle position obtained after each iteration, the fitness of the current particle position is determined using the objective function, and the retaining structure information and foundation pit concrete information corresponding to the current particle position are determined.

[0076] The information on the retaining structure includes the bending moment and shear force of the retaining structure, and the information on the foundation pit concrete includes the compressive and tensile values ​​of the concrete in the foundation pit.

[0077] Specifically, after each iteration, the corresponding particle position can be obtained. Substituting the supporting axial force, such as N, corresponding to that particle position into the objective function yields the objective function value (the fitness of the current particle position). As mentioned above, simulation software can be used to simulate the bending moment and shear force of the retaining structure corresponding to the supporting axial force, as well as the compressive and tensile values ​​of the concrete in the foundation pit.

[0078] S208. After each iteration, determine whether the current retaining structure information and the current foundation pit concrete information both meet the constraints in the preset particle swarm algorithm and whether the current fitness is less than the preset fitness threshold. If yes, then determine the particle position corresponding to the current fitness as the particle position to be determined. If no, then continue the iteration process.

[0079] The constraints include bending moment constraints, shear force constraints, compression constraints, and tension constraints.

[0080] Specifically, after each iteration, it is determined whether the retaining structure information and foundation pit concrete information corresponding to the current particle position meet the requirements of the constraints, and whether the fitness of the particle position is less than a preset fitness threshold. If yes, the particle position can be designated as a particle position to be determined; otherwise, the particle position can be discarded, and the iteration process can continue. The stopping condition for the iteration process can be that the number of iterations exceeds a preset maximum number of iterations.

[0081] S209. The minimum value of the fitness corresponding to the position of the particle to be determined is determined as the target fitness, and the supporting axial force corresponding to the target fitness is determined as the target supporting axial force.

[0082] For example, if the fitness corresponding to the position of the particle to be determined of one of the feasible particles is a, b and c, and a is the minimum value, then a is the target fitness. If the supporting axial force corresponding to a is 500 N per meter, then the target supporting axial force is 500 N per meter.

[0083] The method for determining the axial force of foundation pit support provided in this invention first determines the feasible region and constraints of the particles in the preset particle swarm optimization algorithm, accurately and reasonably limiting the range of particle positions, ensuring the accuracy and precision of the determined support axial force, and ensuring that the determined support axial force can meet the risk management requirements of the foundation pit construction site. Then, by using the preset particle swarm optimization algorithm to iteratively process the particle positions and particle velocities, it ensures precise control of the support axial force. After each iteration, the particle positions are verified using the constraints and objective function, realizing fine optimization of the support axial force under different working conditions, improving control efficiency, solving the problem of low control precision in traditional control methods, and avoiding deformation of the foundation pit.

[0084] Example 3

[0085] Figure 3 This is a structural schematic diagram of a device for determining the axial force of a foundation pit support provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a quantity and function determination module 301, a particle position determination module 302, and a target axial force determination module 303, wherein:

[0086] The quantity and function determination module is used to determine the number of adjustable supports with axial force in the foundation pit and the target function of the foundation pit, wherein the target function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit;

[0087] The particle position determination module is used to determine the position of feasible particles based on the number of adjustable supports, and to perform multiple iterations on the position of feasible particles using a preset particle swarm algorithm to obtain the particle position.

[0088] The target axial force determination module is used to determine the fitness based on the particle position obtained from the current iteration and the objective function for each iteration, and to determine the target support axial force based on the magnitude of the fitness.

[0089] The device for determining the axial force of foundation pit support provided in this embodiment of the invention uses a preset particle swarm optimization algorithm to iteratively process the positions of feasible particles multiple times. Based on the fitness of the processing results (particle positions), it accurately selects the required and optimal axial force of foundation pit support. This solves the problem of low control accuracy in traditional control methods, realizes fine control of the axial force of foundation pit support, and avoids deformation of foundation pit.

[0090] Optionally, the device may also include:

[0091] The feasible region determination module is used to determine the maximum and minimum bearing capacity of the support axial force before determining the position of the feasible particles based on the number of adjustable supports, and to determine the maximum bearing capacity as the upper limit of the particle feasible region in the preset particle swarm algorithm, and the minimum bearing capacity as the lower limit of the particle feasible region.

[0092] The constraint determination module is used to determine the first limit information of the retaining structure and the second limit information of the concrete in the foundation pit, and to determine the constraint conditions in the preset particle swarm algorithm based on the first limit information and the second limit information. The first limit information includes the ultimate bending moment and ultimate shear force of the retaining structure, and the second limit information includes the ultimate compressive value and ultimate tensile value of the concrete in the foundation pit.

[0093] Optionally, the particle position determination module includes:

[0094] The first position determination unit is used to determine the position of a feasible particle based on the particle feasible region, the constraint conditions, and the number of adjustable supports.

[0095] Optionally, determining the position of a feasible particle based on the particle feasible region, the constraint condition, and the adjustable number of supports includes: determining a first initial position of a first initial particle in the particle feasible region, and determining the position difference between the sum of the upper limit and lower limit of the particle feasible region and the first initial position as the second initial position of a second initial particle; determining an initial number of feasible particles based on the adjustable number of supports, and determining the position of the initial number of feasible particles from the particle feasible region based on the constraint condition, the objective function, and the initial position, wherein the initial position includes a first initial position and a second initial position.

[0096] Optionally, the particle position determination module includes:

[0097] A velocity determination unit is used to determine the initial particle velocity within a preset velocity range, wherein the upper and lower limits of the preset velocity range are determined based on the maximum bearing capacity of the supporting axial force.

[0098] The second position determination unit is used to iteratively process the initial particle velocity, the position of the feasible particle, the inertia weight, and the optimal particle position using the preset particle swarm algorithm to obtain the particle velocity, and determine the particle position according to the preset time step and the particle velocity. The optimal particle position is determined based on both the individual optimal position and the global optimal position, and is determined according to the objective function and the constraints in the preset particle swarm algorithm. The inertia weight is determined based on the preset maximum number of iterations.

[0099] Optionally, determining the particle position based on the preset time step and the particle velocity includes: for each iteration, determining the product of the current particle velocity and the preset time step, and calculating the sum of the product and the historical particle position obtained in the previous iteration to obtain the particle position corresponding to the current iteration.

[0100] Optionally, the determination of the inertia weight includes: determining a preset maximum inertia weight, a preset minimum inertia weight, and a preset maximum number of iterations; determining a first weight difference between the preset maximum inertia weight and the preset minimum inertia weight, and determining the quotient of the first weight difference and the preset maximum number of iterations; for each iteration, determining the product of the quotient and the number of iterations of the current iteration, and determining a second weight difference between the preset maximum inertia weight and the product, to obtain the inertia weight.

[0101] Optionally, the constraint determination module includes:

[0102] The information determination unit is used to determine the fitness of the current particle position using an objective function for the particle position obtained after each iteration, and to determine the retaining structure information and foundation pit concrete information corresponding to the current particle position. The retaining structure information includes the bending moment and shear force of the retaining structure, and the foundation pit concrete information includes the compressive and tensile values ​​of the concrete in the foundation pit.

[0103] The unit for determining the position to be determined is used to determine, after each iteration, whether the current retaining structure information and the current foundation pit concrete information both meet the constraints in the preset particle swarm algorithm and whether the current fitness is less than the preset fitness threshold. If so, the particle position corresponding to the current fitness is determined as the particle position to be determined. The constraints include bending moment constraints, shear force constraints, compression constraints, and tension constraints.

[0104] The axial force determination unit is used to determine the minimum value of the fitness corresponding to the position of the particle to be determined as the target fitness, and to determine the supporting axial force corresponding to the target fitness as the target supporting axial force.

[0105] The device for determining the axial force of the foundation pit support provided in the embodiments of the present invention can execute the method for determining the axial force of the foundation pit support provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0106] Example 4

[0107] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0108] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0109] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0110] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the method for determining the axial force of the foundation pit support.

[0111] In some embodiments, the method for determining the axial force of the foundation pit support can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the method for determining the axial force of the foundation pit support described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the method for determining the axial force of the foundation pit support by any other suitable means (e.g., by means of firmware).

[0112] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0114] The computer equipment provided above can be used to execute the method for determining the axial force of the foundation pit support provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0115] Example 5

[0116] In the context of this invention, the computer-readable storage medium may be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to perform a method for determining the axial force of the foundation pit support, the method comprising:

[0117] The number of adjustable supports for supporting axial force in the foundation pit and the objective function of the foundation pit are determined, wherein the objective function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit;

[0118] The positions of feasible particles are determined based on the number of adjustable supports, and the positions of the feasible particles are iteratively processed multiple times using a preset particle swarm algorithm to obtain the particle positions.

[0119] For each iteration, the fitness is determined based on the particle position obtained from the current iteration and the objective function, and the target support axial force is determined based on the magnitude of the fitness.

[0120] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by, or in conjunction with, an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0121] The computer equipment provided above can be used to execute the method for determining the axial force of the foundation pit support provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0122] It is worth noting that in the embodiments of the above-mentioned foundation pit support axial force determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0123] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for determining the axial force of a foundation pit support, characterized in that, include: The number of adjustable supports for supporting axial force in the foundation pit and the objective function of the foundation pit are determined, wherein the objective function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit; Determine the maximum and minimum bearing capacity of the supporting axial force, and set the maximum bearing capacity as the upper limit of the particle feasible region in the preset particle swarm algorithm, and set the minimum bearing capacity as the lower limit of the particle feasible region; The first limit information of the retaining structure and the second limit information of the concrete support in the foundation pit are determined, and the constraint conditions in the preset particle swarm algorithm are determined based on the first limit information and the second limit information. The first limit information includes the ultimate bending moment and ultimate shear force of the retaining structure, and the second limit information includes the ultimate compressive value and ultimate tensile value of the concrete support in the foundation pit. Determining the position of a feasible particle based on the particle feasible region, the constraints, and the adjustable number of supports includes: determining a first initial position of a first initial particle within the particle feasible region, and determining the position difference between the sum of the upper and lower limits of the particle feasible region and the first initial position as the second initial position of a second initial particle; determining an initial number of feasible particles based on the adjustable number of supports, and determining the position of the initial number of feasible particles from the particle feasible region based on the constraints, the objective function, and the initial positions, wherein the initial positions include a first initial position and a second initial position; The system iterates through the positions of the feasible particles using a preset particle swarm optimization algorithm to obtain the particle positions. This includes: determining an initial particle velocity within a preset velocity range, where the upper and lower limits of the preset velocity range are determined based on the maximum bearing capacity of the supporting axis force; iterating through the initial particle velocity, the positions of the feasible particles, the inertia weight, and the optimal particle position using the preset particle swarm optimization algorithm to obtain the particle velocity; and determining the particle position based on a preset time step and the particle velocity, where the optimal particle position is determined based on both the individual optimal position and the global optimal position, and is determined according to the objective function and the constraints in the preset particle swarm optimization algorithm; the inertia weight is determined based on a preset maximum number of iterations. For each iteration, a fitness is determined based on the particle position obtained from the current iteration and the objective function, and the target support axial force is determined based on the magnitude of the fitness, including: For each particle position obtained after the iteration, the fitness of the current particle position is determined using the objective function, and the retaining structure information and foundation pit concrete support information corresponding to the current particle position are determined. The retaining structure information includes the bending moment and shear force of the retaining structure, and the foundation pit concrete support information includes the compressive and tensile values ​​of the concrete support in the foundation pit. After each iteration, it is determined whether the current retaining structure information and the current foundation pit concrete support information both meet the constraints in the preset particle swarm algorithm and whether the current fitness is less than the preset fitness threshold. If so, the particle position corresponding to the current fitness is determined as the particle position to be determined. The constraints include bending moment constraints, shear force constraints, compression constraints, and tension constraints. The minimum value of the fitness corresponding to the position of the particle to be determined is determined as the target fitness, and the supporting axial force corresponding to the target fitness is determined as the target supporting axial force.

2. The method according to claim 1, characterized in that, The step of determining the particle position based on a preset time step and the particle velocity includes: For each iteration, the product of the current particle velocity and the preset time step is determined, and the sum of the product and the historical particle position obtained from the previous iteration is calculated to obtain the particle position corresponding to the current iteration.

3. The method according to claim 1, characterized in that, The determination of the inertia weight includes: Determine the preset maximum inertia weight, the preset minimum inertia weight, and the preset maximum number of iterations; Determine the first weight difference between the preset maximum inertia weight and the preset minimum inertia weight, and determine the quotient of the first weight difference and the preset maximum number of iterations; For each iteration, the product of the quotient and the iteration number of the current iteration is determined, and the difference between the preset maximum inertia weight and the second weight of the product is determined to obtain the inertia weight.

4. A device for determining the axial force of a foundation pit support, used to perform the method for determining the axial force of a foundation pit support as described in any one of claims 1-3, characterized in that, include: The quantity and function determination module is used to determine the number of adjustable supports with axial force in the foundation pit and the target function of the foundation pit, wherein the target function is used to determine the lateral horizontal displacement of the retaining structure of the foundation pit; The particle position determination module is used to determine the position of feasible particles based on the number of adjustable supports, and to perform multiple iterations on the position of feasible particles using a preset particle swarm algorithm to obtain the particle position. The target axial force determination module is used to determine the fitness based on the particle position obtained from the current iteration and the objective function for each iteration, and to determine the target support axial force based on the magnitude of the fitness. The particle position determination module includes: A velocity determination unit is used to determine the initial particle velocity within a preset velocity range, wherein the upper and lower limits of the preset velocity range are determined based on the maximum bearing capacity of the supporting axial force. The second position determination unit is used to iteratively process the initial particle velocity, the position of the feasible particle, the inertia weight, and the optimal particle position using the preset particle swarm algorithm to obtain the particle velocity, and determine the particle position according to the preset time step and the particle velocity. The optimal particle position is determined based on both the individual optimal position and the global optimal position, and is determined according to the objective function and the constraints in the preset particle swarm algorithm. The inertia weight is determined based on the preset maximum number of iterations.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the axial force of the foundation pit support as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the axial force of the foundation pit support as described in any one of claims 1-3.

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