Simulation optimization method and system for cable tower of steel rigid frame composite structure of sea area bridge

Abstract

The invention discloses a method and system for simulating and optimizing a cable tower of a steel-rigid skeleton composite structure of a sea area bridge. The method comprises: obtaining a cable tower information set, wherein the cable tower information set comprises: an effective cross-sectional area of ​​the cable tower, a maximum compression force that the cable tower can withstand, an elastic modulus of steel, a polar moment of inertia of a cable tower section about a central axis, a length of the cable tower, a shear modulus of steel, a maximum torque that the cable tower can withstand, a polar moment of inertia of a cable tower section about a longitudinal axis, an external torque received by the cable tower, and a cross-sectional area of ​​the cable tower; setting a first cable tower stability model and a second cable tower stability model, and optimizing the first cable tower stability model and the second cable tower stability model by random search according to the cable tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean; searching for a cable tower information set corresponding to the maximum stability value mean, thereby completing the cable tower simulation optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of cable tower simulation optimization, and more specifically, relates to a cable tower simulation optimization method and system for a marine bridge steel rigid frame composite structure. Background Art

[0002] Offshore bridge tower simulation refers to the process of virtual simulation and analysis of the supporting structure of offshore bridges - towers. This simulation usually involves technologies such as computer-aided design (CAD), computer-aided engineering (CAE) and computer-aided manufacturing (CAM).

[0003] At present, the current status of sea bridge tower simulation can be summarized as follows:

[0004] Application of CAD software: Using CAD software to perform 3D modeling of the cable tower is the first step in simulation. These software include AutoCAD, SolidWorks, CATIA, etc., which can help engineers create cable tower models with accurate dimensions and geometric shapes.

[0005] Finite element analysis (FEA): Finite element analysis is a commonly used engineering simulation technology used to evaluate the stress, deformation, etc. of a structure under different loads. Finite element analysis of offshore bridge towers can help engineers understand the stress conditions of the towers in the marine environment and possible safety hazards.

[0006] Fluid dynamics simulation: Sea bridges are affected by the marine environment, such as waves, tides, etc. Through fluid dynamics simulation, the impact of these external environments on the towers can be evaluated, including the impact of water flow on the structure, vortex effects, etc.

[0007] However, the simulation accuracy of existing technologies is insufficient, resulting in limited reference value for bridge construction. Summary of the invention

[0008] In order to solve the above technical problems, the present invention proposes a simulation optimization method for a cable tower of a marine bridge steel rigid skeleton composite structure, comprising:

[0009] Acquire a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of a tower section about a central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of a tower section about a longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower;

[0010] Setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean;

[0011] The tower information set corresponding to the mean of the maximum stability values ​​is found, thereby completing the tower simulation optimization.

[0012] Furthermore, the first tower stability model includes:

[0013]

[0014] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum allowable force of the tower design, α is the first weight of the first tower stability model, β is the second weight of the first tower stability model, γ is the third weight of the first tower stability model, G is the shear modulus of steel, δ is the fourth weight of the first tower stability model, T max is the maximum torque that the tower can withstand, ∈ is the fifth weight of the first model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0015] Furthermore, the second tower stability model includes:

[0016]

[0017] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum force allowed in the tower design, α′ is the first weight of the second tower stability model, β′ is the second weight of the second tower stability model, γ′ is the third weight of the second tower stability model, G is the shear modulus of steel, δ′ is the fourth weight of the second tower stability model, T max is the maximum torque that the tower can withstand, ∈′ is the fifth weight of the second model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0018] Further, optimizing the first tower stability model and the second tower stability model by random search includes:

[0019] Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set;

[0020] Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ;

[0021] Step 3: Set the initial search radius R (0) and the initial step length δ (0) ;

[0022] Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set;

[0023] Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model;

[0024] Step 6: If the maximum number of iterations is reached, stop the optimization process; otherwise, return to step 3 to continue searching.

[0025] Furthermore, step 3 includes:

[0026] In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of in r is a random vector that follows a uniform distribution and ranges from [-R (k) ,R (k) ], R (k) is the current search radius;

[0027] Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value at

[0028] If the function value Less than the current tower information set x (k) The function value f( x(k) ), then accept the new tower information set Otherwise, the probability Accept a new tower information set, where δ (k)is the current step size. If p is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information collection Otherwise remain unchanged.

[0029] The present invention also proposes a cable tower simulation optimization system for a marine bridge steel rigid skeleton composite structure, comprising:

[0030] An information acquisition module is used to acquire a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of the tower section about the central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of the tower section about the longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower;

[0031] Setting a model module, used for setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean;

[0032] The optimization module is used to find the tower information set corresponding to the mean value of the maximum stability value, so as to complete the tower simulation optimization.

[0033] Furthermore, the first tower stability model includes:

[0034]

[0035] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum allowable force of the tower design, α is the first weight of the first tower stability model, β is the second weight of the first tower stability model, γ is the third weight of the first tower stability model, G is the shear modulus of steel, δ is the fourth weight of the first tower stability model, T max is the maximum torque that the tower can withstand, ∈ is the fifth weight of the first model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0036] Furthermore, the second tower stability model includes:

[0037]

[0038] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum force allowed in the tower design, α′ is the first weight of the second tower stability model, β′ is the second weight of the second tower stability model, γ′ is the third weight of the second tower stability model, G is the shear modulus of steel, δ′ is the fourth weight of the second tower stability model, T max is the maximum torque that the tower can withstand, ∈′ is the fifth weight of the second model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0039] Further, optimizing the first tower stability model and the second tower stability model by random search includes:

[0040] Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set;

[0041] Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ;

[0042] Step 3: Set the initial search radius R (0) and the initial step length δ (0) ;

[0043] Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set;

[0044] Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model;

[0045] Step 6: If the maximum number of iterations is reached, stop the optimization process; otherwise, return to step 3 to continue searching.

[0046] Furthermore, step 3 includes:

[0047] In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of in r is a random vector that follows a uniform distribution and ranges from [-R (k) ,R (k) ], R (k) is the current search radius;

[0048] Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value at

[0049] If the function value Less than the current tower information set x (k) The function value f(x (k) ), then accept the new tower information set Otherwise, the probability Accept new tower information collection, where is the current step size. If p is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information collection Otherwise remain unchanged.

[0050] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0051] The present invention obtains a tower information set, wherein the tower information set includes: the effective cross-sectional area of ​​the tower, the maximum compression force that the tower can withstand, the elastic modulus of the steel, the polar moment of inertia of the tower section about the central axis, the length of the tower, the maximum force allowed by the tower design, the shear modulus of the steel, the maximum torque that the tower can withstand, the polar moment of inertia of the tower section about the longitudinal axis, the external torque received by the tower and the cross-sectional area of ​​the tower; a first tower stability model and a second tower stability model are set, and according to the tower information set, the first tower stability model and the second tower stability model are optimized by random search, the first maximum stability value and the second maximum stability value are determined, and finally the maximum stability value mean is obtained; the tower information set corresponding to the maximum stability value mean is searched, thereby completing the tower simulation optimization. According to the above technical scheme, the present invention can realize accurate simulation optimization of tower stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0053] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0054] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0056] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0057] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0058] The display is used to show the interactive cross-section of each application.

[0059] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0060] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0061] Example 1

[0062] like Figure 1 As shown, an embodiment of the present invention provides a method for simulating and optimizing a cable tower of a steel rigid skeleton composite structure of a sea area bridge, comprising:

[0063] Step 101, obtaining a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of a tower section about a central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of a tower section about a longitudinal axis, an external torque on the tower, and a cross-sectional area of ​​the tower;

[0064] Step 102, setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean;

[0065] Specifically, the first tower stability model includes:

[0066]

[0067] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum allowable force of the tower design, α is the first weight of the first tower stability model, β is the second weight of the first tower stability model, γ is the third weight of the first tower stability model, G is the shear modulus of steel, δ is the fourth weight of the first tower stability model, T max is the maximum torque that the tower can withstand, ∈ is the fifth weight of the first model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0068] Specifically, the second tower stability model includes:

[0069]

[0070] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum force allowed in the tower design, α′ is the first weight of the second tower stability model, β′ is the second weight of the second tower stability model, γ′ is the third weight of the second tower stability model, G is the shear modulus of steel, δ′ is the fourth weight of the second tower stability model, T max is the maximum torque that the tower can withstand, ∈′ is the fifth weight of the second model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0071] Specifically, optimizing the first tower stability model and the second tower stability model by random search includes:

[0072] Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set;

[0073] Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ;

[0074] Step 3: Set the initial search radius R (0) and the initial step length δ (0) ;

[0075] Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set;

[0076] Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model;

[0077] Step 6: If the maximum number of iterations is reached, stop the optimization process; otherwise, return to step 3 to continue searching.

[0078] Specifically, step 3 includes:

[0079] In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of in r is a random vector that follows a uniform distribution and ranges from [-R (k) ,R (k) ], R (k) is the current search radius;

[0080] Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value at

[0081] If the function value Less than the current tower information set x (k) The function value f( x(k) ), then accept the new tower information set Otherwise, the probability Accept a new tower information set, where δ (k) is the current step size. If p is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information collection Otherwise remain unchanged.

[0082] Step 103, searching for the tower information set corresponding to the mean of the maximum stability values, thereby completing the tower simulation optimization.

[0083] Example 2

[0084] like Figure 2 As shown, the embodiment of the present invention also proposes a cable tower simulation optimization system for a marine bridge steel rigid skeleton composite structure, comprising:

[0085] An information acquisition module is used to acquire a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of the tower section about the central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of the tower section about the longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower;

[0086] Setting a model module, used for setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean;

[0087] Specifically, the first tower stability model includes:

[0088]

[0089] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum allowable force of the tower design, α is the first weight of the first tower stability model, β is the second weight of the first tower stability model, γ is the third weight of the first tower stability model, G is the shear modulus of steel, δ is the fourth weight of the first tower stability model, T max is the maximum torque that the tower can withstand, ∈ is the fifth weight of the first model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0090] Specifically, the second tower stability model includes:

[0091]

[0092] Among them, Seff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum force allowed in the tower design, α′ is the first weight of the second tower stability model, β′ is the second weight of the second tower stability model, γ′ is the third weight of the second tower stability model, G is the shear modulus of steel, δ′ is the fourth weight of the second tower stability model, T max is the maximum torque that the tower can withstand, ∈′ is the fifth weight of the second model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0093] Specifically, optimizing the first tower stability model and the second tower stability model by random search includes:

[0094] Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set;

[0095] Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ;

[0096] Step 3: Set the initial search radius R (0) and the initial step length δ (0) ;

[0097] Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set;

[0098] Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model;

[0099] Step 6: If the maximum number of iterations is reached, stop the optimization process; otherwise, return to step 3 to continue searching.

[0100] Specifically, step 3 includes:

[0101] In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of in r is a random vector that follows a uniform distribution and ranges from [-R (k) , R(k) ], R (k) is the current search radius;

[0102] Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value at

[0103] If the function value Less than the current tower information set x (k) The function value f( x(k) ), then accept the new tower information set Otherwise, the probability Accept a new tower information set, where δ (k) is the current step size. If p is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information collection Otherwise remain unchanged.

[0104] The optimization module is used to find the tower information set corresponding to the mean value of the maximum stability value, so as to complete the tower simulation optimization.

[0105] Example 3

[0106] The embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the simulation optimization method for a cable tower of a steel rigid skeleton composite structure of a sea area bridge.

[0107] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0108] Optionally, in this embodiment, the storage medium is configured to store a program code for executing the following steps: Step 101, obtaining a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of a tower section about a central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of a tower section about a longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower;

[0109] Step 102, setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean;

[0110] Specifically, the first tower stability model includes:

[0111]

[0112] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum allowable force of the tower design, α is the first weight of the first tower stability model, β is the second weight of the first tower stability model, γ is the third weight of the first tower stability model, G is the shear modulus of steel, δ is the fourth weight of the first tower stability model, T max is the maximum torque that the tower can withstand, ∈ is the fifth weight of the first model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0113] Specifically, the second tower stability model includes:

[0114]

[0115] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum force allowed in the tower design, α′ is the first weight of the second tower stability model, β′ is the second weight of the second tower stability model, γ′ is the third weight of the second tower stability model, G is the shear modulus of steel, δ′ is the fourth weight of the second tower stability model, T max is the maximum torque that the tower can withstand, ∈′ is the fifth weight of the second model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0116] Specifically, optimizing the first tower stability model and the second tower stability model by random search includes:

[0117] Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set;

[0118] Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ;

[0119] Step 3: Set the initial search radius R (0) and the initial step length δ (0) ;

[0120] Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set;

[0121] Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model;

[0122] Step 6: If the maximum number of iterations is reached, stop the optimization process; otherwise, return to step 3 to continue searching.

[0123] Specifically, step 3 includes:

[0124] In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of in r is a random vector that follows a uniform distribution and ranges from [-R (k) ,R (k) ], R (k) is the current search radius;

[0125] Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value at

[0126] If the function value Less than the current tower information set x (k) The function value f( x(k) ), then accept the new tower information set Otherwise, the probability Accept a new tower information set, where δ (k) is the current step size. If p is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information collection Otherwise remain unchanged.

[0127] Step 103, searching for the tower information set corresponding to the mean of the maximum stability values, thereby completing the tower simulation optimization.

[0128] Example 4

[0129] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a simulation optimization method for a cable tower of a steel rigid frame composite structure of a marine bridge.

[0130] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0131] Among them, the storage medium can be used to store software programs and modules, such as a simulation optimization method for a cable tower of a steel rigid frame composite structure of a sea area bridge in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned simulation optimization method for a cable tower of a steel rigid frame composite structure of a sea area bridge. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0132] The processor can call the information and application program stored in the storage medium through the transmission system to execute the steps: Step 101, obtaining a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of the tower section about the central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of the tower section about the longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower;

[0133] Step 102, setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean;

[0134] Specifically, the first tower stability model includes:

[0135]

[0136] Among them, S eff is the effective cross-sectional area of ​​the tower, Fmax is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum allowable force of the tower design, α is the first weight of the first tower stability model, β is the second weight of the first tower stability model, γ is the third weight of the first tower stability model, G is the shear modulus of steel, δ is the fourth weight of the first tower stability model, T max is the maximum torque that the tower can withstand, ∈ is the fifth weight of the first model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0137] Specifically, the second tower stability model includes:

[0138]

[0139] Among them, S eff is the effective cross-sectional area of ​​the tower, F max is the maximum compression force that the tower can withstand, E is the elastic modulus of the steel, I is the polar moment of inertia of the tower section about the central axis, L is the length of the tower, P is max is the maximum force allowed in the tower design, α′ is the first weight of the second tower stability model, β′ is the second weight of the second tower stability model, γ′ is the third weight of the second tower stability model, G is the shear modulus of steel, δ′ is the fourth weight of the second tower stability model, T max is the maximum torque that the tower can withstand, ∈′ is the fifth weight of the second model of tower stability, J is the polar moment of inertia of the tower section about the longitudinal axis, M is the external torque on the tower, and A is the cross-sectional area of ​​the tower.

[0140] Specifically, optimizing the first tower stability model and the second tower stability model by random search includes:

[0141] Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set;

[0142] Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ;

[0143] Step 3: Set the initial search radius R (0) and the initial step length δ (0) ;

[0144] Step 4: Based on the initial search radius R (0) and the initial step length δ (0)Perform random search and update the tower information set;

[0145] Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model;

[0146] Step 6: If the maximum number of iterations is reached, stop the optimization process; otherwise, return to step 3 to continue searching.

[0147] Specifically, step 3 includes:

[0148] In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of in r is a random vector that follows a uniform distribution and ranges from [-R (k) ,R (k) ], R (k) is the current search radius;

[0149] Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value at

[0150] If the function value Less than the current tower information set x (k) The function value f( x(k) ), then accept the new tower information set Otherwise, the probability of Accept a new tower information set, where δ (k) is the current step size. If p is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information collection Otherwise remain unchanged.

[0151] Step 103, searching for the tower information set corresponding to the maximum stability value mean, thereby completing the tower simulation optimization.

[0152] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0153] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0158] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A simulation optimization method for a cable tower of a steel rigid frame composite structure of a sea bridge, characterized in that: include: Acquire a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of a tower section about a central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of a tower section about a longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower; Setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean; The first tower stability model includes: ， in, is the effective cross-sectional area of ​​the tower, is the maximum compression force that the tower can withstand. is the elastic modulus of steel, is the polar moment of inertia of the tower section about the median axis, is the length of the tower, is the maximum force allowed in the tower design, is the first weight of the first model of tower stability, is the second weight of the first model of tower stability, is the third weight of the first model of tower stability, is the shear modulus of steel, is the fourth weight of the first model of tower stability, The maximum torque that the tower can withstand. is the fifth weight of the first model of tower stability, is the polar moment of inertia of the tower section about the longitudinal axis, is the external moment acting on the tower, is the cross-sectional area of ​​the tower; The second tower stability model includes: ， in, is the effective cross-sectional area of ​​the tower, is the maximum compression force that the tower can withstand. is the elastic modulus of steel, is the polar moment of inertia of the tower section about the median axis, is the length of the tower, is the maximum force allowed in the tower design, is the first weight of the second model of tower stability, is the second weight of the second model of tower stability, is the third weight of the second model of tower stability, is the shear modulus of steel, is the fourth weight of the second model of tower stability, The maximum torque that the tower can withstand. is the fifth weight of the second model of tower stability, is the polar moment of inertia of the tower section about the longitudinal axis, is the external moment acting on the tower, is the cross-sectional area of ​​the tower; Optimizing the first tower stability model and the second tower stability model by random search includes: Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set; Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ; Step 3: Set the initial search radius R (0) and the initial step length δ (0) ; Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set; Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model; Step 6: If the maximum number of iterations is reached, stop the optimization process, otherwise return to step 3 to continue searching; The tower information set corresponding to the mean of the maximum stability values ​​is found, thereby completing the tower simulation optimization.

2. A method for simulating and optimizing the cable tower of a marine bridge steel rigid skeleton composite structure as claimed in claim 1, characterized in that: Step 3 includes: In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of ,in =x (k) +r, r is a random vector that follows a uniform distribution and ranges from [−R (k) ,R (k) ], R (k) is the current search radius; Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value f( ); If the function value f( ) is less than the current tower information set x (k) The function value f( x(k) ), then accept the new tower information set x (k+1) = , otherwise, by probability Accept new tower information collection, where is the current step size, if If it is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information set x (k+1) = , otherwise remain unchanged.

3. A simulation optimization system for cable towers of a marine bridge steel rigid frame composite structure, characterized in that: include: An information acquisition module is used to acquire a tower information set, wherein the tower information set includes: an effective cross-sectional area of ​​the tower, a maximum compression force that the tower can withstand, an elastic modulus of steel, a polar moment of inertia of the tower section about the central axis, a length of the tower, a maximum force allowed by the tower design, a shear modulus of steel, a maximum torque that the tower can withstand, a polar moment of inertia of the tower section about the longitudinal axis, an external moment on which the tower is subjected, and a cross-sectional area of ​​the tower; Setting a model module, used for setting a first tower stability model and a second tower stability model, and optimizing the first tower stability model and the second tower stability model by random search according to the tower information set, determining a first maximum stability value and a second maximum stability value, and finally obtaining a maximum stability value mean; The first tower stability model includes: ， in, is the effective cross-sectional area of ​​the tower, is the maximum compression force that the tower can withstand. is the elastic modulus of steel, is the polar moment of inertia of the tower section about the median axis, is the length of the tower, is the maximum force allowed in the tower design, is the first weight of the first model of tower stability, is the second weight of the first model of tower stability, is the third weight of the first model of tower stability, is the shear modulus of steel, is the fourth weight of the first model of tower stability, The maximum torque that the tower can withstand. is the fifth weight of the first model of tower stability, is the polar moment of inertia of the tower section about the longitudinal axis, is the external moment acting on the tower, is the cross-sectional area of ​​the tower; The second tower stability model includes: ， in, is the effective cross-sectional area of ​​the tower, is the maximum compression force that the tower can withstand. is the elastic modulus of steel, is the polar moment of inertia of the tower section about the median axis, is the length of the tower, is the maximum force allowed in the tower design, is the first weight of the second model of tower stability, is the second weight of the second model of tower stability, is the third weight of the second model of tower stability, is the shear modulus of steel, is the fourth weight of the second model of tower stability, The maximum torque that the tower can withstand. is the fifth weight of the second model of tower stability, is the polar moment of inertia of the tower section about the longitudinal axis, is the external moment acting on the tower, is the cross-sectional area of ​​the tower; Optimizing the first tower stability model and the second tower stability model by random search includes: Step 1, using the first tower stability model and the second tower stability model as the objective function f(x), wherein x is the tower information set; Step 2: Initialize the tower information set x and randomly generate an initial tower information set x (0) ; Step 3: Set the initial search radius R (0) and the initial step length δ (0) ; Step 4: Based on the initial search radius R (0) and the initial step length δ (0) Perform random search and update the tower information set; Step 5: Adjust the initial search radius R (0) and the initial step length δ (0) , increasing the search range, thereby improving the accuracy of the first tower stability model and the second tower stability model; Step 6: If the maximum number of iterations is reached, stop the optimization process, otherwise return to step 3 to continue searching; The optimization module is used to find the tower information set corresponding to the mean value of the maximum stability value, so as to complete the tower simulation optimization.

4. A sea area bridge steel rigid skeleton composite structure cable tower simulation optimization system as claimed in claim 3, characterized in that: Step 3 includes: In the current tower information set x (k) Randomly generate a new candidate tower information set in the neighborhood of ,in =x (k) +r, r is a random vector that follows a uniform distribution and ranges from [−R (k) ,R (k) ] ,R (k) is the current search radius; Calculate the first tower stability model and the second tower stability model in the candidate tower information set The function value f( ); If the function value f( ) is less than the current tower information set x (k) The function value f(x (k) ), then accept the new tower information set x (k+1) = , otherwise, by probability Accept new tower information collection, where is the current step size, if If it is greater than a randomly generated uniformly distributed random number u, where 0≤u≤1, then the candidate tower information set is accepted. And update the new tower information set x (k+1) = , otherwise remain unchanged.

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