A method and related device for optimizing the spatial structure of a steel structure net rack

Abstract

The application discloses a kind of steel structure net rack's space structure optimization method and related equipment, method includes: according to the design parameter of steel structure net rack, constructs three-dimensional model;Based on three-dimensional model, the mode shape period and mass participation coefficient of mode shape are calculated, and mode shape sensitivity analysis is carried out, to determine optimization variable;According to load information, load combination analysis is carried out, to determine the most unfavorable load combination;Based on the most unfavorable load combination, the linear parameter of steel structure net rack is calculated;According to linear parameter and section information, determine function constraint condition, and based on function constraint condition, construct objective function;Using target genetic algorithm, the objective function is iteratively optimized in combination with optimization variable, and the optimal objective function value and optimal structure scheme are output;The optimal structure scheme that checking passes through is used as the space structure design scheme of steel structure net rack.The application can greatly improve the space structure optimization efficiency and global optimization reliability of steel structure net rack, and can be widely applied in steel structure design technical field.

Description

Technical Field

[0001] This application relates to the field of steel structure design technology, and in particular to a spatial structure optimization method and related equipment for steel structure space frames. Background Technology

[0002] Spatial structural optimization of steel space frames refers to the process of minimizing material usage or optimizing structural performance while meeting constraints such as strength, stability, and displacement, by adjusting design variables such as the cross-sectional parameters, node positions, and member connection methods of the space frame. It is widely used in engineering fields such as large-span buildings and industrial plants, and is a key technical link in improving the economy and safety of steel structures. Among related technologies, spatial structural optimization of steel space frames often employs intelligent optimization methods such as genetic algorithms and particle swarm optimization algorithms. However, the most critical drawback of these technologies is that the selected optimization variables include parameters with negligible impact on the structural dynamic response. This not only increases the optimization dimensionality and reduces iteration efficiency but also makes it difficult to achieve precise optimization for key vibration modes, affecting the structural performance under dynamic loads.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The embodiments of this application aim to at least partially solve one of the technical problems in the related art. Therefore, the main objective of the embodiments of this application is to propose a spatial structure optimization method and related equipment for steel space frames, capable of outputting the optimal solution set of quantitative indicators and actual structural schemes, balancing scientific rigor and engineering practicality, and significantly improving the spatial structure optimization efficiency and global optimization reliability of steel space frames.

[0005] To achieve the above objectives, one aspect of this application proposes a spatial structure optimization method for steel space frames, the method comprising the following steps: Obtain the basic design parameters of the steel structure space frame, and construct a three-dimensional model based on the basic design parameters; wherein, the basic design parameters include cross-sectional information, material properties, load information, and seismic resistance level parameters; Based on the three-dimensional model, the modal period and mass participation coefficient of a set number of vibration modes are calculated, and modal sensitivity analysis is performed on the modal period and the mass participation coefficient to determine the optimization variables; Based on the load information, perform load combination analysis to determine the most unfavorable load combination; Based on the most unfavorable load combination, calculate the linear parameters of the steel structure space frame; wherein, the linear parameters include linear reaction force, internal force and displacement value; Based on the linear parameters and the cross-sectional information, the function constraints are determined, and the objective function is constructed based on the function constraints. A target genetic algorithm integrating modal sensitivity analysis is used to iteratively optimize the objective function in combination with the optimization variables, and output the optimal solution set; wherein, the optimal solution set includes the optimal objective function value and the optimal structural scheme; The optimal structural scheme is verified. If the optimal structural scheme satisfies all verification constraints, then the optimal structural scheme is adopted as the spatial structural design scheme of the steel structure space frame.

[0006] To achieve the above objectives, another aspect of this application proposes a spatial structure optimization device for steel space frames, the device comprising the following modules: A 3D model building module is used to obtain the basic design parameters of the steel structure space frame and build a 3D model based on the basic design parameters; wherein, the basic design parameters include cross-sectional information, material properties, load information and seismic resistance level parameters; The mode shape parameter analysis module is used to calculate the mode shape period and mass participation coefficient of a set number of mode shapes based on the three-dimensional model, and to perform mode shape sensitivity analysis on the mode shape period and the mass participation coefficient to determine the optimization variables; The load combination analysis module is used to perform load combination analysis based on the load information and determine the most unfavorable load combination. The linear parameter calculation module is used to calculate the linear parameters of the steel structure space frame based on the most unfavorable load combination; wherein, the linear parameters include linear reaction force, internal force and displacement value; The objective function construction module is used to determine the function constraints based on the linear parameters and the cross-sectional information, and to construct the objective function based on the function constraints. The objective function optimization module is used to iteratively optimize the objective function by employing a target genetic algorithm that integrates modal sensitivity analysis and the optimization variables, and outputs an optimal solution set; wherein, the optimal solution set includes the optimal objective function value and the optimal structural scheme; The design scheme determination module is used to verify the optimal structural scheme. If the optimal structural scheme meets all verification constraints, then the optimal structural scheme is adopted as the spatial structural design scheme of the steel structure space frame.

[0007] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0008] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0009] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0010] The embodiments of this application include at least the following beneficial effects: This application provides a spatial structure optimization method and related equipment for steel space frames. This method obtains the basic design parameters of the steel space frame and constructs a three-dimensional model based on these parameters. The basic design parameters include cross-sectional information, material properties, load information, and seismic resistance level parameters. Based on the three-dimensional model, the modal periods and mass participation coefficients of a set number of vibration modes are calculated, and modal sensitivity analysis is performed on the modal periods and mass participation coefficients to determine optimization variables. Load combination analysis is performed based on the load information to determine the most unfavorable load combination. Based on the most unfavorable load combination, the linear parameters of the steel space frame are calculated. The linear parameters include linear reactions, internal forces, and displacement values. Based on the linear parameters and cross-sectional information, functional constraints are determined, and an objective function is constructed based on these constraints. A target genetic algorithm integrating modal sensitivity analysis is used, combined with optimization variables, to iteratively optimize the objective function and output the optimal solution set. The optimal solution set includes the optimal objective function value and the optimal structural scheme. The optimal structural scheme is verified; if the optimal structural scheme satisfies all verification constraints, then the optimal structural scheme is adopted as the spatial structure design scheme for the steel space frame. This application's embodiments link modal period, mass participation coefficient, and modal sensitivity analysis to select parameters that significantly affect dynamic characteristics as optimization variables, reducing the involvement of invalid parameters. Simultaneously, combining these optimization variables with iterative optimization of the objective function makes the optimization more focused on key modalities, significantly improving iteration efficiency and targeting, thus solving the problems of redundant and insufficient targeting in traditional algorithms. Furthermore, by integrating modal sensitivity analysis with a target genetic algorithm, the algorithm's iteration strategy is optimized using sensitivity analysis results, addressing the slow convergence and tendency to get trapped in local optima issues of traditional genetic algorithms, improving optimization efficiency and the probability of obtaining the global optimum. Additionally, by outputting a solution set containing the optimal objective function value and the optimal structural scheme, it provides both quantitative optimization performance indicators and practical structural optimization schemes for engineers to design steel structure space frames, balancing the scientific validity and engineering practicality of the optimization results and meeting the selection needs of different design scenarios. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the steps of a spatial structure optimization method for a steel space frame provided in an embodiment of this application. Figure 2 This is a schematic flowchart of a spatial structure optimization method for a steel space frame provided in an embodiment of this application; Figure 3 This is a schematic diagram of a spatial structure optimization device for a steel structure space frame provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0013] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0014] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0016] Spatial structural optimization of steel space frames refers to the process of minimizing material usage or optimizing structural performance by adjusting design variables such as the cross-sectional parameters, node positions, and member connection methods of the space frame, while meeting constraints such as strength, stability, and displacement. It is widely used in engineering fields such as large-span buildings and industrial plants, and is a key technical link to improve the economy and safety of steel structures.

[0017] In related technologies, spatial structure optimization of steel space frames often employs intelligent optimization methods such as genetic algorithms and particle swarm optimization algorithms. Typically, a three-dimensional model containing parameters such as cross-sectional dimensions and node coordinates is first established. Then, the stress, displacement, and other response indices of the structure are calculated based on load combinations. An objective function is constructed with material usage or stress ratio as the core, and a better solution is obtained through algorithmic iteration. Some methods introduce sensitivity analysis to initially screen optimization variables, but the screening criteria are mostly based on static response indices (such as stress and displacement). However, the most critical shortcoming of these technologies is the disconnect between the selection of optimization variables and dynamic characteristics. The dynamic characteristic parameters such as the modal period and mass participation factor of the structure, as mentioned in the embodiments of this application, are not fully considered. This results in the selection of optimization variables including parameters with minimal impact on the structure's dynamic response, increasing the optimization dimensionality, reducing iteration efficiency, and making it difficult to achieve precise optimization for key modes, thus affecting the performance guarantee of the structure under dynamic loads.

[0018] In view of this, this application provides a spatial structure optimization method and related equipment for steel space frames. This method links modal period, mass participation coefficient and modal sensitivity analysis to select parameters that significantly affect dynamic characteristics as optimization variables, thereby reducing the participation of invalid parameters. At the same time, iterative optimization of the objective function is performed in combination with optimization variables, making the optimization more focused on key modes, greatly improving iteration efficiency and pertinence, and solving the problems of redundant optimization variables and insufficient pertinence in traditional algorithms. Furthermore, by integrating modal sensitivity analysis with the target genetic algorithm, the algorithm iteration strategy is optimized through sensitivity analysis results, which can solve the problems of slow convergence and easy getting trapped in local optima in traditional genetic algorithms, improving optimization efficiency and the probability of obtaining the global optimal solution. In addition, by outputting a solution set containing the optimal objective function value and the optimal structural scheme, it provides both quantitative optimization effect indicators and practical structural optimization schemes for engineers to carry out actual construction design of steel space frames, taking into account the scientific nature and engineering practicality of the optimization results, and meeting the selection needs of different design scenarios.

[0019] This application provides a spatial structure optimization method for a steel structure space frame, relating to the field of steel structure design technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle-mounted terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing a spatial structure optimization method for a steel structure space frame, but is not limited to the above forms.

[0020] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0021] Please see Figure 1 , Figure 1 This is an optional flowchart of a spatial structure optimization method for a steel space frame provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S107.

[0022] Step S101: Obtain the basic design parameters of the steel structure space frame, and construct a three-dimensional model based on the basic design parameters; wherein, the basic design parameters include cross-sectional information, material properties, load information, and seismic resistance level parameters; Optionally, the basic design parameters include cross-section information, material properties, load information, and seismic resistance level parameters. The cross-section information includes the type of circular tube cross-section, the corresponding cross-section name, and the total number of components of each type. Material properties include the elastic modulus, Poisson's ratio, coefficient of linear expansion, and design strength determined according to specifications for steel. Load information includes the type, value, and mode of action of dead load, roof live load, wind load, and snow load, where the self-weight coefficient for dead load is 1, and the self-weight coefficients for roof live load, wind load, and snow load are 0. Seismic resistance level parameters include the seismic resistance level corresponding to the structural type and the seismic resistance level of the structural measures.

[0023] Step S102: Based on the three-dimensional model, calculate the mode period and mass participation coefficient of the set number of mode shapes, and perform mode sensitivity analysis on the mode period and the mass participation coefficient to determine the optimization variables; In some embodiments, the step of calculating the modal period and mass participation factor of a set number of vibration modes based on a three-dimensional model may include: constructing the structural dynamic characteristic equations corresponding to the set number of vibration modes based on the three-dimensional model; solving the structural dynamic characteristic equations to obtain the eigenvalues ​​and eigenvectors corresponding to the set number of vibration modes; wherein the eigenvectors are used to characterize the vibration mode type; calculating the modal period based on the eigenvalues; and calculating the mass participation factor based on the eigenvectors.

[0024] In the specific implementation, based on the three-dimensional model constructed in S101, the period and mass participation factor of the set number of vibration modes are calculated. Specifically, in the dynamic characteristic analysis in step S102, the number of vibration modes is set to 9, and the vibration mode type is an eigenvector (i.e., the vibration mode type is an eigenvector obtained by solving the dynamic characteristic equation of the structure based on the space frame information collected in step S101). In the process of calculating the period and mass participation factor of the set number of vibration modes, the Timoshenko beam element properties are used to consider the shear effect, and the torsional moment of inertia of the beam is the free torsional moment of inertia, without considering P-Δ or second-order effects.

[0025] The three-dimensional model constructed in step S101 includes the geometric dimensions of the space frame (such as node coordinates and member lengths), material properties (such as elastic modulus and density), member cross-sectional parameters (such as cross-sectional area and moment of inertia), and boundary support conditions. The eigenvectors of the output mode shape are obtained by solving the dynamic characteristic equations of the structure (i.e., the eigenvalue problem).

[0026] In structural engineering, the P-Δ effect (a core type of second-order effect) refers to the additional internal forces and additional displacements caused by lateral displacements when a structure is subjected to axial pressure.

[0027] For a mode shape, it is the vibration pattern (displacement distribution law) of each mass point (such as a space frame node) during free vibration of the structure, and is a direct reflection of the inherent vibration characteristics of the structure. For the mode period, it is the vibration period corresponding to a certain mode shape (the time required for the structure to complete one full vibration). For the mass participation factor, it is the proportion of the structural mass corresponding to a certain mode shape that participates in the seismic action (or dynamic load) in that direction.

[0028] Among them, the structural dynamic characteristic equation is the core equation used to calculate the inherent dynamic characteristics (mode shape and mode period) of the structure. It is a mathematical expression of the balance between the inertial force generated by the structural mass and the elastic restoring force generated by the structural stiffness. It is used to solve the inherent vibration mode (mode shape) and vibration speed (mode period) of the structure itself.

[0029] In some embodiments, modal sensitivity analysis is performed on the modal period and mass participation coefficient to determine optimization variables, including: calculating the modal sensitivity of the modal period to obtain a first sensitivity coefficient of the modal period; calculating the modal sensitivity of the mass participation coefficient to obtain a second sensitivity coefficient of the mass participation coefficient; performing data integration processing on the first sensitivity coefficient and the second sensitivity coefficient to obtain a comprehensive sensitivity coefficient; and determining optimization variables based on the comprehensive sensitivity coefficient and the comprehensive sensitivity threshold.

[0030] Among them, the mode shape sensitivity analysis calculates the degree of influence of changes in each parameter on the mode shape period and mass participation coefficient, and selects parameters whose influence exceeds a preset threshold as optimization variables. The optimization variables are used to iteratively optimize the objective function constructed subsequently.

[0031] Step S103: Perform load combination analysis based on the load information to determine the most unfavorable load combination; In some embodiments, step S103 may include: determining a load combination set based on load information and in conjunction with structural design specifications; calculating the structural response of each load combination in the load combination set; selecting the peak response values ​​of key control indicators from the structural responses of each load combination; and constructing the most unfavorable load combination based on the peak response values ​​corresponding to each load combination.

[0032] Key control indicators may include, but are not limited to: maximum axial force, maximum bending moment, maximum shear force, maximum displacement, and minimum stability safety factor.

[0033] Structural response refers to all the mechanical behaviors and performance characteristics of a structure under loads (such as dead loads, live loads, wind loads, etc.).

[0034] Peak response values ​​are the "maximum values ​​or most unfavorable extreme values" of key control indicators (such as maximum axial force, maximum displacement, maximum support reaction force, etc.) in the structural response (such as internal forces, reactions, and displacements) calculated for each load combination. The core is to extract the "limit quantification values" that are most decisive for structural safety / deformation control under each load combination, as the core basis for judging whether the load combination is the "most unfavorable".

[0035] Optionally, the most unfavorable load combination refers to the set of load superposition forms that are most unfavorable to the structural stress and deformation (i.e. most likely to cause the structure to exceed design limits, affect safety or normal use) among all possible load combinations.

[0036] For example, each member will have a "most unfavorable tensile force value" and a "most unfavorable compressive force value", and each node will have a "most unfavorable displacement value". These selected extreme values ​​together constitute the "most unfavorable load combination" used for design.

[0037] Step S104: Based on the most unfavorable load combination, calculate the linear parameters of the steel structure space frame; wherein, the linear parameters include linear reaction force, internal force and displacement value; In some embodiments, step S104 may include: constructing a structural finite element model based on the most unfavorable load combination and a three-dimensional model; constructing an overall structural stiffness matrix based on finite element theory and according to the spatial position and connection relationship of the members of the steel structure space frame; converting the most unfavorable load combination into the force applied to the structural finite element model to generate a load vector; constructing a linear static equilibrium equation by combining the load vector and the overall structural stiffness matrix, and solving the linear static equilibrium equation to calculate the linear parameters of the steel structure space frame.

[0038] The linear parameters include linear reaction forces, internal forces, and displacement values. Linear reaction forces include axial forces, shear forces, and bending moments at the support nodes in different directions; internal forces include axial forces, shear forces, torques, and bending moments at different positions of each member; and displacement values ​​include the maximum and minimum displacements of each node in the X, Y, and Z directions, as well as spatial displacements.

[0039] Step S105: Determine the function constraints based on the linear parameters and the cross-sectional information, and construct the objective function based on the function constraints; In the specific implementation, based on the magnitude and location of all external forces borne by the structure (i.e., linear parameters) calculated in step S104, and combined with the design parameters of the member cross-section size in the current iteration, the objective function is established with the constraints of "stress ratio not exceeding 1", "displacement value within the allowable range of the specification" and "minimum total material usage".

[0040] Among them, the stress ratio constraint condition is determined based on the stress ratio limit of the member; the allowable range of displacement value is determined according to the structural type and function; the total material usage is calculated by the quantity, length and mass density of each type of member.

[0041] Optionally, a stress ratio of 1 indicates that the actual stress is exactly equal to the allowable stress of the material, the component is in a critical safety state, and its load-bearing capacity is fully utilized; a stress ratio of <1 indicates that the actual stress is less than the allowable stress of the material, the component is safe, and there is a certain safety reserve; a stress ratio of >1 indicates that the actual stress has exceeded the allowable stress of the material, the component is in an unsafe state, does not meet the design requirements, and will be damaged in actual engineering.

[0042] Step S106: Using a target genetic algorithm that integrates modal sensitivity analysis, the objective function is iteratively optimized in combination with the optimization variables to output the optimal solution set; wherein, the optimal solution set includes the optimal objective function value and the optimal structural scheme; In some embodiments, step S106 may include: encoding the optimization variables with real numbers to generate an initial population; calculating the fitness of each individual in the initial population based on the objective function and the dynamic weighting coefficients of the objective function, and using tournament selection to select the optimal set of individuals as the parent population based on the fitness calculation results of each individual in the initial population; wherein, the value of the dynamic weighting coefficients of the objective function is corrected by the dynamic weighting coefficients of the load combination, and the dynamic weighting coefficients of the load combination are determined according to the mass participation coefficients of each vibration mode and the load effect; using an arithmetic crossover algorithm to linearly combine the gene loci of each target individual pair in the parent population to generate a new offspring population; and using... The non-uniform mutation algorithm mutates the gene loci of each individual in the new offspring population to obtain a mutated offspring population. If the current iteration does not meet the iteration termination condition, the mutated offspring population is used as the initial population, and the algorithm returns to perform fitness calculations on each individual in the initial population based on the objective function and the dynamic weight coefficients of the objective function. Then, a tournament selection method is used to select the optimal set of individuals as the parent population based on the fitness calculation results of each individual in the initial population. This process continues until the current iteration meets the iteration termination condition. When the current iteration meets the iteration termination condition, all high-quality individuals that satisfy the function constraints during the iteration process are used to construct the optimal solution set.

[0043] In this embodiment, the improved genetic algorithm uses real-number encoding, converting cross-sectional parameters into continuous values ​​within a corresponding size range and node positions into three-dimensional coordinate values. The selection operator employs tournament selection, randomly selecting a preset number of individuals from the population for comparison and selecting the optimal individual. The crossover operator in the improved genetic algorithm uses arithmetic crossover, linearly combining the corresponding gene loci of the selected individuals to generate new individuals. The mutation operator uses non-uniform mutation, where the mutation length decreases as the number of iterations increases.

[0044] The dynamic weighting coefficient of the load combination is determined based on the mass participation coefficient of each vibration mode and the load effect.

[0045] In the specific implementation, an improved genetic algorithm that integrates modal sensitivity analysis is used to iteratively optimize the objective function constructed in step S105. First, the parameters that have a significant impact on the structural response are identified through modal sensitivity analysis as optimization variables. Then, based on the genetic algorithm framework, encoding, selection, crossover, and mutation operations are performed to simultaneously adjust the cross-sectional parameters, node positions, and member connection methods. Furthermore, a dynamic weighting coefficient for load combination is introduced during the iteration process to real-time adjust the weight of the objective function according to the contribution of load effects under different modes.

[0046] The optimal objective function value is the lowest total material usage that the algorithm can find while satisfying all constraints (such as stress ratio, displacement, etc.). The optimal structural scheme is a dataset, which is the specific design scheme that generates the above "optimal objective function value".

[0047] Step S107: Verify the optimal structural scheme. If the optimal structural scheme satisfies all verification constraints, then the optimal structural scheme shall be adopted as the spatial structural design scheme of the steel structure space frame.

[0048] The content of the verification constraints may include, but is not limited to: strength stress ratio verification of members, stability stress ratio verification about a 2-axis, stability stress ratio verification about a 3-axis, bolt stress ratio verification of nodes, sleeve stress ratio verification, collision inspection of bolt balls, and bearing stress ratio verification of sleeve ends.

[0049] In the specific implementation, the structure optimized in step S106 is verified. If the verified structure meets all constraints, the optimal structural scheme is output as the spatial structural design scheme of the steel structure space frame; otherwise, the process returns to step S106 for re-optimization.

[0050] Steps S101 to S107 of this embodiment link modal period, mass participation coefficient, and modal sensitivity analysis to select parameters that significantly affect dynamic characteristics as optimization variables. This reduces the participation of invalid parameters. Simultaneously, iterative optimization of the objective function using these optimization variables makes the optimization more focused on key modalities, significantly improving iteration efficiency and targeting. This solves the problems of redundant and insufficient targeting in traditional algorithms. Furthermore, by integrating modal sensitivity analysis with a target genetic algorithm, the algorithm's iteration strategy is optimized using the sensitivity analysis results. This addresses the slow convergence and tendency to get trapped in local optima problems of traditional genetic algorithms, improving optimization efficiency and the probability of obtaining the global optimal solution. Additionally, by outputting a solution set containing the optimal objective function value and the optimal structural scheme, it provides both quantitative optimization performance indicators and practical structural optimization schemes for engineers to design steel structure space frames. This balances the scientific validity and engineering practicality of the optimization results, meeting the selection needs of different design scenarios.

[0051] To explain in detail the principles of the technical solution of this application, the overall process of this application will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principles of this application and should not be regarded as a limitation of this application.

[0052] To address the issue that some optimization variables selected in related technologies include parameters with negligible impact on the structural dynamic response, thus increasing the optimization dimensionality and reducing iteration efficiency, this application provides a spatial structure optimization method for steel space frames. By linking mode shape period, mass participation factor, and mode shape sensitivity analysis, parameters with significant impact on dynamic characteristics are selected as optimization variables, reducing the participation of invalid parameters. Simultaneously, the mass participation factor is used to correct dynamic weights, making the optimization more focused on key mode shapes, significantly improving iteration efficiency and targeting, and solving the problems of redundant optimization variables and insufficient targeting in traditional algorithms.

[0053] Please see Figure 2 , Figure 2 This is a flowchart illustrating a spatial structure optimization method for a steel space frame provided in an embodiment of this application, as shown below. Figure 2 As shown, the overall implementation process of a spatial structure optimization method for a steel space frame provided in this application mainly includes steps S201 to S207: Step S201, Initial modeling; In the specific implementation, firstly, the cross-sectional information, material properties, load information, and seismic resistance level parameters of the steel structure space frame are obtained; then, a three-dimensional model is constructed based on the cross-sectional information, material properties, load information, and seismic resistance level parameters of the steel structure space frame.

[0054] Step S202, Dynamic Characteristics Analysis; In the specific implementation, based on the three-dimensional model constructed in S201, the period and mass participation factor of the set number of vibration modes are calculated. Specifically, in the dynamic characteristic analysis in step S202, the number of vibration modes is set to 9, and the vibration mode type is an eigenvector (i.e., the vibration mode type is an eigenvector obtained by solving the dynamic characteristic equation of the structure based on the space frame information collected in step S201). In the process of calculating the period and mass participation factor of the set number of vibration modes, the Timoshenko beam element properties are used to consider the shear effect, and the torsional moment of inertia of the beam is the free torsional moment of inertia, without considering P-Δ or second-order effects.

[0055] In this embodiment of the application, a modal sensitivity analysis is also performed on the modal period and mass participation coefficient to determine the optimization variables. The modal sensitivity analysis calculates the degree of influence of changes in each parameter on the modal period and mass participation coefficient, and selects parameters whose influence exceeds a preset threshold as optimization variables. The specific details are as follows: For calculating the sensitivity of the modal period, the sensitivity coefficient of the modal period is... The calculation formula is as follows: ; in, For the first Mode Period (Taken from the modal period calculation results of step S202) For the first Design parameters (e.g., cross-sectional diameter) Node coordinates The sensitivity coefficient of ) The change in the mode shape period (obtained through parameter perturbation based on the mode shape calculation model in step S202). For design parameters A tiny change (1% of the original parameter value).

[0056] For the sensitivity calculation of the mass participation factor, the sensitivity coefficient of the mass participation factor. The calculation formula is as follows: ; in, For the first Modal mass participation factor (Taken from the mass participation factor results of S202) For parameters The sensitivity coefficient; The change in the quality participation coefficient (derived from the calculation model in step S202). For design parameters A tiny change (1% of the original parameter value).

[0057] In the specific implementation, the comprehensive sensitivity threshold is set by optimizing variable selection. (like ),when When, determine parameters As a key optimization variable (because this parameter has a significant impact on the structural dynamic characteristics reflected in step S202), it is included in the genetic algorithm optimization scope of step S206 below.

[0058] Step S203: Determine the load combination; Based on the load information in step S201, a load combination analysis is performed to determine the most unfavorable load combination. In the load combination determination in step S203, the load combination analysis is based on the physical nature and code definition requirements of different types of loads. By comparing the structural response under different load combinations, the load combination that is most unfavorable to the structural stress is selected.

[0059] Step S204, linear parameter calculation; Based on the most unfavorable load combination determined in step S203, the linear reactions, internal forces, and displacements of the space frame are calculated. Specifically, firstly, a finite element model of the structure is established based on the most unfavorable load combination determined in step S203; then, based on finite element theory, the stiffness matrices of these elements are combined according to the spatial position and connection relationship of all members in the overall structure; finally, the most unfavorable load combination determined in step S203 is transformed into specific forces applied to the finite element model of the structure, describing the magnitude and location of all external forces borne by the structure under the most unfavorable condition.

[0060] Step S205, construct the objective function; In the specific implementation, based on the magnitude and location of all external forces borne by the structure (i.e., linear parameters) calculated in step S204, and combined with the design parameters of the member cross-section size in the current iteration, the objective function is established with the constraints of "stress ratio not exceeding 1", "displacement value within the allowable range of the specification" and "minimum total material usage".

[0061] Step S206: Optimize parameter adjustments; An improved genetic algorithm incorporating modal sensitivity analysis was used to iteratively optimize the objective function constructed for S205. First, the parameters that significantly affect the structural response were identified through modal sensitivity analysis as optimization variables. Then, based on the genetic algorithm framework, encoding, selection, crossover, and mutation operations were performed to simultaneously adjust the cross-sectional parameters, node positions, and member connection methods (such as hinged or rigid connections). Furthermore, a dynamic weighting coefficient for load combination was introduced during the iteration process to real-time adjust the weights of the objective function according to the contribution of load effects under different modes. In step S206, parameter adjustment, modal sensitivity analysis calculates the impact of changes in each parameter on the modal period and mass participation coefficient, selecting parameters whose impact exceeds a preset threshold as optimization variables. The modal sensitivity analysis and optimization variable selection in step S206, building upon the modal sensitivity analysis in step S202, represents a variable selection stage before actual optimization. This stage reduces the number of variables, selecting parameters worthy of optimization from all possible design parameters to lower the complexity of the subsequent genetic algorithm. By calculating the sensitivity of the structural response index to the design parameters, parameters significantly affecting structural performance are selected as optimization variables. The specific implementation process of modal sensitivity analysis and optimization variable selection is as follows: First, determine the set of structural response indicators. , including the period of 9 vibration modes Key node displacements (e.g., maximum displacement in the X / Y / Z directions), maximum stress ratio of the member Design parameter set This includes the cross-sectional dimensions of the circular tube (such as diameter). Wall thickness ), node three-dimensional coordinates ; Then, calculate the first... Response metrics For the Design parameters The sensitivity is calculated using the following formula: ; in, This is the sensitivity coefficient; (or ) as a response indicator The minute changes; (or () is the design parameter A tiny change (usually 1% of the original parameter value).

[0062] For example, if the diameter of a certain circular tube cross-section An increase of 1% leads to the first mode period If it increases by 0.5%, then .

[0063] Finally, set the sensitivity threshold. (like ),when When, determine parameters Key optimization variables are included in subsequent optimizations; otherwise, they are excluded to reduce the number of optimization variables and improve efficiency. In step S206, the optimization parameter adjustment improves the genetic algorithm by using real number encoding, converting cross-sectional parameters into continuous values ​​within the corresponding size range, converting node positions into three-dimensional coordinate values, and employing tournament selection as the selection operator. A preset number of individuals are randomly selected from the population for comparison, and the optimal individual is selected. The encoding steps include the following algorithms: To improve the encoding and initial population generation of the genetic algorithm, the selected optimization variables are transformed into chromosomes that the genetic algorithm can process, and the initial population is generated. Specifically: First, each optimization variable is encoded using real numbers. Mapped to gene loci in chromosomes, the chromosome vector is represented as: ; in, For the first The chromosomes of an individual; , The first The diameter and wall thickness of a circular tube cross-section; For the first The coordinates of each node; Number of cross-section types; This represents the number of nodes.

[0064] Then, during the initial population generation, the value range of each gene locus must conform to engineering specifications (such as cross-sectional diameter). mm, node coordinates m), generated by random sampling Individuals (population size) Typically, 50-100 are selected to form the initial population. : ; Specifically, for the construction of the fitness function (including dynamic weights for load combinations), the individual fitness is calculated based on the objective function and dynamic weight coefficients, using the following formula: ; in, For individuals Fitness value (the smaller the value, the better); The total material usage (unit: kg) is calculated based on the number of components of each type. ,length and mass density (such as Q235 steel) Calculated using kg / mm³, i.e. , For the first Cross-sectional area of ​​a certain type of section (such as a circular tube) ); The maximum stress ratio of the member (must be ≤1); The maximum spatial displacement of the node (unit: mm); To specify the allowable displacement limit (e.g., take 1 / 250 of the span according to the structural type); For dynamic weighting coefficients, satisfying Its value is corrected by the dynamic weighting coefficient of the load combination, and the correction formula is: ; in, , , Based on weights (e.g.) ), For the first The contribution of the mode shape to the load effect is calculated as follows: ; in, For the first The second mode Load values ​​at each node (unit: kN); For the first The second mode Displacement values ​​of each node (unit: m), This represents the total number of nodes.

[0065] For example, if the total load effect of the fourth mode accounts for 3596% of the total load effect of all modes, then Corresponding weight .

[0066] In the S6 optimization parameter adjustment, the crossover operator of the improved genetic algorithm adopts arithmetic crossover, which linearly combines the corresponding gene loci of the selected individuals to generate new individuals. The mutation operator adopts non-uniform mutation, and the variable asynchronous length decreases with the increase of the number of iterations. The dynamic weight coefficient of the load combination is calculated and determined based on the mass participation coefficient of each vibration mode and the load effect. The genetic algorithm operations (selection, crossover, mutation) include the following algorithms: For the selection operation: the tournament selection method is adopted. k individuals (k=3-5) are randomly selected from the population, and the individual with the smallest fitness value is selected to enter the offspring population. This process is repeated N times to generate a new population, ensuring that superior genes are preserved. For cross operations: select the parent individual =( , ,…, )and =( , ,…, (L is the chromosome length), using arithmetic crossover to generate offspring, the formula is: ; ; in, , For offspring individuals; λ∈[0,1] is the crossover factor (randomly selected, such as λ=0.6); , Parent individuals , The i-th gene location; For mutation operations: non-uniform mutation is used for individuals. The i-th gene position The mutation is performed using the following formula: ; in, These are the mutated parameter values; δ is the upper limit of parameter Pi; δ is the random sign (take +1 or -1); r∈[0,1] is a random number; t is the current iteration number; T is the maximum iteration number (e.g. T=100); β is the shape parameter (usually taken as 2, controlling the rate at which the variable asynchronous length decays with iteration).

[0067] For example, if the current iteration t=50 and T=100, then (1-t / T)=(0.5)2=0.25, the variable asynchronous length gradually decreases as the iteration increases, enhancing the local search capability.

[0068] Furthermore, based on the mode period provided in step S202 and quality participation coefficient This provides a "dynamic characteristic response index" for the sensitivity analysis of step S206, ensuring that the selected optimization variables not only affect static performance (such as stress and displacement), but also accurately regulate the dynamic characteristics of the structure (such as vibration frequency and resonance risk).

[0069] based on Corrected dynamic weights This allows the optimization process in step S206 to prioritize the mode shapes with high mass participation coefficients from step S202 (these mode shapes contribute more to the overall structural response), achieving deep coupling between "dynamic characteristics - load effects - optimization objectives." This overcomes the limitation of traditional algorithms where static and dynamic optimization are disconnected, and includes the following algorithms: Dynamic weight correction algorithm based on mass participation coefficient in step S202: utilizing the modal mass participation coefficient in step S202 The contribution of each vibration mode to the load effect is quantified, and the objective function weights in step S206 are adjusted in real time. The formula is as follows: (1) The formula for calculating the contribution of modal load effect is as follows: ; in, ηt is the dynamic weighting coefficient of the t-th mode; ηt is the mass participation coefficient of the t-th mode calculated by S2 (reflecting the proportion of the contribution of this mode to the overall dynamic response); Let be the load value at the m-th node under the t-th vibration mode; This is the displacement value of the m-th node under the t-th vibration mode (based on the mode derivation in step S202). This represents the total number of nodes.

[0070] (2) The calculation formula for the objective function weight correction is as follows: ; ; in, Based on weights (e.g.) Revised Fitness function used in step S206 This allows the weights to be dynamically matched with the importance of the mode shape reflected in step S202.

[0071] Step S207, structural verification output.

[0072] The optimized structure in step S206 is verified. If all constraints are met, the optimal structural scheme is output; otherwise, the process returns to step S206 for re-optimization.

[0073] It should be noted that this embodiment is only a brief illustrative description of the overall process of a spatial structure optimization method for a steel structure space frame. Detailed descriptions of each step can be found in the relevant content of the foregoing embodiments, and will not be repeated here. It is understood that this application embodiment does not impose any limitations on this.

[0074] In summary, the spatial structure optimization method for steel space frame provided in this application has the following beneficial effects: (1) By linking the mode period and mass participation coefficient in the dynamic characteristic analysis step with the mode sensitivity analysis in the optimization parameter adjustment step, parameters that have a significant impact on dynamic characteristics are selected as optimization variables, reducing the participation of invalid parameters. At the same time, the dynamic weight is corrected by using the mass participation coefficient, making the optimization more focused on the key mode, greatly improving the iteration efficiency and pertinence, and solving the problems of redundant optimization variables and insufficient pertinence of traditional algorithms.

[0075] (2) By improving the non-uniform mutation strategy of the genetic algorithm, the variable asynchronous length decreases as the number of iterations increases. Combined with the arithmetic crossover operator, it takes into account both global search and local fine optimization, greatly improving the convergence accuracy. At the same time, the dynamic weight adapts to the load effects of different vibration modes, balances the structural safety and economy, and overcomes the defect that fixed step size is prone to getting trapped in local optima.

[0076] (3) By constructing a multi-objective fitness function that integrates material usage, stress ratio, and displacement value, and combining dynamic characteristics and load combinations, the static and dynamic performance are optimized in a coordinated manner. This ensures that the optimization results meet the strength and displacement constraints while reducing the resonance risk under dynamic loads, thus breaking through the limitations of traditional single-objective optimization and improving the overall performance of the structure.

[0077] In addition, to verify the effectiveness of the spatial structure optimization method for steel space frame provided in this application embodiment, this application embodiment also provides an example of a specific experiment and its results, the specific example of which is as follows: In the experiments of this application embodiment, the data required before the experiment includes, but is not limited to, the node information, cross-sectional information, seismic resistance level, material information (including material properties, etc.), and load information of the steel structure space frame. The node information prepared before the experiment is shown in Table 1, and the cross-sectional information prepared before the experiment is shown in Table 2. Table 1

[0078] Table 2

[0079] The calculation parameters designed in the experiment include: (1) Calculation parameters for dynamic characteristics: Number of vibration modes calculated: 9; Mode type: eigenvector; (2) Linear calculation parameters: Beam element properties: Shear effect considered (Temsenko beam); Beam torsional moment of inertia: Free torsional moment of inertia; Consider P-Δ / second-order effects: No; The design parameters include: Structural importance coefficient: 1.000; Support critical angle: 15.000°; The seismic resistance level information is shown in Table 3 below: Table 3

[0080] The material properties information in the material information section is shown in Table 4: Table 4

[0081] The material statistics in the material information are shown in Table 5: Table 5

[0082] For the load and combination, the specific experimental parameters are as follows: Operating conditions are shown in Table 6: Table 6

[0083] Regarding load information, taking member-guided loads as an example, member-guided loads can include, but are not limited to, dead loads and roof live loads. Specifically, the list of member-guided loads (force: kN; distributed force: kN / m; bending moment: kN.m; distributed bending moment: kN.m / m) is shown in Table 7: Table 7

[0084] Among them, the loads conducted on the members with a dead load of 0 are shown in Table 8: Table 8

[0085] Table 9 shows the member load transfer for (roof) live load 1: Table 9

[0086] Based on the experimental data prepared above, the modal periods and mass participation factors of the multiple vibration modes were set, and the most unfavorable load combination was determined. Among them, "1.300 dead load + 1.50 roof load 1" was used as one of the basic combinations in the load combination calculation; the final most unfavorable combination was determined by taking the maximum effect from the calculation results of all the basic combinations specified in the code (such as those including wind, snow, earthquake, temperature, etc.). The periodicity and quality participation coefficients are shown in Table 10: Table 10

[0087] Taking nodes 27, 25, 9, and 7 as examples, the most unfavorable reaction forces for nodes 27, 25, and 9 are shown in Table 11: Table 11

[0088] The most unfavorable reaction force at node 7 is shown in Table 12: Table 12

[0089] For linear combinations, this can include, but is not limited to, the internal forces of the top 10 elements with the largest axial force N, the top 10 elements with the smallest axial force N, the top 10 elements with the largest bending moment M2, the top 10 elements with the smallest bending moment M2, the top 10 elements with the largest bending moment M3, and the top 10 elements with the smallest bending moment M3. Specifically, the internal forces of the top 10 elements with the largest axial force N (units: m, kN, kN.m) are shown in Table 13: Table 13

[0090] The internal forces (units: m, kN, kN.m) of the 10 elements with the smallest axial force N are shown in Table 14: Table 14

[0091] The internal forces (units: m, kN, kN.m) of the 10 elements with the largest bending moment M2 are shown in Table 15: Table 15

[0092] The internal forces (units: m, kN, kN.m) of the 10 elements with the smallest bending moment M2 are shown in Table 16: Table 16

[0093] The internal forces (units: m, kN, kN.m) of the 10 elements with the largest bending moment M3 are shown in Table 17: Table 17

[0094] The internal forces (units: m, kN, kN.m) of the 10 elements with the smallest bending moment M3 are shown in Table 18: Table 18

[0095] The maximum and minimum displacements of the linear combination are shown in Table 19: Table 19

[0096] Finally, the above calculation results (optimal structural scheme) were verified. The verification included stress ratio verification, verification of the top 10 elements with the highest "strength-stress ratio", verification of the top 10 elements with the highest "stability stress ratio around the 2-axis", verification of the top 10 elements with the highest "stability stress ratio around the 3-axis", verification of the top 10 bolts with the highest "stress ratio", bolt ball verification, and verification of the top 10 bolt balls with the highest "stress ratio". The verification results are as follows: The stress ratio calculation results are shown in Table 20: Table 20

[0097] The verification results (combination number / case number) of the 10 elements with the highest "strength-stress ratio" are shown in Table 21: Table 21

[0098] The verification results (combination number / case number) of the top 10 elements with the largest "stability stress ratio around the 2-axis" are shown in Table 22: Table 22

[0099] The verification results (combination number / case number) of the top 10 elements with the largest "stability stress ratio around 3 axes" are shown in Table 23: Table 23

[0100] Stress ratio control is shown in Table 24: Table 24

[0101] The calculation results for the 10 bolts with the highest "stress ratio" are shown in Table 25: Table 25

[0102] The calculation results for the bolt ball are shown in Table 26: Table 26

[0103] The calculation results for the 10 bolt balls with the highest "stress ratio" are shown in Table 27: Table 27

[0104] The experimental data and results above demonstrate that the spatial structure optimization method for steel space frames provided in this application can solve for the optimal structural scheme of the steel space frame. Experimental verification shows that the optimal scheme meets multiple verification requirements. Therefore, the optimal structural scheme output by the algorithm can be directly used as the spatial structure design scheme for the steel space frame. In other words, the spatial structure optimization method for steel space frames provided in this application can offer practical structural optimization schemes for engineers to design and construct steel space frames, balancing the scientific validity and engineering practicality of the optimization results, and meeting the selection needs of different design scenarios.

[0105] Please see Figure 3 This application also provides a spatial structure optimization device 300 for steel structure space frames, which can implement the above-mentioned method. The device includes the following modules: The three-dimensional model construction module 301 is used to obtain the basic design parameters of the steel structure space frame and construct a three-dimensional model based on the basic design parameters; wherein, the basic design parameters include cross-sectional information, material properties, load information and seismic resistance level parameters; The mode shape parameter analysis module 302 is used to calculate the mode shape period and mass participation coefficient of a set number of mode shapes based on the three-dimensional model, and to perform mode shape sensitivity analysis on the mode shape period and the mass participation coefficient to determine the optimization variables; The load combination analysis module 303 is used to perform load combination analysis based on the load information and determine the most unfavorable load combination. The linear parameter calculation module 304 is used to calculate the linear parameters of the steel structure space frame based on the most unfavorable load combination; wherein the linear parameters include linear reaction force, internal force and displacement value; The objective function construction module 305 is used to determine the function constraints based on the linear parameters and the cross-sectional information, and to construct the objective function based on the function constraints. The objective function optimization module 306 is used to iteratively optimize the objective function by employing a target genetic algorithm that integrates modal sensitivity analysis and the optimization variables, and output an optimal solution set; wherein, the optimal solution set includes the optimal objective function value and the optimal structural scheme; The design scheme determination module 307 is used to verify the optimal structural scheme. If the optimal structural scheme meets all verification constraints, then the optimal structural scheme is adopted as the spatial structural design scheme of the steel structure space frame.

[0106] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0107] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0108] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0109] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 402 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and is called and executed by the processor 401 using the methods described in the embodiments of this application. Input / output interface 403 is used to implement information input and output; The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0110] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0111] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0112] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0113] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0115] This application provides a spatial structure optimization method and related equipment for steel space frames. By linking modal period, mass participation coefficient, and modal sensitivity analysis, it selects parameters that significantly affect dynamic characteristics as optimization variables, reducing the participation of invalid parameters. Simultaneously, it iteratively optimizes the objective function using these optimization variables, making the optimization more focused on key modes, significantly improving iteration efficiency and targeting, and solving the problems of redundant and insufficient targeting in traditional algorithms. Furthermore, by integrating modal sensitivity analysis with a target genetic algorithm, it optimizes the algorithm's iteration strategy through sensitivity analysis results, solving the problems of slow convergence and susceptibility to local optima in traditional genetic algorithms, improving optimization efficiency and the probability of obtaining the global optimal solution. In addition, by outputting a solution set containing the optimal objective function value and the optimal structural scheme, it provides both quantitative optimization performance indicators and practical structural optimization schemes for engineers to design and construct steel space frames, balancing the scientific validity and engineering practicality of the optimization results and meeting the selection needs of different design scenarios.

[0116] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for optimizing the spatial structure of a steel space frame, characterized in that, The method includes the following steps: Obtain the basic design parameters of the steel structure space frame, and construct a three-dimensional model based on the basic design parameters; wherein, the basic design parameters include cross-sectional information, material properties, load information, and seismic resistance level parameters; Based on the three-dimensional model, the modal period and mass participation coefficient of a set number of vibration modes are calculated, and modal sensitivity analysis is performed on the modal period and the mass participation coefficient to determine the optimization variables; Based on the load information, perform load combination analysis to determine the most unfavorable load combination; Based on the most unfavorable load combination, calculate the linear parameters of the steel structure space frame; wherein, the linear parameters include linear reaction force, internal force and displacement value; Based on the linear parameters and the cross-sectional information, the function constraints are determined, and the objective function is constructed based on the function constraints. A target genetic algorithm integrating modal sensitivity analysis is used to iteratively optimize the objective function in combination with the optimization variables, and output the optimal solution set; wherein, the optimal solution set includes the optimal objective function value and the optimal structural scheme; The optimal structural scheme is verified. If the optimal structural scheme satisfies all verification constraints, then the optimal structural scheme is adopted as the spatial structural design scheme of the steel structure space frame.

2. The method according to claim 1, characterized in that, The calculation of the modal period and mass participation factor of a set number of vibration modes based on the three-dimensional model includes: Based on the three-dimensional model, construct the structural dynamic characteristic equations corresponding to a set number of vibration modes; The structural dynamic characteristic equation is solved to obtain a set number of eigenvalues ​​and eigenvectors corresponding to the mode shapes; wherein the eigenvectors are used to characterize the mode shape type. Based on the aforementioned characteristic values, the mode period is calculated. The quality participation coefficient is calculated based on the feature vector.

3. The method according to claim 1, characterized in that, The modal sensitivity analysis of the modal period and the mass participation coefficient to determine the optimization variables includes: The mode sensitivity is calculated for the mode period to obtain the first sensitivity coefficient of the mode period; The modal sensitivity of the mass participation coefficient is calculated to obtain the second sensitivity coefficient of the mass participation coefficient; The first sensitivity coefficient and the second sensitivity coefficient are integrated to obtain the comprehensive sensitivity coefficient. The optimization variables are determined based on the comprehensive sensitivity coefficient and the comprehensive sensitivity threshold.

4. The method according to claim 1, characterized in that, The step of performing load combination analysis based on the load information to determine the most unfavorable load combination includes: Based on the load information and in conjunction with structural design specifications, determine the load combination set; Calculate the structural response for each load combination in the set of load combinations; Peak response values ​​of key control indicators are selected from the structural responses of each load combination. The most unfavorable load combination is constructed based on the peak response value corresponding to each load combination.

5. The method according to claim 1, characterized in that, The calculation of the linear parameters of the steel structure space frame based on the most unfavorable load combination includes: Based on the most unfavorable load combination and the three-dimensional model, a structural finite element model is constructed. Based on the finite element theory, the overall stiffness matrix of the structure is constructed according to the spatial position and connection relationship of the members of the steel structure space frame. The most unfavorable load combination is transformed into the force applied to the finite element model of the structure, generating a load vector. A linear static equilibrium equation is constructed by combining the load vector and the overall stiffness matrix of the structure, and the linear static equilibrium equation is solved to calculate the linear parameters of the steel structure space frame.

6. The method according to claim 1, characterized in that, The target genetic algorithm employing fusion mode sensitivity analysis iteratively optimizes the objective function by incorporating the optimization variables, outputting an optimal solution set, including: The optimization variables are encoded with real numbers to generate an initial population; The fitness of each individual in the initial population is calculated based on the objective function and the dynamic weighting coefficient of the objective function. The optimal set of individuals is selected as the parent population based on the fitness calculation results of each individual in the initial population using the tournament selection method. The value of the dynamic weighting coefficient of the objective function is corrected by the dynamic weighting coefficient of the load combination. The dynamic weighting coefficient of the load combination is determined based on the mass participation coefficient of each vibration mode and the load effect. The arithmetic crossover algorithm is used to linearly combine the gene loci of each target individual pair in the parent population to generate a new offspring population; A non-uniform mutation algorithm is used to mutate the gene loci of each individual in the new offspring population to obtain a mutated offspring population. If the current iteration process does not meet the iteration termination condition, then the mutated offspring population is used as the initial population, and the process of calculating the fitness of each individual in the initial population based on the objective function and the dynamic weight coefficient of the objective function is returned, and the optimal set of individuals is selected as the parent population based on the fitness calculation results of each individual in the initial population using the tournament selection method, until the current iteration process meets the iteration termination condition. When the current iteration process satisfies the iteration termination condition, all high-quality individuals that satisfy the function constraints during the iteration process are constructed into the optimal solution set.

7. A spatial structure optimization device for a steel space frame, characterized in that, The device includes the following modules: A 3D model building module is used to obtain the basic design parameters of the steel structure space frame and build a 3D model based on the basic design parameters; wherein, the basic design parameters include cross-sectional information, material properties, load information and seismic resistance level parameters; The mode shape parameter analysis module is used to calculate the mode shape period and mass participation coefficient of a set number of mode shapes based on the three-dimensional model, and to perform mode shape sensitivity analysis on the mode shape period and the mass participation coefficient to determine the optimization variables; The load combination analysis module is used to perform load combination analysis based on the load information and determine the most unfavorable load combination. The linear parameter calculation module is used to calculate the linear parameters of the steel structure space frame based on the most unfavorable load combination; wherein, the linear parameters include linear reaction force, internal force and displacement value; The objective function construction module is used to determine the function constraints based on the linear parameters and the cross-sectional information, and to construct the objective function based on the function constraints. The objective function optimization module is used to iteratively optimize the objective function by employing a target genetic algorithm that integrates modal sensitivity analysis and the optimization variables, and outputs an optimal solution set; wherein, the optimal solution set includes the optimal objective function value and the optimal structural scheme; The design scheme determination module is used to verify the optimal structural scheme. If the optimal structural scheme meets all verification constraints, then the optimal structural scheme is adopted as the spatial structural design scheme of the steel structure space frame.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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