A method, system, and related equipment for selecting fulcrums in irregular grid structures.

Abstract

This invention discloses a method, system, and related equipment for selecting support points in irregular mesh structures. The solution includes: first, establishing a finite element model of the irregular mesh structure, counting and numbering the nodes; then, considering on-site construction conditions, providing intelligent algorithm parameters such as ① the range of selectable support points; ② the range of the number of support points; and ③ the maximum number of iterations; next, selecting a structural optimization index as the population fitness function; then, selecting a constraint processing function; finally, iterating through the finite element model of the irregular mesh structure using the corresponding parameters, automatically calling a finite element software platform to calculate the structural forces and outputting the calculation results, calculating the structural optimization index based on the force results, and constraining individuals that do not meet the constraints, and outputting the optimal support point arrangement scheme after repeated iterations. This invention effectively overcomes the cumbersome calculation process of existing support point determination methods, quickly determining the optimal support point scheme for the structure, and is convenient for engineers to use.

Description

Technical Field

[0001] This invention relates to the construction and installation process of large-span grid structures, and more particularly to the selection of structural support points in the overall installation methods such as jacking and hoisting of large-span grid structures. Background Technology

[0002] With the rapid development of the social economy, the construction of large public buildings is becoming more and more frequent. At the same time, with the innovation and upgrading of design concepts and aesthetics, asymmetrical, long cantilevered, and high-clearance building shapes are increasing. Irregular large-span grid structures have become an important structural form for realizing design concepts. In the construction process of such grid structures, installation methods such as jacking and hoisting are widely used.

[0003] All of these installation methods involve determining the support points for irregular structures. The number and location of the support points directly affect structural safety, support reaction force, and the selection of construction equipment. However, the determination of support point schemes for irregular grid structures often relies on the experience of engineers, who select from multiple support point schemes based on structural response and construction site conditions. This involves repeated calculations of the number and location of the support points.

[0004] Therefore, existing methods for determining the support points of irregular structures are extremely inefficient because they rely on the experience of engineers. It is evident that improving the efficiency of determining the support points of irregular structures is a problem that needs to be solved in this field. Summary of the Invention

[0005] To address the problems of existing solutions that rely on human experience to select and determine the support points for irregular mesh structures, the present invention aims to provide a support point selection scheme for irregular mesh structures. This scheme, based on an innovative intelligent algorithm, can automatically provide the most suitable support points for irregular mesh structures for engineers' reference, greatly reducing reliance on engineers' personal experience and improving efficiency.

[0006] To achieve the above objectives, the present invention provides a method for selecting support points in irregular mesh structures, the method comprising the following steps:

[0007] Step (1): For the target irregular mesh structure, establish the corresponding structural finite element model and set the corresponding load and other conditions;

[0008] Step (2): Based on the on-site construction conditions, determine the range parameters of the available support points, the range parameters of the number of support points, and the iterative operation parameters of the structural finite element model;

[0009] Step (3): Construct the population fitness function in the established structural finite element model based on the structural optimization index;

[0010] Step (4): Construct the constraint processing function in the established finite element model of the target irregular mesh structure based on the performance requirements of the target irregular mesh structure;

[0011] Step (5): Introduce the parameters determined in step (2), the population fitness function constructed in step (3), and the constraint processing function constructed in step (4) into the structural finite element model established in step (1), and iterate accordingly to calculate the structural force and output the calculation results. Calculate the structural optimization index based on the force results, and perform constraint processing on individuals that do not meet the constraints. After repeated iterations, output the optimal support arrangement scheme.

[0012] In some embodiments of the present invention, the structural finite element model is constructed based on a genetic algorithm in step (1).

[0013] In some embodiments of the present invention, the iterative operation parameters of the structural finite element model determined in step (2) specifically include four parameters: population size n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm.

[0014] In some embodiments of the present invention, in step (3), a population fitness function is constructed based on structural performance indicators such as structural strain energy and maximum reaction force at the fulcrum.

[0015] In some embodiments of the present invention, in step (4), a constraint processing function is constructed based on performance indicators such as the strength of structural members, the maximum deformation of the structure as a whole, and stability.

[0016] In some embodiments of the present invention, the iterative operation of the structural finite element model in step (5) specifically includes the following steps:

[0017] (5.1) The structural finite element model is first initialized based on the input parameters of the range of selectable support points, the range of the number of support points, and various iterative operation parameters to form the distribution of support points corresponding to the initial population.

[0018] (5.2) Set the corresponding boundary conditions for the formed support points, and then combine them with the load and other conditions set in advance when constructing the structural finite element model to perform structural stress calculation;

[0019] (5.3) Based on the structural stress calculation results in step (5.2), the structural optimization index is calculated in combination with the population fitness function constructed in step (3);

[0020] (5.4) The new population with the fulcrum distribution is updated based on the structural optimization index determined in step (5.3);

[0021] (5.5) Based on the constraint processing function constructed in step (4), the fulcrum distribution population formed in step (5.4) is constrained to form a new population;

[0022] (5.6) Based on the above steps, the maximum number of iterations m is used to iterate repeatedly, and finally the optimal fulcrum arrangement scheme is generated.

[0023] To achieve the above objectives, the present invention also provides a selection system for pivot points of irregular mesh structures. The selection system is composed of a finite element model construction module, a parameter setting module, a population fitness function construction module, a constraint processing function construction module, and an iteration and constraint processing module working together.

[0024] The finite element model building module is configured to build a corresponding structural finite element model for the target irregular mesh structure and set corresponding loads and other conditions.

[0025] The parameter setting module is configured to determine the range parameters of selectable support points, the range parameters of the number of support points, and the iterative operation parameters of the structural finite element model, based on the on-site construction conditions.

[0026] The population fitness function construction module is configured to construct the population fitness function in the established structural finite element model based on structural optimization indices.

[0027] The constraint processing function construction module is configured to construct constraint processing functions adapted to the established structural finite element model based on the performance requirements of the target irregular mesh structure.

[0028] The iteration and constraint processing module works in conjunction with the finite element model construction module, parameter setting module, population fitness function construction module, and constraint processing function construction module to interact with each other. It can retrieve the structural finite element model established by the finite element model construction module and introduce the parameters determined by the parameter setting module, the population fitness function constructed by the population fitness function construction module, and the constraint processing function constructed by the constraint processing function construction module as input parameters. Based on these input parameters, it performs iteration, calculates the structural forces, outputs the calculation results, calculates the structural optimization index based on the force results, and performs constraint processing on individuals that do not meet the constraints. After repeated iterations, it outputs the optimal support arrangement scheme.

[0029] In some embodiments of the present invention, the iteration and constraint processing module is composed of a fulcrum distribution location calculation submodule, a structural force calculation submodule, a structural optimization index calculation submodule, a population update submodule, a constraint processing submodule, and an iteration processing submodule working together.

[0030] The fulcrum distribution location calculation submodule is configured to call the structural finite element model and first perform initialization based on the input fulcrum selectable range parameters, fulcrum number range parameters, and various iterative operation parameters to form the fulcrum distribution location corresponding to the initial population.

[0031] The structural stress calculation submodule is configured to interact with the support point distribution location calculation submodule, and can set corresponding boundary conditions for the formed support point locations, and then combine the load and other conditions preset when the structural finite element model is constructed to perform structural stress calculation.

[0032] The structural optimization index calculation submodule is configured to interact with the structural stress calculation submodule, and can perform structural optimization index calculation based on the structural stress calculation results calculated by the structural stress calculation submodule and the population fitness function constructed by the population fitness function construction module.

[0033] The population update submodule is configured to interact with the structure optimization index calculation submodule, and can update and form a new population with a fulcrum distribution based on the structure optimization index calculated and determined by the structure optimization index calculation submodule.

[0034] The constraint processing submodule is configured to interact with the population update submodule and can perform constraint processing on the fulcrum distribution population formed by the population update submodule based on the constraint processing function constructed by the constraint processing function construction module to form a new population.

[0035] The iterative processing submodule is configured to interact with the fulcrum distribution location calculation submodule, structural stress calculation submodule, structural optimization index calculation submodule, population update submodule, and constraint processing submodule. Based on the iterative operation parameters set by the parameter setting module, it coordinates the fulcrum distribution location calculation submodule, structural stress calculation submodule, structural optimization index calculation submodule, population update submodule, and constraint processing submodule to perform repeated iterations and finally output the optimal fulcrum arrangement scheme.

[0036] To achieve the above objectives, the present invention also provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the above-described fulcrum selection method.

[0037] To achieve the above objectives, the present invention also provides a processor for running a program that executes the steps of the above-described pivot selection method during runtime.

[0038] To achieve the above objectives, the present invention also provides a terminal device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program code is loaded and executed by the processor to implement the steps of the above-described fulcrum selection method.

[0039] To achieve the above objectives, the present invention also provides a computer program product that, when executed on a data processing device, is adapted to perform the steps of the above-described fulcrum selection method.

[0040] The fulcrum selection scheme for irregular grid structures provided by this invention is based on an intelligent fulcrum selection method. It can automatically provide the most suitable fulcrum for the structure based on the initial fulcrum parameters through an innovative intelligent selection algorithm, which reduces the reliance on the engineer's personal experience and overcomes the cumbersome calculation process of existing fulcrum determination methods. It is easy for engineers to use and greatly saves their time and energy.

[0041] The support point selection scheme for irregular grid structures provided by this invention is applicable to the selection and determination of structural support points in overall installation projects such as jacking and hoisting of large-span grid structures. It can quickly provide support point layout schemes and corresponding structural calculation results, reducing reliance on engineers' subjective judgment and saving time; at the same time, it can also reduce construction equipment and avoid construction risks.

[0042] Furthermore, in its specific implementation, the solution provided by this invention innovatively uses the finite element calculation of complex rod structures as the basis for fitness function calculation, based on the self-iterative automatic optimization function of the intelligent optimization algorithm. It further calls the finite element calculation and intelligent optimization algorithm, which can effectively overcome the cumbersome calculation process of existing support point determination methods. At the same time, the resulting support point layout and structural verification results are directly related, with high reliability and easy engineering application. Attached Figure Description

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 This is a basic flowchart of the fulcrum selection method for irregular mesh structures in this invention;

[0045] Figure 2 This is a system schematic diagram of the pivot selection system for irregular grid structures in this invention;

[0046] Figure 3 A flowchart illustrating the preferred support points for the irregular mesh structure in this invention example;

[0047] Figure 4 This is a schematic diagram of an irregular mesh structure in an example of the present invention where the fulcrum needs to be determined;

[0048] Figure 5 This is a schematic diagram of the initial spatial distribution of points in the grid structure in an example of the present invention;

[0049] Figure 6 This is a schematic diagram of the optimized distribution structure of the fulcrum arrangement in an example of the present invention;

[0050] Figure 7 This is the calculation result corresponding to the optimized fulcrum arrangement in the example of the present invention. Detailed Implementation

[0051] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below with reference to specific illustrations.

[0052] In response to the structural characteristics of irregular large-span grid structures, and to quickly and accurately obtain the support points of such structures, this invention abandons conventional methods and instead innovatively constructs a finite element model of the irregular grid structure based on an improved genetic algorithm. Based on this model and combined with preliminary support point parameters, intelligent algorithms are used for intelligent calculation and automatic optimization to determine the optimal support point arrangement scheme.

[0053] Accordingly, the present invention provides a method for selecting support points for irregular mesh structures, which mainly consists of the following steps in combination:

[0054] Step (1): For the target irregular mesh structure, establish the corresponding structural finite element model and set the corresponding load and other conditions;

[0055] Step (2): Based on the on-site construction conditions, determine the range parameters of the available support points, the range parameters of the number of support points, and the iterative operation parameters of the structural finite element model;

[0056] Step (3): Construct the population fitness function in the established structural finite element model based on the structural optimization index;

[0057] Step (4): Construct the constraint processing function in the established finite element model of the target irregular mesh structure based on the performance requirements of the target irregular mesh structure;

[0058] Step (5): Introduce the parameters determined in step (2), the population fitness function constructed in step (3), and the constraint processing function constructed in step (4) into the structural finite element model established in step (1), and iterate accordingly to calculate the structural force and output the calculation results. Calculate the structural optimization index based on the force results, and perform constraint processing on individuals that do not meet the constraints. After repeated iterations, output the optimal support arrangement scheme.

[0059] Based on this, the implementation scheme and corresponding technical features of the present invention will be described in detail below.

[0060] In some embodiments of the present invention, when constructing the structural finite element model in step (1), each node in the selectable support range of the constructed structural finite element model is provided with a clear and fixed node number for the algorithm to retrieve; furthermore, the constructed structural finite element model is configured to run successfully independently outside each support selection program, and the supports in the support range need to be deleted before entering the support selection program.

[0061] In some embodiments of the present invention, when determining the corresponding fulcrum selection range parameters in step (2), the fulcrum targeted is a set of fulcrum nodes in the irregular grid structure that can be used as structural support points during construction. At the same time, the fulcrum range is determined according to the actual construction site and excludes areas where support systems such as formwork and jacks cannot be installed due to ground facilities, foundation bearing capacity or structural collisions. This ensures the accuracy of the determined fulcrum selection range parameters.

[0062] As an example, the range of possible fulcrum parameters here is N points, and each point contains spatial coordinates.

[0063] Furthermore, the range of the number of support points determined in step (2) is specifically composed of a positive integer interval including a minimum number of support points and a maximum number of support points. Here, the minimum number of support points is 1, and the maximum number is determined comprehensively based on historical data and the actual situation on the construction site. Generally speaking, the more support points there are, the better the structural stress condition, but the higher the construction cost. Therefore, in this scheme, the preferred number of support points can be selected gradually from 3 upwards until the structural stress meets the requirements.

[0064] Furthermore, this scheme further improves the speed of convergence in subsequent calculations by reducing the interval length.

[0065] As an example, the range of the number of fulcrums determined here is [m1, m2], where m1 = 2 and m2 = 5.

[0066] Furthermore, the iterative operation parameters of the structural finite element model determined in step (2) specifically include four parameters: population size n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm. These four parameters can effectively adapt to the iterative operation of the structural finite element model.

[0067] The population size n is configured to determine the number of possible solutions to the problem. To ensure the accuracy of subsequent running results, the population size n is preferably above 20.

[0068] The maximum number of iterations, *m*, is configured to control the length of the intelligent algorithm's search process during the iterative execution of the structural finite element model. When the maximum number of iterations is reached, the iteration of the structural finite element model will stop searching and output the currently found optimal solution. To ensure the accuracy of subsequent results, the maximum number of iterations, *m*, is preferably greater than 50.

[0069] The crossover probability Pc is configured to determine the frequency of crossover operations between two individuals during the iterative run of the structural finite element model. A higher crossover probability is beneficial for maintaining population diversity and promoting the generation of new individuals, but it may also destroy superior individuals. A lower crossover probability may lead to a slow search speed. To ensure the accuracy of subsequent running results, the crossover probability Pc is preferably set between 0.4 and 0.99.

[0070] The mutation probability Pm is configured to determine the frequency of individual gene mutations during the iterative run of the structural finite element model. For mutation operations during iterative run, an excessively high mutation probability may cause the search to become randomized, disrupting the optimal pattern of the population. To ensure the accuracy of subsequent run results, the mutation probability Pm is preferably set at 0.05.

[0071] In some embodiments of the present invention, when constructing the population fitness function based on structural optimization indices in step (3), it can be specifically constructed based on structural performance indices such as structural strain energy and maximum reaction force of fulcrum.

[0072] As an example, the fitness function is set here based on the reciprocal of the maximum reaction force of the construction support group as follows:

[0073]

[0074] In the formula, Fitness is the fitness value of the population; P i,max Let i be the nodal reaction force at the point of maximum reaction.

[0075] In some embodiments of the present invention, when constructing the constraint processing function based on the performance requirements of the target irregular mesh structure in step (4), the function is specifically constructed based on performance indicators such as the strength of structural members, the maximum deformation of the overall structure, and stability.

[0076] As an example, a multi-condition constraint function for structural safety is established here based on structural strength and deformation. If any aspect exceeds the limit, constraint processing is performed:

[0077] σ max >σ limits (Equation 2)

[0078] u max >u limits (Equation 3)

[0079] Where σ max σ represents the maximum stress of the structure. limits The maximum allowable stress of the structure; u max The maximum deformation force of the structure; u limits The maximum allowable deformation of the structure.

[0080] In some embodiments of the present invention, the structural finite element model is iteratively run in step (5), specifically including the following steps:

[0081] (5.1) The structural finite element model is first initialized based on the input parameters of the range of selectable support points, the range of the number of support points, and various iterative operation parameters to form the distribution of support points corresponding to the initial population.

[0082] (5.2) Set the corresponding boundary conditions for the formed support points, and then combine them with the load and other conditions set in advance when constructing the structural finite element model to perform structural stress calculation;

[0083] (5.3) Based on the structural stress calculation results in step (5.2), the structural optimization index is calculated in combination with the population fitness function constructed in step (3);

[0084] (5.4) The new population with the fulcrum distribution is updated based on the structural optimization index determined in step (5.3);

[0085] (5.5) Based on the constraint processing function constructed in step (4), the fulcrum distribution population formed in step (5.4) is constrained to form a new population;

[0086] (5.6) Based on the above steps, the maximum number of iterations m is used to iterate repeatedly, and finally the optimal fulcrum arrangement scheme is generated.

[0087] Furthermore, in step (5.1), when forming the fulcrum distribution position corresponding to the initial population, the present invention includes:

[0088] First, an N-bit ordered list with all original values ​​of 0 is formed using the input range parameter N of the pivot point, where each bit corresponds to a real node in the finite element model.

[0089] Next, randomly change m of them to 1, and configure the current position to have a support point. The boundary conditions corresponding to the support point, such as hinged or fixed, are determined by the actual conditions of the construction site.

[0090] Next, the above random process is repeated n times to form n 0-1 lists, which constitute the initial population and then form the initial positions of the pivot distribution.

[0091] Furthermore, in step (5.2), the present invention includes the following steps when performing structural stress calculations:

[0092] First, read the 0-1 list formed in step (5.1) and set the corresponding boundary conditions for the corresponding pivot positions based on the API method;

[0093] Next, based on the loads and other conditions pre-set in step 1, the structural stress is calculated using the finite element method, and the calculation results are output. For example, the finite element module here can perform structural calculations based on elements such as rod elements and beam elements, and output stress, strain, and other results.

[0094] Furthermore, when calculating the structural optimization index in step (5.3) of this invention, the corresponding structural optimization index can be selected as structural strain energy or maximum reaction force of the support point, etc.

[0095] Based on this, the structural optimization index is calculated using the structural stress value obtained in step (5.2) and the population fitness function determined in step (3) (such as formula 1). When the population fitness function is used for calculation, its population fitness is the reciprocal of the maximum stress on the fulcrum. Therefore, the smaller the stress on the fulcrum, the higher the population fitness.

[0096] Furthermore, when constructing a new population with fulcrum distribution in step (5.4), based on the structural optimization index calculated in step (5.3), the 0-1 list of fulcrum distribution formed in step (5.1) is updated and a new population with fulcrum arrangement is formed by sequentially performing selection, crossover, and mutation operations.

[0097] The specific implementation schemes for selection, crossover, and mutation operations are not limited here and can be determined according to actual needs.

[0098] Furthermore, in step (5.5), when the present invention performs constraint processing on the new population of fulcrum distribution established in step (5.4), it first analyzes and judges each individual in the new population to determine whether it satisfies the structural fulcrum constraint conditions formed by the corresponding constraint processing function in step (4).

[0099] Next, for individuals that do not meet the constraints, the corresponding constraint processing function in step (4) is used to process the constraints until all individuals meet the constraints, thereby forming a new population.

[0100] Specifically, the constraint processing process in the present invention is as follows:

[0101] (5.4.1) First, sort the existing supports in order of reaction force from low to high, remove the support with the smallest reaction force (sorted as 1), randomly add new supports, update the chromosome code, perform finite element calculation, examine the structural calculation results of the new support arrangement, and if the constraint conditions are met, the chromosome repair is complete.

[0102] (5.4.2) If the constraint conditions are still not met, remove the second smallest support point (ranked as 2), randomly add a new support point, update the chromosome code, perform finite element calculation, repeat the process until the constraint conditions are met and the chromosome repair is completed.

[0103] (5.4.3) If the requirements are still not met after the traversal of the fulcrum, analyze and determine whether the number of fulcrums has reached the maximum number m2. If not, add one fulcrum randomly, perform finite element calculation, and perform chromosome repair until the constraint conditions are met.

[0104] The constraint processing scheme here is designed with a two-stage constraint processing process: First, without increasing the number of fulcrums, the positions of the fulcrums are dynamically adjusted to try to meet the structural safety limits; second, if adjusting the positions of the fulcrums alone cannot meet the requirements, the number of fulcrums in the structure is gradually increased to accelerate the speed of chromosome repair, thereby achieving the technical effect of reducing the number of fulcrums and rapidly repairing chromosomes.

[0105] The fulcrum selection method for irregular mesh structures provided in this invention can be configured into a corresponding software program to form a fulcrum selection system for irregular mesh structures. When running, this software program executes the aforementioned fulcrum selection method for irregular mesh structures and stores it in a corresponding storage medium for the processor to retrieve and execute.

[0106] like Figure 2 As shown, the pivot selection system 100 for irregular mesh structures formed therefrom is composed of a finite element model construction module 110, a parameter setting module 120, a population fitness function construction module 130, a constraint processing function construction module 140, and an iteration and constraint processing module 150 working together.

[0107] The finite element model construction module 110 in this system is configured to establish a corresponding structural finite element model for the target irregular mesh structure and set the corresponding load and other conditions.

[0108] The parameter setting module 120 in this system is configured to determine the range of selectable support points, the range of the number of support points, and the iterative operation parameters of the structural finite element model, based on the on-site construction conditions.

[0109] The population fitness function construction module 130 in this system is configured to construct the population fitness function in the established structural finite element model based on the structural optimization index.

[0110] The constraint processing function construction module 140 in this system is configured to construct constraint processing functions adapted to the established structural finite element model based on the performance requirements of the target irregular mesh structure.

[0111] In this system, the iteration and constraint processing module 150 works in conjunction with the finite element model construction module 110, parameter setting module 120, population fitness function construction module 130, and constraint processing function construction module 140 to interact with each other. It can retrieve the structural finite element model established by the finite element model construction module 110 and introduce the parameters determined by the parameter setting module 120, the population fitness function constructed by the population fitness function construction module 130, and the constraint processing function constructed by the constraint processing function construction module 140 as parameter inputs. Based on these inputs, iterative calculations are performed to calculate the structural forces and output the calculation results. Based on the force results, structural optimization indices are calculated, and constraints are applied to individuals that do not meet the constraints. After repeated iterations, the optimal support arrangement scheme is output.

[0112] As further explanation, in the specific implementation of the finite element model construction module 110 in this system, when constructing the structural finite element model, each node in the selectable support range of the structural finite element model is set with a clear and fixed node number for the algorithm to retrieve; furthermore, the constructed structural finite element model is configured to run successfully independently outside of each support selection program, and the supports in the support range need to be deleted before entering the support selection program.

[0113] In this system, the population fitness function construction module 130 can be specifically constructed based on structural performance indicators such as structural strain energy and maximum reaction force at the support.

[0114] As an example, the population fitness function is constructed using the reciprocal of the maximum reaction force at the structural fulcrum.

[0115]

[0116] In the formula, Fitness is the fitness value of the population; P i,max Let i be the nodal reaction force at the point of maximum reaction force in the structure.

[0117] In this system, the constraint processing function construction module 140 is specifically constructed based on performance indicators such as the strength of structural members, the maximum deformation of the overall structure, and stability.

[0118] As an example, the strength of structural members and the maximum deformation of the structure as a whole can be used to establish a constraint processing function. This processing function is a multi-condition constraint: if any of the conditions in (Equation 2) and (Equation 3) are satisfied, constraint processing is performed.

[0119] σ max >σ limits (Equation 2)

[0120] u max >u limits (Equation 3)

[0121] Where σ max σ represents the maximum stress of the structure. limits The maximum allowable stress of the structure; u max The maximum deformation force of the structure; u limits The maximum allowable deformation of the structure.

[0122] In the specific implementation of the iteration and constraint processing module 150 in this system, it is composed of a support point distribution location calculation submodule, a structural force calculation submodule, a structural optimization index calculation submodule, a population update submodule, a constraint processing submodule, and an iteration processing submodule working together.

[0123] The fulcrum distribution location calculation submodule is configured to call the structural finite element model and perform initialization based on the input fulcrum selectable range parameters, fulcrum number range parameters, and various iterative operation parameters to form the fulcrum distribution location corresponding to the initial population.

[0124] The structural stress calculation submodule is configured to interact with the support distribution location calculation submodule. It can set corresponding boundary conditions for the support locations formed by the support distribution location calculation submodule, and then perform structural stress calculations in combination with the load and other conditions preset when the structural finite element model is constructed.

[0125] The structural optimization index calculation submodule is configured to interact with the structural stress calculation submodule. It can calculate the structural optimization index based on the structural stress calculation results calculated by the structural stress calculation submodule and by calling the population fitness function constructed by the population fitness function construction module.

[0126] The population update submodule is configured to interact with the structure optimization index calculation submodule, and can update and form a new population with a fulcrum distribution based on the structure optimization index calculated by the structure optimization index calculation submodule.

[0127] The constraint processing submodule is configured to interact with the population update submodule and can perform constraint processing on the fulcrum distribution population formed by the population update submodule based on the constraint processing function constructed by the constraint processing function construction module 140 to form a new population.

[0128] The iterative processing submodule is configured to interact with the fulcrum distribution location calculation submodule, structural stress calculation submodule, structural optimization index calculation submodule, population update submodule, and constraint processing submodule. Based on the iterative operation parameters set by the parameter setting module 120 (such as the four parameters: population size n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm), it coordinates the fulcrum distribution location calculation submodule, structural stress calculation submodule, structural optimization index calculation submodule, population update submodule, and constraint processing submodule to iterate repeatedly and finally output the optimal fulcrum arrangement scheme.

[0129] The following specific application example illustrates the implementation process of the pivot selection scheme for irregular grid structures provided by this invention.

[0130] This example builds a corresponding pivot selection system for irregular grid structures based on the aforementioned scheme, and sets up the corresponding operating environment and deploys the system.

[0131] See Figure 3 In this example, the process of selecting the optimal support point for an irregular grid structure based on the support point selection system is as follows:

[0132] (1) First, an irregular mesh structure finite element model is established for the target irregular mesh structure, such as... Figure 4 As shown.

[0133] (2) Based on the constructed finite element model, chromosome encoding is performed: chromosomes are generated according to the possible positions of the fulcrum distribution in the input parameters, such as... Figure 5 As shown, the number of chromosome positions is equal to the number of possible fulcrum distribution positions N. Each number in a chromosome can have two values: 1 or 0. 1 corresponds to the existence of a fulcrum at that point, while 0 means there is no fulcrum.

[0134] (3) Establish multi-condition constraints; generate chromosome constraints based on structural strength, deformation and stability verification. If the generated chromosome code and the corresponding fulcrum calculation results do not meet the constraints, perform constraint processing.

[0135] Specifically, the constraint handling process in this example is as follows:

[0136] ① Sort the existing fulcrums in order of increasing reaction force, remove the fulcrum with the smallest reaction force (sorted as 1), randomly add new fulcrums, update the chromosome code, perform finite element calculation, examine the structural calculation results of the new fulcrum arrangement, and if the constraint conditions are met, the chromosome repair is complete.

[0137] ② If the constraints are still not met, remove the second smallest reaction point (ranked as 2), randomly add a new point, update the chromosome code, perform finite element calculation, repeat the process until the constraints are met and the chromosome repair is complete.

[0138] ③ If the requirements are still not met after the traversal of the fulcrums, check whether the number of fulcrums has reached the maximum number m2. Then, randomly add 1 fulcrum, perform finite element calculation, and perform chromosome repair until the constraint conditions are met.

[0139] (5) To facilitate construction and reduce risks, the fitness function in this example is set as follows based on the reciprocal of the maximum reaction force of the construction support group:

[0140]

[0141] In the formula, Fitness is the fitness value of the population; P i,max Let i be the nodal reaction force at the point of maximum reaction force in the structure.

[0142] (6) Based on the finite element model of the engineering structure and the input parameters, calculate the fitness index of the fulcrum arrangement; then perform the selection operation based on the roulette wheel strategy, and then perform crossover, mutation and recombination operations to form a new fulcrum arrangement population; then perform constraint processing to form a new population, iterate repeatedly, and converge to obtain the optimal chromosome arrangement of the population.

[0143] (7) Decode the obtained chromosome into a pivot arrangement, and output the corresponding number and position of pivots, as well as the corresponding finite element calculation results, such as Figure 6 and Figure 7 The above is provided for reference by engineering personnel.

[0144] Based on the above-described pivot selection scheme for irregular mesh structures, this embodiment of the invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the steps of the above-described pivot selection method for irregular mesh structures.

[0145] This invention also provides a processor for running a program, wherein the program executes the steps of the above-described method for selecting fulcrums for irregular grid structures.

[0146] This invention also provides a terminal device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. The program code is loaded and executed by the processor to implement the steps of the above-described method for selecting fulcrums for irregular grid structures.

[0147] The present invention also provides a computer program product, which, when executed on a data processing device, is adapted to perform the steps of the above-described method for selecting fulcrums for irregular grid structures.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0150] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0154] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0155] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0156] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0157] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0158] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] The method, specific system unit, or part thereof of the present invention described above is a pure software architecture. It can be deployed via program code on physical media, such as hard disks, optical discs, or any electronic device (such as smartphones or computer-readable storage media). When a machine loads and executes the program code (e.g., a smartphone loads and executes it), the machine becomes an apparatus for implementing the present invention. The method and apparatus of the present invention can also be transmitted in program code form via transmission media, such as cables, optical fibers, or any transmission method. When the program code is received, loaded, and executed by a machine (e.g., a smartphone), the machine becomes an apparatus for implementing the present invention.

[0160] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for selecting fulcrums in irregular grid structures, characterized in that, The fulcrum selection method includes the following steps: Step (1): For the target irregular mesh structure, establish the corresponding structural finite element model and set the corresponding loads; Step (2): Based on the on-site construction conditions, determine the range parameters of the available support points, the range parameters of the number of support points, and the iterative operation parameters of the structural finite element model; Step (3): Construct the population fitness function in the established structural finite element model based on the structural optimization index; Step (4): Construct the constraint handling function in the established finite element model of the structure based on the performance requirements of the target irregular mesh structure; Step (5): Introduce the parameters determined in step (2), the population fitness function constructed in step (3), and the constraint processing function constructed in step (4) into the structural finite element model established in step (1), and iterate accordingly to calculate the structural force and output the calculation results. Calculate the structural optimization index based on the force results, and perform constraint processing on individuals that do not meet the constraints. After repeated iterations, output the optimal support arrangement scheme.

2. The method for selecting support points for irregular mesh structures according to claim 1, characterized in that, In step (1), a structural finite element model is constructed based on a genetic algorithm.

3. The method for selecting support points for irregular mesh structures according to claim 1, characterized in that, The iterative operation parameters of the structural finite element model determined in step (2) specifically include four parameters: population size n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm.

4. The method for selecting support points for irregular mesh structures according to claim 1, characterized in that, In step (3), the population fitness function is constructed based on the structural strain energy and the maximum reaction force of the fulcrum.

5. The method for selecting support points for irregular mesh structures according to claim 1, characterized in that, In step (4), the constraint processing function is constructed based on the strength of the structural members, the maximum deformation of the structure as a whole, and its stability.

6. The method for selecting support points for irregular mesh structures according to claim 1, characterized in that, The iterative operation of the structural finite element model in step (5) specifically includes the following steps: (5.1) The structural finite element model is first initialized based on the input range parameters of the selectable range of the support points, the range parameters of the number of support points, and the various iterative operation parameters to form the distribution position of the support points corresponding to the initial population; (5.2) Set the corresponding boundary conditions for the formed support points, and then combine them with the loads set in advance when constructing the structural finite element model to perform structural stress calculation; (5.3) Based on the structural stress calculation results in step (5.2), the structural optimization index is calculated in combination with the population fitness function constructed in step (3); (5.4) Update the new population with the fulcrum distribution based on the structural optimization index determined in step (5.3); (5.5) Based on the constraint processing function constructed in step (4), the fulcrum distribution population formed in step (5.4) is constrained to form a new population; (5.6) Based on the above steps, the maximum number of iterations m is used to iterate repeatedly, and finally the optimal fulcrum arrangement scheme is generated.

7. A system for selecting support points in an irregular grid structure, characterized in that, The selection system is composed of a finite element model construction module, a parameter setting module, a population fitness function construction module, a constraint processing function construction module, and an iteration and constraint processing module working together. The finite element model building module is configured to build a corresponding structural finite element model for the target irregular mesh structure and set the corresponding loads. The parameter setting module is configured to determine the range parameters of selectable support points, the range parameters of the number of support points, and the iterative operation parameters of the structural finite element model, based on the on-site construction conditions. The population fitness function construction module is configured to construct the population fitness function in the established structural finite element model based on structural optimization indices. The constraint processing function construction module is configured to construct constraint processing functions adapted to the established structural finite element model based on the performance requirements of the target irregular mesh structure. The iteration and constraint processing module works in conjunction with the finite element model construction module, parameter setting module, population fitness function construction module, and constraint processing function construction module to interact with each other. It can retrieve the structural finite element model established by the finite element model construction module and introduce the parameters determined by the parameter setting module, the population fitness function constructed by the population fitness function construction module, and the constraint processing function constructed by the constraint processing function construction module as input parameters. Based on these input parameters, it performs iteration, calculates the structural forces, outputs the calculation results, calculates the structural optimization index based on the force results, and performs constraint processing on individuals that do not meet the constraints. After repeated iterations, it outputs the optimal support arrangement scheme.

8. The system for selecting support points for irregular grid structures according to claim 7, characterized in that, The iteration and constraint processing module consists of a supporting submodule for calculating the fulcrum distribution location, a structural force calculation submodule, a structural optimization index calculation submodule, a population update submodule, a constraint processing submodule, and an iteration processing submodule, which work together to form the module. The fulcrum distribution location calculation submodule is configured to call the structural finite element model and first perform initialization based on the input fulcrum selectable range parameters, fulcrum number range parameters, and various iterative operation parameters to form the fulcrum distribution location corresponding to the initial population. The structural stress calculation submodule is configured to interact with the support point distribution location calculation submodule, and can set corresponding boundary conditions for the formed support point locations, and then perform structural stress calculation in combination with the loads preset when the structural finite element model is constructed. The structural optimization index calculation submodule is configured to interact with the structural stress calculation submodule, and can perform structural optimization index calculation based on the structural stress calculation results calculated by the structural stress calculation submodule and the population fitness function constructed by the population fitness function construction module. The population update submodule is configured to interact with the structure optimization index calculation submodule, and can update and form a new population with a fulcrum distribution based on the structure optimization index calculated and determined by the structure optimization index calculation submodule. The constraint processing submodule is configured to interact with the population update submodule and can perform constraint processing on the fulcrum distribution population formed by the population update submodule based on the constraint processing function constructed by the constraint processing function construction module to form a new population. The iterative processing submodule is configured to interact with the fulcrum distribution location calculation submodule, structural stress calculation submodule, structural optimization index calculation submodule, population update submodule, and constraint processing submodule. Based on the iterative operation parameters set by the parameter setting module, it coordinates the fulcrum distribution location calculation submodule, structural stress calculation submodule, structural optimization index calculation submodule, population update submodule, and constraint processing submodule to perform repeated iterations and finally output the optimal fulcrum arrangement scheme.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the pivot selection method for irregular mesh structures as described in any one of claims 1-6.

10. A computer program product, characterized in that, When executed on a data processing device, it is suitable for performing the steps of the pivot selection method for irregular grid structures as described in any one of claims 1-6.

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