A method for optimizing the section of a large-span steel truss diagonal web member
By optimizing the cross-section of the diagonal web members of large-span steel trusses using a genetic algorithm that enhances elite retention, the problem of lack of automated optimization in existing technologies is solved, achieving efficient and economical cross-section optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SICHUAN COAL MINE CONSTRUCT 6TH ENG DIVISION
- Filing Date
- 2026-01-14
- Publication Date
- 2026-06-05
AI Technical Summary
In existing technologies, the optimization of the diagonal web section of large-span steel trusses lacks automated programs, resulting in complex designs and large computational loads, making efficient optimization impossible.
A genetic algorithm with enhanced elite retention is adopted, combined with objective function and constraints, to optimize the cross-section of the diagonal web members of a large-span steel truss. The optimization of the diagonal web member cross-section is achieved by using data acquisition module, calculation module and visualization module.
The system achieves automated optimization of the cross-section of the diagonal web members of large-span steel trusses, reducing computational complexity, improving computational efficiency, and enhancing the rationality and economy of the optimization results.
Smart Images
Figure CN122154015A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of structural design in building engineering, and in particular to a method for optimizing the cross-section of diagonal web members in large-span steel trusses. Background Technology
[0002] Currently, there are two main forms of structural optimization design for buildings: one is optimization based on optimization techniques used in building structural design, and the other is optimization using architectural design software. The first method primarily occurs during the structural design phase, aiming at optimizing structural functionality, form, and overall layout. It uses indicators such as stiffness-to-weight ratio, period ratio, and displacement ratio as references, and is performed manually by the structural designer. This optimization method is complex, requires a high level of skill from the designer, and involves a large amount of calculation when optimizing component cross-sections, resulting in low efficiency. The second method typically utilizes BIM technology to adjust unreasonable issues in building structures, avoiding serious design deviations. However, this method lacks optimization programs for the cross-sectional dimensions of structural components (especially for the diagonal web members of large-span steel trusses), and cannot achieve automatic optimization of component cross-sectional dimensions.
[0003] Therefore, there is an urgent need to develop a method for optimizing the cross-section of the diagonal web members of large-span steel trusses. Summary of the Invention
[0004] The purpose of this invention is to provide a method for optimizing the cross-section of the diagonal web members of a large-span steel truss, so as to solve the problems existing in the prior art.
[0005] The technical solution adopted to achieve the purpose of this invention is as follows: a method for optimizing the cross-section of the diagonal web members of a large-span steel truss, comprising the following steps:
[0006] 1) Obtain the initial parameter values of the steel truss structure to be optimized. The parameter values include the steel truss structural form, steel truss dimensions, material properties of the steel truss, steel truss height, top chord section parameters, bottom chord section parameters, side web member section parameters, and vertical web member section parameters.
[0007] 2) Determine the search space for the cross-sectional area of the inclined web member based on the geometric and mechanical parameters.
[0008] 3) Establish a mathematical model with the objective function of minimizing the total mass of the truss and the constraints of truss deflection and member stress.
[0009] 4) An enhanced elite-preserving genetic algorithm is used for optimization calculation. Step 4 specifically includes the following sub-steps:
[0010] 4.1) Initialize the population parameters and initialize the maneuver control parameters represented by each individual in the population.
[0011] 4.2) Statistical analysis of population fitness values.
[0012] 4.3) Select individuals from the population as the mutation parent according to the operator selection method.
[0013] 4.4) Perform chromosome crossover on the selected mutant mothers.
[0014] 4.5) Perform gene mutations on the mutated mother after chromosome crossing.
[0015] 4.6) After the parent gene mutates, a new generation of individuals is generated, and this new generation forms a new population. The fitness values of the individuals in the new population are calculated.
[0016] 4.7) Repeat steps 4.3) to 4.6) to iteratively optimize the new generation population until the set number of iterations is reached.
[0017] Furthermore, the objective function is shown in equation (1).
[0018] (1)
[0019] In the formula, Let be the density of the i-th member in the truss structure. Let be the cross-sectional area of the i-th member in the truss structure. Let be the length of the i-th member in the truss structure. Let n be the total number of truss members.
[0020] Furthermore, the design variables of the mathematical model are shown in equation (2).
[0021] (2)
[0022] In the formula, X is the design variable, and H is the truss height. Let be the cross-sectional height of the i-th member, and n be the total number of truss members.
[0023] Furthermore, the constraints are shown in equation (3).
[0024] (3)
[0025] In the formula, , These are the truss deflection values obtained from structural calculations and the allowable deflection values specified in the standards, respectively. , These represent the stress values of the members obtained from the structural analysis of the truss structure under the i-th working condition and the allowable stress values specified in the code, respectively.
[0026] Furthermore, pattern H is used to define genes that have the same string. The average fitness in the current race is calculated using equation (4).
[0027] (4)
[0028] In the formula, Let t represent the t-th population. Indicates pattern H in the population The total number of occurrences. for The degree of adaptability.
[0029] Furthermore, in steps 4.3) and 4.4), a tournament selection operator is used to randomly select a certain number of individuals in the current population, compare their genetic fitness pairwise, and then select the best individual as the seed for the next generation. This process is repeated until the required number of individuals are selected.
[0030] Furthermore, in step 4.5), a new individual is generated by mutation using the Gaussian mutation operator.
[0031] Furthermore, in step 4.6), a two-point crossover recombination operator is used as a recombination operator to select the crossover point of two chromosomes, and the gene segments between the two crossover points are exchanged to generate two new offspring chromosomes.
[0032] Furthermore, in step 4), the penalty function mechanism prevents some individuals with the potential to become the optimal solution from being prematurely eliminated due to slight violations of constraints in the early stages. The penalty function is shown in equation (5) or equation (6).
[0033] (5)
[0034] (6)
[0035] In the formula, F is the ten-year penalty function value. V is the total volume of the truss. W is the total weight of the truss. , is the penalty factor. Q is the degree of constraint violation by the truss structure. Q is determined as shown in equation (7).
[0036] (7)
[0037] This invention also discloses a system for optimizing the cross-sectional area of diagonal web members in large-span steel trusses, comprising a data acquisition module, a calculation module, a storage module, and a visualization module. The monitoring module is used to implement step 1) of the above method, acquiring the initial parameter values of the steel truss structure to be optimized, and inputting the collected data as the first sequence information into the calculation module. The calculation module is used to implement steps 2) to 4) of the above method, finding the optimal cross-sectional area of the diagonal web members under a specified environment. The storage module is used to store the parameter values and the sequence information generated during the operation of the calculation module. The visualization module is used to extract all information from the storage device and display the optimal cross-sectional area result of the diagonal web members.
[0038] The technical effects of this invention are beyond doubt:
[0039] A. Optimize the cross-section of the diagonal web members for different structural forms and dimensions of large-span steel trusses, the materials of the steel trusses, and the cross-sectional parameters of the lower chord and lower chord members;
[0040] B. By optimizing the cross-section, the structure can achieve the effects of the lightest total structural mass, the best design rationality, and the lowest engineering cost while meeting the constraints.
[0041] C. It has many customizable parameters, which can optimize the cross-sectional dimensions of the diagonal web members of various large-span steel truss structures, and has good versatility.
[0042] D. Compared to the optimized design method for reinforced concrete frame structures based on improved genetic algorithms, this invention overcomes the problems of excessive coupling of optimizable variables and complex optimization processes. The optimization method of this invention has a smaller search space, higher computational efficiency, and faster convergence speed. Attached Figure Description
[0043] Figure 1 Flowchart for optimizing the diagonal web members of a large-span steel truss;
[0044] Figure 2 This is a flowchart of the genetic program algorithm. Detailed Implementation
[0045] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.
[0046] Example 1:
[0047] See Figure 1 This embodiment provides a method for optimizing the cross-section of the diagonal web members of a large-span steel truss, including the following steps:
[0048] 1) Obtain the initial parameter values of the steel truss structure to be optimized. The parameter values include the steel truss structural form, steel truss dimensions, material properties of the steel truss, steel truss height, top chord section parameters, bottom chord section parameters, side web member section parameters, and vertical web member section parameters.
[0049] 2) Determine the search space for the cross-sectional area of the inclined web member based on the geometric and mechanical parameters.
[0050] 3) Establish a mathematical model with the objective function of minimizing the total mass of the truss and the constraints of truss deflection and member stress.
[0051] The objective function is shown in equation (1).
[0052] (1)
[0053] In the formula, Let be the density of the i-th member in the truss structure. Let be the cross-sectional area of the i-th member in the truss structure. Let be the length of the i-th member in the truss structure. Let n be the total number of truss members.
[0054] The design variables of the mathematical model are shown in equation (2).
[0055] (2)
[0056] In the formula, X is the design variable, and H is the truss height. Let be the cross-sectional height of the i-th member, and n be the total number of truss members.
[0057] The constraints are shown in equation (3).
[0058] (3)
[0059] In the formula, , These are the truss deflection values obtained from structural calculations and the allowable deflection values specified in the standards, respectively. , These represent the stress values of the members obtained from the structural analysis of the truss structure under the i-th working condition and the allowable stress values specified in the code, respectively.
[0060] 4) An enhanced elite-preserving genetic algorithm is used for optimization calculation. See [link / reference] Figure 2 Step 4 specifically includes the following sub-steps:
[0061] 4.1) Initialize the population parameters and initialize the maneuver control parameters represented by each individual in the population.
[0062] 4.2) Calculate the fitness value of the population. Genes with the same string are defined using pattern H. The average fitness of the current population is calculated using equation (4).
[0063] (4)
[0064] In the formula, Let t represent the t-th population. Indicates pattern H in the population The total number of occurrences. for The degree of adaptability.
[0065] 4.3) Select individuals from the population as mutation parents according to the operator selection method. Use the tournament selection operator to randomly select a certain number of individuals in the current population, compare their genetic fitness pairwise, and then select the best individual as the seed for the next generation. Repeat this process until the required number of individuals are selected.
[0066] 4.4) Perform chromosome crossover on the selected mutant mothers.
[0067] 4.5) Perform gene mutations on the mutated parent chromosome after the crossing over. Use the Gaussian mutation operator to generate new individuals.
[0068] 4.6) After the mutation of the mother's gene, a new generation of individuals is generated, forming a new population. The fitness values of the individuals in the new population are calculated. The two-point crossover recombination operator is used as the recombination operator to select the crossover point of two chromosomes, and the gene segments between these two crossover points are exchanged, thereby generating two new offspring chromosomes.
[0069] 4.7) Repeat steps 4.3) to 4.6) to iteratively optimize the new generation population until the set number of iterations is reached.
[0070] It is worth noting that in step 4), the penalty function mechanism prevents some individuals with the potential to become the optimal solution from being prematurely eliminated due to slight violations of constraints in the early stages. The penalty function is shown in equation (5) or equation (6).
[0071] (5)
[0072] (6)
[0073] In the formula, F is the ten-year penalty function value. V is the total volume of the truss. W is the total weight of the truss. , is the penalty factor. Q is the degree of constraint violation by the truss structure. Q is determined as shown in equation (7).
[0074] (7)
[0075] In the formula, The truss deflection value obtained from structural calculations represents the value of the truss. This represents the allowable deflection value specified in the standard. This represents the stress value of the i-th component in the truss structure under the specified working condition. This represents the allowable stress value specified in the standard.
[0076] Example 2:
[0077] This embodiment provides a system for optimizing the cross-sectional area of diagonal web members in a large-span steel truss, including a data acquisition module, a calculation module, a storage module, and a visualization module. The monitoring module is used to implement step 1) of the method described in Embodiment 1, acquiring the initial parameter values of the steel truss structure to be optimized, and inputting the collected data as the first sequence information into the calculation module. The calculation module is used to implement steps 2) to 4) of the method described in Embodiment 1, finding the optimal cross-sectional area of the diagonal web members under a specified environment. The storage module is used to store the parameter values and the sequence information generated during the operation of the calculation module. The visualization module is used to extract all information from the storage device and display the optimal cross-sectional area result of the diagonal web members.
Claims
1. A method for optimizing the cross-section of diagonal web members in a large-span steel truss, characterized in that, Includes the following steps: 1) Obtain the initial parameter values of the steel truss structure to be optimized; the parameter values include the steel truss structure form, steel truss dimensions, material properties of the steel truss, steel truss height, upper chord section parameters, lower chord section parameters, side web member section parameters, and vertical web member section parameters; 2) Determine the search space for the cross-sectional area of the inclined web member based on geometric and mechanical parameters; 3) Establish a mathematical model with the objective function of minimizing the total mass of the truss and the constraints of truss deflection and member stress; 4) An enhanced elite-preserving genetic algorithm is used for optimization calculation; step 4 specifically includes the following sub-steps: 4.1) Initialize the population parameters and initialize the maneuver control parameters represented by each individual in the population; 4.2) Calculate the fitness value of the population; 4.3) Select individuals from the population as the mutation parent based on the operator selection method; 4.4) Perform chromosome crossover on the selected mutant mothers; 4.5) Perform gene mutations on the mutated mother chromosomes after crossing over; 4.6) After the mutation of the parent gene, a new generation of individuals is generated, and the new generation of individuals forms a new population; the fitness value of the individuals in the new population is calculated. 4.7) Repeat steps 4.3) to 4.6) to iteratively optimize the new generation population until the set number of iterations is reached.
2. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: The objective function is shown in equation (1); (1) In the formula, Let be the density of the i-th member in the truss structure; Let be the cross-sectional area of the i-th member in the truss structure; is the length of the i-th member in the truss structure; n is the total number of truss members.
3. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: The design variables of the mathematical model are shown in equation (2); (2) In the formula, X is the design variable, and H is the truss height. Let be the cross-sectional height of the i-th member, and n be the total number of truss members.
4. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: The constraints are shown in equation (3); (3) In the formula, , These are the truss deflection values obtained from structural calculations and the allowable deflection values specified in the code, respectively. , These represent the stress values of the members obtained from the structural analysis of the truss structure under the i-th working condition and the allowable stress values specified in the code, respectively.
5. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: Genes with the same string are defined using pattern H; the average fitness in the current race is calculated using equation (4); (4) In the formula, Denotes the t-th population; Indicates pattern H in the population The total number of occurrences in; for The degree of adaptability.
6. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: In steps 4.3) and 4.4), a tournament selection operator is used to randomly select a certain number of individuals in the current population, compare their genetic fitness pairwise, and then select the best individual as the seed for the next generation. This process is repeated until the required number of individuals are selected.
7. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: In step 4.5), a new individual is generated by mutation using the Gaussian mutation operator.
8. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: In step 4.6), the two-point crossover recombination operator is used as the recombination operator to select the crossover point of two chromosomes, and the gene segments between the two crossover points are exchanged to generate two new offspring chromosomes.
9. The method for optimizing the cross-section of the diagonal web members of a large-span steel truss according to claim 1, characterized in that: In step 4), the penalty function processing mechanism avoids some individuals with the potential to become the optimal solution being eliminated prematurely due to slight violations of constraints in the early stage; the penalty function is shown in equation (5) or equation (6); (5) (6) In the formula, F is the ten-year penalty function value; V is the total volume of the truss; W is the total weight of the truss; , is the penalty factor; Q is the degree of constraint violation of the truss structure; Q is determined as shown in equation (7); (7)。 10. A cross-sectional optimization system for diagonal web members of a large-span steel truss, characterized in that: The system includes a data acquisition module, a calculation module, a storage module, and a visualization module. The monitoring module is used to implement step 1) of the method as described in claim 1, acquiring the initial parameter values of the steel truss structure to be optimized, and inputting the collected data as the first sequence information into the calculation module. The calculation module is used to implement steps 2) to 4) of the method as described in claim 1, finding the optimal cross-sectional area of the diagonal brace under a specified environment. The storage module is used to store the parameter values and the sequence information generated during the operation of the calculation module. The visualization module is used to extract all the information from the storage device and display the optimal cross-sectional area result of the diagonal brace.