RBF-NSGA-II-based acoustic optimization method for reinforced concrete composite bridge

Abstract

The invention provides a reinforced concrete composite bridge acoustic optimization method based on RBF-NSGA-II, and relates to the technical field of rail transit bridge vibration and noise reduction, and the method comprises the steps: building a structural acoustic numerical model of a reinforced concrete composite bridge; setting a uniform design table of the steel-concrete combined bridge; performing a simulation test based on the uniform design table and the structural acoustic numerical model to obtain multiple groups of acoustic response data; training a preset RBF neural network based on the multiple groups of acoustic result data to obtain an agent model; performing multi-objective optimization on the design variables of the steel-concrete combined bridge through an NSGA-I optimization algorithm and the proxy model to obtain an optimal solution set of the design variables; and evaluating each solution in the design variable optimal solution set to obtain a design variable optimal solution, and taking a design variable in the design variable optimal solution as an optimization result of the steel-concrete combined bridge. According to the invention, the problem that the prior art cannot meet the requirements of optimized structure acoustic response, structural mechanics and economical efficiency at the same time is solved.

Description

Technical Field

[0001] The invention relates to the technical field of vibration and noise reduction of rail transit bridges, and in particular to an acoustic optimization method for a steel-concrete composite bridge based on RBF-NSGA-II. Background Art

[0002] Steel-concrete composite beam bridges play an important role in modern transportation infrastructure. With their superior span capacity and excellent mechanical properties, they have become an indispensable part of the railway network. Compared with traditional bridge structures, steel-concrete composite bridge structures have excellent performance in bearing capacity, seismic performance and durability, making them widely used in key projects such as long-span bridges and high-speed railway bridges.

[0003] However, the noise problem of steel-concrete composite bridges cannot be ignored. The existing control of the noise of steel-concrete composite bridges is often achieved through external means such as constrained damping layers and vibration-damping tracks, while the influence of the cross-sectional parameters of steel-concrete composite bridges on the noise characteristics is still based on a single parameter analysis. How to adjust the cross-sectional parameters of steel-concrete composite bridges to meet the structural mechanics requirements and economic requirements while optimizing the structural acoustic characteristics is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0004] The purpose of the present invention is to provide an acoustic optimization method for a steel-concrete composite bridge based on RBF-NSGA-II to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] The present application provides an acoustic optimization method for a steel-concrete composite bridge based on RBF-NSGA-II, comprising:

[0006] Acquiring parameter information of the steel-concrete composite bridge, and establishing a structural acoustic numerical model of the steel-concrete composite bridge according to the parameter information;

[0007] Setting a uniform design table for a steel-concrete composite bridge, wherein the design variables of the uniform design table include concrete slab thickness, steel web thickness, steel web height, steel flange thickness, and steel flange width;

[0008] Inputting multiple sets of design variables in the uniform design table into the structural acoustic numerical model for simulation testing to obtain multiple sets of acoustic response data;

[0009] Training a preset RBF neural network based on the multiple groups of acoustic result data to obtain a proxy model;

[0010] The multi-objective optimization of the design variables of the steel-concrete composite bridge is performed by using the NSGA-II optimization algorithm and the surrogate model to obtain the optimal solution set of the design variables, wherein the objective function of the multi-objective optimization is composed of the acoustic response and the total material cost of the steel-concrete composite bridge;

[0011] Each solution in the optimal solution set of the design variables is evaluated to obtain the optimal solution of the design variables, and the design variables in the optimal solution of the design variables are used as the optimization results of the steel-concrete composite bridge.

[0012] The beneficial effects of the present invention are:

[0013] The present invention can accurately simulate the acoustic response of the bridge by designing a structural acoustic numerical model of the steel-concrete composite bridge, and simultaneously uses a combined proxy model and the NSGA-II optimization algorithm to perform multi-objective optimization on the acoustic characteristics of the bridge and the total material cost, so that the steel-concrete composite bridge can achieve a balance between the acoustic response and the material cost. Finally, each solution is evaluated, and the parameter combination of the design variables with the highest score is selected, which can be directly applied in actual engineering, thereby improving the design quality and construction efficiency of the steel-concrete composite bridge, and the optimized design variables meet the actual needs of the steel-concrete composite bridge, which not only ensures the structural acoustic performance, but also reduces the material cost. Therefore, the method of the present invention provides technical support for the acoustic optimization design of the steel-concrete composite bridge.

[0014] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 It is a schematic flow chart of the acoustic optimization method for a steel-concrete composite bridge based on RBF-NSGA-II described in an embodiment of the present invention;

[0017] Figure 2 A side view of a steel-concrete composite bridge to be optimized in an embodiment of the present invention;

[0018] Figure 3 is a cross-sectional view of a steel-concrete composite bridge to be optimized in an embodiment of the present invention;

[0019] Figure 4 is a schematic diagram of a structural acoustics numerical model in an embodiment of the present invention;

[0020] Figure 5 A comparison diagram of the prediction results of the proxy model and the calculation results of the structural acoustics numerical model in an embodiment of the present invention;

[0021] Figure 6 It is a schematic diagram of iterative convergence of the NSGA-II optimization algorithm in an embodiment of the present invention;

[0022] Figure 7 Schematic diagram of the optimal solution set of design variables in an embodiment of the present invention;

[0023] Figure 8 3 is a comparison diagram of acoustic responses of design variables before and after optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0026] Embodiment 1:

[0027] This embodiment provides an acoustic optimization method for a steel-concrete composite bridge based on RBF-NSGA-II.

[0028] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400, step S500, and step S600.

[0029] Step S100: acquiring parameter information of the steel-concrete composite bridge, and establishing a structural acoustic numerical model of the steel-concrete composite bridge according to the parameter information;

[0030] The step S100 includes:

[0031] Step S101: acquiring parameter information of a steel-concrete composite bridge, wherein the parameter information includes excitation parameters and material parameters of the steel-concrete composite bridge;

[0032] In this embodiment, the material parameters include density, elastic modulus, Poisson's ratio and damping loss factor.

[0033] Step S102: establishing a finite element model and a statistical energy analysis model of the steel-concrete composite bridge according to the parameter information;

[0034] The step S102 includes:

[0035] Step A100: performing modal density analysis on the steel-concrete composite bridge to obtain the modal density of each component of the steel-concrete composite bridge;

[0036] Step A200: dividing all components of the steel-concrete composite bridge into first components and second components based on the modal density, wherein the modal density of the first component is less than the modal density of the second component;

[0037] Step A300: performing finite element modeling on the first component according to the material parameters to obtain a finite element model;

[0038] Step A400: Perform statistical energy analysis modeling on the second component according to the material parameters to obtain a statistical energy analysis model.

[0039] Step S103: coupling the finite element modeling and the statistical energy analysis model to obtain a numerical model of the steel-concrete composite bridge;

[0040] Step S104: assigning the excitation parameters to the numerical model to obtain a structural acoustic numerical model of the steel-concrete composite bridge.

[0041] In this embodiment, the structural acoustic numerical model can directly calculate the sound pressure level at a certain point in the steel-concrete composite bridge and the sound radiation efficiency and energy of each component. The total sound power level of the steel-concrete composite bridge structure can be calculated through the sound pressure level, sound radiation efficiency and energy. In this embodiment, the total sound power level of the steel-concrete composite bridge is used as the acoustic response, that is, the acoustic characteristics of the steel-concrete composite bridge.

[0042] Step S200: setting a uniform design table for a steel-concrete composite bridge, wherein the design variables of the uniform design table include concrete slab thickness, steel web thickness, steel web height, steel flange thickness, and steel flange width;

[0043] In this embodiment, the cross-sectional parameters of the steel-concrete composite bridge are designed. First, the design variables of the cross-sectional parameters are set. The design variables actually represent the combination of multiple cross-sectional optimization parameters. The optimization range and learning range of the design variables are set, and the learning range is made larger than the optimization range to reduce boundary errors.

[0044] Set the uniform design table for the design variables. In this embodiment, there are 5 parameters. You can choose the uniform design table U 16* (16 12 ). At the same time, the uniform design table is designed to distribute the design variables reasonably, which not only avoids the redundancy of the experiment, but also reduces the number of simulation runs and reduces the computational cost.

[0045] Step S300: inputting multiple sets of design variables in the uniform design table into the structural acoustic numerical model to perform simulation tests, thereby obtaining multiple sets of acoustic response data;

[0046] Step S400: training a preset RBF neural network based on the multiple groups of acoustic result data to obtain a proxy model;

[0047] In this embodiment, the RBF neural network includes an input layer, a hidden layer and an output layer, wherein the transformation from the input layer to the hidden layer is nonlinear, and the transformation from the hidden layer to the output layer is linear. In order to achieve nonlinear transformation, the hidden layer usually selects a Gaussian function as a radial basis function, and the output result of the output layer can be expressed as a linear superposition of the hidden layer.

[0048] In this embodiment, within the learning range of the design variables, the Adam optimizer is selected to train the RBF neural network to obtain a proxy model, and the proxy model greatly reduces the amount of calculation for subsequent multiple optimization solutions, significantly improving the optimization efficiency.

[0049] Step S500: performing multi-objective optimization on the design variables of the steel-concrete composite bridge by using the NSGA-II optimization algorithm and the proxy model to obtain an optimal solution set of the design variables, wherein the objective function of the multi-objective optimization is composed of the acoustic response and the total material cost of the steel-concrete composite bridge;

[0050] In this embodiment, the NSGA-II optimization algorithm is used for multi-objective optimization, which integrates fast non-dominated sorting and congestion, has low computational complexity, more uniform distribution of each solution in the optimal solution set of design variables, and better robustness.

[0051] The step S500 includes:

[0052] Step S501: randomly generating an initial population, wherein the initial population includes a plurality of individuals, and the individuals include design variables of a steel-concrete composite bridge;

[0053] In this embodiment, the individual is a set of design variables and total material cost of the steel-concrete composite bridge, wherein the total material cost is calculated by the design variable. An initial population is randomly generated, that is, multiple individuals are initialized, and each cross-sectional optimization parameter in the design variable is randomly generated within the optimization range.

[0054] Step S502: input each individual into the proxy model to calculate the individual's acoustic response;

[0055] Step S503: Perform fast non-dominated sorting on the population, calculate the violation value of the individual, and divide all individuals according to the violation value to obtain the Pareto optimal solution set;

[0056] The step S503 specifically includes:

[0057] Step B100: Calculate individual violation values;

[0058] Step B200: Dividing all individuals into feasible solutions and infeasible solutions based on the violation value;

[0059] Step B300: performing fast non-dominated sorting on the feasible solutions, and dividing the feasible solutions into multiple Pareto frontier layers;

[0060] Step B400: Select the first Pareto frontier layer as the Pareto optimal solution set.

[0061] In this embodiment, the violation value of the individual is first calculated, and all individuals are divided into feasible solutions and infeasible solutions according to the violation value. The feasible solutions are quickly non-dominated sorted, and the feasible solutions are divided into multiple Pareto frontier layers. The first Pareto frontier layer containing multiple optimal individuals is selected as the Pareto optimal solution set.

[0062] Step S504: Calculate the crowding degree of each individual in the Pareto optimal solution set based on the objective function, the individual's level and the individual's acoustic response;

[0063] The step S504 includes:

[0064] Step C100: Calculate the objective function value of each individual in the Pareto optimal solution set based on the objective function;

[0065] Step C200: sorting all individuals in ascending order of the objective function value;

[0066] Step C300: Obtain the objective function values ​​of the two adjacent individuals of each individual, and calculate the crowding degree of each individual based on the objective function values ​​of the two adjacent individuals.

[0067] In this embodiment, the calculation formula of the congestion degree is:

[0068]

[0069] Where, d i represents the crowding degree of the ith individual, f k (i+1) represents the objective function value of the kth objective function of the i+1th individual, f k (i-1) represents the objective function value of the kth objective function of the i-1th individual, represents the maximum value of the kth objective function in the Pareto optimal solution set, The minimum value of the kth objective function in the Pareto optimal solution set, and m represents the number of objective functions.

[0070] Step S505: performing selection operation, crossover operation and mutation operation on the individuals in the Pareto optimal solution set in sequence according to the crowding degree and the violation value to obtain a new population;

[0071] The step S505 includes:

[0072] Step D100: Calculating a selection probability based on the congestion degree and the violation value;

[0073] Step D200: performing a selection operation on individuals in the Pareto optimal solution set using the selection probability to obtain a first population;

[0074] Step D300: Select some design variables of each individual in the first population to perform a crossover operation to obtain a second population;

[0075] Step D400: Calculating the mutation probability based on the congestion degree and the violation value;

[0076] Step D500: performing mutation operation on individuals in the second population based on the mutation probability to obtain a new population.

[0077] Step S506: Determine whether the number of iterations reaches a preset threshold. If so, stop the iteration and use the Pareto optimal solution as the optimal solution set of the design variables. Otherwise, use the new population as the initial population for the next round of iteration and perform the next iteration.

[0078] In this embodiment, the steps of constructing the objective function are:

[0079] Step E100: Calculate the acoustic response under the design variable through the proxy model, and calculate the total material cost corresponding to the design variable through the design variable;

[0080] Step E200: minimizing the acoustic response and the total material cost as the objective function;

[0081] Step E300: defining constraint conditions of the objective function, wherein the constraint conditions include an optimization range of design variables, a maximum tensile stress constraint function, and a bridge fundamental frequency constraint function.

[0082] In this embodiment, multiple objective functions are included, namely minimizing the acoustic response and the total material cost, wherein the formula of the objective function is:

[0083] min{W(x),C(x)}x=(tc , t w , l w , t f , l f )

[0084] Where W(x) represents the total sound power level when the design variable is x, C(x) represents the total material cost when the design variable is x, min{·} represents the minimum value, and t c represents the thickness of the concrete slab, t w Indicates the thickness of the steel web, l w Indicates the height of the steel web, t f Indicates the thickness of the steel flange, l f Indicates the steel flange width.

[0085] At the same time, the optimization range of each design variable, the maximum tensile stress constraint function and the bridge fundamental frequency constraint function are set. The expression of the maximum tensile stress constraint function is:

[0086] σ(x)-σ(x0)≤0

[0087] Where σ(·) represents the maximum tensile stress in the span of a steel-concrete composite bridge under the action of uniformly distributed static load on the entire bridge, x represents the design variable, and x0 represents the original design variable.

[0088] The expression of the bridge fundamental frequency constraint function is:

[0089] f(x0)-f(x)≤0

[0090] Where f(·) represents the fundamental frequency of the bridge, x represents the design variable, and x0 represents the original design variable.

[0091] In this embodiment, the maximum tensile stress is conveniently calculated by the structural mechanics equation, the fundamental frequency of the bridge can be calculated by the calculation formula of the relevant specifications, and the original design variables are the initial cross-sectional parameters of the steel-concrete composite bridge to be optimized.

[0092] Step S600: Evaluate each solution in the optimal solution set of the design variables to obtain the optimal solution of the design variables, and use the design variables in the optimal solution of the design variables as the optimization result of the steel-concrete composite bridge.

[0093] The step S600 includes:

[0094] Step S601: calculating the index of each solution in the optimal solution set of the design variables, wherein the index includes the acoustic response and the total material cost;

[0095] Step S602: Calculate the distance between each solution and the ideal optimal solution and the ideal worst solution based on the indicator;

[0096] The step S602 includes:

[0097] Step F100: Perform negative processing and standardization on all indicators to obtain the standard indicator corresponding to each solution;

[0098] In this embodiment, the indicator is processed negatively to obtain a negative indicator. A negative indicator means that the smaller the value, the better the indicator. Both the acoustic response and the total material cost are negative indicators.

[0099] The formula for standardization is:

[0100]

[0101] In the formula, z ab′ represents the bth negative index of the ath solution, z ab represents the bth standard index of the ath solution, M represents the number of solutions in the optimal solution set of the design variables, and N represents the number of indexes.

[0102] Step F200: Calculate the distance between each solution and the ideal optimal solution and the ideal worst solution based on the standard index.

[0103] In this embodiment, for each standard indicator, the ideal optimal solution is the minimum value of the standard indicator, and the ideal worst solution is the maximum value of the standard indicator.

[0104] The calculation formula of the distance from the ideal optimal solution is:

[0105]

[0106] Where, d a,+ represents the distance between the ath solution and the ideal optimal solution, z ab represents the bth standard index of the ath solution, represents the ideal optimal solution for the bth standard indicator, and N represents the number of indicators.

[0107] The calculation formula of the distance from the ideal worst solution is:

[0108]

[0109] Where, d a，- represents the distance between the ath solution and the ideal optimal solution, z ab represents the bth standard index of the ath solution, represents the ideal worst solution for the bth standard indicator, and N represents the number of indicators.

[0110] Step S603: Based on the distance between each solution and the ideal optimal solution and the ideal worst solution, the relative closeness of each solution is calculated, and the solution with the largest relative closeness is selected as the optimal solution of the design variables;

[0111] In this embodiment, the degree of closeness between each solution and the ideal solution of the design variable is determined, so the relative closeness is calculated to measure the closeness, and the solution with the largest relative closeness is selected as the optimal solution of the design variable, wherein the calculation formula of the relative closeness is:

[0112]

[0113] In the formula, S a Relative proximity, d a，+ represents the distance between the ath solution and the ideal optimal solution, d a,- Represents the distance between the ath solution and the ideal optimal solution.

[0114] Step S604: taking the design variables in the optimal solution of the design variables as the optimization results of the steel-concrete composite bridge.

[0115] Embodiment 2:

[0116] In this embodiment, Figure 2 and Figure 3 Taking the steel-concrete composite bridge to be optimized as an example, as shown in Table 1, it is a material parameter table designed in this embodiment.

[0117] Table 1

[0118]

[0119] As shown in Table 2, it is an excitation parameter table designed for this embodiment.

[0120] Table 2

[0121]

[0122]

[0123] In this embodiment, a structural acoustic numerical model is established as follows: Figure 4 As shown, the concrete slab is modeled using finite element FE, the steel web and steel flange are modeled using statistical energy analysis SEA, and the force transmitted to the bridge can be calculated by frequency domain wheel-rail force program, vehicle-bridge coupling program or obtained by actual measurement.

[0124] After the structural acoustic numerical model is established, the design variables, optimization range and learning range are determined according to the actual situation. In this embodiment, the design variables include the thickness of the concrete slab, the thickness of the steel web, the height of the steel web, the thickness of the steel flange and the width of the steel flange. Table 3 shows the optimization range of the design variables.

[0125] Table 3

[0126]

[0127] A suitable uniform design table is selected to design multiple groups of design variables, and the acoustic response of each group of design variables is calculated. In this embodiment, the total sound power level is used as the acoustic response.

[0128] In this embodiment, the preset REF neural network is trained using the acoustic response results to obtain a proxy model, and the proxy model is used to obtain an acoustic response that can accurately predict the acoustic response of a steel-concrete composite bridge with different design variable combinations.

[0129] exist Figure 5 The comparison between the prediction of the total sound power level in the frequency range of 20-2000 Hz for a set of design variables and the calculation of the structural acoustic numerical model is shown in Figure 2. The small deviation shows that the surrogate model can well predict the acoustic response of the steel-concrete composite bridge.

[0130] In this embodiment, the NSGA-II optimization algorithm was used for 200 iterations, that is, the population algebra was 200, and its convergence status and the optimal solution set of the design variables were respectively as follows: Figure 6 and Figure 7 As shown, the material cost change rate represents the change rate of the total material cost.

[0131] In this embodiment, Figure 7 As shown in FIG. 1 , a cutoff limit is set, and solutions with a total sound power less than 137.4 dB are taken as the optimal solution set of the design variables. These are all design variables that further reduce the structure-borne noise compared to the original design. In theory, these solutions can be selected according to actual conditions. However, in order to obtain the optimal solution that balances noise and total material cost, this embodiment evaluates all solutions of the optimal solution set of the design variables and obtains the optimal solution X of the design variables, where X∈x, x=(t c ,t w ,l w ,t f ,l f ), X = (0.5328, 0.0200, 2.9719, 0.03002, 0.9562) m, x represents the design variable.

[0132] The sound power level comparison between the optimal solution of the design variable and the original design variable is as follows: Figure 8 As shown, the sound power level drops by 5 dB and the total material cost drops by 21%.

[0133] In summary, the method of the present invention achieves the purpose of reducing noise by changing the cross-sectional design parameters of the steel-concrete composite bridge. At the same time, the design can meet the stiffness strength and has a low cost. Therefore, the present invention realizes the acoustic optimization for the steel-concrete composite bridge under the premise of considering multiple constraints, which can provide help for the design of the steel-concrete composite bridge.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0135] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An acoustic optimization method for a steel-concrete composite bridge based on RBF-NSGA-II, characterized in that: include: Acquiring parameter information of the steel-concrete composite bridge, and establishing a structural acoustic numerical model of the steel-concrete composite bridge according to the parameter information; Setting a uniform design table for a steel-concrete composite bridge, wherein the design variables of the uniform design table include concrete slab thickness, steel web thickness, steel web height, steel flange thickness, and steel flange width; Inputting multiple sets of design variables in the uniform design table into the structural acoustic numerical model for simulation testing to obtain multiple sets of acoustic response data; Training a preset RBF neural network based on the multiple groups of acoustic result data to obtain a proxy model; The multi-objective optimization of the design variables of the steel-concrete composite bridge is carried out by using the NSGA-II optimization algorithm and the proxy model to obtain the optimal solution set of the design variables, wherein the objective function of the multi-objective optimization is composed of the acoustic response and the total material cost of the steel-concrete composite bridge; Each solution in the optimal solution set of the design variables is evaluated to obtain the optimal solution of the design variables, and the design variables in the optimal solution of the design variables are used as the optimization results of the steel-concrete composite bridge.

2. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 1, characterized in that The step of obtaining parameter information of the steel-concrete composite bridge and establishing a structural acoustic numerical model of the steel-concrete composite bridge according to the parameter information includes: Acquiring parameter information of the steel-concrete composite bridge, wherein the parameter information includes excitation parameters and material parameters of the steel-concrete composite bridge; Establish the finite element model and statistical energy analysis model of the steel-concrete composite bridge based on the parameter information; The finite element modeling and the statistical energy analysis model are coupled to obtain a numerical model of the steel-concrete composite bridge; The excitation parameters are assigned to the numerical model to obtain the structural acoustic numerical model of the steel-concrete composite bridge.

3. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 1, characterized in that ,The finite element model and statistical energy analysis model of the steel-concrete composite bridge are established according to the parameter information, including: Performing modal density analysis on the steel-concrete composite bridge to obtain the modal density of each component of the steel-concrete composite bridge; Based on the modal density, all components of the steel-concrete composite bridge are divided into a first component and a second component, wherein the modal density of the first component is less than the modal density of the second component; Performing finite element modeling on the first component according to the material parameters to obtain a finite element model; According to the material parameters, statistical energy analysis modeling is performed on the second component to obtain a statistical energy analysis model.

4. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 1, characterized in that ,The multi-objective optimization of the steel-concrete composite bridge is carried out through the NSGA-II optimization algorithm and the agent model, and the optimal solution set of design variables is obtained, including: Randomly generate an initial population, wherein the initial population includes a plurality of individuals, and the individuals include design variables of the steel-concrete composite bridge; Each individual is input into the proxy model to calculate the individual's acoustic response; Perform fast non-dominated sorting on the population, calculate the violation value of the individual, and divide all individuals according to the violation value to obtain the Pareto optimal solution set; Based on the objective function, the individual's rank and the individual's acoustic response, the crowding degree of each individual in the Pareto optimal solution set is calculated; According to the crowding degree and the violation value, selection operation, crossover operation and mutation operation are performed on the individuals in the Pareto optimal solution set in sequence to obtain a new population; Determine whether the number of iterations reaches the preset threshold. If so, stop the iteration and use the Pareto optimal solution as the optimal solution set of the design variables. Otherwise, use the new population as the initial population for the next round of iteration and perform the next iteration.

5. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 4 is characterized in that , the population is quickly non-dominated sorted, the violation value of the individual is calculated, and all individuals are divided according to the size of the violation value to obtain the Pareto optimal solution set, including: Calculate individual violation values; Based on the violation value, all individuals are divided into feasible solutions and infeasible solutions; Performing fast non-dominated sorting on the feasible solutions, and dividing the feasible solutions into multiple Pareto frontier layers; Select the first Pareto frontier layer as the Pareto optimal solution set.

6. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 4 is characterized in that ,The calculation of the crowding degree of each individual in the Pareto optimal solution set includes: Calculate the objective function value of each individual in the Pareto optimal solution set based on the objective function; Sort all individuals in order from small to large according to the objective function value; Obtain the objective function values ​​of the two adjacent individuals of each individual, and calculate the crowding degree of each individual based on the objective function values ​​of the two adjacent individuals.

7. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 1, characterized in that ,According to the crowding degree and the violation value, the individuals in the Pareto optimal solution set are sequentially subjected to selection operation, crossover operation and mutation operation to obtain a new population, including: calculating a selection probability based on the congestion degree and the violation value; The individuals in the Pareto optimal solution set are selected by using the selection probability to obtain a first population; Selecting part of the design variables of each individual in the first population to perform a crossover operation to obtain a second population; Calculating a mutation probability based on the congestion degree and the violation value; Based on the mutation probability, a mutation operation is performed on the individuals in the second population to obtain a new population.

8. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 1 is characterized in that ,The steps of constructing the objective function are: The acoustic response under the design variable is calculated by the surrogate model, and the total material cost corresponding to the design variable is calculated by the design variable; Minimizing the acoustic response and the total material cost is taken as the objective function; The constraint conditions of the objective function are defined, wherein the constraint conditions include an optimization range of the design variables, a maximum tensile stress constraint function, and a bridge fundamental frequency constraint function.

9. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 1 is characterized in that , evaluate each solution in the optimal solution set of the design variables to obtain the optimal solution of the design variables, and use the design variables in the optimal solution of the design variables as the optimization result of the steel-concrete composite bridge, including: Calculating an index of each solution in the optimal solution set of the design variables, the index including acoustic response and total material cost; Calculate the distance between each solution and the ideal optimal solution and the ideal worst solution based on the indicator; Based on the distance between each solution and the ideal optimal solution and the ideal worst solution, the relative closeness of each solution is calculated, and the solution with the largest relative closeness is selected as the optimal solution of the design variables; The design variables in the optimal solution of the design variables are taken as the optimization results of the steel-concrete composite bridge.

10. The acoustic optimization method for steel-concrete composite bridge based on RBF-NSGA-II according to claim 9, characterized in that , the distance between each solution and the ideal optimal solution and the ideal worst solution is calculated based on the index, and also includes: All indicators are negatively processed and standardized to obtain the standard indicators corresponding to each solution; The distance between each solution and the ideal optimal solution and the ideal worst solution is calculated based on the standard index.

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