Noise reduction method, system and medium for steel-concrete composite bridge based on multi-parameter optimization

Abstract

The present application provides a noise reduction method, system and medium for a steel-concrete composite bridge based on multi-parameter optimization. The method includes: using a preset finite element-boundary element model to calculate the low-frequency noise acoustic contribution of each component of the steel-concrete composite bridge, using a preset SEA model to calculate the high-frequency noise acoustic contribution of each component of the steel-concrete composite bridge, screening out low-frequency target bridge components based on the low-frequency noise acoustic contribution and calculating the parameter sensitivity of each component parameter in the low-frequency band, screening out high-frequency target bridge components based on the high-frequency noise acoustic contribution and calculating the parameter sensitivity of each component parameter in the high-frequency band, and determining the parameters to be optimized in the low-frequency band and the parameters to be optimized in the high-frequency band, processing the parameters to be optimized in the low-frequency band and the parameters to be optimized in the high-frequency band based on the preset response surface method and genetic algorithm, obtaining the optimal parameter combination, and adjusting the bridge component parameters according to the optimal parameters. Thus, the purpose of precise noise reduction of the steel-concrete composite bridge is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of bridge noise reduction, and in particular to a noise reduction method, system and medium for a steel-concrete composite bridge based on multi-parameter optimization. Background Art

[0002] In the field of noise control for steel-concrete composite bridges, the generation mechanisms and propagation paths of low-frequency and high-frequency noise differ significantly. Low-frequency noise is primarily caused by the structural vibration of load-bearing components such as the bridge deck and beams, which is transmitted through solid media as acoustic radiation. High-frequency noise, on the other hand, originates from aerodynamic excitations such as tire-bridge friction and air turbulence during vehicle operation, and propagates as airborne sound. Due to these significant differences in their propagation paths and mechanisms, existing noise reduction technologies are often limited to analyzing a single frequency band and lack a systematic quantitative assessment of noise contributions across the entire frequency range. This results in noise reduction measures that are insufficiently targeted and ineffective overall. With advances in numerical simulation technology, finite element-boundary element (FE-BE) coupled models and statistical energy analysis (SEA) models have been applied to predict and analyze low-frequency structure-borne noise and high-frequency airborne noise, respectively. However, current research often focuses on optimizing the design of isolated frequency bands or local components, without establishing a model for the coupled effects of low- and high-frequency noise or implementing a hierarchical parameter sensitivity screening based on the differences in component acoustic contributions. Furthermore, traditional parameter optimization methods often rely on empirical trial-and-error methods and single-objective optimization models, making it difficult to balance conflicting multi-band noise reduction objectives under complex constraints. This limits parameter control accuracy and engineering applicability. To address these issues, a systematic noise reduction approach is urgently needed that integrates multi-band acoustic contribution analysis, parameter sensitivity screening, and multi-objective optimization to accurately identify key noise sources and achieve efficient and coordinated parameter control. Summary of the Invention

[0003] This application proposes a noise reduction technology for steel-concrete composite bridges based on multi-parameter optimization. First, through frequency-band modeling and multi-band acoustic contribution analysis, the bridge components that contribute most to noise are precisely identified. Parameter sensitivity analysis is then used to identify key design parameters that significantly impact noise from these selected components. Finally, multi-objective optimization is used to achieve efficient and coordinated parameter control. Compared to traditional full-parameter analysis methods, this hierarchical screening method focuses on key parameters, significantly reducing computational complexity while ensuring effective noise reduction. This achieves the dual goals of conserving computational resources and improving optimization efficiency, providing a highly effective solution for noise control in large bridge structures.

[0004] This application also provides a noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization, comprising the following steps:

[0005] The preset finite element-boundary element model is used to calculate the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge;

[0006] The preset SEA model is used to calculate the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge;

[0007] Low-frequency target bridge components are screened out based on the acoustic contribution of low-frequency noise, and high-frequency target bridge components are screened out based on the acoustic contribution of high-frequency noise;

[0008] Calculate the parameter sensitivity of each component parameter in the low-frequency band based on the low-frequency noise acoustic contribution of the low-frequency target bridge component, and determine the parameters to be optimized in the low-frequency band;

[0009] Calculate the parameter sensitivity of each component parameter in the high-frequency band based on the high-frequency noise acoustic contribution of each high-frequency bridge component, and determine the parameters to be optimized in the high-frequency band;

[0010] Based on the preset response surface method and genetic algorithm, the parameters to be optimized in the low-frequency band and the high-frequency band are processed to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

[0011] Optionally, in the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, the calculation of the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge using a preset finite element-boundary element model includes:

[0012] The vibration velocity of each bridge component is calculated using a preset finite element model;

[0013] The vibration velocity is input into the preset boundary element model to obtain the radiated sound pressure, and the sound pressure level of each bridge component is calculated based on the radiated sound pressure;

[0014] The vibration contribution of each bridge component is shielded in turn, and the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is obtained based on the sound pressure level calculation.

[0015] Optionally, in the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, the calculation of the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge using a preset SEA model includes:

[0016] The vibration energy density of each bridge component in different noise frequency bands at high frequencies is obtained according to the preset SEA model.

[0017] The radiated sound power of each bridge component in different noise frequency bands at high frequencies is calculated based on the vibration energy density and using a preset energy density-radiation efficiency model.

[0018] The ratio of the radiated sound power of each bridge component to the total radiated sound power is taken as the acoustic contribution of each bridge component to the noise in different frequency bands in the high frequency band.

[0019] Optionally, in the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, screening out low-frequency target bridge components according to the acoustic contribution of low-frequency noise, and screening out high-frequency target bridge components according to the acoustic contribution of high-frequency noise, includes:

[0020] Compare the acoustic contribution of each bridge component to low-frequency noise of different frequencies with the preset acoustic contribution threshold, and count the number of frequencies whose acoustic contribution exceeds the threshold;

[0021] The frequency quantity is compared with a preset frequency quantity threshold, and the bridge component whose frequency quantity exceeds the threshold is regarded as a low-frequency target bridge component;

[0022] Compare the acoustic contribution of each bridge component to noise in different frequency bands in the high-frequency band with the preset acoustic contribution threshold, and count the number of frequency bands whose acoustic contribution exceeds the threshold;

[0023] The frequency band number is compared with a preset frequency band number threshold, and the bridge components whose frequency band number exceeds the threshold are regarded as high-frequency target bridge components.

[0024] Optionally, in the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, the step of calculating the parameter sensitivity of each component parameter in the low-frequency band based on the acoustic contribution of the low-frequency noise of the low-frequency target bridge component and determining the parameters to be optimized in the low-frequency band includes:

[0025] According to the acoustic contribution of the low-frequency target bridge component to the noise of different frequencies in the low-frequency band, the acoustic contribution change rate of each component parameter of the low-frequency target bridge component at each frequency point is calculated;

[0026] The low-frequency parameter sensitivity of each component parameter is obtained by performing a weighted average calculation based on the acoustic contribution change rate of each frequency point and the preset weight coefficient;

[0027] The low-frequency band parameter sensitivity is compared with a preset low-frequency band parameter sensitivity threshold, and component parameters with a sensitivity higher than the threshold are used as low-frequency band parameters to be optimized.

[0028] Optionally, in the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, the step of calculating the parameter sensitivity of each component parameter in the high-frequency band according to the acoustic contribution of each high-frequency bridge component to high-frequency noise, and determining the parameters to be optimized in the high-frequency band, includes:

[0029] Calculate the average value of the acoustic contribution of each frequency band noise in the high frequency band to obtain the acoustic contribution of the high frequency band;

[0030] Calculating the acoustic contribution change rate of each component parameter according to the high-frequency band acoustic contribution to obtain the high-frequency band parameter sensitivity;

[0031] The high-frequency band parameter sensitivity is compared with a preset high-frequency band parameter sensitivity threshold, and the component parameters with a sensitivity higher than the threshold are used as high-frequency band parameters to be optimized.

[0032] Optionally, in the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, the low-frequency band parameters to be optimized and the high-frequency band parameters to be optimized are processed based on a preset response surface method and a genetic algorithm to obtain an optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters, including:

[0033] Determine the value ranges of the parameters to be optimized in the low-frequency band and the high-frequency band, and generate multiple sets of parameter combinations to be optimized based on the CCD experimental design method;

[0034] For each set of parameter combinations to be optimized, the preset finite element-boundary element model and SEA model are used to process them respectively to obtain the corresponding low-frequency sound pressure level and high-frequency sound pressure level;

[0035] Using the preset response surface method, response surface models of low-frequency sound pressure level and high-frequency sound pressure level are constructed respectively;

[0036] The response surface model is processed using genetic algorithm to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

[0037] In a second aspect, the present application provides a noise reduction system for a steel-concrete composite bridge based on multi-parameter optimization. The system includes: a memory and a processor. The memory stores a program for a noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization. When the program for the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization is executed by the processor, the following steps are implemented:

[0038] The preset finite element-boundary element model is used to calculate the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge;

[0039] The preset SEA model is used to calculate the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge;

[0040] Low-frequency target bridge components are screened out based on the acoustic contribution of low-frequency noise, and high-frequency target bridge components are screened out based on the acoustic contribution of high-frequency noise;

[0041] Calculate the parameter sensitivity of each component parameter in the low-frequency band based on the low-frequency noise acoustic contribution of the low-frequency target bridge component, and determine the parameters to be optimized in the low-frequency band;

[0042] Calculate the parameter sensitivity of each component parameter in the high-frequency band based on the high-frequency noise acoustic contribution of each high-frequency bridge component, and determine the parameters to be optimized in the high-frequency band;

[0043] Based on the preset response surface method and genetic algorithm, the parameters to be optimized in the low-frequency band and the high-frequency band are processed to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

[0044] Optionally, in the noise reduction system for a steel-concrete composite bridge based on multi-parameter optimization described in the present application, the calculation of the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge using a preset finite element-boundary element model includes:

[0045] The vibration velocity of each bridge component is calculated using a preset finite element model;

[0046] The vibration velocity is input into the preset boundary element model to obtain the radiated sound pressure, and the sound pressure level of each bridge component is calculated based on the radiated sound pressure;

[0047] The vibration contribution of each bridge component is shielded in turn, and the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is obtained based on the sound pressure level calculation.

[0048] In a third aspect, the present application also provides a computer-readable storage medium, which stores a program for a steel-concrete composite bridge noise reduction method based on multi-parameter optimization. When the program for a steel-concrete composite bridge noise reduction method based on multi-parameter optimization is executed by a processor, the steps of the steel-concrete composite bridge noise reduction method based on multi-parameter optimization as described in any one of the above items are implemented.

[0049] As can be seen from the above, the noise reduction method, system, and medium for steel-concrete composite bridges based on multi-parameter optimization provided in this application first selects bridge components that contribute most to noise based on acoustic contribution assessment. Then, through parameter sensitivity analysis, key design parameters with significant noise impact are identified from these selected components. Finally, multi-objective optimization design is carried out for these core parameters. Compared with traditional full-parameter analysis methods, this method, by focusing on key parameters through hierarchical screening, significantly reduces computational complexity while ensuring effective noise reduction, achieving the dual goals of conserving computational resources and improving optimization efficiency, and providing a highly efficient solution for noise control in large bridge structures.

[0050] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A flowchart of a noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization provided in an embodiment of the present application;

[0053] Figure 2 A flowchart of calculating the acoustic contribution of low-frequency noise in a noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization provided in an embodiment of the present application;

[0054] Figure 3 A flowchart of calculating the acoustic contribution of high-frequency noise in a steel-concrete composite bridge noise reduction method based on multi-parameter optimization provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application 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 application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0056] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0057] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for reducing noise in a steel-concrete composite bridge based on multi-parameter optimization in some embodiments of the present application. This method for reducing noise in a steel-concrete composite bridge based on multi-parameter optimization is used in a terminal device, such as a computer or mobile phone terminal. This method for reducing noise in a steel-concrete composite bridge based on multi-parameter optimization includes the following steps:

[0058] S11. Calculate the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge using a preset finite element-boundary element model;

[0059] S12. Calculate the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge using the preset SEA model;

[0060] S13. Screening out low-frequency target bridge components based on the acoustic contribution of low-frequency noise, and screening out high-frequency target bridge components based on the acoustic contribution of high-frequency noise;

[0061] S14. Calculate the parameter sensitivity of each component parameter in the low-frequency band according to the low-frequency noise acoustic contribution of the low-frequency target bridge component, and determine the parameters to be optimized in the low-frequency band;

[0062] S15. Calculate the parameter sensitivity of each component parameter in the high frequency band according to the high frequency noise acoustic contribution of each high frequency bridge component, and determine the parameters to be optimized in the high frequency band;

[0063] S16. Based on the preset response surface method and genetic algorithm, the parameters to be optimized in the low-frequency band and the parameters to be optimized in the high-frequency band are processed to obtain the optimal parameter combination, and the parameters of the bridge components are adjusted according to the optimal parameters.

[0064] It should be noted that this application first conducts a frequency band analysis of the noise response of the target steel-concrete composite bridge based on the numerical simulation method of hybrid FE-BE and SEA to determine the acoustic contribution of important components in the steel-concrete composite bridge, and based on the acoustic contribution analysis, determines the sensitivity of the parameters of important components to different frequency bands, thereby selecting the component parameters that need to be optimized. Then, based on the central composite design (CDD) and response surface method (RSM), the selected optimized design component parameters are expressed, and finally a second-order polynomial characterizing the noise response is generated (the dependent variable is the optimization target, and the independent variable is the optimization parameter). The second-order polynomial is used as the fitness function of the genetic algorithm (GA), and constraint processing technology is implemented in the genetic algorithm, especially the feasibility rule of Deb, to ensure that the optimized solution meets actual engineering requirements.

[0065] Please refer to Figure 2 , Figure 2 This is a flow chart for calculating the acoustic contribution of low-frequency noise in a steel-concrete composite bridge noise reduction method based on multi-parameter optimization in some embodiments of the present application. According to an embodiment of the present invention, calculating the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge using a preset finite element-boundary element model includes:

[0066] S21. Calculate the vibration velocity of each bridge component using a preset finite element model;

[0067] S22. Input the vibration velocity into a preset boundary element model to obtain a radiation sound pressure, and calculate the sound pressure level of each bridge component based on the radiation sound pressure;

[0068] S23. Shield the vibration contribution of each bridge component in turn, and calculate the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band based on the sound pressure level.

[0069] It should be noted that the low-frequency (0-200Hz) noise response of the target steel-concrete composite bridge was analyzed based on the numerical simulation method of hybrid finite element-boundary element (FE-BE). The vibration contribution of each bridge component was shielded in turn to obtain the acoustic contribution of each bridge component to the low-frequency noise of different frequencies.

[0070] The calculation formula for the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is:

[0071] ;

[0072] in, is the acoustic contribution of the i-th bridge component at frequency f, is the sound pressure level of the complete bridge structure at frequency f, is the sound pressure level at frequency f after the i-th bridge component is removed.

[0073] Please refer to Figure 3 , Figure 3 This is a flow chart for calculating the acoustic contribution of high-frequency noise in a steel-concrete composite bridge noise reduction method based on multi-parameter optimization in some embodiments of the present application. According to an embodiment of the present invention, calculating the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge using a preset SEA model includes:

[0074] S31. Obtain the vibration energy density of each bridge component in different noise frequency bands in the high frequency band according to the preset SEA model;

[0075] S32. Calculate the radiated sound power of each bridge component in different noise frequency bands in the high frequency band based on the vibration energy density and using a preset energy density-radiation efficiency model;

[0076] S33. The ratio of the radiated sound power of each bridge component to the total radiated sound power is used as the acoustic contribution of each bridge component to the noise of different frequency bands in the high frequency band.

[0077] It should be noted that the SEA-based numerical simulation method analyzes the high-frequency noise response (200-2000Hz) of the target steel-concrete composite bridge to determine the acoustic contribution of each bridge component to the noise in different frequency bands in this high-frequency range. Based on the statistical energy analysis (SEA) model, the vibration energy density of each bridge subsystem in each 1 / 3 octave frequency band in the high-frequency range is calculated. A preset energy density-radiation efficiency model is then used to calculate the radiated sound power of each bridge component in different noise bands in the high-frequency range, thereby quantifying the acoustic contribution of each bridge component to the noise in different frequency bands.

[0078] According to an embodiment of the present invention, screening out low-frequency target bridge components according to the acoustic contribution of low-frequency noise, and screening out high-frequency target bridge components according to the acoustic contribution of high-frequency noise, includes:

[0079] Compare the acoustic contribution of each bridge component to low-frequency noise of different frequencies with the preset acoustic contribution threshold, and count the number of frequencies whose acoustic contribution exceeds the threshold;

[0080] The frequency quantity is compared with a preset frequency quantity threshold, and the bridge component whose frequency quantity exceeds the threshold is regarded as a low-frequency target bridge component;

[0081] Compare the acoustic contribution of each bridge component to noise in different frequency bands in the high-frequency band with the preset acoustic contribution threshold, and count the number of frequency bands whose acoustic contribution exceeds the threshold;

[0082] The frequency band number is compared with a preset frequency band number threshold, and the bridge components whose frequency band number exceeds the threshold are regarded as high-frequency target bridge components.

[0083] It should be noted that the bridge components that need to be optimized are selected based on their acoustic contribution.

[0084] According to an embodiment of the present invention, the step of calculating the parameter sensitivity of each component parameter in the low-frequency band based on the low-frequency noise acoustic contribution of the low-frequency target bridge component and determining the parameters to be optimized in the low-frequency band includes:

[0085] According to the acoustic contribution of the low-frequency target bridge component to the noise of different frequencies in the low-frequency band, the acoustic contribution change rate of each component parameter of the low-frequency target bridge component at each frequency point is calculated;

[0086] The low-frequency parameter sensitivity of each component parameter is obtained by performing a weighted average calculation based on the acoustic contribution change rate of each frequency point and the preset weight coefficient;

[0087] The low-frequency band parameter sensitivity is compared with a preset low-frequency band parameter sensitivity threshold, and component parameters with a sensitivity higher than the threshold are used as low-frequency band parameters to be optimized.

[0088] It should be noted that the acoustic contribution change rate of each component parameter of the low-frequency target bridge component is calculated when the component parameters change, and the low-frequency parameter sensitivity of each component parameter is obtained by weighted averaging, so as to select the component parameters that need to be optimized.

[0089] According to an embodiment of the present invention, the step of calculating the parameter sensitivity of each component parameter in the high frequency band based on the high frequency noise acoustic contribution of each high frequency bridge component and determining the parameters to be optimized in the high frequency band includes:

[0090] Calculate the average value of the acoustic contribution of each frequency band noise in the high frequency band to obtain the acoustic contribution of the high frequency band;

[0091] Calculating the acoustic contribution change rate of each component parameter according to the high-frequency band acoustic contribution to obtain the high-frequency band parameter sensitivity;

[0092] The high-frequency band parameter sensitivity is compared with a preset high-frequency band parameter sensitivity threshold, and the component parameters with a sensitivity higher than the threshold are used as high-frequency band parameters to be optimized.

[0093] It should be noted that the acoustic contribution change rate of each component when the parameters change is calculated based on the acoustic contribution of the high-frequency band, and the high-frequency band parameter sensitivity is obtained, which can be expressed as the ratio of the change value of the acoustic contribution before and after the change to the acoustic contribution before the change.

[0094] According to an embodiment of the present invention, the low-frequency band parameters to be optimized and the high-frequency band parameters to be optimized are processed based on a preset response surface method and a genetic algorithm to obtain an optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters, including:

[0095] Determine the value ranges of the parameters to be optimized in the low-frequency band and the high-frequency band, and generate multiple sets of parameter combinations to be optimized based on the CCD experimental design method;

[0096] For each set of parameter combinations to be optimized, the preset finite element-boundary element model and SEA model are used to process them respectively to obtain the corresponding low-frequency sound pressure level and high-frequency sound pressure level;

[0097] Using the preset response surface method, response surface models of low-frequency sound pressure level and high-frequency sound pressure level are constructed respectively;

[0098] The response surface model is processed using genetic algorithm to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

[0099] It should be noted that in actual use, bridges must meet acoustic requirements in low-frequency environments while not neglecting their acoustic performance in high-frequency environments. Furthermore, the low-frequency and high-frequency bands are mutually restrictive, so the low-frequency and high-frequency sound pressure levels need to be comprehensively considered to achieve optimal overall acoustic performance. In this application, the low-frequency and high-frequency response surface models are formulated as a multi-objective optimization problem and assigned to a genetic algorithm. The genetic algorithm searches the parameter space to find a set of parameter combinations that makes the low-frequency and high-frequency sound pressure levels as close to their respective ideal states as possible. Specifically, a central composite design (CCD) experimental design method is used to generate multiple sets of optimized parameter combinations that include both low-frequency and high-frequency parameters to be optimized. For each generated set of optimized parameter combinations, the noise response (i.e., the low-frequency sound pressure level and the high-frequency sound pressure level) is calculated. The radiated sound power is obtained according to the SEA model, converted to the radiated sound power level using a conversion formula, and then the high-frequency sound pressure level is calculated using the sound power level to sound pressure level conversion formula. Then, a preset response surface method (RSM) is used to construct response surface models for the low-frequency band and the high-frequency band, respectively. In this embodiment, the response surface model is a second-order polynomial model, wherein the dependent variable of the polynomial model is the sound pressure level, and the independent variable is the parameter to be optimized, which is used to characterize the change in the sound pressure level when the parameter to be optimized changes. The low-frequency and high-frequency response surface models are then submitted to a genetic algorithm as a multi-objective optimization problem. The genetic algorithm is used to search the parameter space to find a parameter combination that can achieve the optimal state of the low-frequency and high-frequency sound pressure levels. In this embodiment of the application, the second-order polynomial model is used as the fitness function of the genetic algorithm, and the Deb feasibility rule is used to process the parameter constraints. The parameter space is multi-objective iteratively optimized by the genetic algorithm to obtain the Pareto optimal parameter solution set. Finally, the actual parameters of the bridge components are adjusted according to the optimal parameter combination to achieve better acoustic effects of the bridge.

[0100] According to an embodiment of the present invention, the further embodiment includes:

[0101] Obtain the low-frequency sound pressure level and high-frequency sound pressure level of bridge components under multiple working conditions before and after bridge component parameter adjustment;

[0102] Calculate the mean of the low-frequency sound pressure level and the high-frequency sound pressure level before and after the adjustment, compare the mean of the low-frequency sound pressure level after the adjustment with the mean of the low-frequency sound pressure level before the adjustment, and compare the mean of the high-frequency sound pressure level after the adjustment with the mean of the high-frequency sound pressure level before the adjustment;

[0103] If the comparison results of the low-frequency sound pressure levels before and after adjustment and the comparison results of the high-frequency sound pressure levels before and after adjustment do not meet the preset comparison result requirements, it is prompted to review and adjust the parameters of the component.

[0104] It should be noted that after adjustment, the low-frequency sound pressure level and the high-frequency sound pressure level should be lower than before adjustment, and they must each drop by a preset proportion compared to their respective values ​​before adjustment to meet the comparison result requirements. The preset proportion can be determined based on actual conditions.

[0105] The present invention also discloses a noise reduction system for a steel-concrete composite bridge based on multi-parameter optimization, comprising a memory and a processor. The memory stores a noise reduction method program for a steel-concrete composite bridge based on multi-parameter optimization. When the noise reduction method program for a steel-concrete composite bridge based on multi-parameter optimization is executed by the processor, the following steps are implemented:

[0106] The preset finite element-boundary element model is used to calculate the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge;

[0107] The preset SEA model is used to calculate the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge;

[0108] Low-frequency target bridge components are screened out based on the acoustic contribution of low-frequency noise, and high-frequency target bridge components are screened out based on the acoustic contribution of high-frequency noise;

[0109] Calculate the parameter sensitivity of each component parameter in the low-frequency band based on the low-frequency noise acoustic contribution of the low-frequency target bridge component, and determine the parameters to be optimized in the low-frequency band;

[0110] Calculate the parameter sensitivity of each component parameter in the high-frequency band based on the high-frequency noise acoustic contribution of each high-frequency bridge component, and determine the parameters to be optimized in the high-frequency band;

[0111] Based on the preset response surface method and genetic algorithm, the parameters to be optimized in the low-frequency band and the high-frequency band are processed to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

[0112] It should be noted that this application first conducts a frequency band analysis of the noise response of the target steel-concrete composite bridge based on the numerical simulation method of hybrid FE-BE and SEA to determine the acoustic contribution of important components in the steel-concrete composite bridge, and based on the acoustic contribution analysis, determines the sensitivity of the parameters of important components to different frequency bands, thereby selecting the component parameters that need to be optimized. Then, based on the central composite design (CDD) and response surface method (RSM), the selected optimized design component parameters are expressed, and finally a second-order polynomial characterizing the noise response is generated (the dependent variable is the optimization target, and the independent variable is the optimization parameter). The second-order polynomial is used as the fitness function of the genetic algorithm (GA), and constraint processing technology is implemented in the genetic algorithm, especially the feasibility rule of Deb, to ensure that the optimized solution meets actual engineering requirements.

[0113] According to an embodiment of the present invention, the calculation of the acoustic contribution of low-frequency noise of each component of the steel-concrete composite bridge using a preset finite element-boundary element model includes:

[0114] The vibration velocity of each bridge component is calculated using a preset finite element model;

[0115] The vibration velocity is input into the preset boundary element model to obtain the radiated sound pressure, and the sound pressure level of each bridge component is calculated based on the radiated sound pressure;

[0116] The vibration contribution of each bridge component is shielded in turn, and the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is obtained based on the sound pressure level calculation.

[0117] It should be noted that the low-frequency (0-200Hz) noise response of the target steel-concrete composite bridge was analyzed based on the numerical simulation method of hybrid finite element-boundary element (FE-BE). The vibration contribution of each bridge component was shielded in turn to obtain the acoustic contribution of each bridge component to the low-frequency noise of different frequencies.

[0118] The calculation formula for the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is:

[0119] ;

[0120] in, is the acoustic contribution of the i-th bridge component at frequency f, is the sound pressure level of the complete bridge structure at frequency f, is the sound pressure level at frequency f after the i-th bridge component is removed.

[0121] According to an embodiment of the present invention, the calculation of the acoustic contribution of high-frequency noise of each component of the steel-concrete composite bridge using a preset SEA model includes:

[0122] The vibration energy density of each bridge component in different noise frequency bands at high frequencies is obtained according to the preset SEA model.

[0123] The radiated sound power of each bridge component in different noise frequency bands at high frequencies is calculated based on the vibration energy density and using a preset energy density-radiation efficiency model.

[0124] The ratio of the radiated sound power of each bridge component to the total radiated sound power is taken as the acoustic contribution of each bridge component to the noise in different frequency bands in the high frequency band.

[0125] It should be noted that the SEA-based numerical simulation method analyzes the high-frequency noise response (200-2000Hz) of the target steel-concrete composite bridge to determine the acoustic contribution of each bridge component to the noise in different frequency bands in this high-frequency range. Based on the statistical energy analysis (SEA) model, the vibration energy density of each bridge subsystem in each 1 / 3 octave frequency band in the high-frequency range is calculated. A preset energy density-radiation efficiency model is then used to calculate the radiated sound power of each bridge component in different noise bands in the high-frequency range, thereby quantifying the acoustic contribution of each bridge component to the noise in different frequency bands.

[0126] According to an embodiment of the present invention, screening out low-frequency target bridge components according to the acoustic contribution of low-frequency noise, and screening out high-frequency target bridge components according to the acoustic contribution of high-frequency noise, includes:

[0127] Compare the acoustic contribution of each bridge component to low-frequency noise of different frequencies with the preset acoustic contribution threshold, and count the number of frequencies whose acoustic contribution exceeds the threshold;

[0128] The frequency quantity is compared with a preset frequency quantity threshold, and the bridge component whose frequency quantity exceeds the threshold is regarded as a low-frequency target bridge component;

[0129] Compare the acoustic contribution of each bridge component to noise in different frequency bands in the high-frequency band with the preset acoustic contribution threshold, and count the number of frequency bands whose acoustic contribution exceeds the threshold;

[0130] The frequency band number is compared with a preset frequency band number threshold, and the bridge components whose frequency band number exceeds the threshold are regarded as high-frequency target bridge components.

[0131] It should be noted that the bridge components that need to be optimized are selected based on their acoustic contribution.

[0132] According to an embodiment of the present invention, the step of calculating the parameter sensitivity of each component parameter in the low-frequency band based on the low-frequency noise acoustic contribution of the low-frequency target bridge component and determining the parameters to be optimized in the low-frequency band includes:

[0133] According to the acoustic contribution of the low-frequency target bridge component to the noise of different frequencies in the low-frequency band, the acoustic contribution change rate of each component parameter of the low-frequency target bridge component at each frequency point is calculated;

[0134] The low-frequency parameter sensitivity of each component parameter is obtained by performing a weighted average calculation based on the acoustic contribution change rate of each frequency point and the preset weight coefficient;

[0135] The low-frequency band parameter sensitivity is compared with a preset low-frequency band parameter sensitivity threshold, and component parameters with a sensitivity higher than the threshold are used as low-frequency band parameters to be optimized.

[0136] It should be noted that the acoustic contribution change rate of each component parameter of the low-frequency target bridge component is calculated when the component parameters change, and the low-frequency parameter sensitivity of each component parameter is obtained by weighted averaging, so as to select the component parameters that need to be optimized.

[0137] According to an embodiment of the present invention, the step of calculating the parameter sensitivity of each component parameter in the high frequency band based on the high frequency noise acoustic contribution of each high frequency bridge component and determining the parameters to be optimized in the high frequency band includes:

[0138] Calculate the average value of the acoustic contribution of each frequency band noise in the high frequency band to obtain the acoustic contribution of the high frequency band;

[0139] Calculating the acoustic contribution change rate of each component parameter according to the high-frequency band acoustic contribution to obtain the high-frequency band parameter sensitivity;

[0140] The high-frequency band parameter sensitivity is compared with a preset high-frequency band parameter sensitivity threshold, and the component parameters with a sensitivity higher than the threshold are used as high-frequency band parameters to be optimized.

[0141] It should be noted that the acoustic contribution change rate of each component when the parameters change is calculated based on the acoustic contribution of the high-frequency band, and the high-frequency band parameter sensitivity is obtained, which can be expressed as the ratio of the change value of the acoustic contribution before and after the change to the acoustic contribution before the change.

[0142] According to an embodiment of the present invention, the low-frequency band parameters to be optimized and the high-frequency band parameters to be optimized are processed based on a preset response surface method and a genetic algorithm to obtain an optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters, including:

[0143] Determine the value ranges of the parameters to be optimized in the low-frequency band and the high-frequency band, and generate multiple sets of parameter combinations to be optimized based on the CCD experimental design method;

[0144] For each set of parameter combinations to be optimized, the preset finite element-boundary element model and SEA model are used to process them respectively to obtain the corresponding low-frequency sound pressure level and high-frequency sound pressure level;

[0145] Using the preset response surface method, response surface models of low-frequency sound pressure level and high-frequency sound pressure level are constructed respectively;

[0146] The response surface model is processed using genetic algorithm to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

[0147] It should be noted that in actual use, bridges must meet acoustic requirements in low-frequency environments while not neglecting their acoustic performance in high-frequency environments. Furthermore, the low-frequency and high-frequency bands are mutually restrictive, so the low-frequency and high-frequency sound pressure levels need to be comprehensively considered to achieve optimal overall acoustic performance. In this application, the low-frequency and high-frequency response surface models are formulated as a multi-objective optimization problem and assigned to a genetic algorithm. The genetic algorithm searches the parameter space to find a set of parameter combinations that makes the low-frequency and high-frequency sound pressure levels as close to their respective ideal states as possible. Specifically, a central composite design (CCD) experimental design method is used to generate multiple sets of optimized parameter combinations that include both low-frequency and high-frequency parameters to be optimized. For each generated set of optimized parameter combinations, the noise response (i.e., the low-frequency sound pressure level and the high-frequency sound pressure level) is calculated. The radiated sound power is obtained according to the SEA model, converted to the radiated sound power level using a conversion formula, and then the high-frequency sound pressure level is calculated using the sound power level to sound pressure level conversion formula. Then, a preset response surface method (RSM) is used to construct response surface models for the low-frequency band and the high-frequency band, respectively. In this embodiment, the response surface model is a second-order polynomial model, wherein the dependent variable of the polynomial model is the sound pressure level, and the independent variable is the parameter to be optimized, which is used to characterize the change in the sound pressure level when the parameter to be optimized changes. The low-frequency and high-frequency response surface models are then submitted to a genetic algorithm as a multi-objective optimization problem. The genetic algorithm is used to search the parameter space to find a parameter combination that can achieve the optimal state of the low-frequency and high-frequency sound pressure levels. In this embodiment of the application, the second-order polynomial model is used as the fitness function of the genetic algorithm, and the Deb feasibility rule is used to process the parameter constraints. The parameter space is multi-objective iteratively optimized by the genetic algorithm to obtain the Pareto optimal parameter solution set. Finally, the actual parameters of the bridge components are adjusted according to the optimal parameter combination to achieve better acoustic effects of the bridge.

[0148] According to an embodiment of the present invention, the further embodiment includes:

[0149] Obtain the low-frequency sound pressure level and high-frequency sound pressure level of bridge components under multiple working conditions before and after bridge component parameter adjustment;

[0150] Calculate the mean of the low-frequency sound pressure level and the high-frequency sound pressure level before and after the adjustment, compare the mean of the low-frequency sound pressure level after the adjustment with the mean of the low-frequency sound pressure level before the adjustment, and compare the mean of the high-frequency sound pressure level after the adjustment with the mean of the high-frequency sound pressure level before the adjustment;

[0151] If the comparison results of the low-frequency sound pressure levels before and after adjustment and the comparison results of the high-frequency sound pressure levels before and after adjustment do not meet the preset comparison result requirements, it is prompted to review and adjust the parameters of the component.

[0152] It should be noted that after adjustment, the low-frequency sound pressure level and the high-frequency sound pressure level should be lower than before adjustment, and they must each drop by a preset proportion compared to their respective values ​​before adjustment to meet the comparison result requirements. The preset proportion can be determined based on actual conditions.

[0153] A third aspect of the present invention provides a readable storage medium, which stores a program for a steel-concrete composite bridge noise reduction method based on multi-parameter optimization. When the program for a steel-concrete composite bridge noise reduction method based on multi-parameter optimization is executed by a processor, the steps of the steel-concrete composite bridge noise reduction method based on multi-parameter optimization as described in any one of the above items are implemented.

[0154] The disclosed method, system, and medium for noise reduction in steel-concrete composite bridges based on multi-parameter optimization first screen out the bridge components that contribute most to noise based on acoustic contribution assessment. Parameter sensitivity analysis then identifies the key design parameters that significantly impact noise within these selected components. Finally, multi-objective optimization design is performed on these core parameters. Compared to traditional full-parameter analysis methods, this hierarchical screening method focuses on key parameters, significantly reducing computational complexity while ensuring effective noise reduction. This achieves the dual goals of conserving computational resources and improving optimization efficiency, providing a highly effective solution for noise control in large bridge structures.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0156] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0157] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0158] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0159] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A noise reduction method for steel-concrete composite bridges based on multi-parameter optimization, characterized in that: The following steps are involved: The vibration velocity of each bridge component is calculated using a preset finite element model; The vibration velocity is input into the preset boundary element model to obtain the radiated sound pressure, and the sound pressure level of each bridge component is calculated based on the radiated sound pressure; The vibration contribution of each bridge component is shielded in turn, and the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is obtained based on the sound pressure level calculation; The vibration energy density of each bridge component in different noise frequency bands at high frequencies is obtained according to the preset SEA model. The radiated sound power of each bridge component in different noise frequency bands at high frequencies is calculated based on the vibration energy density and using a preset energy density-radiation efficiency model. The ratio of the radiated sound power of each bridge component to the total radiated sound power is used as the acoustic contribution of each bridge component to the noise in different frequency bands in the high frequency band; Compare the acoustic contribution of each bridge component to low-frequency noise of different frequencies with the preset acoustic contribution threshold, and count the number of frequencies whose acoustic contribution exceeds the threshold; The frequency quantity is compared with a preset frequency quantity threshold, and the bridge component whose frequency quantity exceeds the threshold is regarded as a low-frequency target bridge component; Compare the acoustic contribution of each bridge component to noise in different frequency bands in the high-frequency band with the preset acoustic contribution threshold, and count the number of frequency bands whose acoustic contribution exceeds the threshold; The frequency band number is compared with a preset frequency band number threshold, and the bridge component whose frequency band number exceeds the threshold is regarded as a high-frequency target bridge component; According to the acoustic contribution of the low-frequency target bridge component to the noise of different frequencies in the low-frequency band, the acoustic contribution change rate of each component parameter of the low-frequency target bridge component at each frequency point is calculated; The low-frequency parameter sensitivity of each component parameter is obtained by performing a weighted average calculation based on the acoustic contribution change rate of each frequency point and the preset weight coefficient; Comparing the low-frequency parameter sensitivity with a preset low-frequency parameter sensitivity threshold, and taking the component parameters with a sensitivity higher than the threshold as the low-frequency parameter to be optimized; Calculate the average value of the acoustic contribution of each frequency band noise in the high frequency band to obtain the acoustic contribution of the high frequency band; Calculating the acoustic contribution change rate of each component parameter according to the high-frequency band acoustic contribution to obtain the high-frequency band parameter sensitivity; Comparing the high-frequency band parameter sensitivity with a preset high-frequency band parameter sensitivity threshold, and taking the component parameters with a sensitivity higher than the threshold as the high-frequency band parameters to be optimized; Based on the preset response surface method and genetic algorithm, the parameters to be optimized in the low-frequency band and the high-frequency band are processed to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

2. The noise reduction method for steel-concrete composite bridge based on multi-parameter optimization according to claim 1 is characterized in that: The method processes the parameters to be optimized in the low-frequency band and the parameters to be optimized in the high-frequency band based on the preset response surface method and the genetic algorithm to obtain the optimal parameter combination, and adjusts the parameters of the bridge components according to the optimal parameters, including: Determine the value ranges of the parameters to be optimized in the low-frequency band and the high-frequency band, and generate multiple sets of parameter combinations to be optimized based on the CCD experimental design method; For each set of parameter combinations to be optimized, the preset finite element-boundary element model and SEA model are used to process them respectively to obtain the corresponding low-frequency sound pressure level and high-frequency sound pressure level; Using the preset response surface method, response surface models of low-frequency sound pressure level and high-frequency sound pressure level are constructed respectively; The response surface model is processed using genetic algorithm to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

3. The noise reduction system of steel-concrete composite bridge based on multi-parameter optimization is characterized by: The invention comprises a memory and a processor, wherein the memory stores a program of a noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization, and when the program of the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization is executed by the processor, the following steps are implemented: The vibration velocity of each bridge component is calculated using a preset finite element model; The vibration velocity is input into the preset boundary element model to obtain the radiated sound pressure, and the sound pressure level of each bridge component is calculated based on the radiated sound pressure; The vibration contribution of each bridge component is shielded in turn, and the acoustic contribution of each bridge component to noise of different frequencies in the low-frequency band is obtained based on the sound pressure level calculation; The vibration energy density of each bridge component in different noise frequency bands at high frequencies is obtained according to the preset SEA model. The radiated sound power of each bridge component in different noise frequency bands at high frequencies is calculated based on the vibration energy density and using a preset energy density-radiation efficiency model. The ratio of the radiated sound power of each bridge component to the total radiated sound power is used as the acoustic contribution of each bridge component to the noise in different frequency bands in the high frequency band; Compare the acoustic contribution of each bridge component to low-frequency noise of different frequencies with the preset acoustic contribution threshold, and count the number of frequencies whose acoustic contribution exceeds the threshold; The frequency quantity is compared with a preset frequency quantity threshold, and the bridge component whose frequency quantity exceeds the threshold is regarded as a low-frequency target bridge component; Compare the acoustic contribution of each bridge component to noise in different frequency bands in the high-frequency band with the preset acoustic contribution threshold, and count the number of frequency bands whose acoustic contribution exceeds the threshold; The frequency band number is compared with a preset frequency band number threshold, and the bridge component whose frequency band number exceeds the threshold is regarded as a high-frequency target bridge component; According to the acoustic contribution of the low-frequency target bridge component to the noise of different frequencies in the low-frequency band, the acoustic contribution change rate of each component parameter of the low-frequency target bridge component at each frequency point is calculated; The low-frequency parameter sensitivity of each component parameter is obtained by performing a weighted average calculation based on the acoustic contribution change rate of each frequency point and the preset weight coefficient; Comparing the low-frequency parameter sensitivity with a preset low-frequency parameter sensitivity threshold, and taking the component parameters with a sensitivity higher than the threshold as the low-frequency parameter to be optimized; Calculate the average value of the acoustic contribution of each frequency band noise in the high frequency band to obtain the acoustic contribution of the high frequency band; Calculating the acoustic contribution change rate of each component parameter according to the high-frequency band acoustic contribution to obtain the high-frequency band parameter sensitivity; Comparing the high-frequency band parameter sensitivity with a preset high-frequency band parameter sensitivity threshold, and taking the component parameters with a sensitivity higher than the threshold as the high-frequency band parameters to be optimized; Based on the preset response surface method and genetic algorithm, the parameters to be optimized in the low-frequency band and the high-frequency band are processed to obtain the optimal parameter combination, and the bridge component parameters are adjusted according to the optimal parameters.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a noise reduction program for a steel-concrete composite bridge based on multi-parameter optimization. When the noise reduction program for a steel-concrete composite bridge based on multi-parameter optimization is executed by a processor, the steps of the noise reduction method for a steel-concrete composite bridge based on multi-parameter optimization as described in any one of claims 1 to 2 are implemented.

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