Method for regulating and controlling classroom acoustic environment by wood material based on genetic algorithm

Abstract

The invention provides a method for regulating and controlling a classroom acoustic environment through a wood material based on a genetic algorithm, and relates to the technical field of building physics and acoustic engineering, and the method comprises the four steps: building a classroom acoustic simulation model, defining material parameters and optimizing constraint conditions, carrying out genetic algorithm multi-objective optimization, and outputting an optimization scheme. According to the method, the genetic algorithm is combined with acoustic simulation, intelligent optimization of the classroom acoustic environment is achieved, a traditional design process depending on experience is converted into a data-driven scientific decision, and the cost is remarkably reduced while the acoustic quality is guaranteed. Meanwhile, acoustic performance and economical efficiency are balanced through multi-objective optimization, the material combination with the most cost performance can be automatically screened, the construction constraint that one material is arranged on each wall is strictly followed, it is guaranteed that the scheme can directly fall to the ground, and a universal frame is suitable for various classroom spaces; and an innovative solution with scientificity, economical efficiency and practicability is provided for architectural acoustic design.

Description

Technical Field

[0001] This invention relates to the fields of building physics and acoustic engineering technology, and in particular to a method for controlling the acoustic environment of a classroom using wood materials based on a genetic algorithm. Background Technology

[0002] As a crucial venue for knowledge dissemination, the acoustic environment of the classroom directly impacts teaching effectiveness and learning efficiency. An ideal classroom acoustic environment requires both moderate reverberation time and high speech intelligibility. However, many classrooms in reality suffer from significant acoustic deficiencies, primarily manifested in excessively long reverberation times and low speech transmission indices. For example, in a typical classroom without acoustic treatment, the reverberation time is usually above 2.0 seconds, severely affecting speech intelligibility.

[0003] Traditional classroom acoustic environment design methods rely heavily on the designer's experience and simple calculation rules, which has significant limitations. Designers typically select acoustic materials based on past project experience and gradually adjust the scheme through trial and error, lacking systematic optimization methods. This experience-dependent design process often struggles to find the optimal balance between acoustic performance, material costs, and construction feasibility.

[0004] With the development of computer simulation technology, tools such as finite element analysis have been introduced into the field of architectural acoustic design, enabling designers to conduct preliminary simulations of classroom acoustic environments. However, existing simulation technologies are mostly limited to the analysis and verification of acoustic performance, lacking automated optimization capabilities. Designers still need to manually adjust material parameters and layout schemes, repeatedly conduct simulation tests, and the entire process is time-consuming, labor-intensive, and it is difficult to guarantee obtaining the optimal solution.

[0005] In terms of material application, traditional acoustic design often tends to overuse expensive materials or simply adopt a single material for comprehensive coverage. This not only wastes material costs but may also lead to unsatisfactory acoustic effects in certain frequency bands due to mismatched sound absorption characteristics of the materials. In particular, while wood-based acoustic materials have unique advantages in controlling the acoustic environment of classrooms, traditional methods struggle to fully leverage their synergistic effects with other materials.

[0006] Furthermore, existing acoustic design methods typically consider acoustic performance as the sole factor, neglecting the economic constraints of material costs. In practical engineering, this single-objective design approach often leads to budget overruns, especially in educational settings with limited funding, making large-scale application difficult. Therefore, this invention proposes a method for controlling the acoustic environment of classrooms using wood materials based on a genetic algorithm to address the problems existing in the prior art. Summary of the Invention

[0007] To address the aforementioned problems, the present invention aims to propose a method for regulating the acoustic environment of classrooms using wood materials based on genetic algorithms. This method integrates architectural acoustics, materials engineering, computational simulation, and intelligent algorithms, providing an innovative solution to practical architectural environmental problems.

[0008] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a method for controlling the acoustic environment of a classroom using wood materials based on a genetic algorithm, comprising the following steps:

[0009] Step 1: Establish a classroom acoustic simulation model

[0010] A three-dimensional acoustic model of the classroom was constructed using finite element analysis software. Then, geometric parameters, sound source location, and receiver location were set, and air attenuation parameters and surface scattering coefficients were imported to simulate the sound wave propagation behavior.

[0011] Step 2: Define material parameters and optimization constraints

[0012] The physical parameters of various sound-absorbing materials are imported, including the sound absorption coefficient, size and unit cost of wood-based and non-wood-based sound-absorbing materials. Acoustic quality constraints and material selection constraints are defined in the optimization software, where acoustic quality constraints include reverberation time and speech transmission index.

[0013] Step 3: Multi-objective optimization using genetic algorithm

[0014] A genetic algorithm is used for iterative calculations to optimize material type, installation location, and coverage area with the objective function of minimizing total material cost, while also satisfying acoustic quality constraints.

[0015] Step 4: Output Optimization Scheme

[0016] Generate the optimal acoustic material configuration scheme, including material selection and specific layout parameters for the front wall, ceiling, and rear wall.

[0017] Further improvements include the following steps:

[0018] Step 5: Verify the optimization results

[0019] The optimized scheme was applied to an actual classroom, and the improvement effects on reverberation time and speech transmission index were verified by installing acoustic materials and measuring acoustic parameters.

[0020] Further improvements are made in the following steps: In step one, the three-dimensional classroom model is constructed based on the standard classroom geometry, the positions of the sound source and receiver are consistent with the actual measurements, and the acoustic ray tracing method is used for simulation. The number of rays is set according to the model size to ensure simulation accuracy.

[0021] A further improvement is made in step two, where the wood-based sound-absorbing materials include slotted wood-based sound-absorbing boards, perforated wood-based sound-absorbing boards, and wood fiber sound-absorbing boards, and the non-wood-based sound-absorbing materials include polyester fiber sound-absorbing boards, mineral wool sound-absorbing boards, fabric sound-absorbing boards, perforated calcium silicate sound-absorbing boards, perforated composite gypsum sound-absorbing boards, and PVC sound-absorbing boards.

[0022] A further improvement is made in step two, where the material selection constraints include area constraints and material limitation constraints. The area constraints ensure that the material coverage area does not exceed the available area, while the material limitation constraints stipulate that only one type of acoustic material is used on each wall.

[0023] The further improvement lies in the following: In step three, the specific parameters of the genetic algorithm are: population size of 200, number of iterations of 50000, crossover rate of 0.8, mutation rate of 0.2, and the function tolerance is set to 1e-6.

[0024] The further improvement lies in the following: In step three, the optimization involves balancing multiple objectives through the Pareto front, with the objective function being to minimize the total material cost, calculated using the following formula:

[0025]

[0026] In the formula, CAM represents the total cost of all acoustic materials, and P... m and P a The unit costs of wood and non-wood materials are respectively, L n and L b This represents the area of ​​the corresponding material.

[0027] A further improvement is that, in step four, the configuration strategy of the configuration scheme includes: selecting material combinations with complementary sound absorption frequency characteristics and applying them to different surfaces of the classroom to achieve balanced optimization of the acoustic effect across the entire frequency band.

[0028] A further improvement is that, in step four, the material combination with complementary sound absorption frequency characteristics is specifically: a wood-based sound-absorbing material with outstanding mid-to-low frequency sound absorption performance, and a non-wood-based sound-absorbing material with outstanding high-frequency sound absorption performance.

[0029] The beneficial effects of this invention are as follows: By combining genetic algorithms with acoustic simulation, this invention achieves intelligent optimization of the classroom acoustic environment, transforming the traditional experience-based design process into data-driven scientific decision-making, significantly reducing costs while ensuring acoustic quality. Simultaneously, through multi-objective optimization, it balances acoustic performance and economy, automatically selecting the most cost-effective material combinations and strictly adhering to the construction constraint of one material per wall, ensuring the solution can be directly implemented. Its universal framework is applicable to various classroom spaces, providing an innovative solution for architectural acoustic design that combines scientific rigor, economy, and practicality. Practical applications show that this invention can reduce reverberation time by 73% (from 2.42s to 0.78s), improve speech intelligibility by 45%, and reduce material usage costs. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0031] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0032] according to Figure 1 As shown, this embodiment proposes a method for controlling the acoustic environment of a classroom using wood materials based on a genetic algorithm, including the following steps:

[0033] Step 1: Establish a classroom acoustic simulation model

[0034] A three-dimensional acoustic model of the classroom was constructed using the finite element analysis software COMSOL Multiphysics. Geometric parameters, sound source and receiver locations were then set, and air attenuation parameters and surface scattering coefficients were imported to simulate sound wave propagation. The 3D classroom model was built based on standard classroom geometry, and the sound source and receiver locations were consistent with actual measurements. The acoustic ray tracing method was used for simulation, with the number of rays set according to the model size to ensure accuracy. Specifically, a classroom geometric model was created in CAD or Rhinoceros software, the sound source and receiver locations were set to match actual measurements, and air attenuation parameters (at 20°C and 50% humidity) and scattering coefficients of various object surfaces (0.6 for tables and chairs, and 0.8 for curtains) were imported to accurately simulate sound wave propagation. The simulation employed the acoustic ray tracing method, releasing a sufficient number of rays (6000) to ensure accuracy.

[0035] Step 2: Define material parameters and optimization constraints

[0036] Import the physical parameters of various sound-absorbing materials, including the absorption coefficient, size, and unit cost of both wood-based and non-wood-based materials, and define acoustic quality constraints and material selection constraints in the optimization software. Specifically, import a database containing various sound-absorbing materials, whose physical parameters include absorption coefficient, size, and unit cost. The material library mainly includes two categories: wood-based sound-absorbing materials (including slotted wood sound-absorbing panels, perforated wood sound-absorbing panels, and wood fiber sound-absorbing panels) and non-wood-based sound-absorbing materials (including polyester fiber sound-absorbing panels, mineral wool sound-absorbing panels, fabric sound-absorbing panels, perforated calcium silicate sound-absorbing panels, perforated composite gypsum sound-absorbing panels, and PVC sound-absorbing panels). Define two types of constraints in the optimization software (such as MATLAB):

[0037] Acoustic quality constraints: T20≤1s, Speech Transmission Index (STI)≤0.11;

[0038] Material selection constraints include area constraints and material limitation constraints. Area constraints ensure that the area covered by the material does not exceed the available area, that is, ensure that the area covered by the material on the front wall, ceiling, and back wall does not exceed the available area of ​​the wall. Material limitation constraints stipulate that only one type of acoustic material can be used on each wall. That is, to ensure aesthetics and construction convenience, only one type of acoustic material is allowed to be used on each wall.

[0039] Step 3: Multi-objective optimization using genetic algorithm

[0040] A genetic algorithm is used for iterative calculations to minimize the total material cost, optimizing material type, installation location, and coverage area while meeting acoustic quality constraints. Specifically, the genetic algorithm parameters are: population size of 200, number of iterations of 50,000, crossover rate of 0.8, mutation rate of 0.2, and a function tolerance of 1e-6.

[0041] In step three, the optimization involves balancing multiple objectives (the relationship between acoustic performance and cost) through the Pareto front. The objective function is to minimize the total material cost, and the calculation formula is as follows:

[0042]

[0043] In the formula, CAM represents the total cost of all acoustic materials, and P... m and P a The unit costs of wood and non-wood materials are respectively, L n and L b This represents the area of ​​the corresponding material.

[0044] Step 4: Output Optimization Scheme

[0045] The optimal acoustic material configuration scheme is generated, including material selection and specific layout parameters on the front wall, ceiling, and rear wall. Specifically, after the genetic algorithm runs, it outputs a globally optimal solution or a Pareto optimal solution set. This scheme explicitly provides the optimal acoustic material configuration, including the specific material selection, installation locations on the front wall, ceiling, and rear wall of the classroom, and the coverage area. Its core strategy lies in selecting material combinations with complementary sound absorption frequency characteristics (e.g., utilizing the excellent mid-to-low frequency sound absorption performance of wood and the high frequency sound absorption performance of non-wood materials), and applying them to different surfaces of the classroom to achieve balanced optimization of the acoustic effect across the entire frequency range.

[0046] Step 5: Verify the optimization results

[0047] The optimized scheme was applied to an actual classroom, and the improvement effects on reverberation time and speech transmission index were verified by installing acoustic materials and measuring acoustic parameters.

[0048] Furthermore, before the acoustic modifications to the classroom, six reverberation points were selected in the seating area. The reverberation times ranged from 2.42 seconds to 1.76 seconds, with the low-frequency reverberation time being excessively long, exceeding 2.4 seconds. The speech transmission index was 0.19, significantly exceeding the range stipulated by national standards. This long reverberation time leads to blurred and overlapping sounds, reducing students' comprehension of the teacher's speech, and the low speech transmission index negatively impacts the quality of classroom communication.

[0049] Meanwhile, in the untreated acoustic environment of the classroom, sound propagation exhibits a clear energy concentration phenomenon at 10ms and 20ms. At 10ms, the sound wave spreads in a high-energy form, mainly concentrated around the sound source, indicating that the sound energy is high at close range. By 20ms, the sound wave has spread throughout the classroom, and the energy gradually decays to a moderate level, but there is still a lot of reflected sound, resulting in insufficient attenuation of sound energy in the space, exhibiting obvious reverberation, and a long reverberation time. The speech transmission index (STI) is low, which is not conducive to the clear propagation of speech.

[0050] After calculating the optimal combination of acoustic materials, the classroom was renovated, and the results were measured. After the renovation, the reverberation time at all frequencies was improved. The optimized design completely covered the ceiling with mineral wool sound-absorbing panels, covering an area of ​​72m². 2Meanwhile, slotted wooden sound-absorbing panels were installed outside the blackboard area on the front wall of the classroom, while the rear wall remained untreated. In optimizing the classroom's acoustic environment, the structure and physical properties of materials directly affect their acoustic performance. Wood, due to its unique porous structure and fibrous nature, possesses excellent mid-to-low frequency sound absorption performance, effectively reducing sound wave reflection. Slotted wooden sound-absorbing panels are resonant sound absorbers, and their sound absorption characteristics are related to the perforation rate of the panel, the cavity, and the sound-absorbing material filling the back of the panel. In this embodiment, the reverberation time of the untreated classroom ranged from 2.42s to 1.76s, with a particularly long low-frequency reverberation time, necessitating increased sound absorption. Wooden sound-absorbing panels with a thickness of 15mm and 8mm perforations were selected. The classroom's reverberation time significantly decreased, falling between 0.78s and 0.43s. In contrast, materials such as mineral wool exhibited more significant sound absorption coefficients in the high-frequency range, capable of absorbing a wider range of sound wave spectra. Experimental and simulation results show that wood materials exhibit relatively balanced performance in terms of speech transmission index, especially in the mid-frequency sound wave transmission range, where their porous structure helps improve speech clarity and intelligibility. The speech transmission index was improved to 0.05, all within the range specified by national standards. Therefore, combining wood materials with mineral wool, which has a high sound absorption coefficient, can achieve optimal sound absorption effects at different frequencies.

[0051] A cost comparison with traditional acoustic design schemes shows that the optimal solution generated by this model has a material cost of 1512 RMB, while other acoustic design companies and related scholars estimate material costs between 2000 and 4000 RMB. This comparative analysis further proves that this model can effectively reduce project costs while ensuring acoustic quality. The optimized classroom not only meets national standards in terms of reverberation time and speech transmission index, but also has significant economic benefits in practical applications.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its framework and scope of application, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the acoustic environment of a classroom using wood materials based on a genetic algorithm, characterized in that: Includes the following steps: Step 1: Establish a classroom acoustic simulation model A three-dimensional acoustic model of the classroom was constructed using finite element analysis software. Then, geometric parameters, sound source location, and receiver location were set, and air attenuation parameters and surface scattering coefficients were imported to simulate the sound wave propagation behavior. Step 2: Define material parameters and optimization constraints The physical parameters of various sound-absorbing materials are imported, including the sound absorption coefficient, size and unit cost of wood-based and non-wood-based sound-absorbing materials. Acoustic quality constraints and material selection constraints are defined in the optimization software, where acoustic quality constraints include reverberation time and speech transmission index. Step 3: Multi-objective optimization using genetic algorithm A genetic algorithm is used for iterative calculations to optimize material type, installation location, and coverage area with the objective function of minimizing total material cost, while also satisfying acoustic quality constraints. Step 4: Output Optimization Scheme Generate the optimal acoustic material configuration scheme, including material selection and specific layout parameters for the front wall, ceiling, and rear wall.

2. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: It also includes the following steps: Step 5: Verify the optimization results The optimized scheme was applied to an actual classroom, and the improvement effects on reverberation time and speech transmission index were verified by installing acoustic materials and measuring acoustic parameters.

3. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: In step one, the three-dimensional classroom model is constructed based on the standard classroom geometry, the positions of the sound source and receiver are consistent with the actual measurements, and the acoustic ray tracing method is used for simulation. The number of rays is set according to the model size to ensure simulation accuracy.

4. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: In step two, the wood-based sound-absorbing materials include slotted wood-based sound-absorbing boards, perforated wood-based sound-absorbing boards, and wood fiber sound-absorbing boards, while the non-wood-based sound-absorbing materials include polyester fiber sound-absorbing boards, mineral wool sound-absorbing boards, fabric sound-absorbing boards, perforated calcium silicate sound-absorbing boards, perforated composite gypsum sound-absorbing boards, and PVC sound-absorbing boards.

5. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: In step two, the material selection constraints include area constraints and material limitation constraints. The area constraints ensure that the material coverage area does not exceed the available area, while the material limitation constraints stipulate that only one type of acoustic material is used on each wall.

6. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: In step three, the specific parameters of the genetic algorithm are: population size of 200, number of iterations of 50,000, crossover rate of 0.8, mutation rate of 0.2, and the function tolerance is set to 1e-6.

7. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: In step three, the optimization involves a Pareto front approach that balances multiple objectives. The objective function is to minimize the total material cost, and the calculation formula is as follows: In the formula, CAM represents the total cost of all acoustic materials, and P... m and P a L represents the unit cost of wood and non-wood materials, respectively. n and L b This represents the area of ​​the corresponding material.

8. The method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials according to claim 1, characterized in that: In step four, the configuration strategy of the configuration scheme includes: selecting material combinations with complementary sound absorption frequency characteristics and applying them to different surfaces of the classroom to achieve balanced optimization of acoustic effects across the entire frequency band.

9. A method for controlling the acoustic environment of a classroom based on a genetic algorithm using wood materials, as described in claim 8, characterized in that: In step four, the material combination with complementary sound absorption frequency characteristics specifically includes: wood-based sound-absorbing materials with outstanding mid-to-low frequency sound absorption performance, and non-wood-based sound-absorbing materials with outstanding high-frequency sound absorption performance.

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