Prediction optimization method for exposure level of traffic noise in residential quarter

Abstract

The invention belongs to the technical field of noise control, discloses a prediction optimization method for a traffic noise exposure level in a residential quarter, and is mainly used for solving the problem that the traffic noise exposure level in the residential quarter cannot be predicted and optimized from the planning and design level of the residential quarter. According to the method, a sound environment simulation model is established, statistical analysis is performed on form factors of existing residential quarters and internal sound environment parameters of the residential quarters based on urban road noise sources, and form factors influencing the internal traffic noise exposure level of the residential quarters are extracted; a sound environment prediction model based on the form factors of the residential quarter is constructed through regression analysis, direct correspondence between the design parameters of the residential quarter and the sound environment parameters is achieved, and the sound environment parameters in the residential quarter can be rapidly calculated through the form factors of the residential quarter in the planning and design stage of the residential quarter; the method can play a good role in assisting the planning and design of the residential quarter, and facilitates the timely adjustment of the design scheme of the residential quarter.

Description

Technical Field

[0001] The present invention belongs to the technical field of noise control, and in particular relates to a method for predicting and optimizing the exposure level of traffic noise within a residential area. Background Art

[0002] With the continuous improvement of the national economic level, from the end of the 20th century to the beginning of the 21st century, my country has experienced the largest and fastest urbanization process in world history. At present, my country's urbanization rate has exceeded 60%, and nearly 850 million permanent residents live in cities.

[0003] Residential communities carry important functions in urban development and constitute a major part of urban development. Improving the quality of residential communities is essential for improving the quality of cities. At the same time, residents have varying demands for improved spatial quality in their residential communities. Improving residents' housing conditions and living environment, and enhancing their quality of life, with a people-oriented approach, is currently the primary task of urban renewal. However, with the rapid expansion of urban scale, residential areas are impacted by urban road networks, population density, and vehicle volume, resulting in severe traffic noise pollution. This severely impacts the acoustic environment in residential communities and conflicts with improvements in their quality. Traffic noise has become one of the main constraints on improving the quality of residential communities. Therefore, improving the acoustic environment in residential communities plays a significant role in urban renewal. Given the increasing demand for the environmental quality of building spaces driven by relevant policies and standards over the past two years, improving the acoustic environment in building spaces is essential for improving residents' living standards and enhancing their sense of well-being.

[0004] The planning and design of residential communities are crucial to noise nuisance, yet mandatory regulations for noise prevention and reduction are lacking. For example, in regulatory detailed planning, building setbacks from road boundaries lack consideration for noise impacts; and architectural design rarely considers the benefits of shielding traffic noise. Current noise pollution control efforts rely too heavily on technology and materials, while neglecting the role of residential community planning and design in noise prevention.

[0005] Therefore, there is an urgent need for a prediction and optimization method for the internal traffic noise exposure level of residential communities based on the impact mechanism of the spatial morphological structure of residential communities on traffic noise, which can provide a basis and entry point for the planning and design of residential communities, and thereby form a rational planning and design of building groups. Summary of the Invention

[0006] The purpose of the present invention is to provide a prediction and optimization method for the internal traffic noise exposure level of residential communities. The method simulates the acoustic environment of a large number of residential community models, statistically analyzes the morphological factor parameters and acoustic environment parameter indicators of each residential community, and then constructs an acoustic environment prediction model based on the morphological factors of the residential community. This facilitates the preliminary prediction of the various acoustic environment parameter indicators of the residential community during the planning and design stage of the residential community, so as to timely adjust and optimize the design plan.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for predicting and optimizing traffic noise exposure levels within a residential area comprises the following steps: S1, based on the different building layout types of residential communities, obtain the morphological factors of several existing residential communities, and construct a residential community acoustic environment simulation model based on different urban road noise sources to obtain the acoustic environment parameters within each residential community; S2: Correlation analysis is performed between the morphological factors of residential communities with the same building layout and the same urban road noise source and the obtained acoustic environment parameters, and the morphological factors that have a significant impact on the traffic noise exposure level within the residential communities are extracted; S3, based on the building layout type of the residential area and the type of urban road adjacent to the residential area, using the extracted morphological factors as independent variables and the acoustic environment parameters within the residential area as dependent variables, establish a regression equation for obtaining the acoustic environment parameters within the residential area, and thereby construct a prediction model for the acoustic environment within the residential area based on the morphological factors of the residential area; S4, selecting a corresponding residential community internal acoustic environment prediction model based on the building layout type of the target residential community and the type of urban road adjacent to the target residential community, substituting morphological factors that affect the traffic noise exposure level within the target residential community into the residential community internal acoustic environment prediction model, and calculating the acoustic environment parameters within the target residential community; S5. Obtain the distribution of traffic noise exposure levels within the target residential area based on the acoustic environment parameters within the target residential area, and optimize the acoustic environment quality of the target residential area based on the distribution of traffic noise exposure levels.

[0008] Preferably, step S1 includes the following specific steps: S11, obtaining images of multiple typical existing residential communities with different building layout types, and constructing simulation models of the residential communities respectively; S12, acquiring traffic data of multiple typical urban road sections of different urban road types, and constructing urban road simulation models respectively; S13, replacing the plurality of urban road simulation models with the external road noise sources of the residential area simulation model in sequence, and outputting the acoustic environment parameters inside the residential area using noise analysis software.

[0009] Preferably, in step S2, before performing the correlation analysis, a KS single sample test is performed on each data group, and the data group with normality is analyzed using the Pearson correlation coefficient, while the data group without normality is analyzed using the Spearman rank correlation coefficient.

[0010] Preferably, in step S3, before performing regression analysis, the KMO test and Bartlett's sphericity test are performed on the extracted residential area morphological factors of each building layout type, and it is determined based on the test results whether the extracted residential area morphological factors of each building layout type meet the conditions for factor dimensionality reduction analysis.

[0011] Preferably, factor analysis is performed on the morphological factors of the residential quarters that meet the conditions, and the specific steps include: The initial eigenvalues ​​of each morphological factor are calculated; Extract the common factors whose initial eigenvalues ​​are greater than the first preset value and name them X1, X2, ...X p , where p is the number of common factors; The extracted common factors are orthogonally rotated with each morphological factor to generate a rotation component matrix, and the correlation coefficient r between each morphological factor and each common factor is obtained. 11 、r 12 ……r pn , where n is the number of morphological factors; The rotation component matrix is ​​screened, and each common factor is interpreted using a morphological factor whose correlation coefficient with each common factor is greater than a second preset value.

[0012] Preferably, according to the results of factor analysis, a linear regression analysis is performed with the extracted common factors as independent variables and the acoustic environment parameters inside the residential area as dependent variables to calculate the constant value β0 and the regression equation coefficients β1, β2, ..., β p , eliminating the common factors that are not significant, and establishing the regression equation of the internal acoustic environment parameters of the residential quarters based on the common factors according to the regression equation coefficients of the remaining common factors: ; Among them, β0 is a constant value, β p is the regression equation coefficient of the common factor, X p is the value of the common factor, and p is the number of common factors.

[0013] Preferably, the influence weight of each morphological factor on the internal acoustic environment parameters of the residential area is calculated by calculating the regression equation coefficient of each common factor and the correlation coefficient between each morphological factor and the common factor. W fi : ; in, W fi is the influence weight of each morphological factor, β p is the regression equation coefficient of the common factor, r pn is the correlation coefficient between the morphological factor and its common factor, p is the number of common factors, and n is the number of morphological factors; According to the influence weight, the prediction model of the internal acoustic environment of the residential area based on the residential area morphological factors is obtained: ; in ，W fi is the weight of the morphological factor, V fi is the value of the morphological factor, and n is the number of morphological factors.

[0014] Preferably, a regression analysis is directly performed on the morphological factors of residential communities that do not meet the conditions, and a stepwise regression analysis is performed with the extracted morphological factors of residential communities of each building layout type as independent variables and the acoustic environment parameter index inside the residential community as dependent variables to calculate the constant value β0 and the regression equation coefficients β1, β2, ..., β p , eliminating the insignificant morphological factors, and establishing a prediction model for the internal acoustic environment of a residential area based on the morphological factors of the residential area according to the regression equation coefficients of the remaining morphological factors: ; Among them, β0 is a constant value, β p is the regression equation coefficient of the morphological factor, V fi is the value of the morphological factor, and p is the number of morphological factors.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention establishes a residential community sound environment simulation model based on different building layout types and different types of urban roads, conducts a correlation analysis on the morphological factors of existing residential communities and the sound environment parameters inside the communities, extracts the morphological factors that affect the traffic noise exposure level inside the residential communities, and obtains a regression equation for calculating the sound environment parameters inside the residential communities based on the morphological factors of the residential communities through regression analysis, thereby constructing a residential community sound environment prediction model based on the morphological factors, achieving a direct correspondence between the design parameters of the residential communities and the sound environment parameters, so that the sound environment parameters inside the residential communities can be quickly calculated through their own morphological factors during the planning and design stage of the residential communities, thereby grasping the distribution of traffic noise exposure levels inside the residential communities, playing a good auxiliary role in the planning and design of residential communities, facilitating timely adjustment of community design plans, and filling the gap of currently no prediction and optimization of traffic noise exposure levels inside residential communities from the planning and design stage of residential communities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flow chart of a method for predicting and optimizing traffic noise exposure levels within a residential area provided in a preferred embodiment of the present invention; Figure 2 Schematic diagram of a specific flow of step S1 in a method for predicting and optimizing the internal traffic noise exposure level of a residential area provided in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Example 1 like Figure 1 A method for predicting and optimizing the exposure level of traffic noise within a residential area is shown, comprising the following steps: S1, based on the different building layout types of residential communities, obtain the morphological factors of several existing residential communities, and construct a residential community acoustic environment simulation model based on different urban road noise sources to obtain the acoustic environment parameters within each residential community.

[0019] The purpose of this step is to construct acoustic environment simulation models for residential communities based on the different building layout types and the types of urban roads adjacent to them, using a large number of existing residential communities as data samples, so as to obtain the acoustic environment parameters within each residential community and provide sample data for the subsequent correlation analysis between residential community morphological factors and acoustic environment parameters.

[0020] It should be noted that, in this embodiment, step S1 uses noise analysis software to construct a residential community sound environment simulation model. When selecting the noise analysis software, it is necessary to consider that the noise source adapted by this embodiment is urban road noise, and the adapted sound environment simulation scene is a residential community. Common noise analysis software includes: CadnaA and SoundPlan.

[0021] The building layout types of the above-mentioned residential quarters include enclosure, point group, matrix and mixed types; the morphological factors of the residential quarters include building density, volume ratio, floor height, enclosure and other building indicators, and the values ​​of the morphological factors are known parameters after the residential quarters are designed; the acoustic environment parameters are based on the pedestrian height level and building facades of the residential quarters, and the acoustic environment parameters are corresponding noise evaluation indicators selected according to the "Acoustic Environment Quality Standard" (GB 3096-2008), including the proportion of quiet areas - SPL (L Aeq ) The ratio of the outdoor space area of ​​the residential area with less than 50dBA to the total outdoor space area of ​​the residential area, and the proportion of quiet areas - within 50dBA <SPL(L Aeq )<55dBA in the ratio of the outdoor space area of ​​the residential area to the overall outdoor space area of ​​the residential area, and the proportion of the noise area - SPL (L Aeq )>60dBA outdoor space area of ​​residential area to the total outdoor space area of ​​residential area, the proportion of low noise area - in 55 <SPL(L Aeq ) <60dBA in the residential area where the outdoor space area accounts for the total outdoor space area of ​​the residential area and L 10 —The value that the A sound level exceeds for 10% of the time during the measurement period is equivalent to the average peak value of the noise, L 50 —The value that the A sound level exceeds for 50% of the time during the measurement period, which is equivalent to the average median value of the noise, L 90 —The value that the A sound level exceeds for 90% of the time during the measurement period is equivalent to the average background value of the noise, L avg —The average value of all grid sound pressure level data.

[0022] S2, conduct a correlation analysis between the morphological factors of residential communities with the same building layout and based on the same urban road noise source and the obtained acoustic environment parameters, and extract the morphological factors that have a significant impact on the traffic noise exposure level within the residential communities.

[0023] The purpose of this step is to perform a correlation analysis on the sample data in step S1 above to obtain the correlation between the various morphological factors and the acoustic environment parameters exhibited by each residential area with each building layout type when facing different urban road noise sources, thereby extracting the morphological factors that have a significant impact on the traffic noise exposure level within the residential area according to the correlation value, and providing logical support for the subsequent construction of the acoustic environment parameter calculation equation based on the morphological factors of the residential area.

[0024] It should be noted that in step S2, for residential communities with the same building layout, the morphological factor values ​​of each residential community are different. Under the influence of the noise of the same urban road noise source, the internal sound environment parameters of the residential communities reflected are also different. Therefore, this step is used to perform correlation data analysis on the morphological factors of residential communities with the same building layout and based on the same urban road noise source and the obtained sound environment parameters of each residential community, and obtain the correlation between each morphological factor and the sound environment parameter, and then extract the morphological factors that have a significant impact on the internal traffic noise exposure level of the residential community.

[0025] S3, based on the building layout type of the residential area and the type of urban road adjacent to the residential area, using the extracted morphological factors as independent variables and the acoustic environment parameters within the residential area as dependent variables, a regression equation for the acoustic environment parameters within the residential area based on different building layouts and different urban roads is established through regression analysis, and a prediction model for the acoustic environment within the residential area based on the residential area morphological factors is constructed; The purpose of this step is to use the various morphological factors extracted in step S2 as a basis to find out, through regression analysis, the specific correlation between the morphological factors that can have a significant impact on the traffic noise exposure level within the residential community of each building layout type when facing different urban road noise sources and the acoustic environment parameters within the residential community, thereby obtaining a regression equation for calculating the acoustic environment parameters within the residential community based on the morphological factors of the residential community, and constructing an acoustic environment prediction model based on the regression equation, providing a prediction path for subsequent prediction optimization of the target residential community.

[0026] S4. According to the building layout type of the target residential area and the type of urban road adjacent to it, a corresponding residential area internal sound environment prediction model is selected, and the morphological factors that affect the internal traffic noise exposure level of the target residential area are substituted into the residential area internal sound environment prediction model to calculate the internal sound environment parameters of the target residential area.

[0027] According to this step, users can directly calculate the acoustic environment parameters inside the target residential area through the morphological factors of the area, thereby achieving a direct correspondence between the morphological factors and the acoustic environment parameters, and filling the gap of the current lack of prediction and optimization of the traffic noise exposure level inside the residential area from the planning and design stage of the residential area.

[0028] S5. Obtain the distribution of traffic noise exposure levels within the target residential area based on the acoustic environment parameters within the target residential area, and optimize the acoustic environment quality of the target residential area based on the distribution of traffic noise exposure levels.

[0029] According to this step, after the user has mastered the distribution of traffic noise exposure levels in the target residential area, he or she can optimize the acoustic environment of the area in a targeted manner based on the building layout, design parameters and noise prevention.

[0030] In general, the prediction and optimization method for the internal traffic noise exposure level of residential communities provided in this embodiment is to simulate the sound environment of a large number of existing residential communities to obtain the sound environment parameters inside each residential community, and group them according to the building layout type of the residential community and the type of urban road adjacent to it. The morphological factors of the residential communities and the sound environment parameters are correlated with each other, and the morphological factors that have a significant impact on the internal traffic noise exposure level of the residential communities are extracted. A sound environment prediction model based on the morphological factors of the residential communities is constructed through regression analysis. The sound environment prediction model can mainly be used to predict the following two situations: one is to predict the sound environment parameters inside the residential community after the residential community building layout design is completed, and the other is to compare the building layout plans of two residential communities, with the goal of improving the sound environment quality of the residential community and selecting a better plan.

[0031] The creativity of this embodiment lies in: relying on the above-mentioned sound environment prediction model, it is possible to achieve a direct correspondence between the morphological factor design parameters of the residential community and the sound environment parameters. When using it, the user only needs to obtain the relevant morphological factor values ​​of the target residential community to quickly calculate the sound environment parameters inside the residential community, so that the distribution of traffic noise exposure levels inside the residential community can be preliminarily predicted during the planning and design stage of the residential community, so as to adjust the design plan of the residential community to improve the quality of the sound environment. This can greatly improve the operability of the sound environment evaluation of the residential community and play a good auxiliary role in the planning and design of the residential community.

[0032] Example 2 This embodiment is a further preferred embodiment of embodiment 1. Figure 2 As shown, in this embodiment, step S1 includes the following specific steps: S11, obtaining images of multiple typical existing residential communities with different building layout types, and constructing simulation models of the residential communities respectively; S12, acquiring traffic data of multiple typical urban road sections of different urban road types, and constructing urban road simulation models respectively; S13, replacing the plurality of urban road simulation models with the external road noise sources of the residential area simulation model in sequence, and outputting the acoustic environment parameters inside the residential area using noise analysis software.

[0033] This embodiment uses noise analysis software to construct a residential community acoustic environment simulation model and output the acoustic environment parameters inside the residential community. Compared with the manual measurement method, it has simulation accuracy and is also faster.

[0034] Specifically, the noise analysis software is Cadna / A, which is highly applicable to the simulation of the acoustic environment of domestic residential areas and can be widely used in the prediction and control of urban and regional noise.

[0035] The specific method of constructing a residential community simulation model using Cadna / A is as follows: use map software to select and download typical residential community images with different building layout types, and extract the residential community base map based on the image map. During the extraction process, the original green area of ​​the residential community is restored as much as possible. In order to ensure that these communities are within the radiation range of road sound sources, the land area and aspect ratio of the residential community are controlled to keep them within the appropriate range. Finally, the extracted base map is imported into Cadna / A, and the residential community simulation model is established by automatically generating floors.

[0036] The specific method of constructing an urban road simulation model through Cadna / A is: setting road parameters based on the residential area simulation model. The sound source type settings in Cadna / A are divided into Line Source, Area Source, Point Source, Vertical Area Source, Road Source, Railway, Parking Lot, Power Plant and Tennis. This embodiment is to simulate the impact of urban road traffic noise on the sound environment of residential areas, so the road noise source (Road Source) is selected as the sound source in Cadna / A. In the process of setting the Road Source, according to different urban road types, with reference to the traffic data of existing typical urban road sections, the width, traffic volume, speed, etc. of the urban roads required in Cadna / A are set.

[0037] It should be noted that common urban road types include main roads, elevated composite roads, expressways, etc. The traffic data of typical sections of the above urban road types are input into Cadna / A to establish multiple urban road simulation models. The urban road simulation model is used to replace the main road adjacent to the residential area simulation model. For other roads adjacent to the residential area simulation model, they are replaced with secondary roads. The road data of the secondary roads are set according to the "Urban Road Engineering Design Code" (CJJ 37-2012).

[0038] Example 3 This embodiment is a further preferred embodiment of embodiment 1. In step S2 of this embodiment, before performing correlation analysis, a KS single sample test is performed on each data group. The data group with normality is analyzed using the Pearson correlation coefficient, and the data group without normality is analyzed using the Spearman rank correlation coefficient.

[0039] The purpose of the KS single sample test is to test whether each morphological factor of residential quarters with the same building layout type and the acoustic environment parameters of the residential quarters obey the normal distribution, so as to select the appropriate correlation analysis method.

[0040] The above-mentioned data correlation analysis using the Pearson correlation coefficient or the Spearman rank correlation coefficient is existing technology and will not be described in detail here.

[0041] Example 4 This embodiment is a further preferred embodiment of embodiment 1. In step S3, before performing regression analysis, the KMO test and Bartlett's sphericity test are performed on the extracted residential area morphological factors of each building layout type, and it is determined based on the test results whether the extracted residential area morphological factors of each building layout type meet the conditions for factor dimensionality reduction analysis.

[0042] The purpose of conducting the KMO test and Bartlett's sphericity test is to test whether the morphological factors extracted based on different building layout types can be subjected to factor dimensionality reduction analysis. Factor dimensionality reduction analysis can aggregate multiple morphological factors into a few independent common factors, which can reduce the complexity of data correlation analysis and simplify the subsequent regression calculation equation.

[0043] The test criteria of the KMO test and Bartlett's sphericity test are: if KMO ≥ 0.6 and Bartlett's test p < 0.05, factor dimensionality reduction analysis can be performed, and vice versa.

[0044] The calculation of the KMO value and the Bartlett test p-value described above is prior art and will not be described in detail here.

[0045] It should be noted that before participating in the KMO test and Bartlett's sphericity test, in order to more scientifically demonstrate the influence of each morphological factor and eliminate the influence caused by the order of magnitude and unit differences of the morphological factor's own values, all morphological factor data are standardized. The standardization method adopts existing technologies, such as deviation standardization and Z-score standardization.

[0046] Example 5 This embodiment, as a further supplementary solution to embodiment 4, performs factor analysis on the morphological factors of residential communities that meet the requirements. The specific steps include: The initial eigenvalues ​​of each morphological factor are calculated; Extract the common factors whose initial eigenvalues ​​are greater than the first preset value and name them X1, X2, ...X p , where p is the number of common factors; The extracted common factors are orthogonally rotated with each morphological factor to generate a rotation component matrix, and the correlation coefficient r between each morphological factor and each common factor is obtained. 11 、r 12 ……r pn , where n is the number of morphological factors; The rotation component matrix is ​​screened, and each common factor is interpreted using a morphological factor whose correlation coefficient with each common factor is greater than a second preset value.

[0047] The purpose of the above factor analysis is to condense the extracted multiple morphological factors into a few common factors based on the correlation analysis results of the morphological factors and acoustic environment parameters of residential areas with various building layout types and urban road types, simplify the variables, and reduce the complexity of the analysis; new variables can be generated by sorting the initial eigenvalues ​​of each morphological factor for subsequent regression analysis.

[0048] In this embodiment, the specific setting value of the first preset value needs to ensure that the extracted common factor has a strong explanatory power for all the original extracted morphological factors; the second preset value is set according to the correlation ranking between the extracted morphological factors and the common factor.

[0049] Example 6 This embodiment is a further supplement to the embodiment 5. Based on the results of factor analysis, a linear regression analysis is performed with the extracted common factors as independent variables and the acoustic environment parameters inside the residential area as dependent variables to calculate the constant value β0 and the regression equation coefficients β1, β2, ..., β p , eliminating the common factors that are not significant, and establishing the regression equation of the internal acoustic environment parameters of the residential quarters based on the common factors according to the regression equation coefficients of the remaining common factors: ; Among them, β0 is a constant value, β p is the regression equation coefficient of the common factor, X p is the value of the common factor, and p is the number of common factors.

[0050] The above linear regression analysis is to establish a linear relationship model between the dependent variable and one or more independent variables through the least squares method.

[0051] When constructing a prediction model for the acoustic environment of a residential area after factor dimensionality reduction analysis, it is first necessary to consider the correlation between each common factor and the acoustic environment parameters. Based on the basic form of the linear regression model, the relationship between the independent variable and the dependent variable, including the degree of correlation and influence intensity between them, is revealed, thereby establishing a regression equation for the acoustic environment parameters within the residential area based on the common factors.

[0052] The influence weight of each morphological factor on the internal acoustic environment parameters of the residential area is obtained by calculating the regression equation coefficient of each common factor and the correlation coefficient between each morphological factor and the common factor. W fi : ; in, W fi is the influence weight of each morphological factor, β p is the regression equation coefficient of the common factor, rpn is the correlation coefficient between the morphological factor and its common factor, p is the number of common factors, and n is the number of morphological factors; According to the influence weight, the prediction model of the internal acoustic environment of the residential area based on the residential area morphological factors is obtained: ; in ， W fi is the weight of the shape factor, V fi is the value of the morphological factor, and n is the number of morphological factors.

[0053] After obtaining the regression equation of the internal sound environment parameters of the residential community based on common factors, it is necessary to consider the correlation between each common factor and the morphological factors it contains. Therefore, according to the correlation coefficient between each morphological factor and each common factor obtained during the factor analysis and the regression equation coefficient of each common factor obtained during the regression analysis, the influence weight of each morphological factor is calculated to express the degree of influence of a single morphological factor on the internal sound environment parameters of the residential community, and then a prediction model of the internal sound environment of the residential community based on the morphological factors of the residential community is obtained, so as to realize the direct correspondence between the design parameters of the residential community and the sound environment parameters.

[0054] Example 7 This embodiment, as a further supplement to the fourth embodiment, directly performs regression analysis on the morphological factors of residential quarters that do not meet the requirements. The extracted morphological factors of residential quarters of various building layout types are used as independent variables, and the acoustic environment parameter index inside the residential quarters is used as the dependent variable. A stepwise regression analysis is performed to calculate the constant value β0 and the regression equation coefficients β1, β2, ..., β p , eliminating the insignificant morphological factors, and establishing a prediction model for the internal acoustic environment of a residential area based on the morphological factors of the residential area according to the regression equation coefficients of the remaining morphological factors: ; Among them, β0 is a constant value, β p is the regression equation coefficient of the morphological factor, V fi is the value of the morphological factor, and p is the number of morphological factors.

[0055] The stepwise regression analysis mentioned above is used to exclude morphological factors with large autocorrelation between independent variables. For residential community morphological factors that have not been subjected to factor dimensionality reduction analysis, it is only necessary to calculate the constant value and the regression equation coefficient of each morphological factor through regression analysis to establish a residential community internal sound environment prediction model based on the residential community morphological factors, thereby achieving a direct correspondence between the residential community design parameters and the sound environment parameters.

[0056] Example 8 This embodiment combines the technical solutions of the above embodiments and further illustrates the method for predicting and optimizing the traffic noise exposure level within a residential area of ​​the present invention through a specific example.

[0057] In this embodiment, the building layout of the residential area includes enclosure type, point group type, matrix type and mixed type; the residential area morphological factors that need to be obtained are morphological factors that may be related to the level of traffic noise exposure and its distribution, which are extracted based on the spatial environment information composition of high-rise residential areas and the summary of existing research, combined with commonly used indicators in planning and design, including 14 items: floor height, volume ratio, greening rate, street opening width, building density, total length of reflecting surface, average shape coefficient, main road line-contact rate, enclosure degree, number of street openings, maximum continuous width along the street, average height of green plants, distance from the sound source, and average area of ​​each building; the road types of urban roads include main roads, elevated composite roads, and expressways.

[0058] Based on the architectural layout of existing residential communities, 30 high-rise residential communities with enclosed, point group, matrix and mixed layouts were selected, totaling 120 independent and complete closed communities. The specific values ​​of the morphological factors of each residential community were obtained, the community base map was extracted, and a residential community simulation model was established. According to the road type of urban roads, typical sections of main roads, elevated composite roads and expressways were selected, and the road parameters were set with reference to their traffic data to establish an urban road simulation model.

[0059] According to the residential area information and urban road data obtained above, the acoustic environment simulation model inside the residential area was constructed through Cadna / A, and the acoustic environment parameters - the proportion of quiet areas, the proportion of relatively quiet areas, the proportion of low noise areas, the proportion of noisy areas and L 10 、L 50 、L 90 、L avg .

[0060] Before selecting the correlation analysis method, a KS single-sample test was performed on all data that may be normally distributed in the scatter plot. The final test results showed that the morphological factor data such as building density, floor area ratio, number of street openings, and total length of reflective surfaces in residential areas were normally distributed, so the Pearson correlation coefficient needed to be used for analysis when conducting correlation analysis. The morphological factor data of the number of street openings in residential areas were not normally distributed, so the Spearman rank correlation coefficient needed to be used for analysis when conducting correlation analysis.

[0061] The form factors and proportions of quiet areas, relatively quiet areas, low noise areas, and noisy areas of residential areas with the same building layout and based on the same urban road noise source are calculated. 10 、L 50 、L 90, L avg The data were then imported into SPSS for correlation analysis.

[0062] Taking an enclosed residential area adjacent to a main road as an example, the correlation analysis results between various morphological factors and horizontal noise exposure at pedestrian height are shown in Table 1 below: The results of the correlation analysis between various morphological factors and building facade noise exposure are shown in Table 2 below: Table 1 shows that in high-rise residential communities with an enclosed layout along main roads, there is no obvious correlation between floor height, floor area ratio, greening ratio, average height of green plants, number of street openings, and average shape coefficient and the distribution of noise at the pedestrian level. Therefore, the size of the above-mentioned morphological factors will not affect the noise exposure at the pedestrian level of the residential community. However, the morphological factors that are significantly correlated with the noise exposure at the pedestrian level within the residential community include enclosure degree, building density, maximum continuous width along the street, average area of ​​each building, width of street openings, total length of reflecting surface, main road alignment rate, and distance from the sound source.

[0063] Table 2 shows that in high-rise residential communities with an enclosed layout along main roads, there is no significant correlation between the floor area ratio, greening ratio, street opening width, average shape coefficient, main road alignment ratio, number of street openings, and average height of green plants and building facade noise exposure. Therefore, the size of these morphological factors will not affect the noise exposure of residential community building facades. However, the morphological factors that are significantly correlated with the noise exposure of building facades within residential communities include enclosure degree, building density, total length of reflecting surface, distance from the sound source, average area of ​​each building, maximum continuous width along the street, and floor height.

[0064] By integrating the correlation data in Table 1 and Table 2, we can extract nine morphological factors that significantly affect the internal traffic noise exposure level of enclosed residential communities along main roads, namely building density, enclosure degree, maximum continuous width along the street, average area of ​​each building, width of street openings, total length of reflecting surfaces within the residential community, main road alignment rate, distance from the sound source, and floor height.

[0065] The KMO test and Bartlett's sphericity test were performed on the nine morphological factors extracted that significantly affected the internal traffic noise exposure level of enclosed residential communities along main roads. The KMO value of the morphological factor of enclosed residential communities was 0.678>0.6, and the Bartlett's sphericity test Sig. value was 0.000, which met the conditions for factor dimensionality reduction analysis. Therefore, it is necessary to perform factor dimensionality reduction analysis on the morphological factors of enclosed residential communities.

[0066] The initial eigenvalues ​​of each morphological factor were calculated, as shown in Table 3 below. Five common factors with initial eigenvalues ​​greater than 0.5 were extracted, that is, the first preset value was 0.5, which explained 90.989% of the original morphological factor system and had a strong explanatory power. This proves that these five common factors have a strong representation of the original nine morphological factors. These five common factors are named X1, X2...X5 in sequence. The extracted common factors are orthogonally rotated with each morphological factor to generate a rotation component matrix, as shown in Table 4 below. The correlation coefficient r between each morphological factor and each common factor is obtained. 11 、r 12 ……r 18 、r 19 ……r 51 、r 52 ……r 58 、r 59 ; The rotated component matrix is ​​screened, and the data with a correlation coefficient with the common factor less than 0.6 is eliminated, and the morphological factors with a correlation coefficient greater than 0.6 are used to explain the common factor, that is, the second preset value is 0.6.

[0067] As can be seen from Tables 3 and 4, the variance contribution rate of the first common factor X1 is 46.046%, which includes two morphological factors, namely the maximum continuous width along the street and the average area of ​​each building; the variance contribution rate of the second common factor X2 is 20.703%, which includes two morphological factors, namely building density and enclosure; the variance contribution rate of the third common factor X3 is 10.821%, which includes two morphological factors, namely the main road alignment rate and the width of the street opening; the variance contribution rate of the fourth common factor X4 is 7.506%, which includes one morphological factor, namely the total length of the reflective surface of the residential area; the variance contribution rate of the fifth common factor X5 is 5.911%, which includes one morphological factor, namely floor height.

[0068] Based on the results of factor dimensionality reduction analysis, regression analysis was performed using SPSS. Specifically, linear regression analysis was adopted. The extracted common factors were used as independent variables, and the acoustic environment parameters within the residential area were used as dependent variables. Linear regression analysis was performed to calculate the constant value β0 and the regression equation coefficients β1, β2, ... β p , eliminate the common factors that are not significant, and establish a regression equation for the internal acoustic environment parameters of the residential area based on the common factors according to the regression equation coefficients of the remaining common factors.

[0069] Taking the construction of a regression equation based on the proportion of quiet areas at the pedestrian height level in enclosed residential communities along the main roads as an example, a linear regression analysis was performed with the proportion of quiet areas at the pedestrian height level in residential communities with an enclosed layout along the main roads as the dependent variable and the five common factors as independent variables, and the regression equation coefficient table was obtained, as shown in Table 5 below.

[0070] Among them, the significance sig. value of the common factor X5 = 0.111> 0.05. After eliminating it, the regression equation based on the proportion of highly quiet pedestrian areas in enclosed residential communities along main roads is as follows: Percentage of quiet area = 0.134 + 0.069X1 + 0.079X2 + 0.036X3 + 0.055X4; When the regression equation coefficients of each common factor and the correlation coefficients between each morphological factor and the common factor are known, the influence weight of each morphological factor on the proportion of highly quiet pedestrian areas in enclosed residential communities along main roads is calculated, as shown in Table 6 below. Through the above weights, we can obtain the calculation model of the proportion of high quiet pedestrian areas in enclosed residential communities along main roads based on the residential community morphological factors: ; in ，W fi is the weight of the morphological factor, V fi is the value of the morphological factor, n is the number of morphological factors, and in the above calculation model, n=7.

[0071] According to the above-mentioned example of establishing the sound environment prediction model, the calculation model of other sound environment parameters of the enclosed residential area along the main road is obtained. Similarly, the calculation model of the sound environment parameters inside the residential area with other building layouts and close to other urban roads can also be obtained. By integrating the various calculation models, a sound environment prediction model based on the morphological factors of the residential area can be constructed, which realizes the direct correspondence between the morphological factors of the residential area and the sound environment parameters, and provides a path for the sound environment prediction of the target residential area.

[0072] There is a target residential community in the planning and design stage. The building layout of the target residential community is enclosed, and the urban road adjacent to it is a main road. Then, according to the building layout type of the target residential community and the type of urban road adjacent to it, the sound environment parameter calculation model of the enclosed residential community next to the main road should be selected, including the calculation models of sound environment parameters such as the proportion of quiet areas at pedestrian height, the proportion of quiet areas on building facades, the proportion of relatively quiet areas at pedestrian height, the proportion of relatively quiet areas on building facades, the proportion of noisy areas at pedestrian height, and the proportion of noisy areas on building facades.

[0073] According to the morphological factors required in the calculation model of various acoustic environment parameters, taking the calculation model of the proportion of pedestrian high quiet zones in enclosed residential communities along main roads as an example, it is necessary to obtain specific parameters such as the maximum continuous width along the street, the average area of ​​each building, the building density, the enclosure degree, the main road alignment rate, the width of the street openings and the total length of the reflecting surface from the morphological factors of the target residential community.

[0074] The specific values ​​of the various morphological factors of the above-mentioned target residential area are shown in Table 7 below. In order to eliminate the influence caused by the order of magnitude and unit differences of the morphological factors themselves, the specific values ​​of the above-mentioned morphological factors are standardized. The standardization method is consistent with the standardization method used when constructing the sound environment prediction model. The deviation standardization method is specifically selected, and the standardization results of the above-mentioned morphological factors are shown in Table 8 below. Substituting the standardized values ​​in the table above into the calculation model for the proportion of highly quiet pedestrian zones in enclosed residential communities along main roads, the proportion of highly quiet pedestrian zones within the target residential communities is calculated as follows: ; Among them, n=7; the final result shows that the proportion of highly quiet pedestrian areas within the target residential community is about 19.8%.

[0075] Similarly, the various acoustic environment parameters inside the target residential community are calculated in turn, and the distribution of traffic noise exposure levels inside the residential community is grasped based on the regional proportion values ​​in each acoustic environment parameter. The acoustic environment quality of the target residential community is optimized in a targeted manner based on the building layout, morphological factor design parameters and noise prevention and control of the community, and the optimized residential community data is re-verified to ensure that the acoustic environment quality inside the target residential community meets the expected requirements.

[0076] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for predicting and optimizing traffic noise exposure levels within residential areas, characterized in that: The following steps are involved: S1, based on the different building layout types of residential communities, obtain the morphological factors of several existing residential communities, and construct a residential community acoustic environment simulation model based on different urban road noise sources to obtain the acoustic environment parameters within each residential community; S2: Correlation analysis is performed between the morphological factors of residential communities with the same building layout and the same urban road noise source and the obtained acoustic environment parameters, and the morphological factors that have a significant impact on the traffic noise exposure level within the residential communities are extracted; S3, based on the building layout type of the residential area and the type of urban road adjacent to the residential area, using the extracted morphological factors as independent variables and the acoustic environment parameters within the residential area as dependent variables, establish a regression equation for obtaining the acoustic environment parameters within the residential area, and thereby construct a prediction model for the acoustic environment within the residential area based on the morphological factors of the residential area; S4, selecting a corresponding residential community internal acoustic environment prediction model based on the building layout type of the target residential community and the type of urban road adjacent to the target residential community, substituting morphological factors that affect the traffic noise exposure level within the target residential community into the residential community internal acoustic environment prediction model, and calculating the acoustic environment parameters within the target residential community; S5. Obtain the distribution of traffic noise exposure levels within the target residential area based on the acoustic environment parameters within the target residential area, and optimize the acoustic environment quality of the target residential area based on the distribution of traffic noise exposure levels.

2. The prediction optimization method according to claim 1, characterized in that: The step S1 includes the following specific steps: S11, obtaining images of multiple typical existing residential communities with different building layout types, and constructing simulation models of the residential communities respectively; S12, acquiring traffic data of multiple typical urban road sections of different urban road types, and constructing urban road simulation models respectively; S13, replacing the plurality of urban road simulation models with the external road noise sources of the residential area simulation model in sequence, and outputting the acoustic environment parameters inside the residential area using noise analysis software.

3. The prediction optimization method according to claim 1, characterized in that: In step S2, before performing correlation analysis, a KS single sample test is performed on each data group, and the data group with normality is analyzed using the Pearson correlation coefficient, while the data group without normality is analyzed using the Spearman rank correlation coefficient.

4. The prediction optimization method according to claim 1, characterized in that: In step S3, before performing regression analysis, the KMO test and Bartlett's sphericity test are performed on the extracted residential area morphological factors of each building layout type, and it is determined based on the test results whether the extracted residential area morphological factors of each building layout type meet the conditions for factor dimensionality reduction analysis.

5. The prediction optimization method according to claim 4, characterized in that: Factor analysis is conducted on the morphological factors of residential communities that meet the requirements. The specific steps include: The initial eigenvalues ​​of each morphological factor are calculated; Extract the common factors whose initial eigenvalues ​​are greater than the first preset value and name them X1, X2, ...X p , where p is the number of common factors; The extracted common factors are orthogonally rotated with each morphological factor to generate a rotation component matrix, and the correlation coefficient r between each morphological factor and each common factor is obtained. 11 、r 12 ……r pn , where n is the number of morphological factors; The rotation component matrix is ​​screened, and each common factor is interpreted using a morphological factor whose correlation coefficient with each common factor is greater than a second preset value.

6. The prediction optimization method according to claim 5, characterized in that: According to the results of factor analysis, the extracted common factors were used as independent variables and the acoustic environment parameters inside the residential area were used as dependent variables. Linear regression analysis was performed to calculate the constant value β0 and the regression equation coefficients β1, β2, ... β p , eliminating the common factors that are not significant, and establishing the regression equation of the internal acoustic environment parameters of the residential quarters based on the common factors according to the regression equation coefficients of the remaining common factors: ； Among them, β0 is a constant value, β p is the regression equation coefficient of the common factor, X p is the value of the common factor, and p is the number of common factors.

7. The prediction optimization method according to claim 6, characterized in that: The influence weight of each morphological factor on the internal acoustic environment parameters of the residential area is obtained by calculating the regression equation coefficient of each common factor and the correlation coefficient between each morphological factor and the common factor. W fi : ； in, W fi is the influence weight of each morphological factor, β p is the regression equation coefficient of the common factor, r pn is the correlation coefficient between the morphological factor and its common factor, p is the number of common factors, and n is the number of morphological factors; According to the influence weight, the prediction model of the internal acoustic environment of the residential area based on the residential area morphological factors is obtained: ； in ，W fi is the weight of the morphological factor, V fi is the value of the morphological factor, and n is the number of morphological factors.

8. The prediction optimization method according to claim 4, characterized in that: A direct regression analysis was performed on the morphological factors of residential communities that did not meet the requirements. The extracted morphological factors of residential communities of various building layout types were used as independent variables, and the acoustic environment parameter indicators within the residential communities were used as dependent variables. A stepwise regression analysis was performed to calculate the constant value β0 and the regression equation coefficients β1, β2, ... β p , eliminating the insignificant morphological factors, and establishing a prediction model for the internal acoustic environment of a residential area based on the morphological factors of the residential area according to the regression equation coefficients of the remaining morphological factors: ； Among them, β0 is a constant value, β p is the regression equation coefficient of the morphological factor, V fi is the value of the morphological factor, and p is the number of morphological factors.

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