CN115099516A - Test and Evaluation Method of Track Noise Influence Degree in Near-track Residential Areas - Google Patents

Abstract

The invention relates to the field of noise testing methods, in particular to a method for testing and evaluating the impact degree of track noise in residential areas adjacent to the track. Location and rail-adjacent residential areas, collect noise data as objective evaluation information; collect subjective evaluation information on the impact of track noise at test points, and perform correlation analysis and cross-analysis on subjective evaluation information; AHP model determination based on particle swarm optimization algorithm The weight and evaluation index of the evaluation index; establish an evaluation model of the track noise impact degree of the adjacent rail residential area based on the weight, subjective evaluation information and objective evaluation information; draw the spatial distribution map of the track noise impact degree of the adjacent rail residential area. The invention improves the accuracy and reliability of the track noise evaluation.

Description

Test and Evaluation Method of Track Noise Impact Degree in Near-track Residential Areas

technical field

The invention relates to the field of noise testing methods, in particular to a method for testing and evaluating the influence degree of track noise in a residential area adjacent to the track.

Background technique

Along with the needs of development and production, the scale of cities continues to expand, and the drawbacks brought about by urban expansion are gradually revealed. From air pollution to water pollution to noise pollution, these are the "by-products" of the rapid urban development. "It pervasively interferes with people living in the city. A major source of noise pollution is traffic noise. Traffic noise, as a long-lasting noise source that is relatively close to people, interferes with people's hearing, sleep, daily life and work. , 40-45dB(A) noise will make the human brain emit wake-up waves, and sudden 60dB(A) noise will cause 72% of people to be awakened. Long-term exposure to 80dB(A) noise will cause deafness. The probability of occurrence is significantly increased.

In order to solve urban congestion and accelerate urban development, rail construction has become an important measure for urban transportation. Under the condition of not increasing more traffic noise, the rail construction in the central area of the city mostly chooses straddle-type monorail transportation, which will make some cross The area around the straddle-type monorail is a residential area. Although the noise of the straddle-type monorail itself is very small, it will still cause this part of the residential area to be disturbed by the running noise of the straddle-type monorail.

For the evaluation of track noise, some scholars put forward the generation mechanism of track noise and the propagation law under fixed test conditions. Focuses on the effects of occupational exposure on physical health. However, due to the fact that the actual running conditions of trains are different under different track conditions and the specific conditions of residential areas, the existing track noise assessment has not been oriented to the environment and layout characteristics of residential areas affected by track noise, and lacks public attention. , resulting in poor accuracy and reliability of the existing track noise evaluation methods.

SUMMARY OF THE INVENTION

The present invention aims to provide a method for testing and evaluating the influence degree of track noise in a residential area near the track, so as to solve the problems of poor accuracy and reliability of the existing track noise evaluation method.

The method for testing and evaluating the impact degree of track noise in the adjacent track residential area in this scheme includes the following steps:

Step 1: Select multiple residential areas in the vicinity of the track as test points, and collect noise data as objective evaluation information at the position of the track noise sound source and the adjacent track residential area at each test point;

Step 2: Collect subjective evaluation information on the impact of track noise at the test point, where the subjective evaluation information includes basic information, residence information, work and rest information, acoustic environment satisfaction, individual characteristics, and multi-dimensional information of the degree of influence. Correlation analysis and cross-analysis of information;

Step 3: Determine the weight and the evaluation index of the evaluation index based on the AHP model of the particle swarm optimization algorithm;

Step 4, based on the weight, subjective evaluation information and objective evaluation information, establish an evaluation model of the track noise impact degree of the track-adjacent residential area;

Step 5, draw the spatial distribution diagram of the impact degree of the track noise in the track-adjacent residential area.

The beneficial effects of this program are:

By carrying out subjective and objective evaluations of the track noise in the residential areas adjacent to the track, and combining the subjective and objective evaluations to establish a track noise evaluation model, the noise impact evaluation can be carried out from the actual objective and subjective aspects, and the accuracy of the noise evaluation can be improved. sturdiness and reliability.

Further, in the step 1, the non-traffic rush hour during the daytime on weekdays is selected as the measurement period of the noise data.

The beneficial effect is that the measurement period of the noise data can reduce the interference of road traffic noise.

Further, when collecting the noise data of the residential area near the rail, measure the noise in the horizontal direction and the noise in the vertical direction as the noise data. Collect from the preset distance from the window and from the ground; when measuring the noise data at the sound source position of the track noise, collect the noise data of a preset number of times for each measurement point at the preset position, and take the noise data of the preset number of times. The range value and average value of the data.

The beneficial effects are: the noise data of the noise sound source position is measured for many times, and the average value is obtained, thereby improving the data accuracy of the noise sound source position.

Further, the step 3 also includes the following sub-steps:

Sub-step 3.1, using AHP to construct the hierarchical structure of the evaluation model;

Sub-step 3.2, using the analytic hierarchy process to construct the judgment matrix of the problem to be evaluated;

Sub-step 3.3, construct the objective function to be optimized;

Sub-step 3.4, based on the particle swarm optimization algorithm to solve the weight and evaluation index of the evaluation model.

The beneficial effects are: by combining the analytic hierarchy process and the particle swarm optimization algorithm to solve the weight, the accuracy and reliability of the subsequent comprehensive evaluation based on the subjective evaluation information and the objective evaluation information are improved.

Further, in the sub-step 3.1, the problem to be evaluated is divided into the target layer, the criterion layer and the index layer according to the logical relationship, the target layer is marked as the A layer, the criterion layer is marked as the B layer, and the index layer is marked as Layer C, and the number of factors in layer A is 1, the number of factors in layer B is n _b , and the number of factors in layer C is n _c ;

In the sub-step 3.2, a judgment matrix is established for the B layer and the C layer respectively, and the judgment of the importance degree of the factors in the judgment matrix is performed based on the above-layer factors as a standard, and obtains:

C层判断矩阵为

The judgment matrix of layer B is:

The judgment matrix of layer C is:

The beneficial effect is that the consistency of the judgment matrix can be improved by performing hierarchical analysis on the problems that need to be evaluated.

的权重为w_k(k＝1～n_b)，当

时，A_k表现为完全一致，即有：Further, in the sub-step 3.3, the construction method of the objective function of layer B and layer C is the same, assuming that the factor of layer B is

The weight of is w _k (k=1～n _b ), when

When , _Ak appears to be completely consistent, that is:

表达为：Convert to solve the optimal solution and get the consistency index function

Expressed as:

Its constraints are:

The beneficial effect is that the function for solving the optimal solution is used as the objective function, so that the subsequent weight solving is more accurate.

Further, in the sub-step 3.4, define the particle swarm optimization algorithm: m=20, N=10, C ₁ =C ₂ =2, and obtain the weight, according to the hierarchical relationship between the target layer, the criterion layer and the index layer , construct the evaluation index of the impact degree of the track noise on the adjacent track residential area.

The beneficial effects are that the consistency of the judgment matrix of each layer is improved, and the obtained weight result has high reliability.

Further, in the step 4, the low-frequency index of the low-frequency noise contribution rate in the objective evaluation information in step 1 is calculated according to a preset formula, and the preset formula is:

Among them, η _vj is the proportion of low-frequency noise at the j-th measuring point of the v-th measured cell to the overall noise, EL is the sum of the energy of the low-frequency noise at the measuring point ( _W /m ² ), and E _T is the measured The sum of the energy of noise in the frequency range of 20Hz-20kHz at the point position (W/m ² ), _PL is the low-frequency noise sound pressure (Pa) at the measurement point position, and P _T is the total noise sound pressure in the frequency range of 20Hz-20kHz at the measurement point position (Pa), L _L is the sum of the low-frequency noise sound pressure levels at the measuring point (dB), L _T is the sum of the noise sound pressure levels in the frequency range of 20Hz-20kHz at the measuring point position (dB), and L _k is the first noise in the low-frequency range. k 1/3 octave center frequency sound pressure level (dB);

Calculate the sound pressure level index as:

Calculate the evaluation index as:

Among them, μ _vj is the sound pressure level index of the jth measuring point in the vth cell, L _track is the equivalent continuous sound pressure level (dB) of the rail train during the measurement period, and L _is the noise source intensity during the measurement period. The equivalent continuous sound pressure level (dB) above the track, L _is the equivalent continuous sound pressure level (dB) below the track at the noise source intensity during the measurement period, a _vi is the impact of the track noise in the ith scene of the vth cell The subjective evaluation index of the degree, A _vi is the subjective score of the influence degree of the track noise in the i-th scene of the v-th cell.

The beneficial effects are: the influence of the track noise on the adjacent track community and the residents in the community is multi-angle, and the parameter indicators obtained from the multi-angle are converted into dimensionless relative quantities, so as to be introduced into the evaluation model for accurate and comprehensive evaluation.

Further, in the step 4, the evaluation model of the influence degree of the track noise on the adjacent track residential area is expressed as:

Among them, I _RTN is the track noise impact degree index, indicating the severity of the track noise impact, O is the track noise objective evaluation index, S is the track noise subjective evaluation index, wo is the weight of the objective evaluation index, and ws is the subjective evaluation index. Occupied weight, x _oi is the quantified value of each objective evaluation index, w _oi is the weight occupied by each objective evaluation index, x _si is the quantified value of each subjective evaluation index, and w _si is the weight occupied by each subjective evaluation index.

The beneficial effects are: an evaluation model is established by combining subjective and objective evaluation, and the comprehensiveness and accuracy of evaluation are improved.

Further, in the step 5, the track noise maps of a plurality of track-adjacent residential areas are drawn through ArcGIS.

The beneficial effect is: the visualization of orbital noise is more intuitive.

Description of drawings

Fig. 1 is a flow chart of an embodiment of a method for testing and evaluating the impact degree of track noise in a near-track residential area;

Fig. 2 is the flow chart of solving weight based on PSO algorithm in the method embodiment of the present invention;

FIG. 3 is a diagram of the horizontal distance between the measuring point and the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;

4 is a diagram of the horizontal distance from the measuring point in the B cell to the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;

5 is a diagram of the horizontal distance from the measuring point in the C cell to the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;

6 is a diagram of the horizontal distance from the measuring point in the D cell to the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;

7 is a diagram of the horizontal distance from the measuring point of the E cell to the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;

FIG. 8 is a spectrum diagram of track noise in Building 4 of B cell in an embodiment of the method of the present invention;

Fig. 9 is the frequency spectrum diagram of the track noise of Building No. 2 in the C cell in the embodiment of the method of the present invention;

10 is a spectrum diagram of the track noise of Building 1 in D cell in an embodiment of the method of the present invention;

11 is a spectrum diagram of track noise in Building 4 of E cell in an embodiment of the method of the present invention;

12 is an orbital noise map of cell A in a method embodiment of the present invention;

13 is an orbit noise map of cell B in a method embodiment of the present invention;

14 is an orbital noise map of cell C in a method embodiment of the present invention;

Fig. 15 is the orbit noise map of D cell in the method embodiment of the present invention;

FIG. 16 is an orbit noise map of the E cell in the method embodiment of the present invention.

Detailed ways

The following is further described in detail through specific embodiments.

Example

As shown in Figure 1, the method for testing and evaluating the impact degree of track noise in residential areas adjacent to the track includes the following steps:

Step 1: Select multiple residential areas in the vicinity of the straddle-type monorail running route as test points, and use sound detection equipment to collect noise data at the location of the track noise sound source at each test point and in the adjacent rail residential area. objectively evaluate information. In order to reduce road traffic noise interference, the actual measurement time is selected as the non-peak traffic time during the daytime on weekdays. The model of the sound detection equipment is AWA6228+, and the counting frequency is set to 1s/time to ensure the accuracy of the actual measurement to the greatest extent. In the actual measurement process, in order to reduce the impact of the ambient wind on the data, a ball is reinforced on the microphone of the sound detection device. The sound detection equipment should be calibrated according to the regulations before each use. The deviation of the indication value of the sound calibration of the equipment before and after use should not be greater than 0.5dB, otherwise the measurement will be invalid. During the actual measurement, it should be ensured that the weather conditions meet the conditions of no rain, no snow, no thunder, and no electricity, and the wind speed is less than 5m/s. When collecting the noise data of the residential area near the rail, measure the noise in the horizontal direction and the noise in the vertical direction as the noise data. Window and a preset distance from the ground to collect.

The actual measurement plan in the residential area is: when measuring the noise in the horizontal direction, the measuring points near the building are arranged at 1m away from the building and 1.5m above the ground; the measuring points far away from the building (more than 3.5m away from the building) are arranged at 1.5m above the ground. When measuring the noise in the vertical direction, the measuring points are arranged at a distance of 1.5m from the window and a height of 1.5m from the ground. The actual measurement plan of the train noise source intensity is as follows: two measuring points are arranged at a horizontal distance of 7.5m from the track center line, 1.5m above the rail top surface and 1.5m below the rail top surface, and 10 measuring points are carried out at each measuring point. Measurements, take the range value and the average value. And measure the background noise when the trackless train passes through each measuring point, measure 10 times, and take the average value. The test scenario "when the trackless train passes through" is used as the measurement scenario of the background noise of this test. When the trackless train passes through, the background noise is measured at each measuring point for 30s, and 10 groups are measured at each measuring point. Background noise data, averaged.

Taking Chongqing Railway Line 3 as an example, the track laying method at the source intensity test point of the track line is in an elevated form, the track running speed is 72km/h, and there are no large-volume obstructions within 30m around the measuring point. The curvature radius and slope meet the specification requirements, and the source intensity test can be carried out. According to the high occupancy rate, mature development and certain scale, five typical communities on the track line were selected for actual measurement. Through multiple measurements during the pre-test, it was found that the time required for a single 6-car marshalling train to pass through the measurement point at a uniform speed was approximately 8s. When the train speed is in the range of 20-40km/h, 12s is selected as the time length for obtaining the equivalent continuous sound level value of the track noise; when the train speed is in the range of 40-60km/h, 10s is selected as the time for obtaining the equivalent track noise level The time length of the continuous sound level value; when the train speed is in the range of 60-80km/h, 8s is selected as the time length for obtaining the equivalent continuous sound level value of the track noise, and the noise source intensity of the train is measured as shown in Table 1. the result of.

Table 1 Track noise source intensity

The five cells are respectively represented as A cell, B cell, C cell, D cell, and E cell. The length of cell A parallel to the track is about 150m, and the length of the vertical direction to the track is about 125m. 27 measuring points (D8-D41) are arranged on the same line in the vertical direction of cell A and the track line. These 27 measuring points are far from the center of the track. The distance of the line is 30-155m. The position information and noise data of measuring points D8-D41 are shown in Table 2. The horizontal distance between the measuring point in A cell and the sound source and the equivalent continuous A sound level are shown in Figure 3.

Table 2 Location information and noise measurement results of measuring points D8-D41 in Residential Area A

It can be seen from Table 2 and Figure 3 that with the increase of the horizontal distance between the measurement point in the A cell and the track centerline, the equivalent continuous A sound level of the track noise at the measurement point shows a downward trend.

The total length of cell B parallel to the track is about 160m, and the total length of the vertical direction to Line 3 is about 72m. Select 13 measuring points (A1-A13) on the same line perpendicular to the track line. The distance between these 13 measuring points The distance from the center line of the track is 30-102m. The location information and noise data of the measuring points A1-A13 in cell B are shown in Table 3. The horizontal distance between the measuring points in cell B and the sound source and the equivalent continuous A sound level are shown in Figure 4. .

Table 3B cell A1-A13 measurement point location information and noise measurement results

It can be seen from Table 3 and Figure 4 that with the increase of the horizontal distance between the measuring point in the B cell and the track centerline, the equivalent continuous A sound level of the track noise at the measuring point shows a downward trend.

The total length of cell C parallel to the track is about 300m, and the total length to the vertical direction of the track is about 40m. Select 9 measuring points (B1-B9) on the same line as the vertical direction of the track line. The distances are 25m, 30m, 35m, 40m, 45m, 50m, 55m, 60m, and 65m, respectively. The location information and noise data information of the measuring points B1-B9 in cell C are shown in Table 4. The horizontal distance between the measuring points in cell C and the sound source The equivalent continuous A sound level is shown in Figure 5. It can be seen from Table 4 and Figure 5 that with the increase of the horizontal distance between the measuring point in the C cell and the track centerline, the equivalent continuous A sound level of the track noise at the measuring point shows a downward trend.

Table 4 Location information and noise measurement results of B1-B9 measuring points in cell C

The distance between D cell and the track in the parallel direction is short, and the total length is about 70m. The distance between D cell and the vertical direction of the track is long, with a total length of about 105m. Select 22 measuring points (C1-C22) on the same line as the vertical direction of the track line. The distance between the 22 measuring points and the center line of the track is 25-130m. The position information and noise data of the measuring points C1-C22 are shown in Table 5. The horizontal distance between the measuring point in D cell and the sound source and the equivalent continuous A sound level are shown in Table 5. shown in Figure 6.

Table 5D cell C1-C22 measurement point location information and noise measurement results

It can be seen from Table 5 and Figure 6 that with the increase of the horizontal distance between the measurement point in the D cell and the track centerline, the equivalent continuous A sound level of the track noise at the measurement point shows a downward trend.

The total length of the E cell parallel to the track is about 257m, and the total length of the vertical direction to Line 3 is about 60m. Select 7 measuring points (E1-E7) on the same line perpendicular to the track line. The distance between these 7 measuring points The distance from the centerline of the track is 85-115m. The position information and noise data of measuring points E1-E7 are shown in Table 6. The horizontal distance from the measuring point in cell E to the sound source and the equivalent continuous A sound level are shown in Figure 7.

Table 6 Location information and noise measurement results of measuring points E1-E7 in E cell

It can be seen from Table 6 and Figure 7 that with the increase of the horizontal distance between the measuring point in the E cell and the center line of the track, the equivalent continuous A sound level of the track noise at the measuring point shows a downward trend.

Select 4 representative buildings in the tested communities, B community, C community, D community, E community, such as the building with the most track-adjacent characteristics, the vertical distribution law and spectral characteristics of track noise Explore.

The measuring points are arranged on the 1-12 floors of Building No. 4 in Community B, and the horizontal distance between the measuring points and the center line of the track is 35m. The location information of the measuring point and the measured data are shown in Table 7, and the frequency spectrum is shown in Figure 8.

Table 7. Measured data of track noise on each floor of Building 4 in Community B

It can be seen from Figure 9 that the track noise energy of each floor is concentrated in the frequency range of 500Hz-2500Hz, and reaches its peak at 1250Hz, and forms a small peak at 630Hz that is slightly lower than the peak frequency at 1250Hz. Affected by the vertical height, the attenuation of the noise in the middle and high frequency bands is greater than that in the low frequency band.

The measuring points are arranged on the 1st to 28th floors of Building 2 in the C community, and all the measuring points are 35m horizontally away from the track center line. The vertical distribution of the track noise is shown in Table 8, and the frequency spectrum is shown in Figure 9.

Table 8. Measured data of track noise on each floor of Building 2 in Community C

It can be seen from Figure 9 that the equivalent continuous A sound level at the measuring point generally decreases first, then increases and then decreases with the increase of the floor. It decreases in the range of 5.8m-5.6m, increases in the range of 5.6m-22.5m, and decreases in the range of 22.5m-67.8m. The A sound level at the 2nd floor measuring point is higher than that of the 4th floor, and the A sound level at the 4th floor measuring point is higher than that of the 6th floor. The absolute value of the height difference between the upper and the top of the rail is not much different from that of the 2nd floor. The A sound level reaches its peak at the 12th floor, and the vertical height difference between the measuring point and the track surface at the 12th floor is 22.5m. The position below the 12th floor is affected by the sound shadow area formed by street trees, etc., coupled with the attenuation of noise by road surface absorption and road reflection, so that the position below the 12th floor is closer to the track, but the A sound level does not reach the peak; the 12th floor The above positions are no longer affected by the sound shadow area, road surface absorption and reflection, etc. As the height of the floor increases, the distance from the sound source increases, and the noise level decreases accordingly.

The measuring points are arranged on the floors 1-18 of Building 1 in D Community, and the horizontal distance between all the measuring points and the center line of the track is 40m. The vertically arranged measuring point position information and track noise data are shown in Table 9, and the frequency spectrum is shown in Figure 10.

Table 9D Measured data of track noise on each floor of Building 1 in Community D

The peak frequency of each floor appears at 1250Hz, and there is a small peak at 630Hz that is slightly lower than 1250Hz. The track noise energy of each floor is mainly distributed in the range of 500Hz-2500Hz. The noise in the middle and high frequency bands is attenuated more than the low frequency band due to the influence of the vertical height.

The measuring points are arranged on the floors 1-24 of Building 4 in E Community, and the horizontal distance between the measuring points and the center line of the track is 85m. Table 10 shows the location information and measured data of the measurement points in the vertical direction of the E cell, and the frequency spectrum is shown in Figure 11.

Table 10 Measured data of track noise on each floor of Building 4 in Community E

It can be seen from Figure 11 that most of the track noise energy of each floor is distributed in the frequency range of 630Hz-2000Hz, the peak frequency is 1250Hz, and a small peak appears at 630Hz, which is slightly lower than 1250Hz.

Step 2: Collect subjective evaluation information on the impact of track noise at the test point, and perform correlation analysis and cross-analysis on the subjective evaluation information, where the subjective evaluation information includes basic information, residence information, work and rest information, acoustic environment satisfaction, individual characteristics, The multi-dimensional information of the noise impact degree measure, the subjective evaluation information is obtained by means of a questionnaire, and the subjective evaluation information is listed on the questionnaire.

Basic information includes gender, age, education, and occupation; residential information includes residential area, whether it is adjacent to the track, floor, and type of windows. Whether the window of the bedroom and living room is directly facing the track and there is no obstruction in the middle shall prevail; the acoustic environment satisfaction includes Satisfaction with the overall acoustic environment of the community, and the type of noise that interferes the most with life; work and rest information includes the time to get up, the time to fall asleep, the length of stay in the community during weekday orbital operation, and the length of stay in the community during weekend orbital operation; individual characteristics include The degree of trouble with insomnia, the degree of trouble with work, study and life pressure or anxiety, and the degree of sensitivity to noise; the measure of noise impact includes subjective evaluation of the impact of track noise in different scenarios.

The respondents of the questionnaire include: the gender distribution is evenly distributed; the age distribution includes teenagers (under 20 years old), youth (20-30 years old), prime-age (31-40 years old), middle-aged (41-50 years old), Middle-aged and elderly (51-60 years old) and elderly (over 60 years old) multiple age groups; the range of floors included covers low-rise, middle-low-rise, middle-rise, middle-rise and high-rise; window types include a small amount of installation of soundproof glass and most installations of ordinary Glass.

The questions designed in the questionnaire include: the degree of trouble with insomnia, the degree of stress or anxiety in work, study or life, the degree of concern about the quality of life, and the degree of sensitivity to noise.

The subjective evaluation information is described by the Likert scale. The Likert scale expresses a fact in the questions of the questionnaire. The respondents need to choose the degree of their recognition of the fact from the options. The degree of recognition is usually divided into 5 Each level corresponds to a score of 1-5 points.

The Likert scale designed to understand the influence of orbital noise on respondents in different scenarios is tested by Cronbach's Alpha. The value range of Cronbach's Alpha is 0-1, the closer it is to 1, the higher the reliability of the questionnaire results. When the value range of Cronbach's Alpha is 0.7-0.8, it means that the reliability of the questionnaire results is average; when the value range of Cronbach's Alpha is 0.8-0.9, it means that the reliability of the questionnaire results is high; when the value range of Cronbach's Alpha is high When it is 0.9-1, it means that the reliability of the questionnaire results is very high, and the results are shown in Table 2.

Table 2 Reliability analysis of track noise impact degree in different scenarios

In the correlation analysis, the Spearman coefficient is used as a test coefficient to explore the internal connection and influence relationship between the basic information, residence information, work and rest information, individual characteristics of the respondents in the questionnaire and the subjective influence degree of track noise. In this paper, the significance level is set as 0.05 (when the significance is less than 0.05, it is considered that there is a significant difference between the test objects).

Crossover analysis was performed using the Kruskal-Wallis one-way ANOVA test and the Mann-Whitney U test. The Kruskal-Wallis one-way variance test belongs to the category of non-parametric tests, which can test whether there are significant differences in a continuous variable among multiple independent groups (3 or more groups); the Mann-Whitney U test also belongs to the category of non-parametric tests and is used for The test group is less than 3 and the data within the group does not meet the test of normal distribution and homogeneity of variance.

Step 3: Determine the weight and evaluation index of the evaluation index based on the AHP model of the particle swarm optimization algorithm, including the following sub-steps:

In sub-step 3.1, the hierarchical structure of the evaluation model is constructed by the AHP method, and the problems to be evaluated are divided into the target layer, the criterion layer and the index layer according to the logical relationship. The problems to be evaluated are the problems in the subjective evaluation process. The layer is marked as layer A, the criterion layer is marked as layer B, the index layer is marked as layer C, and the number of factors in layer A is 1, the number of factors in layer B is n _b , and the number of factors in layer C is n _c .

In sub-step 3.2, the analytic hierarchy process is used to construct the judgment matrix of the problem to be evaluated, and the judgment matrix P is:

Among them, a _ij is the degree of importance of index i relative to index j (a _ij >0; when i=j, a _ij =1; a _ij *a _ji =1).

The weight calculation formula based on the judgment matrix is:

为判断矩阵第i行元素乘积的n次方根，a_ij为判断矩阵中第i行j列元素。Among them, _Wi is the calculated index weight,

is the n-th root of the product of the elements in the ith row of the judgment matrix, and a _ij is the element in the ith row and j column of the judgment matrix.

A judgment matrix is established for the B layer and the C layer, and the judgment of the importance of the factors in the judgment matrix is carried out as a standard for the above-layer factors, and the following results are obtained:

C层判断矩阵为

The judgment matrix of layer B is:

The judgment matrix of layer C is:

的权重为w_k(k＝1～n_b)，当

时，A_k表现为完全一致，即有：Sub-step 3.3, construct the objective function to be optimized, the construction method of the objective function of layer B and layer C is the same, assuming the factor of layer B

The weight of is w _k (k=1～n _b ), when

When , _Ak appears to be completely consistent, that is:

表达为，一致性指标函数即目标函数：Convert to solve the optimal solution and get the consistency index function

Expressed as, the consistency index function is the objective function:

Its constraints are:

Sub-step 3.4, based on the particle swarm optimization algorithm to solve the weights and evaluation indicators of the evaluation model. The basic principle of the particle swarm algorithm is: there are m particles in an N-dimensional space, and the particles are represented by i. Each particle has its own position vector and adjacent velocity, the position vector is x _i =(x _i1 ,x _i2 ,...,x _in ), and the velocity is adjacent to v _i =(v _i1 ,v _i2 ,.. .,v _in ), both the position vector and the velocity vector are n-dimensional. The process of particles finding the optimal solution is an iterative process, and _pi is the optimal position found in the process of finding the optimal solution. Iteration to when the position of the particle is optimal is measured by the fitness function f(x).

Among them, c ₁ , c ₂ are learning factors (values in non-negative constants), r ₁ , r ₂ are random numbers between 0 and 1, i=1, 2,...,m; n =1,2,...,N; v _in ∈(-v _max , v _max ), v _max is a constant.

Define the particle swarm optimization algorithm: m=20, N=10, C ₁ =C ₂ =2, and calculate the weight based on the PSO algorithm. The specific steps are shown in Figure 2. Generate the initial particle solution: generate in the solution space (0,1) random number and normalized. Substitute the feasible solution into the objective function, calculate the initial particle fitness, and select the best particle. Update and iterate the particles to determine whether the constraints are met. If not, return to generating the initial solution of the particles. If so, calculate the fitness of the updated particles, and select the optimal position and the global optimal position of the particles. Determine whether the termination conditions are met. , if not, iteratively update the particle, if so, output the optimal solution. Substitute the optimal solution into the objective function to find the consistency index value. Determine whether the consistency requirements are met. If not, return to generating the initial solution of the particle. If so, output the global optimal position and the corresponding weight and consistency index, and construct the orbit noise according to the hierarchical relationship between the target layer, the criterion layer and the index layer. Evaluation index for the evaluation of the impact degree of the adjacent rail residential area.

Step 4, based on the weight, subjective evaluation information and objective evaluation information, establish an evaluation model of the track noise impact degree of the track-adjacent residential area, specifically:

The low-frequency index of the low-frequency noise contribution rate in the objective evaluation information in step 1 is calculated according to the preset formula, and the preset formula is:

Among them, η _vj is the proportion of low-frequency noise at the j-th measuring point of the v-th measured cell to the overall noise, EL is the sum of the energy of the low-frequency noise at the measuring point ( _W /m ² ), and E _T is the measured The sum of the energy of noise in the frequency range of 20Hz-20kHz at the point position (W/m ² ), _PL is the low-frequency noise sound pressure (Pa) at the measurement point position, and P _T is the total noise sound pressure in the frequency range of 20Hz-20kHz at the measurement point position (Pa), L _L is the sum of the low-frequency noise sound pressure levels at the measuring point (dB), L _T is the sum of the noise sound pressure levels in the frequency range of 20Hz-20kHz at the measuring point position (dB), and L _k is the first noise in the low-frequency range. k 1/3 octave center frequency sound pressure level (dB);

Calculate the sound pressure level index as:

Calculate the evaluation index as:

Among them, μ _vj is the sound pressure level index of the jth measuring point in the vth cell, L _track is the equivalent continuous sound pressure level (dB) of the rail train during the measurement period, and L _is the noise source intensity during the measurement period. The equivalent continuous sound pressure level (dB) above the track, L _is the equivalent continuous sound pressure level (dB) below the track at the noise source intensity during the measurement period, a _vi is the impact of the track noise in the ith scene of the vth cell The subjective evaluation index of the degree, A _vi is the subjective score of the influence degree of the track noise in the i-th scene of the v-th cell.

The evaluation model of the influence degree of track noise on the residential area adjacent to the track is expressed as:

Among them, I _RTN is the track noise impact degree index, indicating the severity of the track noise impact, O is the track noise objective evaluation index, S is the track noise subjective evaluation index, wo is the weight of the objective evaluation index, and ws is the subjective evaluation index. Occupied weight, x _oi is the quantified value of each objective evaluation index, w _oi is the weight occupied by each objective evaluation index, x _si is the quantified value of each subjective evaluation index, and w _si is the weight occupied by each subjective evaluation index.

Step 5: According to the evaluation model in Step 4, draw the spatial distribution map of the track noise impact degree of the adjacent rail residential areas, and draw the track noise maps of multiple adjacent rail residential areas through ArcGIS, and obtain Figure 12, Figure 13, Figure 14, and Figure 1. 15 and the orbital noise map shown in Figure 16.

It can be seen from Figure 12 that cell A is relatively close to the station, and cell A is generally polluted by track noise. The maximum track noise level in the cell is 69.7dB, and the minimum is 50.1dB. Because in cell A, the train has obvious noise. The acceleration behavior is affected by the speed of the train. In area I, the track noise level of Building 1 is significantly higher than that of Building 3; in Area III, the track noise level of Building 2 is slightly higher than that of Building 4. According to the measured data, there is little difference in the sound level of the track noise at the measuring points in the two buildings; in Area IV, there is basically no difference in the sound level of the track noise in Building 2 and Building 4.

It can be seen from Figure 13 that cell B is located between the two stations, and the speed of the train does not change significantly when the train passes by. Cell B is more seriously polluted by track noise. The sound level of track noise is 71.1dB at the maximum and 54.7dB at the minimum. As the distance increases, the sound level of the track noise generally shows a downward trend. Buildings 1-4 have the same floor height, and the track noise levels of these 4 buildings are roughly similar. However, in Area II, since the volume of Buildings 2 and 3 is slightly smaller than that of Buildings 1 and 4, compared with Buildings 1 and 4, Buildings 2 and 3 are located at the same level. The volume of the obstruction between the measuring point and the sound source is small, so that the track noise level of Buildings 2 and 3 in Area II is slightly higher than that of Buildings 1 and 4. In Area I and Area II, when the distance from the track center line is the same, the average sound level of the track noise of the measuring points in the building is greater than that of the ground measuring points; The average sound level of the track noise is not much different from that on the ground. This is due to the weakening effect on the track noise level of the measuring points in the building with the increase of the occlusion volume of the building. In area IV, when the distance from the center line of the track is the same, the average sound level of the track noise of the measuring points in the building is smaller than that of the ground measuring points. With the continuous increase of the volume of the obstructions in the building, the weakening effect on the noise is more obvious. . At the ground measuring points in Area IV, the track noise level of the measuring points with building occlusion is significantly lower than that of the unobstructed measuring point, which proves that the building occlusion has a relatively obvious hindering effect on the transmission of sound.

It can be seen from Figure 14 that the C cell is seriously polluted by the track noise, and the sound level is 74.4dB at the maximum and 60.4dB at the minimum. It is worth noting that the lowest track noise in this cell also exceeds the requirement of less than 55dB for the Class 1 acoustic environment functional area in the "Acoustic Environment Quality Standard". The noise level of the track in the community shows a downward trend on the whole with the increase of the distance between the measuring point and the center line of the track. The speed of the train does not change significantly when passing through the community. The sound level and attenuation law of the track noise at the measuring points in Building 5 are roughly the same. In Area IV, when the horizontal distance from the center line of the track is the same, the noise level at the position where the buildings are shielded at the far track end of Buildings 1-5 is significantly lower than that at the position without shielding on the same straight line. Comparing the sound level of the track noise from the measuring points at different horizontal distances from the track centerline in the same building of Buildings 1-5, it can be found that the track noise sound level of the buildings adjacent to the track is significantly higher than that of the non-track buildings in the same building. And the decay speed of the track noise sound level value of the measuring points in the building is much faster than that of the unobstructed ground measuring points at the same distance. The ability of building occlusion to hinder noise transmission is much stronger than that of air absorption, ground absorption, and geometric divergence. In area I, combined with the measured data, when the horizontal distance from the track center line is the same, the average sound level of the track noise of the measuring points in the building is higher than that of the ground measuring points in the community; in areas II and III, when the distance from the track center line is horizontal When the distance is the same, the average sound level of the track noise of the measuring points in the building is lower than that of the ground measuring points in the community. It is again confirmed that the attenuation effect of the noise is more obvious when there is a building block.

It can be seen from Figure 15 that the D cell is seriously polluted by the track noise. The maximum track noise level is 74.3dB and the minimum is 53.5dB. The closer to the track line, the higher the sound level of the track noise, and with the increase of the distance, the pollution of the track noise is alleviated. The two buildings most seriously polluted by track noise in the community are Building 1 and Building 2, which are closest to the track line. The sound level of track noise at more than 70% of the measuring points in these two buildings is above 66dB; Building 4 is less polluted by rail noise, which is inseparable from the noise blocking effect of Buildings 1 and 2. In area I, when the horizontal distance from the centerline of the track is the same, the average sound level of the track noise at the measuring points in Buildings 1 and 2 is slightly higher than that at the ground measuring points; in area II, when the horizontal distance from the centerline of the track is the same When the same, the average sound level of the track noise at the measuring points in Buildings 1 and 2 is not much different from the ground measuring points; in Areas III and IV, when the horizontal distance from the track centerline is the same, Buildings 3 and 3 and The average sound level of the track noise at the measuring points in Building 4 is lower than that of the ground measuring points. In area IV, when the horizontal distance from the track center line is the same, the noise level of the track with building occlusion is significantly lower than that of the non-building occluded measuring point.

It can be seen from Figure 16 that the E cell is less polluted by the track noise. The maximum sound level of the track noise in the cell is 61.6dB, and the minimum sound level is 51.1dB. When the rail train passes through the E district, there is no obvious speed change behavior, and the buildings 1-4 in the district are arranged in a line along the track line. Because the distance between Building 4 and the track line is less than that of Buildings 1-3, the sound level of the track noise of Building 4 is slightly higher than that of Buildings 1-3. In area I, when the distance from the center line of the track is the same, the average noise level of the track noise at the measuring points in Building 4 is greater than that at the ground measuring points; The mean value of the track noise sound level of the points is smaller than that of the ground measuring points, and the mean value of the track noise sound level of the measuring points in Buildings 2-3 is roughly the same as that of the ground measuring points; in Area III, the orbital noise of the measuring points in Buildings 1-3 The average sound level is slightly smaller than that of the ground measurement points. In Region IV, the attenuation effect of building occlusion on the sound level of track noise is more obvious.

Due to the strict planning and design of the extension route of the track during the track establishment period and the subsequent operation period, the noise during the track operation has been protected to a certain extent, or the noise may be constructed in places where there may be residential areas. Smaller track types, such as single track. However, due to urban development in some areas, residential areas will be built in places where there are no residential areas along the track, so that the newly built residential areas will still be affected by noise. Therefore, for the small number of residential areas affected by noise, and the residential areas near the monorail without noise protection equipment, the noise impact of the residential areas adjacent to the monorail area is ignored under the premise of the limited understanding of the low noise of the monorail. , it is conceivable to install sound insulation devices, so it is generally not studied that it is affected by track noise.

In this embodiment, the noise generated by the running of the rail train affects the households in the residential area built along the rail line. The influence of the rail noise on the adjacent rail residential area and the residential households in the residential area is multi-angle, through the actual test The obtained track noise frequency characteristics and objective sound level values can only reflect the level of track noise in each community from a numerical point of view. There are differences in the perception of track noise by households with different individual characteristics and different living characteristics. This difference is the track noise. that cannot be described by objective values. The magnitude of the impact degree of track noise is the result of the combined effect of objective numerical value and subjective perception. This embodiment is based on combining objective evaluation and subjective evaluation to evaluate the impact degree of track noise in residential areas adjacent to the track, which can more scientifically and comprehensively evaluate the impact of track noise on the adjacent track. The impact level of residential areas in order to improve the noise situation.

The above descriptions are only embodiments of the present invention, and common knowledge such as well-known specific structures and characteristics in the solution are not described too much here. It should be pointed out that for those skilled in the art, some modifications and improvements can be made without departing from the structure of the present invention. These should also be regarded as the protection scope of the present invention, and these will not affect the implementation of the present invention. Effectiveness and utility of patents. The scope of protection claimed in this application shall be based on the content of the claims, and the descriptions of the specific implementation manners in the description can be used to interpret the content of the claims.

Claims (10)

1. A method for testing and evaluating the impact degree of rail noise in a residential area adjacent to a rail track, characterized in that it comprises the following steps: Step 1: Select multiple residential areas in the vicinity of the track as test points, and collect noise data as objective evaluation information at the position of the track noise sound source and the adjacent track residential area at each test point; Step 2: Collect subjective evaluation information on the impact of track noise at the test point, where the subjective evaluation information includes basic information, residence information, work and rest information, acoustic environment satisfaction, individual characteristics, and multi-dimensional information of the degree of influence. Correlation analysis and cross-analysis of information; Step 3: Determine the weight and the evaluation index of the evaluation index based on the AHP model of the particle swarm optimization algorithm; Step 4, based on the weight, subjective evaluation information and objective evaluation information, establish an evaluation model of the track noise impact degree of the track-adjacent residential area; Step 5, draw the spatial distribution diagram of the impact degree of the track noise in the track-adjacent residential area. 2 . The method for testing and evaluating the impact degree of rail noise in a near-rail residential area according to claim 1 , wherein in the step 1, the non-traffic rush hour during the working day is selected as the measurement period of the noise data. 3 . 3. the track noise impact degree test and evaluation method of the adjacent rail residential area according to claim 2, is characterized in that: when collecting the noise data of the adjacent rail residential area, measure the horizontal direction noise and the vertical direction noise as noise data, the described The horizontal direction noise is collected from the near building measuring point and the far building measuring point, and the vertical direction noise is collected from the preset distance from the window and the ground; when measuring the noise data of the sound source position of the track noise, Each measuring point at the preset position collects noise data for a preset number of times, and obtains a range value and an average value of the noise data for the preset number of times. 4. The track noise impact degree test and evaluation method of near-track residential area according to claim 3, is characterized in that: described step 3 also comprises the following sub-steps: Sub-step 3.1, using the analytic hierarchy process to construct the hierarchical structure of the evaluation model; Sub-step 3.2, using the analytic hierarchy process to construct the judgment matrix of the problem to be evaluated; Sub-step 3.3, construct the objective function to be optimized; Sub-step 3.4, based on the particle swarm optimization algorithm to solve the weight and evaluation index of the evaluation model. 5. The method for testing and evaluating the impact degree of track noise in near-track residential areas according to claim 4, characterized in that: in the sub-step 3.1, the problem to be evaluated is divided into target layer, criterion layer and In the indicator layer, the target layer is marked as layer A, the criterion layer is marked as layer B, the indicator layer is marked as layer C, and the number of factors in layer A is 1, the number of factors in layer B is n _b , and the number of factors in layer C is n b . The number of factors is n _c ; In the sub-step 3.2, a judgment matrix is established for the B layer and the C layer respectively, and the judgment of the importance degree of the factors in the judgment matrix is performed based on the above-layer factors as a standard, and obtains: The judgment matrix of layer B is:

The judgment matrix of layer C is:

6. The method for testing and evaluating the influence degree of track noise in near-track residential areas according to claim 5, it is characterized in that: in described sub-step 3.3, the objective function construction method of layer B and layer C is the same, and it is assumed that the factor of layer B is the same.

The weight of is w _k (k=1～n _b ), when

When , _Ak appears to be completely consistent, that is:

Convert to solve the optimal solution and get the consistency index function

Expressed as:

Its constraints are:

7. The method for testing and evaluating the influence degree of track noise in near-track residential areas according to claim 5, characterized in that: in the sub-step 3.4, a definition is made for the particle swarm optimization algorithm: m=20, N=10, C ₁ =C ₂ =2, and the weights are calculated. According to the hierarchical relationship between the target layer, the criterion layer and the index layer, an evaluation index for evaluating the impact degree of the track noise on the adjacent track residential area is constructed. 8. The method for testing and evaluating the impact degree of track noise in a near-track residential area according to claim 5, wherein in the step 4, the low-frequency index of the low-frequency noise contribution rate in the objective evaluation information in step 1 is according to a preset formula To calculate, the preset formula is:

Among them, η _vj is the proportion of low-frequency noise at the j-th measuring point of the v-th measured cell to the overall noise, EL is the sum of the energy of the low-frequency noise at the measuring point ( _W /m ² ), and E _T is the measured The sum of the energy of noise in the frequency range of 20Hz-20kHz at the point position (W/m ² ), _PL is the low-frequency noise sound pressure (Pa) at the measurement point position, and P _T is the total noise sound pressure in the frequency range of 20Hz-20kHz at the measurement point position (Pa), L _L is the sum of the low-frequency noise sound pressure levels at the measuring point (dB), L _T is the sum of the noise sound pressure levels in the frequency range of 20Hz-20kHz at the measuring point position (dB), and L _k is the first noise in the low-frequency range. k 1/3 octave center frequency sound pressure level (dB); Calculate the sound pressure level index as:

Calculate the evaluation index as:

Among them, μ _vj is the sound pressure level index of the jth measuring point in the vth cell, L _track is the equivalent continuous sound pressure level (dB) of the rail train during the measurement period, and L _is the noise source intensity during the measurement period. The equivalent continuous sound pressure level (dB) above the track, L _is the equivalent continuous sound pressure level (dB) below the track at the noise source intensity during the measurement period, a _vi is the impact of the track noise in the ith scene of the vth cell The subjective evaluation index of the degree, A _vi is the subjective score of the influence degree of the track noise in the i-th scene of the v-th cell. 9. The method for testing and evaluating the degree of influence of rail noise in adjacent rail residential areas according to claim 5, characterized in that: in the step 4, the evaluation model for the degree of influence of rail noise on adjacent rail residential areas is expressed as:

Among them, I _RTN is the track noise impact degree index, indicating the severity of the track noise impact, O is the track noise objective evaluation index, S is the track noise subjective evaluation index, wo is the weight of the objective evaluation index, and ws is the subjective evaluation index. Occupied weight, x _oi is the quantified value of each objective evaluation index, w _oi is the weight occupied by each objective evaluation index, x _si is the quantified value of each subjective evaluation index, and w _si is the weight occupied by each subjective evaluation index. 10 . The method for testing and evaluating the influence degree of rail noise in adjacent rail residential areas according to claim 5 , wherein in the step 5 , multiple rail noise maps of adjacent rail residential areas are drawn by ArcGIS. 11 .

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