CN115099516A - Method for testing and evaluating influence degree of track noise of adjacent-track residential area - Google Patents
Method for testing and evaluating influence degree of track noise of adjacent-track residential area Download PDFInfo
- Publication number
- CN115099516A CN115099516A CN202210803398.7A CN202210803398A CN115099516A CN 115099516 A CN115099516 A CN 115099516A CN 202210803398 A CN202210803398 A CN 202210803398A CN 115099516 A CN115099516 A CN 115099516A
- Authority
- CN
- China
- Prior art keywords
- noise
- track
- layer
- adjacent
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012360 testing method Methods 0.000 title claims abstract description 41
- 238000011156 evaluation Methods 0.000 claims abstract description 91
- 239000002245 particle Substances 0.000 claims abstract description 26
- 238000013210 evaluation model Methods 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 15
- 238000005457 optimization Methods 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000009826 distribution Methods 0.000 claims abstract description 8
- 238000010219 correlation analysis Methods 0.000 claims abstract description 5
- 238000005259 measurement Methods 0.000 claims description 32
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000011002 quantification Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 abstract description 6
- 238000010998 test method Methods 0.000 abstract 1
- 210000004027 cell Anatomy 0.000 description 62
- 239000010410 layer Substances 0.000 description 62
- 230000006870 function Effects 0.000 description 11
- 230000009286 beneficial effect Effects 0.000 description 10
- 210000003771 C cell Anatomy 0.000 description 9
- 210000003719 b-lymphocyte Anatomy 0.000 description 8
- 210000004128 D cell Anatomy 0.000 description 6
- 238000001228 spectrum Methods 0.000 description 6
- 238000011161 development Methods 0.000 description 5
- 238000010521 absorption reaction Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 230000007423 decrease Effects 0.000 description 4
- 230000003247 decreasing effect Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 239000011229 interlayer Substances 0.000 description 4
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 description 3
- 208000019901 Anxiety disease Diseases 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 2
- 238000000585 Mann–Whitney U test Methods 0.000 description 2
- 208000013738 Sleep Initiation and Maintenance disease Diseases 0.000 description 2
- 230000009471 action Effects 0.000 description 2
- 230000036506 anxiety Effects 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 206010022437 insomnia Diseases 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- IJJWOSAXNHWBPR-HUBLWGQQSA-N 5-[(3as,4s,6ar)-2-oxo-1,3,3a,4,6,6a-hexahydrothieno[3,4-d]imidazol-4-yl]-n-(6-hydrazinyl-6-oxohexyl)pentanamide Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)NCCCCCC(=O)NN)SC[C@@H]21 IJJWOSAXNHWBPR-HUBLWGQQSA-N 0.000 description 1
- 206010011878 Deafness Diseases 0.000 description 1
- 206010073310 Occupational exposures Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010367 cloning Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 231100000895 deafness Toxicity 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 208000016354 hearing loss disease Diseases 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 231100000675 occupational exposure Toxicity 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention relates to the field of noise test methods, in particular to a method for testing and evaluating the influence degree of track noise of adjacent track residential areas, which comprises the steps of selecting a plurality of residential areas in the adjacent area of a track as test points, collecting noise data as objective evaluation information at the track noise sound source position and the adjacent track residential areas at each test point; acquiring subjective evaluation information on the influence of track noise of the test points, and performing correlation analysis and cross analysis on the subjective evaluation information; determining the weight and the evaluation index of the evaluation index based on an analytic hierarchy process model of a particle swarm optimization algorithm; establishing an evaluation model of the influence degree of the track noise of the adjacent-track residential area based on the weight, the subjective evaluation information and the objective evaluation information; and drawing a spatial distribution diagram of the influence degree of the track noise of the residential area of the adjacent track. The method improves the accuracy and reliability of the rail noise evaluation.
Description
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 an adjacent-rail residential area.
Background
Along with the development demand and production demand, the urban scale is continuously enlarged, the disadvantages caused by urban expansion are gradually revealed, from air pollution to water pollution and then to noise pollution, and the byproducts from the high-speed urban development can interfere people living in cities without holes. One of the main sources of noise pollution is traffic noise, which is a noise source with long duration and close distance to people, and interferes with many aspects of people's hearing, sleep, daily life and work, for example, after entering a sleep state, 40-45db (a) noise can make people's brain emit wake waves, and the sudden 60db (a) noise can cause 72% of people to be awakened, and the occurrence probability of deafness lesion is obviously increased after long-term exposure to 80db (a) noise.
In order to solve the congestion of a city and accelerate the development of the city, the track construction becomes an important measure of urban traffic, under the condition that more traffic noise is not increased, the track construction in the central area of the city selects straddle type monorail traffic more, so that part of the surrounding area of the straddle type monorail is a residential area, although the noise of the straddle type monorail is small, the part of the residential area is interfered by the running noise of the straddle type monorail train.
Aiming at the evaluation of the orbital noise, partial scholars provide a propagation rule under the generation mechanism and the fixed test condition of the orbital noise, the research on the characteristics of the orbital noise is guided by the test requirements, and the research on the influence and the invasion of the noise on human bodies mainly focuses on the influence of occupational exposure on the physiological health. However, because the actual running condition of the train is different under different rail road conditions and different specific conditions of residential areas, the existing rail noise evaluation is not researched by taking the residential area environment and layout characteristics affected by the rail noise as a guide, and attention on the human mind level is lacked, so that the accuracy and reliability of the existing rail noise evaluation method are poor.
Disclosure of Invention
The invention aims to provide a method for testing and evaluating the influence degree of track noise in an adjacent-rail residential area, so as to solve the problem that the conventional method for evaluating the track noise is poor in accuracy and reliability.
The method for testing and evaluating the influence degree of the track noise of the adjacent-rail residential area in the scheme comprises the following steps:
and 5, drawing a spatial distribution diagram of the track noise influence degree of the adjacent track residential area.
The beneficial effect of this scheme is:
the method comprises the steps of carrying out subjective evaluation and objective evaluation on the rail noise of the adjacent rail residential area, and establishing an evaluation model of the rail noise by combining the subjective evaluation and the objective evaluation so as to carry out noise influence evaluation from the actual objective aspect and the subjective aspect, thereby improving the accuracy and the reliability of the noise evaluation.
Further, in step 1, the non-traffic peak time during the working day is selected as the measurement period of the noise data.
The beneficial effects are that: the measurement period of the noise data can reduce the interference of the road traffic noise.
Further, when noise data of an adjacent rail residential area are collected, measuring horizontal direction noise and vertical direction noise as noise data, wherein the horizontal direction noise is collected from a near building measuring point and a far building measuring point, and the vertical direction noise is collected from a preset distance from a window and the ground; when measuring the noise data of the track noise sound source position, collecting the noise data of preset times for each measuring point at the preset position, and taking the range value and the average value of the noise data of the preset times.
The beneficial effects are that: and measuring the noise data of the noise source position for multiple times, and averaging to improve the data accuracy of the noise source position.
Further, the step 3 further comprises the following substeps:
substep 3.1, constructing a hierarchical structure of an evaluation model by using an analytic hierarchy process;
substep 3.2, constructing a judgment matrix of the problem to be evaluated by using an analytic hierarchy process;
substep 3.3, constructing an objective function to be optimized;
and substep 3.4, solving the weight and the evaluation index of the evaluation model based on a particle swarm optimization algorithm.
The beneficial effects are that: the weights are solved by combining a layer analysis method and a particle swarm optimization algorithm, so that the accuracy and reliability of subsequent comprehensive evaluation based on subjective evaluation information and objective evaluation information are improved.
Further, in the substep 3.1, the problem to be evaluated is divided into a target layer, a criterion layer and an index layer according to a logical relationship, the target layer is marked as a layer a, the criterion layer is marked as a layer B, the index layer is marked as a layer C, the number of the factors in the layer a is 1, and the number of the factors in the layer B is n b The number of factors in the C layer is n c ；
In the substep 3.2, judgment matrixes are respectively established for the layer B and the layer C, and judgment of the importance degree of the factors in the judgment matrixes is carried out by taking the above-layer factors as the standard, so that the following results are obtained:
the B layer judgment matrix is
The C layer judgment matrix is
The beneficial effects are that: by performing hierarchical analysis on the problems to be evaluated, the consistency of the judgment matrix can be improved.
Further, in the substep 3.3, the target function construction methods of the B layer and the C layer are the same, and B layer factors are assumed
Has a weight of w k (k＝1～n b ) When is coming into contact with
When, A k It appears to be completely identical, namely:
converting into the optimal solution to obtain the consistency index function
Expressed as:
the constraint conditions are as follows:
the beneficial effects are that: and taking the function for solving the optimal solution as an objective function, so that the subsequent weight solution is more accurate.
Further, in the sub-step 3.4, a particle swarm optimization algorithm is defined: m 20, N10, C 1 ＝C 2 And 2, calculating the weight, and constructing an evaluation index for evaluating the influence degree of the track noise on the adjacent track residential area according to the hierarchical relationship of the target layer, the standard layer and the index layer。
The beneficial effects are that: the consistency of the judgment matrixes of all layers is improved, and the reliability of the obtained weight result is high.
Further, in 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, where the preset formula is:
wherein eta is vj The proportion of low-frequency noise at the jth measuring point position of the v measured cell in the whole noise, E L The sum of the energies (W/m) of the low frequency noise at the measuring point position 2 )，E T The sum of the energy (W/m) of the noise in the frequency range of 20Hz-20kHz at the measuring point position 2 )，P L Low frequency noise sound pressure (Pa), P for the measured point position T The total noise sound pressure (Pa), L in the frequency range of 20Hz-20kHz for the measuring point position L Is the sum of the low frequency noise sound pressure levels (dB), L T Is the sum (dB), L of the sound pressure level of the noise in the frequency range of 20Hz-20kHz at the measuring point position k Sound pressure level (dB) of the kth 1/3 octave center frequency in the low frequency range;
and calculating the sound pressure level index as follows:
and calculating an evaluation index as follows:
wherein, mu vj Is the sound pressure level index, L, of the jth measuring point position of the v cell Track For measuring equivalent continuous sound pressure level (dB), L of the rail train during a time period On the upper part For measuring equivalent continuous sound pressure level (dB), L, above the orbit of strong noise source in the time period Lower part For measuring the equivalent under the strong track of the noise source in the time periodContinuous sound pressure level (dB), a vi Is a subjective evaluation index of the ith scene orbit noise influence degree of the vth cell, A vi And the subjective score of the ith scene orbit noise influence degree of the vth cell is obtained.
The beneficial effects are that: the influence of the track noise on the adjacent track cell and resident households in the cell is multi-angle, and parameter indexes obtained from multiple angles are converted into dimensionless relative quantities, so that the accurate and comprehensive evaluation is conveniently conducted in an evaluation model.
Further, in step 4, the evaluation model of the degree of influence of the track noise on the adjacent track residential area is represented as:
wherein, I RTN Is track noise influence degree index representing the severity of track noise influence, O is track noise objective evaluation index, S is track noise subjective evaluation index, wo is the weight of the objective evaluation index, ws is the weight of the subjective evaluation index, and x oi For each objective evaluation index quantification, w oi Weight, x, for each objective evaluation index si For each subjective evaluation index quantification value, w si And the weight of each subjective evaluation index is occupied.
The beneficial effects are that: an evaluation model is established by combining subjective evaluation and objective evaluation, and the comprehensiveness and accuracy of evaluation are improved.
Further, in the step 5, a track noise map of a plurality of adjacent track residential areas is drawn through the ArcGIS.
The beneficial effects are that: the rail noise is visualized and more visual.
Drawings
FIG. 1 is a block diagram of a flow chart of an embodiment of a method for testing and evaluating the influence degree of track noise in an adjacent-rail residential area according to the present invention;
FIG. 2 is a block diagram of a process for solving weights based on a PSO algorithm in an embodiment of the method of the present invention;
FIG. 3 is a graph of the horizontal distance between a measuring point and a sound source and the equivalent continuous A sound level in the embodiment of the method of the invention;
FIG. 4 is a graph of the distance between a measuring point of a cell B and a sound source and an equivalent continuous sound level A in the embodiment of the method of the present invention;
FIG. 5 is a graph of the horizontal distance from the C cell measuring point to the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;
FIG. 6 is a graph of the horizontal distance from the measuring point of the D cell to the sound source and the equivalent continuous A sound level in the embodiment of the method of the present invention;
FIG. 7 is a graph 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 graph of the orbit noise spectrum of cell B, floor 4 in accordance with an embodiment of the present invention;
fig. 9 is a graph of the noise spectrum of the floor track of cell C, floor 2 in the embodiment of the method of the present invention;
fig. 10 is a graph of the orbit noise spectrum of the floor 1 of cell D in the embodiment of the method of the present invention;
FIG. 11 is a graph of the noise spectrum of the E-cell floor 4 orbit in the embodiment of the method of the present invention;
FIG. 12 is a track noise map of cell A in an embodiment of the method of the present invention;
FIG. 13 is a graph of orbital noise maps of cell B in an embodiment of the method of the present invention;
FIG. 14 is a track noise map of cell C in an embodiment of the method of the present invention;
FIG. 15 is a track noise map of cell D in an embodiment of the method of the present invention;
fig. 16 is a track noise map of cell E in an embodiment of the method of the present invention.
Detailed Description
The following is a more detailed description of the present invention by way of specific embodiments.
Examples
As shown in fig. 1, the method for testing and evaluating the influence degree of track noise in an adjacent-rail residential area comprises the following steps:
The actual measurement scheme in the residential area is as follows: when the noise in the horizontal direction is measured, measuring points near the building are arranged at the position 1m away from the building and 1.5m away from the ground; distant building stations (more than 3.5m away from the building) are arranged 1.5m above the ground. When the noise in the vertical direction is measured, the measuring points are arranged at the position 1.5m away from the window and 1.5m higher than the ground, and in the actual measurement, the noise measuring points in the vertical direction are arranged by adopting an interlayer measuring method. The actual measurement scheme of the strong part of the train noise source is as follows: two measuring points are arranged at a horizontal distance of 7.5m from the central line of the rail, 1.5m above the top surface of the rail and 1.5m below the top surface of the rail, 10 times of measurement is carried out at each measuring point, and a range value and an average value are taken. And measuring the background noise when no rail train passes at each measuring point for 10 times, and taking an average value. Taking the test scene of 'when the railless train passes' as the measurement scene of the background noise of the test, when the railless train passes, measuring the background noise at each measuring point for 30s, measuring 10 groups of background noise data at each measuring point, and averaging.
Taking the third line of track in Chongqing as an example, the track laying mode at the source strength test point of the track line is in an overhead form, the track running speed is 72km/h, no large-volume shelter is arranged within 30m around the test point, the curvature radius and the gradient of the track at the test point both meet the standard requirements, and the source strength test can be carried out. Five typical cells on a track line are selected according to high survival rate, mature development and certain scale for actual measurement, and the time required for 6 marshalling trains to pass a measuring point at constant speed in a single time is approximately 8s through multiple measurements in a pre-test. When the train speed is within the range of 20-40km/h, selecting 12s as the time length for obtaining the equivalent continuous sound level value of the track noise; when the train speed is within the range of 40-60km/h, selecting 10s as the time length for obtaining the equivalent continuous sound level value of the track noise; when the train speed is within 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 to obtain the result shown in the table 1.
TABLE 1 high noise Source of the orbit
The five cells are respectively expressed as a cell A, a cell B, a cell C, a cell D and a cell E. The length of the cell A in the direction parallel to the track is about 150m, the length of the cell A in the direction perpendicular to the track is about 125m, 27 measuring points (D8-D41) are arranged on the same straight line in the direction perpendicular to the track line of the cell A, the distance between the 27 measuring points and the central line of the track is 30-155m, the position information and noise data of the measuring points D8-D41 are shown in Table 2, and the horizontal distance between the measuring points of the cell A and the sound source and the equivalent continuous A sound level are shown in FIG. 3.
Table 2A cell D8-D41 measuring point position information and noise actual measurement result
As can be seen from table 2 and fig. 3, as the horizontal distance between the measurement point in the cell a and the center line of the track increases, the equivalent continuous a sound level of the track noise at the measurement point tends to decrease.
The total length of the B cell in the parallel direction with the track is about 160m, the total length of the B cell in the vertical direction with the No. 3 line is about 72m, 13 measuring points (A1-A13) are arranged on the same straight line in the vertical direction with the track line, the distance between the 13 measuring points and the central line of the track is 30-102m, the position information and noise data of the measuring points A1-A13 of the B cell are shown in the table 3, and the horizontal distance between the measuring points of the B cell and the sound source and the equivalent continuous A sound level are shown in the graph 4.
Table 3B cell A1-A13 measuring point position information and noise actual measurement result
As can be seen from table 3 and fig. 4, as the horizontal distance between the measurement point in the B cell and the track center line increases, the track noise equivalent continuous a sound level at the measurement point decreases.
The total length of the C cell and the track in the parallel direction is about 300m, the total length of the C cell and the track in the vertical direction is about 40m, 9 measuring points (B1-B9) are arranged on the same straight line in the vertical direction of the track line, the distances from the 9 measuring points to the track center line are respectively 25m, 30m, 35m, 40m, 45m, 50m, 55m, 60m and 65m, the position information and the noise data information of the C cell measuring points B1-B9 are shown in the table 4, and the horizontal distance from the C cell measuring points to the sound source and the equivalent continuous A sound level are shown in the graph 5. As can be seen from table 4 and fig. 5, as the horizontal distance between the measurement point in the C cell and the center line of the track increases, the equivalent continuous a sound level of the track noise at the measurement point tends to decrease.
TABLE 4C cell B1-B9 survey point location information and noise measurement results
The distance between the D cell and the track in the parallel direction is short, the total length is about 70m, the distance between the D cell and the track in the vertical direction is long, the total length is about 105m, 22 measuring points (C1-C22) are selected and arranged on the same straight line in 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 the noise data of the measuring points C1-C22 are shown in the table 5, and the horizontal distance between the measuring point of the D cell and the sound source and the equivalent continuous A sound level are shown in the table 6.
Table 5D cell C1-C22 measuring point position information and noise actual measurement result
As can be seen from Table 5 and FIG. 6, as the horizontal distance between the measuring point in the D cell and the center line of the track increases, the equivalent continuous A sound level of the track noise at the measuring point shows a descending trend.
The total length of the E cell in the parallel direction with the track is about 257m, the total length of the E cell in the vertical direction with the No. 3 line is about 60m, 7 measuring points (E1-E7) are arranged on the same straight line in the vertical direction with the track line, the distance between the 7 measuring points and the central line of the track is 85-115m, the position information and noise data of the measuring points E1-E7 are shown in the table 6, and the horizontal distance between the measuring point of the E cell and the sound source and the equivalent continuous A sound level are shown in the figure 7.
Table 6E cell E1-E7 measuring point position information and noise actual measurement result
As can be seen from table 6 and fig. 7, as the horizontal distance between the measuring point in the E cell and the track center line increases, the track noise equivalent continuous a sound level at the measuring point decreases.
Selecting 4 representative buildings, a B cell, a C cell, a D cell and an E cell from the tested cells, for example, the building with the most adjacent rail characteristic, and exploring the vertical distribution rule and the frequency spectrum characteristic of the rail noise.
And measuring points are arranged on 1-12 layers of interlayer of floor 4 in the district B, and the horizontal distances from the measuring points to the center line of the track are 35 m. The station position information and the measured data are shown in table 7, and the spectrogram is shown in fig. 8.
TABLE 7B measured data of track noise of 4 th floor in residential quarter
As can be seen from FIG. 9, the track noise energy of each floor is concentrated in the frequency band of 500Hz-2500Hz, and reaches a peak at 1250Hz, and forms a small peak at 630Hz which is slightly lower than the peak frequency at 1250 Hz. The attenuation degree of the middle and high frequency band noise is larger than that of the low frequency band noise under the influence of vertical height.
Measuring points are arranged on the 1-28 floor layers of the No. 2 building in the C cell, the horizontal distance between each measuring point and the center line of the track is 35m, the vertical distribution condition of the track noise is obtained and is shown in the table 8, and the frequency spectrum diagram is shown in the figure 9.
TABLE 8C measured data of track noise of each floor of No. 2 building in residential district
As can be seen from fig. 9, the equivalent continuous a sound level at the measuring point shows a trend of first decreasing, then increasing and then decreasing as the floor increases; the vertical height difference change between the measuring point and the rail top surface shows the trend of decreasing in the range of-5.8 m to 5.6m, increasing in the range of 5.6m to 22.5m and decreasing in the range of 22.5m to 67.8m on the whole. The sound level A of the 2-layer measuring points is higher than that of the 4-layer measuring points, and the sound level A of the 4-layer measuring points is higher than that of the 6-layer measuring points, because the 2-layer measuring points are positioned below the rail surface, the 4-layer measuring points are approximately as high as the rail surface, and the 6-layer measuring points are positioned on the rail surface and have little difference with the 2-layer measuring points in the absolute value of the height difference with the rail top surface. The sound level a peaks at 12 levels, and the vertical height difference between the measuring point and the rail surface at 12 levels is 22.5 m. The positions below 12 layers are influenced by a sound shadow area formed by a street tree and the like, and the noise is weakened by road absorption, road reflection and the like, so that the sound level A does not reach a peak value although the positions below 12 layers are closer to a track; the positions above 12 floors are not affected by sound shadow areas, road absorption and reflection and the like any more, and the distance between the positions and a sound source is increased along with the increase of floors, so that the noise level is reduced.
And measuring points are arranged on 1-18 layers of interlayer of the district No. 1 building, and the horizontal distance between each measuring point and the center line of the track is 40 m. The position information of the vertically arranged measuring points and the track noise data are shown in table 9, and the spectrogram is shown in fig. 10.
TABLE 9D measured data of track noise of each floor of No. 1 building in residential district
The frequency peak value of each floor appears at 1250Hz, and a small peak value slightly lower than 1250Hz appears at 630Hz, and the track noise energy of each floor is mainly distributed in the range of 500Hz-2500 Hz. The attenuation degree of the middle and high frequency band noise influenced by the vertical height is larger than that of the low frequency band noise.
And measuring points are arranged on 1-24 layers of interlayer of the No. 4 building of the residential quarter, and the horizontal distance between each measuring point and the central line of the track is 85 m. Table 10 shows the position information and measured data of the points distributed in the vertical direction of the E-cell, and the spectrogram is shown in fig. 11.
TABLE 10E measured data of track noise of 4 th floor in residential district
As can be seen from FIG. 11, the noise energy of each floor track is mostly distributed in the frequency band from 630Hz to 2000Hz, the peak frequency is 1250Hz, and a small peak slightly lower than 1250Hz appears at 630 Hz.
And 2, collecting subjective evaluation information on the track noise influence of the test points, and performing correlation analysis and cross analysis on the subjective evaluation information, wherein the subjective evaluation information comprises multi-dimensional information of basic information, living information, work and rest information, acoustic environment satisfaction, individual characteristics and noise influence level measurement, and is obtained in a questionnaire mode, and the subjective evaluation information is listed on the questionnaire.
The basic information comprises gender, age, academic calendar and occupation; the living information comprises living communities, whether the living communities face the rail, floors and window types, and whether the living rails face the rail by the fact that the windows of the bedroom and the living room face the rail without a shelter in the middle; the acoustic environment satisfaction degree comprises the overall acoustic environment satisfaction degree of the cell and the type of the most serious noise to the life interference; the work and rest information comprises the time of getting up, the time of falling asleep, the stay time of the cell in the orbital operation period of the working day and the stay time of the cell in the orbital operation period of the weekend; individual characteristics include the degree of suffering from insomnia problems, the degree of suffering from stress or anxiety problems in work, study and life, the degree of sensitivity to noise; the noise impact level quantity includes subjective evaluation of the impact level of the orbit noise under different scenes.
The interviewees for which the questionnaires are directed include: the sex proportion is uniformly distributed; the age distribution range comprises a plurality of age groups of teenagers (below 20 years), young people (20-30 years), strong people (31-40 years), middle-aged people (41-50 years), middle-aged and aged people (51-60 years) and old people (above 60 years); the range of the floors comprises a low floor, a middle and low floor, a middle and high floor and a high floor; the window type includes a small amount of installed soundproof glass and a large amount of installed general glass.
Questions designed in the questionnaire include: the degree of suffering from the problem of insomnia, the degree of feeling stress or anxiety at work, study or life, the degree of interest in quality of life, and the degree of sensitivity to noise.
Subjective assessment information is described using the Likter scale, which describes a fact in the question of a questionnaire, and the interviewee needs to select their acceptance of the fact in the options, which are typically divided into 5 levels, each level corresponding to a score of 1-5 points.
The Liktet scale, designed to understand the degree of impact of orbital noise on the interviewees in different scenarios, was tested for confidence by cloning the Bach's Alpha. The value range of Cronbach's Alpha is 0-1, and the closer to 1, the higher the reliability of the represented questionnaire results. When the value range of Cronbach's Alpha is 0.7-0.8, the reliability of the questionnaire result is general; when the numeric area of Cronbach's Alpha is 0.8-0.9, the reliability of the questionnaire result is high; when the value range of Cronbach's Alpha is 0.9-1, the reliability of the questionnaire results is very high, and the results are shown in Table 2.
TABLE 2 analysis of the reliability of the degree of influence of track noise in different scenarios
In the correlation analysis, the Spearman coefficient is used as a test coefficient to search the intrinsic connection and influence relationship among the basic information, living information, work and rest information, individual characteristics and the subjective influence degree of the track noise of the interviewee in the questionnaire. The significance level is set herein to 0.05 (significance less than 0.05 is considered to be a significant difference between test subjects).
The cross-analyses were analyzed using a Kruskal-Wallis one-way variance test and a Mann-Whitney U test. The Kruskal-Wallis single-factor variance test belongs to the field of nonparametric test, and can test whether a plurality of independent groups (3 or more groups) have obvious difference on a certain continuous variable; the Mann-Whitney U test also belongs to the non-parametric test category and is used for testing that groups are less than 3 and that data in the groups do not satisfy the problems of normal distribution and homogeneity of variance.
substep 3.1, constructing a hierarchical structure of an evaluation model by using an analytic hierarchy process, dividing the problem to be evaluated into a target layer, a standard layer and an index layer according to a logical relation, wherein the problem to be evaluated is a problem in the subjective evaluation process, the target layer is marked as an A layer, the standard layer is marked as a B layer, the index layer is marked as a C layer, the factor number in the A layer is 1, and the factor number in the B layer is n b The number of factors in the C layer is n c 。
And substep 3.2, constructing a judgment matrix of the problem to be evaluated by using an analytic hierarchy process, wherein the judgment matrix P is as follows:
wherein, a ij Is the degree of importance (a) of index i relative to index j ij >0; when i is j, a ij ＝1；a ij *a ji ＝1)。
The weight calculation formula based on the judgment matrix is as follows:
wherein, W i In order to calculate the resulting weight of the index,
to determine the n-th root, a, of the product of the elements of the ith row of the matrix ij To judge the ith row and j column elements in the matrix.
Respectively establishing judgment matrixes aiming at the layer B and the layer C, and judging the importance degree of the factors in the matrixes by taking the above layer of factors as a standard to obtain:
the B layer judgment matrix is
C layer judgment matrix of
Substep 3.3, constructing the objective function to be optimized, wherein the construction methods of the objective functions of the B layer and the C layer are the same, and the B layer factor is assumed
Has a weight of w k (k＝1～n b ) When is coming into contact with
When, A k It appears to be completely identical, namely:
converting into the optimal solution to obtain the consistency index function
Expressed as the consistency indicator function, the objective function:
the constraint conditions are as follows:
substep 3.4, solving the weight and the evaluation index of the evaluation model based on a particle swarm optimization algorithm, wherein the particle swarm optimization algorithm has the basic principle that: there are m particles in an N-dimensional space, the particles being denoted by i. Each particle has its own position vector and velocity neighbor, the position vector being x i ＝(x i1 ,x i2 ,...,x in ) Velocity is adjacent to v i ＝(v i1 ,v i2 ,...,v in ) The position vector and the velocity vector are both n-dimensional. The process of finding the optimal solution by the particles is an iterative process, p i The optimal position found in the process of finding the optimal solution by the particles is provided. The position of the particles up to when the iteration is best measured by the fitness function f (x).
Wherein, c 1 ,c 2 As learning factor (taking values in non-negative constants), r 1 ,r 2 Is a random number between 0 and 1, i ═ 1, 2.., m; n is 1,2,. cndot.n; v. of in ∈(-v max ,v max )，v max Is a constant.
Defining a particle swarm optimization algorithm: m 20, N10, C 1 ＝C 2 The weight is found based on the PSO algorithm at 2, and the specific procedure is as shown in fig. 2, generating the particle initial solution: random numbers of (0,1) are generated in the solution space and normalized. And substituting the feasible solution into the objective function, calculating the initial particle fitness and selecting the optimal particles. Updating and iterating the particles, judging whether constraint conditions are met, if not, returning to the generation of an initial particle solution, and if so, calculating the adaptation of the updated particlesSelecting the optimal positions of the particles and the global optimal position; and judging whether a termination condition is met, if not, updating and iterating the particles, and if so, outputting an optimal solution. And substituting the optimal solution into the objective function to obtain a consistency index value. And judging whether the consistency requirement is met, if not, returning to the generation of the particle initial solution, if so, outputting the global optimal position, the corresponding weight and the consistency index, and constructing an evaluation index for evaluating the influence degree of the track noise on the adjacent track residential area according to the hierarchical relation of the target layer, the criterion layer and the index layer.
calculating the low-frequency index of the low-frequency noise contribution rate in the objective evaluation information in the step 1 according to a preset formula, wherein the preset formula is as follows:
wherein eta vj The proportion of low-frequency noise at the jth measuring point position of the v measured cell in the whole noise, E L The sum of the energies (W/m) of the low frequency noise at the measuring point position 2 )，E T The sum of the energy (W/m) of the noise in the frequency range of 20Hz-20kHz at the measuring point position 2 )，P L Low frequency noise sound pressure (Pa), P for the measured point position T The total noise sound pressure (Pa), L in the frequency range of 20Hz-20kHz for the measuring point position L Is the sum (dB) of the low frequency noise sound pressure level at the measurement point location, L T Is the sum (dB), L of the noise sound pressure level in the frequency range of 20Hz-20kHz at the measuring point position k Sound pressure level (dB) of the kth 1/3 octave center frequency in the low frequency range;
calculating the sound pressure level index as follows:
and calculating an evaluation index as follows:
wherein, mu vj Is the sound pressure level index, L, of the jth measuring point position of the v cell Track For measuring equivalent continuous sound pressure level (dB), L of the rail train during a time period On the upper part For measuring equivalent continuous sound pressure level (dB), L, above the orbit of strong noise source in the time period Lower part Is the equivalent continuous sound pressure level (dB), a, below the orbit where the noise source is strong in the measurement period vi Is a subjective evaluation index of the ith scene orbit noise influence degree of the vth cell, A vi And the subjective score of the ith scene orbit noise influence degree of the vth cell is obtained.
The evaluation model of the influence degree of the track noise on the adjacent track residential area is expressed as follows:
wherein, I RTN Is track noise influence degree index representing the severity of track noise influence, O is track noise objective evaluation index, S is track noise subjective evaluation index, wo is the weight of the objective evaluation index, ws is the weight of the subjective evaluation index, and x oi For each objective evaluation index quantification, w oi Weight, x, for each objective evaluation index si For each subjective evaluation index quantification value, w si And the weight of each subjective evaluation index is occupied.
And step 5, drawing a spatial distribution diagram of the influence degree of the track noise of the adjacent track residential areas according to the evaluation model in the step 4, and drawing a track noise map of a plurality of adjacent track residential areas through ArcGIS to obtain the track noise map shown in the figures 12, 13, 14, 15 and 16.
As can be seen from fig. 12, the distance between the cell a and the station is relatively close, the pollution condition of the cell a caused by the track noise is general, the maximum track noise sound level value in the cell is 69.7dB, the minimum track noise sound level value in the cell a is 50.1dB, because the train has an obvious acceleration behavior at the cell a and is influenced by the train speed, the track noise sound level of the floor 1 in the area i is obviously higher than that of the floor 3; in the area III, the level of the track noise level of the floor 2 is slightly higher than that of the floor 4, and the level difference of the track noise levels at measuring points in the two floors is small by combining the measured data; within zone iv, there is substantially no difference in the level of floor 2 and floor 4 orbital noise.
As can be seen from fig. 13, the B cell is located between the two stations, the speed of the train passing through does not change significantly, the B cell is seriously polluted by the track noise, the maximum sound level of the track noise reaches 71.1dB, the minimum sound level of the track noise reaches 54.7dB, and the sound level of the track noise is reduced as the distance increases. The floor heights of the buildings 1-4 are the same, and the track noise level of the 4 buildings is approximately similar. However, in the area II, because the volume of the floor 2 and the floor 3 is slightly smaller than that of the floor 1 and the floor 4, compared with the floor 1 and the floor 4, the volume of the shelters between the measuring points of the floor 2 and the floor 3 and the sound source at the same horizontal position is smaller, so that the track noise level of the floor 2 and the floor 3 in the area II is slightly higher than that of the floor 1 and the floor 4. In the area I and the area II, when the track center lines are at the same horizontal distance, the track noise average sound level value of the measuring points in the building is larger than that of the ground measuring points; and in the area III, when the track center line is the same horizontal distance, the difference between the track noise average sound level value of the measuring point in the building and the ground is not large. This is because as the sheltered volume of the building increases, the sound level of the track noise of the measuring point in the building is weakened. And in the area IV, when the distance from the center line of the track is the same horizontal distance, the track noise average sound level value of the measuring point in the building is smaller than that of the measuring point on the ground, and the attenuation effect on the noise is more obvious along with the continuous increase of the volume of the shelter in the building. And at the ground measuring point in the area IV, the track noise level of the measuring point with the building shelter is obviously lower than that of the measuring point without the shelter, and the building shelter is proved to have obvious blocking effect on sound transmission.
As can be seen from fig. 14, the C cell is severely polluted by the track noise, and the maximum sound level value reaches 74.4dB, and the minimum sound level value reaches 60.4 dB. It is noted that the lowest orbit noise of the cell exceeds the requirement that the functional area of class 1 acoustic environment in the acoustic environment quality standard is lower than 55 dB. The track noise level in the cell shows a descending trend along with the increase of the distance between the measuring point and the track central line on the whole, the speed of the train does not change obviously when the train passes through the cell, the horizontal distance between the No. 1-5 buildings in the cell and the track central line has little difference, and the track noise level and the attenuation rule of the measuring point in the No. 1-5 buildings are approximately the same. In the area IV, the horizontal distances from the center line of the track are the same, and the noise level of the building shielding position at the track end far away from the No. 1-5 building is obviously lower than that of the non-shielding position on the same straight line. Comparing the sound level of the track noise at the measuring points with different horizontal distances from the central line of the track in the same building of the No. 1-5 buildings can find that the sound level of the track noise of the house facing the track is obviously higher than that of the house facing the track in the same building, and the attenuation speed of the sound level of the track noise at the measuring points in the building is much higher than that of the measuring points on the ground without shielding at the same distance. The obstruction capability of building shelters to noise propagation is much stronger than air absorption, ground absorption, geometric divergence and the like. In the area I, the measured data is combined, and when the horizontal distances from the central line of the track are the same, the track noise average sound level of a measuring point in the building is higher than that of a measuring point in the cell; in the areas II and III, when the horizontal distances from the central line of the track are the same, the average sound level of track noise at a measuring point in the building is lower than that at a measuring point in the district, and the attenuation effect of the noise is more obvious when the shielding of the building is verified again.
As can be seen from fig. 15, the pollution of the cell D by the track noise is serious, and the maximum sound level value of the track noise reaches 74.3dB, and the minimum sound level value reaches 53.5 dB. The noise level of the track noise at the measuring points closer to the track line is higher, and the pollution condition of the track noise is relieved along with the increase of the distance. The two buildings which are most seriously polluted by the track noise in the residential area are the No. 1 building and the No. 2 building which are closest to the track line, and the track noise sound level values of more than 70 percent of measuring points in the two buildings are above 66 dB; the pollution of the No. 3 building and the No. 4 building by track noise is light, and the noise is inseparable from the noise blocking effect of the No. 1 building and the No. 2 building. In the area I, when the horizontal distances from the central line of the track are the same, the track noise average sound level of the measuring points in the No. 1 building and the No. 2 building is slightly higher than the ground measuring points; in the area II, when the horizontal distances from the central line of the track are the same, the difference between the track noise average sound level levels of the measuring points in the No. 1 building and the No. 2 building and the ground measuring points is not large; in the region III and the region IV, when the horizontal distances from the central line of the track are the same, the average sound level of track noise at the measuring points in the No. 3 building and the No. 4 building is lower than that at the measuring points on the ground. In the area IV, when the horizontal distances from the central line of the track are the same, the level of the track noise sound level of the measuring point shielded by the building is obviously lower than that of the measuring point shielded by no building.
As can be seen from fig. 16, the pollution of the E cell by the track noise is light, the maximum sound level value of the track noise in the E cell is 61.6dB, and the minimum sound level value is 51.1 dB. When the rail train passes through the E cell, no obvious speed change action exists, and No. 1-4 buildings in the cell are arranged in a straight line along a rail line. Because the distance between the 4 th floor and the track line is less than the 1-3 th floor, the track noise level of the 4 th floor is slightly higher than that of the 1-3 th floor. In the area I, when the distances from the center line of the track are the same, the track noise sound level mean value of a measuring point in a No. 4 building is larger than that of a ground measuring point; in the area II, when the distances from the center line of the track are the same, the track noise level mean value of the measuring points in the No. 4 building is smaller than that of the ground measuring points, and the track noise level mean value of the measuring points in the No. 2-3 building is approximately the same as that of the ground measuring points; in the area III, the average value of the track noise sound level of the measuring points in the No. 1-3 buildings is slightly smaller than the ground measuring points. In the region IV, the attenuation effect of building shielding on the sound level of track noise is obvious.
In the track building period and the subsequent operation period, the extension route of the track is strictly planned and designed, certain protection is carried out on noise in the track operation process, or a track type with low noise, such as a monorail, is built aiming at a place where a residential area is possible. However, some areas may be built with residential areas along the track where there are no residential areas due to urban development, so that the residential areas of the newly built areas may still be affected by noise. Therefore, to this residential area that receives noise interference a small amount to and do not build near noise protection equipment single track's residential area, under the prerequisite of the restriction understanding such as single track small in noise, the noise influence of the regional residential area of neighbouring single track is neglected, can think of through installing noise insulation additional, so, generally can not go to study its condition that receives the influence of track noise.
In this embodiment, noise generated by the running of a rail train affects residents in a cell established along a track line when the rail train passes through, the influence of rail noise on adjacent rail cells and resident residents in the cell is multi-angle, the frequency characteristic and the objective sound level value of the rail noise obtained through actual tests can only reflect the level of the rail noise of each cell from the angle of a numerical value, and the residents with different individual characteristics and different resident characteristics sense the rail noise differently, which cannot be described by an objective numerical value of the rail noise. The size of track noise influence degree is objective numerical value and subjective perception combined action's result, and this embodiment is based on combining objective evaluation and subjective evaluation to facing rail residential block track noise influence degree and appraising, can evaluate the influence degree of track noise to facing rail residential block more scientifically comprehensively to improve to the noise condition.
The above description is only an example of the present invention, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. The method for testing and evaluating the influence degree of the track noise of the adjacent-track residential area is characterized by comprising the following steps of:
step 1, selecting a plurality of residential areas in an adjacent area of a track as test points, and collecting noise data as objective evaluation information at the track noise sound source position and the adjacent track residential area at each test point;
step 2, collecting subjective evaluation information of the test point orbit noise influence, wherein the subjective evaluation information comprises basic information, residence information, work and rest information, acoustic environment satisfaction, individual characteristics and multi-dimensional information of influence measure, and performing correlation analysis and cross analysis on the subjective evaluation information;
step 3, determining the weight and the evaluation index of the evaluation index based on an analytic hierarchy process model of the particle swarm optimization algorithm;
step 4, establishing an evaluation model of the influence degree of the track noise of the adjacent-track residential area based on the weight, the subjective evaluation information and the objective evaluation information;
and step 5, drawing a spatial distribution map of the track noise influence degree of the adjacent track residential area.
2. The method according to claim 1, wherein the method comprises the following steps: in step 1, the non-traffic peak time during the working day is selected as the measurement period of the noise data.
3. The method according to claim 2, wherein the method comprises the following steps: when noise data of an adjacent-rail residential area are collected, measuring horizontal direction noise and vertical direction noise as noise data, wherein the horizontal direction noise is collected from a near building measuring point and a far building measuring point, and the vertical direction noise is collected from a preset distance from a window and the ground; when measuring the noise data of the track noise sound source position, collecting the noise data of preset times for each measuring point at the preset position, and taking the range value and the average value of the noise data of the preset times.
4. The method according to claim 3, wherein the method comprises the following steps: the step 3 further comprises the following substeps:
substep 3.1, constructing a hierarchical structure of an evaluation model by using an analytic hierarchy process;
substep 3.2, constructing a judgment matrix of the problem to be evaluated by using an analytic hierarchy process;
substep 3.3, constructing an objective function to be optimized;
and substep 3.4, solving the weight and the evaluation index of the evaluation model based on a particle swarm optimization algorithm.
5. The method according to claim 4, wherein the method comprises the following steps: in substep 3.1, the problem to be evaluated is divided into a target layer, a criterion layer and an index layer according to a logical relationship, the target layer is marked as a layer A, the criterion layer is marked as a layer B, the index layer is marked as a layer C, the factor number in the layer A is 1, and the factor number in the layer B is n b The number of factors in the C layer is n c ；
In the substep 3.2, judgment matrixes are respectively established for the layer B and the layer C, and judgment of the importance degree of the factors in the judgment matrixes is carried out by taking the above-layer factors as the standard, so that the following results are obtained:
b layer decision matrix of
The C layer judgment matrix is
6. The method according to claim 5, wherein the method comprises the following steps: in the substep 3.3, the target function construction methods of the B layer and the C layer are the same, and B layer factors are assumed
Has a weight of w k (k＝1～n b ) When is coming into contact with
When, A k It appears to be completely identical, namely:
converting into the optimal solution to obtain consistencyIndex function
Expressed as:
the constraint conditions are as follows:
7. the method according to claim 5, wherein the method comprises the following steps: in the substep 3.4, a particle swarm optimization algorithm is defined: m 20, N10, C 1 ＝C 2 And 2, calculating the weight, and constructing an evaluation index for evaluating the influence degree of the track noise on the adjacent track residential area according to the hierarchical relationship of the target layer, the criterion layer and the index layer.
8. The method for testing and evaluating the influence degree of track noise in an adjacent-rail 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 the step 1 is calculated according to a preset formula, wherein the preset formula is as follows:
wherein eta is vj The proportion of low-frequency noise at the jth measuring point position of the v measured cell in the whole noise, E L The sum of the energies (W/m) of the low frequency noise at the measuring point position 2 )，E T The sum of the energy (W/m) of the noise in the frequency range of 20Hz-20kHz at the measuring point position 2 )，P L Low frequency noise sound pressure (Pa), P for the measured point position T For measuring point position with frequency of 20Hz-20kHzTotal noise sound pressure (Pa), L in the range L Is the sum (dB) of the low frequency noise sound pressure level at the measurement point location, L T Is the sum (dB), L of the sound pressure level of the noise in the frequency range of 20Hz-20kHz at the measuring point position k Sound pressure level (dB) of the kth 1/3 octave center frequency in the low frequency range;
and calculating the sound pressure level index as follows:
and calculating an evaluation index as follows:
wherein, mu vj Is the sound pressure level index, L, of the jth measuring point position of the v cell Track For measuring the equivalent continuous sound pressure level (dB), L of the rail train during a time interval Upper part of For measuring equivalent continuous sound pressure level (dB), L, above the orbit of strong noise source in the time period Lower part Is the equivalent continuous sound pressure level (dB), a, below the orbit where the noise source is strong in the measurement period vi Is a subjective evaluation index of the ith scene orbit noise influence degree of the vth cell, A vi And the subjective score of the ith scene orbit noise influence degree of the vth cell is obtained.
9. The method according to claim 5, wherein the method comprises the following steps: in the step 4, the evaluation model of the degree of influence of the track noise on the adjacent track residential area is represented as follows:
wherein, I RTN Is an index of the degree of influence of the track noise, which indicates the degree of severity of the influence of the track noise, wherein O is an objective evaluation index of the track noise, S is a subjective evaluation index of the track noise, and wo isThe objective evaluation index is the weight, ws is the weight of the subjective evaluation index, x oi For each objective evaluation index quantification, w oi Weight, x, for each objective evaluation index si For each subjective evaluation index quantification value, w si And the weight of each subjective evaluation index is occupied.
10. The method according to claim 5, wherein the method comprises the following steps: in the step 5, a track noise map of a plurality of adjacent track residential areas is drawn through ArcGIS.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210803398.7A CN115099516A (en) | 2022-07-07 | 2022-07-07 | Method for testing and evaluating influence degree of track noise of adjacent-track residential area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210803398.7A CN115099516A (en) | 2022-07-07 | 2022-07-07 | Method for testing and evaluating influence degree of track noise of adjacent-track residential area |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115099516A true CN115099516A (en) | 2022-09-23 |
Family
ID=83297869
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210803398.7A Pending CN115099516A (en) | 2022-07-07 | 2022-07-07 | Method for testing and evaluating influence degree of track noise of adjacent-track residential area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115099516A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703452A (en) * | 2023-06-21 | 2023-09-05 | 江苏精加至信医疗科技有限公司 | Customer management method and system based on intelligent marketing |
-
2022
- 2022-07-07 CN CN202210803398.7A patent/CN115099516A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703452A (en) * | 2023-06-21 | 2023-09-05 | 江苏精加至信医疗科技有限公司 | Customer management method and system based on intelligent marketing |
| CN116703452B (en) * | 2023-06-21 | 2024-03-12 | 江苏精加至信医疗科技有限公司 | Customer management method and system based on intelligent marketing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220405655A1 (en) | Contribution identification method for noise at boundary of urban substation | |
| Kim et al. | Evaluation of the Weather Research and Forecast/urban model over Greater Paris | |
| Zou et al. | Field measurement of the urban pedestrian level wind turbulence | |
| Watts et al. | Identifying tranquil environments and quantifying impacts | |
| Cao et al. | A case study of gust factor of a strong typhoon | |
| CN115099516A (en) | Method for testing and evaluating influence degree of track noise of adjacent-track residential area | |
| Souri et al. | Wind-driven rain on buildings: Accuracy of the ISO semi-empirical model | |
| Yu et al. | Urban exposure upstream fetch and its influence on the formulation of wind load provisions | |
| Jain et al. | Performance based design extreme wind loads on a tall building | |
| Kragh et al. | The Nord2000 prediction method for road traffic noise–outline and validation, and application in environmental noise mapping | |
| Zhen et al. | Combined effects of thermal and acoustic environments on outdoor human comfort in university campus | |
| KR102297183B1 (en) | Heatwave prediction system using learning based on cluster analysis by land cover | |
| Pan | Damage prediction of low-rise buildings under hurricane winds | |
| Kim | Urban form, wind, comfort, and sustainability: the San Francisco experience | |
| Li et al. | Duration of extreme synoptic wind speeds for North America in a changing climate and its engineering implementation | |
| Li et al. | The impact of twisted wind on pedestrian comfort around two non-identical-height buildings in tandem arrangement: a wind tunnel study | |
| Kumar et al. | Quantifying the influence of transmission path characteristics on urban railway noise | |
| He et al. | Observation of downburst wind characteristics using the Doppler profiler and near-ground measurements | |
| Yadav et al. | Analysis of spatial and temporal variation of noise level at intersections of a mid-sized City in India | |
| CN118863122A (en) | Village sound environment evaluation method based on multi-source traffic noise and noise annoyance degree | |
| Kerry et al. | Considering uncertainty when performing environmental noise measurements | |
| Haipeng | Research and Application of Lightning Disaster Risk Assessment of Gas Station | |
| Handayani et al. | The Noise Level in Residential Areas Bordering Jagorawi Highway | |
| Lawrence et al. | Measurement of traffic noise shielding provided by buildings | |
| Souri | Effectiveness of roof overhang on mid-rise buildings: field measurements and improved assessment based on ISO standard |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |