Cold region tunnel thermal insulation layer thickness calculation method based on region actual measurement data

Abstract

The invention relates to the technical field of cold region tunnel anti-freezing, in particular to a cold region tunnel thermal insulation layer thickness calculation method based on region actual measurement data, which comprises the following steps: S1, selecting at least one section of cold region tunnel as a test tunnel, and taking a cold region where the test tunnel is located as a research region; s2, setting a plurality of monitoring points at intervals in the test tunnel hole along the longitudinal direction, and monitoring and calculating the annual average temperature and the annual temperature amplitude of all the monitoring points; s3, calculating the laying thickness of the thermal insulation layer of each monitoring point through a numerical calculation method; s4, performing multiple regression analysis on the thermal insulation layer thicknesses, the annual average temperatures and the annual temperature amplitudes of all the monitoring points to obtain a fitting function of the thermal insulation layer thicknesses of all the tunnels in the research area about the annual average temperatures and the annual temperature amplitudes; and S5, substituting the air temperature parameters in the portal of the proposed tunnel or the existing tunnel in the research area into the fitting function, and calculating to obtain the thickness of the thermal insulation layer of the proposed tunnel or the existing tunnel. The thickness of the thermal insulation layer of the tunnel in the cold region of the research area can be calculated more accurately.

Description

Technical Field

[0001] The invention relates to the technical field of cold region tunnel antifreeze, and in particular to a method for calculating the thickness of a cold region tunnel insulation layer based on regional measured data. Background Art

[0002] Cold region tunnels refer to tunnels built in cold regions, which usually have special geological conditions such as low temperature and frozen soil. With the rapid development of China's infrastructure, the number of cold region tunnels is increasing, and the problem of tunnel freezing damage is becoming more and more obvious. If the anti-freezing measures of the tunnel are not in place, in serious cases, it is easy to have ice hanging on the tunnel vault, ice on the road surface, cracking of the lining, freezing of the drainage system and other freezing damage phenomena, which will pose a huge threat to the safe operation of the line and the safety of the tunnel structure.

[0003] Setting up an insulation layer is one of the effective means to prevent freezing damage in cold-region tunnels. By setting up an insulation layer in areas of the tunnel that are susceptible to freezing and thawing, the heat exchange between the lining and surrounding rock and the cold air in the tunnel can be effectively weakened, and frequent freezing and thawing of the surrounding rock and drainage system behind the lining can be avoided, thereby achieving the purpose of preventing freezing damage.

[0004] At present, the main calculation methods for the thickness of the insulation layer of tunnels in cold regions are numerical calculation method, analytical method and engineering analogy method. The numerical calculation method can determine the thickness of the insulation layer, but the calculation is relatively complex and the assumptions used are more restrictive. The numerical calculation method is an effective method to determine the thickness of the insulation layer. When the thermophysical parameters and boundary condition parameters in the calculation model are reliable, the results are relatively accurate. However, if the thermophysical parameters and boundary condition parameters based on them are greatly different from the actual situation, the insulation layer thickness obtained will not be accurate enough. In addition, there is also an analytical method, but the calculation is relatively complex and the assumptions used are more restrictive. At the same time, the insulation layer thickness obtained by experience and engineering analogy is still not scientific and reasonable enough.

[0005] Therefore, there is an urgent need for a calculation method that can more accurately obtain the thickness of the insulation layer of planned or existing tunnels in cold regions. Summary of the invention

[0006] The purpose of the present invention is to provide a method for calculating the thickness of the thermal insulation layer of a tunnel in a cold region based on regional measured data, aiming to improve the problem in the prior art that it is inconvenient to accurately obtain the thickness of the thermal insulation layer of a planned or existing tunnel in a cold region.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for calculating the thickness of a tunnel insulation layer in a cold region based on regional measured data comprises the following steps:

[0009] S1. Select at least one cold region tunnel as a test tunnel, and take the cold region where the test tunnel is located as a research area;

[0010] S2, setting a plurality of monitoring points at longitudinal intervals in the test tunnel, and obtaining the annual average temperature and annual temperature amplitude data at each of the monitoring points through monitoring and calculation;

[0011] S3, calculating and obtaining the laying thickness data of the thermal insulation layer at each of the monitoring points by numerical calculation method;

[0012] S4. Perform multiple regression analysis on the insulation thickness, annual average temperature and annual temperature amplitude data of all monitoring points to obtain the fitting function of the insulation thickness of all tunnels in the study area with respect to the annual average temperature and annual temperature amplitude;

[0013] S5. Based on the annual average temperature and annual temperature amplitude data of the tunnel site area of ​​the planned or existing tunnel in the study area, the thickness of the insulation layer of the planned or existing tunnel in the study area is calculated using the fitting function.

[0014] Furthermore, in step S2, each monitoring point is provided with a temperature sensor capable of automatically storing temperature data, each temperature sensor monitors temperature data multiple times at even intervals every day, and the monitoring time of all temperature sensors is at least one full year.

[0015] Furthermore, in step S2, the daily average temperature of all monitoring points in a year is calculated, and the sine function or cosine function of the daily average temperature of all monitoring points in a year with respect to time is fitted, and then the annual average temperature data and annual temperature amplitude data of all monitoring points are calculated.

[0016] Furthermore, in step S3, the formation parameters, tunnel burial depth, geothermal gradient and hydrological parameters of the test tunnel are measured or consulted, a temperature field calculation model for all monitoring points is constructed, and the temperature monitoring data of all monitoring points are combined to obtain the insulation layer thickness data at each monitoring point through numerical calculation method.

[0017] Furthermore, in step S4, the multivariate regression analysis includes linear fitting analysis, exponential fitting analysis and big data fitting analysis, and the function obtained by the fitting method with the highest fitting degree is selected as the fitting function of the insulation layer thickness with respect to the annual average temperature and the annual temperature amplitude.

[0018] Furthermore, when performing multivariate linear regression analysis on the insulation layer thickness, annual average temperature and annual temperature amplitude data of all monitoring points, the fitting function is in the form of: H=Ax1+Bx2+C, wherein H is the thickness of the tunnel insulation layer, x1 is the annual average temperature, x2 is the annual temperature amplitude, and A, B, and C are all constants of the fitting function.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. In the present invention, based on the temperature characteristics of existing cold-zone tunnels in the study area, a numerical calculation model is established, and based on multiple linear regression, a fitting function relationship between the thickness of the thermal insulation layer and the annual average temperature and the annual temperature amplitude in the regional range is obtained. To a certain extent, the fitting function performs fitting correction on the relationship between the thermal insulation layer thickness data and related parameters obtained in the numerical calculation method, which can reduce the calculation error caused by unexpected related parameter errors. Based on the meteorological parameters in the planned tunnel or existing tunnel in the study area (i.e., the annual average temperature and annual temperature amplitude data), the value of the thermal insulation layer laying thickness can be obtained more accurately. Compared with the numerical calculation method, the traditional experience and engineering analogy method, the thermal insulation layer thickness obtained by this scheme is more reasonable and scientific; and the more the number of test tunnels selected and the more monitoring points set, the more scientific and reasonable the calculation results obtained;

[0021] 2. In the present invention, the annual average temperature and annual temperature amplitude of the tunnel site are taken into consideration, thus avoiding the traditional method of relying only on the average temperature of the coldest month while ignoring the influence of the annual average temperature. At the same time, based on the functional relationship between the thickness of the insulation layer and the annual average temperature and the annual temperature amplitude, and according to the longitudinal temperature distribution characteristics of the proposed cold-region tunnel, zone insulation can be carried out, and the thickness of the insulation layer in different sections of the tunnel can be calculated to avoid waste caused by excessive thickness of the insulation layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0023] Figure 1 It is a flowchart of calculating the thickness of the insulation layer of a tunnel in a cold region in the present invention;

[0024] Figure 2 It is a schematic diagram of the two-dimensional temperature field numerical model constructed for the test tunnel in the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] A method for calculating the thickness of a tunnel insulation layer in a cold region based on regional measured data comprises the following steps:

[0027] S1. Select at least one section of an existing cold-region tunnel as a test tunnel, and take the cold region where the test tunnel is located as the study area; the study area is a cold region with a higher altitude or higher latitude. The study area can be a province with lower temperatures in winter, or a plateau area with a higher altitude. Tunnels in the study area are called cold-region tunnels because of the geological conditions of low temperature and frozen soil. More than half of my country's land belongs to the cold region, which mainly includes permafrost areas and seasonal frozen soil areas, accounting for 20% and 55% of the country's land area respectively;

[0028] S2. Conduct continuous longitudinal temperature testing in the test tunnel within the study area, set up multiple monitoring points along the longitudinal intervals of the test tunnel, and monitor and calculate the annual average temperature and annual temperature amplitude data at each monitoring point;

[0029] The annual average temperature refers to the arithmetic mean of the daily average temperatures of all days throughout the year. In fact, in addition to using the arithmetic mean of the daily average temperatures of all days throughout the year to calculate the annual average temperature, you can also add up the average temperatures of all months in a year and divide it by 12. This is a very simple calculation, and the calculation result has a small error.

[0030] Among them, the annual temperature amplitude refers to the difference between the highest and lowest monthly average temperatures in a year, also known as the annual temperature difference or annual temperature amplitude; its size is related to factors such as latitude, land and sea distribution, topography and weather; the higher the latitude, the larger the annual temperature amplitude, because the higher the latitude, the greater the annual change in the altitude of the sun at noon and the annual change in the length of day and night, resulting in a larger annual temperature difference; the farther away from the sea, the greater the annual temperature amplitude, the land has a smaller heat capacity than the ocean, it warms up faster in summer and cools down faster in winter, so the annual temperature amplitude in inland areas is larger than that in coastal areas; the temperature amplitudes of highlands, depressions and valleys are different, and the higher the altitude, the larger the annual temperature amplitude The smaller the temperature, for example, the Qinghai-Tibet Plateau has a low summer temperature and a low winter temperature due to its high altitude, so the annual temperature amplitude is smaller; the annual temperature amplitude in areas with more sunny days is greater than that in areas with more rainy days; for example, the annual temperature amplitude in Xinjiang is larger than that in coastal areas at the same latitude; the annual temperature amplitude in areas with high vegetation coverage is smaller than that in bare land; the annual temperature amplitude is one of the important signs for distinguishing the climate type of each region. For example, areas with large daily and annual temperature differences belong to continental climate, while areas with small daily and annual temperature differences belong to marine climate. By measuring and analyzing the annual temperature amplitude, we can better understand climate change and regional climate characteristics;

[0031] S3. The theoretically minimum required thickness of the thermal insulation layer at each monitoring point is obtained by numerical calculation. The numerical calculation method is a commonly used method for calculating the thickness of the thermal insulation layer of an existing tunnel. When the thermophysical parameters and boundary condition parameters in the calculation model are reliable, the obtained results are relatively accurate.

[0032] S4. Collect relevant data on the thickness of the thermal insulation layer, the annual average temperature and the annual temperature amplitude at all monitoring points, perform multivariate linear regression analysis on the data of the thickness of the thermal insulation layer, the annual average temperature and the annual temperature amplitude at all monitoring points, and obtain the fitting function of the thickness of the thermal insulation layer of all tunnels in the study area with respect to the annual average temperature and the annual temperature amplitude;

[0033] S5. Based on the annual average temperature data and annual temperature amplitude data at the entrance of the proposed tunnel or in the existing tunnel in the study area, the annual average temperature data and annual temperature amplitude data of the proposed tunnel or the existing tunnel are substituted into the above fitting function to calculate the thickness of the insulation layer of the proposed tunnel.

[0034] In the present invention, based on the temperature characteristics of existing cold-zone tunnels in the study area, a numerical calculation model is established, and based on multiple linear regression, a fitting function relationship between the thickness of the insulation layer and the annual average temperature and the annual temperature amplitude in the regional range is obtained. To a certain extent, the fitting function fits and corrects the relationship between the insulation layer thickness data and related parameters obtained in the numerical calculation method, which can reduce the calculation error caused by unexpected related parameter errors. Based on the meteorological parameters of the proposed tunnel entrance in the study area (that is, the annual average temperature and annual temperature amplitude data of the proposed tunnel entrance), the insulation layer laying thickness value can be obtained more accurately. Compared with the numerical calculation method, the traditional reliance on experience and engineering analogy method, the insulation layer thickness obtained by this scheme is more reasonable and scientific.

[0035] In the present invention, the annual average temperature and annual temperature amplitude of the tunnel site are taken into consideration, avoiding the traditional method of relying only on the average temperature of the coldest month and ignoring the influence of the annual average temperature. At the same time, based on the functional relationship between the thickness of the insulation layer and the annual average temperature and the annual temperature amplitude, according to the longitudinal temperature distribution characteristics of the proposed cold zone tunnel, zone insulation can be carried out, and the thickness of the insulation layer of the tunnel in different sections can be calculated to avoid waste caused by excessive thickness of the insulation layer.

[0036] Specifically, in step S2, a temperature sensor that can automatically store temperature data is set at each monitoring point inside the test tunnel. Each temperature sensor monitors the temperature data of the corresponding monitoring point once every interval. The monitoring time of all temperature sensors is at least one full year, and the monitoring frequency of each temperature sensor is 1 time / 2h. By monitoring several temperature data of each monitoring point every day through the temperature sensor, it is convenient to calculate the daily average temperature value of each detection point every day, and then calculate the annual average temperature value of each monitoring point. Among them, the calculation methods of the daily average temperature are mainly the following:

[0037] 1. Four-time average method: This method involves four specific time points in a day, namely 2 a.m., 8 a.m., 2 p.m., and 8 p.m. Take the temperature values ​​at these four time points, add them up and divide by 4. The average value is the daily average temperature;

[0038] 2. Daily maximum and minimum temperature average method: This method is relatively simple. You only need to add the maximum and minimum temperatures of the day and then divide by 2. The average value is the daily average temperature.

[0039] 3. 24-hour average method: This method requires adding up all the temperature values ​​in 24 hours of a day, and then dividing by 24. The average value obtained is the daily average temperature.

[0040] Furthermore, in step S2, a sine function or cosine function related to temperature and time is used to fit the sine function or cosine function of the daily average temperature of all monitoring points in a year with respect to time, thereby obtaining the annual average temperature data and annual temperature amplitude data of all monitoring points. Among them, based on the above sine function or cosine function, the annual average temperature can be calculated using the integration method. Compared with the annual average temperature obtained by the arithmetic mean, the integral average of the trigonometric function is more accurate, which is convenient for obtaining more accurate annual average temperature data; the annual temperature amplitude data can be obtained by subtracting the minimum value from the maximum value of the above sine function or cosine function. This scheme takes into account the macroscopic influence of the annual average temperature data and the annual temperature amplitude data on the surrounding rock of the tunnel in a complete annual cycle. The annual average temperature data and the annual temperature amplitude data are themselves comprehensive expression data of higher temperature and lower temperature. The two comprehensively reflect the temperature conditions of different monitoring points in the test tunnel in the study area in a complete year.

[0041] Furthermore, in step S3, the formation parameters, tunnel burial depth, geothermal gradient and hydrological parameters of the test tunnel are measured or consulted, and a temperature field calculation model for all monitoring points is constructed. In combination with the temperature monitoring data of all monitoring points, the insulation thickness data at each monitoring point is calculated by numerical calculation. When the thermophysical parameters and boundary condition parameters in the calculation model are relatively reliable, the insulation thickness data required by the theory calculated is also relatively reliable. In this embodiment, by monitoring the air temperature data of multiple monitoring points and calculating the insulation thickness of multiple monitoring points at the same time, the individual obvious errors generated in the numerical calculation of the insulation thickness can be corrected to a certain extent by multiple regression analysis, which is conducive to reducing the errors generated by directly using numerical calculation methods.

[0042] Further, in step S4, the multiple regression analysis includes linear fitting analysis, exponential fitting analysis and big data fitting analysis, and the function obtained by the fitting method with the highest degree of fitting is selected as the fitting function of the thickness of the insulation layer with respect to the annual average temperature and the annual temperature amplitude. When the data is less, the insulation layer thickness, annual average temperature and annual temperature amplitude data of all monitoring points are subjected to multiple linear regression analysis, and the form of the fitting function is: H=Ax1+Bx2+C, wherein H is the thickness of the tunnel insulation layer, x1 is the annual average temperature, x2 is the annual temperature amplitude, and A, B, and C are all constants of the fitting function. In regression analysis, if there are two or more independent variables, it is called multiple regression. In fact, a phenomenon is often related to multiple factors. It is more effective and more practical to predict or estimate the dependent variable by the optimal combination of multiple independent variables than to predict or estimate with only one independent variable. Therefore, multiple linear regression has greater practical significance than univariate linear regression. The basic principles and basic calculation process of multiple linear regression are the same as those of univariate linear regression, but due to the large number of independent variables, the calculation is more complicated, so statistical software is generally required in practical applications.

[0043] When there is a lot of data, exponential fitting analysis can also be used; when the amount of data is very abundant, big data fitting analysis can also be used.

[0044] Example 1

[0045] In this embodiment, taking the tunnels in the extremely cold area in Jilin Province (40°50′~46°19′N, 121°38′~131°19′E) as an example, this area is used as the study area of ​​this embodiment, and a number of existing operating tunnels in the study area are selected for testing, and the annual temperature data at different positions in the tunnel are obtained. After trigonometric function fitting, the annual average temperature and annual temperature amplitude at different longitudinal positions in each tunnel are obtained.

[0046] Measure or consult the burial depth and stratum thermal physical parameters at different locations of the target test tunnel, and construct a two-dimensional temperature field numerical model, such as Figure 1 shown.

[0047] The thickness of the insulation layer at different locations of the test tunnel in the study area was calculated by numerical calculation method. The insulation layer was laid externally, and the relevant data of the thickness of the insulation layer at different locations in the study area and the annual average temperature and annual temperature amplitude at the calculation location were summarized and calculated, and a multivariate linear regression analysis was performed on the thickness of the insulation layer, the annual average temperature and the annual temperature amplitude.

[0048] Table 1 Calculated values ​​of insulation layer thickness under different working conditions in the study area

[0049]

[0050] By fitting the data in Table 1, the fitting function formula of the thickness of the tunnel insulation layer in the study area (40°50′~46°19′N, 121°38′~131°19′E) under the annual average temperature and annual temperature amplitude is obtained:

[0051] H=-0.0840x1+0.6323x2-5.7224,

[0052] Wherein, H is the thickness of the insulation layer, in cm, rounded to the nearest integer;

[0053] x1 is the annual average temperature of the proposed or existing tunnel entrance / ℃, 5.51℃≤x1≤8.92℃;

[0054] x2 is the annual temperature amplitude of the air temperature at the entrance of the planned or existing tunnel / ℃; 10.28℃≤x2≤17.32℃.

[0055] Based on the annual average temperature and annual temperature amplitude parameters of the tunnel site area of ​​the proposed or existing tunnel portal obtained by monitoring, the above fitting function formula can be used to design the thickness of the insulation layer of the proposed or existing tunnel in the study area (40°50′~46°19′N, 121°38′~131°19′E).

[0056] Example 2

[0057] In this embodiment, taking the cold zone tunnels in the western Sichuan Plateau (97°21′~106°3′ east longitude, 27°10′~34°19′ north latitude) as an example, the area of ​​97°21′~106°3′ east longitude, 27°10′~34°19′ north latitude is taken as the study area of ​​this embodiment, and the G317 and G318 high-altitude tunnels along the study area are selected to carry out in-tunnel temperature tests, and the annual average temperature and annual temperature amplitude of different tunnel entrance areas are obtained. By constructing the corresponding numerical calculation model, the laying thickness of the insulation layer under different working conditions is obtained.

[0058] Table 2 Calculated values ​​of insulation layer thickness under different working conditions in the study area

[0059]

[0060] By fitting the data in Table 2, the fitting function formula of the thickness of the tunnel insulation layer in the study area (97°21′~106°3′, 27°10′~34°19′N) under the annual average temperature and annual temperature amplitude is obtained as follows:

[0061] H=-1.4675x1+0.2647x2+4.9672,

[0062] Wherein, H is the insulation layer thickness, in cm, rounded to the nearest integer;

[0063] x1 is the annual average temperature at the cave entrance / ℃, -0.77℃≤x1≤4.12℃;

[0064] x2 is the annual temperature amplitude of the cave entrance air temperature / ℃, 5.87℃≤x2≤9.15℃.

[0065] Based on the annual average temperature and annual temperature amplitude parameters of the proposed tunnel portal, the above fitting function formula is used to design the thickness of the insulation layer of the proposed and existing tunnels in the study area (97°21′~106°3′, 27°10′~34°19′N).

[0066] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating the thickness of the insulation layer of a cold region tunnel based on regional measured data, characterized in that: The following steps are involved: S1. Select at least one cold region tunnel as a test tunnel, and take the cold region where the test tunnel is located as a research area; S2, setting a plurality of monitoring points at longitudinal intervals in the test tunnel, and obtaining the annual average temperature and annual temperature amplitude data at each of the monitoring points through monitoring and calculation; S3, calculating and obtaining the laying thickness data of the thermal insulation layer at each of the monitoring points by numerical calculation method; S4. Perform multiple regression analysis on the insulation thickness, annual average temperature and annual temperature amplitude data of all monitoring points to obtain the fitting function of the insulation thickness of all tunnels in the study area with respect to the annual average temperature and annual temperature amplitude; S5. Based on the annual average temperature and annual temperature amplitude data at the entrance of the proposed tunnel or inside the existing tunnel in the study area, the thickness of the insulation layer of the proposed or existing tunnel in the study area is calculated using the fitting function.

2. The method for calculating the thickness of the insulation layer of a cold region tunnel based on regional measured data according to claim 1 is characterized in that: In step S2, each monitoring point is provided with a temperature sensor that can automatically store temperature data, each temperature sensor monitors temperature data multiple times at even intervals every day, and the monitoring time of all temperature sensors is at least one full year.

3. The method for calculating the thickness of the insulation layer of a cold region tunnel based on regional measured data according to claim 2 is characterized in that: In step S2, the daily average temperature of all monitoring points in a year is calculated, and the sine function or cosine function of the daily average temperature of all monitoring points in a year with respect to time is fitted, and then the annual average temperature data and annual temperature amplitude data of all monitoring points are calculated.

4. The method for calculating the thickness of the insulation layer of a cold region tunnel based on regional measured data according to claim 1 is characterized in that: In step S3, the formation parameters, tunnel burial depth, geothermal gradient and hydrological parameters of the test tunnel are measured or consulted, and a temperature field calculation model for all monitoring points is constructed. Combined with the temperature monitoring data of all monitoring points, the insulation layer thickness data at each monitoring point is calculated by numerical calculation method.

5. The method for calculating the thickness of the insulation layer of a cold region tunnel based on regional measured data according to claim 1 is characterized in that: In step S4, the multivariate regression analysis includes linear fitting analysis, exponential fitting analysis and big data fitting analysis, and the function obtained by the fitting method with the highest fitting degree is selected as the fitting function of the insulation layer thickness with respect to the annual average temperature and the annual temperature amplitude.

6. The method for calculating the thickness of the insulation layer of a cold region tunnel based on regional measured data according to claim 5 is characterized in that: When performing multivariate linear regression analysis on the insulation layer thickness, annual average temperature and annual temperature amplitude data of all monitoring points, the fitting function is in the form of: H=Ax1+Bx2+C, where H is the thickness of the tunnel insulation layer, x1 is the annual average temperature, x2 is the annual temperature amplitude, and A, B, and C are all constants of the fitting function.

Images