CN119063693A - A method for monitoring and early warning of surface subsidence at tunnel entrances - Google Patents
A method for monitoring and early warning of surface subsidence at tunnel entrances Download PDFInfo
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Abstract
The invention provides a tunnel portal earth surface subsidence monitoring and safety early warning method which is suitable for the field of tunnel construction monitoring and comprises the steps of arrangement of monitoring points, data acquisition and transmission, data processing and analysis and safety early warning.
Description
Technical Field
The invention relates to a tunnel portal earth surface subsidence monitoring and safety early warning method, which is suitable for the field of tunnel construction monitoring.
Background
Tunnel engineering is used as an important infrastructure construction, and has important significance for promoting regional economic development and improving traffic conditions. However, in the tunnel construction process, due to complex and changeable geological conditions and high construction technical requirements, the stratum traversed by the tunnel may have adverse factors such as weakness, breakage, water content and the like, so that the tunnel construction faces a plurality of safety risks, and the problem of surface subsidence of tunnel openings is particularly remarkable. The ground surface subsidence of the tunnel portal not only affects the stability and the service life of the tunnel structure, but also can seriously affect surrounding buildings, roads and environments, and even causes safety accidents. Therefore, the method effectively monitors and pre-warns the subsidence of the ground surface of the tunnel portal, and plays an important role in ensuring construction safety and preventing accidents.
In the process of measuring the settlement of a tunnel portal, the current common practice in the industry is to arrange a plurality of measuring lines above the tunnel portal and perpendicular to the axis direction of the tunnel, each measuring line is provided with a monitoring point at a certain distance, the purpose of monitoring the settlement at the top of the tunnel portal is achieved by measuring the settlement data of the monitoring point, meanwhile, with the continuous development and popularization of computer technology, the industry personnel also begin to adopt some algorithms to build a model, analyze the obtained monitoring data, and obtain a predicted value according to the analysis rule so as to achieve the purpose of pre-warning.
However, at present, practitioners neglect the special characteristics of the tunnel portal subsidence monitoring data, the tunnel portal subsidence monitoring data always presents high-frequency changes, particularly when construction activities such as tunnel portal area excavation and support are frequent, the tunnel portal subsidence monitoring data can obviously generate local subsidence, because the tunnel portal area belongs to a stress concentration area and is very easy to generate abnormal local subsidence, aiming at the characteristics of the tunnel portal subsidence monitoring data, a method for monitoring and safety pre-warning of the surface subsidence of the tunnel portal is needed, and aiming at the characteristics of the high-frequency changes and the local subsidence, the targeted analysis is carried out.
Disclosure of Invention
The invention aims to solve the problem that a tunnel vault settlement analysis method aiming at the characteristics of high-frequency change and local settlement is needed at present, and provides a tunnel portal earth surface settlement monitoring and safety early warning method,
The aim of the invention can be achieved by adopting the following technical scheme:
The tunnel portal earth surface subsidence monitoring and safety early warning method is characterized by comprising the following steps of:
S101, monitoring the arrangement of the points;
The arrangement of the monitoring points comprises arranging ground surface subsidence monitoring points of the tunnel portal according to the topography and geological conditions of the tunnel portal, wherein the ground surface subsidence monitoring points are divided into a plurality of measuring lines which are arranged perpendicular to the axis direction of the tunnel, and the number of the ground surface subsidence monitoring points is n and is expressed as Di, i=1, 2.
S102, data acquisition and transmission;
the data acquisition and transmission are carried out according to the earth surface subsidence monitoring points, measurement work is carried out according to a certain time interval, corresponding earth surface subsidence monitoring data are obtained, and the monitoring data are transmitted to a data processing center in a wired or wireless mode;
S103, data processing and analysis;
The data processing and analysis comprises the steps of carrying out a data partition modeling analysis method on the earth surface subsidence monitoring data to obtain prediction data of earth surface subsidence monitoring;
The data processing and analyzing method further comprises the steps of:
a) Obtaining a minimum horizontal distance Hi and a minimum vertical distance Bi of each ground surface subsidence monitoring point Di from the central line of the tunnel, wherein i=1, 2.
B) Dividing the earth surface monitoring points into two types, namely I type and II type, wherein the classification index is that when Hi corresponding to an earth surface subsidence monitoring point I is larger than 2.5 times of the design width of a tunnel section or the vertical distance Bi corresponding to the earth surface subsidence monitoring point I is larger than 2.5 times of the design height of the tunnel section, the monitoring point is II type, otherwise, the monitoring point is I type, k total earth surface subsidence monitoring points of I type are finally obtained, the k total monitoring points are expressed as D 1 x, x=1, 2, & gt, k total obtained monitoring data sets are expressed as C 1 x, l total obtained earth surface subsidence monitoring points of II type are simultaneously obtained as D 2 y, y=1, 2, & gt, l total obtained monitoring data sets are expressed as C 2 y, and k+l=n;
c) Obtaining 1 earth surface subsidence monitoring point location i to be analyzed,
D) When the classification result of the earth surface subsidence monitoring point position I is I type, constructing a prediction model based on a long-short-period memory neural network to obtain a predicted value y, continuously using the long-short-period memory neural network to construct the prediction model to obtain the predicted value x times, calculating the mean value and standard deviation of the x predicted values, calculating 90% confidence interval according to the calculation result of the mean value and standard deviation and assuming that the calculation result is subjected to normal distribution, finally obtaining 90% confidence interval of the predicted value,
E) When the classification result of the earth surface subsidence monitoring point position i is II type, adopting linear regression to construct a prediction model to obtain a prediction value,
F) Repeating the steps c-e until all the monitoring points are analyzed, finally obtaining 95% confidence intervals based on the predicted values of all the class I surface subsidence monitoring points, and finally obtaining the predicted values based on all the class II surface subsidence monitoring points;
s104, safety precaution;
The safety early warning comprises the steps of processing and analyzing based on the data, and respectively formulating an early warning standard 1 and an early warning standard 2 for the I class and the II class of the ground surface subsidence monitoring point according to the topography and geology of the tunnel portal and the classification standard;
The safety early warning further comprises the step of sending out the safety early warning when the 95% confidence interval of the predicted value of a certain class I earth surface subsidence monitoring point is displayed to exceed the early warning standard 1, and sending out the safety early warning when the predicted value of a certain class II earth surface subsidence monitoring point is displayed to exceed the early warning standard 2;
further, in the step S103, the prediction model constructed by the long-term and short-term memory neural network adopts a random seed which is not fixed in the weight initialization process;
further, in S103, the value of x is 100-1000;
Further, in the step S103, the 90% confidence interval is obtained by a) calculating the mean value of the x predicted values, b) calculating the standard deviation of the x predicted values, c) determining the standard normal distribution critical value corresponding to the 90% confidence level, d) calculating the confidence interval, wherein the formula is formula (1),
In the formula,And z is a standard normal distribution critical value, and s is a standard deviation.
The beneficial effects of the invention are as follows:
The method has the advantages that all the earth surface subsidence monitoring points are classified into two types according to actual conditions, meanwhile, a data analysis modeling analysis method is adopted, and particularly an uncertain analysis method is introduced to the earth surface subsidence monitoring points of the class I, and whether the earth surface subsidence monitoring points exceed the early warning standard or not is considered by adopting a 90% confidence interval, so that the method has the advantages of high safety and accuracy.
Drawings
FIG. 1 is a flow chart of a tunnel portal earth surface subsidence monitoring and safety pre-warning method;
FIG. 2 is a schematic diagram of a hole surface subsidence monitoring point and a reference point according to an embodiment of the present invention;
FIG. 3 is a vertical plot of predicted value distributions for an embodiment of the present invention.
Detailed Description
The following detailed description of specific embodiments of the invention is provided in connection with the accompanying drawings, and it is to be understood that the specific embodiments presented herein are for purposes of illustration and explanation only and are not to be construed as limiting the invention.
The following is a concrete embodiment of a tunnel portal earth surface subsidence monitoring and safety pre-warning method.
Fig. 1 is a flowchart of a method for monitoring and safety pre-warning ground subsidence at a tunnel portal according to the present invention.
The tunnel portal earth surface subsidence monitoring and safety early warning method is characterized by comprising the following steps of:
S101, monitoring the arrangement of the points;
The arrangement of the monitoring points comprises arranging ground surface subsidence monitoring points of the tunnel portal according to the topography and geological conditions of the tunnel portal, wherein the ground surface subsidence monitoring points are divided into a plurality of measuring lines which are arranged perpendicular to the axis direction of the tunnel, and the number of the ground surface subsidence monitoring points is n and is expressed as D i, i=1, 2.
In this embodiment, the supporting project is a tunnel construction project of an eighth subsection of the eastern iron project, the whole length of the tunnel is 3595m, the entrance mileage is ch000+606, the exit mileage is ch004+201, the line is from 3 permillage of ascending slope to 6 permillage of descending slope of CH002+400 change slope at the entrance end, the maximum burial depth is 335m, the minimum burial depth is 5m, the tunnel is designed to be a ballasted composite lining structure, and the standard line spacing is 4.2m. Auxiliary construction and rescue channels of inclined shafts at the position of the double Wen Dan #1 tunnel design 1 are 261m in length, and the clearance section size of the inclined shafts is 5mm 8m;
in this embodiment, in order to ensure the safety of tunnel portal excavation, 10 monitoring points and 1 reference point are arranged at the tunnel portal, and a layout diagram of a ground surface subsidence measuring point of the tunnel portal is shown in fig. 1, and is marked as D 1～D10;
S102, data acquisition and transmission;
the data acquisition and transmission are carried out according to the earth surface subsidence monitoring points, measurement work is carried out according to a certain time interval, corresponding earth surface subsidence monitoring data are obtained, and the monitoring data are transmitted to a data processing center in a wired or wireless mode;
In this embodiment, the measuring tool is performed according to a fixed time interval at the earth surface subsidence detection point, the requirement of the time interval is shown in table 1 and table 2, in principle, a higher measurement frequency specified in table 1 and table 2 is selected, and meanwhile, the monitoring value obtained by measuring with the total station instrument is transmitted to the data processing center in a wireless mode.
Table 1 monitoring and measuring frequency table determined by distance from excavation working face
| Measuring the distance (m) between the section and the excavation surface | Measuring frequency |
| (0~1)B | 2 Times/d |
| (1~2)B | 1 Time/d |
| (2~5)B | 1 Time/2-3 d |
| >5B | 1 Time/7 d |
Note that B represents the tunnel excavation width
Table 2 monitoring and measuring frequency table determined by displacement speed
| Rate of displacement (mm/d) | Measuring frequency |
| ≥5 | 2 Times/d |
| 1~5 | 1 Time/d |
| 0.5~1 | 1 Time/2-3 d |
| 0.2~0.5 | 1 Time/3 d |
| <0.2 | 1 Time/7 d |
S103, data processing and analysis;
The data processing and analysis comprises the steps of carrying out a data partition modeling analysis method on the earth surface subsidence monitoring data to obtain prediction data of earth surface subsidence monitoring;
The data processing and analyzing method further comprises the steps of:
a) Obtaining a minimum horizontal distance Hi and a minimum vertical distance Bi of each ground surface subsidence monitoring point Di from the central line of the tunnel, wherein i=1, 2.
B) Dividing the earth surface monitoring points into two types, namely I type and II type, wherein the classification index is that when Hi corresponding to an earth surface subsidence monitoring point I is larger than 2.5 times of the design width of a tunnel section or the vertical distance Bi corresponding to the earth surface subsidence monitoring point I is larger than 2.5 times of the design height of the tunnel section, the monitoring point is II type, otherwise, the monitoring point is I type, k total earth surface subsidence monitoring points of I type are finally obtained, the k total monitoring points are expressed as D 1 x, x=1, 2, & gt, k total obtained monitoring data sets are expressed as C 1 x, l total obtained earth surface subsidence monitoring points of II type are simultaneously obtained as D 2 y, y=1, 2, & gt, l total obtained monitoring data sets are expressed as C 2 y, and k+l=n;
c) Obtaining 1 earth surface subsidence monitoring point location i to be analyzed,
D) When the classification result of the earth surface subsidence monitoring point position I is I type, constructing a prediction model based on a long-short-period memory neural network to obtain a predicted value y, continuously using the long-short-period memory neural network to construct the prediction model to obtain the predicted value x times, calculating the mean value and standard deviation of the x predicted values, calculating 90% confidence interval according to the calculation result of the mean value and standard deviation and assuming that the calculation result is subjected to normal distribution, finally obtaining 90% confidence interval of the predicted value,
E) When the classification result of the earth surface subsidence monitoring point position i is II type, adopting linear regression to construct a prediction model to obtain a prediction value,
F) Repeating the steps c-e until all the monitoring points are analyzed, finally obtaining 95% confidence intervals based on the predicted values of all the class I surface subsidence monitoring points, and finally obtaining the predicted values based on all the class II surface subsidence monitoring points;
further, in the step S103, the prediction model constructed by the long-term and short-term memory neural network adopts a random seed which is not fixed in the weight initialization process;
further, in S103, the value of x is 100-1000;
Further, in the step S103, the 90% confidence interval is obtained by a) calculating the mean value of the x predicted values, b) calculating the standard deviation of the x predicted values, c) determining the standard normal distribution critical value corresponding to the 90% confidence level, d) calculating the confidence interval, wherein the formula is formula (1),
In the formula,The mean value is z, the standard normal distribution critical value and s is standard deviation;
In this embodiment, according to the classification method of the settlement monitoring points, the settlement monitoring points D 1～D10 are classified into 2 classes, wherein class I points are D1, D2, D3, D9, D10, and one of 5 points is marked as D 1 x, x=1, 2, and..5 in turn, class II points are D4, D5, D6, D7, and D8 in turn, and are marked as D 2 y, y=1, 2, and..5, and the monitoring points D 1 1 are studied, the D 1 1 is a class I monitoring point, existing monitoring data thereof is obtained, a prediction model is constructed based on a long-short term memory neural network to obtain a prediction value, the prediction model is continuously constructed by using the long-short term memory neural network to obtain a prediction value, 100 prediction values are obtained 100 times in total, the 100 prediction value distribution is as shown in fig. 2, the 100 prediction values are calculated as a mean value and a standard deviation, and a final confidence interval is calculated according to the calculation of the mean value and the standard deviation, and the final confidence interval is calculated as 5390% of the final confidence interval is obtained as 15.042,15.449%.
S104, safety precaution;
The safety early warning comprises the steps of respectively making an early warning standard 1 and an early warning standard 2 for the I class and the II class of the earth surface subsidence monitoring point positions based on the data processing and the analysis and according to the geological condition of the tunnel portal terrain and combining the classification standard, and further comprises the steps of sending out the safety early warning when the 95% confidence interval of the predicted value of the I class earth surface subsidence monitoring point position is displayed to exceed the early warning standard 1, and sending out the safety early warning when the predicted value of the II class earth surface subsidence monitoring point position exceeds the early warning standard 2.
In this embodiment, the monitoring point D 1 1 is a class I monitoring point, and the 90% confidence interval of the predicted value is [15.042,15.449] finally, and on the basis of the predicted value being [15.042,15.449] which is smaller than the early warning standard of the class I monitoring point, the total settlement is not greater than 25mm, and the early warning is not required to be sent.
In the embodiment, the invention discloses a tunnel portal earth surface subsidence monitoring and safety early warning method which comprises the steps of arrangement of monitoring points, data acquisition and transmission, data processing and analysis and safety early warning.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the particular embodiments disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
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