CN113931636B - Tunnel underpass existing operation subway line grouting construction parameter selection method and application thereof - Google Patents
Tunnel underpass existing operation subway line grouting construction parameter selection method and application thereof Download PDFInfo
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- 238000010276 construction Methods 0.000 title claims abstract description 65
- 238000010187 selection method Methods 0.000 title description 5
- 238000006073 displacement reaction Methods 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000009412 basement excavation Methods 0.000 claims abstract description 24
- 230000000704 physical effect Effects 0.000 claims abstract description 7
- 230000035515 penetration Effects 0.000 claims abstract description 3
- 238000003062 neural network model Methods 0.000 claims description 7
- 239000011800 void material Substances 0.000 claims description 3
- 230000007547 defect Effects 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 description 6
- 230000000149 penetrating effect Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000002002 slurry Substances 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 210000003462 vein Anatomy 0.000 description 2
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- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/001—Improving soil or rock, e.g. by freezing; Injections
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D11/00—Lining tunnels, galleries or other underground cavities, e.g. large underground chambers; Linings therefor; Making such linings in situ, e.g. by assembling
- E21D11/04—Lining with building materials
- E21D11/10—Lining with building materials with concrete cast in situ; Shuttering also lost shutterings, e.g. made of blocks, of metal plates or other equipment adapted therefor
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G—PHYSICS
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Abstract
The invention discloses a method for selecting grouting construction parameters of a tunnel underpass existing operation subway line and application thereof, and the method comprises the following steps: s1: collecting physical property parameters of stratum close to the lower penetration section which is excavated on site, grouting construction parameters and stratum displacement change values caused by excavation; s2: utilizing the collected data to perform nonlinear relation fitting on physical property parameters of the excavated stratum close to the underpass section, stratum displacement change values caused by excavation and grouting construction parameters, and constructing a nonlinear model; s3: and inputting stratum physical parameters of the underpass section to be excavated and the vertical displacement change limit value of the existing operation subway line specified by related specifications into a nonlinear model, so as to obtain grouting construction parameters corresponding to the excavated section. The method can be well adapted to obtaining reasonable grouting construction parameters under different stratum conditions through the constructed nonlinear model, prevents overlarge stratum displacement change caused by tunnel excavation, and overcomes the defect that the on-site grouting construction parameters are selected empirically.
Description
Technical Field
The invention relates to the technical field of tunnel construction, in particular to a method for selecting grouting construction parameters of a tunnel underpass existing operation subway line and application thereof.
Background
Along with the rapid development of the economic society in China, the scale and population number of cities are rapidly expanded, and subways are certainly ideal ways for solving the urban traffic jam phenomenon. The total mileage of subways in first-line cities such as Beijing, shanghai, guangzhou, shenzhen and the like is continuously increased, a large-scale subway network is formed, and the line overlapping condition of subway lines is quite common. When the existing operation subway tunnel is penetrated under the tunnel by the mine method, the construction of the existing operation subway tunnel can cause a certain influence on the upper existing operation subway tunnel, and the displacement change of the existing operation subway line is often controlled by carrying out advanced pre-grouting on the tunnel in engineering. However, the complexity and uncertainty of the formation conditions are trapped, so that the selection of grouting parameters is often based on experience, and excessive grouting pressure or grouting amount in the grouting construction process can cause the linear displacement change of the existing operation ground to exceed the standard requirement, and even cause engineering accidents. Therefore, how to obtain reasonable grouting construction parameters to ensure the safe construction of the downward penetrating process is always a difficult problem puzzling the grouting engineering.
At present, some students at home and abroad develop research on grouting construction parameters of a newly built tunnel penetrating through the existing tunnel, and research means are mainly focused on two aspects of indoor and outdoor tests and numerical simulation. A large number of engineering practices show that the pre-grouting reinforcement control of the tunnel face of the newly-built tunnel is influenced by various factors such as grouting pressure, grouting materials, stratum parameters and the like, and the grouting construction parameters, stratum physical parameters and the linear displacement of the existing operation subway are in nonlinear relation. The artificial neural network method has strong nonlinear fitting capability, can fit the relation among a plurality of influencing factors and construct a nonlinear model, and has been widely applied to the field of engineering construction by domestic and foreign scholars in recent years. The invention patent (CN 111119902A) uses the earth surface subsidence value calculated by a numerical simulation method as an input layer, uses the measured earth surface subsidence value and grouting mechanical parameters as an output layer, constructs a nonlinear model, uses the earth surface subsidence value caused by an excavation section as an input value, outputs the earth surface subsidence value and grouting mechanical parameters of the excavation section, carries out numerical simulation on the earth surface subsidence value of the excavation section by using the mechanical parameters of the earth surface subsidence value and the grouting body of the excavation section, and finally repeatedly adjusts grouting construction parameters by predicting the earth surface subsidence value of the excavation section to meet construction requirements.
In the existing method, proper grouting construction parameters are repeatedly regulated by predicting the earth surface subsidence value, the obtained parameters are lagged, and reasonable grouting construction parameters cannot be obtained to guide construction due to the fact that grouting pressure, stratum parameters and other factors are not combined. In addition, the existing method for predicting various parameters through nonlinear function simulation combines some calculated values or simulation values, has certain data uncertainty, has larger error on the output predicted parameter values, and is difficult to be used for guiding engineering practice. Therefore, the development of the simulation method which is simple and reliable in sampling and capable of directly predicting grouting construction parameters has great application value.
Disclosure of Invention
The invention provides a method for selecting grouting construction parameters of an existing operation subway line under a tunnel and application thereof, aiming at solving the technical problems by fitting a nonlinear relation through on-site excavated stratum physical parameters close to the underpass section, grouting construction parameters and stratum displacement change values caused by excavation, constructing a nonlinear relation model, inputting the stratum physical parameters of the underpass section to be excavated and the existing operation subway line displacement limit values specified by related specifications into the nonlinear model, outputting the needed grouting construction parameters corresponding to an excavation section, thereby improving the defect that the on-site grouting construction parameters are selected empirically and guiding engineering practice.
In order to achieve the above purpose, the invention provides a method for selecting grouting construction parameters of a tunnel underpass existing operation subway line, which comprises the following steps:
S1: collecting physical property parameters of stratum close to the lower penetration section which is excavated on site, grouting construction parameters and stratum displacement change values caused by excavation;
S2: utilizing the collected data to perform nonlinear relation fitting on physical property parameters of the excavated stratum close to the underpass section, stratum displacement change values caused by excavation and grouting construction parameters, and constructing a nonlinear model;
S3: and inputting stratum physical parameters of the underpass section to be excavated and the vertical displacement limit value of the existing operation subway line specified by related specifications into a nonlinear model, so as to obtain grouting construction parameters corresponding to the excavated section.
Preferably, the stratum physical parameters in S1 include: natural density, cohesion, internal friction angle, void ratio, elastic modulus.
Preferably, the grouting construction parameters in S1 include: grouting pressure, grouting time and grouting amount.
Preferably, the displacement change value of the stratum caused by the excavation in the step S1 is a maximum value of displacement change during the period from the beginning to the end of the tunnel excavation.
Preferably, the nonlinear model in S2 is one of a BP neural network model, a radial basis function model, and a self-organizing neural network model.
Preferably, the relevant specification in the step S3 is "urban rail transit Structure safety protection technical Specification" (CJJ/T202-2013).
Based on a general inventive concept, the invention also provides application of the grouting construction parameter selection method in tunnel underpass existing operation subway line construction.
The scheme of the invention has the following beneficial effects:
1. By utilizing the strong nonlinear mapping capability of the nonlinear relation, grouting construction parameters required by the underpass section can be accurately reflected through on-site monitoring of stratum physical parameters of the adjacent underpass section and stratum displacement change values caused by excavation, and the method is applied to guiding engineering practice.
2. All the construction parameters obtained by on-site monitoring are adopted for establishing the nonlinear function model, so that the constructed nonlinear function model simulates the grouting construction parameters which are output and has more practical guiding value.
3. The input values adopted for predicting grouting construction parameters are as follows: the stratum physical parameters of the underpass section and the vertical displacement limit value of the existing operation subway line specified in the technical Specification for protecting urban rail transit structure (CJJ/T202-2013) are accurate parameters which can be obtained, and the predicted grouting construction parameters can more accurately reflect the grouting construction parameters required by the underpass section.
Drawings
In order to more clearly illustrate the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, and it will be apparent that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort to those skilled in the art.
Fig. 1 is a flow chart of a method for selecting grouting construction parameters of a tunnel underpass existing operation subway line.
Fig. 2 is a real view of a tunnel face constructed by the grouting parameter selection method in the tunnel underpass subway excavation sections YDK43+754.550 to YDK43+ 763.100.
Fig. 3 shows the change value of the linear displacement of the existing operation ground iron at the upper part after the underpass tunnel is excavated.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The following examples are illustrative of the invention and are not intended to limit the scope of the invention. Modifications and substitutions to methods, procedures, or conditions of the present invention without departing from the spirit and nature of the invention are intended to be within the scope of the present invention.
Example 1
Constructing a nonlinear model with selectable grouting construction parameters
A certain subway engineering section tunnel is constructed by adopting a mining method, an existing subway operation tunnel is penetrated down according to a design plan, and the starting and stopping mileage of a stacked section is YDK43+ 722.000-YDK43+ 763.100, and the starting and stopping mileage of an adjacent excavated section is YDK 722.000-YDK 754.550. In order to ensure the safety of tunnel excavation and existing operation subway lines, advanced pre-grouting is adopted to strengthen the tunnel face. According to the technical Specification for protecting urban rail transit structure safety (CJJ/T202-2013), the displacement change value of an operation subway in the construction process must be effectively controlled, so that the safety construction in the downward penetrating process can be ensured only by selecting reasonable grouting construction parameters. The embodiment selects BP neural network to construct nonlinear model, and the main steps are as follows:
S1: the physical property parameters (natural density, cohesive force, internal friction angle, pore ratio and elastic modulus) of the stratum of 60 groups of adjacent excavated sections (YDK 722.000-YDK 754.550), the displacement change values (shown in table 1) of the corresponding stratum and grouting construction parameters (grouting pressure, grouting time and grouting amount) are randomly collected (shown in table 2).
Table 1 input layer training samples
Table 2 output layer training samples
S2: taking stratum physical parameters and stratum displacement change values caused by excavation in the collected 60 groups of data as input layers and grouting construction parameters as output layers to construct a BP neural network model;
Example 2
Suitability evaluation of grouting construction parameters selected by constructed nonlinear model
In addition, the stratum physical parameters (natural density, cohesive force, internal friction angle, void ratio and elastic modulus), grouting construction parameters (grouting pressure, grouting time and grouting amount) and stratum displacement change values caused by excavation of the adjacent excavated sections (YDK 722.000-YDK 754.550) are collected, and 20 groups of data are randomly selected (shown in table 3).
Table 3 input layer prediction samples
The feasibility of the nonlinear model was verified by using the collected 20 sets of data, and the relative error between the predicted value of the grouting construction parameter and the measured grouting construction parameter obtained by outputting the constructed BP neural network was shown in Table 4
TABLE 4 comparison of predicted and actual values
As can be seen from Table 4, the maximum relative error between the grouting parameters selected by the prediction of the constructed BP neural network model and the actual construction grouting parameters is 19%, the average error is not more than 13%, and the BP neural network model has higher prediction accuracy as a whole and can be used for guiding construction practice.
Example 3
Application practice for selecting grouting parameters in tunnel underpass existing subway operation construction by using nonlinear model
And outputting grouting construction parameters (grouting pressure, grouting time and grouting amount) required by the corresponding excavated section by taking the stratum physical parameters (natural density, cohesive force, internal friction angle, aperture ratio and elastic modulus) of the downward penetrating section (YDK43+ 754.550-YDK43+ 763.100) and the vertical displacement limit value of the existing operation subway line specified by the urban rail transit structure safety protection technical specification (CJJ/T202-2013) as input layer data, and predicting the obtained grouting construction parameters by adopting a grouting construction parameter selection method. The result of fig. 2 shows that the tunnel face is stable, and the main slurry vein and the branch slurry vein with better ductility are formed, so that the accidents such as tunnel collapse and the like do not occur in the process of excavation. Meanwhile, as shown in fig. 3, after the underpass tunnel is excavated according to the on-site monitoring data, the linear displacement change value of the upper existing operation ground is between-0.5 mm and 1.0mm, and all the requirements of relevant specifications are met.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above examples. Modifications and variations which would be obvious to those skilled in the art without departing from the technical spirit of the present invention are also considered to be within the scope of the present invention.
Claims (3)
1. The method for selecting the grouting construction parameters of the existing operation subway line of the tunnel underpass is characterized by comprising the following steps:
S1: collecting physical property parameters of stratum close to the lower penetration section which is excavated on site, grouting construction parameters and stratum displacement change values caused by excavation; the stratum physical parameters include: natural density, cohesion, internal friction angle, void ratio, elastic modulus; the grouting construction parameters comprise: grouting pressure, grouting time and grouting amount;
The stratum displacement change value caused by the excavation is the maximum value of displacement change in the period from the beginning to the end of tunnel excavation;
S2: utilizing the collected data to perform nonlinear relation fitting on physical property parameters of the excavated stratum close to the underpass section, stratum displacement change values caused by excavation and grouting construction parameters, and constructing a nonlinear model;
S3: and inputting stratum physical parameters of the underpass section to be excavated and the vertical displacement limit value of the existing operation subway line specified by related specifications into a nonlinear model, so as to obtain grouting construction parameters corresponding to the excavated section.
2. The method for selecting the grouting construction parameters of the tunnel underpass existing operation subway line according to claim 1, wherein the nonlinear model in the step S2 is one of a BP neural network model, a radial basis function model and a self-organizing neural network model.
3. Use of a method for selecting grouting construction parameters of a tunnel underpass existing operation subway line according to any one of claims 1-2 in tunnel underpass existing operation subway line construction.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101344389A (en) * | 2008-08-20 | 2009-01-14 | 中国建筑第八工程局有限公司 | Method for estimating tunnel surrounding rock displacement by neural network |
| JP2011256611A (en) * | 2010-06-09 | 2011-12-22 | Central Japan Railway Co | Soil improvement method and underpass method |
| CN109978226A (en) * | 2019-01-24 | 2019-07-05 | 同济大学 | Shield construction ground settlement prediction method based on Recognition with Recurrent Neural Network |
| CN111119902A (en) * | 2019-12-16 | 2020-05-08 | 北京科技大学 | Tunnel dynamic construction method based on BP neural network |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101344389A (en) * | 2008-08-20 | 2009-01-14 | 中国建筑第八工程局有限公司 | Method for estimating tunnel surrounding rock displacement by neural network |
| JP2011256611A (en) * | 2010-06-09 | 2011-12-22 | Central Japan Railway Co | Soil improvement method and underpass method |
| CN109978226A (en) * | 2019-01-24 | 2019-07-05 | 同济大学 | Shield construction ground settlement prediction method based on Recognition with Recurrent Neural Network |
| CN111119902A (en) * | 2019-12-16 | 2020-05-08 | 北京科技大学 | Tunnel dynamic construction method based on BP neural network |
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