CN111119902A - Tunnel dynamic construction method based on BP neural network - Google Patents
Tunnel dynamic construction method based on BP neural network Download PDFInfo
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- CN111119902A CN111119902A CN201911295340.0A CN201911295340A CN111119902A CN 111119902 A CN111119902 A CN 111119902A CN 201911295340 A CN201911295340 A CN 201911295340A CN 111119902 A CN111119902 A CN 111119902A
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- tunnel
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- E—FIXED CONSTRUCTIONS
- E21—EARTH 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
- E21D9/003—Arrangement of measuring or indicating devices for use during driving of tunnels, e.g. for guiding machines
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B25/00—Apparatus for obtaining or removing undisturbed cores, e.g. core barrels, core extractors
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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- E—FIXED CONSTRUCTIONS
- E21—EARTH 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
Abstract
The invention provides a tunnel dynamic construction method based on a BP neural network, and belongs to the technical field of tunnel construction. The method comprises the steps of firstly segmenting a tunnel, arranging ground surface settlement monitoring points in the center of each construction segment, training to generate a BP (back propagation) neural network by taking a ground surface settlement value obtained through numerical calculation as an input layer data and taking mechanical parameters of a soil layer and a grouting body as output layer data, inputting an actually measured ground surface settlement value of each construction segment into the trained BP neural network, inverting to obtain mechanical parameters of the soil layer and the grouting body, predicting the ground surface settlement of a segment to be excavated through numerical simulation, continuously optimizing grouting parameters until engineering requirements are met, and guiding the construction of the tunnel of the segment to be excavated by using the optimized grouting parameters. The construction method adopts the modes of real-time monitoring, advanced prediction, sectional construction and timely adjustment, and can better control the surface settlement.
Description
Technical Field
The invention relates to the technical field of tunnel construction, in particular to a dynamic tunnel construction method based on a BP neural network.
Background
Because subway tunnels are mostly built in urban central areas, buildings stand upright and have large pedestrian flow, the construction of the traditional open excavation method is difficult to be normally carried out, and compared with the open excavation method, the shallow-buried underground excavation method is widely applied because the shallow-buried underground excavation method has small influence on urban environment, surrounding residents, traffic and the like, but the overlarge ground surface settlement is easily caused in the construction process, and even engineering accidents are caused. How to accurately predict the surface subsidence before tunnel excavation and adjust the construction parameters in real time has always been a problem that engineering technicians pay attention to. The numerical simulation is the most common prediction means, however, when the numerical simulation analysis is performed, the prediction result is often far from the actual situation because the identification of the parameters of the soil layer and the grouting body is difficult. Aiming at the problem, the invention provides a tunnel dynamic construction method based on a BP neural network, which is characterized in that soil layer and grouting body parameters are obtained in a segmented mode through a parameter inversion mode, and the grouting parameters are continuously optimized according to the surface subsidence prediction result of a section to be excavated, so that the surface subsidence can be well controlled.
Disclosure of Invention
The invention provides a tunnel dynamic construction method based on a BP (back propagation) neural network, aiming at the problems that the mechanical parameters of a soil layer and a grouting body cannot be accurately identified in the construction process of the existing subway tunnel, the ground surface settlement is difficult to be well predicted, corresponding measures are taken and the like.
The method comprises the following steps:
s1: dividing the tunnel into a plurality of construction sections along the axial direction of the tunnel, and arranging ground surface settlement monitoring points in the middle of each construction section;
s2: carrying out geological drilling exploration in the area of the tunnel, extracting a soil body drill core and measuring mechanical parameters of the soil body drill core, layering the soil body according to the mechanical parameters, respectively measuring mechanical parameters of a grout pulse and the soil body within a grouting design range, and then solving the mechanical parameters of the equivalent grouting body according to an equivalent action principle;
s3: taking the mechanical parameters of each soil layer and the grouting body measured in the S2 as a reference set, generating output layer data by a Monte Carlo method, calculating by using a numerical simulation method to obtain a surface subsidence value as input layer data, and training to generate a BP neural network;
s4: performing pre-grouting reinforcement, excavation and support on the tunnel by adopting a multi-cycle mode, performing segmented construction and monitoring a surface subsidence value;
s5: inputting a ground surface settlement monitoring value caused by tunnel excavation into a BP neural network, and performing inversion to obtain mechanical parameters of each soil layer and grouting body;
s6: predicting the surface subsidence of the next construction section by using a numerical simulation method according to mechanical parameters of each soil layer and grouting body obtained by inversion in S5, and increasing the grouting range or grouting pressure when the maximum subsidence exceeds a surface subsidence control value; when the maximum sedimentation amount is less than 20% of the surface sedimentation control value, reducing the grouting range or grouting pressure; repeatedly adjusting, and optimizing grouting parameters;
s7: performing pre-grouting reinforcement by using the grouting parameters optimized by S6, excavating and supporting in time, and monitoring the surface subsidence value in real time;
s8: and repeating the steps from S5 to S7, and constructing forward section by section.
And in S1, the length of the construction section is 2-5 times of the tunnel diameter.
Ground surface settlement monitoring points are arranged on each construction section of the tunnel, and each row of ground surface settlement monitoring points in the S1 is 7-15 and is symmetrically distributed about the center of the tunnel.
Geological drilling holes are axially arranged in the tunnel at the front of construction and are subjected to geological exploration, geological drilling holes in S2 are axially arranged in the tunnel, the number of the drilling holes is not less than 3, and the hole bottoms of the geological drilling holes are positioned below 3 times of the hole diameter of the tunnel bottom plate.
The mechanical parameters in S2 include compression modulus, Poisson' S ratio, cohesion and internal friction angle.
And the mechanical parameters of each soil layer and the grouting body obtained by inversion in the S5 comprise elastic modulus, Poisson ratio, cohesive force and internal friction angle.
The technical scheme of the invention has the following beneficial effects:
in the scheme, based on the BP neural network, the mechanical parameters of the soil layer and the grouting body can be accurately obtained by inversion by utilizing the strong nonlinear mapping capability of the BP neural network, so that the precision of predicting the surface subsidence through numerical simulation is improved to a great extent; the dynamic construction method of real-time monitoring, advanced prediction, segmented construction and timely adjustment of grouting parameters is adopted, so that the method can better adapt to complicated and variable stratum conditions. The method can accurately identify the mechanical parameters of the soil layer and the grouting body, so that the accuracy of surface settlement prediction is improved to a certain extent, and the grouting parameters are adjusted in time and used for guiding tunnel construction of an unexcavated section.
Drawings
FIG. 1 is a flow chart of a dynamic tunnel construction method based on a BP neural network according to the present invention;
FIG. 2 is a diagram of a three-dimensional tunnel model according to an embodiment of the present invention;
FIG. 3 is a transverse cross-sectional view of a tunnel in an embodiment of the present invention;
FIG. 4 is a diagram of the arrangement of surface subsidence monitoring points in an embodiment of the present invention;
fig. 5 is a longitudinal cross-sectional view of a tunnel in an embodiment of the invention.
Wherein: 1-surface settlement monitoring point, 2-grouting body, 3-tunnel, 4-supporting structure, 5-geological drilling and 6-soil layer.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a tunnel dynamic construction method based on a BP (back propagation) neural network, aiming at the problems that the mechanical parameters of a soil layer and a grouting body cannot be accurately identified in the construction process of the existing subway tunnel, the ground surface settlement is difficult to be well predicted, corresponding measures are taken and the like.
As shown in fig. 1, the method comprises the steps of:
s1: dividing the tunnel into a plurality of construction sections along the axial direction of the tunnel, and arranging ground surface settlement monitoring points in the middle of each construction section;
s2: carrying out geological drilling exploration in the area of the tunnel, extracting a soil body drill core and measuring mechanical parameters of the soil body drill core, layering the soil body according to the mechanical parameters, respectively measuring mechanical parameters of a grout pulse and the soil body within a grouting design range, and then solving the mechanical parameters of the equivalent grouting body according to an equivalent action principle;
s3: taking the mechanical parameters of each soil layer and the grouting body measured in the S2 as a reference set, generating output layer data by a Monte Carlo method, calculating by using a numerical simulation method to obtain a surface subsidence value as input layer data, and training to generate a BP neural network;
s4: performing pre-grouting reinforcement, excavation and support on the tunnel by adopting a multi-cycle mode, performing segmented construction and monitoring a surface subsidence value;
s5: inputting a ground surface settlement monitoring value caused by tunnel excavation into a BP neural network, and performing inversion to obtain mechanical parameters of each soil layer and grouting body;
s6: predicting the surface subsidence of the next construction section by using a numerical simulation method according to mechanical parameters of each soil layer and grouting body obtained by inversion in S5, and increasing the grouting range or grouting pressure when the maximum subsidence exceeds a surface subsidence control value; when the maximum sedimentation amount is less than 20% of the surface sedimentation control value, reducing the grouting range or grouting pressure; repeatedly adjusting, and optimizing grouting parameters;
s7: performing pre-grouting reinforcement by using the grouting parameters optimized by S6, excavating and supporting in time, and monitoring the surface subsidence value in real time;
s8: and repeating the steps from S5 to S7, and constructing forward section by section.
The following description is given with reference to specific examples.
In the concrete construction, the process mainly comprises the following steps:
s1: dividing the tunnel 3 into a plurality of construction sections along the axial direction of the tunnel, and arranging a ground surface settlement monitoring point 1 in the middle of each construction section; as shown in FIG. 2;
s2: carrying out geological drilling exploration in the area of the tunnel 3, extracting a soil body drill core, measuring mechanical parameters of the soil body drill core, and layering the soil body according to mechanical characteristics;
s3: taking the measured mechanical parameters of each soil layer 6 and the grouting body 2 as a reference set, generating output layer data by a Monte Carlo method, calculating by using a numerical simulation method to obtain a surface subsidence value as input layer data, and training to generate a BP neural network;
s4: pre-grouting reinforcement, excavation and supporting are carried out on the tunnel 3 in a multi-cycle mode, wherein the excavation adopts an upper step method and a lower step method, the supporting structure 4 adopts a steel grating and net-sprayed concrete, the construction is carried out in sections, and the surface subsidence value is monitored; as shown in fig. 3;
s5: inputting a ground surface settlement monitoring value caused by excavation of the tunnel 3 into a BP neural network, and performing inversion to obtain mechanical parameters of each soil layer and the grouting body 2;
s6: predicting the surface subsidence of the next construction section by using a numerical simulation method according to the mechanical parameters of each soil layer 6 and the grouting body 2 obtained by inversion in the S5, and increasing the grouting range or the grouting pressure when the maximum subsidence exceeds a surface subsidence control value; when the maximum sedimentation amount is less than 20% of the surface sedimentation control value, reducing the grouting range or grouting pressure; repeatedly adjusting, and optimizing grouting parameters;
s7: performing pre-grouting reinforcement by using the grouting parameters optimized in the S6, excavating and supporting in time, and monitoring the surface subsidence value in real time;
s8: and repeating the steps from S5 to S7, and constructing forward section by section.
And (3) adopting a dynamic construction method based on the BP neural network, and controlling the length of the segments of the tunnel 3 to be 2-5 times of the tunnel diameter when the tunnel 3 is segmented.
As shown in fig. 4, ground surface settlement monitoring points 1 are arranged at each construction section of the tunnel 3, and the number of the ground surface settlement monitoring points in each row is 7-15, and the ground surface settlement monitoring points are symmetrically distributed around the center of the tunnel.
Mechanical parameters of the soil layer 6 and the grouting body 2 involved in the measurement comprise compression modulus, Poisson ratio, cohesive force and internal friction angle; soil layer 6 and grouting force 2 optical parameters involved in inversion comprise elastic modulus, Poisson ratio, cohesive force and internal friction angle.
As shown in fig. 5, geological boreholes 5 are axially arranged in the tunnel at the front of the construction and geological exploration is performed, wherein the number of the geological boreholes 5 is not less than 3, and the bottoms of the geological boreholes 5 are located below 3 times the hole diameter of the tunnel bottom plate.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. A tunnel dynamic construction method based on a BP neural network is characterized in that: the method comprises the following steps:
s1: dividing the tunnel into a plurality of construction sections along the axial direction of the tunnel, and arranging ground surface settlement monitoring points in the middle of each construction section;
s2: carrying out geological drilling exploration in the area of the tunnel, extracting a soil body drill core and measuring mechanical parameters of the soil body drill core, layering the soil body according to the mechanical parameters, respectively measuring mechanical parameters of a grout pulse and the soil body within a grouting design range, and then solving the mechanical parameters of the equivalent grouting body according to an equivalent action principle;
s3: taking the mechanical parameters of each soil layer and the grouting body measured in the S2 as a reference set, generating output layer data by a Monte Carlo method, calculating by using a numerical simulation method to obtain a surface subsidence value as input layer data, and training to generate a BP neural network;
s4: performing pre-grouting reinforcement, excavation and support on the tunnel by adopting a multi-cycle mode, performing segmented construction and monitoring a surface subsidence value;
s5: inputting a ground surface settlement monitoring value caused by tunnel excavation into a BP neural network, and performing inversion to obtain mechanical parameters of each soil layer and grouting body;
s6: predicting the surface subsidence of the next construction section by using a numerical simulation method according to mechanical parameters of each soil layer and grouting body obtained by inversion in S5, and increasing the grouting range or grouting pressure when the maximum subsidence exceeds a surface subsidence control value; when the maximum sedimentation amount is less than 20% of the surface sedimentation control value, reducing the grouting range or grouting pressure; repeatedly adjusting, and optimizing grouting parameters; s7: performing pre-grouting reinforcement by using the grouting parameters optimized by S6, excavating and supporting in time, and monitoring the surface subsidence value in real time;
s8: and repeating the steps from S5 to S7, and constructing forward section by section.
2. The dynamic tunnel construction method based on the BP neural network according to claim 1, wherein: and the length of the construction section in the S1 is 2-5 times of the tunnel diameter.
3. The dynamic tunnel construction method based on the BP neural network according to claim 1, wherein: and each row of the ground surface settlement monitoring points in the S1 is 7-15 and is symmetrically distributed about the center of the tunnel.
4. The dynamic tunnel construction method based on the BP neural network according to claim 1, wherein: the geological drill holes in the S2 are arranged along the axial direction of the tunnel, and the number of the drill holes is not less than 3.
5. The dynamic tunnel construction method based on the BP neural network according to claim 1, wherein: and the bottom of the geological drilling hole in the S2 is positioned below 3 times of the hole diameter of the tunnel bottom plate.
6. The dynamic tunnel construction method based on the BP neural network according to claim 1, wherein: the mechanical parameters in S2 include compression modulus, Poisson' S ratio, cohesion and internal friction angle.
7. The dynamic tunnel construction method based on the BP neural network according to claim 1, wherein: and the mechanical parameters of each soil layer and the grouting body obtained by inversion in the S5 comprise elastic modulus, Poisson ratio, cohesive force and internal friction angle.
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| CN113309527A (en) * | 2021-06-11 | 2021-08-27 | 北京科技大学 | Upper pilot tunnel pre-camber construction method of double-layer pilot tunnel |
| CN113553761A (en) * | 2021-06-28 | 2021-10-26 | 山西省交通科技研发有限公司 | Method for analyzing influence of subway construction on adjacent loaded pile foundations |
| CN113931636A (en) * | 2021-10-22 | 2022-01-14 | 广州地铁设计研究院股份有限公司 | Tunnel underpass existing operation subway line grouting construction parameter selection method and application thereof |
| CN115563831A (en) * | 2022-10-20 | 2023-01-03 | 北京云庐科技有限公司 | Tunnel stratum mechanical parameter obtaining method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115563831B (en) * | 2022-10-20 | 2023-07-21 | 北京云庐科技有限公司 | Tunnel stratum mechanical parameter acquisition method and device, electronic equipment and storage medium |
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