A tunnel unfavorable geological body advanced geological prediction detection simulation device and method

Abstract

The application discloses a tunnel unfavorable geological body advanced geological prediction detection simulation device and method, relates to the field of tunnel engineering and advanced geological prediction technology, and comprises a tunnel model, a fault model, a karst model and a boulder model. Tunnel model side walls are respectively excavated with a working face A pilot tunnel, a working face B pilot tunnel and a working face C pilot tunnel. The fault model is arranged opposite the working face A pilot tunnel. The karst model is arranged opposite the working face B pilot tunnel. The boulder model is arranged opposite the working face C pilot tunnel. The device can simulate the unfavorable geological body of the fault, the karst and the boulder respectively, has the advantages of ingenious design, strong pertinence, convenience, easy use and strong adaptability, and has a great promoting effect on tunnel advanced geological prediction. The method uses the device to perform a test first, obtains optimized detection parameters of various unfavorable geological bodies, and then uses the detection parameters to perform field detection, so that various unfavorable geological bodies in an unexcavated area can be detected and predicted at one time, and the method is convenient, fast and high in accuracy.

Description

Technical Field

[0001] The present invention relates to the technical fields of tunnel engineering and advanced geological prediction, and in particular to a device and method for detecting and simulating advanced geological prediction of unfavorable geological bodies in tunnels. Background Art

[0002] As the technological level of road construction in my country continues to improve, the construction of railways and highways continues to penetrate into various complex geological environments, making geological problems more and more complicated and safety hazards more and more varied during tunnel construction. The requirements for tunnel design surveys and advanced geological forecasts during construction are becoming increasingly higher.

[0003] Existing geological advance prediction technologies suffer from complex forecasting, conflicting forecast results, and lengthy tunnel construction time constraints, making them difficult to meet market development needs. Furthermore, most existing detection methods rely solely on theoretical research and numerical simulation. Even when experiments are conducted in actual tunnel engineering projects, they lack a clear understanding of actual surrounding rock geological anomalies and only provide a rough assessment of the prediction results based on the general condition of the surrounding rock after excavation. This makes it difficult to provide more accurate feedback for the data processing and inversion algorithms used in the prediction technology, hindering adaptive algorithm adjustments. Furthermore, existing detection methods rarely consider factors that influence detection effectiveness, a key factor in the poor performance of current tunnel advance geological prediction and surface detection.

[0004] Long-distance tunnel geological prediction technology is a key area of ​​research in advanced tunnel geological prediction. Its results are directly related to multiple factors, including the safety, quality, stability, and reliability of tunnel construction. Advanced tunnel geological prediction, particularly long-distance prediction technology, has always been a difficult technical challenge in this field. Summary of the Invention

[0005] The purpose of the present invention is to provide a simulation device and method for advanced geological prediction detection of poor geological bodies in tunnels. By burying known poor geological bodies, relevant experimental work on geological prediction is carried out, and various factors affecting the detection effect are fully considered to improve the accuracy of geological prediction detection. It can provide basic research conditions for the study of tunnel geological prediction in complex geological environments and lay the foundation for the development of industry technology.

[0006] The object of the present invention is achieved through the following technical solutions:

[0007] A tunnel unfavorable geological body advanced geological prediction detection simulation device, including a tunnel model, a fault model, a karst model and a rock model,

[0008] The tunnel model is a square hole excavated downwards, and the center positions of the three side walls of the tunnel model are respectively excavated with a tunnel face A pilot tunnel, a tunnel face B pilot tunnel and a tunnel face C pilot tunnel in a direction away from the tunnel model;

[0009] The fault model is a rectangular hole excavated downwards, and the fault model is arranged opposite to the pilot hole A of the tunnel face;

[0010] The karst model is a circular hole excavated downwards, and the karst model is arranged opposite to the pilot hole B of the tunnel face;

[0011] The boulder model is a buried concrete block, and the boulder model is arranged opposite to the C pilot tunnel of the tunnel face.

[0012] Furthermore, the square hole of the tunnel model has a side length of 2.5 m and a depth of 3.5 m;

[0013] The lower parts of the pilot tunnels of the tunnel face A and the tunnel face B are square holes with a length and width of 1m, and the upper parts are semicircular holes with a diameter of 1m. The top of the semicircular holes is 2m away from the ground surface.

[0014] The C pilot tunnel of the tunnel face is a circular hole with a diameter of 1m, and the circular arc top of the C pilot tunnel of the tunnel face is 2m away from the ground surface;

[0015] The depths of the pilot tunnels of face A, face B and face C are all 1 m.

[0016] Furthermore, the fault model is 4m long, 2.5m wide, and 3.5m deep. The center of the long side of the fault model is arranged opposite the axis of the pilot tunnel of the tunnel face A. The distance between the fault model and the pilot tunnel of the tunnel face A is 3.5m.

[0017] Furthermore, the diameter of the boulder model is 1 m, the buried depth is 3 m, the center of the boulder model is arranged opposite to the axis of the C pilot tunnel of the tunnel face, and the distance between the boulder model and the C pilot tunnel of the tunnel face is 3 m.

[0018] Furthermore, the karst model is a vertical shaft with a diameter of 1 m, the well depth of the karst model is 3 m, the axis of the karst model is arranged perpendicularly and coplanar with the axis of the pilot tunnel of the tunnel face B, and the distance between the karst model and the pilot tunnel of the tunnel face B is 3 m.

[0019] A method for advanced geological prediction and detection of unfavorable geological bodies in tunnels, comprising the following steps:

[0020] S1. Construct the above-mentioned advanced geological prediction and detection simulation device for unfavorable geological bodies in the tunnel in the laboratory or on site;

[0021] S2. Conducting geological prediction experiments on faults, karst, boulders, and stratigraphic interfaces using detection equipment to obtain optimal detection parameters corresponding to different geological environments. The geological prediction experiment on faults refers to filling the fault model with different media used to simulate actual faults and then performing detection using the detection equipment. The geological prediction experiment on karst refers to filling the karst model with different media used to simulate actual karst and then performing detection using the detection equipment. The geological prediction experiment on boulders refers to performing detection on the buried boulder model using the detection equipment. The geological prediction experiment on stratigraphic interfaces refers to directly detecting the stratigraphic interface at the bottom of the tunnel using the detection equipment.

[0022] S3. Adjust the parameters of the detection equipment to the optimal detection parameters corresponding to each geological environment obtained in step S2, and use the detection model to detect the actual geological environment to achieve advanced prediction.

[0023] The beneficial effects of the present invention are:

[0024] The present invention provides a tunnel unfavorable geological body advanced geological prediction detection simulation device which can simulate the unfavorable geological bodies of faults, karsts and boulders respectively by setting a tunnel model, a fault model, a karst model and a boulder model; at the same time, through optimized size design, the size of the simulation device is made as small as possible while meeting the functional requirements, and the construction is convenient when it is set up in the unexcavated area of ​​the tunnel which actually needs to be detected. At the same time, it can also be used for indoor simulation detection experiments in the laboratory. The design is ingenious, highly targeted, easy to use and highly adaptable, and it has a huge promoting effect on the development of the tunnel advanced geological prediction industry.

[0025] At the same time, the present invention provides a method for advanced geological prediction and detection of unfavorable geological bodies in tunnels, which first builds the above-mentioned simulation device; then conducts a detection test to obtain geological map characteristics of various unfavorable geological bodies, and based on this, obtains preferred detection parameters of the detection equipment for various unfavorable geological bodies; then uses the obtained preferred detection parameters to detect the actual unexcavated area, which can accurately and effectively predict various unfavorable geological bodies in the unexcavated area at one time, and at the same time, combined with the geological map characteristics obtained before, the position of the unfavorable geological body can be accurately determined, and according to the propagation characteristics of the excited physical field in the medium, the lithology, structure and other characteristics of the unfavorable geological body can be determined. It is convenient, fast and highly accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a structural schematic diagram of a device for advanced geological prediction and detection simulation of unfavorable geological bodies in tunnels according to the present invention;

[0027] Figure 2 A schematic diagram of the plane distribution and dimensions of a device for advanced geological prediction and detection of unfavorable geological bodies in tunnels according to the present invention;

[0028] Figure 3 The fault detection experiment of the HSP method was carried out using a 38Hz frequency seismic detector to receive information, and the results were obtained.

[0029] Figure 4 The fault detection experiment using the HSP method uses a 2000Hz frequency seismometer to receive information, and the results are shown in the figure.

[0030] Figure 5 The HSP method was used to conduct karst detection experiments, and a 38Hz frequency seismic detector was used to receive information. The results were obtained.

[0031] Figure 6 The HSP method was used to conduct karst detection experiments. A 2000Hz frequency seismic detector was used to receive information. The results were obtained.

[0032] Figure 7 The HSP method was used to detect rocks, and a 38Hz frequency geophone was used to receive information.

[0033] Figure 8 The HSP method was used to conduct a boulder detection experiment, and a 2000Hz frequency seismic detector was used to receive information. The results were obtained. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0035] like Figure 1 、 Figure 2 As shown, a tunnel unfavorable geological body advanced geological prediction detection simulation device includes a tunnel model 1, a fault model 2, a karst model 3 and a boulder model 4.

[0036] The tunnel model 1 is a square hole excavated downwards, and the center positions of the three side walls of the tunnel model 1 are respectively excavated with a tunnel face A pilot tunnel 5, a tunnel face B pilot tunnel 6 and a tunnel face C pilot tunnel 7 in a direction away from the tunnel model 1;

[0037] The fault model 2 is a rectangular hole excavated downwards, and the fault model 2 is arranged facing the pilot hole 5 of the tunnel face A;

[0038] The karst model 3 is a circular hole excavated downwards, and the karst model 3 is arranged opposite to the pilot hole 6 of the tunnel face B;

[0039] The rock model 4 is a buried concrete block, and the rock model 4 is arranged opposite to the C pilot tunnel 7 of the tunnel face.

[0040] During application, the fault model 2 and the karst model 3 can be filled with different media such as water, sand, mud, etc. to simulate the actual fault and karst conditions, so that the detection simulation device can carry out geological prediction experiments on stratum interfaces and can carry out stratum prediction experiments on unfavorable geological bodies in tunnels such as faults, karst and boulders.

[0041] The method for advanced geological prediction detection of unfavorable geological bodies in tunnels based on the above detection simulation device includes the following steps:

[0042] S1. Build the above-mentioned tunnel unfavorable geological body advanced geological prediction detection simulation device in the laboratory, which can move the complex geological prediction preliminary work indoors; or directly build the unfavorable geological body advanced geological prediction detection simulation device on site to simulate the actual geological environment and improve the accuracy of the prediction.

[0043] S2. Use detection equipment to conduct geological prediction experiments on faults, karst, boulders, and stratum interfaces to obtain the optimal detection parameters corresponding to different geological environments.

[0044] The above-mentioned geological prediction experiment for faults refers to filling the fault model 2 with different media used to simulate actual faults and then using detection equipment to detect them. The fault model 2 in front is detected through the position of the tunnel face A in the tunnel model 1. The fault model 2 can be filled with different media such as water, sand, mud, etc., and the spatial wave field characteristics, spectral characteristics, detection resolution and other information are analyzed to provide basic information for geological prediction. Figure 3 、 Figure 4 Figure 2 shows an experiment using the horizontal seismic profiling method (HSP) to detect faults. 38Hz and 2000Hz geophones were used to receive information, and data processing and inversion were performed to obtain the resulting images. This simulation demonstrates that the HSP method is effective for fault detection. However, when detecting faults, the signals received by geophones of different frequencies produce different spectral characteristics after processing. The results obtained with low-frequency geophones more intuitively reflect the actual solid model and are less affected by boundaries.

[0045] The above geological prediction experiment for karst refers to the use of detection equipment to detect after filling the karst model 3 with different media used to simulate actual karst, and detecting the karst model 3 in front through the position of the tunnel face B in the tunnel model 1. The model can be filled with different media such as water, sand, mud, etc., and the spatial wave field characteristics, spectral characteristics, detection resolution and other information are analyzed to provide basic information for geological prediction. Figure 5 、 Figure 6Figure 2 shows the results of a karst exploration experiment using the horizontal seismic profiling method (HSP). 38Hz and 2000Hz geophones were used to receive information, and data processing and inversion were performed. This simulation demonstrates that the HSP method is effective for karst exploration. However, the signals received by geophones of different frequencies exhibit slight differences in the resulting spectral characteristics. While the results obtained with geophones of both frequencies intuitively reflect the real-world model and are less affected by boundaries, they exhibit multiple reflections, resulting in anomalous alternation in the spectral characteristics.

[0046] The above-mentioned geological prediction experiment for boulders refers to using detection equipment to detect the buried boulder model 4, detecting the front boulder model 4 from the left position in the tunnel model 1, analyzing the spatial wave field characteristics, spectrum characteristics, detection resolution and other information to provide basic information for geological prediction. Figure 7 、 Figure 8 Figure 2 shows the results of an experiment using the horizontal seismic profiling method (HSP) on a boulder. 38Hz and 2000Hz geophones were used to receive information, and data processing and inversion were performed. This simulation demonstrates that the HSP method is effective for detecting boulders. Signals received by geophones of different frequencies produce different spectral characteristics after processing. High-frequency geophones provide a more intuitive representation of the actual solid model and are less affected by boundaries.

[0047] The above-mentioned geological prediction experiment on the stratum interface refers to the direct detection of the stratum interface at the bottom of the tunnel using detection equipment. The bottom stratum interface is detected directly through the bottom of the tunnel model 1 (simulating the tunnel face), and the spatial wave field characteristics, spectral characteristics, detection resolution and other information are analyzed to provide basic information for geological prediction.

[0048] According to the above, by using the detection equipment to carry out the simulation detection experiment of the above step S2, it can be determined whether the detection equipment can detect adverse geological conditions, and at the same time, the detection parameters and data processing methods required for various adverse geological conditions can be simulated and matched.

[0049] S3. Adjust the parameters of the detection equipment to the optimal detection parameters corresponding to each geological environment obtained in step S2, use the detection model to detect the actual geological environment, analyze the spatial wave field characteristics, spectral characteristics, detection resolution and other information of the actual detection, and realize the advanced prediction of geologically unfavorable bodies.

[0050] When implementing it specifically, Figure 2As shown, the square hole of the tunnel model 1 has a side length of 2.5m and a depth of 3.5m; the lower parts of the pilot tunnel 5 on the tunnel face A and the pilot tunnel 6 on the tunnel face B are square holes with a length and width of 1m and a semicircular hole with a diameter of 1m on the upper part, and the arc top of the semicircular hole is 2m above the ground surface; the pilot tunnel 7 on the tunnel face C is a circular hole with a diameter of 1m, and the arc top of the semicircular hole is 2m above the ground surface; the depth of the pilot tunnel 5 on the tunnel face A, the pilot tunnel 6 on the tunnel face B, and the pilot tunnel 7 on the tunnel face C are all 1m.

[0051] Fault model 2 is 4m long, 2.5m wide, and 3.5m deep. The center of its long side is aligned with the axis of pilot tunnel 5 on face A, and the distance between fault model 2 and pilot tunnel 5 is 3.5m. Boulder model 4 has a diameter of 1m and a burial depth of 3m. The center of boulder model 4 is aligned with the axis of pilot tunnel 7 on face C, and the distance between boulder model 4 and pilot tunnel 7 is 3m. Karst model 3 is a vertical shaft with a diameter of 1m and a depth of 3m. The axis of karst model 3 is aligned perpendicularly and coplanarly with the axis of pilot tunnel 6 on face B, and the distance between them is 3m.

[0052] When the geotechnical plate is rectangular, the formula for the minimum geotechnical plate thickness is:

[0053]

[0054] Where: X-plate thickness (mm), β-variable coefficient, Y-transverse dimension (mm), Z-longitudinal dimension (mm), γ-tensile strength (KN / mm2), ρ-groundwater density, g-gravitational acceleration, H-head height difference at the load center of the geotechnical disk, a, b, c, d are power exponents.

[0055] By calculating using the formula for the minimum rock and soil plate thickness, it can be determined that the distance between the fault model 2 and the pilot tunnel 5 at the tunnel face A is 3.5 m.

[0056] When the geotechnical disk is circular, the formula for the minimum geotechnical disk thickness is:

[0057]

[0058] Where: X-plate thickness (mm), β-variable coefficient, r-interface size radius (mm), γ-tensile strength (KN / mm2), ρ-groundwater density, g-gravitational acceleration, H-head height difference at the load center of the geotechnical disk, a, b, c are power exponents, and π-pi.

[0059] By calculating using the minimum rock-soil disk thickness formula, it can be determined that the distance between the rock model 4 and the pilot tunnel 7 at the tunnel face C is 3 m, and the distance between the karst model 3 and the pilot tunnel 6 at the tunnel face B is 3 m.

[0060] To minimize the impact of ground boundary reflections on model detection, the tunnel depth must be greater than or equal to the distance between the unfavorable geological body and the tunnel face (as previously determined, the maximum is 3.5 meters). Therefore, the depth of Tunnel Model 1 was set to 3.5 meters. Considering the operating space required for model testing, the length and width of Tunnel Model 1 were both set to 2.5 meters.

[0061] In actual application, this simulation device is mainly used for detection simulation of HSP geological prediction. Since the HSP geological prediction method requires the deployment of at least 3 detectors and 2 seismic source excitation points on the left and right sides of the tunnel face, and the minimum deployment spacing must be 0.25m, the tunnel face length is set to 1m; the tunnel face width needs to be larger than the width of the HSP acquisition host (0.6m), so it is set to 1m.

[0062] In terms of the four dimensions of the fault, cave and boulder models,

[0063] The calculation formula for the limit of vertical resolution of seismic wave exploration is:

[0064] h=λ / 4

[0065] Where:

[0066] h-thickness of exploration target body,

[0067] λ-wavelength of the sub-wave, the calculation formula is as follows:

[0068] λ=v / f

[0069] v-media speed

[0070] f-wavelet dominant frequency

[0071] Since the poured filling material is plain fill, the minimum velocity is about 1000m / s, and the maximum main frequency of the struck seismic wavelet is about 550Hz. Therefore, the minimum vertical resolution of detection can be calculated to be 0.45m. Therefore, the width of fault model 2 is set to be slightly larger than the minimum detectable thickness, which is 0.5m. The radius of karst model 3 and boulder model 4 is also 0.5m.

[0072] To ensure the effectiveness of the model test, fault model 2, karst model 3, and boulder model 4 must all be located directly in front of the tunnel face, so their burial depth must be at least 3 m.

[0073] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A tunnel unfavorable geological body advanced geological prediction detection simulation device, characterized in that: Including tunnel model (1), fault model (2), karst model (3) and rock model (4), The tunnel model (1) is a square hole excavated downwards, and the center positions of the three side walls of the tunnel model (1) are respectively excavated with a tunnel face A pilot tunnel (5), a tunnel face B pilot tunnel (6) and a tunnel face C pilot tunnel (7) in a direction away from the tunnel model (1); The fault model (2) is a rectangular hole excavated downwards, and the fault model (2) is arranged facing the pilot hole (5) of the tunnel face A; The karst model (3) is a circular hole excavated downwards, and the karst model (3) is arranged facing the pilot hole (6) of the tunnel face B; The boulder model (4) is a buried concrete block, and the boulder model (4) is arranged opposite to the C pilot hole (7) of the tunnel face; The distance between the fault model (2) and the tunnel face A pilot tunnel (5), the distance between the karst model (3) and the tunnel face B pilot tunnel (6), and the distance between the boulder model (4) and the tunnel face C pilot tunnel (7) are calculated using the minimum rock and soil plate thickness formula; When the geotechnical plate is rectangular, the formula for the minimum geotechnical plate thickness is: Where: X is plate thickness, β is coefficient of variation, Y is transverse dimension, Z is longitudinal dimension, γ is tensile strength, ρ is groundwater density, g is acceleration of gravity, H is water head difference at the load center of the geotechnical disk, and a, b, c, and d are power exponents. When the geotechnical disk is circular, the formula for the minimum geotechnical disk thickness is: Where: X is plate thickness, β is coefficient of variation, r is interface radius, γ is tensile strength, ρ is groundwater density, g is acceleration of gravity, H is water head difference at the load center of the geotechnical disk, a, b, c are power exponents, and π is pi. The square hole of the tunnel model (1) has a side length of 2.5 m and a depth of 3.5 m; The lower parts of the tunnel face A pilot tunnel (5) and the tunnel face B pilot tunnel (6) are square holes with a length and width of 1m, and the upper parts are semicircular holes with a diameter of 1m. The top of the semicircular holes is 2m away from the ground surface. The tunnel face C pilot tunnel (7) is a circular hole with a diameter of 1m, and the circular arc top of the tunnel face C pilot tunnel (7) is 2m away from the ground surface; The depths of the tunnel face A pilot tunnel (5), the tunnel face B pilot tunnel (6) and the tunnel face C pilot tunnel (7) are all 1 m; The fault model (2) is 4 m long, 2.5 m wide, and 3.5 m deep. The center of the long side of the fault model (2) is arranged opposite to the axis of the pilot tunnel (5) of the tunnel face A. The distance between the fault model (2) and the pilot tunnel (5) of the tunnel face A is 3.5 m. The diameter of the boulder model (4) is 1m, the buried depth is 3m, the center of the boulder model (4) is opposite to the axis of the tunnel face C guide tunnel (7), the distance between the boulder model (4) and the tunnel face C guide tunnel (7) is 3m; In terms of the size of the fault, cave and boulder model 4, The calculation formula for the limit of vertical resolution of seismic wave exploration is: Where: h-thickness of exploration target body, - Wavelength of the wavelet, calculated as follows: v -Media speed f -Main frequency of sub-wave Since the poured filling material is plain fill, the minimum velocity is about 1000m / s, and the maximum main frequency of the struck seismic wavelet is about 550Hz. Therefore, the minimum vertical resolution of detection can be calculated to be 0.45m. Therefore, the width of fault model 2 is set to be slightly larger than the minimum detectable thickness, which is 0.5m. The radius of karst model 3 and boulder model 4 is also 0.5m.

2. The device for advanced geological prediction and detection simulation of unfavorable geological bodies in tunnels according to claim 1, characterized in that: The karst model (3) is a vertical shaft with a diameter of 1m. The depth of the karst model (3) is 3m. The axis of the karst model (3) is perpendicular to and coplanar with the axis of the tunnel face B pilot tunnel (6). The distance between the karst model (3) and the tunnel face B pilot tunnel (6) is 3m.

3. A method for advanced geological prediction and detection of unfavorable geological bodies in tunnels, characterized by: The steps include: S1. Constructing a tunnel unfavorable geological body advanced geological prediction and detection simulation device as claimed in claim 1 in a laboratory or on site; S2. Using detection equipment to conduct geological prediction experiments on faults, karst, boulders, and stratum interfaces respectively, and obtain the best detection parameters corresponding to different geological environments; wherein the geological prediction experiment on faults refers to filling the fault model (2) with different media used to simulate actual faults and then performing detection using detection equipment; the geological prediction experiment on karst refers to filling the karst model (3) with different media used to simulate actual karst and then performing detection using detection equipment; the geological prediction experiment on boulders refers to using detection equipment to detect the buried boulder model (4); the geological prediction experiment on stratum interfaces refers to using detection equipment to directly detect the stratum interface at the bottom of the tunnel; S3. Adjust the parameters of the detection equipment to the optimal detection parameters corresponding to each geological environment obtained in step S2, and use the detection model to detect the actual geological environment to achieve advanced geological prediction of the tunnel.

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