阅读说明：本技术 一种隧道不良地质体超前地质预报探测模拟装置及方法 (Advanced geological forecast detection simulation device and method for tunnel poor geologic body ) 是由 卢松 肖洋 李春林 汪旭 杨玲洁 张优 王福亮 李苍松 丁建芳 于 2020-06-04 设计创作，主要内容包括：本发明公开了一种隧道不良地质体超前地质预报探测模拟装置及方法,涉及隧道工程、超前地质预报技术领域,该装置包括隧道模型、断层模型、岩溶模型和孤石模型,隧道模型侧壁方向分别挖掘有掌子面A导洞、掌子面B导洞和掌子面C导洞；断层模型正对掌子面A导洞设置；岩溶模型正对掌子面B导洞设置；孤石模型正对掌子面C导洞设置。该装置可分别模拟断层、岩溶、孤石的不良地质体,其设计巧妙、针对性强、方便易用、适应性强,对隧道超前地质预报有着巨大的推助作用。该方法利用上述装置先进行试验,获取各类不良地质体的优选探测参数；其后利用该探测参数实地探测,可一次性探测预报未开挖区域内的各类不良地质体,其方便快捷,准确性高。(The invention discloses a device and a method for simulating advanced geological forecast detection of poor geologic bodies of tunnels, which relate to the technical field of tunnel engineering and advanced geological forecast, and the device comprises a tunnel model, a fault model, a karst model and a boulder model, wherein a tunnel face A pilot tunnel, a tunnel face B pilot tunnel and a tunnel face C pilot tunnel are respectively excavated in the direction of the side wall of the tunnel model; the fault model is arranged right opposite to the tunnel face A pilot tunnel; the karst model is arranged right opposite to the tunnel face B pilot tunnel; the boulder model is arranged right opposite to the tunnel face C pilot tunnel. The device can simulate the unfavorable geologic bodies of faults, karsts and boulders respectively, is ingenious in design, strong in pertinence, convenient and easy to use, strong in adaptability, and has a huge boosting effect on advanced geological forecast of tunnels. The method comprises the steps of firstly carrying out tests by utilizing the device to obtain preferred detection parameters of various poor geologic bodies; and then, the detection parameters are utilized for on-site detection, so that various bad geologic bodies in an un-excavated area can be detected and forecasted at one time, and the method is convenient, quick and high in accuracy.)

1. A tunnel unfavorable geologic body advanced geological forecast detection simulation device is characterized by comprising a tunnel model (1), a fault model (2), a karst model (3) and a boulder model (4),

the tunnel model (1) is a square hole excavated downwards, and a tunnel face A pilot hole (5), a tunnel face B pilot hole (6) and a tunnel face C pilot hole (7) are respectively excavated at the central positions of three side walls of the tunnel model (1) in the direction far away from the tunnel model (1);

the fault model (2) is a rectangular hole dug downwards, and the fault model (2) is arranged right opposite to the tunnel face A pilot tunnel (5);

the karst model (3) is a circular hole dug downwards, and the karst model (3) is arranged right opposite to the tunnel face B pilot tunnel (6);

the boulder model (4) is an embedded concrete block, and the boulder model (4) is arranged right opposite to the tunnel face C pilot tunnel (7).

2. The advanced geological prediction and detection simulation device for the poor geologic body of the tunnel according to claim 1, wherein the side length of the square hole of the tunnel model (1) is 2.5m, and the depth is 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 the length and width of 1m, the upper parts of the tunnel face A pilot tunnel and the tunnel face B pilot tunnel are semicircular holes with the diameter of 1m, and the distance between the arc tops of the semicircular holes and the ground surface is 2 m;

the tunnel face C pilot tunnel (7) is a round tunnel with the diameter of 1m, and the distance between the round arc top of the tunnel face C pilot tunnel (7) and the ground surface is 2 m;

the tunnel face A pilot tunnel (5), the tunnel face B pilot tunnel (6) and the tunnel face C pilot tunnel (7) are all 1m in depth.

3. The advanced geological prediction and detection simulation device for the poor geologic body of the tunnel as claimed in claim 2, wherein the fault model (2) has a length of 4m, a width of 2.5m and a depth of 3.5m, the center of the long side of the fault model (2) is arranged opposite to the axis of the tunnel face A pilot tunnel (5), and the distance from the fault model (2) to the tunnel face A pilot tunnel (5) is 3.5 m.

4. The advanced geological prediction and detection simulation device for the poor geologic body of the tunnel according to claim 2, wherein the diameter of the boulder model (4) is 1m, the burial depth is 3m, the center of the boulder model (4) is arranged opposite to the axis of the tunnel face C pilot tunnel (7), and the distance between the boulder model (4) and the tunnel face C pilot tunnel (7) is 3 m.

5. The advanced geological prediction and detection simulation device for the poor geologic body of a tunnel as claimed in claim 2, wherein the karst model (3) is a vertical shaft with a diameter of 1m, the well 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), and the distance between the karst model (3) and the tunnel face B pilot tunnel (6) is 3 m.

6. A method for advanced geological forecast detection of poor geologic bodies of tunnels is characterized by comprising the following steps:

s1, building a tunnel poor geologic body advanced geological forecast detection simulation device according to claim 1 in a laboratory or in the field;

s2, performing geological forecast experiments on fault, karst, boulder and stratum interfaces by using detection equipment respectively to obtain optimal detection parameters corresponding to different geological environments; the geological prediction experiment of the fault refers to that different media used for simulating an actual fault are filled in the fault model (2) and then are detected by using detection equipment, the geological prediction experiment of the karst refers to that different media used for simulating an actual karst are filled in the karst model (3) and then are detected by using the detection equipment, the geological prediction experiment of the boulder refers to that the buried boulder model (4) is detected by using the detection equipment, and the geological prediction experiment of the stratum interface refers to that the detection equipment is used for directly detecting the stratum interface at the bottom of the tunnel;

and S3, adjusting the parameters of the detection equipment to the optimal detection parameters corresponding to the geological environments obtained in the step S2, and detecting the actual geological environment by using the detection model to realize the advanced geological forecast of the tunnel.

Technical Field

The invention relates to the technical field of tunnel engineering and advanced geological prediction, in particular to a device and a method for simulating advanced geological prediction and detection of poor geological bodies of tunnels.

Background

With the continuous improvement of the technical level of road construction in China, the construction of railways and expressways is continuously deepened into various complicated geological environments, so that the geological problems are more and more complicated, the potential safety hazards are more and more abundant and varied in tunnel engineering construction, and the requirements on tunnel design investigation and geological advanced prediction in the construction period are more and more high.

Disclosure of Invention

The invention aims to provide a device and a method for simulating advanced geological forecast detection of tunnel unfavorable geologic bodies, which can develop relevant experimental work related to geological forecast by burying known unfavorable geologic bodies, fully consider various factors influencing the detection effect, improve the accuracy of geological forecast detection, provide basic research conditions for the research of tunnel geological forecast in complex geological environments and lay a foundation for the development of industrial technology.

The purpose of the invention is realized by the following technical scheme:

a tunnel unfavorable geologic body advanced geological forecast detection simulation device comprises a tunnel model, a fault model, a karst model and a boulder model,

the tunnel model is a square hole excavated downwards, and a tunnel face A pilot hole, a tunnel face B pilot hole and a tunnel face C pilot hole are respectively excavated at the central positions of three side walls of the tunnel model in the direction far away from the tunnel model;

the fault model is a rectangular hole dug downwards, and the fault model is arranged right opposite to the tunnel face A pilot tunnel;

the karst model is a circular hole dug downwards, and the karst model is arranged right opposite to the tunnel face B pilot tunnel;

the boulder model is a buried concrete block, and the boulder model is arranged right opposite to the tunnel face C pilot tunnel.

Furthermore, the side length of a square hole of the tunnel model is 2.5m, and the depth of the square hole is 3.5 m;

the lower parts of the tunnel face A pilot tunnel and the tunnel face B pilot tunnel are square holes with the length and the width of 1m, the upper parts of the tunnel face A pilot tunnel and the tunnel face B pilot tunnel are semicircular holes with the diameter of 1m, and the distance between the arc tops of the semicircular holes and the ground surface is 2 m;

the tunnel face C pilot tunnel is a round tunnel with the diameter of 1m, and the distance between the round arc top of the tunnel face C pilot tunnel and the ground surface is 2 m;

the tunnel depth of the tunnel face A pilot tunnel, the tunnel face B pilot tunnel and the tunnel face C pilot tunnel is 1 m.

Furthermore, the fault model is 4m long, 2.5m wide and 3.5m deep, the long edge center of the fault model is over against the axis of the tunnel face A pilot tunnel, and the distance between the fault model and the tunnel face A pilot tunnel is 3.5 m.

Further, the diameter of the boulder model is 1m, the burial depth is 3m, the center of the boulder model is arranged right opposite to the axis of the tunnel face C pilot tunnel, and the distance between the boulder model and the tunnel face C pilot tunnel is 3 m.

Further, the karst model is a vertical shaft with the diameter of 1m, the well depth of the karst model is 3m, the axis of the karst model and the axis of the tunnel face B pilot tunnel are perpendicular and coplanar, and the distance between the karst model and the tunnel face B pilot tunnel is 3 m.

A method for advanced geological forecast detection of poor geologic bodies of tunnels comprises the following steps:

s1, building the advanced geological prediction detection simulation device for the poor geologic body of the tunnel in a laboratory or on the spot;

s2, performing geological forecast experiments on fault, karst, boulder and stratum interfaces by using detection equipment respectively to obtain optimal detection parameters corresponding to different geological environments; the geological prediction experiment of the fault refers to that different mediums for simulating an actual fault are filled in the fault model and then are detected by using detection equipment, the geological prediction experiment of the karst refers to that different mediums for simulating an actual karst are filled in the karst model and then are detected by using detection equipment, the geological prediction experiment of the boulder refers to that the buried boulder model is detected by using detection equipment, and the geological prediction experiment of the stratum interface refers to that the detection equipment is used for directly detecting the stratum interface at the bottom of the tunnel;

and S3, adjusting the parameters of the detection equipment to the optimal detection parameters corresponding to the geological environments obtained in the step S2, and detecting the actual geological environment by using the detection model to realize advanced prediction.

The invention has the beneficial effects that:

the device for simulating the advanced geological forecast and detection of the poor geologic body of the tunnel can respectively simulate the poor geologic body of a fault, a karst and a boulder by arranging a tunnel model, a fault model, a karst model and a boulder model; meanwhile, through optimized size design, the size of the simulation device is as small as possible on the premise of meeting functional requirements, construction is convenient when a tunnel to be actually detected is built in an un-excavated area, meanwhile, the simulation device can also be used for implementing indoor simulation detection experiments in a laboratory, and the simulation device is ingenious in design, strong in pertinence, convenient and easy to use, strong in adaptability and has a huge boosting effect on the development of the tunnel advanced geological prediction industry.

Meanwhile, the invention provides a method for advanced geological prediction and detection of poor geologic bodies of tunnels, which comprises the steps of firstly building the simulation device; then, carrying out a detection test to obtain geological map characteristics of various unfavorable geologic bodies, and accordingly obtaining preferred detection parameters of the detection equipment on the various unfavorable geologic bodies; and then, the obtained optimal detection parameters are used for detecting the actual unearthed area, various bad geologic bodies in the unearthed area can be accurately and effectively forecasted at one time, meanwhile, the position of the bad geologic body can be accurately determined by combining with the geological map characteristics obtained before, and the characteristics of the lithology, the structure and the like of the bad geologic body can be known according to the propagation characteristics of the excited physical field in a medium, so that the method is convenient, rapid and high in accuracy.

Drawings

FIG. 1 is a schematic structural diagram of a device for simulating advanced geological prediction and detection of poor geologic bodies in tunnels according to the present invention;

FIG. 2 is a schematic plane distribution diagram and a schematic size diagram of the advanced geological prediction detection simulation device for poor geologic bodies in tunnels according to the present invention;

FIG. 3 is a result diagram obtained by using an HSP method to perform fault detection experiments and using a 38Hz frequency geophone to receive information;

FIG. 4 is a result diagram obtained by using an HSP method to perform fault detection experiments and using a 2000Hz frequency geophone to receive information;

FIG. 5 is a result diagram obtained by performing a karst detection experiment by using an HSP method and receiving information by using a 38Hz frequency geophone;

FIG. 6 is a result diagram obtained by performing a karst detection experiment by the HSP method and receiving information by using a 2000Hz frequency geophone;

FIG. 7 is a result diagram obtained by performing an experiment for detecting boulders by the HSP method and receiving information by using a 38Hz frequency geophone;

fig. 8 is a result diagram obtained by performing an experiment of detecting boulders by using the HSP method and receiving information by using a 2000Hz frequency geophone.

Detailed Description

The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.

As shown in fig. 1 and fig. 2, a tunnel poor geologic body advanced geological prediction detection simulation device comprises a tunnel model 1, a fault model 2, a karst model 3 and a boulder model 4,

the tunnel model 1 is a square hole excavated downwards, and a tunnel face A pilot hole 5, a tunnel face B pilot hole 6 and a tunnel face C pilot hole 7 are respectively excavated at the central positions of three side walls of the tunnel model 1 towards the direction far away from the tunnel model 1;

the fault model 2 is a rectangular hole dug downwards, and the fault model 2 is arranged right opposite to the tunnel face A pilot tunnel 5;

the karst model 3 is a circular hole dug downwards, and the karst model 3 is arranged right opposite to the tunnel face B pilot tunnel 6;

the boulder model 4 is an embedded concrete block, and the boulder model 4 is arranged right opposite to the tunnel face C pilot tunnel 7.

When the device is applied, different media such as water, sandy soil, mud and the like can be filled in the fault model 2 and the karst model 3 to simulate the conditions of actual faults and karsts, so that the detection simulation device can be used for developing geological forecast experiments of a stratum interface and can be used for developing geological forecast experiments of tunnel bad geologic bodies such as faults, karsts and boulders.

The method for forecasting and detecting the advance geology of the poor geologic body of the tunnel based on the detection simulation device comprises the following steps:

s1, building the advanced geological prediction detection simulation device for the poor geologic body of the tunnel in a laboratory, and moving the early-stage work of complex geological prediction into a room to be completed; or the unfavorable geologic body advanced geological prediction detection simulation device is directly set up in the field to simulate the geological environment in the field and improve the accuracy of prediction.

And S2, performing geological forecast experiments on the interfaces of the fault, the karst, the boulder and the stratum by using detection equipment respectively to obtain optimal detection parameters corresponding to different geological environments.

The geological forecasting experiment on the fault is to fill different media for simulating the actual fault in the fault model 2 and then detect the fault model 2 in front by using detection equipment, fill different media such as water, sand, mud and the like in the fault model 2 through the position of the tunnel face A in the tunnel model 1, analyze information such as space wave field characteristics, map characteristics, detection resolution and the like, and provide basic information for geological forecasting. As shown in fig. 3 and 4, a horizontal seismic wave profile method (HSP method) is used to perform an experiment on a fault, geophones with frequencies of 38Hz and 2000Hz are respectively used to receive information, and data processing and inversion are performed to obtain a result diagram. According to the simulation experiment, the fault can be effectively detected by using the HSP method, but when the fault is detected, results obtained by processing signals received by detectors with different frequencies have different spectrum characteristics, wherein the results obtained by the low-frequency detectors can more intuitively reflect a real entity model, and the influence of boundaries is small.

The geological forecast experiment of the karst is that different media used for simulating actual karst are filled in the karst model 3 and then detected by using detection equipment, the front karst model 3 is detected through the position of the tunnel face B in the tunnel model 1, different media such as water, sand, mud and the like can be filled in the model, and information such as space wave field characteristics, map characteristics, detection resolution and the like is analyzed, so that basic information can be provided for geological forecast. As shown in fig. 5 and 6, the results are obtained by performing experiments on the karst by using a horizontal seismic wave profile method (HSP method), receiving information by using geophones with frequencies of 38Hz and 2000Hz, and performing data processing and inversion. The simulation experiment shows that the HSP method can effectively detect the karst, but the results obtained by detectors with different frequencies are visually reflected to a real entity model by virtue of slight difference of the graph characteristics of the results obtained by the detectors with the two frequencies, the influence of the boundary is small, but the phenomenon of multiple reflections exists, and the abnormal alternation phenomenon is reflected in the graph.

The geological prediction experiment on the boulder refers to the steps of detecting the buried boulder model 4 by using detection equipment, detecting the boulder model 4 in front by the left side position in the tunnel model 1, analyzing information such as space wave field characteristics, map characteristics and detection resolution, and providing basic information for geological prediction. As shown in fig. 7 and 8, the horizontal seismic wave profile method (HSP method) is used to perform an experiment on the boulder, and geophones with frequencies of 38Hz and 2000Hz are respectively used to receive information and perform data processing and inversion to obtain a result diagram. According to the simulation experiment, the HSP method can effectively detect the boulder, the results obtained by processing signals received by detectors with different frequencies have different spectrum characteristics, wherein the results obtained by the high-frequency detectors can more intuitively reflect a real entity model and are less influenced by boundaries.

The geological prediction experiment for the stratum interface refers to the steps of directly detecting the stratum interface at the bottom of the tunnel by using detection equipment, directly detecting the stratum interface at the bottom through the bottom (a simulation palm surface) in the tunnel model 1, analyzing information such as space wave field characteristics, map characteristics, detection resolution and the like, and providing basic information for geological prediction.

Accordingly, the simulation test of step S2 is performed by the probe apparatus to determine whether the probe apparatus can detect the unfavorable geological conditions, and the simulation matching can be performed on the probe parameters and the data processing method required for various unfavorable geological conditions.

And S3, adjusting the parameters of the detection equipment to the optimal detection parameters corresponding to the geological environments obtained in the step S2, detecting the actual geological environment by using a detection model, and analyzing the information such as the spatial wave field characteristics, the map characteristics, the detection resolution and the like of the actual detection to realize the advanced prediction of the geological bad body.