A method and system for evaluating the stability of a tunnel cave roof through intelligent detection

Abstract

The application provides a tunnel karst cave roof stability evaluation method and system for intelligent detection, wherein the geological state of the tunnel karst cave roof is explored through deep drilling, ranging is performed through a surface leveling laser range finder, signals reflected by natural surfaces are received for ranging, and a three-dimensional entity model of the tunnel karst cave is constructed; original point cloud data of the karst cave are processed through 3D analysis, filtering and interpretation, and a numerical analysis model of the karst cave is established based on a finite element simulation program; mechanical calculation and finite element simulation calculation and analysis of the tunnel karst cave roof are performed according to actual external loads of the tunnel and serialized virtual external loads of the tunnel, and sequence data of the stability analysis of the karst cave roof are generated; and whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel is judged according to the sequence data of the mechanical calculation of the tunnel karst cave roof and statistical analysis of the maximum principal stress data of the tunnel karst cave roof.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of magnetic induction detection laser detection tunnel geology analysis, more particularly, the present application relates to a tunnel karst roof stability evaluation system and method for intelligent detection. BACKGROUND

[0002] In tunnel engineering, the stability of the tunnel karst roof is very important, and is complex and diverse due to factors such as the location, size and geology of the karst, and the location, type and filling degree of the tunnel, which makes the construction of the tunnel karst section a high-risk operation, and the karst and the tunnel interact with each other, so it is very critical to analyze the stability of the tunnel under the karst roof thickness, how to explore the geological state of the tunnel karst roof and construct a three-dimensional entity model of the tunnel karst, how to process and filter data, interpret and establish a numerical analysis model of the karst, how to accurately calculate and simulate the mechanical calculation of the tunnel karst roof and analyze the finite element, how to statistically analyze the stress data of the tunnel karst roof and judge the stability of the karst roof, and other problems remain to be solved. SUMMARY

[0003] A series of simplified concepts are introduced in the summary section, which will be further described in detail in the detailed description section; the summary section of the present application does not mean to attempt to limit the key features and necessary technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0004] To at least partially solve the above problems, the present application provides a tunnel karst roof stability evaluation method for intelligent detection, comprising:

[0005] S10, the geological state of the tunnel karst roof is explored by deep drilling, and the ranging is performed by a surface leveling laser range finder, and the ranging is performed by accepting the signal reflected by the natural object surface, and a three-dimensional entity model of the tunnel karst is constructed;

[0006] S20, the original point cloud data of the karst is processed and filtered by 3D analysis, and a numerical analysis model of the karst is established based on a finite element simulation program;

[0007] S30, according to the actual tunnel external load and the serialized virtual tunnel external load, the mechanical calculation of the tunnel karst roof and the finite element simulation calculation analysis are performed, and the karst roof stability analysis sequence data is generated;

[0008] S40, according to the mechanical calculation sequence data of the tunnel karst roof, the maximum principal stress data of the tunnel karst roof is statistically analyzed, and whether the stability of the karst roof meets the requirement of the maximum load of the tunnel is judged.

[0009] Preferably, S10 comprises:

[0010] S101, the geological state of the tunnel cave roof is explored by deep drilling;

[0011] S102, a multi-directional acoustic magnetic vibration probe group is arranged at the end of the drilling exploration to detect the local vibration resistance strength of the tunnel cave roof;

[0012] S103, ranging is performed by a surface leveling laser range finder, the surface leveling laser range finder actively emits laser light, at the same time, signals reflected by the surface of natural objects are received to perform ranging, and a three-dimensional entity model of the tunnel cave is constructed.

[0013] Preferably, S20 comprises:

[0014] S201, after the original point cloud data of the cave is processed by 3D analysis, the cave 3D analysis processing point cloud data is obtained;

[0015] S202, the cave 3D analysis processing point cloud data is filtered and processed, and the filtered and processed data is interpreted to obtain cave 3D point cloud interpretation information;

[0016] S203, according to the cave 3D point cloud interpretation information, a cave numerical analysis model is established based on a finite element simulation program.

[0017] Preferably, S30 comprises:

[0018] S301, according to the actual tunnel external load, the load upper limit and lower limit range are sequentially virtually expanded to generate a sequential virtual tunnel external load;

[0019] S302, according to the actual tunnel external load and the sequential virtual tunnel external load, tunnel cave roof mechanical calculation is performed to obtain tunnel cave roof mechanical calculation sequence data;

[0020] S303, according to the sequential virtual tunnel external load, finite element simulation calculation and analysis are performed to generate cave roof stability analysis sequence data.

[0021] Preferably, S40 comprises:

[0022] S401, according to the tunnel cave roof mechanical calculation sequence data, tunnel cave roof maximum principal stress data is statistically analyzed;

[0023] S402, according to the cave roof stability analysis sequence data, whether the cave roof stability meets the tunnel external maximum load requirement is judged through the cave roof maximum principal stress data.

[0024] The application provides a tunnel cave roof stability evaluation system for intelligent detection, comprising:

[0025] The roof geological surface leveling monitoring subsystem explores the tunnel cave roof geological state through deep drilling, measures the distance through the surface leveling laser range finder, and receives the signals reflected by the natural surface to measure the distance, thereby constructing a three-dimensional entity model of the tunnel cave;

[0026] The filtered and interpreted numerical model subsystem processes and filters the original point cloud data of the cave through 3D analysis, and establishes a numerical analysis model of the cave based on a finite element simulation program;

[0027] The cave roof mechanical analysis subsystem performs mechanical calculation and finite element simulation calculation and analysis of the tunnel cave roof according to the actual tunnel external load and the serialized virtual tunnel external load, and generates sequence data for stability analysis of the cave roof;

[0028] The cave roof stress evaluation subsystem statistically analyzes the maximum principal stress data of the tunnel cave roof according to the sequence data for mechanical calculation of the tunnel cave roof, and determines whether the stability of the cave roof meets the requirements of the maximum external load of the tunnel.

[0029] Preferably, the roof geological surface leveling monitoring subsystem comprises:

[0030] The deep drilling exploration subsystem explores the tunnel cave roof geological state through deep drilling;

[0031] The multi-directional acoustic magnetic vibration detection subsystem is provided with a multi-directional acoustic magnetic vibration probe group at the end of the drilling exploration, and detects the local vibration resistance strength of the tunnel cave roof;

[0032] The surface leveling laser ranging modeling subsystem measures the distance through the surface leveling laser range finder, and receives the signals reflected by the natural surface to measure the distance, thereby constructing a three-dimensional entity model of the tunnel cave.

[0033] Preferably, the filtered and interpreted numerical model subsystem comprises:

[0034] The original point cloud 3D analysis subsystem obtains 3D analysis processing point cloud data of the cave after processing the original point cloud data of the cave through 3D analysis;

[0035] The filtered and interpreted numerical model subsystem processes and filters the original point cloud data of the cave through 3D analysis, and establishes a numerical analysis model of the cave based on a finite element simulation program;

[0036] The cave roof mechanical analysis subsystem performs mechanical calculation and finite element simulation calculation and analysis of the tunnel cave roof according to the actual tunnel external load and the serialized virtual tunnel external load, and generates sequence data for stability analysis of the cave roof;

[0037] Preferably, the cave roof mechanical analysis subsystem comprises:

[0038] The tunnel external load sequence subsystem serializes the virtual expansion according to the actual tunnel external load, generates the serialized virtual tunnel external load according to the upper limit and lower limit range of the load, and generates the serialized virtual tunnel external load according to the actual tunnel external load.

[0039] The tunnel cave mechanics calculation subsystem performs tunnel cave roof mechanics calculation according to the actual tunnel external load and the serialized virtual tunnel external load, and obtains tunnel cave roof mechanics calculation sequence data.

[0040] The cave roof stability analysis subsystem performs finite element simulation calculation and analysis according to the serialized virtual tunnel external load, and generates cave roof stability analysis sequence data.

[0041] Preferably, the cave roof stress evaluation subsystem comprises:

[0042] The principal stress data statistical subsystem statistically analyzes the tunnel cave roof maximum principal stress data according to the tunnel cave roof mechanics calculation sequence data.

[0043] The cave roof stability determination subsystem determines whether the cave roof stability meets the tunnel external maximum load requirement according to the cave roof stability analysis sequence data and the cave roof maximum principal stress data.

[0044] Compared with the prior art, the present application at least has the following beneficial effects:

[0045] The tunnel karst cave roof stability evaluation method and system for intelligent detection can analyze the tunnel karst cave state under the conditions of different tunnel positions, types and filling degrees, significantly reduce the construction safety risk, can analyze the tunnel karst cave roof thickness stability, can perform deep drilling exploration on the tunnel karst cave roof geological state, can perform ranging by the surface leveling laser range finder and accept the signal reflected by the natural object surface, the three-dimensional entity model of the tunnel karst cave is more accurate, the tunnel karst cave original point cloud data is processed by 3D analysis, filtering and interpretation, the numerical analysis model of the tunnel karst cave is established based on the finite element simulation program, the model accuracy is significantly improved, the tunnel karst cave roof mechanical calculation and finite element simulation calculation analysis are performed according to the actual tunnel external load and the serialized virtual tunnel external load, the tunnel karst cave roof stability analysis sequence data is generated, the data expansion comprehensiveness is significantly increased, the tunnel karst cave roof maximum principal stress data can be statistically analyzed, whether the tunnel karst cave roof stability meets the tunnel external maximum load requirement can be judged, vibration protection can be performed, the tunnel and karst cave construction operation process safety is significantly improved, and the application has important technical significance and remarkable effect.

[0046] The tunnel karst cave roof stability evaluation method and system for intelligent detection, other advantages, objects and features of the application will be embodied in part through the following description, and part will be understood by those skilled in the art through research and practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, and are used to explain the application together with embodiments of the application, and do not constitute a limitation on the application. In the drawings:

[0048] Figure 1 An embodiment diagram of the tunnel karst cave roof stability evaluation method for intelligent detection.

[0049] Figure 2 An embodiment diagram of the tunnel karst cave roof stability evaluation system for intelligent detection.

[0050] Figure 3 Figure 1 is a schematic diagram of one embodiment of the tunnel karst cave roof stability evaluation system of the present application. DETAILED DESCRIPTION

[0051] The present application will be further described below in conjunction with the accompanying drawings and examples, so that those skilled in the art can implement the present application according to the description; as shown in the drawings, the present application provides a tunnel karst cave roof stability evaluation method, comprising: Figure 1

[0052] S10, the geological state of the tunnel karst cave roof is explored by deep drilling, ranging is performed by a surface leveling laser range finder, and ranging is performed by accepting signals reflected by natural surfaces to construct a three-dimensional entity model of the tunnel karst cave;

[0053] S20, the original point cloud data of the karst cave is processed by 3D analysis, filtering and interpretation, and a numerical analysis model of the karst cave is established based on a finite element simulation program;

[0054] S30, according to actual tunnel external loads and serialized virtual tunnel external loads, mechanical calculation and finite element simulation calculation and analysis of the tunnel karst cave roof are performed to generate karst cave roof stability analysis sequence data;

[0055] S40, according to the tunnel karst cave roof mechanical calculation sequence data, maximum principal stress data of the tunnel karst cave roof is statistically analyzed to determine whether the karst cave roof stability meets the requirements of the maximum external load of the tunnel.

[0056] ​The principle and effect of the above technical solution are as follows: the tunnel karst cave roof stability evaluation method provided by the application can detect intelligently, can explore the geological state of the tunnel karst cave roof through deep drilling, can measure the distance by means of the surface leveling laser range finder and accept the signals reflected by the natural surface at the same time, and can construct the three-dimensional entity model of the tunnel karst cave; the original point cloud data of the karst cave are processed and filtered by 3D analysis, are interpreted, and the numerical analysis model of the karst cave is established based on the finite element simulation program; the mechanical calculation and finite element simulation calculation analysis of the tunnel karst cave roof are carried out according to the actual tunnel external load and the serialized virtual tunnel external load, and the sequence data of the stability analysis of the karst cave roof are generated; the maximum principal stress data of the tunnel karst cave roof are statistically analyzed according to the sequence data of the mechanical calculation of the tunnel karst cave roof, and whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel is judged; the position, size, and geological conditions of the karst cave and the state of the tunnel karst cave under different conditions of the position, type, and filling degree of the tunnel can be analyzed, the safety risk of construction can be significantly reduced, the thickness stability of the karst cave roof can be analyzed, the geological state of the tunnel karst cave roof can be explored by deep drilling, the distance can be measured by means of the surface leveling laser range finder and the signals reflected by the natural surface at the same time, the three-dimensional entity model of the tunnel karst cave is more accurate, the original point cloud data of the karst cave are processed and filtered by 3D analysis, are interpreted, and the numerical analysis model of the karst cave is established based on the finite element simulation program, the accuracy of the model is significantly improved, the mechanical calculation and finite element simulation calculation analysis of the tunnel karst cave roof are carried out according to the actual tunnel external load and the serialized virtual tunnel external load, and the sequence data of the stability analysis of the karst cave roof are generated, the data expansion is significantly increased, the maximum principal stress data of the tunnel karst cave roof can be statistically analyzed, whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel can be judged, vibration protection can be carried out, and the safety of the tunnel and the karst cave in the construction and operation process is significantly improved; the application has important technical significance and remarkable effects.

[0057] In one embodiment, S10 comprises:

[0058] S101, exploring the geological state of the tunnel karst cave roof by deep drilling;

[0059] S102, setting a multi-directional acoustic-magnetic vibration probe group at the end of the drilling exploration to detect the local vibration resistance strength of the tunnel karst cave roof;

[0060] S103, measuring the distance by means of the surface leveling laser range finder, the surface leveling laser range finder actively emits laser, and at the same time, accepts the signals reflected by the natural surface to measure the distance, and constructs the three-dimensional entity model of the tunnel karst cave.

[0061] The principle and effect of the above technical solution are: the geological state of the tunnel cave roof is explored through deep drilling; a multi-directional acoustic-magnetic-vibration probe group is arranged at the end of the drilling to detect the local vibration resistance strength of the tunnel cave roof; a surface leveling laser range finder is used for ranging, the surface leveling laser range finder actively emits laser and simultaneously receives signals reflected by the surface of natural objects for ranging, and a three-dimensional entity model of the tunnel cave is constructed; the ranging is performed by the surface leveling laser range finder, the surface leveling laser range finder actively emits laser and simultaneously receives signals reflected by the surface of natural objects for ranging, and the three-dimensional entity model of the tunnel cave is constructed, including: the ranging is performed by the surface leveling laser range finder; the surface leveling laser range finder includes: a tunnel surface adhering and extending base plate 1031, a precise leveling tooth seat 1032, a laser range finder probe group 1033, and a laser range finding monitoring device 1034; the tunnel surface adhering and extending base plate is adhered to the tunnel surface at any position; the precise leveling tooth seat precisely levels the laser range finder probe group to keep all laser probes of the laser range finder probe group consistent with the horizontal plane reference angle; the laser range finding monitoring device controls automatic leveling, laser emission reflection sensing detection of the laser range finder probe, and laser ranging of the laser range finder; the laser range finder probe group of the surface leveling laser range finder actively emits laser from multiple laser probes, synchronously ranges multiple scanning points to obtain the slant distance from the coordinate center of the precise leveling tooth seat to the scanning points, obtains the spatial relative coordinates of the multiple scanning points and the coordinate center of the precise leveling tooth seat in the horizontal and vertical direction angles of the scanning, simultaneously receives signals reflected by the surface of natural objects for ranging, verifies the spatial relative coordinate accuracy of the multiple scanning points and the coordinate center of the precise leveling tooth seat, selects the spatial relative coordinates of the multiple scanning points and the coordinate center of the precise leveling tooth seat closest to the mean value, obtains the multiple-point-probe mean value mutual verification precise three-dimensional measurement data, and further constructs the three-dimensional entity model of the tunnel cave.

[0062] In one embodiment, S20 includes:

[0063] S201, after the original point cloud data of the cave is processed by 3D analysis, the 3D analysis processed point cloud data of the cave is obtained;

[0064] S202, the 3D analysis processed point cloud data of the cave is filtered and processed, and the filtered and processed data is interpreted to obtain 3D point cloud interpretation information of the cave;

[0065] S203, according to the 3D point cloud interpretation information of the cave, a numerical analysis model of the cave is established based on a finite element simulation program.

[0066] The principle and effect of the above technical solution are that: the original point cloud data of the karst cave is processed by 3D analysis, and karst cave 3D analysis processing point cloud data is obtained; the karst cave 3D analysis processing point cloud data is filtered and processed, and the filtered and processed data is interpreted to obtain karst cave 3D point cloud interpretation information; and a karst cave numerical analysis model is established based on a finite element simulation program according to the karst cave 3D point cloud interpretation information.

[0067] In one embodiment, S30 comprises:

[0068] S301, according to the actual tunnel external load, the virtual expansion is sequenced according to the upper and lower limit range of the load, and the sequenced virtual tunnel external load is generated;

[0069] S302, according to the actual tunnel external load and the sequenced virtual tunnel external load, tunnel karst cave roof mechanical calculation is carried out, and tunnel karst cave roof mechanical calculation sequence data is obtained;

[0070] S303, according to the sequenced virtual tunnel external load, finite element simulation calculation analysis is carried out, and karst cave roof stability analysis sequence data is generated.

[0071] The principle and effect of the above technical solution are that: according to the actual tunnel external load, the virtual expansion is sequenced according to the upper and lower limit range of the load, and the sequenced virtual tunnel external load is generated; according to the actual tunnel external load and the sequenced virtual tunnel external load, tunnel karst cave roof mechanical calculation is carried out, and tunnel karst cave roof mechanical calculation sequence data is obtained; and according to the sequenced virtual tunnel external load, finite element simulation calculation analysis is carried out, and karst cave roof stability analysis sequence data is generated.

[0072] In one embodiment, S40 comprises:

[0073] S401, according to the tunnel karst cave roof mechanical calculation sequence data, tunnel karst cave roof maximum principal stress data is statistically analyzed;

[0074] S402, according to the karst cave roof stability analysis sequence data, whether the karst cave roof stability meets the tunnel external maximum load requirement is judged through the karst cave roof maximum principal stress data.

[0075] The principle and effect of the above technical solution are as follows: according to the tunnel karst cave roof mechanical calculation sequence data, the maximum principal stress data of the tunnel karst cave roof is statistically analyzed; according to the karst cave roof stability analysis sequence data, whether the karst cave roof stability meets the tunnel external maximum load requirement is judged through the maximum principal stress data of the karst cave roof; the maximum principal stress data of the karst cave roof includes the maximum tensile stress data of the karst cave roof and the maximum compressive stress data of the karst cave roof; whether the karst cave roof stability meets the maximum tensile stress data of the karst cave roof or the maximum compressive stress data of the karst cave roof is judged; and under the tunnel external maximum load, a vibration level simulation is resisted; under the maximum tensile stress of the karst cave roof, a plurality of vibration frequencies are simulated, the resonance frequency of the karst cave roof is monitored, under the resonance frequency of the karst cave roof, the vibration frequency is continuously vibrated until the simulated karst cave roof falling state is reached, and the simulated karst cave roof falling resonance time is counted; under the maximum compressive stress of the karst cave roof, a plurality of vibration frequencies are simulated, the resonance frequency of the karst cave roof is monitored, under the resonance frequency of the karst cave roof, the vibration frequency is continuously vibrated until the simulated karst cave roof collapse state is reached, and the simulated karst cave roof collapse resonance time is counted; a resonance frequency damper is arranged to prevent the karst cave roof from falling when the resonance frequency of the karst cave roof is reached.

[0076] As shown in Figure 2 The present application provides a tunnel karst cave roof stability evaluation system for intelligent detection, comprising:

[0077] A roof geological surface leveling monitoring subsystem is used to explore the geological state of the tunnel karst cave roof through deep drilling, measure the distance through a surface leveling laser range finder, and accept signals reflected by the surface of natural objects to measure the distance, and construct a three-dimensional entity model of the tunnel karst cave;

[0078] A filtered interpreted numerical model subsystem is used to process and filter and interpret the original point cloud data of the karst cave through 3D analysis, and establish a numerical analysis model of the karst cave based on a finite element simulation program;

[0079] A karst cave roof mechanical analysis subsystem is used to perform tunnel karst cave roof mechanical calculation and finite element simulation calculation analysis according to the actual tunnel external load and the serialized virtual tunnel external load, and generate karst cave roof stability analysis sequence data;

[0080] A karst cave roof stress evaluation subsystem is used to statistically analyze the maximum principal stress data of the tunnel karst cave roof according to the tunnel karst cave roof mechanical calculation sequence data, and judge whether the karst cave roof stability meets the tunnel external maximum load requirement.

[0081] The principle and effect of the above technical solution are that the tunnel karst cave roof stability evaluation system provided by the application comprises: a roof geological surface leveling monitoring subsystem, which explores the geological state of the tunnel karst cave roof through deep drilling, measures the distance through a surface leveling laser range finder, simultaneously receives signals reflected by the surface of natural objects for distance measurement, and constructs a three-dimensional entity model of the tunnel karst cave; a filtered interpreted numerical model subsystem, which, through 3D analysis processing and filtering and interpretation of original point cloud data of the karst cave, establishes a numerical analysis model of the karst cave based on a finite element simulation program; a karst cave roof mechanical analysis subsystem, which, according to actual external loads of the tunnel and serialized virtual external loads of the tunnel, performs mechanical calculation and finite element simulation calculation analysis on the tunnel karst cave roof to generate sequence data for stability analysis of the karst cave roof; and a karst cave roof stress evaluation subsystem, which, according to the sequence data for mechanical calculation of the tunnel karst cave roof, statistically analyzes maximum principal stress data of the tunnel karst cave roof to determine whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel. The system can analyze the tunnel karst cave state under different conditions of the position, size, and geological complexity of the karst cave and the position, type, and filling degree of the tunnel, significantly reduces the risk of construction safety, can perform stability analysis on the thickness of the tunnel karst cave roof, can explore the geological state of the tunnel karst cave roof through deep drilling and measure the distance through a surface leveling laser range finder while receiving signals reflected by the surface of natural objects for distance measurement, and the three-dimensional entity model of the tunnel karst cave is more accurate. The original point cloud data of the karst cave is processed through 3D analysis, filtering, and interpretation, and a numerical analysis model of the karst cave is established based on a finite element simulation program, and the model accuracy is significantly improved. According to the actual external loads of the tunnel and the serialized virtual external loads of the tunnel, mechanical calculation and finite element simulation calculation analysis are performed on the tunnel karst cave roof to generate sequence data for stability analysis of the karst cave roof, and the data expansion is significantly increased in comprehensiveness. The system can statistically analyze the maximum principal stress data of the tunnel karst cave roof to determine whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel and can perform vibration protection, significantly improving the safety of the tunnel and the karst cave during construction and operation. The system has important technical significance and significant effects.

[0082] In one embodiment, the roof geological surface leveling monitoring subsystem comprises:

[0083] A deep drilling exploration subsystem explores the geological state of the tunnel karst cave roof through deep drilling.

[0084] A multi-directional acoustic magnetic vibration detection subsystem is provided with a multi-directional acoustic magnetic vibration probe group at the end of the drilling exploration to detect the local vibration resistance strength of the tunnel karst cave roof.

[0085] A surface leveling laser ranging modeling subsystem measures the distance through a surface leveling laser range finder, the surface leveling laser range finder actively emits laser light, simultaneously receives signals reflected by the surface of natural objects for distance measurement, and constructs a three-dimensional entity model of the tunnel karst cave.

[0086] The principle and effect of the above technical solution are that the tunnel karst cave roof stability evaluation system provided by the application comprises: a roof geological surface leveling monitoring subsystem, which explores the geological state of the tunnel karst cave roof through deep drilling, measures the distance through a surface leveling laser range finder, simultaneously receives signals reflected by the surface of natural objects for distance measurement, and constructs a three-dimensional entity model of the tunnel karst cave; a filtered interpreted numerical model subsystem, which, through 3D analysis processing and filtering and interpretation of original point cloud data of the karst cave, establishes a numerical analysis model of the karst cave based on a finite element simulation program; a karst cave roof mechanical analysis subsystem, which, according to actual external loads of the tunnel and serialized virtual external loads of the tunnel, performs mechanical calculation and finite element simulation calculation analysis on the tunnel karst cave roof to generate sequence data for stability analysis of the karst cave roof; and a karst cave roof stress evaluation subsystem, which, according to the sequence data for mechanical calculation of the tunnel karst cave roof, statistically analyzes maximum principal stress data of the tunnel karst cave roof to determine whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel. The system can analyze the tunnel karst cave state under different conditions of the position, size, and geological complexity of the karst cave and the position, type, and filling degree of the tunnel, significantly reduces the risk of construction safety, can perform stability analysis on the thickness of the tunnel karst cave roof, can explore the geological state of the tunnel karst cave roof through deep drilling and measure the distance through a surface leveling laser range finder while receiving signals reflected by the surface of natural objects for distance measurement, and the three-dimensional entity model of the tunnel karst cave is more accurate. The original point cloud data of the karst cave is processed through 3D analysis, filtering, and interpretation, and a numerical analysis model of the karst cave is established based on a finite element simulation program, and the model accuracy is significantly improved. According to the actual external loads of the tunnel and the serialized virtual external loads of the tunnel, mechanical calculation and finite element simulation calculation analysis are performed on the tunnel karst cave roof to generate sequence data for stability analysis of the karst cave roof, and the data expansion is significantly increased in comprehensiveness. The system can statistically analyze the maximum principal stress data of the tunnel karst cave roof to determine whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel and can perform vibration protection, significantly improving the safety of the tunnel and the karst cave during construction and operation. The system has important technical significance and significant effects.Figure 3 As shown, the roof geological surface leveling monitoring subsystem includes: a deep drilling exploration subsystem for exploring the geological state of the tunnel cave roof through deep drilling; a multi-directional acoustic magnetic vibration detection subsystem for setting a multi-directional acoustic magnetic vibration probe group at the end of the drilling exploration to detect the local vibration resistance strength of the tunnel cave roof; a surface leveling laser ranging modeling subsystem for ranging by a surface leveling laser range finder, the surface leveling laser range finder actively emits laser light and simultaneously receives signals reflected by the natural object surface to range, and a three-dimensional entity model of the tunnel cave is constructed; ranging by the surface leveling laser range finder, the surface leveling laser range finder actively emits laser light and simultaneously receives signals reflected by the natural object surface to range, and a three-dimensional entity model of the tunnel cave is constructed, including: ranging by the surface leveling laser range finder; the surface leveling laser range finder includes: a tunnel surface adhering extension base plate 1031, a precision leveling tooth seat 1032, a laser range finder probe group 1033, and a laser ranging monitoring device 1034; the tunnel surface adhering extension base plate is adhered to the tunnel surface at any position; the precision leveling tooth seat precisely levels the laser range finder probe group to keep all laser probes of the laser range finder probe group consistent with the horizontal plane reference angle; the laser ranging monitoring device controls automatic leveling, laser emission reflection sensing detection of the laser range finder probe, and ranging of the laser range finder; the laser range finder probe group of the surface leveling laser range finder actively emits laser light from multiple laser probes, synchronously ranges multiple scanning points to obtain the slant distance from the coordinate center of the precision leveling tooth seat to the scanning points, obtains the spatial relative coordinates of the multiple scanning points and the coordinate center of the precision leveling tooth seat in the horizontal and vertical direction angles of the scanning, simultaneously receives signals reflected by the natural object surface to range, and verifies the spatial relative coordinate accuracy of the multiple scanning points and the coordinate center of the precision leveling tooth seat, selects the spatial relative coordinates of the multiple scanning points and the coordinate center of the precision leveling tooth seat closest to the mean value, obtains the multiple-point-probe mean value mutual verification precision three-dimensional measurement data, and further constructs a three-dimensional entity model of the tunnel cave.

[0087] In one embodiment, the filtered interpreted numerical model subsystem includes:

[0088] The original point cloud 3D analysis subsystem obtains cave 3D analysis processing point cloud data after 3D analysis processing of cave original point cloud data.

[0089] The filtered data interpretation subsystem filters and interprets the cave 3D analysis processing point cloud data to obtain cave 3D point cloud interpretation information.

[0090] The cave numerical analysis model subsystem establishes a cave numerical analysis model based on a finite element simulation program according to the cave 3D point cloud interpretation information.

[0091] The principle and effect of the above technical solution are: the filtering interpretation numerical model subsystem includes: an original point cloud 3D analysis subsystem, after the original point cloud data of a karst cave is processed by 3D analysis, the karst cave 3D analysis processed point cloud data is obtained; a filtering processing data interpretation subsystem, which performs filtering processing on the karst cave 3D analysis processed point cloud data, interprets the filtering processing data, and obtains karst cave 3D point cloud interpretation information; and a karst cave numerical analysis model subsystem, which establishes a karst cave numerical analysis model based on a finite element simulation program according to the karst cave 3D point cloud interpretation information.

[0092] In one embodiment, the karst cave roof mechanical analysis subsystem includes:

[0093] The tunnel external load sequence subsystem virtually extends the actual tunnel external load according to the upper limit and lower limit range of the load, and generates a sequence of virtual tunnel external loads.

[0094] The tunnel karst cave mechanical calculation subsystem performs mechanical calculation on the tunnel karst cave roof according to the actual tunnel external load and the sequence of virtual tunnel external loads, and obtains tunnel karst cave roof mechanical calculation sequence data.

[0095] The karst cave roof stability analysis subsystem performs finite element simulation calculation and analysis according to the sequence of virtual tunnel external loads, and generates karst cave roof stability analysis sequence data.

[0096] The principle and effect of the above technical solution are: the karst cave roof mechanical analysis subsystem includes: a tunnel external load sequence subsystem, which virtually extends the actual tunnel external load according to the upper limit and lower limit range of the load, and generates a sequence of virtual tunnel external loads; a tunnel karst cave mechanical calculation subsystem, which performs mechanical calculation on the tunnel karst cave roof according to the actual tunnel external load and the sequence of virtual tunnel external loads, and obtains tunnel karst cave roof mechanical calculation sequence data; and a karst cave roof stability analysis subsystem, which performs finite element simulation calculation and analysis according to the sequence of virtual tunnel external loads, and generates karst cave roof stability analysis sequence data.

[0097] In one embodiment, the karst cave roof stress evaluation subsystem includes:

[0098] The principal stress data statistical subsystem statistically analyzes the maximum principal stress data of the tunnel karst cave roof according to the tunnel karst cave roof mechanical calculation sequence data.

[0099] The karst cave roof stability determination subsystem determines whether the stability of the karst cave roof meets the requirements of the maximum external load of the tunnel according to the karst cave roof stability analysis sequence data and the maximum principal stress data of the karst cave roof.

[0100] The principle and effect of the technical scheme are as follows: the cave roof stress evaluation system comprises a principal stress data statistical subsystem, which statistically analyzes tunnel cave roof maximum principal stress data according to tunnel cave roof mechanics calculation sequence data; a cave roof stability determination subsystem, which determines whether the cave roof stability meets tunnel external maximum load requirements by the cave roof maximum principal stress data according to cave roof stability analysis sequence data; the cave roof maximum principal stress data comprises cave roof maximum tensile stress data and cave roof maximum compressive stress data; whether the cave roof stability meets the cave roof maximum tensile stress data or the cave roof maximum compressive stress data is determined; and under the tunnel external maximum load, vibration resistance level simulation is carried out; under the cave roof maximum tensile stress, various vibration frequencies are simulated, the cave roof resonance frequency is monitored, under the cave roof resonance frequency, the vibration frequency is continuously carried out until the simulated cave roof falling state is reached, and the simulated cave roof falling resonance time is counted; under the cave roof maximum compressive stress, various vibration frequencies are simulated, the cave roof resonance frequency is monitored, under the cave roof resonance frequency, the vibration frequency is continuously carried out until the simulated cave roof collapse state is reached, and the simulated cave roof collapse resonance time is counted; a resonance frequency damper is arranged to prevent the cave roof from falling when the cave roof resonance frequency is reached.

[0101] Although the embodiments of the present application have been disclosed as above, they are not limited to the application listed in the specification and the embodiments, and can be fully applied to various fields suitable for the present application, and additional modifications can be easily made by those skilled in the art, and thus the present application is not limited to specific details and the figures shown and described herein, without departing from the general concept defined by the claims and the equivalent scope.

Claims

1. A method for evaluating the stability of the roof slab of a tunnel karst cave using intelligent detection, characterized in that, include: S10 involves exploring the geological conditions of the tunnel's karst roof through deep drilling, measuring distances using a surface-leveling laser rangefinder, and simultaneously receiving signals reflected from natural surfaces to measure distances, thereby constructing a three-dimensional solid model of the tunnel's karst cave. S20, the original point cloud data of the karst cave is processed and filtered and interpreted through 3D analysis, and a numerical analysis model of the karst cave is established based on the finite element simulation program; S30: Based on the actual external load of the tunnel and the serialized virtual external load of the tunnel, perform mechanical calculations and finite element simulation analysis of the tunnel karst cave roof, and generate sequence data for stability analysis of the karst cave roof. S40. Based on the mechanical calculation sequence data of the tunnel karst cave roof, statistically analyze the maximum principal stress data of the tunnel karst cave roof to determine whether the stability of the karst cave roof meets the requirements of the maximum external load of the tunnel. S10 includes: S103 uses a surface-leveling laser rangefinder to measure distances. The surface-leveling laser rangefinder actively emits lasers and simultaneously receives signals reflected from the surface of natural objects to measure distances, thus constructing a three-dimensional solid model of the tunnel cave. Distance measurement is performed using a surface-leveling laser rangefinder. This rangefinder actively emits laser light and simultaneously receives signals reflected from natural surfaces for distance measurement. The construction of a three-dimensional solid model of the tunnel / karst cave includes: distance measurement using a surface-leveling laser rangefinder; the surface-leveling laser rangefinder comprises: a tunnel surface bonding extension plate, a precision leveling gear, a laser rangefinder probe assembly, and a laser distance measurement monitoring device; the tunnel surface bonding extension plate is fitted to the shape of the tunnel surface at any position; the precision leveling gear precisely levels the laser rangefinder probe assembly, maintaining the consistency of the horizontal reference angle between all laser probes in the laser rangefinder probe assembly; the laser distance measurement monitoring device controls automatic leveling and the laser emission and reflection sensing of the laser probes. The system should be tested and measured using a laser rangefinder. The laser rangefinder probe group of the surface-leveling laser rangefinder actively emits lasers to simultaneously measure the slant distance from the coordinate center of the precision leveling tooth base to multiple scanning points. By coordinating the horizontal and vertical angles of the scan, the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling tooth base are obtained. Simultaneously, signals reflected from natural surfaces are received for distance measurement, and the accuracy of the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling tooth base is mutually verified. The closest average spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling tooth base are selected to obtain multi-point detection average cross-validation precise three-dimensional measurement data, which is then used to construct a three-dimensional solid model of the tunnel / karst cave.

2. The intelligent detection method for evaluating the stability of tunnel karst cave roof slabs according to claim 1, characterized in that, S10 includes: S101, the geological condition of the tunnel's karst roof was explored through deep drilling; S102, a multi-directional acoustic-magnetic resonance probe group is set at the end of the borehole exploration to detect the local vibration resistance of the tunnel karst roof.

3. The method for evaluating the stability of tunnel karst cave roof slabs using intelligent detection according to claim 1, characterized in that, S20 includes: S201, the original point cloud data of the karst cave is processed through 3D analysis to obtain the karst cave 3D analysis processed point cloud data; S202, filter the 3D point cloud data of the karst cave and interpret the filtered data to obtain the 3D point cloud interpretation information of the karst cave; S203. Based on the 3D point cloud interpretation information of the karst cave, a numerical analysis model of the karst cave is established using a finite element simulation program.

4. The method for evaluating the stability of tunnel karst cave roof slabs using intelligent detection according to claim 1, characterized in that, S30 includes: S301, Based on the actual external load of the tunnel, the virtual external load of the tunnel is serialized and virtually extended according to the upper and lower limits of the load range to generate a serialized virtual external load of the tunnel; S302, based on the actual external load of the tunnel and the serialized virtual external load of the tunnel, perform mechanical calculations on the roof of the tunnel karst cave and obtain the mechanical calculation sequence data of the roof of the tunnel karst cave; S303: Based on the serialized virtual tunnel external load, finite element simulation calculation and analysis are performed to generate sequence data for stability analysis of the cave roof.

5. The method for evaluating the stability of tunnel karst cave roof slabs using intelligent detection according to claim 1, characterized in that, S40 includes: S401, based on the mechanical calculation sequence data of the tunnel karst cave roof, statistically analyze the maximum principal stress data of the tunnel karst cave roof; S402. Based on the stability analysis sequence data of the karst cave roof, the maximum principal stress data of the karst cave roof is used to determine whether the stability of the karst cave roof meets the requirements of the maximum external load of the tunnel.

6. A smart detection system for evaluating the stability of tunnel karst cave roof slabs, characterized in that, include: The roof geological surface leveling and monitoring subsystem explores the geological condition of the tunnel karst cave roof through deep drilling, measures distances using a surface leveling laser rangefinder, and simultaneously receives signals reflected from natural object surfaces for distance measurement, thus constructing a three-dimensional solid model of the tunnel karst cave. The filtering and interpretation numerical model subsystem processes the original point cloud data of the karst cave through 3D analysis, filtering, and interpretation, and establishes a numerical analysis model of the karst cave based on the finite element simulation program. The karst cave roof mechanical analysis subsystem performs mechanical calculations and finite element simulation analysis of the tunnel karst cave roof based on the actual external load of the tunnel and the serialized virtual external load of the tunnel, and generates a sequence of karst cave roof stability analysis data. The stress evaluation subsystem for the karst cave roof plate, based on the mechanical calculation sequence data of the tunnel karst cave roof plate, statistically analyzes the maximum principal stress data of the tunnel karst cave roof plate, and judges whether the stability of the karst cave roof plate meets the requirements of the maximum external load of the tunnel. The roof geological surface leveling monitoring subsystem includes: The surface-leveling laser ranging modeling subsystem uses a surface-leveling laser rangefinder to measure distances. The surface-leveling laser rangefinder actively emits lasers and simultaneously receives signals reflected from the surface of natural objects to measure distances and construct a three-dimensional solid model of the tunnel cave. Distance measurement is performed using a surface-leveling laser rangefinder. This rangefinder actively emits laser light and simultaneously receives signals reflected from natural surfaces for distance measurement. The construction of a three-dimensional solid model of the tunnel / karst cave includes: distance measurement using a surface-leveling laser rangefinder; the surface-leveling laser rangefinder comprises: a tunnel surface bonding extension plate, a precision leveling gear, a laser rangefinder probe assembly, and a laser distance measurement monitoring device; the tunnel surface bonding extension plate is fitted to the shape of the tunnel surface at any position; the precision leveling gear precisely levels the laser rangefinder probe assembly, maintaining the consistency of the horizontal reference angle between all laser probes in the laser rangefinder probe assembly; the laser distance measurement monitoring device controls automatic leveling and the laser emission and reflection sensing of the laser probes. The system should be tested and measured using a laser rangefinder. The laser rangefinder probe group of the surface-leveling laser rangefinder actively emits lasers to simultaneously measure the slant distance from the coordinate center of the precision leveling tooth base to multiple scanning points. By coordinating the horizontal and vertical angles of the scan, the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling tooth base are obtained. Simultaneously, signals reflected from natural surfaces are received for distance measurement, and the accuracy of the spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling tooth base is mutually verified. The closest average spatial relative coordinates of multiple scanning points and the coordinate center of the precision leveling tooth base are selected to obtain multi-point detection average cross-validation precise three-dimensional measurement data, which is then used to construct a three-dimensional solid model of the tunnel / karst cave.

7. The intelligent detection system for evaluating the stability of tunnel karst cave roof as described in claim 6, characterized in that, The roof geological surface leveling monitoring subsystem includes: The deep borehole exploration subsystem explores the geological conditions of the roof of tunnel karst caves through deep borehole drilling. The multi-directional acoustic-magnetic resonance detection subsystem sets up a multi-directional acoustic-magnetic resonance probe group at the end of the borehole exploration to detect the local vibration resistance of the tunnel karst cave roof.

8. The intelligent detection system for evaluating the stability of tunnel karst cave roof as described in claim 6, characterized in that, The filtering and interpretation numerical model subsystem includes: The original point cloud 3D analysis subsystem processes the original point cloud data of the karst cave into 3D analyzed point cloud data. The filtering and data interpretation subsystem filters the 3D point cloud data of the karst cave and interprets the filtered data to obtain the 3D point cloud interpretation information of the karst cave. The numerical analysis model subsystem for karst caves establishes a numerical analysis model of the karst caves based on the interpretation information of the 3D point cloud of the karst caves and the finite element simulation program.

9. The intelligent detection system for evaluating the stability of tunnel karst cave roof as described in claim 6, characterized in that, The mechanical analysis subsystem for the cave roof includes: The tunnel external load sequence subsystem generates a serialized virtual tunnel external load by serializing and virtually expanding the actual tunnel external load according to the upper and lower limits of the load range. The tunnel karst cave mechanical calculation subsystem performs mechanical calculations on the tunnel karst cave roof based on the actual external load of the tunnel and the serialized virtual external load of the tunnel, and obtains the mechanical calculation sequence data of the tunnel karst cave roof. The stability analysis subsystem for the cave roof performs finite element simulation calculations and analyses based on the serialized virtual tunnel external loads, generating a sequence of stability analysis data for the cave roof.

10. A smart detection system for evaluating the stability of tunnel karst cave roofs according to claim 6, characterized in that, The stress evaluation subsystem for the roof of a karst cave includes: The principal stress data statistics subsystem statistically analyzes the maximum principal stress data of the tunnel karst cave roof based on the mechanical calculation sequence data of the tunnel karst cave roof. The stability assessment subsystem for the karst cave roof determines whether the stability of the karst cave roof meets the requirements of the maximum external load of the tunnel based on the stability analysis sequence data of the karst cave roof and the maximum principal stress data of the karst cave roof.

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