Method and apparatus for determining safe distance for karst cavity, device, and storage medium

Abstract

Disclosed are a method and an apparatus for determining a safe distance for a karst cavity, a device, and a storage medium. The method comprises: acquiring geological survey data of a target karst region, and constructing a geological simulation model of the target karst region on the basis of the geological survey data; on the basis of feature information of a target karst cavity, constructing a spatial simulation distribution model of the target karst cavity; on the basis of tunnel design parameters, integrating the geological simulation model and the spatial simulation distribution model to obtain a tunnel construction model of the target karst cavity; embedding preselected vehicle information into the tunnel construction model to obtain a corresponding tunnel operation model; and creating disaster simulation events, performing risk prediction for the tunnel operation model, and determining a safe distance for the target karst cavity on the basis of risk prediction results. The method and apparatus for determining a safe distance for a karst cavity, the device, and the storage medium in the present invention optimize tunnel design and construction, ensure the safety of tunnel construction and operation, and provide accurate data model support for actual tunnel engineering.

Description

Cave safety distance determination method, device and equipment and storage medium TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel construction, in particular to a cave safety distance determination method, device, equipment and storage medium. BACKGROUND

[0002] With the continuous development of tunnel engineering, the safety of tunnel engineering construction in karst areas has become a problem that cannot be ignored. In karst geology engineering, karst caves have an important influence on the stability and safety of tunnel structures.

[0003] The construction of tunnel engineering may lead to inaccurate judgment of the safety distance of karst caves, resulting in failure, deformation, even collapse and water and mud inrush of tunnel structures, which brings serious safety hazards to the construction and operation of tunnels. Accurate assessment of the interaction between karst caves and tunnels and reasonable determination of the safety distance are of great significance to the design and construction of tunnel engineering in karst areas.

[0004] The existing method for determining the safety distance of karst caves mainly relies on empirical formula or empirical judgment, lacks systematic analysis and quantitative method, ignores a large number of main control factors, and brings safety hazards to the construction, design and operation and maintenance of karst tunnels. Moreover, the traditional evaluation method often fails to fully consider the comprehensive influence of geological conditions, karst characteristics, tunnel construction, vehicle operation and other factors, resulting in inaccurate evaluation results or lack of scientific basis.

[0005] Therefore, how to use quantitative methods to improve the safety, reliability and economy of tunnel engineering design, construction and operation in karst areas has become a technical problem to be solved by those skilled in the art.

[0006] SUMMARY

[0007] The present application provides a cave safety distance determination method, device, equipment and storage medium, by designing specific steps of the cave safety distance determination method, optimizing the tunnel design and construction, ensuring the safety of tunnel construction and operation, and providing accurate data model support for actual tunnel engineering.

[0008] In order to solve the above technical problems, the present application provides a cave safety distance determination method, which comprises:

[0009] Obtaining geological survey data of a target karst area, and constructing a geological simulation model of the target karst area according to the geological survey data;

[0010] According to the characteristic information of the target karst cave, a spatial simulation distribution model of the target karst cave is constructed;

[0011] Integrate the geological simulation model and the spatial simulation distribution model based on tunnel design parameters to obtain a tunnel construction model of the target karst cave;

[0012] Embed pre-selected vehicle information into the tunnel construction model to obtain a corresponding tunnel operation model;

[0013] Create various catastrophic simulation events and perform risk prediction on the tunnel operation model, and determine a safety distance of the target karst cave according to a risk prediction result.

[0014] Further, the geological survey data at least includes engineering geological data and hydrogeological data; then

[0015] The geological survey data of the target karst area is obtained, and a geological simulation model of the target karst area is constructed according to the geological survey data, comprising:

[0016] According to the engineering geological data and the hydrogeological data, a fissure-surrounding rock geological model and a fault-surrounding rock geological model of the target karst area are constructed respectively.

[0017] Further, the construction of the fissure-surrounding rock geological model of the target karst area comprises:

[0018] Based on the fissure attribute data in the engineering geological data and the fissure attribute data in the hydrogeological data respectively, corresponding fissure rose diagrams are generated;

[0019] Taking the occurrence, direction and number of fissures in the fissure rose diagram as independent variables, and taking the spatial coordinates of the fissures and the total number of fissure surfaces as dependent variables, random fissure surfaces are generated by a random fissure generation method.

[0020] Fissure bodies are generated according to the random fissure surfaces and imported into a 3D model to form the fissure-surrounding rock geological model.

[0021] Further, the construction of the fault-surrounding rock geological model of the target karst area comprises:

[0022] Based on the fault information data in the engineering geological data, an initial geometric model of the fault is established;

[0023] According to a preset simulation software, the initial geometric model is processed, and an upper fault wedge, a fracture zone and a lower fault wedge are sequentially generated on the initial geometric model to obtain the fault-surrounding rock geological model.

[0024] Further, the characteristic information of the target karst cave at least includes diameter distribution information, cave height distribution information, point karst rate information, line karst rate information, surface karst rate information, volume karst rate information and karst cave burial depth distribution information.

[0025] The method further comprises:

[0026] statistically analyzing each of the characteristic information to construct a spatial simulation distribution model of the target cave.

[0027] Further, the statistically analyzing each of the characteristic information comprises:

[0028] sequentially drawing a distribution histogram corresponding to each of the characteristic information;

[0029] selecting a preset fitting mode to fit each of the distribution histograms to obtain a prior probability density function corresponding to each of the characteristic information, wherein the preset fitting mode at least includes exponential distribution fitting, normal distribution fitting, Poisson distribution fitting, lognormal distribution fitting or F distribution function fitting;

[0030] sequentially screening and updating the prior probability density function corresponding to each of the characteristic information according to a Bayesian updating algorithm to obtain a posterior probability density function of each of the characteristic information;

[0031] The posterior probability density function is used to construct the spatial simulation distribution model of the target cave.

[0032] Further, the constructing the spatial simulation distribution model of the target cave according to the characteristic information of the target cave comprises:

[0033] adopting a cellular automaton algorithm based on each of the characteristic information after statistical analysis to construct the spatial simulation distribution model of the target cave; or,

[0034] adopting a three-dimensional surface reconstruction algorithm based on a Poisson equation based on each of the characteristic information after statistical analysis to construct the spatial simulation distribution model of the target cave.

[0035] Further, the integrating the geological simulation model and the spatial simulation distribution model based on the tunnel design parameters to obtain a tunnel construction model of the target cave comprises:

[0036] converting user-selected tunnel design information data into the tunnel design parameters;

[0037] determining integration target data and evaluation standard data based on the tunnel design parameters;

[0038] selecting a corresponding integration method to integrate the geological simulation model and the spatial simulation distribution model to obtain the tunnel construction model of the target cave, wherein the integration method at least includes any one of the following methods: average fusion, weighted average fusion, voting fusion or stacking fusion.

[0039] Further, the embedding of the pre-selected vehicle information into the tunnel construction model to obtain a corresponding tunnel operation model comprises:

[0040] establishing a vehicle dynamic load function according to the vehicle type, the vehicle running speed and the vehicle load distribution of the pre-selected vehicle;

[0041] embedding the vehicle dynamic load function into the tunnel construction model to obtain the tunnel operation model.

[0042] Further, the creating of each disaster simulation event and the risk prediction on the tunnel operation model, and the determination of the safety distance of the target karst cave according to the risk prediction result comprises:

[0043] performing a sensitivity factor analysis on the tunnel construction model and the tunnel operation model respectively;

[0044] generating a plurality of simulation conditions according to the sensitivity factor analysis result;

[0045] creating a corresponding disaster simulation event for each simulation condition, and calculating the occurrence probability of each disaster simulation event;

[0046] quantifying the disaster risk corresponding to each disaster simulation event based on a preset quantitative disaster formula;

[0047] drawing a mapping relationship diagram between each quantified disaster risk and different karst cave distances;

[0048] determining the maximum karst cave treatment distance corresponding to each disaster simulation event according to the mapping relationship diagram, and taking the maximum karst cave treatment distance as the safety distance of the target karst cave.

[0049] Further, the sensitivity factor analysis on the tunnel construction model and the tunnel operation model comprises:

[0050] adopting an orthogonal analysis method to select uncertain factors in the tunnel construction model and the tunnel operation model; the uncertain factors at least include surrounding rock grade, lateral pressure coefficient, groundwater level, water pressure in the solution cavity, karst cave depth, karst cave shape, excavation footage, vehicle load, relative distance and position of the karst cave and the tunnel;

[0051] performing a sensitivity factor analysis on each of the uncertain factors, and sorting each of the uncertain factors according to the obtained sensitivity degree.

[0052] Further, the generating of a plurality of simulation conditions according to the sensitivity factor analysis result, and the creating of a corresponding disaster simulation event for each simulation condition and the calculation of the occurrence probability of each disaster simulation event comprise:

[0053] selecting a plurality of qualified uncertain factors and determining a corresponding attribute value range of each of the uncertain factors;

[0054] generating a plurality of different simulation conditions by using a Monte Carlo method based on the qualified uncertain factors and the corresponding attribute value ranges;

[0055] creating a catastrophe simulation event for each of the simulation conditions and calculating a probability of occurrence of each of the catastrophe simulation events; the catastrophe simulation event at least includes water and mud inrush, collapse, shield machine head drop, large deformation in a tunnel construction process, and collapse, large deformation, and adjacent structure collapse in a tunnel operation process.

[0056] Further, the preset quantitative catastrophe formula is: R=P x (C1+C2)

[0057] wherein R is a catastrophe risk, P is a catastrophe probability, C1 and C2 are respectively a direct loss and an indirect loss corresponding to the catastrophe simulation event.

[0058] Another embodiment of the present application provides a cave safety distance determination device, comprising:

[0059] a data acquisition module configured to acquire geological survey data of a target karst region, and construct a geological simulation model of the target karst region according to the geological survey data;

[0060] a cave space module configured to construct a spatial simulation distribution model of a target cave according to characteristic information of the target cave;

[0061] a tunnel construction module configured to integrate the geological simulation model and the spatial simulation distribution model based on tunnel design parameters, and obtain a tunnel construction model of the target cave;

[0062] a tunnel operation module configured to embed preselected vehicle information into the tunnel construction model, and obtain a corresponding tunnel operation model;

[0063] a safety distance determination module configured to create each catastrophe simulation event, predict a risk of the tunnel operation model, and determine a safety distance of the target cave according to a risk prediction result.

[0064] Still another embodiment of the present application provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the cave safety distance determination method as described above when executing the computer program.

[0065] Another embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, wherein when a device where the computer readable storage medium is located executes the computer program, the computer program implements the karst cave safety distance determination method as described above.

[0066] Compared with the prior art, the embodiment of the present application has at least one of the following advantages:

[0067] (1) By comprehensively considering the comprehensive influence of geology, karst cave, tunnel and operation factors, the tunnel construction is quantitatively analyzed and evaluated, so as to guarantee the safety and reliability of the tunnel construction in karst areas;

[0068] (2) By accurately determining the safety distance of the karst cave, the safety of the tunnel after being built and put into operation can be ensured, and safety accidents in driving can be effectively avoided;

[0069] (3) According to the calculation result of the model, the safety distance is reasonably set, so as to reduce the engineering quantity of the tunnel as much as possible under the premise of ensuring safety, thereby reducing the engineering cost;

[0070] (4) Based on the organic combination of different data models, by using data processing simulation services and advanced information network technology, many safety data of the tunnel karst cave are effectively analyzed, the karst cave safety distance intelligent analysis platform composed of various models is built, the accurate safety distance is obtained, and the tunnel design and construction are optimized, the safety of the tunnel construction and operation is ensured, the hidden danger of the tunnel disaster is excluded, and accurate data model support is provided for actual tunnel engineering. BRIEF DESCRIPTION OF DRAWINGS

[0071] Fig. 1 is a step flowchart of the karst cave safety distance determination method provided by the embodiment of the present application;

[0072] Fig. 2 is a fracture-surrounding rock geological model construction process diagram of the karst cave safety distance determination method provided by the embodiment of the present application;

[0073] Fig. 3 is a fault-surrounding rock geological model construction process diagram of the karst cave safety distance determination method provided by the embodiment of the present application;

[0074] Fig. 4 is a process schematic diagram of establishing a spatial simulation distribution model of a karst cave provided by the embodiment of the present application;

[0075] Fig. 5 is a karst cave processing distance determination schematic diagram provided by the embodiment of the present application;

[0076] Fig. 6 is a structural block diagram of a karst cave safety distance determination device based on adversarial learning provided by the embodiment of the present application;

[0077] Fig. 7 is a structural diagram of a computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0079] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third", etc. can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0080] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of description, and cannot be understood as indicating or implying that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood in specific cases.

[0081] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood in specific cases.

[0082] An embodiment of the present application provides a solution cave safety distance determination method, specifically, please refer to figure 1, figure 1 shows the step flow chart of the solution cave safety distance determination method provided by the embodiment of the present application, including steps S11 to S15:

[0083] In step S11, geological survey data of the target karst region is acquired, and a geological simulation model of the target karst region is constructed according to the geological survey data.

[0084] Preferably, all relevant information data is first collected uniformly, and for ease of display, please refer to Table 1, which shows a summary table of all data in one embodiment of the present application. For example, core drilling equipment and an X-ray diffractometer (XRD) are used to obtain core samples by drilling and perform mineral composition analysis to obtain rock type data; an ultrasonic flaw detector or a geological radar is used to scan and detect the presence and distribution of fissures, and then obtain a fissure rose diagram; a triaxial compression apparatus or a direct shear apparatus is used to measure the internal friction angle of the soil; an automatic water level gauge is used to continuously record the change of the underground water level in the borehole or monitoring well; a portable pH meter or a laboratory chemical analyzer is used to measure the pH value of the water sample on site or in the laboratory; information of the karst cave is obtained by advanced prediction; a speed sensor is used to monitor the running speed of the vehicle, and a dynamic weighing device is used to record the axle load data of the vehicle, and the like. The engineering geological conditions and hydrogeological conditions in Table 1 refer to the geological survey data in step S11, the karst cave information in Table 1 refers to the target karst cave feature information in step S12, the tunnel construction in Table 1 refers to the tunnel design parameters in step S13, and the tunnel operation in Table 1 refers to the vehicle information in step S14. Specifically, the acquisition process of the target karst cave feature information is performed synchronously with the acquisition of the geological survey data, and the same or similar equipment is used for collection. The consistency of these data in time and equipment ensures the comprehensiveness and accuracy of all geological feature information. In addition, the process of obtaining the karst cave feature information is not limited to the geological survey stage, but will continue to the construction stage to ensure that the karst cave feature information can be monitored and updated in a timely manner during the construction process, thereby ensuring the safety and reliability of the project.

[0085] Table 1 Data Summary Table

[0086] Preferably, geological exploration, borehole measurement and other methods can be used to collect the geological survey data of the target karst region, and the geological survey data at least includes engineering geological data and hydrogeological data; then the engineering geological information and the hydrogeological information are quantified to obtain the engineering geological data and the hydrogeological data, and a fissure-fault-surrounding rock geological model is constructed.

[0087] Specifically, the construction of the fissure-fault-surrounding rock geological model includes two parts:

[0088] 1) Construction of a fissure-surrounding rock geological model: as shown in FIG. 2, which shows a fissure-surrounding rock geological model construction process diagram of the karst cave safety distance determination method provided by the embodiment of the present application, the specific steps are as follows:

[0089] Generate a corresponding fracture rose diagram based on the fracture attribute data in the engineering geological data and the fracture attribute data in the hydrogeological data respectively; specifically, the fracture attribute data includes fracture direction, inclination, occurrence and quantity.

[0090] With the occurrence, direction and quantity position distribution rule of the fracture in the fracture rose diagram as the independent variable, and with the spatial coordinates of the fracture and the total number of the fracture surface as the dependent variable, generate a random fracture surface through a random fracture generation method. Specifically, the random fracture generation method can be to use the Monte Carlo method to construct a random fracture generation program, and generate a random fracture surface in the autoCAD software through the random fracture generation program.

[0091] Finally, generate a fracture body in the 3DEC software according to the random fracture surface, and import it into the 3D model in the finite difference software Flac to form a fracture-surrounding rock geological model.

[0092] 2) Construct a fault-surrounding rock geological model: as shown in FIG. 3, FIG. 3 shows a fault-surrounding rock geological model construction process diagram of the karst safety distance determination method provided by the embodiment of the present application, and the specific steps are as follows:

[0093] Based on the fault information data in the engineering geological data, an initial geometric model of the fault is established in the Gocad software.

[0094] Import the 3D model in the finite difference software Flac to generate an upper fault wedge; define a fracture zone according to the fault geometric model; and finally generate a lower fault wedge on the other side of the fracture zone to complete the fault-surrounding rock geological model.

[0095] By pre-investigating geological information and quantifying, a three-dimensional geological model of the karst area is established according to the geological data obtained by the investigation, which can accurately and completely express the geological conditions and various geological structures of the complex karst landform, intuitively reflect the spatial distribution development of the karst area and the information of faults, fractures and their mutual relationship, and improve the accuracy and intuitiveness of geological engineering investigation, so as to facilitate subsequent evaluation and analysis combined with tunnel design data.

[0096] Step S12, according to the characteristic information of the target karst cave, a spatial simulation distribution model of the target karst cave is constructed.

[0097] Preferably, the characteristic information at least includes diameter distribution information, hole height distribution information, point karst rate information, line karst rate information, surface karst rate information, volume karst rate information and karst cave burial depth distribution information. Of course, the characteristic information can also include other types of information data, which is not specifically limited in the embodiment of the present application.

[0098] In the above embodiment, statistical analysis is performed on each of the feature information. Specifically, the embodiment takes the hole height distribution as an example, and other types of feature information can be correspondingly referred to. The statistical analysis steps are as follows:

[0099] 1) Collect the hole height data of the same rock series as the project, and determine the range, number of groups, and group distance of the hole height data.

[0100] 2) Draw a hole height distribution histogram, and use exponential distribution, normal distribution, Poisson distribution, lognormal distribution, F distribution, and other functions for fitting to obtain the hole height prior probability density function PDFf(l, λ) of the macroscopic site, wherein the parameter to be updated is λ, and the prior distribution PDF of the parameter λ is f'(λ) according to the empirical conjugate distribution table shown in Table 2.

[0101] Table 2

[0102] 3) Using the Bayesian updating algorithm, the hole height data of the actual project is used to obtain the posterior distribution PDF of the updated parameter λ, f"(λ) = KL(λ)f'(λ), wherein K is a normalization constant, and L(λ) is a likelihood function.

[0103] 4) The hole height posterior probability distribution function f'(l, λ) of the specific site is obtained through the Bayesian updating algorithm.

[0104] As shown in FIG. 4, FIG. 4 shows a process diagram for establishing a spatial simulation distribution model of a karst cave according to an embodiment of the present application. After completing the statistical analysis of the karst geological feature information, the spatial simulation distribution model of the karst cave group is established according to the above feature information.

[0105] Specifically, the spatial simulation distribution model of the target karst cave is constructed. The present embodiment assumes that the karst cave is spherical, and provides two methods for establishing the spatial simulation distribution model, as follows:

[0106] 1) Based on the statistical analysis of each of the feature information, a cellular automaton algorithm is used to construct the spatial simulation distribution model of the target karst cave, including:

[0107] Selecting a target rock mass, determining the size and geometric shape of the target rock mass.

[0108] Establishing a three-dimensional space model based on the cellular automaton algorithm and dividing the cellular grid.

[0109] Inputting the karst cave generation rules into the three-dimensional space model, which will guide the behavior of the cellular automaton. The karst cave generation rules specifically include the karst cave diameter distribution, the karst cave hole height distribution, the point karst rate, the line karst rate, the surface karst rate, the volume karst rate, the karst cave burial depth distribution, and the rock layer solubility coefficient parameter.

[0110] Set the iteration time step for the three-dimensional space model and perform iterative simulation, at each iteration time step, the cell determines whether to generate a cave according to the surrounding conditions and rules.

[0111] Finally, the generated cave group is used as a spatial simulation distribution model of the cave, and is exported in the form of a grid file, which can be selected as.f3grid or.flac3d.

[0112] 2) Based on the statistical analysis of each feature information, a three-dimensional surface reconstruction algorithm based on Poisson equation is used to construct a spatial simulation model of the target cave, including:

[0113] Select the target rock mass and determine the size and geometric shape of the target rock mass.

[0114] According to the point dissolution rate of karst and the horizontal area of rock mass, the number of caves in the rock mass space is calculated.

[0115] According to the buried depth distribution of karst and the position distribution of cave, the spherical center three-dimensional coordinates of cave are generated.

[0116] Assuming that the cave is spherical, the cave height is taken as the radius, the cave model is generated according to the cave height distribution, and the filler parameters and water pressure parameters in the cave are given.

[0117] Through the above process, a cave spatial simulation distribution model reflecting the spatial distribution of the cave is established, which can accurately grasp the structure and characteristics of the cave before tunnel construction, predict and evaluate the influence of the cave on tunnel construction, develop targeted construction scheme, improve construction efficiency and reduce cost.

[0118] Step S13, based on the tunnel design parameters, integrating the geological simulation model and the spatial simulation distribution model to obtain the tunnel construction model of the target cave.

[0119] Collect and quantify the tunnel design parameters, and determine the integration target data and evaluation standard data based on the tunnel design parameters.

[0120] Based on the tunnel design parameters, using any one of the integration methods of average fusion, weighted average fusion, voting fusion or stacking fusion, the geological simulation model and the spatial simulation distribution model of the cave are integrated to obtain the tunnel construction model.

[0121] According to the tunnel construction model, analyze the influence of mechanical vibration and surrounding rock stress unloading on the tunnel and cave during tunnel excavation and its response law, and specify the corresponding measures.

[0122] The geological simulation model reflecting the karst geology and the spatial simulation distribution model reflecting the spatial distribution in the karst cave are integrated by combining the tunnel design parameters, so that the properties of the geology and the karst cave are comprehensively evaluated, the prediction accuracy is improved, the advantages of the two are combined and closely combined with the tunnel design, so that the generalization ability and overall performance of the model are improved.

[0123] In step S14, the pre-selected vehicle information is embedded into the tunnel construction model to obtain a corresponding tunnel operation model.

[0124] According to the vehicle type, the average speed of vehicle operation, the maximum speed, the acceleration and the vehicle load distribution, a vehicle dynamic load function is established and embedded into the model for analyzing the stress and vibration level applied to different parts of the tunnel to form the tunnel operation model.

[0125] In addition, the establishment of the vehicle dynamic load model can also be achieved by monitoring the dynamic load of trains of similar grades during operation, fitting a dynamic load function, and embedding the vehicle dynamic load finite element simulation software to form the vehicle dynamic load model.

[0126] Considering the future vehicle operation in the tunnel design can improve the traffic capacity and safety of the tunnel, so that the tunnel design considers the operation during the construction phase, which can reduce the safety hazards of the subsequent tunnel after completion and put into use, optimize the design scheme, reduce the operation cost, and meet the future traffic development needs.

[0127] In step S15, various disaster simulation events are created and risk prediction is performed on the tunnel operation model, and the safety distance of the target karst cave is determined according to the risk prediction result.

[0128] The orthogonal analysis method is used to perform sensitivity analysis on the tunnel construction model and the tunnel operation model. Specifically, the uncertain factors include the surrounding rock grade, the lateral pressure coefficient, the groundwater level, the water pressure in the solution cavity, the solution cavity depth, the solution cavity shape, the excavation footage, the vehicle load, the relative distance and position of the solution cavity and the tunnel, etc.

[0129] According to the sensitivity analysis result, each uncertain factor is sorted.

[0130] According to the actual demand, the uncertain factors with higher sensitivity are selected, and the value range of each selected uncertain factor is determined.

[0131] According to the selected uncertain factors, the Monte Carlo method is used to generate several different simulation working conditions, and the simulation working conditions that do not meet the actual engineering are deleted, and the simulation working conditions that meet the actual situation are left.

[0132] Disaster simulation events are created for each simulation condition, and whether the karst tunnel has a disaster is determined according to local strain, local stress, plastic strain criterion and the like.

[0133] Specifically, the disaster simulation events include disasters such as water and mud inrush, collapse, shield machine head falling, large deformation and the like in the tunnel construction process, and disasters such as collapse, large deformation, collapse of adjacent structures and the like in the tunnel operation process.

[0134] In the embodiment, the judgment criterion of the disaster is:

[0135] Strength failure criterion: based on the Griffth and Hoek-Brown criterion, when the stress level of the surrounding rock exceeds the strength range of the surrounding rock, it is judged that the karst tunnel has a disaster.

[0136] Plastic zone penetration criterion: when the plastic zone area exceeds the cross-sectional area of the tunnel, or the plastic zone penetrates between the tunnel and the karst cave, it is judged that the karst tunnel has a disaster.

[0137] Strain failure criterion: when the rock mass forms a continuous plastic zone or sliding surface, produces strain concentration, or the strain exceeds the limit strain of the surrounding rock, it is judged that the karst tunnel has a disaster.

[0138] According to the type of the above disaster simulation event and the disaster judgment criterion, the occurrence probability of each disaster simulation event is calculated, assuming that there are n simulation conditions, and the number of times of occurrence of the disaster simulation event is f, then the disaster probability is p = f / n.

[0139] In combination with the disaster probability and the corresponding disaster consequence, the disaster risk is quantified, and the embodiment quantifies the disaster process based on a preset quantification disaster formula, specifically: R = P x (C1 + C2)

[0140] Wherein, R is the disaster risk, P is the disaster probability, C1 and C2 are direct loss and indirect loss corresponding to the disaster simulation event respectively.

[0141] As shown in FIG. 5, FIG. 5 shows a karst cave treatment distance determination schematic diagram provided by the embodiment of the present application, according to the relationship between the karst cave distance and the risk, the mapping relationship diagram between each quantified disaster risk and different karst cave distances is drawn.

[0142] According to the mapping relationship diagram, the karst cave treatment distance corresponding to each disaster simulation event is determined, and the maximum value of the karst cave treatment distance corresponding to each disaster simulation event is taken as the safety distance of the karst cave.

[0143] The karst cave safety distance determination method provided by the present application can quantitatively analyze and evaluate the tunnel construction by comprehensively considering the comprehensive influence of geology, karst cave, tunnel and operation factors, so as to guarantee the safety and reliability of the tunnel construction in karst areas; by accurately determining the safety distance of the karst cave and taking corresponding measures, the safety of the tunnel after being built and put into operation can be ensured, so as to effectively avoid safety accidents in driving; according to the model calculation result, the safety distance can be reasonably set, so as to reduce the engineering quantity of the tunnel as much as possible under the premise of ensuring safety, thereby reducing the engineering cost.

[0144] The embodiment of the present application also provides a karst cave safety distance determination device for executing the karst cave safety distance determination method as described above, and Fig. 6 is a structural block diagram of the karst cave safety distance determination device according to the embodiment of the present application, which comprises:

[0145] A data acquisition module 21 is configured to acquire geological survey data of a target karst area, and construct a geological simulation model of the target karst area according to the geological survey data.

[0146] A karst cave space module 22 is configured to construct a spatial simulation distribution model of a target karst cave according to characteristic information of the target karst cave.

[0147] A tunnel construction module 23 is configured to integrate the geological simulation model and the spatial simulation distribution model based on tunnel design parameters, so as to obtain a tunnel construction model of the target karst cave.

[0148] A tunnel operation module 24 is configured to embed preselected vehicle information into the tunnel construction model, so as to obtain a corresponding tunnel operation model.

[0149] A safety distance determination module 25 is configured to create various disaster simulation events and perform risk prediction on the tunnel operation model, and determine the safety distance of the target karst cave according to the risk prediction result.

[0150] The technical features and technical effects of the system provided by the embodiment of the present application are the same as those of the method provided by the embodiment of the present application, and will not be repeated here. Each module in the above system can be realized by software, hardware and combinations thereof in whole or in part. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0151] In the embodiments of the present application, the data acquisition module 21, the cave space module 22, the tunnel construction module 23, the tunnel operation module 24, and the safety distance determination module 25 can be one or more processors or chips with a communication interface capable of implementing a communication protocol, and can further include a data acquisition device, a memory and related interfaces, a system transmission bus, etc. if necessary; the processor or chip executes program-related codes to realize corresponding functions. Alternatively, the data acquisition module 21, the cave space module 22, the tunnel construction module 23, the tunnel operation module 24, and the safety distance determination module 25 can share an integrated chip or processor, as well as a memory, a data acquisition device, a transmission line, etc. The shared processor or chip executes program-related codes to realize corresponding functions.

[0152] The embodiments of the present application also provide a computer readable storage medium, which comprises a stored computer program; wherein the computer program, when executed, controls a device where the computer readable storage medium is located to perform the cave safety distance determination method.

[0153] The embodiments of the present application also provide a computer device, and Fig. 7 is a structural block diagram of a preferred embodiment of a computer device provided by the present application. The computer device comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The processor realizes the cave safety distance determination method when executing the computer program.

[0154] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, …), which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

[0155] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is a control center of the computer device, and connects various parts of the computer device through various interfaces and lines.

[0156] The memory mainly includes a program storage area and a data storage area. The program storage area can store an operating system, at least one application required by a function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, and can also be a non-volatile memory such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., or the memory can also be other volatile solid-state storage devices.

[0157] It should be noted that the above computer device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the structure block diagram of FIG. 7 is only an example of the computer device, and does not constitute a limitation on the computer device. The computer device can include more or fewer components than those shown in the figure, or some components can be combined or different components can be included.

[0158] In summary, the karst cave safety distance determination method, device, equipment and storage medium provided by the embodiment of the present application have at least one of the following beneficial effects:

[0159] (1) By comprehensively considering the comprehensive influence of geology, karst cave, tunnel and operation factors, quantitative analysis and evaluation of tunnel construction is performed, so that the safety and reliability of tunnel construction in karst areas can be ensured;

[0160] (2) By accurately determining the safety distance of the karst cave, the safety of the tunnel after being built and put into operation can be ensured, and safety accidents in driving can be effectively avoided;

[0161] (3) According to the calculation result of the model, the safety distance is reasonably set, so that the engineering quantity of the tunnel can be reduced as much as possible under the premise of ensuring safety, and the engineering cost can be reduced;

[0162] (4) Based on the organic combination of different data models, using data processing simulation services, advanced information network technology, effectively analyzing various safety data of tunnel karst cave, building a unified karst cave safety distance intelligent analysis platform, realizing the accurate acquisition of safety distance, thereby optimizing the tunnel design and construction, ensuring the safety of tunnel construction and operation, eliminating the hidden danger of tunnel disaster, and providing accurate data model support for actual tunnel engineering.

[0163] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for determining the safe distance from a karst cave, characterized in that, include: Obtain geological survey data of the target karst area, and construct a geological simulation model of the target karst area based on the geological survey data; Based on the characteristic information of the target karst cave, a spatial simulation distribution model of the target karst cave is constructed; Based on the tunnel design parameters, the geological simulation model and the spatial simulation distribution model are integrated to obtain the tunnel construction model of the target karst cave; By embedding pre-selected vehicle information into the tunnel construction model, a corresponding tunnel operation model is obtained; Various disaster simulation events are created and risk predictions are performed on the tunnel operation model. Based on the risk prediction results, the safe distance to the target karst cave is determined.

2. The method for determining the safe distance to a karst cave as described in claim 1, characterized in that, The geological exploration data includes at least engineering geological data and hydrogeological data; therefore, The process of acquiring geological survey data of the target karst area and constructing a geological simulation model of the target karst area based on the geological survey data includes: Based on the engineering geological data and the hydrogeological data, a fracture-surrounding rock geological model and a fault-surrounding rock geological model of the target karst region are constructed respectively.

3. The method for determining the safe distance to a karst cave as described in claim 2, characterized in that, The construction of the fracture-surrounding rock geological model of the target karst region includes: Based on the fracture attribute data in the engineering geological data and the fracture attribute data in the hydrogeological data, corresponding fracture rose diagrams are generated respectively. Using the orientation, direction, and quantity distribution of fractures in the fracture rose diagram as independent variables, and the spatial coordinates of the fractures and the total number of fracture surfaces as dependent variables, random fracture surfaces are generated using a random fracture generation method. The fractured body is generated based on the random fracture surface and imported into the 3D model to form the fracture-surrounding rock geological model.

4. The method for determining the safe distance to a karst cave as described in claim 2, characterized in that, The construction of the fault-surrounding rock geological model of the target karst region includes: Based on the fault information data in the engineering geological data, an initial geometric model of the fault is established. The initial geometric model is processed by the preset simulation software, and an upper fault wedge, a fracture zone, and a lower fault wedge are generated sequentially on the initial geometric model to obtain the fault-surrounding rock geological model.

5. The method for determining the safe distance to a karst cave as described in claim 1, characterized in that, The characteristic information of the target karst cave includes at least diameter distribution information, cave height distribution information, point karst ratio information, line karst ratio information, surface karst ratio information, volume karst ratio information, and karst cave burial depth distribution information; The method further includes: Statistical analysis is performed on each of the aforementioned feature information to construct a spatial simulation distribution model of the target karst cave.

6. The method for determining the safe distance to a karst cave as described in claim 5, characterized in that, The statistical analysis of each of the aforementioned feature information includes: Draw the distribution histogram corresponding to each of the aforementioned feature information in sequence; For each of the aforementioned distribution histograms, a preset fitting method is used to fit the data to obtain the prior probability density function corresponding to each of the aforementioned feature information. The preset fitting method includes at least exponential distribution fitting, normal distribution fitting, Poisson distribution fitting, log-normal distribution fitting, or F distribution function fitting. According to the Bayesian update algorithm, the prior probability density function corresponding to each feature information is sequentially filtered and updated to obtain the posterior probability density function of each feature information. The posterior probability density function is used to construct the spatial simulation distribution model of the target cave.

7. The method for determining the safe distance to a karst cave as described in claim 6, characterized in that, The step of constructing a spatial simulation distribution model of the target karst cave based on its characteristic information includes: Based on the statistically analyzed feature information, a spatial simulation distribution model of the target karst cave is constructed using a cellular automata algorithm; or, Based on the statistical analysis of the various feature information, a spatial simulation model of the target cave is constructed using a three-dimensional surface reconstruction algorithm based on the Poisson equation.

8. The method for determining the safe distance to a karst cave as described in claim 1, characterized in that, The process involves integrating the geological simulation model and the spatial simulation distribution model based on tunnel design parameters to obtain the tunnel construction model for the target karst cave, including: Convert the user-selected tunnel design information data into the tunnel design parameters; Based on the tunnel design parameters, the integrated target data and evaluation standard data are determined; The geological simulation model and the spatial simulation distribution model are integrated using a corresponding integration method to obtain the tunnel construction model of the target karst cave. The integration method includes at least one of the following methods: average integration, weighted average integration, voting integration, or stacked integration.

9. The method for determining the safe distance to a karst cave as described in claim 1, characterized in that, The step of embedding pre-selected vehicle information into the tunnel construction model to obtain the corresponding tunnel operation model includes: Establish a vehicle dynamic load function based on the pre-selected vehicle type, vehicle speed, and vehicle load distribution; The vehicle dynamic load function is embedded into the tunnel construction model to obtain the tunnel operation model.

10. The method for determining the safe distance to a karst cave as described in claim 1, characterized in that, The process of creating various disaster simulation events and performing risk prediction on the tunnel operation model, and determining the safe distance to the target cave based on the risk prediction results, includes: Sensitivity factor analysis was performed on the tunnel construction model and the tunnel operation model respectively; Several simulation conditions are generated based on the results of sensitivity factor analysis. For each of the aforementioned simulation conditions, a corresponding disaster simulation event is created, and the probability of occurrence of each of the aforementioned disaster simulation events is calculated; Based on a preset quantitative disaster risk formula, the disaster risk corresponding to each of the disaster simulation events is quantified. Draw a mapping relationship between each quantified catastrophic risk and different cave distances; Based on the mapping diagram, the maximum value of the cave treatment distance corresponding to each of the disaster simulation events is determined, and the maximum value of the cave treatment distance is taken as the safe distance of the target cave.

11. The method for determining the safe distance to a karst cave as described in claim 10, characterized in that, The sensitivity factor analysis of the tunnel construction model and the tunnel operation model includes: Orthogonal analysis was used to select uncertainties in the tunnel construction model and the tunnel operation model, respectively. The uncertainties included at least the surrounding rock grade, lateral pressure coefficient, groundwater level, water pressure in the karst cavity, karst cave burial depth, karst cave shape, excavation advance, vehicle load, and the relative distance and location between the karst cave and the tunnel. Sensitivity factor analysis was performed on each of the uncertain factors, and the uncertain factors were ranked according to the obtained sensitivity.

12. The method for determining the safe distance to a karst cave as described in claim 11, characterized in that, Several simulation conditions are generated based on the results of sensitivity factor analysis. For each of the aforementioned simulation conditions, corresponding disaster simulation events are created, and the probability of occurrence of each of the aforementioned disaster simulation events is calculated, including: Select several uncertain factors that meet the conditions and determine the range of attribute values ​​corresponding to each uncertain factor; Based on the uncertain factors that meet the conditions and the corresponding attribute value ranges, several different simulation conditions are generated using the Monte Carlo method. For each of the aforementioned simulation conditions, create disaster simulation events and calculate the probability of occurrence of each disaster simulation event; the disaster simulation events include at least: water and mud inrush, collapse, shield machine head drop, and large deformation during tunnel construction, as well as collapse, large deformation, and collapse of adjacent structures during tunnel operation.

13. The method for determining the safe distance to a karst cave as described in claim 12, characterized in that, The preset quantification disaster formula is: R = P × (C1 + C2) Where R represents the disaster risk; P represents the disaster probability; and C1 and C2 represent the direct and indirect losses corresponding to the disaster simulation event, respectively.

14. A device for determining the safe distance to a karst cave, characterized in that, include: The data acquisition module is used to acquire geological survey data of the target karst area and construct a geological simulation model of the target karst area based on the geological survey data. The cave space module is used to construct a spatial simulation distribution model of the target cave based on its characteristic information. The tunnel construction module is used to integrate the geological simulation model and the spatial simulation distribution model based on tunnel design parameters to obtain the tunnel construction model of the target karst cave; The tunnel operation module is used to embed pre-selected vehicle information into the tunnel construction model to obtain the corresponding tunnel operation model; The safe distance determination module is used to create various disaster simulation events and perform risk prediction on the tunnel operation model, and determine the safe distance of the target cave based on the risk prediction results.

15. A computer device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method for determining the safe distance of a karst cave as described in any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the method for determining the safe distance of a karst cave as described in any one of claims 1 to 13.

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