Method for monitoring tunnel geology in karst area and related equipment

Abstract

The invention discloses a karst area tunnel geology monitoring method and related equipment, and relates to the technical field of tunnel monitoring, and the method comprises the steps: determining the water content of rock based on resistivity information; determining a seepage velocity and a water head height based on the hydrological seepage model; performing time-frequency domain analysis on the water content and the water head height, and determining hydrological frequency spectrum characteristics; and based on the seepage velocity and the hydrological frequency spectrum characteristics, a grout diffusion coupling model and an intelligent grouting optimization model are constructed, and the grouting amount and the grouting pressure in the grouting process are controlled. According to the method, the conductivity in tunnel construction in the karst area is monitored in real time, the hydrological permeation model and the intelligent optimization control method are combined, the grouting amount and pressure are dynamically adjusted, it is ensured that the grout effectively fills the cavity, and the overall stability and construction safety of the tunnel are improved.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel monitoring, and in particular to a method for geological monitoring of tunnels in karst areas and related equipment. Background Art

[0002] During the construction of tunnels in karst areas, geological stability and hydrological permeability are key factors to ensure construction safety and long-term stable operation of tunnels. Traditional geological monitoring methods mainly rely on geological exploration and static monitoring methods, such as drilling sampling and regular water level measurement. However, these methods often cannot reflect the dynamic changes of geological and hydrological conditions around the tunnel in real time, resulting in the inability to take effective response measures in time in the event of sudden geological disasters or hydrological anomalies. Therefore, there is an urgent need for a geological monitoring method for tunnels in karst areas to achieve real-time monitoring and dynamic optimization control of the geological conditions of tunnels in karst areas, and to improve the safety and efficiency of tunnel construction. Summary of the invention

[0003] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0004] In a first aspect, the present application provides a method for monitoring tunnel geology in a karst area, comprising:

[0005] Determining the water content of the rock based on the resistivity information, wherein the resistivity information is determined based on the conductivity information;

[0006] Determine the seepage velocity and water head height based on the hydrological infiltration model;

[0007] Performing time-frequency domain analysis on the water content and the water head height to determine hydrological spectrum characteristics, wherein the hydrological spectrum characteristics include water content spectrum characteristics and water head height spectrum characteristics;

[0008] Based on the seepage velocity and the hydrological spectrum characteristics, a slurry diffusion coupling model and an intelligent grouting optimization model are constructed to control the grouting volume and grouting pressure during the grouting process.

[0009] In some embodiments, the specific steps of obtaining the conductivity information include:

[0010] monitoring electric field signals at electrode locations;

[0011] Performing multi-scale wavelet transform on the electric field signal to obtain wavelet coefficients;

[0012] Calculating the conductivity change at the electrode position based on the mapping function and the wavelet coefficients;

[0013] Based on the conductivity change, a spatial three-dimensional reconstruction is performed to obtain a spatial conductivity change;

[0014] Based on the spatial conductivity variation, conductivity information is determined.

[0015] In some embodiments, the water content is determined based on the following formula, expressed as:

[0016]

[0017] Among them, ρ(r, t) is the resistivity; a is the rock characteristic constant; φ is the porosity; S w (r, t) is the water content; m is the rock cementation index; l is the saturation index; t is the time.

[0018] In some embodiments, the water content spectrum characteristics and the water head height spectrum characteristics are determined based on the following formula, expressed as:

[0019]

[0020] Among them, S w (r, τ) is the water content S w h(r, τ) is the signal of the water head height h changing with time τ; ω(τ-t) is the window function; e -j2πfτ is the kernel of Fourier transform, representing the sinusoidal component of frequency f; is the spectrum characteristic of water content; is the frequency spectrum characteristic of water head height.

[0021] In some embodiments, the slurry diffusion coupling model is determined based on the following formula, expressed as:

[0022]

[0023] Among them, C(r, t) is the slurry concentration; v(r, t) is the flow velocity field of the slurry; q(r, t) is the seepage velocity; D(r, t) is the diffusion coefficient; R(C(r, t)) is the slurry solidification reaction term.

[0024] In some embodiments, it further comprises:

[0025] Based on the hydrological spectrum characteristics, the permeability coefficient and the diffusion coefficient are dynamically updated.

[0026] In some embodiments, it further comprises:

[0027] The objective function of the intelligent grouting optimization model is determined based on a concentration deviation control item, a grouting smoothness control item, a water flow influence suppression item, and a water content dynamic optimization item.

[0028] In the second aspect, the present application proposes a karst area tunnel geological monitoring device, comprising:

[0029] A water content determination unit, used to determine the water content of the rock based on the resistivity information, wherein the resistivity information is determined based on the conductivity information;

[0030] A hydrological parameter determination unit, used to determine the seepage velocity and water head height based on a hydrological infiltration model;

[0031] A spectrum feature determination unit, used for performing time-frequency domain analysis on the water content and the water head height to determine hydrological spectrum features, wherein the hydrological spectrum features include water content spectrum features and water head height spectrum features;

[0032] The grouting process control unit is used to construct a slurry diffusion coupling model and an intelligent grouting optimization model based on the seepage velocity and the hydrological spectrum characteristics, and control the grouting amount and grouting pressure during the grouting process.

[0033] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for geological monitoring of tunnels in karst areas of any one of the first aspects when executing the computer program stored in the memory.

[0034] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for monitoring karst area tunnel geology according to any one of the first aspects is implemented.

[0035] In summary, this application realizes high-precision and real-time monitoring of electrical conductivity during tunnel construction in karst areas by designing a multipolar electrode array, and combines the hydrological permeability model and time-frequency domain analysis technology to construct a slurry diffusion coupling model and an intelligent grouting optimization model. The system can dynamically adjust the grouting volume and grouting pressure to ensure that the slurry effectively fills the stratum voids and prevents excessive diffusion or insufficient grouting of the slurry, thereby significantly improving the overall stability and construction safety of the tunnel. In addition, the dynamic parameter update mechanism based on spectral characteristics enables the system to adaptively respond to complex and changeable geological and hydrological conditions. For example, when the spectrum analysis detects the enhancement of a certain frequency component, indicating a rapid change in the groundwater level or a potential change in the stratum structure, the system will adjust the grouting strategy in time to prevent potential geological disasters. This application has a high level of intelligence and precision, and can provide reliable monitoring and optimization control means in a complex karst geological environment, significantly improving the safety and efficiency of tunnel construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0037] Figure 1 A schematic diagram of a method for monitoring geological conditions in a karst area tunnel provided in an embodiment of the present application;

[0038] Figure 2 A schematic diagram of the structure of a tunnel geological monitoring device in a karst area provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of the structure of an electronic device for geological monitoring of tunnels in karst areas provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0041] See also Figure 1 , which is a flow chart of a method for monitoring geological conditions in a karst area tunnel provided in an embodiment of the present application, which may specifically include:

[0042] S110, determining the water content of the rock based on the resistivity information, wherein the resistivity information is determined based on the conductivity information;

[0043] Exemplarily, there is an inverse relationship between resistivity ρ(r, t) and conductivity σ(r, t), expressed as:

[0044] ρ(r, t) = [σ(r, t)] -1

[0045] Among them, ρ(r, t) is resistivity, which represents the impedance of the medium to the electric current. The higher the resistivity, the less conductive the medium is; through resistivity, the physical properties of the medium, such as the density and water content of the rock, can be inferred. σ(r, t) is conductivity, which describes the conductivity of the medium in a certain area. The stronger the conductivity of the medium, the higher the conductivity, reflecting the change in water content or the distribution of minerals in the area. For example, areas with higher water content usually have higher conductivity because water has good conductivity.

[0046] When the conductivity σ(r, t) increases, the resistivity ρ(r, t) decreases, indicating that the conductivity of the medium has become stronger and the resistance has become weaker. For example, rocks or soils with a higher water content will generally exhibit higher conductivity and lower resistivity. When the conductivity σ(r, t) decreases, the resistivity ρ(r, t) increases, indicating that the conductivity of the medium has weakened and its ability to hinder the flow of electricity has increased. For example, in dry rocks, the conductivity will decrease and the resistivity will increase. If the conductivity of an area suddenly increases, this may indicate that the water content of the area has increased, perhaps due to groundwater infiltration or accumulation of water. If the resistivity changes significantly, this may also indicate a change in the geological structure, perhaps due to the expansion or contraction of cracks in the rock formation, causing the conductive properties to change.

[0047] By using a multipolar electrode array to monitor the conductivity changes of the rock formation during tunnel construction in real time, the system can obtain conductivity data at different depths and locations with high accuracy. After processing and analysis, it is converted into resistivity information, which is used to further deduce the water content of the rock. The water content is determined based on the following formula and is expressed as:

[0048]

[0049] Among them, ρ(r, t) is the resistivity, which reflects the resistance of the rock to the electric current; a is the rock characteristic constant, which is related to the mineral composition and structure of the rock; φ is the porosity, which indicates the ratio of the void volume to the total volume in the rock, reflecting the ability of the rock to store fluid; S w (r, t) is the saturation, that is, the water content, which indicates the proportion of rock pores filled with water and reflects the amount of water in the rock. A saturation of 1 indicates that the pores are completely filled with water; a saturation of 0 indicates that no water exists; as the saturation increases, the number of conductive pathways formed by water in the rock increases and the resistivity decreases; as the saturation decreases, the rock becomes drier and the resistivity increases; m is the rock cementation index; l is the saturation index, which indicates the nonlinear relationship between saturation and resistivity.

[0050] The steps to obtain conductivity data include:

[0051] During tunnel construction in karst areas, changes in the electrical conductivity of the tunnel walls and surrounding rock formations are important parameters for assessing geological stability and hydrological permeability. In order to accurately monitor these changes, a multi-polarization electrode array system is designed, which combines high-frequency electromagnetic wave transmission and echo analysis to obtain conductivity information at different depths and locations in real time. The system forms a three-dimensional, multi-scale monitoring network by laying a series of electrodes with different polarization directions and frequency responses on the tunnel walls and inside the karst area. Using a custom electromagnetic wave propagation model and signal processing algorithm, the electrical characteristics of the karst medium are extracted, and the stability of the tunnel wall structure and the hydrological permeability are analyzed.

[0052] The process of constructing a multipolar electrode array includes:

[0053] The electrode array consists of N electrodes, each of which has a polarization direction and frequency characteristics. The position of the electrode is represented by the coordinate r n =(x n ,y n , z n ) represents, where n = 1, 2, ..., N, and the electrode layout determines the spatial distribution of the entire electrode array, ensuring comprehensive coverage of the target area.

[0054] The polarization direction of the electrode is given by the unit vector describe, It is expressed as:

[0055]

[0056] in, is the unit polarization vector of the nth electrode. The polarization direction determines the directionality of the electromagnetic waves emitted and received by the electrode. n and φ n They are the azimuth and elevation angles respectively. The polarization direction vector is composed of three components, namely the component in the X-axis direction, the component in the Y-axis direction and the vector in the Z-axis direction.

[0057] To describe the propagation process of high-frequency electromagnetic waves in karst media, the Maxwell equations for anisotropic media are established, which can be expressed as:

[0058]

[0059] Where r is an arbitrary position vector in three-dimensional space, representing any point in the monitoring area, not limited to the position of the electrode, t is time, E(r, t) is the electric field strength vector, reflects the rotation of the electric field in space, H(r, t) is the magnetic field intensity vector, μ0 is the vacuum magnetic permeability, which indicates the coupling ability between the magnetic field and the electric field, ε0 is the vacuum dielectric constant, which indicates the coupling ability between the electric field and the magnetic field, and ε r(r) is the relative dielectric constant. Due to the geological complexity of karst areas, this parameter is anisotropic, indicating that the medium has different electrical properties in different directions. σ(r, t) is the electrical conductivity. In karst media, different minerals and water contents will cause changes in electrical conductivity. M(r, t) is the magnetization intensity. J s (r, t) is the source current density.

[0060] In karst media, due to the inhomogeneity and anisotropy of the medium, the propagation of electromagnetic waves will be affected by the following: the conductivity at different locations will cause the attenuation and reflection effects of electromagnetic waves. For example, when electromagnetic waves encounter areas with high water content or containing metal minerals, the areas with high conductivity will cause rapid attenuation of the waves. The geological structure in the karst area may have different dielectric properties in different directions, causing electromagnetic waves to propagate at different speeds in all directions. This will cause the bending or deflection of electromagnetic waves in different directions. For example, geological structures containing iron ore may have strong magnetization, which affects the propagation and reflection characteristics of electromagnetic waves. The electric field and magnetic field interact with each other, and changes in the electric field will cause changes in the magnetic field, and vice versa. The propagation of electromagnetic waves in karst media is a complex coupling process that is affected by multiple physical quantities.

[0061] Source current density J s (r, t) is provided by the electrode array and is expressed as:

[0062]

[0063] Among them, I n (t) is the current intensity of the nth electrode, δ(rr n ) is the current source located at r n , ensuring that the current source is only at the electrode position r n exists when r=r n When δ(rr n ) is infinite, but its integral is 1; when r≠r n When δ(rr n ) is 0, concentrating the current density at the exact location of the nth electrode. In other words, the current source only exists at the spatial point r where the electrode is located. n , there is no current at other locations. Therefore, the delta function ensures the spatial localization of the current density, with current only at the electrode locations; The direction of the current emitted by the electrode defines the polarization direction of the current, which affects the directional propagation of the electric and magnetic fields; J s (r, t) is the source current density provided by the electrode array, which represents the current distribution at spatial position r and time t, and it is composed of the current contributed by all electrodes.

[0064] In karst areas, an electrode array is used to send electromagnetic waves and monitor echo signals. The electrode array excites electromagnetic waves through source current density. When electromagnetic waves encounter different media during propagation, they will be reflected, refracted, and scattered. The goal of high-frequency echo analysis is to reflect the conductivity and other physical properties of the tunnel or surrounding geological structures by analyzing the electromagnetic wave echo signals. In this process, each electrode not only emits electromagnetic waves, but also receives electric field signals. n At , the received electric field signal is:

[0065]

[0066] Among them, E n (t) = E(r n , t), represents the electric field strength E measured by the nth electrode at time t n (t), equal to the position r n The electric field strength measured at the location changes with time. n , t), V is the rock layer area around the tunnel, covering the volume affected by electromagnetic wave propagation, G(r n , r, t) is the Green's function of the medium, indicating the transition from r to r n The propagation response includes possible reflection, scattering, attenuation, and delay of electromagnetic waves in the medium, indicating how the electromagnetic wave emitted by the current source position r affects the receiving point r after propagating through the medium. n The electric field signal at .

[0067] In order to monitor the dynamic changes in the conductivity of the rock formation around the tunnel, it is necessary to analyze the electric field signal received by each electrode. n (t) is time-varying and has complex frequency and time components. Time-frequency analysis methods are used to extract features that are helpful in evaluating the dynamic changes of conductivity.

[0068] For each electrode n, the received electric field signal is subjected to multi-scale wavelet transform, and the wavelet coefficient W is obtained. n (a, b), the wavelet transform formula is as follows:

[0069]

[0070] Among them, W n(a, b) is the wavelet coefficient of the nth electrode, which indicates the characteristics of the electric field signal received by the nth electrode at different scales a and translations b after wavelet transformation; ψ(t) is the mother wavelet function, which indicates the basis function used to decompose the signal. The mother wavelet generates a set of functions by scaling and translating. These functions are used to match different characteristics of the signal; a is the scale parameter, which controls the frequency resolution of the wavelet. The smaller scale parameter a is used to capture the fast-changing components (i.e., the high-frequency part) in the signal, while the larger scale a is used to capture the slow-changing components (i.e., the low-frequency part) in the signal. By adjusting the scale parameter a, the characteristics in different frequency ranges are extracted from the electric field signal, thereby reflecting the dynamic behavior of the conductivity change; b is the translation parameter, which determines the position of the wavelet function on the time axis. By adjusting b, the signal can be analyzed at different time points; * is the complex conjugate. The use of the complex conjugate can ensure that the phase information of the signal is not lost during the transformation process. Phase information is crucial for understanding the propagation characteristics of electromagnetic waves and the change of conductivity.

[0071] By establishing the relationship between the wavelet coefficient and the conductivity change, the conductivity change is obtained, which is expressed as:

[0072] Δσ n (t) = F(W n (a, b)

[0073] Among them, Δσ n (t) is the change of conductivity at the nth electrode, which means the change of conductivity at the nth electrode over time. The conductivity reflects the conductivity of the medium, which is closely related to factors such as geological structure, mineral content, and water content. By analyzing the dynamic change of conductivity, we can infer the change of physical properties of the underground medium, and then judge whether the geological structure around the tunnel is stable and whether there are potential geological disasters. n (a, b) are the time-frequency characteristics obtained by performing multi-scale wavelet transform on the electric field signal received by the electrode; F is the mapping function, which represents the relationship between the wavelet coefficients and the conductivity.

[0074] In the process of spatial interpolation and three-dimensional conductivity reconstruction, the core idea is to use the point data collected by the electrode array and convert the discrete data into a continuous spatial conductivity distribution through the interpolation algorithm. The radial basis function interpolation method is used. This method is suitable for karst geological environments with uneven and complex conductivity changes. It can use the conductivity changes of all electrodes for interpolation. The specific expression is:

[0075]

[0076] Among them, λ n (t) is the interpolation coefficient, by satisfying Δσ n(t) = Δσ(r, t) determines the conductivity change Δσ at the nth electrode n (t) is equal to the position r n The conductivity change Δσ(r, t) measured at the electrode determines the conductivity change Δσ at each electrode position. n (t) Impact on the entire area, ||rr n || is any position r to the nth electrode position r n The distance, radial basis function φ(||rr n ||) is the distance from the electrode position r n The influence degree of the electric field at any position r is calculated by using the Gaussian function as the radial basis function. The Gaussian function is chosen because of its smoothness and good locality, that is, the farther the point is from the electrode, the smaller the influence on the electrode is, while the influence on the point near the electrode is greater. The goal of three-dimensional conductivity reconstruction is to calculate the conductivity change Δσ(r, t) at each position point r around the tunnel through the measured values ​​of all electrodes and the interpolation coefficients. The three-dimensional distribution of conductivity changes in the entire monitoring area is obtained through the interpolation results. The conductivity σ(r, t) is the conductivity distribution model after three-dimensional reconstruction, which represents the conductivity of the rock formation around the tunnel at any position and time point. This three-dimensional conductivity distribution model combines the measurement data and interpolation results of all electrodes to provide a continuous and comprehensive picture of conductivity information.

[0077] S120, determining the seepage velocity and water head height based on the hydrological infiltration model;

[0078] Exemplarily, this step relies on the high-precision three-dimensional conductivity data obtained by the multipolarized electrode array mentioned above, combined with the hydrological permeability model, to comprehensively evaluate the hydrological permeability characteristics of the rock formation around the tunnel. The hydrological permeability model is mainly based on Darcy's law and the continuity equation to describe the flow behavior of groundwater in the karst medium, thereby determining the seepage velocity q(r, t) and the head height h(r, t), expressed as:

[0079]

[0080] Among them, q(r, t) is the seepage velocity, which describes the flow speed and direction of the fluid at different locations; K(r) is the permeability coefficient, which indicates the permeability of the medium and reflects the flow resistance of the fluid in the medium. It may be anisotropic, that is, the permeability of the fluid in different directions is different. Areas with larger permeability coefficients (areas where water flows more easily) will lead to faster water flow velocities, while areas with smaller permeability coefficients (such as dense rocks) will hinder the passage of water, resulting in slower flow rates; h(r, t) reflects the potential energy of water at that location. The larger the head height, the higher the water potential energy at that location, and water flows from a high head position to a low head position. The head height is the result of the combined effects of factors such as water pressure, gravity, and topography; is the gradient of the water head height, which describes the rate of change of the water head in space.

[0081] The continuity equation is used to ensure the conservation of mass, that is, in a certain volume, the difference between the amount of water flowing in and the amount of water flowing out is equal to the rate of change of the water volume, expressed as:

[0082]

[0083] in, is the divergence of the seepage velocity, indicating the convergence or divergence of the water flow. A negative divergence indicates that the fluid flows out of the area, and a positive divergence indicates that the fluid flows into the area. s (r, t) is the water source and sink term, which indicates the newly added water source or water sink at that location. Rainfall or groundwater injection increases the water source in the area, while drainage or pumping reduces the water source in the area.

[0084] In the embodiment of the present application, by combining the water content distribution obtained by inversion of the conductivity data, the hydrological permeability model can dynamically simulate the diffusion and convergence of groundwater in the rock formation. The system solves the above equations by numerical methods to generate a spatiotemporal distribution diagram containing the seepage velocity and head height. These distribution diagrams not only reflect the current groundwater flow state, but also predict the changing trend of future hydrological conditions, providing accurate hydrological parameter support for subsequent intelligent grouting optimization.

[0085] S130, performing time-frequency domain analysis on the water content and the water head height to determine hydrological spectrum characteristics, wherein the hydrological spectrum characteristics include water content spectrum characteristics and water head height spectrum characteristics;

[0086] For example, the time-frequency domain analysis uses methods such as STFT (Short-Time Fourier Transform) and wavelet transform to process the time-varying signals of water content and head height. STFT performs local Fourier transform on the signal by sliding the window on the time axis to capture the changes in frequency components in different time periods; while wavelet transform can provide high-resolution information of time and frequency at the same time through multi-scale decomposition, which is suitable for analyzing hydrological signals with suddenness and non-stationarity.

[0087] The water content S was calculated by STFT algorithm. w The time-frequency domain analysis of the water head height h(r, t) and the water head height h(r, t) is performed to obtain the hydrological spectrum characteristics, which can be expressed as:

[0088]

[0089] Among them, S w (r, τ) is the water content S wh(r, τ) is the signal of the water head height h changing with time τ; ω(τ-t) is the window function, which cuts the signal into a short-time signal around a certain time t, usually a Gaussian window, Hanning window, etc.; e -jπ2πfτ is the kernel of Fourier transform, representing the sinusoidal component of frequency f; is the spectrum characteristic of water content; is the frequency spectrum characteristic of water head height.

[0090] For example, the extracted hydrological spectrum features play a key role in the intelligent grouting optimization model. These spectral features reflect the dynamic behavior of groundwater flow, such as seasonal hydrological changes, sudden seepage events, etc., and can provide an important basis for optimizing grouting volume and grouting pressure. By analyzing the spectral characteristics, the system can identify the main influencing factors and changing trends of hydrological conditions, and then dynamically adjust the grouting strategy to ensure that the slurry is evenly diffused in a complex and changeable geological environment, avoid excessive or insufficient grouting, and significantly improve the overall stability and construction safety of the tunnel. In addition, the dynamic parameter update mechanism based on spectral characteristics enables the intelligent grouting optimization model to adaptively respond to changes in groundwater hydrological conditions, improve the real-time performance and accuracy of the system, and enhance its application effect in complex karst geological environments.

[0091] S140. Based on the seepage velocity and the hydrological spectrum characteristics, a slurry diffusion coupling model and an intelligent grouting optimization model are constructed to control the grouting volume and grouting pressure during the grouting process.

[0092] For example, relying on the seepage velocity determined by the hydrological permeability model and the hydrological spectrum characteristics extracted by time-frequency domain analysis, a coupling model is constructed to comprehensively consider the impact of hydrological changes on slurry diffusion, and the grouting parameters are adjusted in real time through an intelligent optimization algorithm to ensure precise control of the grouting process and the stability of the tunnel structure.

[0093] The slurry diffusion coupled model aims to describe how the diffusion behavior of slurry in the formation is affected by the seepage velocity and hydrological spectrum characteristics. Specifically, the model combines the nonlinear convection-diffusion equation, taking into account the velocity field, seepage velocity and diffusion coefficient of the slurry, expressed as:

[0094]

[0095] Among them, C(r, t) is the slurry concentration; v(r, t) is the slurry flow velocity field, which depends on the grouting pressure; q(r, t) is the water flow velocity; D(r, t) is the diffusion coefficient, which reflects the diffusion characteristics of the slurry under different geological conditions; R(C(r, t)) is the slurry solidification reaction term, which describes the slurry solidification process.

[0096] The solidification process of the slurry has a significant effect on its diffusion. The solidification reaction term R(C(r, t)) is expressed as:

[0097] R(C(r, t)) = k r C(r,t) ro

[0098] Among them, k r is the curing rate constant; ro is the reaction order.

[0099] The flow of the slurry is expressed as:

[0100]

[0101] Among them, K(r, t) is the permeability coefficient; μ is the dynamic viscosity of the slurry, which describes the internal resistance of the slurry, that is, the friction between the internal layers of the slurry when it flows. The greater the viscosity, the more difficult it is for the slurry to flow; P(r, t) is the slurry pressure field during the grouting process.

[0102] By dynamically monitoring the changes in water content and head height, the permeability coefficient K(r, t) and diffusion coefficient D(r, t) are adjusted in real time to ensure that the impact of hydrological changes is taken into account during the grouting process.

[0103] The dynamic adjustment process of permeability coefficient K(r, t) is expressed as:

[0104]

[0105] Where K0(r) is the initial permeability coefficient; F K is a mapping function that converts the spectrum characteristics into an adjustment factor for the permeability coefficient. When high-frequency components appear in the spectrum, it indicates that the water content or water head height changes dramatically, which may lead to an increase in the permeability of the medium.

[0106] The dynamic adjustment process of the diffusion coefficient D(r, t) is expressed as:

[0107]

[0108] Where D0(r) is the initial diffusion coefficient; F D is a mapping function that converts the spectrum characteristics into an adjustment factor of the diffusion coefficient; the changes in water content and head height affect the diffusion ability of the slurry in the medium, reflecting the hindering or promoting effect of the medium on the diffusion of the slurry.

[0109] Based on the constructed slurry diffusion coupling model, the intelligent grouting optimization model optimizes the grouting volume Q in real time through the nonlinear model predictive control method. in (t) and pressure P in(t) to achieve the best distribution of slurry in the formation. The model ensures the grouting effect by minimizing the deviation between the slurry concentration and the target concentration, and controlling the grouting volume and grouting pressure. The optimization objective function is expressed as:

[0110]

[0111] Among them, the first item is the slurry concentration and the target concentration C desired (r); the second and third items are for controlling the smoothness of grouting volume and grouting pressure to prevent violent fluctuations during the grouting process; the fourth item is the influence of water head height gradient on slurry concentration gradient, taking into account the effect of water flow on slurry diffusion. When the water head height gradient is large, groundwater flow may have a significant impact on slurry diffusion. This item is used to minimize the adverse effect of water flow on slurry distribution; the fifth item links the spectral characteristics of water content with slurry concentration, reflecting the impact of dynamic changes in water content on slurry distribution. When the spectral characteristics of water content show abnormal changes, this item guides the optimization process to avoid excessive concentration or loss of slurry in high-risk areas.

[0112] Constraints:

[0113]

[0114] Among them, Q in (t) is the slurry flow rate injected at time t; The minimum allowable grouting volume means that at least a certain amount of slurry needs to be injected during the grouting process to avoid insufficient slurry diffusion; P is the maximum allowable grouting volume, indicating that the grouting volume cannot exceed a certain upper limit to avoid excessive diffusion of the slurry or excessive formation pressure; in (t) is the grouting pressure at time t; To indicate the minimum grouting pressure, below which the grout may not be able to overcome the formation resistance and spread effectively; To indicate the maximum allowable grouting pressure to avoid formation damage caused by excessive pressure.

[0115] If the grouting amount is too little, the slurry may not be able to effectively fill the cracks or voids, and the effect of consolidating the stratum may not be achieved; if the grouting amount is too much, it may cause the slurry to overflow or generate excessive pressure in the stratum, affecting the stability of the tunnel or even damaging the surrounding rock and soil structure; if the grouting pressure is too small, the slurry may not be able to penetrate into the cracks or voids in the stratum, resulting in poor consolidation effect; if the grouting pressure is too high, it may damage the surrounding stratum structure, causing the cracks to expand and even cause serious problems such as surface collapse.

[0116] Dynamically adjust constraints based on spectrum characteristics:

[0117]

[0118] in, is the initial minimum grouting volume; is the initial maximum grouting volume; is the initial minimum grouting pressure; is the initial maximum grouting pressure; is the mapping function of the water head height, and the upper and lower limits of the grouting pressure are adjusted according to the frequency spectrum characteristics of the water head height; is the mapping function of water content, and the upper and lower limits of grouting amount are adjusted according to the spectrum characteristics of water content.

[0119] For example, by solving the optimization objective function, the intelligent grouting optimization model can determine the optimal grouting volume and grouting pressure, and achieve precise control of the grouting process. This process relies on the real-time acquisition of seepage velocity and hydrological spectrum characteristics to ensure that the slurry can effectively fill the stratum voids, while avoiding excessive diffusion or insufficient grouting of the slurry, significantly improving the overall stability and construction safety of the tunnel.

[0120] The slurry diffusion coupling model is mainly responsible for describing the diffusion behavior of slurry in the formation and its interaction with groundwater flow. Based on the nonlinear convection-diffusion equation, the model combines the seepage velocity and hydrological spectrum characteristics to dynamically simulate the diffusion velocity, concentration distribution and pressure changes of slurry under different geological conditions. Through this model, the diffusion of slurry in the formation can be reflected in real time, and the impact of the grouting process on the stability of the formation can be evaluated. The intelligent grouting optimization model is based on the dynamic diffusion data provided by the slurry diffusion coupling model, and uses the nonlinear model predictive control method to optimize the grouting volume and grouting pressure. The optimization model realizes real-time adjustment of grouting parameters by minimizing the objective function, ensuring that the slurry can effectively and evenly fill the formation voids and prevent over-grouting or under-grouting.

[0121] See also Figure 2 , which is a schematic diagram of the structure of a karst area tunnel geological monitoring device provided in an embodiment of the present application, comprising:

[0122] A water content determination unit 21, used to determine the water content of the rock based on the resistivity information, wherein the resistivity information is determined based on the conductivity information;

[0123] A hydrological parameter determination unit 22 determines the seepage velocity and water head height based on a hydrological infiltration model;

[0124] A spectrum feature determination unit 23 is used to perform time-frequency domain analysis on the water content and the water head height to determine hydrological spectrum features, wherein the hydrological spectrum features include water content spectrum features and water head height spectrum features;

[0125] The grouting process control unit 24 is used to construct a slurry diffusion coupling model and an intelligent grouting optimization model based on the seepage velocity and the hydrological spectrum characteristics, and control the grouting amount and grouting pressure during the grouting process.

[0126] See also Figure 3 The embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any one of the above-mentioned methods for tunnel geological monitoring in karst areas are implemented.

[0127] Since the electronic device introduced in this embodiment is a device used to implement a karst area tunnel geological monitoring device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by the technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0128] During the specific implementation process, when the computer program 311 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0129] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems or computer program products. Therefore, the present application adopts the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application adopts the form of a computer program product implemented on one or more computer-readable storage media containing computer-readable program code.

[0131] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes Figure 1 The process of a method for monitoring geological conditions in a karst area tunnel is described in the corresponding embodiment.

[0135] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0141] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0142] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0143] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and modifications of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and modifications.

Claims

1. A method for monitoring tunnel geology in karst areas, characterized in that: The method comprises: Determining the water content of the rock based on the resistivity information, wherein the resistivity information is determined based on the conductivity information; Determine the seepage velocity and water head height based on the hydrological infiltration model; Performing time-frequency domain analysis on the water content and the water head height to determine hydrological spectrum characteristics, wherein the hydrological spectrum characteristics include water content spectrum characteristics and water head height spectrum characteristics; Based on the seepage velocity and the hydrological spectrum characteristics, a slurry diffusion coupling model and an intelligent grouting optimization model are constructed to control the grouting volume and grouting pressure during the grouting process.

2. The method for monitoring karst area tunnel geology according to claim 1, characterized in that: The specific steps of obtaining the conductivity information include: monitoring electric field signals at electrode locations; Performing multi-scale wavelet transform on the electric field signal to obtain wavelet coefficients; Calculating the conductivity change at the electrode position based on the mapping function and the wavelet coefficients; Based on the conductivity change, a spatial three-dimensional reconstruction is performed to obtain a spatial conductivity change; Based on the spatial conductivity variation, conductivity information is determined.

3. The method for monitoring karst area tunnel geology according to claim 1, characterized in that: The water content is determined based on the following formula, expressed as: Among them, ρ(r, t) is the resistivity; a is the rock characteristic constant; φ is the porosity; S w (r, t) is the water content; m is the rock cementation index; l is the saturation index; t is the time.

4. The method for monitoring karst area tunnel geology according to claim 1, characterized in that: The water content spectrum characteristics and the head height spectrum characteristics are determined based on the following formula, expressed as: Among them, S w (r, τ) is the water content S w h(r, τ) is the signal of the water head height h changing with time τ; ω(τ-t) is the window function; e -j2πfτ is the kernel of Fourier transform, representing the sinusoidal component of frequency f; is the spectrum characteristic of water content; is the frequency spectrum characteristic of water head height.

5. The method for monitoring karst area tunnel geology according to claim 1, characterized in that: The slurry diffusion coupling model is determined based on the following formula, expressed as: Among them, C(r, t) is the slurry concentration; v(r, t) is the flow velocity field of the slurry; q(r, t) is the seepage velocity; D(r, t) is the diffusion coefficient; R(C(r, t)) is the slurry solidification reaction term.

6. The method for monitoring karst area tunnel geology according to claim 1, characterized in that: Also includes: Based on the hydrological spectrum characteristics, the permeability coefficient and the diffusion coefficient are dynamically updated.

7. The method for monitoring karst area tunnel geology according to claim 1, characterized in that: Also includes: The objective function of the intelligent grouting optimization model is determined based on a concentration deviation control item, a grouting smoothness control item, a water flow influence suppression item, and a water content dynamic optimization item.

8. A karst area tunnel geological monitoring device, characterized in that: include: A water content determination unit, used to determine the water content of the rock based on the resistivity information, wherein the resistivity information is determined based on the conductivity information; A hydrological parameter determination unit, used to determine the seepage velocity and water head height based on a hydrological infiltration model; A spectrum feature determination unit, used for performing time-frequency domain analysis on the water content and the water head height to determine hydrological spectrum features, wherein the hydrological spectrum features include water content spectrum features and water head height spectrum features; The grouting process control unit is used to construct a slurry diffusion coupling model and an intelligent grouting optimization model based on the seepage velocity and the hydrological spectrum characteristics, and control the grouting amount and grouting pressure during the grouting process.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the karst area tunnel geological monitoring method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring karst area tunnel geology as described in any one of claims 1 to 7 is implemented.

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