Water conservancy tunnel surrounding rock intersection section stability prediction method, device, equipment and medium

Abstract

The application discloses a water conservancy tunnel surrounding rock intersection section stability prediction method, device, equipment and medium, the method comprises the following steps: through collecting and analyzing the broadband microseismic response signal of the rock mass under the construction disturbance, the internal structure dynamic change data of the deep rock mass is inverted and output in real time; the response signal of the optical marker in the surrounding rock is captured and analyzed in real time, the three-dimensional dynamic stress field distribution data and the seepage path data of the surrounding rock of the intersection section are reconstructed and output; the digital twin model of the surrounding rock of the tunnel intersection section is constructed; the internal structure dynamic change data of the deep rock mass and the three-dimensional dynamic stress field distribution data and the seepage path data are input to the digital twin model as dynamic boundary conditions in real time, the subsequent motion state of the three twins is deduced by driving the digital twin model; when the motion state of the three twins deduced by the digital twin model shows chaotic characteristics, a surrounding rock instability early warning is sent. The application realizes real-time perception and dynamic evolution early warning of the internal state of the surrounding rock.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering safety monitoring technology, and in particular to methods, devices, equipment and media for predicting the stability of intersecting sections of surrounding rock in hydraulic tunnels. Background Technology

[0002] In hydraulic tunnel engineering, intersections (such as the junctions of the main tunnel and branch tunnels) are high-risk areas for stress concentration and deformation instability due to complex structural spatial effects, mixed and anisotropic surrounding rock types. The stability of this area directly affects the safety and progress of the entire project.

[0003] Currently, monitoring and prediction technologies for tunnel surrounding rock, such as the deformation prediction method based on an improved gradient boosting decision tree disclosed in Chinese invention patent application CN119475122B, the hierarchical prediction method based on multimodal spatiotemporal fusion disclosed in CN120850217B, and the automatic inversion method for mechanical parameters disclosed in CN114169238B, mainly rely on deploying sensors on the surface or shallow layer of the surrounding rock to collect data such as displacement and stress, and then performing inversion and prediction through machine learning or numerical simulation. However, these methods have the following inherent defects in practical applications: Simplification and distortion of constitutive models: Existing numerical simulation methods rely on pre-set constitutive models (such as the Mohr-Coulomb model and the pervasive joint model). These models are mathematical simplifications of complex rock mass behavior and are difficult to realistically simulate the complete process of surrounding rock from continuous damage to discontinuous failure, especially in the cross-core area of ​​lithological abrupt change.

[0004] The disconnect between prediction and construction disturbance: Mechanical vibration and excavation unloading during construction are the direct causes of deformation and instability of the surrounding rock. However, existing prediction systems mostly treat them as interference noise and filter them out. They fail to use them as the main driving force for model evolution and couple them together, resulting in prediction results that are difficult to respond to engineering dynamics in real time and have obvious lag.

[0005] Therefore, there is an urgent need for a new early warning method that can penetrate the interior of the rock mass, respond to construction disturbances in real time, and predict dynamic evolution. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, device, equipment and medium for predicting the stability of the surrounding rock intersection section of a hydraulic tunnel, aiming to realize real-time perception and dynamic evolution early warning of the internal state of the surrounding rock.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels, including: A passive source microseismic monitoring network is deployed in the deep rock mass around the tunnel intersection. By collecting and analyzing the broadband microseismic response signals of the rock mass under construction disturbance, the dynamic change data of the internal structure of the deep rock mass is inverted and output in real time. Real-time capture and analysis of response signals of optical markers in the surrounding rock, reconstruction and output of three-dimensional dynamic stress field distribution data and seepage path data of the cross section surrounding rock; A digital twin model of the surrounding rock at the tunnel intersection is constructed. The digital twin model includes at least a first twin representing the surrounding rock of the main tunnel, a second twin representing the surrounding rock of the branch tunnel, and a third twin representing the intersection core area. The dynamic changes in the internal structure of deep rock masses, as well as the three-dimensional dynamic stress field distribution data and seepage path data, are input into the digital twin model in real time as dynamic boundary conditions, driving the digital twin model to deduce the subsequent motion state of the three twin bodies. When the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics, an early warning of surrounding rock instability is issued.

[0008] Furthermore, the passive source microseismic monitoring network consists of broadband microseismic detectors deployed in multiple deep boreholes at different depths and azimuths within the rock mass surrounding the tunnel intersection, forming a panoramic monitoring grid covering the three-dimensional space of the intersection. Multiple detectors are spirally deployed along the axial direction within each deep borehole to acquire microseismic response signals at different depth levels of the rock mass. Real-time inversion and output of dynamic changes in the internal structure of the deep rock mass are performed, including: The collected broadband microseismic response signals are subjected to spectral analysis to generate vibration spectrum diagrams; Extract characteristic spectral lines from the vibration spectral line diagram. The characteristic spectral lines include the fundamental frequency representing the inherent vibration characteristics of the rock mass as a whole and the overtones representing the reflections from different lithological interfaces and joint zones. Establish a mapping relationship between characteristic spectral line changes and the dynamic evolution of the internal structure of the rock mass. When the energy amplitude of a specific overtone band exceeds a preset threshold or the center frequency drifts beyond the allowable range, it is determined that rock mass damage or microcrack propagation has occurred at the depth location corresponding to that overtone.

[0009] Furthermore, the establishment of a mapping relationship between characteristic spectral line changes and the dynamic evolution of the internal structure of the rock mass, and the determination that rock mass damage or microfracture propagation occurs at the depth location corresponding to the overtone when the energy amplitude of a specific overtone band exceeds a preset threshold or the center frequency drifts beyond the allowable range, includes: Identify the fundamental frequency in the vibration spectrum diagram and map the frequency value of the fundamental frequency to the inherent vibration frequency characteristics of the rock mass as a whole; Multiple overtones in the vibration spectrum are identified, and the frequency value of each overtone is mapped to the depth location characteristics of a discontinuous structural surface inside the rock mass. The discontinuous structural surface includes lithological layer interfaces, joint zones, and fracture zones. The system monitors the energy amplitude and center frequency of each of the multiple overtones in real time. When the energy amplitude of any overtone exceeds a preset threshold or the center frequency drifts beyond a preset range, it determines that rock mass damage or microcrack propagation has occurred at the depth location mapped by that overtone.

[0010] Furthermore, the real-time capture and analysis of the response signals of optical markers in the surrounding rock, and the reconstruction and output of the three-dimensional dynamic stress field distribution data and seepage path data of the intersecting surrounding rock, include: Tracer materials containing stress-responsive and seepage-responsive optical markers are injected into the surrounding rock at the tunnel intersection, and an optical detection array is deployed on the free face of the tunnel. Based on the intensity of the luminescence signal and the time difference of arrival of stress-responsive optical markers captured by the optical detection array, the three-dimensional coordinates and luminescence intensity of the luminescence event inside the surrounding rock are inverted and calculated, thereby generating three-dimensional dynamic stress field distribution data that reflects the degree and range of stress concentration. Based on the temporal and spatial distribution of fluorescence signals from seepage-responsive optical markers captured by an optical detection array, the three-dimensional migration path and velocity field of groundwater can be depicted in real time to obtain seepage path data.

[0011] Furthermore, the stress-responsive optical marker is a mechanochromic polymer encapsulated in brittle microcapsules. The microcapsules rupture under the influence of microcrack expansion or compression caused by stress concentration in the surrounding rock, and the released polymer molecular chains undergo conformational changes under stress, generating irreversible chemiluminescence. The seepage-responsive optical marker is a water-soluble compound that can generate a fluorescent signal after complexing with ions in groundwater. The optical probe array is arranged in a grid pattern on the tunnel lining or surrounding rock surface, and each probe includes a broadband excitation source and a multi-channel spectrometer for simultaneously capturing mechanoluminescence signals and fluorescent tracer signals in different wavelength bands.

[0012] Furthermore, when constructing the digital twin model, different initial physical and mechanical parameters are assigned to the first, second, and third twins, respectively. These initial physical and mechanical parameters include at least the elastic modulus, Poisson's ratio, cohesion, and internal friction angle. These initial physical and mechanical parameters can be dynamically updated based on the real-time inversion data of the dynamic changes in the internal structure of the deep rock mass, as well as the real-time reconstruction data of the three-dimensional dynamic stress field distribution and seepage path. The step of inputting the dynamic changes in the internal structure of the deep rock mass, as well as the three-dimensional dynamic stress field distribution and seepage path data, as dynamic boundary conditions into the digital twin model in real time, drives the digital twin model to deduce the subsequent motion states of the three twins, including: Define the Lagrangian function of the surrounding rock system simulated by the digital twin model. The Lagrangian function is a function of the kinetic energy, elastic potential energy, damage dissipation energy of the first twin, the second twin, and the third twin, as well as the external input energy defined by the real-time output dynamic change data of the internal structure of the deep rock mass, the real-time output three-dimensional dynamic stress field distribution data, and the seepage path data. By solving the Lagrange equation, we can obtain the motion state at the next moment that causes the functional of the action of the surrounding rock system to reach its extreme value.

[0013] Furthermore, when the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics, an early warning of surrounding rock instability is issued, including: Calculate the maximum Lyapunov index of the surrounding rock system; When the calculated maximum Lyapunov exponent is greater than zero and continues to exceed a preset time threshold, the surrounding rock system is determined to enter a chaotic and unstable state.

[0014] Secondly, the present invention also provides a stability prediction device for the intersection section of surrounding rock in a hydraulic tunnel, comprising: The inversion unit is used to deploy a passive source microseismic monitoring network in the deep rock mass around the tunnel intersection. By collecting and analyzing the broadband microseismic response signal of the rock mass under construction disturbance, it can invert and output the dynamic change data of the internal structure of the deep rock mass in real time. The reconstruction unit is used to capture and analyze the response signals of optical markers in the surrounding rock in real time, and reconstruct and output the three-dimensional dynamic stress field distribution data and seepage path data of the cross section surrounding rock; The model building unit is used to build a digital twin model of the surrounding rock of the tunnel intersection section. The digital twin model includes at least a first twin representing the surrounding rock of the main tunnel, a second twin representing the surrounding rock of the branch tunnel, and a third twin representing the intersection core area. The deduction unit is used to input the dynamic change data of the internal structure of the deep rock mass, as well as the three-dimensional dynamic stress field distribution data and seepage path data, as dynamic boundary conditions into the digital twin model in real time, and drive the digital twin model to deduce the subsequent motion state of the three twin bodies. The judgment unit is used to issue an early warning of surrounding rock instability when the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics.

[0015] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the stability prediction method for the cross section of the surrounding rock of a hydraulic tunnel as described above.

[0016] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the stability prediction method for the surrounding rock intersection section of a hydraulic tunnel as described above.

[0017] The advantages of this invention compared to existing technologies are as follows: By utilizing a broadband microseismic array and spectral analysis, construction vibrations are transformed from interference noise into active signals for detecting the internal structure of the rock mass, enabling the visualization and inversion of deep rock mass damage and structural changes that are inaccessible to traditional surface monitoring. Simultaneously, by adding mechanoluminescent materials and fluorescent tracers to the grouting material, and in conjunction with three-dimensional optical tomography, real-time, dynamic, and visual imaging of three-dimensional stress concentration zones and seepage paths in tunnel surrounding rock is achieved for the first time, greatly enriching the data dimensions used for stability analysis. The complex rock mass system is simplified into a three-body digital twin model, using real-time sensing data as the driving boundary, allowing the prediction model to evolve dynamically in sync with the physical entity. An early warning is issued when the trajectory of the surrounding rock system exhibits chaotic characteristics, which is more physically meaningful and forward-looking than traditional threshold-based warnings or predictions based on statistical laws.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other objectives, features and advantages of the present invention more obvious and understandable, preferred embodiments are described in detail below. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a method for predicting the stability of a hydraulic tunnel surrounding rock intersection section provided in a specific embodiment of the present invention; Figure 2 A schematic block diagram of a stability prediction device for the cross section of surrounding rock in a hydraulic tunnel, provided in a specific embodiment of the present invention; Figure 3 This is a schematic block diagram of a computer device provided for a specific embodiment of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0023] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] like Figure 1 As shown, this embodiment of the invention provides a method for predicting the stability of the cross section of the surrounding rock in a hydraulic tunnel, including the following steps: S10-S50.

[0026] S10. A passive source microseismic monitoring network is deployed in the deep rock mass around the tunnel intersection. By collecting and analyzing the broadband microseismic response signal of the rock mass under construction disturbance, the dynamic change data of the internal structure of the deep rock mass is inverted and output in real time.

[0027] The passive source microseismic monitoring network consists of broadband microseismic detectors installed in multiple deep boreholes at different depths and azimuths in the rock mass surrounding the tunnel intersection, forming a panoramic listening grid covering the three-dimensional space of the intersection. Multiple detectors are spirally arranged along the axial direction in each deep borehole to obtain microseismic response signals at different depth levels of the rock mass.

[0028] In this embodiment, the passive source microseismic monitoring network is first deployed to monitor the stability of the surrounding rock at the intersection of the target hydraulic tunnel. This monitoring network is used to capture micro-fracture signals generated by the rock mass under construction disturbance in real time, providing a data foundation for subsequent inversion of the internal structure of the rock mass.

[0029] Specifically, within the rock mass surrounding the target intersection, based on geological survey data and 3D seismic exploration results, multiple representative spatial azimuth and depth locations will be selected for deep borehole drilling. The borehole depth must penetrate the surface weathering unloading zone and enter the relatively intact rock mass to ensure that the geophones can receive effective microseismic signals from the deep rock mass. The borehole azimuth must cover the entire space of the intersection, including both sides of the main tunnel, the area around the branch tunnels, and the acute-angled rock column area where the main and branch tunnels intersect.

[0030] Within each deep borehole, multiple broadband microseismic geophones are arranged in a spiral pattern along the borehole axis. This spiral arrangement allows the geophones to be distributed in a staggered manner in three-dimensional space, thereby acquiring microseismic response signals from different depth levels of the rock mass and avoiding information blind spots caused by single-depth deployment. The vertical spacing between adjacent geophones is set according to the rock mass wave velocity and the expected monitoring accuracy, for example, it can be set to 5 to 10 meters. All geophones are connected to a surface data acquisition station via signal transmission cables. The data acquisition station is equipped with a high-precision synchronous clock and a high sampling rate data acquisition card to achieve continuous, synchronous, and high-fidelity acquisition of microseismic signals.

[0031] Using the above-described deployment method, broadband microseismic detectors arranged in a spiral pattern within multiple deep boreholes collectively form a panoramic monitoring grid covering the three-dimensional space of the target intersection section. This monitoring grid can capture, in real time and from all directions, rock mass micro-fracture events induced by construction disturbances (such as drill-and-blast excavation, TBM tunneling, heavy machinery operations, etc.), as well as the longitudinal wave (P-wave) and transverse wave (S-wave) signals generated by these events.

[0032] Real-time inversion and output of dynamic changes in the internal structure of deep rock masses includes the following steps: S101. Perform spectral analysis on the collected broadband micro-vibration response signal to generate a vibration spectrum diagram.

[0033] During tunnel construction, a passive source microseismic monitoring network was deployed to continuously collect broadband microseismic response signals from the rock mass. The data acquisition system recorded data in real time at a preset high sampling rate (e.g., not less than 1000 Hz) to ensure that details of high-frequency microfracture signals could be captured.

[0034] The acquired time-domain microseismic signals were subjected to spectral analysis. First, the raw signal underwent preprocessing, including mean removal, trend removal, and denoising to eliminate environmental and instrument noise interference. The preprocessed signal was then converted to a frequency domain signal using a Fast Fourier Transform (FFT), generating a vibration spectrum. The vibration spectrum, with frequency on the x-axis and energy amplitude on the y-axis, visually displays the energy distribution characteristics of the microseismic signal in the frequency domain.

[0035] S102. Extract the characteristic spectral lines from the vibration spectral line diagram. The characteristic spectral lines include the fundamental frequency representing the inherent vibration characteristics of the rock mass as a whole and the overtones representing the reflections from different lithological interfaces and joint zones.

[0036] Feature extraction was performed on the generated vibration spectrum to identify two types of spectral lines with clear physical meaning: the fundamental frequency and overtones. The fundamental frequency represents the inherent vibrational characteristics of the rock mass as a whole. It is represented by the peak with the most concentrated energy and the lowest frequency in the spectrum, and its frequency value is related to the equivalent stiffness, mass, and geometric dimensions of the rock mass as a whole. Overtones, on the other hand, represent the reflection and refraction effects of microwaves on different discontinuous structural surfaces within the rock mass. Overtones are multiple secondary peaks in the spectrum other than the fundamental frequency, and each overtone's frequency value corresponds to a discontinuous structural surface (such as lithological interface, joint zone, fractured zone, or densely fractured zone) at a specific depth or location within the rock mass.

[0037] The extraction of characteristic spectral lines can be achieved using peak detection algorithms. By identifying local maxima in the spectral graph and filtering them in combination with a preset signal-to-noise ratio threshold, false peaks caused by noise can be eliminated.

[0038] S103. Establish a mapping relationship between characteristic spectral line changes and the dynamic evolution of the internal structure of the rock mass. When the energy amplitude of a specific overtone band exceeds a preset threshold or the center frequency drifts beyond the allowable range, it is determined that rock mass damage or microcrack propagation occurs at the depth location corresponding to the overtone.

[0039] In some embodiments, step S103 specifically includes the following steps: S1031-S1033.

[0040] S1031. Identify the fundamental frequency in the vibration spectrum diagram and map the frequency value of the fundamental frequency to the inherent vibration frequency characteristics of the rock mass as a whole.

[0041] The identified fundamental frequency value is used as a representation of the natural vibration frequency of the rock mass as a whole. During construction, the trend of the fundamental frequency change is monitored in real time. When the rock mass as a whole suffers damage, stiffness degradation, or mass loss, its natural vibration frequency will decrease accordingly, which is manifested as a left shift in the fundamental frequency spectrum.

[0042] S1032. Identify multiple overtones in the vibration spectrum diagram and map the frequency value of each overtone to the depth location characteristics of a discontinuous structural surface inside the rock mass. The discontinuous structural surface includes lithological layer interfaces, joint zones, and fracture zones.

[0043] Through pre-conducted finite element numerical simulations and on-site blasting tests, a database of correspondences between different depths and lithological interfaces of the target intersection rock mass and characteristic spectral frequency values ​​was established. Specifically, discontinuous structural surfaces at different locations were preset in the numerical model to simulate the propagation and reflection of microwaves within them, and the theoretical overtone values ​​corresponding to each structural surface were calculated. Simultaneously, an active seismic source with a known source location was generated at the construction site through small-charge blasting. The signals received by each detector were recorded and spectral analysis was performed, and the actual overtone values ​​were compared and corrected with the theoretical values.

[0044] Based on the calibration results above, the frequency value of each identified overtone is mapped to the depth location feature of a discontinuous structural surface within the rock mass. That is, each overtone corresponds to a discontinuous structural surface with a known depth or spatial location.

[0045] S1033. Real-time monitoring of the energy amplitude and center frequency of each of the multiple overtones. When the energy amplitude of any overtone exceeds the preset threshold or the center frequency drifts beyond the preset range, it is determined that rock mass damage or microcrack propagation has occurred at the depth location mapped by the overtone.

[0046] During real-time monitoring, the energy amplitude and center frequency of each overtone identified in S1032 are continuously tracked. When the energy amplitude of a certain overtone continuously rises and exceeds a preset threshold, it indicates that the micro-fracture activity at the discontinuous structural surface corresponding to that overtone intensifies, energy release increases, and rock mass damage or micro-fracture propagation occurs at that location. When the center frequency of a certain overtone drifts beyond a preset range, it indicates that the geometry or physical properties of the structural surface have changed (such as fracture opening, closing, or extension), similarly indicating that rock mass damage or micro-fracture propagation occurs at that location. The preset threshold and preset range can be set according to geological conditions and engineering experience, and dynamically optimized during the monitoring process using machine learning methods.

[0047] Through the aforementioned S10 steps, real-time inversion of the dynamic changes in the internal structure of the deep rock mass at the tunnel intersection is achieved, thus enabling a shift from passive monitoring to active detection. Traditional surface displacement monitoring can only detect the results after rock mass deformation, while this scheme uses construction disturbance as an active seismic source. By analyzing microseismic response signals, it achieves a "vision and auscultation" of changes in the internal structure of the rock mass, enabling early detection of the initiation and evolution of damage in the deep rock mass. Simultaneously, by spirally deploying geophones in multiple deep boreholes, a panoramic monitoring grid covering the three-dimensional space of the intersection is formed, overcoming the spatial blind spots caused by single boreholes or surface deployments, and accurately locating the three-dimensional coordinates of deep damage events.

[0048] S20. Real-time capture and analysis of the response signals of optical markers in the surrounding rock, reconstruct and output three-dimensional dynamic stress field distribution data and seepage path data of the cross section surrounding rock.

[0049] In some embodiments, step S20 specifically includes the following steps: S201-S203.

[0050] S201. Inject tracer material containing stress-responsive optical markers and seepage-responsive optical markers into the surrounding rock of the tunnel intersection, and deploy an optical detection array on the tunnel free face.

[0051] When performing pre-grouting or initial support construction at tunnel intersections, a smart tracer grouting material is formulated. This material is based on cement-based or chemical grout and incorporates two core optical markers: stress-responsive optical markers and seepage-responsive optical markers.

[0052] Stress-responsive optical markers utilize mechanochromic polymers encapsulated in brittle microcapsules. These microcapsules have a core-shell structure; the outer shell is a mechanically designed brittle material (such as urea-formaldehyde resin or melamine resin) capable of rupturing under specific stress; the core is a mechanochromic polymer (such as spiropyran derivatives or diarylethylene derivatives), which does not emit light in the stress-free state. Upon microcapsule rupture, the polymer is released, and under the influence of the surrounding rock stress field, its molecular chains undergo conformational changes, producing irreversible chemiluminescence. The wavelength and intensity of the emitted light are positively correlated with the magnitude of the applied stress.

[0053] The seepage-responsive optical marker is a water-soluble compound that produces a fluorescent signal after complexing with specific ions in groundwater. This tracer does not emit light in its dry state, but when it encounters seepage from groundwater, it dissolves and migrates with the water flow. Upon encountering specific ions in the groundwater (such as calcium and magnesium ions), it undergoes a complexation reaction, producing a fluorescent signal of a specific wavelength.

[0054] The two optical markers are uniformly dispersed in the grouting material at a certain ratio and injected into the surrounding rock fissures and pores of the target intersection section through a grouting pump via pre-set grouting holes. Parameters such as grouting pressure and grouting volume are determined according to the surrounding rock conditions and design drawings to ensure that the intelligent tracer material can be fully diffused to the area to be monitored.

[0055] On the free surfaces of tunnel intersections (including the initial support surface, the inner surface of the secondary lining, or the exposed rock surface), optical detection arrays are deployed in a grid pattern. The optical detection array consists of multiple optical detection units, and the spacing between adjacent detection units is set according to the monitoring accuracy requirements and expected signal attenuation characteristics, for example, it can be set to 1 meter to 3 meters.

[0056] Each optical detection unit includes a broadband excitation source, a multi-channel spectrometer, and a signal transmission module. The broadband excitation source is used to excite the optical markers to generate response signals. The wavelength range of the excitation source needs to cover the excitation wavelengths of both optical markers, and can be a tunable laser or a multi-wavelength LED array. The multi-channel spectrometer is used to simultaneously capture mechanoluminescence signals and fluorescent tracer signals in different wavelength bands. Each spectrometer is equipped with a high-sensitivity photodetector and a narrowband filter, enabling it to accurately distinguish and record the wavelength and intensity of light signals from different markers. The signal transmission module converts the captured light signals into electrical signals and transmits them to the data processing center in real time via wired or wireless means.

[0057] All optical detection units are synchronized using a synchronous clock to ensure accurate calculation of signal arrival time differences during subsequent signal processing.

[0058] S202. Based on the intensity of the luminescent signal and the time difference of arrival of the stress-responsive optical markers captured by the optical detection array, the three-dimensional coordinates and luminescent intensity of the luminescent events inside the surrounding rock are inverted and calculated, thereby generating three-dimensional dynamic stress field distribution data that reflects the degree and range of stress concentration.

[0059] During construction, stress concentration zones within the surrounding rock cause the microcapsules of stress-responsive optical markers to rupture and generate mechanoluminescence. An optical detection array captures these luminescence events in real time and reconstructs the three-dimensional stress field distribution.

[0060] Specifically, when a mechanoluminescence event occurs at a certain location, the emitted light signal propagates in the form of a spherical wave. Multiple optical detection units deployed on the free surface receive this light signal at different times. The data processing center records the arrival time of each detection unit that receives the signal. Based on the speed of light propagation in the rock mass (which can be obtained from field calibration tests) and the arrival time difference of the signals from multiple detection units, the three-dimensional coordinates of the luminescence event within the surrounding rock are calculated using multi-point positioning algorithms (such as the time difference of arrival positioning method, hyperbolic positioning method, etc.). The more detection units involved in the positioning, the higher the positioning accuracy.

[0061] The intensity of the light signal recorded by the optical detection unit is related to factors such as the original luminous intensity of the luminous event, the attenuation coefficient of the light along the propagation path, and the directionality of the detection unit. Based on a preset photon transmission model, the data processing center comprehensively considers the above factors and inversely derives the true luminous intensity of the luminous event from the intensity values ​​recorded by multiple detection units.

[0062] By conducting preliminary mechanical and optical calibration experiments in the laboratory or on-site, a correlation curve between the luminescence intensity of the mechanoluminescent material and the magnitude of the applied stress is established. This correlation curve is stored in a database. Based on the actual luminescence intensity of the luminescent event obtained through inversion, the stress value borne by the surrounding rock at that location is calculated by looking up a table or interpolation.

[0063] All luminescent events occurring within a continuous monitoring period are subjected to the aforementioned location and stress mapping processing to obtain a series of discrete data points with three-dimensional coordinates and stress values. Using spatial interpolation algorithms (such as Kriging interpolation and inverse distance weighted interpolation), these discrete data points are processed into continuous three-dimensional volume data, thereby generating a three-dimensional dynamic stress field distribution cloud map reflecting the degree and range of stress concentration. Different colors or brightness levels are used in the cloud map to represent the stress values, thus visualizing the stress field.

[0064] S203. Based on the time sequence and spatial distribution of fluorescence signals of seepage-responsive optical markers captured by the optical detection array, the three-dimensional migration path and velocity field of groundwater are depicted in real time to obtain seepage path data.

[0065] When seepage or water inrush occurs in the surrounding rock, the water flow dissolves and carries seepage-responsive fluorescent tracers along the fracture channels. An optical detection array captures the fluorescence signals in real time and reconstructs the seepage path.

[0066] Specifically, when seepage occurs, the tracer first appears at a certain location with the water flow, and generates a fluorescent signal after coming into contact with specific ions in the groundwater at that location. The optical detection array detects the appearance of this fluorescent signal and uses a multi-point positioning algorithm to determine the three-dimensional coordinates of the first appearance of the fluorescent signal, i.e., the starting point of the seepage path.

[0067] As groundwater continues to flow, the activated fluorescent tracer migrates downstream along the seepage channels. During this migration, the tracer continuously emits fluorescent signals. An optical detection array continuously detects these fluorescent signals at different times and locations. The data processing center locates the fluorescent signals detected at each moment, obtaining a series of three-dimensional coordinate points that change over time.

[0068] Connecting three-dimensional coordinate points on a continuous time series in chronological order forms a seepage trajectory line, and multiple trajectory lines constitute a seepage path network.

[0069] The average migration velocity of the tracer is calculated based on the spatial distance between two adjacent fluorescence signal localization points and the time interval between their appearances. Performing this calculation on multiple segments along the seepage path yields the velocity distribution along the seepage path. By tracking and statistically analyzing a large number of tracer particles, the three-dimensional velocity field within the entire monitoring area can be further reconstructed.

[0070] Through steps S201 to S203, a leap from point monitoring to field imaging was achieved. By using optical markers distributed throughout the surrounding rock, continuous imaging of the stress and seepage fields across the entire monitoring area was realized, greatly enriching the information dimensions. By employing two optical markers with different response mechanisms (methemoluminescence and ion-responsive fluorescence) and utilizing a multi-channel spectral analyzer to distinguish signals in different bands, stress and seepage field data can be acquired simultaneously in the same time and space without interference. This provides valuable in-situ measured data for analyzing the coupling mechanism between stress and seepage. Furthermore, through a three-dimensional reconstruction algorithm, abstract stress values ​​and seepage trajectories are transformed into intuitive color cloud maps and dynamic trajectory lines. This allows engineers to clearly understand the distribution, range, and evolution trend of stress concentration zones within the surrounding rock, as well as the migration path and speed of groundwater, greatly enhancing the ability to perceive and assess the stability of the surrounding rock.

[0071] S30. Construct a digital twin model of the surrounding rock at the tunnel intersection. The digital twin model shall include at least a first twin representing the surrounding rock of the main tunnel, a second twin representing the surrounding rock of the branch tunnel, and a third twin representing the core area of ​​the intersection.

[0072] A digital twin model of the surrounding rock at the target tunnel intersection is constructed on a cloud server or edge computing node. This digital twin model is a high-fidelity virtual mapping of the surrounding rock mass in the physical world, used to simulate and extrapolate its dynamic response under construction disturbances.

[0073] Based on the structural characteristics and mechanical response properties of the target intersection, the complex surrounding rock system is simplified into three core, interacting twins.

[0074] The first twin represents the surrounding rock of the main tunnel. Its geometric range encompasses the rock mass within a certain area around the main tunnel excavation outline, extending beyond the stress disturbance zone. The second twin represents the surrounding rock of the branch tunnel. Its geometric range also encompasses the rock mass within a certain area around the branch tunnel excavation outline. The third twin represents the intersection core area. This twin specifically refers to the acute-angled rock column region at and around the intersection of the main tunnel and the branch tunnel; it is the area with the most significant stress concentration, the most complex deformation, and the highest risk of instability.

[0075] The three twins are spatially adjacent and mechanically coupled, together forming a complete rock mass system. Their interactions include stress transfer, deformation coordination, and energy exchange.

[0076] Based on geological survey data, indoor rock mechanics test results, and engineering analogy experience, different initial physical and mechanical parameters were assigned to the first, second, and third digital twins. These parameters form the basis for the initial state setting of the digital twin model and include at least: Elastic modulus, used to characterize the ability of a rock mass to resist elastic deformation. Poisson's ratio, used to characterize the ratio of lateral strain to axial strain in a rock mass under uniaxial stress. Cohesion, used to characterize the bonding and interlocking ability between particles or blocks within a rock mass. Angle of internal friction, used to characterize the frictional characteristics between rock particles or blocks.

[0077] Due to differences in lithology, weathering degree, and joint development in different areas of the intersection section, the initial physical and mechanical parameters of the three twins are different. For example, the core area of ​​the intersection is subject to multi-directional excavation disturbance, resulting in a higher degree of rock mass damage. Its initial elastic modulus and cohesion can be set to lower values ​​than the corresponding parameters of the surrounding rock of the main tunnel and the branch tunnel.

[0078] S40. The dynamic change data of the internal structure of the deep rock mass, as well as the three-dimensional dynamic stress field distribution data and seepage path data, are input into the digital twin model in real time as dynamic boundary conditions, driving the digital twin model to deduce the subsequent motion state of the three twin bodies.

[0079] The dynamic change data of the internal structure of the deep rock mass obtained by real-time inversion, as well as the three-dimensional dynamic stress field distribution data and seepage path data obtained by real-time reconstruction, are used as dynamic boundary conditions and are input into the digital twin model in real time at a set time step (e.g., once per minute).

[0080] Dynamic data on the internal structure of deep rock masses are used to update the damage field and fracture network distribution inside the twin; three-dimensional dynamic stress field distribution data are used to correct the load conditions on the twin boundary; and seepage path data are used to define the seepage field and pore water pressure distribution inside the twin.

[0081] These dynamic boundary conditions transform the digital twin model from a static, parameter-based computational model into a dynamic mirror that evolves synchronously with the surrounding rock conditions in the physical world.

[0082] In some embodiments, step S40 specifically includes the following steps: S401-S402.

[0083] S401. Define the Lagrangian function of the surrounding rock system simulated by the digital twin model. The Lagrangian function is a function of the kinetic energy, elastic potential energy, damage dissipation energy of the first twin, the second twin, and the third twin, as well as the external input energy defined by the real-time output dynamic change data of the internal structure of the deep rock mass, the real-time output three-dimensional dynamic stress field distribution data, and the seepage path data.

[0084] The surrounding rock system refers to the physical object jointly represented by the first twin, the second twin, and the third twin.

[0085] To deduce the subsequent motion state of the three twins under dynamic boundary conditions, this embodiment introduces the principle of least action from analytical mechanics, defining the Lagrangian function L of the surrounding rock system composed of the first, second, and third twins. The Lagrangian function is the core function describing the dynamic state of the surrounding rock system, and its general form is the difference between the kinetic energy T and the potential energy V of the surrounding rock system. In this embodiment, this classical form is extended to encompass the complex mechanical behavior of the rock mass system: L = T - (U + D) + W; Wherein: T (kinetic energy) represents the kinetic energy of the three twins due to deformation and motion. For quasi-static rock mass deformation problems, the kinetic energy term can be ignored or simplified to dissipation potential related to the deformation rate. U (elastic potential energy) represents the potential energy stored in the three twins due to elastic deformation. The calculation of elastic potential energy is based on the current elastic modulus, Poisson's ratio, and strain field distribution of the twins. D (damage dissipation energy) represents the energy dissipated during the propagation of microcracks and damage evolution within the rock mass. The calculation of damage dissipation energy is based on the dynamic change data of the deep rock mass internal structure obtained by real-time inversion, mapping the newly added damage area and damage degree to energy dissipation. W (external input energy) represents the energy input into the system by external dynamic boundary conditions. The calculation of external input energy is based on the real-time output of three-dimensional dynamic stress field distribution data and seepage path data. Specifically, stress field data is input into the surrounding rock system in the form of work done by forces and displacements on the boundary; seepage field data is input into the surrounding rock system in the form of effective stress changes caused by pore water pressure changes and seepage volume forces.

[0086] Each term of the Lagrangian function L is a functional in terms of time, spatial coordinates, and twin state variables, and its specific mathematical form can be established based on the theories of continuum mechanics and damage mechanics.

[0087] S402. By solving the Lagrange equation, the motion state at the next moment that makes the functional of the action of the surrounding rock system reach its extreme value is obtained.

[0088] According to the principle of least action, the actual motion is the path that maximizes the action functional S = ∫Ldt. This principle is realized by solving the Lagrange equations: ; Where v represents generalized velocity and x represents generalized coordinates.

[0089] Substituting the Lagrangian function L defined in S401 into the above equations yields a set of governing equations concerning the motion states of the three twin bodies. This set of equations is a system of partial differential equations, which is difficult to solve analytically. This embodiment employs numerical methods such as the finite element method or the finite difference method to solve it.

[0090] The specific solution process is as follows: Discretization: Discretize the continuous spatial domain of the three twins into a finite number of grid cells.

[0091] Substitute dynamic boundary conditions: Use the latest dynamic data input in real time in step S40 as the boundary conditions and loads for the current time step, and apply them to the discretized model.

[0092] Numerical iterative solution: A time-stepping algorithm is used to solve for the displacement, strain, and stress fields at the next time step that maximizes the action functional under the current boundary conditions. Essentially, this solution process seeks the most economical evolution path of the system while satisfying the principle of minimum action.

[0093] State update: The motion state obtained at the next time step (including the displacement of each node, the strain and stress of each element) is used as the new model state for the derivation of the next time step.

[0094] Through the iterative process of S401 and S402 described above, the digital twin model realizes the continuous deduction of the subsequent motion state of the three twins. The deduction results are presented in a dynamic visualization form, including three-dimensional displacement field cloud map, distribution of strain concentration area, and location of potential fracture surface.

[0095] Through steps S30 and S40, the continuous medium rock mass with infinite degrees of freedom is simplified into three physically distinct and interacting twins. This retains the core mechanical characteristics of the intersection section (the interaction between the main tunnel, the branch tunnel, and the intersection core area) while significantly reducing the model's complexity, making real-time calculation possible. Simultaneously, the principle of minimum action and the Lagrange mechanical framework are introduced, replacing the traditional, experience-based constitutive model. This allows the model's evolution to no longer rely on pre-defined, simplified stress-strain curves, but rather, driven by real-time dynamic data, automatically deduce the state at the next moment according to the most fundamental physical principles of nature (extreme values ​​of action). This evolutionary approach is more universal and physically realistic.

[0096] S50. When the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics, an early warning of surrounding rock instability is issued.

[0097] During the continuous simulation of the motion states of the three twins in the digital twin model, when the dynamic behavior of the surrounding rock system exhibits chaotic characteristics, it indicates that the surrounding rock is about to enter an unstable state. The essence of chaotic characteristics is the extreme sensitivity of the surrounding rock system to initial conditions, the so-called butterfly effect, where small differences at the initial moment are amplified exponentially over time. By calculating the maximum Lyapunov exponent of the rock mass system, this abstract chaotic characteristic is transformed into a quantifiable and determinable mathematical indicator, thereby achieving a scientific and objective early warning of instability.

[0098] In some embodiments, step S50 specifically includes the following steps: S501-S502.

[0099] S501, Calculate the maximum Lyapunov index of the surrounding rock system.

[0100] The Maximum Lyapunov Exponent (λ_max) is a characteristic parameter that quantitatively describes the sensitivity of a dynamical system to initial conditions. In phase space, the distance between two trajectories that are infinitely close at initial moments will either separate or converge exponentially over time. The Maximum Lyapunov Exponent is a quantitative indicator of this exponential separation (or convergence) rate. When λ_max > 0, adjacent trajectories separate exponentially, and the system exhibits a chaotic state; when λ_max = 0, the trajectories neither separate nor converge, corresponding to periodic or quasi-periodic motion; when λ_max < 0, the trajectories converge exponentially, and the system tends to a stable equilibrium point.

[0101] In this embodiment, the calculation of the maximum Lyapunov index of the rock mass system is as follows: From the time series data continuously derived from the digital twin model, key variables that characterize the dynamic state of the rock mass system are selected, such as the displacement components, strain energy density, or damage variables of the third twin (cross-core region). Let the observed univariate time series be {x1,x2,...,x_N}. According to Takens' embedding theorem, by selecting an appropriate embedding dimension m and time delay τ, it is reconstructed into an m-dimensional phase space, yielding a vector of state points in the phase space. Y_i=[x_i,x_{i+τ},x_{i+2τ},...,x_{i+(m-1)τ}] Where i = 1, 2, ..., M, and M = N - (m - 1)τ is the total number of state points in the phase space. The embedding dimension m and the time delay τ can be automatically determined by algorithms such as mutual information method and pseudo nearest neighbor method.

[0102] For each state point Y_i in the phase space, search for its nearest neighbor Y_j in the phase space. The requirement is that these two state points are spatially closest but temporally non-adjacent (i.e., |ij| is greater than the average period of the time series) to avoid false proximity caused by temporal correlation. Let the initial distance between the two nearest neighbors be d_i(0) = ||Y_i - Y_j||.

[0103] Starting from the initial moment, track these two state points and their evolution trajectories, and calculate the distance between them after k time steps: d_i(k) = ||Y_{i+k}-Y_{j+k}||. For chaotic systems, d_i(k) increases exponentially with increasing k.

[0104] For each state point i and its nearest neighbor, record the value of ln[d_i(k)] at multiple time steps k. For a given set of k, calculate the average of ln[d_i(k)] for all i.<ln[d_i(k)]> With k as the x-axis,<ln[d_i(k)]> Plot a curve with the vertical axis as the ordinate. There is a linearly increasing region in the curve (called the scaling region). The slope of this region is the maximum Lyapunov exponent λ_max.

[0105] To obtain a robust estimate, the least squares method can be used to perform linear fitting on the scaling region. When chaotic motion exists in the system, the calculated λ_max is a positive value, and its magnitude reflects the severity of the chaotic motion. The larger the λ_max, the more sensitive the surrounding rock system is to the initial conditions, the more significant the chaotic characteristics, and the higher the risk of instability.

[0106] S502. When the calculated maximum Lyapunov exponent is greater than zero and continues to exceed the preset time threshold, it is determined that the surrounding rock system will enter a chaotic and unstable state.

[0107] The calculated maximum Lyapunov exponent λ_max is compared with a preset chaos threshold, and combined with the duration determination, to achieve graded early warning.

[0108] Specifically, based on engineering experience and numerical simulation analysis, a chaos threshold λ_threshold is preset. This threshold can be dynamically adjusted according to different geological conditions, construction stages, and monitoring sections. For example, a lower threshold can be set for rock masses with good integrity, while a higher threshold can be set for fractured rock masses with well-developed joints and fissures. The size of the threshold reflects the project's tolerance for instability risks.

[0109] Within each monitoring time step, the current maximum Lyapunov exponent λ_max(t) of the rock mass system is calculated in real time. When λ_max(t) > λ_threshold, it indicates that the current dynamic behavior of the system has entered a chaotic state, and the surrounding rock system is extremely sensitive to small disturbances. Small disturbances (such as the next blasting or tunneling) may trigger unpredictable chain reactions, leading to the instability of the surrounding rock.

[0110] To avoid false alarms caused by transient noise or random fluctuations, a duration determination mechanism is introduced. When the state of λ_max(t) > λ_threshold continues for more than a preset time window T_hold (e.g., 10 consecutive monitoring cycles or 2 consecutive hours), the surrounding rock system confirms that the chaotic state has stabilized and is not a temporary fluctuation.

[0111] In this embodiment, the surrounding rock system issues different levels of early warning signals based on the degree and duration of λ_max(t) exceeding the threshold.

[0112] Blue alert: λ_max(t) exceeds the threshold for the first time, but the duration does not reach T_hold, prompting engineers to pay attention to changes in the system state.

[0113] Yellow alert: When λ_max(t) exceeds the threshold and the duration reaches T_hold, the system is determined to have entered a chaotic and unstable state. It is recommended that engineers increase the monitoring frequency and prepare emergency measures.

[0114] Orange alert: λ_max(t) continuously exceeds 1.5 times the threshold, and the evolution trend shows that the chaotic characteristics continue to increase, indicating that the surrounding rock is about to undergo significant deformation or local damage, and engineering intervention measures should be taken immediately.

[0115] Red alert: λ_max(t) continues to exceed twice the threshold, and the motion state deduced by the digital twin model diverges, indicating that the surrounding rock is about to experience macroscopic instability, and personnel and equipment need to be evacuated urgently.

[0116] Early warning information is transmitted to project management and technical personnel in real time through visual interfaces, mobile terminal push notifications, and other means, and is also simultaneously sent to the construction control center to provide a scientific basis for emergency decision-making.

[0117] Through steps S501 to S502, the abstract chaotic characteristics are transformed into a computable and quantifiable maximum Lyapunov exponent. A scientific and objective instability criterion system is constructed through threshold and duration determination, avoiding the limitations of traditional methods that rely on subjective judgment based on expert experience. Furthermore, the maximum Lyapunov exponent can detect abnormal changes in dynamic behavior (i.e., λ_max turning from negative to positive) before macroscopic instability occurs, achieving true early warning. In addition, the computational input directly comes from the continuous derivation output of the digital twin model, and the warning results can serve as an important basis for adjusting the twin model parameters and updating boundary conditions, forming a closed-loop control loop and significantly improving the intelligence level of surrounding rock stability early warning.

[0118] In summary, by utilizing broadband microseismic arrays and spectral analysis, construction vibrations are transformed from interference noise into active signals for probing the internal structure of rock masses. This enables the visualization and inversion of deep rock mass damage and structural changes that are inaccessible to traditional surface monitoring. Furthermore, by adding mechanoluminescent materials and fluorescent tracers to the grouting material, and combining this with three-dimensional optical tomography, real-time, dynamic, and visual imaging of three-dimensional stress concentration zones and seepage paths in tunnel surrounding rock is achieved for the first time, significantly enriching the data dimensions used for stability analysis. The complex rock mass system is simplified into a three-body digital twin model, using real-time sensing data as the driving boundary, allowing the prediction model to evolve dynamically in sync with the physical entity. Early warnings are issued when the trajectory of the surrounding rock system exhibits chaotic characteristics, which is more physically meaningful and forward-looking than traditional threshold-based warnings or predictions based on statistical laws.

[0119] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0120] This invention also provides a stability prediction device for rock-surrounding sections of hydraulic tunnels. This device is used to perform the steps in any of the aforementioned embodiments of the stability prediction method for rock-surrounding sections of hydraulic tunnels. Specifically, please refer to... Figure 2 , Figure 2 This illustration shows a schematic block diagram of a stability prediction device 100 for a hydraulic tunnel surrounding rock intersection section according to an embodiment of this application. The hydraulic tunnel surrounding rock intersection section stability prediction device 100 specifically includes: Inversion unit 110 is used to deploy a passive source microseismic monitoring network in the deep rock mass surrounding the tunnel intersection section. By acquiring and analyzing broadband microseismic response signals of the rock mass under construction disturbance, it inverts and outputs real-time data on the dynamic changes in the internal structure of the deep rock mass. Reconstruction unit 120 is used to capture and analyze the response signals of optical markers in the surrounding rock in real-time, reconstructing and outputting three-dimensional dynamic stress field distribution data and seepage path data of the surrounding rock at the intersection section. Model building unit 130 is used to construct a digital twin model of the surrounding rock at the tunnel intersection section. It includes at least a first twin representing the surrounding rock of the main tunnel, a second twin representing the surrounding rock of the branch tunnel, and a third twin representing the cross-core area; the deduction unit 140 is used to input the dynamic change data of the internal structure of the deep rock mass, as well as the three-dimensional dynamic stress field distribution data and seepage path data as dynamic boundary conditions into the digital twin model in real time, driving the digital twin model to deduce the subsequent motion state of the three twins; the judgment unit 150 is used to issue a warning of surrounding rock instability when the motion state of the three twins deduced by the digital twin model shows chaotic characteristics.

[0121] In some embodiments, the inversion unit 110 is specifically applied to: performing spectral analysis on the acquired broadband microseismic response signal to generate a vibration spectrum diagram; extracting characteristic spectral lines from the vibration spectrum diagram, wherein the characteristic spectral lines include a fundamental frequency representing the inherent vibration characteristics of the rock mass as a whole and overtones representing reflections from different lithological interfaces and joint zones; establishing a mapping relationship between the changes in characteristic spectral lines and the dynamic evolution of the internal structure of the rock mass; and determining that rock mass damage or microcrack propagation has occurred at the depth location corresponding to the overtone when the energy amplitude of a specific overtone band exceeds a preset threshold or the center frequency drifts beyond the allowable range.

[0122] In some embodiments, the inversion unit 110 is also specifically applied to: identifying the fundamental frequency in the vibration spectrum diagram and mapping the frequency value of the fundamental frequency to the inherent vibration frequency characteristics of the rock mass as a whole; identifying multiple overtones in the vibration spectrum diagram and mapping the frequency value of each overtone to the depth location characteristics of a discontinuous structural surface inside the rock mass, wherein the discontinuous structural surface includes lithological layer interfaces, joint zones, and fracture zones; and real-time monitoring of the energy amplitude and center frequency of each overtone among the multiple overtones, and determining that rock mass damage or microcrack propagation has occurred at the depth location mapped by the overtone when the energy amplitude of any overtone exceeds a preset threshold or the center frequency drifts beyond a preset range.

[0123] In some embodiments, the reconstruction unit 120 is specifically applied to: identifying the fundamental frequency in the vibration spectrum diagram and mapping the frequency value of the fundamental frequency to the inherent vibration frequency characteristics of the rock mass as a whole; identifying multiple overtones in the vibration spectrum diagram and mapping the frequency value of each overtone to the depth location characteristics of a discontinuous structural surface inside the rock mass, wherein the discontinuous structural surface includes lithological layer interfaces, joint zones, and fracture zones; and real-time monitoring of the energy amplitude and center frequency of each overtone among the multiple overtones, and determining that rock mass damage or microcrack propagation has occurred at the depth location mapped by the overtone when the energy amplitude of any overtone exceeds a preset threshold or the center frequency drifts beyond a preset range.

[0124] In some embodiments, the derivation unit 140 is specifically applied to: defining the Lagrangian function of the surrounding rock system simulated by the digital twin model, wherein the Lagrangian function is a function of the kinetic energy, elastic potential energy, damage dissipation energy of the first twin, the second twin, and the third twin, and the external input energy defined by the real-time output dynamic change data of the deep rock mass internal structure and the real-time output three-dimensional dynamic stress field distribution data and seepage path data; and obtaining the motion state at the next moment that makes the functional of the surrounding rock system action reach its extreme value by solving the Lagrangian equation.

[0125] In some embodiments, the determination unit 150 is specifically applied to: calculating the maximum Lyapunov index of the surrounding rock system; when the calculated maximum Lyapunov index is greater than zero and continues to exceed a preset time threshold, it is determined that the surrounding rock system will enter a chaotic and unstable state.

[0126] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned optical-hydraulic tunnel surrounding rock intersection stability prediction device 100 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0127] The aforementioned stability prediction device for the intersection section of the surrounding rock in a water conservancy tunnel can be implemented as a computer program, which can, for example, Figure 3 It runs on the computer device shown.

[0128] Please see Figure 3 , Figure 3 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 700 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0129] like Figure 3 As shown, the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the stability prediction method for the cross section of the surrounding rock of the hydraulic tunnel as described above.

[0130] The computer device 700 includes a processor 720, a memory, and a network interface 750 connected via a system bus 710. The memory may include a non-volatile storage medium 730 and internal memory 740.

[0131] The non-volatile storage medium 730 can store an operating system 731 and a computer program 732. When the computer program 732 is executed, it enables the processor 720 to perform a method for predicting the stability of the rock-surrounding section of a hydraulic tunnel.

[0132] The processor 720 provides computing and control capabilities to support the operation of the entire computer device 700.

[0133] The internal memory 740 provides an environment for the operation of the computer program 732 in the non-volatile storage medium 730. When the computer program 732 is executed by the processor 720, the processor 720 can execute the stability prediction method for the cross section of the surrounding rock of the hydraulic tunnel.

[0134] This network interface 750 is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 700 to which the present application is applied. The specific computer device 700 may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. The processor 720 is used to run program code stored in memory to implement the stability prediction method for the cross-section of the surrounding rock in a hydraulic tunnel.

[0135] Those skilled in the art will understand that Figure 3 The embodiments of the computer device shown do not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. For example, in some embodiments, the computer device may include only memory and a processor. In such embodiments, the structure and function of the memory and processor are different from those shown. Figure 3 The embodiments shown are consistent and will not be described again here.

[0136] It should be understood that, in the embodiments of this application, the processor 720 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0137] In another embodiment of the present invention, a computer-readable storage medium is provided. This computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when executed by a processor, the computer program implements the stability prediction method for the cross-section of the surrounding rock in a hydraulic tunnel disclosed in this embodiment of the present invention.

[0138] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0139] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Units with the same function may be grouped into one unit. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or may be electrical, mechanical, or other forms of connection.

[0140] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0141] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0142] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting the stability of intersecting sections of surrounding rock in hydraulic tunnels, characterized in that, include: A passive source microseismic monitoring network is deployed in the deep rock mass around the tunnel intersection. By collecting and analyzing the broadband microseismic response signals of the rock mass under construction disturbance, the dynamic change data of the internal structure of the deep rock mass is inverted and output in real time. Real-time capture and analysis of response signals of optical markers in the surrounding rock, reconstruction and output of three-dimensional dynamic stress field distribution data and seepage path data of the cross section surrounding rock; A digital twin model of the surrounding rock at the tunnel intersection is constructed. The digital twin model includes at least a first twin representing the surrounding rock of the main tunnel, a second twin representing the surrounding rock of the branch tunnel, and a third twin representing the intersection core area. The dynamic changes in the internal structure of deep rock masses, as well as the three-dimensional dynamic stress field distribution data and seepage path data, are input into the digital twin model in real time as dynamic boundary conditions, driving the digital twin model to deduce the subsequent motion state of the three twin bodies. When the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics, an early warning of surrounding rock instability is issued.

2. The method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels according to claim 1, characterized in that, The passive source microseismic monitoring network consists of broadband microseismic detectors installed in multiple deep boreholes at different depths and azimuths in the rock mass surrounding the tunnel intersection, forming a panoramic listening grid covering the three-dimensional space of the intersection. Multiple detectors are installed in a spiral pattern along the axial direction in each deep borehole to obtain microseismic response signals at different depth levels of the rock mass. Real-time inversion and output of dynamic changes in the internal structure of deep rock masses, including: The collected broadband microseismic response signals are subjected to spectral analysis to generate vibration spectrum diagrams; Extract characteristic spectral lines from the vibration spectral line diagram. The characteristic spectral lines include the fundamental frequency representing the inherent vibration characteristics of the rock mass as a whole and the overtones representing the reflections from different lithological interfaces and joint zones. Establish a mapping relationship between characteristic spectral line changes and the dynamic evolution of the internal structure of the rock mass. When the energy amplitude of a specific overtone band exceeds a preset threshold or the center frequency drifts beyond the allowable range, it is determined that rock mass damage or microcrack propagation has occurred at the depth location corresponding to that overtone.

3. The method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels according to claim 2, characterized in that, The establishment of a mapping relationship between characteristic spectral line changes and the dynamic evolution of the internal structure of the rock mass, when the energy amplitude of a specific overtone band exceeds a preset threshold or the center frequency drifts beyond the allowable range, determines that rock mass damage or microfracture propagation has occurred at the depth location corresponding to that overtone, including: Identify the fundamental frequency in the vibration spectrum diagram and map the frequency value of the fundamental frequency to the inherent vibration frequency characteristics of the rock mass as a whole; Multiple overtones in the vibration spectrum are identified, and the frequency value of each overtone is mapped to the depth location characteristics of a discontinuous structural surface inside the rock mass. The discontinuous structural surface includes lithological layer interfaces, joint zones, and fracture zones. The system monitors the energy amplitude and center frequency of each of the multiple overtones in real time. When the energy amplitude of any overtone exceeds a preset threshold or the center frequency drifts beyond a preset range, it determines that rock mass damage or microcrack propagation has occurred at the depth location mapped by that overtone.

4. The method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels according to claim 1, characterized in that, The real-time capture and analysis of response signals from optical markers in the surrounding rock, and the reconstruction and output of three-dimensional dynamic stress field distribution data and seepage path data of the intersecting surrounding rock, include: Tracer materials containing stress-responsive and seepage-responsive optical markers are injected into the surrounding rock at the tunnel intersection, and an optical detection array is deployed on the free face of the tunnel. Based on the intensity of the luminescence signal and the time difference of arrival of stress-responsive optical markers captured by the optical detection array, the three-dimensional coordinates and luminescence intensity of the luminescence event inside the surrounding rock are inverted and calculated, thereby generating three-dimensional dynamic stress field distribution data that reflects the degree and range of stress concentration. Based on the temporal and spatial distribution of fluorescence signals from seepage-responsive optical markers captured by an optical detection array, the three-dimensional migration path and velocity field of groundwater can be depicted in real time to obtain seepage path data.

5. The method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels according to claim 4, characterized in that, The stress-responsive optical marker is a mechanochromic polymer encapsulated in brittle microcapsules. The microcapsules rupture under the stress concentration of the surrounding rock, causing the microcracks to expand or be compressed. The released polymer molecular chains undergo conformational changes under stress and produce irreversible chemiluminescence. The seepage-responsive optical marker is a water-soluble compound that can generate a fluorescent signal after complexing with ions in groundwater. The optical probe array is arranged in a grid pattern on the tunnel lining or the surface of the surrounding rock. Each probe includes a broadband excitation source and a multi-channel spectrometer to simultaneously capture mechanoluminescence signals and fluorescent tracer signals in different wavelength bands.

6. The method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels according to claim 1, characterized in that, When constructing a digital twin model, different initial physical and mechanical parameters are assigned to the first, second, and third twins, respectively. These initial physical and mechanical parameters include at least the elastic modulus, Poisson's ratio, cohesion, and internal friction angle. These initial physical and mechanical parameters can be dynamically updated based on real-time inversion data of the dynamic changes in the internal structure of the deep rock mass, as well as real-time reconstruction data of the three-dimensional dynamic stress field distribution and seepage path. The dynamic changes in the internal structure of the deep rock mass, along with the three-dimensional dynamic stress field distribution and seepage path data, are input into the digital twin model in real-time as dynamic boundary conditions, driving the model to deduce the subsequent motion states of the three twins, including: Define the Lagrangian function of the surrounding rock system simulated by the digital twin model. The Lagrangian function is a function of the kinetic energy, elastic potential energy, damage dissipation energy of the first twin, the second twin, and the third twin, as well as the external input energy defined by the real-time output dynamic change data of the internal structure of the deep rock mass, the real-time output three-dimensional dynamic stress field distribution data, and the seepage path data. By solving the Lagrange equation, we can obtain the motion state at the next moment that causes the functional of the action of the surrounding rock system to reach its extreme value.

7. The method for predicting the stability of the intersecting sections of surrounding rock in hydraulic tunnels according to claim 1, characterized in that, When the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics, a warning of surrounding rock instability is issued, including: Calculate the maximum Lyapunov index of the surrounding rock system; When the calculated maximum Lyapunov exponent is greater than zero and continues to exceed a preset time threshold, the surrounding rock system is determined to enter a chaotic and unstable state.

8. A stability prediction device for the intersection section of surrounding rock in a hydraulic tunnel, characterized in that, include: The inversion unit is used to deploy a passive source microseismic monitoring network in the deep rock mass around the tunnel intersection. By collecting and analyzing the broadband microseismic response signal of the rock mass under construction disturbance, it can invert and output the dynamic change data of the internal structure of the deep rock mass in real time. The reconstruction unit is used to capture and analyze the response signals of optical markers in the surrounding rock in real time, and reconstruct and output the three-dimensional dynamic stress field distribution data and seepage path data of the cross section surrounding rock; The model building unit is used to build a digital twin model of the surrounding rock of the tunnel intersection section. The digital twin model includes at least a first twin representing the surrounding rock of the main tunnel, a second twin representing the surrounding rock of the branch tunnel, and a third twin representing the intersection core area. The deduction unit is used to input the dynamic change data of the internal structure of the deep rock mass, as well as the three-dimensional dynamic stress field distribution data and seepage path data, as dynamic boundary conditions into the digital twin model in real time, and drive the digital twin model to deduce the subsequent motion state of the three twin bodies. The judgment unit is used to issue an early warning of surrounding rock instability when the motion states of the three twins deduced by the digital twin model exhibit chaotic characteristics.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the stability prediction method for the surrounding rock intersection section of a hydraulic tunnel as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor performs the stability prediction method for the surrounding rock intersection section of a hydraulic tunnel as described in any one of claims 1 to 7.

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