Geological monitoring method, system and device for tunnel construction in collapse zone

Abstract

This invention discloses a geological monitoring method, system, and device for tunnel construction in landslide areas, relating to the field of risk analysis technology. The method includes: acquiring the location and intensity of micro-fractures at the lower fracture points of the target tunnel at various historical moments; determining the actual fracture center for each historical time period based on the location and intensity of the micro-fractures at the lower fracture points of the target tunnel at various historical moments; determining the trend coefficient of the non-vibration interval based on the changing trend of the actual fracture center in each non-vibration time period within the non-vibration interval; determining the current construction impact coefficient based on the trend coefficient of each non-vibration interval and the actual fracture center in each vibration time period; and assessing the current geological landslide risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures in the current time period. This invention can accurately determine the geological landslide risk and effectively ensure construction safety.

Description

Technical Field

[0001] This invention relates to the field of risk analysis technology, and specifically to geological monitoring methods, systems and devices for tunnel construction in landslide areas. Background Technology

[0002] Landslide zones are geological areas that have experienced landslides in their geological history or have an extremely high risk of landslides. These zones have unstable geological structures and are extremely sensitive to external disturbances; even minor construction activities can trigger geological changes, leading to disasters such as landslides. Given their high risk, real-time monitoring of the geological conditions at the construction site is necessary when carrying out tunnel construction in landslide zones to ensure safety during the construction process.

[0003] Currently, geological monitoring for tunnel construction in landslide areas primarily relies on the frequency and intensity of micro-fractures to construct a risk early warning system. This involves deploying monitoring equipment in the construction area to collect data on micro-fracture activity and analyze trends in their frequency and intensity. Once the data reaches a preset warning threshold, a risk warning is issued, alerting construction personnel to take preventative measures and reduce the likelihood of accidents.

[0004] However, existing methods focus solely on micro-fracture activity itself, neglecting the impact of construction disturbance on soil conditions. This significantly reduces the accuracy of risk warnings and makes it difficult to effectively ensure construction safety. Summary of the Invention

[0005] This invention provides a geological monitoring method, system, and device for tunnel construction in landslide areas, which can more accurately determine the risk of geological landslides and effectively ensure construction safety.

[0006] A first aspect of this invention provides a geological monitoring method for tunnel construction in landslide areas, comprising:

[0007] Obtain the location and intensity of micro-fractures at the fracture points of the target tunnel at various historical moments;

[0008] Based on the micro-fracture location and micro-fracture intensity of the target tunnel at each historical moment, the actual fracture center of each historical time period is determined. The actual fracture center is used to characterize the distribution and concentration of micro-fracture locations within the historical time period.

[0009] Based on the changing trend of the actual fracture center in each non-vibration time period within the non-vibration interval, the trend coefficient of the non-vibration interval is determined. The non-vibration time period is the historical time period that has not been disturbed by vibration during construction operations, and the non-vibration interval is the time interval composed of multiple consecutive non-vibration time periods.

[0010] Based on the trend coefficients of each non-vibration interval and the actual rupture center of each vibration time period, the current construction influence coefficient is determined. The vibration time period is the historical time period affected by vibration interference from construction operations.

[0011] Based on the current construction impact coefficient and the frequency of micro-fractures in the current time period, assess the current geological collapse risk of the target tunnel.

[0012] Furthermore, this invention also proposes obtaining the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments, including:

[0013] Acquire the target microseismic signals of the target tunnel at historical moments, collected by the target sensor;

[0014] The effective rupture signal is extracted from the target microseismic signal. The effective rupture signal includes both shear wave and longitudinal wave signals.

[0015] Based on the time difference between the propagation of shear wave and longitudinal wave signals to the target sensor, the location of the target sensor, and the rock mass wave velocity model, the micro-fracture location of the target tunnel at the target historical time is determined.

[0016] The waveform integration of the effective rupture signal is performed to obtain the micro-rupture intensity of the target tunnel at the rupture point at the target historical time.

[0017] Furthermore, this invention also proposes determining the actual fracture center for each historical time period based on the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments, including:

[0018] Cluster the breakpoints within the target historical time period according to distance to obtain multiple clusters;

[0019] For each cluster, the bias weight of the cluster is determined based on the number of crack points in the cluster and the micro-fracture intensity of each crack point.

[0020] Based on the bias weights of various clusters and the cluster centers of various clusters, the actual rupture center of the target historical time period is determined.

[0021] Furthermore, this invention proposes determining the bias weight of a cluster based on the number of fracture points in the cluster and the microfracture intensity of each fracture point, including:

[0022] Obtain the average micro-fracture strength of the fracture point within the target historical time period;

[0023] The average microfracture strength is subtracted from the microfracture strength of each fracture point in the cluster to obtain multiple microfracture strength differences.

[0024] The bias weights of the clusters are determined by using the differences in the strength of each microfracture.

[0025] Furthermore, the present invention also proposes to determine the trend coefficient of the non-vibration interval based on the changing trend of the actual fracture center in each non-vibration time period within the non-vibration interval, including:

[0026] Based on the distance and direction between the actual fracture center of each non-vibration time period and the next non-vibration time period within the non-vibration interval, migration vectors for multiple non-vibration time periods are obtained.

[0027] A first-order fit is performed on each actual fracture center within the non-vibration interval to obtain the target fitted line;

[0028] Using the direction of the target fitted line as the direction and the square of the distance between adjacent actual fracture centers in the non-vibration interval as the modulus, a trend vector for the non-vibration interval is constructed.

[0029] The trend coefficient of the non-oscillation interval is determined by using the trend vector and each migration vector.

[0030] Furthermore, this invention also proposes determining the current construction influence coefficient based on the trend coefficient of each non-vibration interval and the actual fracture center of each vibration time period, including:

[0031] Based on the trend coefficient of the non-vibration interval and the actual fracture center of the target vibration time period, the construction coefficient and the original coefficient of the target vibration time period are determined. The target vibration time period is the next vibration time period of the non-vibration interval. The construction coefficient is used to characterize the coefficient of the actual fracture center of the target vibration time period affected by construction vibration, and the original coefficient is used to characterize the coefficient of the actual fracture center of the target vibration time period affected by the natural geological changes of the non-vibration interval.

[0032] The vibration weight of the target vibration time period is determined by using the trend coefficient of the non-vibration zone, the construction coefficient of the target vibration time period, and the original coefficient.

[0033] The current construction impact coefficient is determined using the vibration weights.

[0034] Furthermore, this invention also proposes determining the construction coefficient and original coefficient for the target vibration time period based on the trend coefficient of the non-vibration interval and the actual fracture center of the target vibration time period, including:

[0035] Starting from the actual fracture center of the last non-vibration time period in the non-vibration interval, the trend vector of the non-vibration interval is drawn to obtain the theoretical fracture center of the target vibration time period.

[0036] Based on the theoretical fracture center, actual fracture center, and construction influence center of the target vibration time period, the construction coefficient and original coefficient of the target vibration time period are determined.

[0037] Furthermore, this invention also proposes to assess the current geological collapse risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures in the current time period, including:

[0038] Based on the current construction impact coefficient and the frequency of micro-fractures in the current time period, determine the current risk frequency of the target tunnel;

[0039] Divide the risk frequency by a preset frequency threshold to obtain the current risk assessment value of the target tunnel;

[0040] If the risk assessment value is greater than the preset assessment value threshold, it is determined that the target tunnel currently has a risk of geological collapse.

[0041] A second aspect of the present invention provides a geological monitoring system for tunnel construction in landslide areas, comprising:

[0042] The crack point acquisition module is used to acquire the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments;

[0043] The center determination module is used to determine the actual fracture center for each historical time period based on the micro-fracture location and micro-fracture intensity of the target tunnel at each historical moment. The actual fracture center is used to characterize the distribution and concentration of micro-fracture locations within the historical time period.

[0044] The trend determination module is used to determine the trend coefficient of the non-vibration interval based on the changing trend of the actual rupture center in each non-vibration time period within the non-vibration interval. The non-vibration time period is the historical time period that has not been disturbed by vibration from construction operations, and the non-vibration interval is the time interval composed of multiple consecutive non-vibration time periods.

[0045] The impact assessment module is used to determine the current construction impact coefficient based on the trend coefficient of each non-vibration zone and the actual rupture center of each vibration time period. The vibration time period is the historical time period affected by vibration interference from construction operations.

[0046] The risk assessment module is used to assess the current geological collapse risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures in the current time period.

[0047] A third aspect of the present invention provides a geological monitoring device for tunnel construction in a landslide area, the device comprising: a processor and a memory storing computer program instructions;

[0048] When the processor executes computer program instructions, it implements the geological monitoring methods for tunnel construction in collapse zones as provided in any of the above aspects.

[0049] The present invention has the following beneficial effects:

[0050] The geological monitoring method for tunnel construction in landslide areas provided in this invention first acquires the micro-fracture locations and intensities of fracture points at various historical moments in the target tunnel, thereby determining the actual fracture centers for each historical time period and reflecting the concentrated distribution of micro-fracture locations. Next, a trend coefficient is determined based on the changing trends of the actual fracture centers in each non-vibration time period within the non-vibration zone. Since the non-vibration zone is not disturbed by construction vibration, this trend coefficient reflects the changing patterns of micro-fractures under natural conditions. Then, combining the trend coefficients of each non-vibration zone with the actual fracture centers in vibration time periods disturbed by construction vibration, the current construction influence coefficient is determined, incorporating the impact of construction disturbance on the soil state and providing a more comprehensive reflection of the actual situation. Finally, the geological collapse risk is assessed based on the current construction influence coefficient and the frequency of micro-fractures in the current time period. This approach considers both the micro-fracture activity itself and the construction disturbance factor, avoiding the limitations of focusing solely on micro-fracture activity. By comprehensively assessing the risk from multiple perspectives, the geological collapse risk can be more accurately determined, effectively ensuring construction safety. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic flowchart of a geological monitoring method for tunnel construction in a landslide area provided in one embodiment of the present invention;

[0053] Figure 2 This is a schematic flowchart of S200 provided in one embodiment of the present invention;

[0054] Figure 3 This is a schematic flowchart of S400 provided in one embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram illustrating the determination of construction coefficients and original coefficients according to an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of the structure of a geological monitoring system for tunnel construction in a landslide area provided in one embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram of the structure of a geological monitoring device for tunnel construction in a landslide area, provided in one embodiment of the present invention.

[0058] Figure labels: 401 is the actual rupture center of the last non-vibration time period in the non-vibration interval, 402 is the trend vector of the non-vibration interval, 403 is the theoretical rupture center of the target vibration time period, 404 is the actual rupture center of the target vibration time period, and 405 is the construction influence center. Detailed Implementation

[0059] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a geological monitoring method, system, and apparatus for tunnel construction in landslide areas proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0061] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of laws and regulations.

[0062] It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of the present invention. However, they do not mean that the applicant has used or necessarily used the solution.

[0063] In traditional geological monitoring systems for tunnel construction in landslide areas, monitoring models rely solely on the frequency and intensity of micro-fractures to construct risk warning mechanisms. Because a correlation model between construction vibration disturbances and geological state evolution has not been established, it is impossible to effectively distinguish between micro-fractures generated by natural geological activities and those induced by construction disturbances. The monitoring data simultaneously includes instantaneous stress fluctuations caused by construction vibrations and fracture signals triggered by natural relaxation of the geological structure, resulting in the migration patterns of fracture centers being masked by noise, making it difficult to accurately model the evolution trend of the geological state.

[0064] Faced with the aforementioned problems, this invention first recognizes that traditional monitoring methods cannot distinguish between micro-fracture signals caused by natural geological activity and construction vibrations, resulting in the migration pattern of fracture centers being masked by noise. To address this, this invention attempts to divide monitoring data into vibration periods affected by construction vibrations and non-vibration periods undisturbed by construction vibrations, and establishes a baseline model of natural relaxation of geological structures by analyzing the natural migration trend of fracture centers in the non-vibration periods. Based on this, it further studies the offset of fracture centers relative to the natural trend within the vibration periods, thereby quantifying the cumulative impact of construction disturbances on the geological state. The trend coefficient of the non-vibration periods is used to characterize the natural evolution direction of the geological structure, while the deviation between the actual fracture center and the theoretical natural trend during the vibration periods reflects the additional impact of construction operations. By integrating the trend coefficient and the construction impact weight, a risk assessment model that can simultaneously reflect the superimposed effects of natural geological evolution and anthropogenic disturbances is finally constructed.

[0065] In this regard, such as Figure 1 As shown, this invention proposes a geological monitoring method for tunnel construction in landslide areas. This geological monitoring method can be applied to a geological monitoring system or device used for tunnel construction in landslide areas. The geological monitoring method for tunnel construction in landslide areas may include the following steps S100 to S500:

[0066] S100, obtain the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments;

[0067] S200, based on the micro-fracture location and micro-fracture intensity of the target tunnel at each historical moment, determines the actual fracture center of each historical time period. The actual fracture center is used to characterize the distribution and concentration of micro-fracture locations within the historical time period.

[0068] S300, based on the changing trend of the actual fracture center in each non-vibration time period within the non-vibration interval, determines the trend coefficient of the non-vibration interval. The non-vibration time period is the historical time period without vibration interference from construction operations, and the non-vibration interval is the time interval composed of multiple consecutive non-vibration time periods.

[0069] S400, based on the trend coefficient of each non-vibration zone and the actual rupture center of each vibration time period, determines the current construction influence coefficient. The vibration time period is the historical time period affected by vibration interference from construction operations.

[0070] S500 assesses the current geological collapse risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures during the current time period.

[0071] In this embodiment, the micro-fracture location refers to the geographical coordinates of the micro-fracture event. Specifically, it can be calculated by combining the propagation time difference of the microseismic signal with the sensor location and the rock mass wave velocity model, and is used to locate the specific area of ​​rock mass fracture inside the tunnel.

[0072] Microfracture strength refers to the amount of energy released during rupture, which can be obtained by waveform integration of microseismic signals and is used to quantify the intensity of rupture activity.

[0073] The actual fracture center refers to the spatial concentration point of the distribution of micro-fracture locations within a historical time period. Specifically, it can be calculated by combining clustering algorithms with the number of fracture points and the intensity of micro-fractures, and is used to reflect the spatial clustering characteristics of rock mass fracture activity.

[0074] The trend coefficient in the non-vibration range is a quantitative indicator of the migration trend of the fracture center when it is not disturbed by construction. Specifically, it can be calculated by fitting the fracture center migration vector and constructing a trend vector, which is used to characterize the stability change law of the rock mass under natural conditions.

[0075] The current construction impact coefficient refers to the degree of disturbance of construction vibration to the stability of rock mass. Specifically, it can be calculated by comparing the trend in the non-vibration interval with the actual displacement of the fracture center during the vibration period. It is used to assess the dynamic impact of construction operations on the geological structure.

[0076] Geological collapse risk assessment refers to determining the risk level by combining the comprehensive construction impact coefficient and the frequency of micro-fractures, and is used for real-time early warning of potential collapse disasters.

[0077] The core innovation of this invention lies in establishing a benchmark for the natural state of rock mass by analyzing the migration trend of the fracture center within the non-vibration zone, dynamically calculating the construction influence coefficient by combining the fracture center offset caused by construction vibration, and finally integrating the frequency of micro-fractures to achieve geological collapse risk assessment. This method breaks through the limitations of traditional methods that rely solely on the frequency and intensity of micro-fractures, and significantly improves the accuracy of risk warning by quantifying the impact of construction disturbances on rock mass stability.

[0078] The working process and principle of this invention are as follows: First, the micro-fracture locations and micro-fracture intensities of the target tunnel at various historical moments are acquired. This data is collected through a sensor network deployed around the tunnel. Next, based on this micro-fracture data, the actual fracture centers for each historical time period are determined. The actual fracture centers reflect the distribution and concentration of micro-fracture locations within that time period and can be obtained through cluster analysis of the micro-fracture locations.

[0079] Furthermore, the historical time period is divided into non-vibrational intervals and vibrational intervals. Non-vibrational intervals are continuous periods unaffected by construction vibrations, while vibrational intervals are influenced by construction vibrations. For non-vibrational intervals, the changing trends of the actual fracture centers in each non-vibrational interval are analyzed to determine the trend coefficient for that interval. The trend coefficient reflects the direction of geological structure evolution under natural conditions.

[0080] Therefore, based on the trend coefficients of each non-vibration zone and the actual fracture centers of each vibration time period, the current construction influence coefficient is determined. The construction influence coefficient quantifies the degree of impact of construction activities on the geological state. Finally, by combining the current construction influence coefficient with the frequency of micro-fractures in the current time period, the current geological collapse risk of the target tunnel is assessed. This assessment method comprehensively considers the dual effects of natural geological evolution and human construction disturbance, and can more accurately reflect the actual geological state of the tunnel.

[0081] As an example, in a tunnel construction project in a landslide area, a microseismic monitoring network consisting of multiple three-component seismometers was deployed. The monitoring system collected microseismic data every 15 minutes, recording the location and intensity of micro-fractures.

[0082] First, the acquired microseismic data is processed to extract effective microfracture signals. By comparing the arrival time differences of shear waves and longitudinal waves, and combining the sensor location with the rock mass wave velocity model, the precise location of each microfracture event is calculated. The microfracture intensity is obtained by waveform integration of the effective fracture signals.

[0083] Next, the data from 24 consecutive hours was used as a time period, and cluster analysis was performed on the micro-fracture locations within this period. A density-based clustering algorithm was employed to group spatially similar micro-fracture events into one cluster. For each cluster, its centroid location and the number of micro-fracture events it contained were calculated, and the weight of the cluster was determined by combining this with micro-fracture intensity information. Finally, the actual fracture center for this time period was calculated using a weighted average.

[0084] Furthermore, the time period was divided into non-vibration intervals and vibration intervals based on the construction log. For the non-vibration intervals, the changing trend of the actual fracture center over multiple consecutive time periods was analyzed. A least squares method was used to fit a straight line to these points; the direction vector of the fitted line is the trend vector for that non-vibration interval. The trend coefficient was obtained by calculating the average of the cosine values ​​of the angle between the trend vector and the actual fracture center migration vector between each time period.

[0085] For each vibration period, the deviation between the actual rupture center and the predicted trend position of the adjacent non-vibration zone is calculated to obtain the construction impact offset. The construction impact offsets of multiple consecutive vibration periods are weighted and averaged to obtain the current construction impact coefficient.

[0086] Finally, the current construction impact coefficient is multiplied by the frequency of micro-fractures in the current time period to obtain the risk assessment value. If this value exceeds a preset threshold, a geological collapse risk is determined, and an early warning signal will be issued.

[0087] This embodiment first obtains the micro-fracture locations and intensities of fracture points at various historical moments in the target tunnel, thereby determining the actual fracture centers for each historical time period and reflecting the concentrated distribution of micro-fracture locations. Next, a trend coefficient is determined based on the changing trends of the actual fracture centers in each non-vibration time period within the non-vibration zone. Since the non-vibration zone is not affected by construction vibration, this trend coefficient reflects the natural variation patterns of micro-fractures. Then, combining the trend coefficients of each non-vibration zone with the actual fracture centers in vibration time periods affected by construction vibration, the current construction influence coefficient is determined, incorporating the impact of construction disturbance on the soil state for a more comprehensive reflection of the actual situation. Finally, the risk of geological collapse is assessed based on the current construction influence coefficient and the frequency of micro-fractures in the current time period. This approach considers both the micro-fracture activity itself and construction disturbance factors, avoiding the limitations of focusing solely on micro-fracture activity. By comprehensively assessing the risk from multiple perspectives, the risk of geological collapse is more accurately determined, effectively ensuring construction safety.

[0088] In some of the solutions described above in this invention, there are problems such as insufficient positioning accuracy due to signal interference during the process of obtaining the micro-fracture location and micro-fracture intensity, and the intensity calculation method cannot accurately reflect the actual energy release.

[0089] In this regard, the present invention further proposes that S100 may specifically include:

[0090] Acquire the target microseismic signals of the target tunnel at historical moments, collected by the target sensor;

[0091] The effective rupture signal is extracted from the target microseismic signal. The effective rupture signal includes both shear wave and longitudinal wave signals.

[0092] Based on the time difference between the propagation of shear wave and longitudinal wave signals to the target sensor, the location of the target sensor, and the rock mass wave velocity model, the micro-fracture location of the target tunnel at the target historical time is determined.

[0093] The waveform integration of the effective rupture signal is performed to obtain the micro-rupture intensity of the target tunnel at the rupture point at the target historical time.

[0094] In this embodiment, the effective rupture signal is extracted by separating background noise using a filtering algorithm, retaining the dominant frequency band signals of the transverse and longitudinal waves. The time difference is calculated using a cross-correlation algorithm to determine the arrival time delays of the transverse and longitudinal waves, and an equation system is established using the three-dimensional coordinates of the target sensor to solve for the rupture source coordinates. The rock mass wave velocity model dynamically selects the corresponding ratio of longitudinal to transverse wave velocities based on the tunnel surrounding rock type, and the wave velocity data is calibrated through borehole sampling experiments. Waveform integration is performed by extracting the signal envelope using Hilbert transform and then performing area integration; the integration result is linearly related to the energy released by the rupture.

[0095] Specifically, a target sensor array is deployed on the tunnel arch and sidewalls to acquire multi-channel microseismic signals. A bandpass filter removes mechanical vibrations and electromagnetic interference, retaining effective signal components with frequencies between 50-2000 Hz. The arrival time difference between shear and longitudinal waves is detected using the peak value of the cross-correlation function. A hyperbolic positioning equation is established based on the spatial coordinates of the target sensors, and the three-dimensional coordinates of the fracture point are iteratively solved using the least squares method. The rock mass wave velocity model calculates theoretical wave velocity values ​​based on the rock density and elastic modulus obtained from on-site sampling, and establishes a database of wave velocity distributions for different rock layers. During waveform integration, the effective signal is acquired in its entirety. After constructing an analytical signal using Hilbert transform, the instantaneous amplitude integral value is calculated. The integration result is converted into micro-fracture strength parameters using calibration coefficients. This method, combining multi-dimensional signal processing with a physical model, improves the micro-fracture positioning accuracy to within 0.5 meters, and controls the strength calculation error within ±5%, providing a reliable data foundation for subsequent risk analysis.

[0096] As an example, this involves acquiring target microseismic signals of a target tunnel at specific historical moments, collected by target sensors. The target sensors can be seismographs or accelerometers deployed within the rock mass surrounding the tunnel. The target microseismic signals contain information about seismic waves generated by minute fractures within the rock mass.

[0097] The effective fracture signals, including shear wave and longitudinal wave signals, are extracted from the target microseismic signal. This step can be achieved through signal filtering and waveform recognition algorithms to remove noise and irrelevant signals and retain the true rock mass fracture waveform.

[0098] Based on the time difference between the propagation of shear wave and longitudinal wave signals to the target sensor, the location of the target sensor, and the rock mass wave velocity model, the micro-fracture location of the target tunnel at the target historical time can be determined. Specifically, the time difference positioning method can be used, which uses the time difference of waveforms received by multiple sensors, combined with the known rock mass wave velocity model, to invert and calculate the location of the micro-fracture.

[0099] Waveform integration is performed on the effective rupture signal to obtain the micro-fracture intensity at the rupture point of the target tunnel at the target historical time. Waveform integration can yield the seismic moment, which can then be converted into the energy magnitude of the micro-fracture to characterize its intensity.

[0100] This embodiment enables accurate acquisition of the location and intensity information of micro-fracture events in the rock mass surrounding the tunnel. This provides reliable foundational data for subsequent analysis of rock mass stability and assessment of collapse risk, contributing to improved accuracy and reliability of geological monitoring. Furthermore, by extracting effective fracture signals and performing location calculations, this method can effectively filter out the influence of interference factors such as construction noise, obtaining accurate rock mass fracture information. This is of great significance for accurately grasping the rock mass condition in complex construction environments.

[0101] In some of the solutions described above in this invention, the determination of the actual fracture center relies solely on the spatial distribution of micro-fracture locations, without considering the influence of micro-fracture intensity on the degree of fracture concentration. Differences in micro-fracture intensity may lead to the formation of false clusters of some low-intensity fracture points, making it impossible for the actual fracture center to accurately reflect the true geological activity trend, thereby affecting the accuracy of subsequent trend coefficients and construction influence coefficients.

[0102] In this regard, such as Figure 2 As shown, the present invention further proposes that S200 may include the following S210 to S230:

[0103] S210: Cluster the breakpoints within the target historical time period according to distance to obtain multiple clusters;

[0104] S220, For each cluster, the bias weight of the cluster is determined based on the number of crack points in the cluster and the micro-fracture intensity of each crack point;

[0105] S230, based on the bias weights of various clusters and the cluster centers of various clusters, determines the actual rupture center of the target historical time period.

[0106] In this embodiment, the breakpoint clustering can employ the K-means algorithm based on Euclidean distance or the DBSCAN density clustering algorithm to group breakpoints with similar spatial locations into the same cluster. The calculation of the bias weights needs to consider both the number of breakpoints within a cluster and the difference in micro-fracture intensity. For example, the bias weights can be obtained by calculating the sum of the squared differences between the intensity of each breakpoint within a cluster and the global average intensity, and then multiplying this sum by the number of breakpoints within the cluster. The actual fracture centers are generated using a weighted average method, specifically by multiplying the coordinates of each cluster center by its corresponding bias weight, summing these sums, and then dividing by the total weight value.

[0107] Specifically, in the clustering stage, the DBSCAN algorithm is used to cluster the three-dimensional coordinates of all fracture points within the target time period, with a neighborhood radius of 5 meters and a minimum sample size of 3 to exclude isolated noise points. For each valid cluster, the average micro-fracture intensity of all fracture points within the target historical time period is first calculated. Then, the intensity values ​​of each fracture point within the cluster are subtracted from the average micro-fracture intensity, and the sum of the squares of these differences is used as the intensity deviation index. The intensity deviation is multiplied by the number of fracture points within the cluster to obtain the bias weight of that cluster. Finally, the actual fracture center is generated using a weighted average method, specifically by multiplying the coordinates of each cluster center by its corresponding bias weight, summing the results, and then dividing by the total weight value. The total weight is the sum of all bias weights. This method, through the dual constraints of intensity and spatial distribution, eliminates the interference caused by the clustering of low-intensity fracture points, making the actual fracture center closer to the high-intensity fracture activity area and improving the reliability of geological trend analysis.

[0108] As an example, the breakpoints within a target historical time period can be clustered according to distance to obtain multiple clusters. For instance, the K-means clustering algorithm can be used to group breakpoints based on spatial distance. During the clustering process, a cluster radius of 5 meters can be set, meaning that if the distance between two breakpoints is less than 5 meters, they are grouped into the same cluster.

[0109] For each cluster, the bias weight of the cluster is determined based on the number of fracture points and the microfracture intensity of each fracture point. Specifically, firstly, the average microfracture intensity of all fracture points within the target historical time period is calculated. Then, the intensity value of each fracture point within the cluster is subtracted from the average microfracture intensity, and the sum of the squares of the differences is taken as the intensity deviation index. The intensity deviation is multiplied by the number of fracture points within the cluster to obtain the bias weight of that cluster.

[0110] Based on the bias weights of each cluster and the cluster centers of each cluster, the actual rupture center for the target historical time period is determined. Therefore, the coordinates of the cluster centers of each cluster are multiplied by their corresponding bias weights, summed, and then divided by the total weight value. The total weight is the sum of all bias weights, yielding the weighted average coordinates of the actual rupture center.

[0111] This embodiment enables a more accurate determination of the actual fracture centers for each historical time period. By clustering fracture points according to distance, concentrated areas of micro-fracture activity can be effectively identified. Furthermore, by considering the number of fracture points and the intensity of micro-fractures within each cluster, the calculated bias weights reflect the importance of each cluster. Finally, determining the actual fracture centers based on the cluster centers and bias weights provides a more comprehensive reflection of the distribution characteristics of micro-fracture activity. Compared to simply calculating the average location of all fracture points, this method better captures the spatial distribution characteristics of micro-fracture activity, thus providing more reliable basic data for subsequent trend analysis and risk assessment.

[0112] In some of the above-mentioned solutions of the present invention, when determining the bias weight of a cluster based on the number of crack points in the cluster and the micro-fracture intensity of each crack point, the weight is allocated only by the sum of the number of crack points and the intensity, without considering the dispersion of the micro-fracture intensity of each crack point within the cluster relative to the overall average intensity. This results in the bias weight failing to accurately reflect the true impact of the cluster on the actual fracture center.

[0113] In this regard, the present invention further proposes that S220 may include:

[0114] Obtain the average micro-fracture strength of the fracture point within the target historical time period;

[0115] The average microfracture strength is subtracted from the microfracture strength of each fracture point in the cluster to obtain multiple microfracture strength differences.

[0116] The bias weights of the clusters are determined by using the differences in the strength of each microfracture.

[0117] In this embodiment, the average microfracture strength is calculated by dividing the sum of the microfracture strengths of all fracture points within the target historical time period by the total number of fracture points. The microfracture strength difference is the algebraic difference between the microfracture strength of a single fracture point and the average value. When the difference is greater than zero, it indicates that the strength of the fracture point is higher than the average level, and when the difference is less than zero, it indicates that the strength of the fracture point is lower than the average level.

[0118] Specifically, the bias weight of the cluster can be determined by the following formula 1:

[0119] Formula 1

[0120] In formula 1, Used to characterize the bias weight of the t-th cluster within the k-th historical time period. Used to characterize the microfracture intensity of the i-th fracture point in the t-th cluster within the k-th historical time period. ReLU is used to characterize the average micro-fracture intensity of the fracture point within the k-th historical time period, and is used to characterize the ReLU function operation. The function Norm is used to characterize the total number of split points in the t-th cluster within the k-th historical time period.

[0121] Among them, the greater the total number of crack points in a cluster and the greater the micro-fracture intensity of each crack point in the cluster, the stronger the actual fracture center will be biased towards it.

[0122] As an example, the average microfracture intensity of the fracture points within the target historical time period is first obtained. Specifically, the average microfracture intensity can be obtained by calculating the arithmetic mean of the microfracture intensities of all fracture points within the target historical time period. Then, the average microfracture intensity is subtracted from the microfracture intensity of each fracture point in the cluster to obtain multiple microfracture intensity differences. For example, for each fracture point in the cluster, its microfracture intensity is subtracted from the previously calculated average microfracture intensity to obtain the microfracture intensity difference for that fracture point. Finally, the bias weight of the cluster is determined using each microfracture intensity difference according to Formula 1 above.

[0123] This embodiment can more accurately reflect the micro-fracture intensity distribution of fracture points within a cluster, thereby improving the accuracy of determining the actual fracture center. Consequently, it allows for a more precise assessment of geological collapse risks during tunnel construction, facilitating timely preventative measures and enhancing construction safety.

[0124] In some of the above-mentioned solutions of the present invention, when determining the trend coefficient of the non-vibration interval, relying solely on the actual positional changes of the rupture center in adjacent non-vibration time periods makes it difficult to accurately quantify the overall trend direction and intensity of the rupture center migration within the entire non-vibration interval, resulting in deviations in the calculation of the trend coefficient and affecting the accuracy of the subsequent construction impact coefficient assessment.

[0125] In this regard, the present invention further proposes that S300 may specifically include:

[0126] Based on the distance and direction between the actual fracture center of each non-vibration time period and the next non-vibration time period within the non-vibration interval, migration vectors for multiple non-vibration time periods are obtained.

[0127] A first-order fit is performed on each actual fracture center within the non-vibration interval to obtain the target fitted line;

[0128] Using the direction of the target fitted line as the direction and the square of the distance between adjacent actual fracture centers in the non-vibration interval as the modulus, a trend vector for the non-vibration interval is constructed.

[0129] The trend coefficient of the non-oscillation interval is determined by using the trend vector and each migration vector.

[0130] In this embodiment, the migration vector captures the instantaneous change characteristics of the fracture center migration by calculating the spatial displacement of the actual fracture center during adjacent non-vibration time periods. The target fitted line is established using a linear regression method, and its slope reflects the overall direction of the fracture center migration. The magnitude of the trend vector is calculated using the squared distance value to enhance the characterization of the trend intensity by large displacements.

[0131] Specifically, firstly, the direction from the actual fracture center of each non-vibration time period to the actual fracture center of the next non-vibration time period is used as the direction of the migration vector, and the distance between the actual fracture center of the corresponding non-vibration time period and the actual fracture center of the next non-vibration time period is used as the magnitude, thus constructing multiple migration vectors. Next, a first-order linear fitting method is used to fit the coordinates of the actual fracture centers of multiple non-vibration time periods into a straight line. The extension direction of this line is the overall trend direction of fracture center migration. The trend vector is constructed as a vector with the direction of the fitted straight line and a magnitude equal to the square of the distance between adjacent fracture centers. This design ensures that the trend vector not only contains directional information but also reflects the cumulative effect of displacement intensity.

[0132] Finally, using the trend vector and each migration vector, the trend coefficient of the non-oscillation interval is determined by the following formula 2:

[0133] Formula 2

[0134] In formula 2, A trend coefficient used to characterize the m-th non-vibration interval; The cosine similarity between the r-th migration vector and the trend vector in the m-th non-vibration interval is used to characterize the cosine similarity between the two vectors. The smaller the angle between them, the closer the value is to 1. Used to characterize the projection length of the r-th migration vector in the direction of the trend vector in the m-th non-vibration interval; The magnitude of the trend vector used to characterize the m-th non-vibration interval; Used to characterize the number of non-vibration time intervals in the m-th non-vibration interval. This is the number of migration vectors in the m-th non-vibration interval.

[0135] The closer the migration vectors are to the trend vector, and the longer the projection length along the trend vector direction, the larger the trend coefficient of the non-vibration zone. The trend coefficient represents the reliability of the influence of the trend vector; the larger the trend coefficient, the higher the reliability of the trend vector in summarizing the movement characteristics of all actual rupture centers in the non-vibration zone.

[0136] This embodiment accurately captures the changing trend of the actual rupture center within the non-vibration zone, effectively eliminating the influence of random factors and improving the accuracy of the trend coefficient. This provides reliable basic data for subsequent construction impact assessments, thereby improving the accuracy and reliability of geological collapse risk assessments.

[0137] In some of the above-mentioned solutions of the present invention, the current construction influence coefficient is determined based on the trend coefficient of the non-vibration interval and the actual rupture center during the vibration period. However, in this process, there is still a lack of effective means to quantify the specific impact of construction operations on the migration of the rupture center, which leads to deviations in the calculation of the construction influence coefficient and makes it difficult to accurately reflect the dynamic effect of construction disturbance on the geological structure.

[0138] In this regard, such as Figure 3 As shown, the present invention further proposes that S400 may specifically include the following S410 to S430:

[0139] S410, based on the trend coefficient of the non-vibration zone and the actual rupture center of the target vibration time period, determine the construction coefficient and the original coefficient of the target vibration time period. The target vibration time period is the next vibration time period of the non-vibration zone. The construction coefficient is used to characterize the coefficient of the actual rupture center of the target vibration time period affected by construction vibration, and the original coefficient is used to characterize the coefficient of the actual rupture center of the target vibration time period affected by the natural geological changes of the non-vibration zone.

[0140] S420: The vibration weight of the target vibration time period is determined by using the trend coefficient of the non-vibration zone, the construction coefficient and the original coefficient of the target vibration time period.

[0141] S430, using each vibration weight, determines the current construction impact coefficient.

[0142] In this embodiment, when determining the construction coefficient and the original coefficient, the actual fracture center of the last non-vibration time period in the non-vibration interval is used as the starting point to draw a trend vector for the non-vibration interval, thus obtaining the theoretical fracture center of the target vibration time period. Based on the theoretical fracture center, the actual fracture center, and the construction influence center of the target vibration time period, the construction coefficient and the original coefficient are determined. The construction influence center is calculated using the location of the construction operation and a geological structure model; for example, the construction influence center can be derived based on the operating location of the construction machinery and a rock mass stress distribution model. The theoretical fracture center represents the predicted location of the fracture center when not disturbed by construction. The vibration weight is determined by a linear combination of the trend coefficient, the construction coefficient, and the original coefficient.

[0143] Specifically, the vibration weight can be determined using the following formula 3:

[0144] Formula 3

[0145] In formula 3, The vibration weight used to characterize the k-th target vibration time period The trend coefficient used to characterize the previous non-vibration interval of the k-th target vibration time period. Construction coefficient used to characterize the target vibration time period. The original coefficients used to characterize the target vibration time period. It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations in this embodiment of the invention, if the denominator is 0, a parameter adjustment factor greater than 0 needs to be added to the denominator before summing to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and in this application it is set to 0.1.

[0146] Among them, vibration weight, through the characteristics of fracture center migration, reflects the change in the main trend of microfracture evolution after construction influence. The larger the vibration weight, the greater the influence of construction on microfracture evolution, and the more the microfractures tend to evolve towards the construction influence center, thus promoting the occurrence of microfractures in the construction direction. The larger the vibration weight, the stronger the promotion of microfracture by construction.

[0147] The current construction impact coefficient can be determined using the following formula 4:

[0148] Formula 4

[0149] In formula 4, The coefficient is used to characterize the current construction impact factor, and M is used to characterize the total number of target vibration time periods. The vibration weight is used to characterize the vibration time period of the m-th target, where m represents the order of the vibration time periods of the m-th target.

[0150] Among them, with As a weight, it represents how close the target vibration time period is to the current time node; the closer the target vibration time period is to the current time node, the more reliable it is.

[0151] As an example, the trend coefficient of the non-vibration interval is first obtained, reflecting the changing trend of the actual rupture center during the non-vibration period. Then, for the target vibration period immediately following the non-vibration interval, the location information of its actual rupture center is obtained. Based on these two data points, the construction coefficient and the original coefficient for the target vibration period are calculated. The construction coefficient represents the degree of influence of construction activities on the location of the rupture center, while the original coefficient reflects the influence of natural geological changes on the location of the rupture center.

[0152] Furthermore, the trend coefficient of the non-vibration interval is mathematically calculated with the construction coefficient and the original coefficient of the target vibration time period, and the vibration weight of the target vibration time period is obtained through Formula 3 above. The vibration weight reflects the intensity of the impact of construction activities on the geological state during this time period.

[0153] Finally, a comprehensive analysis of the vibration weights for all vibration time periods was conducted, and the current construction impact coefficient was calculated using Formula 4 above. This current construction impact coefficient comprehensively reflects the cumulative impact of construction activities on the geological conditions.

[0154] This embodiment enables accurate quantification of the impact of construction activities on geological conditions. This distinguishes the influence of natural geological changes and human-induced construction disturbances on micro-fracture activity, improving the accuracy of geological risk assessment. Furthermore, by introducing the concept of vibration weighting, this scheme achieves differentiated processing of the impact of construction at different time periods, making the risk assessment results more closely reflect reality. Finally, by calculating the current construction impact coefficient, reliable data support is provided for subsequent geological collapse risk assessment, effectively enhancing tunnel construction safety.

[0155] In some of the solutions described above in this invention, when determining the construction coefficient and the original coefficient, the direct correlation between the trend coefficient and the actual fracture center during the vibration period is relied upon. This fails to effectively distinguish the difference between the fracture displacement caused by construction disturbance and the continuation of the natural geological trend, resulting in a deviation in the assessment of the construction impact.

[0156] In this regard, the present invention further proposes that S410 may specifically include:

[0157] Starting from the actual fracture center of the last non-vibration time period in the non-vibration interval, the trend vector of the non-vibration interval is drawn to obtain the theoretical fracture center of the target vibration time period.

[0158] Based on the theoretical fracture center, actual fracture center, and construction influence center of the target vibration time period, the construction coefficient and original coefficient of the target vibration time period are determined.

[0159] In this embodiment, the theoretical fracture center is determined by extending the trend vector, which includes direction and modulus parameters. The modulus is composed of the square of the distance between adjacent actual fracture centers within the non-vibration zone. The construction influence center is calculated using the coordinates of the construction machinery position and the tunnel axis. The specific coordinates are generated by matching the construction equipment positioning data with the tunnel's three-dimensional model.

[0160] Specifically, such as Figure 4The diagram illustrates the determination of construction coefficients and original coefficients. After the non-vibration period ends, the actual fracture center 401 of the last non-vibration time period is used as a reference point. The modulus of the trend vector 402 is extended along the trend vector direction to generate the theoretical fracture center 403 for the target vibration time period. Then, the first vector between the actual fracture center 404 and the theoretical fracture center 403 of the target vibration time period, the second vector between the actual fracture center 404 and the construction influence center 405 of the target vibration time period, and the third vector between the theoretical fracture center 403 and the construction influence center 405 of the target vibration time period are measured.

[0161] The construction coefficient characterizes the degree to which the actual fracture center is affected by construction vibration during the target vibration period. The influence of construction vibration on the deviation of the actual fracture center from the theoretical fracture center can be measured by calculating the cosine of the angle between the first and second vectors. The original coefficient characterizes the degree to which the actual fracture center is affected by natural geological changes in the non-vibration zone during the target vibration period. The influence of natural geological changes on the deviation of the actual fracture center from the theoretical fracture center can be measured by calculating the cosine of the angle between the first and third vectors.

[0162] This embodiment enables accurate assessment of the impact of construction activities on the distribution of microfractures. By introducing three key points—the theoretical fracture center, the actual fracture center, and the construction influence center—the changes in rock mass state caused by construction disturbance can be quantitatively analyzed. This method considers the specific impacts of construction activities, rather than relying solely on the microfracture activity itself, thereby improving the accuracy and reliability of risk assessment. Furthermore, by calculating the construction coefficient and the original coefficient, the proportion of construction activities in the changes in microfracture distribution can be intuitively reflected, which helps to better understand and predict geological change trends during construction.

[0163] In some of the above-mentioned solutions of the present invention, the construction influence coefficient is determined by analyzing the location and intensity change trend of micro-fractures. However, the correlation between risk frequency and construction disturbance is not established. As a result, when relying solely on the frequency of micro-fractures for risk assessment, the cumulative impact of construction disturbance on geological structure cannot be accurately reflected, and there is a possibility of delayed early warning or misjudgment.

[0164] In this regard, the present invention further proposes that S500 may specifically include:

[0165] Based on the current construction impact coefficient and the frequency of micro-fractures in the current time period, determine the current risk frequency of the target tunnel;

[0166] Divide the risk frequency by a preset frequency threshold to obtain the current risk assessment value of the target tunnel;

[0167] If the risk assessment value is greater than the preset assessment value threshold, it is determined that the target tunnel currently has a risk of geological collapse.

[0168] In this embodiment, the risk frequency is obtained by weighting the micro-fracture frequency with the construction impact coefficient, which reflects the cumulative effect of construction disturbance on the geological structure. The preset frequency threshold is set based on the average micro-fracture frequency during historical safe construction periods, and the risk assessment value quantifies the risk level using a dimensionless ratio. The preset assessment threshold is a fixed value, and an early warning mechanism is triggered when the calculation result exceeds this threshold.

[0169] Specifically, the current risk assessment value of the target tunnel can be determined using the following formula 5:

[0170] Formula 5

[0171] In Formula 5, x represents the current risk assessment value of the target tunnel, D represents the current construction impact coefficient, and n represents the frequency of micro-fractures in the current time period. Used to characterize a preset frequency threshold.

[0172] As an example, during tunnel construction, micro-fracture event data is collected in real time for the current time period, and the frequency of micro-fractures occurring within this time period is counted as 85 times per hour. By recording the start and stop status of construction machinery, the current time period is identified as being affected by construction vibration interference, and a pre-calculated current construction impact coefficient of 0.73 is applied. The micro-fracture frequency is weighted and summed with the construction impact coefficient, resulting in a risk frequency value of 85 × (1 + 0.73) = 147.05 times / hour. This risk frequency is divided by a preset frequency threshold of 100 times / hour, yielding a risk assessment value of 1.47. Compared to the preset assessment threshold of 1.5, since 1.47 is less than the threshold, the current tunnel geological condition is determined to be within a safe range, and no risk warning is triggered. If the risk assessment value is subsequently detected to exceed 1.5, an audible and visual alarm is activated, and construction is suspended.

[0173] This embodiment effectively addresses the shortcomings of existing technologies that rely solely on the frequency of micro-fractures while ignoring the dynamic impact of construction disturbances. By fusing the construction influence coefficient with real-time micro-fracture data, the risk assessment benchmark can be dynamically revised, accurately distinguishing between natural geological activity and construction-induced instability. This method significantly improves the sensitivity of risk warning, enabling timely identification of rock mass stress imbalances caused by construction vibrations, avoiding missed assessments of potential collapse risks, and providing a more reliable basis for construction safety decisions.

[0174] Based on the geological monitoring method for tunnel construction in landslide areas provided by this invention, correspondingly, this invention also provides specific embodiments of a geological monitoring system for tunnel construction in landslide areas.

[0175] like Figure 5As shown, a structural schematic diagram of a geological monitoring system for tunnel construction in a landslide area is provided. The geological monitoring system 500 for tunnel construction in a landslide area includes a crack point acquisition module 510, a center determination module 520, a trend determination module 530, an impact assessment module 540, and a risk assessment module 550.

[0176] The crack point acquisition module 510 is used to acquire the micro-fracture location and micro-fracture intensity of the crack point in the target tunnel at various historical moments.

[0177] The center determination module 520 is used to determine the actual fracture center for each historical time period based on the micro-fracture location and micro-fracture intensity of the target tunnel at each historical moment. The actual fracture center is used to characterize the distribution and concentration of micro-fracture locations within the historical time period.

[0178] The trend determination module 530 is used to determine the trend coefficient of the non-vibration interval based on the changing trend of the actual fracture center in each non-vibration time period within the non-vibration interval. The non-vibration time period is the historical time period that has not been disturbed by vibration during construction operations, and the non-vibration interval is the time interval composed of multiple consecutive non-vibration time periods.

[0179] The impact assessment module 540 is used to determine the current construction impact coefficient based on the trend coefficient of each non-vibration interval and the actual rupture center of each vibration time period. The vibration time period is the historical time period affected by vibration interference from construction operations.

[0180] Risk assessment module 550 is used to assess the current geological collapse risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures in the current time period.

[0181] The geological monitoring system for tunnel construction in landslide areas provided in this invention first acquires the micro-fracture locations and intensities of fracture points at various historical moments in the target tunnel, thereby determining the actual fracture centers for each historical time period and reflecting the concentrated distribution of micro-fracture locations. Next, a trend coefficient is determined based on the changing trends of the actual fracture centers in each non-vibration time period within the non-vibration zone. Since the non-vibration zone is not disturbed by construction vibration, this trend coefficient reflects the changing patterns of micro-fractures under natural conditions. Then, combining the trend coefficients of each non-vibration zone with the actual fracture centers in vibration time periods disturbed by construction vibration, the current construction influence coefficient is determined, incorporating the impact of construction disturbance on the soil state and providing a more comprehensive reflection of the actual situation. Finally, the geological collapse risk is assessed based on the current construction influence coefficient and the frequency of micro-fractures in the current time period. This approach considers both the micro-fracture activity itself and the construction disturbance factor, avoiding the limitations of focusing solely on micro-fracture activity. By comprehensively assessing the risk from multiple perspectives, the geological collapse risk can be more accurately determined, effectively ensuring construction safety.

[0182] Based on the geological monitoring method for tunnel construction in landslide areas provided by this invention, correspondingly, this invention also provides specific embodiments of a geological monitoring device for tunnel construction in landslide areas.

[0183] Figure 6 A schematic diagram of the hardware structure of a geological monitoring device for tunnel construction in a landslide area, provided by an embodiment of the present invention, is shown.

[0184] A geological monitoring device for tunnel construction in a landslide area may include a processor 601 and a memory 602 storing computer program instructions.

[0185] Specifically, the processor 601 may include a central processing unit, a specific integrated circuit, or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0186] Memory 602 may include mass storage for data or instructions. For example, and not limitingly, memory 602 may include hard disk drives, floppy disk drives, flash memory, optical disk drives, magneto-optical disk drives, magnetic tape drives, or Universal Serial Bus drives, or combinations of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 602 is non-volatile solid-state memory.

[0187] Memory 602 may include read-only memory, random access memory, disk storage media device, optical storage media device, flash memory device, electrical, optical or other physical / tangible memory storage device. Therefore, generally, memory 602 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0188] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any of the geological monitoring methods for tunnel construction in landslide areas described in the above embodiments.

[0189] In one example, the geological monitoring device for tunnel construction in a landslide area may also include a communication interface 603 and a bus 610. For example, Figure 6 As shown, the processor 601, memory 602, and communication interface 603 are connected through bus 610 and complete communication with each other.

[0190] The communication interface 603 is mainly used to realize communication between each module, device, unit and / or equipment in the embodiments of the present invention.

[0191] Bus 610 includes hardware, software, or both, that couples together components of a geological monitoring device used for tunnel construction in collapsed areas. For example, and not limitingly, the bus may include an accelerated graphics port or other graphics bus, an enhanced industry standard architecture bus, a front-end bus, an HyperTransport interconnect, an industry standard architecture bus, an unlimited bandwidth interconnect, a low pin count bus, a memory bus, a WeChat architecture bus, a peripheral component interconnect bus, a serial advanced technology accessory bus, a Video Electronics Standards Association local bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 610 may include one or more buses. While specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0192] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0193] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0194] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope 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 protection scope of the present invention.

Claims

1. A geological monitoring method for tunnel construction in landslide areas, characterized in that, The method includes: Obtain the location and intensity of micro-fractures at the fracture points of the target tunnel at various historical moments; Based on the micro-fracture location and micro-fracture intensity of the target tunnel at each historical moment, the actual fracture center of each historical time period is determined. The actual fracture center is used to characterize the distribution and concentration of micro-fracture locations within the historical time period. Based on the changing trend of the actual fracture center in each non-vibration time period within the non-vibration interval, the trend coefficient of the non-vibration interval is determined. The non-vibration time period is a historical time period that has not been disturbed by vibration from construction operations, and the non-vibration interval is a time interval composed of multiple consecutive non-vibration time periods. Based on the trend coefficients of each non-vibration interval and the actual rupture center of each vibration time period, the current construction influence coefficient is determined, where the vibration time period is the historical time period affected by vibration interference from construction operations. Based on the current construction impact coefficient and the frequency of micro-fractures in the current time period, assess the current geological collapse risk of the target tunnel.

2. The geological monitoring method for tunnel construction in landslide areas according to claim 1, characterized in that, The acquisition of the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments includes: Acquire the target microseismic signal of the target tunnel at a historical moment, collected by the target sensor; Effective rupture signals are extracted from the target microseismic signals, and the effective rupture signals include shear wave signals and longitudinal wave signals; Based on the time difference between the propagation of the shear wave signal and the longitudinal wave signal to the target sensor, the position of the target sensor, and the rock mass wave velocity model, the micro-fracture location of the target tunnel at the target historical moment is determined. The effective fracture signal is integrated to obtain the micro-fracture intensity of the target tunnel at the fracture point at the target historical time.

3. The geological monitoring method for tunnel construction in landslide areas according to claim 1, characterized in that, The determination of the actual fracture center for each historical time period based on the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments includes: Cluster the breakpoints within the target historical time period according to distance to obtain multiple clusters; For each of the aforementioned clusters, a bias weight for the cluster is determined based on the number of fracture points in the cluster and the microfracture intensity of each fracture point. Based on the bias weights of each cluster and the cluster center of each cluster, the actual break center of the target historical time period is determined.

4. The geological monitoring method for tunnel construction in landslide areas according to claim 3, characterized in that, The determination of the bias weight of the cluster based on the number of fracture points in the cluster and the microfracture intensity of each fracture point includes: Obtain the average microfracture strength of the fracture point within the target historical time period; Subtracting the average microfracture strength from the microfracture strength of each fracture point in the cluster yields multiple microfracture strength differences. The bias weight of the cluster is determined by using the difference in microfracture strength among the clusters.

5. The geological monitoring method for tunnel construction in landslide areas according to claim 1, characterized in that, The determination of the trend coefficient for the non-vibration interval based on the changing trend of the actual fracture center during each non-vibration time period within the non-vibration interval includes: Based on the distance and direction between each of the non-vibration time periods within the non-vibration interval and the actual fracture center of the next non-vibration time period, a migration vector for multiple non-vibration time periods is obtained. A first-order fit is performed on each of the actual fracture centers within the non-vibration interval to obtain the target fitting line; Using the direction of the target fitted line as the direction and the square of the distance between adjacent actual fracture centers within the non-vibration interval as the modulus, a trend vector for the non-vibration interval is constructed. The trend coefficient of the non-vibration interval is determined using the trend vector and each of the migration vectors.

6. The geological monitoring method for tunnel construction in landslide areas according to claim 1, characterized in that, The determination of the current construction impact coefficient based on the trend coefficient of each non-vibration zone and the actual fracture center of each vibration time period includes: Based on the trend coefficient of the non-vibration interval and the actual fracture center of the target vibration time period, the construction coefficient and the original coefficient of the target vibration time period are determined. The target vibration time period is the next vibration time period after the non-vibration interval. The construction coefficient is used to characterize the coefficient of the actual fracture center of the target vibration time period affected by construction vibration, and the original coefficient is used to characterize the coefficient of the actual fracture center of the target vibration time period affected by the natural geological changes of the non-vibration interval. The vibration weight of the target vibration time period is determined by using the trend coefficient of the non-vibration interval, the construction coefficient and the original coefficient of the target vibration time period; The current construction impact coefficient is determined using the vibration weights mentioned above.

7. The geological monitoring method for tunnel construction in landslide areas according to claim 6, characterized in that, The determination of the construction coefficient and original coefficient for the target vibration time period based on the trend coefficient of the non-vibration zone and the actual fracture center of the target vibration time period includes: Starting from the actual fracture center of the last non-vibration time period in the non-vibration interval, a trend vector of the non-vibration interval is drawn to obtain the theoretical fracture center of the target vibration time period. Based on the theoretical fracture center, actual fracture center, and construction influence center of the target vibration time period, the construction coefficient and original coefficient of the target vibration time period are determined.

8. The geological monitoring method for tunnel construction in landslide areas according to any one of claims 1-7, characterized in that, The assessment of the current geological collapse risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures in the current time period includes: Based on the current construction impact coefficient and the frequency of micro-fractures in the current time period, the current risk frequency of the target tunnel is determined; Divide the risk frequency by a preset frequency threshold to obtain the current risk assessment value of the target tunnel; If the risk assessment value is greater than a preset assessment value threshold, it is determined that the target tunnel currently faces a risk of geological collapse.

9. A geological monitoring system for tunnel construction in landslide areas, characterized in that, The system includes: The crack point acquisition module is used to acquire the micro-fracture location and micro-fracture intensity of the target tunnel at various historical moments; The center determination module is used to determine the actual fracture center for each historical time period based on the micro-fracture location and micro-fracture intensity of the fracture point of the target tunnel at each historical moment. The actual fracture center is used to characterize the distribution and concentration of micro-fracture locations within the historical time period. The trend determination module is used to determine the trend coefficient of the non-vibration interval based on the changing trend of the actual rupture center in each non-vibration time period within the non-vibration interval. The non-vibration time period is a historical time period that has not been disturbed by vibration from construction operations, and the non-vibration interval is a time interval composed of multiple consecutive non-vibration time periods. The impact assessment module is used to determine the current construction impact coefficient based on the trend coefficient of each non-vibration interval and the actual rupture center of each vibration time period, wherein the vibration time period is the historical time period affected by vibration interference from construction operations. The risk assessment module is used to assess the current geological collapse risk of the target tunnel based on the current construction impact coefficient and the frequency of micro-fractures in the current time period.

10. A geological monitoring device for tunnel construction in landslide areas, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the geological monitoring method for tunnel construction in landslide areas as described in any one of claims 1-8.

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