Subway channel underground excavation deformation monitoring method based on ultra-shallow buried large-flow stratum

Abstract

The invention belongs to the technical field of subway channel underground excavation deformation monitoring, and particularly discloses a subway channel underground excavation deformation monitoring method based on an ultra-shallow-buried large-flow stratum, and the method comprises the steps: carrying out the geological radar detection of a construction influence range at the early stage of construction, and generating a geological risk distribution diagram; and the geological risk distribution map is used for carrying out risk grade division on the construction influence area to implement differentiated layout of monitoring points, so that on the premise of guaranteeing the monitoring effect, the deformation capture capability of a key area is improved, the monitoring redundancy of a low-risk area is reduced, and the monitoring efficiency is improved. In the construction process, deformation data collected by the monitoring points in all the sub-areas are dynamically analyzed to identify jumping characteristics and stability characteristics of deformation, whether a monitoring blind area or a redundant area exists or not is judged, encryption or migration operation of the monitoring points is triggered accordingly, conversion of the monitoring system from static layout to dynamic evolution is achieved, and the construction efficiency is improved. And the real-time matching between the monitoring point layout and the stratum deformation evolution is effectively ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of deformation monitoring for the mined - out construction of subway tunnels, and specifically discloses a method for monitoring the deformation of mined - out subway tunnels in a super - shallow - buried and large - flow - rate formation. Background Technique

[0002] With the acceleration of the urbanization process, the development of underground space plays an important role in alleviating traffic pressure. As the core component of the underground transportation system, the construction of subway tunnels is often limited by the complex environmental conditions in the urban core area and is difficult to be constructed by the open - cut method, so the mined - out method needs to be implemented.

[0003] Such projects are mostly used in scenarios such as subway entrances and exits, transfer tunnels, and underground commercial connection tunnels, etc., and generally face complex geological conditions such as super - shallow burial, water - rich soft strata, etc., with characteristics such as thin overburden, loose strata, strong permeability, and poor self - stability. During the construction process, risks such as surrounding rock deformation, settlement, convergence, and even sudden water inrush and local collapse are likely to occur. Therefore, carrying out formation deformation monitoring during the construction process is one of the key technical measures to ensure the safe and smooth implementation of such projects.

[0004] During the mined - out construction of the tunnel, deformation monitoring usually relies on arranging monitoring points in the construction area to obtain key data on structural deformation and formation response. Currently, there are relatively mature technical solutions to support such monitoring. For example, a deformation monitoring method for shallow - buried mined - out tunnel construction disclosed in Chinese invention patent CN104564128B arranges multiple groups of support - state monitoring points on the completed support structure on the basis of synchronous tunnel excavation and initial support, and arranges multiple settlement monitoring points on the ground surface. By continuously collecting monitoring data, the dynamic monitoring of the stability of the tunnel support structure and the ground surface settlement situation is realized.

[0005] However, in the actual application of this technical solution, the layout method of monitoring points mainly adopts a static, uniform or empirical layout, that is, usually arranged at a fixed interval according to engineering experience or specification requirements, without fully considering the geological condition differences at different positions in the construction area, resulting in a mismatch between the distribution of monitoring points and the actual risk distribution. This "one - size - fits - all" layout mode is likely to cause insufficient density of monitoring points in high - risk areas and redundant layout in low - risk areas, affecting the monitoring efficiency and cost control.

[0006] In addition, the mined - out construction process has significant dynamic evolution characteristics. With the advancement of the excavation face, the construction of the support structure, and the redistribution of formation stress, the environment where the monitoring points are located is constantly changing. Continuing to use the initial fixed layout method without adjustment will lead to a mismatch between the layout of monitoring points and the actual deformation development, unable to accurately reflect the true response state of the structure and the formation, thus weakening the early - warning ability of the monitoring system and increasing the construction safety risk. Summary of the Invention

[0007] To this end, one purpose of an embodiment of the present application is to provide a method for monitoring deformation of underground excavation of subway passages in ultra-shallow buried and high-flow strata. Monitoring points are differentially arranged according to the geological exploration results of the construction area before construction, and the layout of monitoring points is dynamically optimized and adjusted during the construction process to ensure that the monitoring system can reflect the structural deformation and stratum response status in real time and accurately, thereby solving the problems mentioned in the background technology.

[0008] The object of the present invention can be achieved by the following technical scheme: a method for monitoring deformation of underground excavation in ultra-shallow buried high-flow strata for subway passages, comprising the following steps: (1) defining a construction impact area with the underground excavation construction face as the center, performing geological radar scanning on the construction impact area to extract geological distribution characteristics including stratum interface distribution, water content distribution and rock mass fissure distribution, and performing geological unit division and risk factor marking to generate a geological risk distribution map.

[0009] (2) According to the geological risk distribution map, the construction affected area is divided into high-risk sub-areas, transition sub-areas and stable sub-areas, and the initial monitoring network is deployed in each sub-area in a differentiated manner.

[0010] (3) During the tunneling process, the deformation data of each monitoring point is collected in real time to construct a spatiotemporal synchronous deformation dataset.

[0011] (4) Based on the spatial distribution characteristics of the deformation data set, the monitoring blind spots and monitoring redundancies are identified. This triggers the deployment of more dense monitoring points when the monitoring blind spots are identified, and triggers the migration of monitoring points when the monitoring redundancies are identified.

[0012] (5) After the dynamic adjustment of the monitoring network is completed, deformation trend analysis is performed based on the time-series deformation data of the monitoring points in each sub-area during the construction process, thereby initiating a graded deformation warning from local to global.

[0013] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. In the early stage of construction, the present invention generates a geological risk distribution map by conducting geological radar detection on the construction impact range, and then uses the geological risk distribution map to divide the risk level of the construction impact area and implement differentiated layout of monitoring points, avoiding the waste of resources caused by traditional uniform layout. On the premise of ensuring the monitoring effect, the deformation capture capability of key areas is improved, the monitoring redundancy of low-risk areas is reduced, and the overall monitoring efficiency is improved.

[0014] 2. During the construction process of the present invention, by dynamically analyzing the deformation data collected at the monitoring points in each sub-region, the jump and stability characteristics of the deformation are identified, whether there are monitoring blind spots or redundant regions is judged, and accordingly, the encryption or migration operation of the monitoring points is triggered, realizing the transformation of the monitoring system from static layout to dynamic evolution, effectively ensuring the real-time matching between the layout of the monitoring points and the evolution of the formation deformation, and providing reliable data support for subsequent deformation early warning.

[0015] 3. By performing trend analysis on the time-series deformation data of the monitoring points in each risk sub-region, identifying the deformation rate and combining it with the deformation coverage range, the present invention triggers a multi-level linkage early warning mechanism from local to global, realizes the gradual response from local anomaly to overall anomaly risk early warning, enhances the logic and accuracy of the early warning system, helps to take targeted engineering measures according to the early warning level, and improves the safety management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0017] Figure 1 It is a flowchart of the method implementation steps of the present invention.

[0018] Figure 2 It is a schematic diagram showing the risk area division according to the geological risk distribution map in the present invention.

[0019] Figure 3 It is a schematic diagram showing the division of the adjacent area corresponding to the monitoring points in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] See Figure 1 As shown, the present invention proposes a method for monitoring the deformation of a subway tunnel mined in a shallow-buried and large-flow formation, including the following steps: (1) Taking the heading face of the mined tunnel as the center, demarcating the construction influence area, performing geological radar scanning on the construction influence area to extract the geological distribution characteristics including the formation interface distribution, water content distribution and rock mass fracture distribution, and performing geological unit division and risk factor marking to generate a geological risk distribution map.

[0022] It should be noted that the heading face is the place where excavation operations are directly carried out. During the construction of underground projects, the rock and soil mass in front of the heading face is the first to be affected by construction activities. Therefore, it is the key starting point for evaluating construction impacts. As the tunneling work progresses, the position of the heading face moves forward continuously, which means that the construction impact area is also constantly updated. Centering on the heading face can ensure that the monitoring range always covers the area where deformation is most likely to occur currently.

[0023] When delimiting the construction impact area, the measured data of historical similar engineering projects and engineering experience can be combined to analyze the impact range of construction activities on the surrounding strata. Based on this, a reasonable construction impact radius can be determined, and the construction impact area of the current project can be delineated accordingly.

[0024] In the optimal implementation of the above scheme, ground penetrating radar scanning is carried out on the construction impact area, and the geological distribution characteristics including the distribution of formation interfaces, water content distribution, and rock mass fissure distribution are extracted as follows: Multiple parallel scanning lines are arranged along the axis direction of the mined tunnel, and each scanning line covers the lateral boundary of the construction impact area.

[0025] It should be noted that in mined tunneling projects, formation deformation mainly propagates along the heading face advancing direction. The geological scanning method of axial layout and lateral coverage can effectively capture the impact range of construction disturbances on the formation, and multiple scanning lines can enhance the spatial continuity and reliability of the data, avoiding the one-sidedness brought by a single path.

[0026] Use ground penetrating radar equipment to perform high-resolution geological scanning on each scanning line, collect the time series data of the reflected waves of underground media, and perform image conversion to obtain the ground penetrating radar image of the construction impact area.

[0027] The above conversion of the original time domain signal detected by radar into a visual ground penetrating radar image provides basic data for subsequent formation boundary identification, lithology discrimination, water content analysis, and fissure detection.

[0028] In the generated ground penetrating radar image, identify the lines with changing reflection intensity as the boundaries between different formations, and determine the spatial distribution of the formations according to the positions of these boundaries.

[0029] It should be understood that due to the differences in physical properties such as mineral composition, density, and water content among different formations, the dielectric constants and electromagnetic wave propagation characteristics are inconsistent, resulting in changes in reflection intensity in ground penetrating radar detection. By identifying and tracking the reflection intensity in the radar image, the position of the formation interface can be effectively discriminated, and thus the spatial distribution of the formations can be obtained.

[0030] Based on the analysis of the reflection wave properties of different formations in the ground penetrating radar image, determine the geological types of the formations, and classify the areas with the same formation distribution and no intervals as the same geological unit.

[0031] It should be noted that due to the differences in physical properties such as mineral composition, water content state, and compactness of different geological types, there are significant differences in their dielectric constants. This difference is manifested in the changes in the reflection waveform characteristics on the radar image in geological radar detection, including the amplitude strength, frequency, and phase continuity.

[0032] For example, sand layers or gravel layers usually have high dielectric constant difference interfaces and are prone to generating strong and continuous reflection signals.

[0033] Due to the high water content in clay layers, the attenuation of electromagnetic waves is obvious, and the reflection signals are weak.

[0034] Therefore, by analyzing and identifying the reflection wave characteristics of each stratum unit in the radar image, the discrimination and classification of the geological types of the strata can be realized.

[0035] The above-mentioned method classifies the areas with the same stratum distribution and no intervals into the same geological unit, so that there should be geological consistency within the same geological unit, without obvious interruptions or transition zones, realizing the preliminary zoning of the construction influence area.

[0036] For each divided geological unit, analyze the attenuation properties of the reflection waves in the corresponding geological radar image to determine the water content rate of the area.

[0037] It should be noted that as the water content rate increases, the conductivity of the medium significantly enhances, resulting in a more rapid energy attenuation of electromagnetic waves during propagation. By quantitatively analyzing the attenuation trend of the reflection wave signals in the geological radar image, the water content rate level in a specific area can be estimated.

[0038] Extract the shape characteristics of the reflection waves from the geological radar images in each geological unit for fracture identification and locate the fracture distribution positions.

[0039] It should be known that in the geological radar image, the fracture structure usually shows strong non - continuous reflection signals, and its reflection waveform presents characteristic geometric shapes, such as hyperbola, intermittent linear or scattered distribution. By identifying and extracting the shape characteristics of the reflection waves, the spatial distribution positions of the fractures in the strata can be effectively located.

[0040] The present invention selects the stratum interface distribution, water content rate distribution, and rock mass fracture distribution as the core geological distribution characteristics in the geological detection of the construction influence area, mainly based on the following technical considerations: The stratum interface distribution reflects the lithological characteristics of different strata and is the basic basis for judging the stratum stability. If there are potential risk strata such as fault fracture zones and weathered rock layers in the strata, it may cause stratum instability under excavation disturbance, thus forming deformation hidden dangers inside the original stratum structure.

[0041] The moisture content distribution characterizes the occurrence state of moisture in underground media and has a significant impact on the physical and mechanical properties of rock and soil masses. In high moisture content areas, rock and soil masses are prone to softening, rheology, and even local liquefaction phenomena, which can lead to a decrease in the bearing capacity of the formation, induce geological disasters such as settlement, water inrush, etc., and significantly increase the deformation risk during the construction process.

[0042] The distribution of rock mass fractures reveals the integrity of the rock mass and the degree of its structural damage. In areas where fractures are densely developed, the strength of the surrounding rock is significantly reduced, the permeability is enhanced, and at the same time, the connection between structural planes is weakened, making it easy to form slip surfaces or collapsed blocks, resulting in the deformation and instability of the surrounding rock.

[0043] The above geological characteristics together constitute the main geological risk sources during the excavation process of underground engineering. Identifying these elements as key geological distribution characteristics not only helps to comprehensively depict the geological conditions of the construction-influenced area but also provides reliable data support for subsequent geological risk zoning.

[0044] In the further optimized implementation of the above scheme, the following content is generated for the geological risk distribution map: layer by layer, extract the formation geological type information of each geological unit and match it with the risk formation database. If the geological type of a certain layer matches the high-risk formation in the database, it is determined that there are formation risk factors in this geological unit.

[0045] It should be added that the risk formation database adopted in the present invention integrates various typical high-risk formation types, including but not limited to silt clay layers, fine silty sand layers, fault fracture zones, strongly weathered rock layers, etc. These formations are extremely prone to geological disasters such as collapse, water inrush, and mud outburst during underground engineering construction due to characteristics such as low strength, high water content, structural fragmentation, or easy deformation. The data sources of the database mainly include the construction accident records caused by specific formations in historical engineering cases and the definition and classification standards of poor formations in relevant industry specifications. Through multi-source information fusion and structured arrangement, a risk formation knowledge base with engineering practical value is constructed.

[0046] In practical applications, by intelligently comparing the geological type of each formation in each detected geological unit with the entries in the database, it is automatically identified whether high-risk formations are included.

[0047] Compare the moisture content of each geological unit with the set safe moisture content threshold. If the moisture content of a certain geological unit exceeds this threshold, it is determined that there are moisture risk factors in this geological unit.

[0048] Based on the distribution position of fractures in each geological unit and the fracture length statistics, the total fracture length per unit area is used as the fracture distribution density, and it is compared with the set safety fracture distribution density. If the fracture distribution density of a certain geological unit is higher than the safety fracture distribution density, it is identified that there are fracture risk factors in this geological unit.

[0049] It should be noted that the above-mentioned safety moisture content threshold and safety fracture distribution density can be comprehensively determined according to construction safety codes and geotechnical engineering investigation standards. These safety thresholds reflect the control boundary conditions of formation stability during the construction of underground projects and are important technical parameters for evaluating whether the rock and soil mass may cause adverse engineering responses such as deformation due to high water content or structural fragmentation.

[0050] Based on the geometric boundary of the construction influence area, a plane model is established, and geological unit identification is carried out based on the demarcation contour line of each geological unit in the constructed model. At the same time, the identified risk factors are marked to generate a geological risk distribution map.

[0051] The above-mentioned generation of the geological risk distribution map can visually display the risk distribution of each geological unit within the construction influence area, thus helping to efficiently and accurately define and classify the risk areas.

[0052] (2) According to the geological risk distribution map, the construction influence area is divided into high-risk sub-areas, transition sub-areas and stable sub-areas, and an initial monitoring network is differentially arranged in each sub-area.

[0053] For the optional implementation of the above scheme, see Figure 2 As shown, the construction influence area is divided into high-risk sub-areas, transition sub-areas and stable sub-areas according to the geological risk distribution map as follows: According to the risk factors identified in each geological unit in the geological risk distribution map, the sub-area division is carried out according to the following zoning determination rules: High-risk sub-area: Two or more types of risk factors exist simultaneously in a single geological unit.

[0054] Transition sub-area: Only one type of risk factor exists in a single geological unit.

[0055] Stable sub-area: No risk factors are identified in a single geological unit.

[0056] The above risk area division method is based on a comprehensive assessment of multiple independent risk factors. Each risk factor represents a different type of potential threat. By comprehensively considering these factors, the overall risk level of the geological unit can be more comprehensively and accurately reflected.

[0057] For a further optional implementation of the above scheme, the differential arrangement of the initial monitoring network in each sub-area is as follows: Monitoring points are arranged in a triangular grid in the high-risk sub-area, and the node spacing between the monitoring points is a fixed preset value.

[0058] It should be noted that the monitoring points are arranged in a triangular grid pattern within the high-risk sub-region because the triangular grid layout can provide higher spatial coverage and redundancy, and a smaller node spacing is set to achieve dense layout, which helps to improve spatial resolution and the timeliness of data feedback.

[0059] In the transition sub-region, the monitoring points are arranged according to a rectangular grid, and the node spacing of the monitoring points is set to a multiple of the node spacing in the high-risk sub-region.

[0060] It should be pointed out that when arranging the monitoring points in the transition sub-region in a rectangular grid pattern, although it is slightly lower than the triangular grid in terms of spatial coverage uniformity and redundancy, it has the advantages of simple layout operation, clear logic, and easy on-site implementation. The node spacing of the monitoring points in this grid is set to an integer multiple of the node spacing in the high-risk sub-region. Exemplarily, it is taken as 2 times. Through reasonable multiple control, it can not only maintain the basic monitoring ability for potential deformation risks but also effectively reduce the density of monitoring points and avoid excessive investment in monitoring resources.

[0061] In the stable sub-region, the monitoring points are arranged along the axis direction of the channel, and the node spacing of the monitoring points is set to a multiple of the node spacing in the transition sub-region.

[0062] It should be noted that in the stable sub-region, the monitoring points are arranged along the axis direction of the channel, and the node spacing is set to a multiple of the node spacing in the transition sub-region. In this way, the linear monitoring network arranged along the axis can still maintain the basic monitoring ability for the overall structural stability while reducing the number of monitoring points.

[0063] The present invention realizes the effective allocation and utilization of resources by adopting a monitoring grid layout method adapted to different risk-level sub-regions. For example, due to its high coverage range and redundancy, the triangular grid is particularly suitable for high-risk areas that require fine monitoring; while the rectangular grid can simplify the operation process without affecting the key monitoring effect and is more suitable for application in the transition area. This method enables an increase in monitoring density in high-risk areas to enhance the monitoring intensity, and an appropriate reduction in monitoring density in relatively stable areas, thereby optimizing resource allocation.

[0064] (3) During the construction of the mined tunnel, the deformation data of each monitoring point are collected in real time to construct a spatio-temporally synchronized deformation data set.

[0065] In the example of the above solution, the deformation data can be settlement displacement, tilt angle, etc.

[0066] (4) Based on the spatial distribution characteristics of the deformation data set, the monitoring blind spots and monitoring redundancy are identified. Thus, when identifying the monitoring blind spots, encryption layout is triggered, and when identifying the monitoring redundancy, the migration of monitoring points is triggered.

[0067] As a way that the above solution can be implemented, identifying monitoring blind spots and monitoring redundancy includes the following: Refer to Figure 3 As shown, S1: In each sub-region, taking each monitoring point as the geometric center, adjacent monitoring points are obtained by spreading around. The local range composed of the adjacent monitoring points is used as the adjacent region of this monitoring point.

[0068] It should be noted that the adjacent monitoring points obtained by spreading each monitoring point around are limited to single-level topological expansion, that is, only the first-level adjacent monitoring points directly adjacent to the monitoring point in the spatial layout relationship are considered, which helps to clarify the spatial boundary of the adjacent region.

[0069] S2: For each adjacent region, based on the spatio-temporal synchronous deformation data set at the same moment, calculate the mean and standard deviation of the deformation data of all adjacent monitoring points in this region, and construct the neighborhood deformation threshold accordingly.

[0070] Specifically, the expression of the neighborhood deformation threshold is , where represents the neighborhood deformation threshold, represents the mean of the deformation data of the adjacent monitoring points, represents the standard deviation of the deformation data of the adjacent monitoring points, represents the empirical coefficient, which controls the determination sensitivity and usually takes a value of 3.

[0071] The construction of the above neighborhood deformation threshold is based on statistical principles: on the premise that the strata are in a stable deformation state, the deformation observation data of multiple monitoring points in the adjacent region should show the statistical characteristics of approximately following a normal distribution. Accordingly, the 3σ criterion (i.e., the three-fold standard deviation principle) is used as the basis for anomaly identification - if the deformation value of a certain monitoring point deviates from the mean of the deformation data in this neighborhood by more than three times the standard deviation, it is determined that it significantly deviates from the normal deformation trend and belongs to an outlier in the statistical sense.

[0072] S3: Compare the deformation data of each monitoring point in each sub-region with the neighborhood deformation threshold of the adjacent region. If there is a certain monitoring point whose deformation data is higher than the neighborhood deformation threshold, execute S4 - S5, otherwise execute S6 - S7.

[0073] It should be added that the above constructed neighborhood deformation threshold is used to characterize the overall deformation average level of the adjacent region of the monitoring point. Since there is usually a certain continuity in the geological structure and consistency in the deformation response between a monitoring point and its surrounding adjacent region during the construction of underground projects, when the deformation observation value of a certain monitoring point significantly deviates from the average deformation trend of its adjacent region, it may indicate that this point is in a local abnormal deformation region.

[0074] A potential reason for such deviations may be that the formation deformation characteristics at the location of the monitoring point are not fully captured by the limited number of neighboring monitoring points, reflecting that the current monitoring density in the area is insufficient and fails to effectively cover the spatial variation details of the formation deformation, resulting in the failure to accurately identify and reflect the local deformation evolution trend.

[0075] S4: Mark the point as a transition point, record the time when the transition occurs, and mark the sub-region to which the point belongs as a transition sub-region.

[0076] S5: Continue to track the transition of the transition sub-area after the transition occurs, and count the duration of the transition. If the duration of the transition reaches a critical duration threshold, it is determined that a monitoring blind area exists in the transition sub-area.

[0077] S6: For the monitoring point that has not experienced a jump, the deformation difference between the point and the monitoring points in the adjacent area is calculated. This operation aims to quantify the difference in deformation behavior between the monitoring point and the surrounding environment, and to count the proportion of monitoring points in the adjacent area whose deformation difference is within the set allowable error range. If the proportion reaches the preset stability ratio threshold, for example, the stability ratio threshold is 80%, it is considered that the monitoring point and most of the monitoring points in its neighborhood show consistent deformation characteristics, indicating that the stratigraphic state of this local area is relatively uniform and stable. The sub-area where the point is located is marked as a stable sub-area, and the adjacent area where the point is located is defined as a local stable area, and the time when the stability occurs is recorded.

[0078] The allowable error range set above is used to determine whether the deformation difference is acceptable.

[0079] It should be understood that if the deformation differences of most of the adjacent monitoring points in the vicinity of a monitoring point are within the set allowable error range, it indicates that the deformation behaviors of the monitoring points in the area are highly consistent and can jointly reflect similar formation deformation characteristics. This indicates that the deformation pattern of the area tends to be uniform, and there is no obvious local disturbance or uncoordinated deformation phenomenon.

[0080] By introducing a judgment method based on deformation difference combined with stability ratio threshold, the interference of measurement error or accidental fluctuation of a single monitoring point on the overall stability judgment can be effectively weakened, thereby improving the robustness and accuracy of anomaly identification. Only when multiple adjacent monitoring points show consistent deformation trends in a statistical sense can the area be judged to be in a stable state. This multi-point collaborative analysis mechanism significantly enhances the credibility of local stability identification results.

[0081] S7: Count the stable duration of the local stable regions within the stable sub-region. If the stable duration reaches the critical duration threshold, it is determined that there is monitoring redundancy in the stable sub-region, that is, the information provided by some monitoring points is repeated and does not affect the judgment of the overall deformation trend.

[0082] It should be noted that stratum deformation is a process that develops over time. Anomaly at a single point in time is not sufficient to determine whether there are systematic problems. Therefore, the concepts of jump duration and stable duration are introduced to further confirm whether it belongs to a true monitoring blind spot or redundancy in combination with the time dimension.

[0083] The present invention realizes the dynamic identification and classification management of blind spots and redundant points in the monitoring system by introducing a local spatial statistical model and a time evolution analysis mechanism. Compared with the traditional static layout method, this method has higher spatial adaptability and time sensitivity, can support the real-time optimization and adjustment of the monitoring network, and improve the early warning ability and resource utilization efficiency of the deformation monitoring system.

[0084] As a further realizable way of the above solution, when identifying the monitoring blind spot, triggering the implementation of densifying the layout of monitoring points is as follows: For the jump points that appear in the sub-region where the monitoring blind spot is located, calculate the deformation difference between the jump point and each adjacent monitoring point in the adjacent region at each moment during the jump duration period. At each moment, identify the adjacent monitoring point with the largest deformation difference from the jump point and define it as a low-correlation monitoring point.

[0085] It should be known that this low-correlation monitoring point reflects the monitoring position with the largest difference in deformation trend and inconsistent response with the jump point.

[0086] Count the frequency distribution of each low-correlation monitoring point during the jump duration period, select the low-correlation monitoring point with the highest frequency of occurrence, and then add new monitoring points in the spatial connection direction between it and the jump point to enhance the monitoring density in this direction and fill the local monitoring blind spot.

[0087] Extracting the low-correlation monitoring point with the highest frequency of occurrence as described above means that there are significant deformation differences between it and the jump point at multiple moments, which is representative in terms of statistics.

[0088] It should be understood that deformation often propagates along the weak surface of the geological structure or the stress release path. Arranging the new monitoring points along the connection direction between the jump point and the low-correlation monitoring point helps to capture the main direction of deformation development. At the same time, it also improves the spatial resolution of the monitoring data on this path and enhances the response ability to abnormal deformation evolution.

[0089] As a further implementable manner of the above solution, when identifying monitoring redundancy, the implementation of monitoring point migration is triggered as follows: For the local stable area of the sub-region where the monitoring redundancy is located, during the stable continuous period, calculate the temporal deformation difference between the geometric center monitoring point and each monitoring point in the adjacent area.

[0090] Select the monitoring points whose deformation differences are always within the preset allowable error range from the temporal deformation differences and define them as high-consistency monitoring points.

[0091] It should be understood that within the area of stable strata, due to similar geological conditions and consistent deformation responses among multiple adjacent monitoring points, their temporal deformation data is highly correlated. This high consistency indicates that the information provided by some monitoring points is repetitive, belonging to information redundancy and not being independent observation points that must be retained.

[0092] Select one monitoring point as the migration object for monitoring point migration at intervals of one monitoring point in the order of spatial distribution from the above high-consistency monitoring points.

[0093] Selecting one monitoring point at intervals as the migration object as described above is a spatial sparsification strategy, which can retain key monitoring nodes while removing redundant points, ensuring the overall identifiability of the deformation trend in this area.

[0094] It should be emphasized that in the analysis of the deformation data of the monitoring points collected in the present invention, it is assumed that the monitoring equipment operates normally and the data collection process is not interfered by equipment failures or measurement errors. Therefore, the monitoring results only reflect the stratum deformation behavior at the location of the monitoring points.

[0095] (4) After completing the dynamic adjustment of the monitoring network, based on the temporal deformation data of the monitoring points in each sub-region during the construction process, conduct deformation trend analysis, and thus initiate hierarchical deformation early warning from local to global.

[0096] It should be noted that after the initial monitoring network is laid out based on the geological risk distribution map, the monitoring network needs to be adaptively adjusted according to the dynamic evolution characteristics of the underground excavation construction. If no monitoring blind area or redundant area is identified during the subsequent monitoring process for a long time, it indicates that the current monitoring network already has good coverage and adaptability and can enter the stable deformation trend analysis stage.

[0097] In the above solution, preferably, the deformation trend analysis refers to the following process: Use the temporal deformation data of each monitoring point in each sub-region to draw a deformation curve within the set observation window, and extract the overall deformation rate from the deformation curve as the deformation trend of each monitoring point.

[0098] In the above example of optimization implementation, the overall deformation rate of the deformation curve can be obtained through the linear fitting method, that is, by performing linear regression fitting on the deformation-time series data within the observation window. The slope of the fitted straight line is the overall deformation rate, which quantifies the change rate of the deformation degree of the monitoring point within the specified observation period.

[0099] Using the overall deformation rate as the basis for subsequent deformation warning, rather than relying on the instantaneous deformation data at a specific time point, is mainly because the construction process of the underground excavation belongs to a long-term and complex geological activity. The deformation behavior of rock and soil masses under external loads usually shows progressive and continuous characteristics and follows certain evolution laws. The deformation data at a single time can only capture the deformation state at the current moment, and it is difficult to reflect the dynamic change characteristics of the deformation development. Therefore, it does not have the value of cross-period comparison. The warning mechanism aims to provide an early warning of the possible deformation risks in the subsequent construction stage, which requires a more stable and predictive index as the basis. By deeply analyzing the deformation trend within the set observation window, more reliable trend information can be extracted, providing a solid scientific support for the warning of potential deformation risks in the future construction process.

[0100] Further optimizing the above solution, the hierarchical deformation warning from local to global includes the following contents: comparing the deformation trend of each monitoring point in each sub-region with the safety threshold, and screening out the monitoring points whose deformation trend reaches the safety threshold, which are recorded as abnormal monitoring points.

[0101] The above-mentioned safety threshold is the maximum allowable deformation rate determined based on the relevant industry guidelines of the construction specifications.

[0102] According to the spatial distribution of the abnormal monitoring points, delimit the abnormal area formed by the surrounding of these points, and calculate the proportion of the abnormal area in the area of the corresponding sub-region.

[0103] Obtain the normalized Euclidean distance between the geometric center of the abnormal area and the center of the corresponding sub-region, and define this as the center deviation distance coefficient of the abnormal area.

[0104] Define the local abnormal tendency factor as: , where represents the local abnormal tendency factor, represents the proportion of the area of the abnormal area in the corresponding sub-region, represents the center deviation distance coefficient of the abnormal area.

[0105] It should be noted that the principle of constructing the above local anomaly tendency factor is as follows: The proportion of the area of the abnormal region in the sub-region to which it belongs reflects the proportion of the abnormal range within the sub-region. The smaller the area proportion, the more concentrated the abnormal region is in the local area and the more it has local characteristics. On the contrary, the larger the value, the wider the abnormal influence range, indicating that it may have entered the stage of overall deformation. The smaller the central deviation distance coefficient of the abnormal region, the closer the abnormal region is to the center of the sub-region, which may reflect the overall deformation trend. The larger the value, the farther it is from the center, that is, the closer it is to the edge and the more it has local characteristics. By multiplying with to obtain the local anomaly tendency factor, it reflects the synchronicity of the area proportion and the central deviation distance coefficient on the local tendency. When both of these values are larger, that is, the abnormal region has both a smaller spatial coverage range and is significantly deviated from the center and tends to the edge, it indicates that the anomaly has a significant local development trend.

[0106] Compare the local anomaly tendency factor obtained according to the above definition with the pre-configured warning tendency. If the local anomaly tendency factor does not reach the warning tendency, it indicates that the abnormal influence range is limited, triggering a local deformation warning, and only taking targeted reinforcement measures in the abnormal region. On the contrary, it indicates that the anomaly has systematic characteristics and it is necessary to initiate a global deformation warning and take overall reinforcement measures for the sub-region.

[0107] Based on the local anomaly tendency factor, the present invention constructs a hierarchical warning mechanism with physical significance and engineering practical value. By setting reasonable warning tendency thresholds, the hierarchical identification and response of the formation deformation behavior from local to overall are realized. The local deformation warning focuses on the rapid intervention and local control of the abnormal region, while the global deformation warning is oriented towards the comprehensive prevention and control of systematic risks. This hierarchical mechanism not only improves the accuracy of risk identification and the timeliness of response.

[0108] All parameters involved in the above formula are dimensionless and only take their numerical values for calculation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0109] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0110] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0111] In addition, each functional module in various embodiments of the present application may be integrated into one processing module, may exist separately as individual physical modules, or two or more modules may be integrated into one module.

[0112] As described above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0113] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring the deformation of a subway tunnel constructed by the mined - out method in a super - shallow - buried and high - flow - rate formation, characterized in that, The following steps are involved: (1) The construction impact area is delineated with the tunnel face as the center. A geological radar scan is performed on the construction impact area to extract geological distribution characteristics including stratum interface distribution, water content distribution, and rock mass fracture distribution, and a geological risk distribution map is generated; (2) Divide the construction impact area into high-risk sub-areas, transitional sub-areas and stable sub-areas according to the geological risk distribution map, and deploy the initial monitoring network in each sub-area in a differentiated manner; (3) During the tunneling process, deformation data of each monitoring point is collected in real time to construct a spatiotemporal synchronous deformation data set; (4) Identify monitoring blind spots and monitoring redundancies based on the spatial distribution characteristics of the deformation data set. This triggers the deployment of more dense monitoring points when blind spots are identified, and triggers the migration of monitoring points when redundancies are identified. (5) After the dynamic adjustment of the monitoring network is completed, deformation trend analysis is performed based on the time-series deformation data of the monitoring points in each sub-area during the construction process, thereby initiating a graded deformation warning from local to global.

2. The deformation monitoring method for the mined - out subway passage with ultra - shallow buried and large flow rate formation as claimed in claim 1, wherein: The specific extraction process of the geological distribution characteristics is as follows: Multiple parallel scanning lines are arranged along the axis of the dark excavation channel, and each scanning line covers the lateral boundary of the construction impact area; Use geological radar equipment to perform high-resolution geological scanning on each scanning line, collect time series data of underground medium reflection waves, and convert them into images to obtain geological radar images of the construction-affected area; In the generated geological radar image, lines with varying reflection intensities are identified as the boundaries between different strata, and the spatial distribution of the strata is determined based on the positions of these boundaries; Determine the geological type of the strata based on the analysis of the reflection wave properties of different strata in the geological radar image, and classify the areas with the same strata distribution and no gaps as the same geological unit; For each divided geological unit, the attenuation property of the reflected wave in the corresponding geological radar image is analyzed to determine the water content of the area; The shape characteristics of the reflected waves are extracted from the geological radar images in each geological unit to identify the cracks and locate the distribution of the cracks.

3. The deformation monitoring method for the mined - out subway passage with ultra - shallow buried and large - flow strata as claimed in claim 2, wherein: The generated geological risk distribution map is as follows: Extract the stratigraphic geological types layer by layer from each divided geological unit and match them with the risk stratigraphic database. If the geological type of a certain layer of a geological unit is successfully matched, it is determined that the geological unit has stratigraphic risk factors. The water content of each geological unit is compared with the set safe water content threshold. If the water content of a geological unit exceeds the threshold, it is determined that the geological unit has water risk factors; The fracture distribution density is obtained based on the distribution position of fractures in each geological unit, and compared with the set safe fracture distribution density. If the fracture distribution density of a geological unit is higher than the safe fracture distribution density, the fracture risk factor of the geological unit is identified; Plane modeling is performed based on the geometric boundaries of the construction-affected area, and geological units are identified based on the boundary contours of each geological unit in the constructed model. At the same time, the identified risk factors are annotated to generate a geological risk distribution map.

4. The deformation monitoring method for the mined - out subway passage with ultra - shallow buried and large - flow formation as claimed in claim 3, wherein: The high-risk sub-areas, transitional sub-areas and stable sub-areas are divided as follows: Based on the risk factors identified in each geological unit in the geological risk distribution map, the sub-regions are divided according to the following zoning judgment rules; High-risk sub-region: There are two or more types of risk factors in a single geological unit; Transition sub-region: There is only one type of risk factor in a single geological unit; Stable sub-region: No risk factors are identified in a single geological unit.

5. The deformation monitoring method for the mined - out subway passage with ultra - shallow burial and large flow rate strata as claimed in claim 1, wherein: The differential layout of the initial monitoring network in each sub-region is as follows: Monitoring points are arranged in a triangular grid in the high-risk sub-region, and the node spacing between monitoring points is a fixed preset value; Monitoring points are arranged in a rectangular grid in the transition sub-region, and the node spacing of the monitoring points is set as a multiple of the node spacing in the high-risk sub-region; Monitoring points are arranged along the channel axis direction in the stable sub-region, and the node spacing of the monitoring points is set as a multiple of the node spacing in the transition sub-region.

6. The deformation monitoring method for the mined - out subway passage with ultra - shallow burial and large flow rate strata as claimed in claim 1, wherein: The identification of monitoring blind spots and monitoring redundancy includes the following: S1: In each sub-region, neighboring monitoring points are obtained by diverging from each monitoring point as the geometric center to the surroundings, and the local range composed of neighboring monitoring points is used as the neighboring area of this monitoring point; S2: For each neighboring area, based on the spatio-temporal synchronous deformation data set at the same moment, calculate the mean and standard deviation of the deformation data of all neighboring monitoring points in this area, and construct a neighborhood deformation threshold accordingly; S3: Compare the deformation data of each monitoring point in each sub-region with the neighborhood deformation threshold of the neighboring area. If the deformation data of a certain monitoring point is higher than the neighborhood deformation threshold, then execute S4 - S5, otherwise execute S6 - S7; S4: Mark this point as a jump point, record the jump occurrence time, and at the same time mark the sub-region to which this point belongs as a jump sub-region; S5: Continuously track the jump in the jump sub-region after the jump occurs, and count the jump duration. If the jump duration reaches the critical duration threshold, it is determined that there is a monitoring blind spot in the jump sub-region; S6: For monitoring points without jumps, calculate the deformation difference between this point and each monitoring point in its neighboring area, and count the proportion of monitoring points in the neighboring area whose deformation difference is within the set allowable error range. If this proportion reaches the preset stable occupancy threshold, mark the sub-region where this point is located as a stable sub-region, define the neighboring area where this point is located as a local stable area, and record the stable occurrence time; S7: Count the stable duration of the local stable area in the stable sub-region. If the stable duration reaches the critical duration threshold, it is determined that there is monitoring redundancy in the stable sub-region.

7. The deformation monitoring method for the mined - out subway passage with ultra - shallow burial and large flow rate strata as described in claim 6, wherein: The implementation of triggering the dense layout of monitoring points when identifying monitoring blind spots is as follows: For the jump points that appear in the sub-region where the monitoring blind spot is located, calculate the deformation difference between this jump point and each neighboring monitoring point in its neighboring area at each moment during the jump duration. At each moment, identify the neighboring monitoring point with the largest deformation difference from this jump point and define it as a low-correlation monitoring point; Statistically analyze the occurrence frequency distribution of each low-correlation monitoring point during the jump duration, select the low-correlation monitoring point with the highest occurrence frequency, and then add new monitoring points in the spatial connection direction between it and the jump point.

8. The deformation monitoring method for the mined - out subway passage with ultra - shallow burial and large flow rate strata as described in claim 6, wherein: The implementation of triggering the relocation of monitoring points when identifying monitoring redundancy is as follows: For the local stable region where the monitoring redundancy is located, during the stable duration, calculate the temporal deformation difference between the geometric center monitoring point and each adjacent monitoring point in the adjacent region; Select the monitoring points whose deformation differences are always within the preset allowable error range from the temporal deformation differences and define them as high-consistency monitoring points; Select one monitoring point as the migration object for monitoring point migration every other monitoring point in the order of spatial distribution from the above high-consistency monitoring points.

9. The deformation monitoring method for the mined - out subway passage with ultra - shallow burial and large flow rate strata as claimed in claim 1, wherein: The deformation trend analysis is as follows: Use the temporal deformation data of each monitoring point in each sub-region to draw a deformation curve within the set observation window, and extract the overall deformation rate from the deformation curve as the deformation trend of each monitoring point.

10. The deformation monitoring method for the mined - out subway passage with ultra - shallow buried and large - flow strata as claimed in claim 1, wherein: The start of the hierarchical deformation warning from local to global includes the following: Compare the deformation trend of each monitoring point in each sub-region with the safety threshold, and select the monitoring points whose deformation trends reach the safety threshold and record them as abnormal monitoring points; Define the abnormal area surrounded by these points according to the spatial distribution of the abnormal monitoring points, and calculate the proportion of the abnormal area in the area of the corresponding sub-region; Obtain the normalized Euclidean distance between the geometric center of the abnormal area and the center of the corresponding sub-region, and define this as the center deviation distance coefficient of the abnormal area; Obtain the local abnormal tendency factor through fusion calculation of the above proportion and the center deviation distance coefficient, and compare it with the warning tendency. If it does not reach the warning tendency, start the local warning, otherwise start the global warning.

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