An intelligent monitoring system for tunnel construction deformation

Abstract

The present invention belongs to the technical field of tunnel construction deformation monitoring, and particularly relates to an intelligent monitoring system for tunnel construction deformation, including a multi-point deformation monitoring module, a spatio-temporal data fusion module, a spatial correlation analysis module, and an intelligent early warning module. By conducting multi-point deformation monitoring on the tunnel surrounding rock, a dynamic spatial deformation database containing time series is constructed, and based on this database, the spatial deformation correlation between monitoring points is analyzed to construct a spatial deformation correlation network of the tunnel surrounding rock. Furthermore, when the spatial deformation correlation network exceeds the preset safety threshold, early warning display is carried out, which can timely capture abnormal deformation trends, significantly shorten the early warning lag time, and improve construction safety. At the same time, when an early warning is triggered based on tunnel surrounding rock deformation monitoring, by distinguishing local early warning and global early warning, and three-dimensionally visualizing the early warning results, it is helpful for priority management, enabling construction personnel to take targeted emergency measures according to the early warning level.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel construction deformation monitoring, and particularly relates to an intelligent monitoring system for tunnel construction deformation volume. Background Art

[0002] With the acceleration of the urbanization process, the demand for traffic efficiency in densely populated areas is increasing day by day. Tunnel construction has become a key means to relieve traffic congestion and optimize the traffic network. During tunnel construction, the surrounding rock, as the main geological medium supporting the tunnel structure, is prone to deformation due to changes in external loads (such as excavation unloading) due to its inherent inhomogeneity and complexity. The deformation of the surrounding rock may further trigger engineering disasters such as collapse, roof fall or water inrush, posing a serious threat to construction safety and project quality. Therefore, in tunnel construction, it is particularly necessary to monitor the deformation of the surrounding rock in real time.

[0003] In the prior art, there have been some technical solutions for monitoring the deformation of tunnel surrounding rock. For example, Chinese Patent Publication No. CN101458069B proposes a method and a monitoring system for monitoring the deformation of tunnel surrounding rock. By fixing a laser on the inner wall of the already stable tunnel surrounding rock and installing a photosensitive displacement signal monitor on the initial support of the unstable surrounding rock, the deformation displacement data of the surrounding rock is collected in real time. After being analyzed by the signal processing terminal, an alarm is triggered when the dangerous deformation value is reached or exceeded. This solution helps to reduce the risk of tunnel collapse during construction and improve the operation safety.

[0004] In addition, Chinese Patent Publication No. CN108050952B provides a method for monitoring the deformation of a tunnel cross-section using a tunnel cross-section deformation monitoring system. This method calculates the settlement value of the tunnel crown, the convergence value of the tunnel crown and the convergence value of the arch waist by the sub-controller, and converts them into language evaluation values, so as to provide early warning information for construction, enabling the staff to grasp the tunnel safety status in real time and detect potential hazards in advance.

[0005] However, whether it is the laser displacement monitoring-based method in the first technical solution above or the quantitative evaluation of tunnel cross-section deformation in the second technical solution, the monitoring of tunnel surrounding rock deformation is limited to the displacement changes at discrete positions, lacking in-depth analysis of the spatial propagation between monitoring points and the overall spatial deformation correlation. Due to the inhomogeneity and complex stress field characteristics of tunnel surrounding rock, its deformation often has a certain propagation correlation between different positions. Ignoring this spatial correlation for deformation early warning may delay the identification and response to potential risks, and even lead to misjudgment of the deformation trend, thus affecting the safety and accuracy of construction decisions. Summary of the Invention

[0006] The object of the present invention is to improve the deficiencies existing in the prior art, and to provide an intelligent monitoring system for tunnel construction deformation. By adding the associated analysis of the deformation spatial propagation of different monitoring points in the tunnel surrounding rock deformation monitoring, the timely and accurate warning of tunnel construction risks can be realized.

[0007] The object of the present invention can be achieved by the following technical solutions: An intelligent monitoring system for tunnel construction deformation includes the following modules: Multi-point deformation monitoring module: Deployed on the surface of the tunnel surrounding rock by deformation monitoring equipment according to the determined area layout density, and used to synchronously collect the deformation data of each monitoring point.

[0008] Space-time data fusion module: Perform spatial mapping on the deformation data of the monitoring points collected by the deformation monitoring equipment, and construct a dynamic spatial deformation database including time series.

[0009] Spatial correlation analysis module: Based on the dynamic spatial deformation database including time series, adopt the dominant-subordinate spatial deformation correlation modeling method to analyze the spatial deformation correlation between monitoring points, and construct the spatial deformation correlation network of the tunnel surrounding rock.

[0010] Intelligent warning module: When the spatial deformation correlation network of the tunnel surrounding rock exceeds the set safety threshold, warning display is carried out through a three-dimensional visualization interface.

[0011] As a preferred implementation of the above solution, the analysis of the spatial deformation correlation between monitoring points by adopting the dominant-subordinate spatial deformation correlation modeling method includes the following steps: Traverse all monitoring points of the tunnel surrounding rock and group them in pairs to form multiple control groups.

[0012] Extract the time series deformation data of each monitoring point from the dynamic spatial deformation database including time series, and perform correlation analysis on the time series deformation data of the two monitoring points in each control group to calculate the deformation correlation index.

[0013] Compare the deformation correlation index calculated for each control group with the preset correlation threshold, and screen out the control groups that meet the conditions to form several associated monitoring point groups.

[0014] Perform causality analysis on the associated monitoring point groups to identify the dominant monitoring points that play a leading role in the deformation and the subordinate monitoring points affected by them, thereby clarifying the dominant-subordinate relationship of the deformation propagation.

[0015] As a preferred implementation of the above solution, the causality analysis of the associated monitoring point groups refers to the following process: In a coordinate system with time as the horizontal axis and deformation as the vertical axis, plot the time series deformation data of the two monitoring points in the associated monitoring point group as two deformation change curves respectively.

[0016] Identify and mark the inflection points on the two deformation quantity change curves, and map the inflection points on the two curves one by one to form several groups of inflection points.

[0017] For each group of inflection points, extract the acquisition times of the corresponding inflection points on the two deformation quantity change curves, count the occurrence proportion of the earlier acquisition time in the group of inflection points for each monitoring point, select the monitoring point with the largest occurrence proportion, and identify it as the leading monitoring point of the associated monitoring point group, while the other monitoring point is identified as the subordinate monitoring point.

[0018] Conduct a comparative analysis on the causal relationship results of all associated monitoring point groups. If it is found that the subordinate monitoring point of a certain associated monitoring point group serves as the leading monitoring point in another associated monitoring point group, then merge these two groups of associated monitoring point groups to form a new associated monitoring point group.

[0019] In the new associated monitoring point group, only retain the first leading monitoring point as the leading monitoring point, and all the remaining monitoring points are identified as subordinate monitoring points.

[0020] As a preferred implementation of the above solution, the process of constructing the spatial deformation association network of the tunnel surrounding rock is as follows: conduct a time lag relationship analysis on the subordinate monitoring points in the associated monitoring point group, and based on this, establish a deformation association line pointing from the leading monitoring point to the subordinate associated monitoring point.

[0021] Assign attribute information to each deformation association line.

[0022] Integrate all the deformation association lines to form a global spatial deformation association network.

[0023] Use a visualization tool to display the spatial deformation association network.

[0024] As a preferred implementation of the above solution, the early warning display through the three-dimensional visualization interface when the spatial deformation association network of the tunnel surrounding rock exceeds the set safety threshold is as follows: set a corresponding safety threshold range for each attribute for the attribute information of the deformation association line.

[0025] During the tunnel construction process, monitor the attribute information of each deformation association line in the spatial deformation association network of the tunnel surrounding rock in real time.

[0026] If the attribute information of a certain deformation association line exceeds the preset safety threshold range, a local early warning is triggered.

[0027] If the attribute information of multiple deformation association lines exceeds the safety threshold simultaneously, a global early warning is triggered.

[0028] When a local or global early warning is triggered, display the early warning information through the three-dimensional visualization interface.

[0029] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. The present invention constructs a dynamic spatial deformation database including time series by performing multi-point deformation monitoring on the tunnel surrounding rock, analyzes the spatial deformation correlation between monitoring points based on this database, constructs a spatial deformation correlation network of the tunnel surrounding rock, and then gives an early warning display when the spatial deformation correlation network exceeds the preset safety threshold, which can timely capture abnormal deformation trends, significantly shorten the early warning lag time, and improve construction safety.

[0030] 2. When the early warning is triggered based on the deformation monitoring of the tunnel surrounding rock, the present invention distinguishes between local early warning and global early warning, and visually displays the early warning results in three dimensions, which helps with priority management, enables construction personnel to take targeted emergency measures according to the early warning level, and at the same time, the three-dimensional visual display makes the early warning information more intuitive, reduces the understanding time, and thus speeds up the decision-making and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be 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, other drawings can be obtained according to the following drawings without creative efforts.

[0032] Figure 1 It is a schematic diagram of the system module connection of the present invention.

[0033] Figure 2 It is a diagram of the implementation steps for analyzing the spatial deformation correlation between monitoring points by using the dominant-subordinate spatial deformation correlation modeling method in the present invention.

[0034] Figure 3 It is a flowchart for analyzing the causal relationship of the associated monitoring point group in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] 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 of 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.

[0036] The present invention provides an intelligent monitoring system for tunnel construction deformation, including a multi-point deformation monitoring module, a spatio-temporal data fusion module, a spatial correlation analysis module, and an intelligent early warning module.

[0037] Referring to Figure 1 As shown, the above multi-point deformation monitoring module collects the deformation data of each monitoring point through deformation monitoring devices and transmits these discrete real-time monitoring data to the spatio-temporal data fusion module.

[0038] The dynamic spatial deformation database generated by the spatio-temporal data fusion module is passed as input data to the spatial correlation analysis module.

[0039] The spatial deformation correlation network and related analysis results generated by the spatial correlation analysis module are transmitted to the intelligent early warning module.

[0040] The several modules that make up the above system flow sequentially from collection, fusion, analysis to early warning, forming a complete processing chain. And there are strong dependencies between the modules. The output of the previous module is the input of the next module. Through the close connection between the modules, the system realizes the full-process intelligent management from data collection to analysis and then to early warning, providing efficient and accurate safety guarantee for tunnel construction.

[0041] In a specific implementation, the multi-point deformation monitoring module is deployed on the surface of the tunnel surrounding rock by deformation monitoring equipment according to the determined regional layout density, and is used to synchronously collect the deformation data of each monitoring point.

[0042] It should be noted that the deformation monitoring equipment refers to a type of professional instrument or device specifically used to real-time sense and record the deformation information on the surface of the tunnel surrounding rock. These devices can accurately capture the deformation characteristics such as displacement, strain and settlement that occur in the surrounding rock during the construction process, providing key data support for evaluating and analyzing the stability of the surrounding rock. Specifically, the deformation monitoring equipment may include but is not limited to the following types: displacement monitoring equipment (such as laser scanners), strain monitoring equipment (such as fiber Bragg grating sensors), and settlement monitoring equipment (such as level gauges), and one or more of these functions can be integrated according to actual needs to achieve multi-dimensional deformation monitoring.

[0043] In the way that the above scheme can be realized, the determined regional layout density is obtained as follows: A three-dimensional geometric model of the tunnel surrounding rock is built according to the design drawings of the tunnel and the geological exploration data.

[0044] It should be understood that the geological exploration data is an important basis for building the three-dimensional geometric model of the tunnel surrounding rock. These data can reflect key information such as the geological structure, rock and soil properties, and hydrogeological conditions in the tunnel area. Specifically, the geological exploration data is shown in Table 1.

[0045] Table 1: Partial geological exploration data

[0046]

[0047] The above geological exploration data can be obtained through a variety of technical means. Exemplary methods include drilling for cores and geophysical exploration. Among them, drilling for cores refers to extracting rock and soil samples through boreholes and conducting detailed analysis of their physical and mechanical properties in the laboratory; while geophysical exploration is based on the changes in physical fields and uses technical means such as seismic wave method, resistivity method or radar detection to conduct non-contact detection and inversion analysis of the underground geological structure and lithology distribution.

[0048] Divide the three-dimensional geometric model into finite element meshes, and each mesh element corresponds to a specific rock area.

[0049] Input the historical load data of similar tunnel construction into the three-dimensional geometric model and carry out numerical simulation of the tunnel construction process according to the set time step.

[0050] It should be added that the similar tunnels mentioned above refer to tunnel projects that are similar or identical to the current tunnel in the following aspects: Geological conditions: similar surrounding rock types, stratum structures, groundwater conditions, etc.

[0051] Design parameters: similar tunnel cross-section shapes, sizes, burial depths, support structure forms, etc.

[0052] Construction methods: adopt similar construction techniques (such as drill and blast method, shield method, TBM method, etc.).

[0053] By selecting "similar tunnels" with similar characteristics to the current tunnel, the reference value and applicability of historical data can be improved.

[0054] It should also be added that historical load data refers to the data set of various external and internal forces and related parameters recorded during the construction of similar tunnels. These data can reflect the actual load conditions borne by the tunnel surrounding rock and support structure during construction, and the specific load data is shown in Table 2.

[0055] Table 2: Partial historical load data

[0056]

[0057] It should be further added that the time step refers to the time interval divided when discretizing the entire construction process in the time dimension. It is an important parameter in numerical simulation and is used to control the time resolution of the calculation, so as to capture the dynamic response of the surrounding rock during construction.

[0058] Extract the deformation gradient distribution of each finite element mesh of the tunnel surrounding rock through the numerical simulation results.

[0059] Match the deformation gradient of the finite element mesh according to the mapping relationship between the preset deformation gradient and the layout density, so as to determine the layout density of deformation monitoring equipment in different regions of the tunnel surrounding rock.

[0060] In a specific example implementation of the above operation, assume that three types of deformation gradients are preset, namely high deformation gradient, medium deformation gradient and low deformation gradient, and according to the predefined mapping relationship between the deformation gradient and the layout density, assign the corresponding layout density of deformation monitoring equipment to each type of deformation gradient. Specifically: The high deformation gradient area corresponds to a higher layout density, and the layout spacing of the deformation monitoring equipment is set to 0.5 meters to ensure high-precision monitoring of the area with severe deformation. The medium deformation gradient area corresponds to a medium layout density, and the layout spacing of the deformation monitoring equipment is set to 1 meter to balance the monitoring accuracy and cost. The low deformation gradient area corresponds to a lower layout density, and the layout spacing of the deformation monitoring equipment is set to 2 meters to meet the basic monitoring requirements of the area with slower deformation.

[0061] Match the deformation gradient of the finite element mesh extracted from the numerical simulation results with the corresponding layout density of the deformation monitoring equipment through the above mapping relationship, so as to determine the sensor layout scheme in different regions of the tunnel surrounding rock. This method can realize the optimal configuration of sensor layout according to the actual deformation characteristics of the tunnel surrounding rock, ensure that key areas are fully monitored, and at the same time reduce the cost and complexity of the overall monitoring system.

[0062] It should be elaborated that the layout density of the above tunnel surrounding rock monitoring points is based on the actual geometric structure, physical and mechanical properties of the surrounding rock and the load change law during the historical construction process. By finite element analysis, the real construction environment and the surrounding rock response are simulated, so as to realize the targeted and optimized layout of deformation monitoring equipment in different regions.

[0063] Specifically, this method emphasizes the use of a high-density layout strategy in key areas with a large deformation gradient (such as the crown, side wall, fault zone, etc.) to comprehensively capture the subtle deformation characteristics of these parts. Compared with the traditional uniform layout method, this method effectively avoids the problems of insufficient monitoring in key areas and data redundancy in non-key areas. While ensuring the monitoring effect, it significantly reduces the number of unnecessary sensors, thereby reducing the hardware investment and installation and maintenance costs, and improving the economy and efficiency of the monitoring system.

[0064] It should be understood that the core idea of the above-mentioned finite element analysis is to discretize a complex continuum (such as tunnel surrounding rock) into multiple simple elements (i.e., finite element meshes), and analyze the mechanical response of each element by solving the governing equations. In this process, the deformation gradient, as a tensor describing the rate of change of deformation in space, is used to quantify the deformation differences between different positions within an object. With the finite element method, the local deformation gradient can be calculated for each mesh. The calculation of the deformation gradient is based on a series of governing equations, including the equilibrium equation, geometric equation, and constitutive equation, which jointly describe the mechanical behavior of the surrounding rock under external loads. The specific calculation method belongs to the category of existing technologies and will not be elaborated in detail here.

[0065] As a specific implementation of the above solution, the deformation data of the monitoring points can include information such as the displacement, strain, and settlement of the monitoring points, and a single type or multiple types of data can be specifically collected according to the monitoring requirements.

[0066] In a further implementation, the spatio-temporal data fusion module performs spatial mapping on the deformation data of the monitoring points collected by the deformation monitoring device to construct a dynamic spatial deformation database containing time series.

[0067] In the preferred operation of the above implementation, the spatial mapping of the deformation data of the monitoring points collected by the deformation monitoring device is as follows: Define a three-dimensional coordinate system on the three-dimensional geometric model of the tunnel surrounding rock.

[0068] It should be noted that for the tunnel surrounding rock model, the three-dimensional coordinate system is usually defined using a rectangular coordinate system or a cylindrical coordinate system because it is convenient to describe the spatial position and deformation characteristics of the tunnel. The selection of the coordinate system origin should be combined with the geometric characteristics of the tunnel and the construction reference point to ensure the intuitiveness of subsequent calculations and analyses. Common ways of defining the origin include: A fixed point on the tunnel center line: For example, the center point directly below the tunnel crown is used as the origin.

[0069] The ground point at the tunnel entrance or exit: Use a specific ground point at the tunnel entrance or exit as the origin.

[0070] The geological exploration reference point: Use the fixed reference point set during the geological exploration process as the origin.

[0071] Exemplarily, when the three-dimensional coordinate system adopts rectangular coordinates, the axis directions are as follows: X-axis: Usually defined along the longitudinal direction of the tunnel (i.e., the tunnel center line direction), representing the extension direction of the tunnel.

[0072] Y-axis: Perpendicular to the longitudinal direction of the tunnel, representing the transverse direction of the tunnel (usually pointing to one side wall of the tunnel).

[0073] Z-axis: Perpendicular to the tunnel plane, representing the vertical direction (usually positive upward, in line with the convention of the gravity direction).

[0074] Locate the three-dimensional spatial coordinates of each monitoring point based on the defined three-dimensional coordinate system to ensure that the coordinates of the monitoring points are consistent with the coordinate system of the three-dimensional geological model.

[0075] Assign a unique identifier to each grid cell divided in the three-dimensional geometric model of the tunnel surrounding rock.

[0076] Perform spatial interpolation operations on the deformation data of the monitoring points using an interpolation algorithm, and then allocate the calculated interpolation results to the corresponding grid cells in the three-dimensional geometric model.

[0077] It should be added that before performing the spatial interpolation operation on the deformation data of the monitoring points using the interpolation algorithm, the collected deformation data needs to be denoised, filtered, corrected, and time synchronized.

[0078] It should be explained that since the data collected by the deformation monitoring equipment is usually discrete point data, while the three-dimensional geological model is continuous, it is necessary to extend the discrete point data to the entire model through an interpolation algorithm. Specifically, one of the following interpolation algorithms can be selected according to requirements: Inverse Distance Weighting Method: Calculate the deformation of any point in the model by weighting according to the distance of the monitoring points, which is suitable for situations where the local change is relatively gentle.

[0079] Kriging Interpolation Method: Considering spatial autocorrelation, it provides more accurate interpolation results and is suitable for complex geological conditions.

[0080] Radial Basis Function Method: Realize interpolation through a fitting function and is suitable for scenarios with high-precision requirements.

[0081] In a further preferred operation of the above implementation, constructing a dynamic spatial deformation database containing time series is implemented as follows: During the tunnel construction period, collect the deformation data of each monitoring point in real time and store it according to the time series.

[0082] Based on the spatial framework of the three-dimensional geometric model, stack the time series deformation data of each monitoring point into the corresponding grid cells one by one, thereby forming a dynamic spatial deformation database that integrates the spatial and time dimensions.

[0083] Through the use of finite element mesh division and interpolation algorithm, the present invention extends the discrete monitoring point data to the entire model, ensuring the spatial continuity and integrity of the data. At the same time, it combines the time series deformation data of the monitoring points with the spatial information of the monitoring points to realize the synchronous superposition of the deformation data in the time and space dimensions, so that the finally formed dynamic spatial deformation database integrates the information of the time and space dimensions and can comprehensively reflect the spatio-temporal evolution characteristics of the tunnel surrounding rock during the construction process.

[0084] In a further implementation, the spatial correlation analysis module is used to analyze the spatial deformation correlation between monitoring points by adopting a dominant-subordinate spatial deformation correlation modeling method based on a dynamic spatial deformation database containing time series, and construct a spatial deformation correlation network of the tunnel surrounding rock.

[0085] As an improved implementation of the above solution, refer to Figure 2 As shown, using the dominant-subordinate spatial deformation correlation modeling method to analyze the spatial deformation correlation between monitoring points includes the following steps: Traverse all monitoring points of the tunnel surrounding rock and group them in pairs to form multiple control groups.

[0086] In the example of the above implementation, assume that 6 monitoring points (A, B, C, D, E, F) are arranged on the surface of the surrounding rock during the construction of a certain tunnel. The 6 monitoring points (A, B, C, D, E, F) are grouped in pairs to form multiple control groups. The possible control groups include: (A, B), (A, C), (A, D), (A, E), (A, F), (B, C), (B, D), (B, E), (B, F), (C, D), (C, E), (C, F), (D, E), (D, F), (E, F).

[0087] Extract the time series deformation data of each monitoring point from the dynamic spatial deformation database containing time series, and perform correlation analysis on the time series deformation data of the two monitoring points in each control group to calculate the deformation correlation index.

[0088] Compare the deformation correlation index calculated for each control group with a preset correlation threshold, and screen out the control groups that meet the conditions to form several associated monitoring point groups.

[0089] Exemplarily, the above-mentioned correlation analysis can adopt the Pearson correlation coefficient, and the obtained deformation correlation index is the correlation coefficient. The value range of the Pearson correlation coefficient is [-1, 1], where the closer the absolute value of the correlation coefficient is to 1, the stronger the linear correlation degree between the time series deformation data of the two monitoring points. This correlation coefficient mainly reflects the deformation synchronization between monitoring points: when the correlation coefficient of two monitoring points is high, it means that when one monitoring point deforms, the other monitoring point also shows significant associated deformation. This phenomenon usually implies that they may be affected by similar external loads or geological conditions, or there is some physical connection in space, such as the effect of stress transmission. This provides an important basis for revealing the deformation propagation law and spatial correlation characteristics inside the tunnel surrounding rock.

[0090] Continuing to apply to the above example, the deformation correlation indexes of some control groups are shown in Table 3.

[0091] Table 3: Deformation correlation indexes of some control groups

[0092]

[0093] When the set correlation threshold is 0.8, the correlation index of each control group is compared with this threshold, and the associated monitoring point groups that meet the conditions are selected as (A, B), (B, C), (C, D), (E, F).

[0094] See Figure 3 As shown, perform a causal relationship analysis on the associated monitoring point groups, identify the dominant monitoring points that play a leading role in the deformation and the subordinate monitoring points affected by them, and thus clarify the dominant-subordinate relationship of the deformation propagation.

[0095] In the above correlation analysis, since the formation of the associated monitoring point groups may be affected by physical mechanisms such as stress transmission, it is necessary to further carry out a causal relationship analysis on the associated monitoring point groups to clarify whether there is a causal effect between each monitoring point and its specific direction of action.

[0096] Specifically, the causal relationship analysis is as follows: In the coordinate system with time as the horizontal axis and the deformation amount as the vertical axis, the time series deformation amount data of two monitoring points in the associated monitoring point group are respectively plotted as two deformation amount change curves.

[0097] Identify and mark the inflection points on the two deformation amount change curves, and map the inflection points on the two curves one by one to form several groups of inflection points.

[0098] It should be noted that the above-mentioned inflection points can be regarded as points where the deformation behavior changes significantly.

[0099] For each group of inflection points, extract the acquisition times of the corresponding inflection points on the two deformation amount change curves, count the proportion of the earlier appearance of each monitoring point in the group of inflection points, select the monitoring point with the largest proportion of appearance, and identify it as the dominant monitoring point of this associated monitoring point group, while the other monitoring point is identified as the subordinate monitoring point.

[0100] It should be understood that the above principle for identifying the main and subordinate monitoring points in the associated monitoring point group is: in several groups of inflection points formed by the deformation amount change curves of the associated monitoring point group, if the deformation change time sequence of a certain monitoring point always leads that of another monitoring point, then it can be determined that this monitoring point is the dominant monitoring point, and the other monitoring point is the subordinate monitoring point. This means that the deformation behavior of the subordinate monitoring point is driven or affected by the deformation of the dominant monitoring point to a certain extent, reflecting the causal relationship and the spatio-temporal dependence of the deformation propagation between the two.

[0101] Continuing to apply to the above example, the causal relationships corresponding to the selected associated monitoring point groups (A, B), (B, C), (C, D), (E, F) are: in the associated monitoring point group (A, B), A is the dominant monitoring point and B is the subordinate monitoring point.

[0102] In the associated monitoring point group (B, C), B is the dominant monitoring point and C is the subordinate monitoring point.

[0103] In the associated monitoring point group (C, D), C is the dominant monitoring point and D is the subordinate monitoring point.

[0104] In the associated monitoring point group (E, F), E is the dominant monitoring point and F is the subordinate monitoring point.

[0105] Compare and analyze the causal relationship results of all associated monitoring point groups. If it is found that the subordinate monitoring point of a certain associated monitoring point group serves as the dominant monitoring point in another associated monitoring point group, then combine these two associated monitoring point groups to form a new associated monitoring point group.

[0106] Further applying to the above example, it can be seen that there is an associated intersection in the above associated monitoring point groups (A, B), (B, C), (C, D) where the subordinate monitoring point serves as the dominant monitoring point in another associated monitoring point group. At this time, combine (A, B), (B, C), (C, D) to form a new associated monitoring point group (A, B, C, D), while the associated monitoring point group (E, F) does not have such an associated intersection and can be used as an independent associated monitoring point group.

[0107] In the new associated monitoring point group, only retain the first dominant monitoring point as the dominant monitoring point, and all the remaining monitoring points are identified as subordinate monitoring points.

[0108] The above method is based on the time series deformation data. Through steps such as inflection point identification and collection time ratio statistics, it objectively identifies the dominant monitoring point and the subordinate monitoring point, avoiding the deviation that may be brought by subjective judgment. At the same time, by comparing the collection time ratio of the inflection points, it can clearly determine which monitoring point's change has an impact on another monitoring point, thereby revealing the directionality of deformation propagation.

[0109] As a further improved implementation of the above solution, the process of constructing the spatial deformation association network of the tunnel surrounding rock is as follows: conduct time lag relationship analysis on the subordinate monitoring points in the associated monitoring point group, and based on this, establish a deformation association line pointing from the dominant monitoring point to the subordinate associated monitoring point.

[0110] Assign attribute information to each deformation association line, where the attribute information includes the deformation propagation direction, the degree of deformation association, and the deformation propagation speed.

[0111] Integrate all the deformation association lines to form a global spatial deformation association network.

[0112] Use a visualization tool to display the spatial deformation association network.

[0113] The process of analyzing the time lag relationship of the subordinate monitoring points in the associated monitoring point group is as follows: For each associated monitoring point group, count the number of subordinate monitoring points it contains.

[0114] If there are multiple subordinate monitoring points in an associated monitoring point group, then for each subordinate monitoring point, calculate the correlation coefficient between its time series deformation data and that of the dominant monitoring point at different lag orders.

[0115] It should be added that the lag order represents the number of time steps of the time series of the subordinate monitoring point relative to that of the dominant monitoring point. Specifically, it quantifies the time difference between the deformation response of the subordinate monitoring point and the occurrence of the deformation of the dominant monitoring point, reflecting the time delay characteristics of the deformation propagation between the two.

[0116] Suppose the time series of the dominant monitoring point is , and the time series of the subordinate monitoring point is .

[0117] For the lag order k, define , that is, the time series of the subordinate monitoring point is delayed backward by k steps.

[0118] Calculate and 's correlation coefficient , where k is the lag order.

[0119] At different lag orders, find the lag order that makes the correlation coefficient reach the maximum value, and take this lag order as the time lag value of this subordinate monitoring point relative to the dominant monitoring point.

[0120] The specific calculation method of the time lag value involved above has been elaborated in detail in the prior art and will not be elaborated here.

[0121] Arrange the time lag values between each subordinate monitoring point and the dominant monitoring point in ascending order to form a time lag relationship sequence of the subordinate monitoring points. This sequence reflects the time response order of the subordinate monitoring points relative to the dominant monitoring point.

[0122] The above method quantifies the time lag relationship of the subordinate monitoring point relative to the dominant monitoring point by calculating the correlation coefficient between two time series (the deformation data of the dominant monitoring point and the subordinate monitoring point) at different lag orders, finds the lag order with the strongest correlation, thereby clarifying the time delay characteristics of the deformation of the subordinate monitoring point driven by the dominant monitoring point, and reflecting the deformation propagation path from the dominant monitoring point to the subordinate monitoring points in the associated monitoring point group composed of multiple subordinate monitoring points.

[0123] In the example of the associated monitoring point group (A, B, C, D) including multiple subordinate monitoring points above, the time lag relationship of B, C, D is B < C < D, so the deformed association line from the dominant monitoring point to the subordinate associated monitoring points is A → B → C → D.

[0124] Furthermore, in the deformed association line attribute information, the deformation propagation direction is the direction from the dominant monitoring point to the subordinate monitoring point; the deformation association degree is the deformation correlation index between the dominant monitoring point and the subordinate monitoring point; the deformation propagation speed can be symbolically represented by the time lag value between the dominant monitoring point and the subordinate monitoring point, where the larger the time lag value, the smaller the deformation propagation speed.

[0125] It should be noted that when there are multiple subordinate monitoring points in the deformed association line, its deformation association degree includes multiple deformation correlation indexes.

[0126] Finally, specifically in implementation, the intelligent early warning module is used to perform early warning display through a three-dimensional visualization interface when the spatial deformation association network of the tunnel surrounding rock exceeds the set safety threshold.

[0127] The specific operation of the above implementation is as follows: For the attribute information of the deformed association line, a corresponding safety threshold range is set for each attribute, and the safety threshold range can set the safety threshold according to the tunnel construction conditions by itself.

[0128] It should be noted that since the deformation propagation direction in the attribute information does not have numerical characteristics, it is difficult to identify anomalies by setting safety thresholds. Therefore, anomaly detection can be carried out by monitoring the change of the deformation propagation direction. Specifically, if the dominant monitoring point no longer points to the subordinate monitoring point, this may reflect the following two situations: one is that the association strength between the dominant monitoring point and the subordinate monitoring point is significantly weakened or even tends to zero, resulting in the loss of the original association relationship between the two; the other is that the causal relationship between the two has changed, indicating that the deformation propagation mechanism may have changed due to the influence of external factors.

[0129] During the tunnel construction process, the attribute information of each deformed association line in the spatial deformation association network of the tunnel surrounding rock is monitored in real time.

[0130] If the attribute information of a certain deformed association line exceeds the preset safety threshold range, a local early warning is triggered.

[0131] If the attribute information of multiple deformed association lines exceeds the safety threshold at the same time, a global early warning is triggered.

[0132] When a local or global early warning is triggered, the early warning information is displayed through a three-dimensional visualization interface.

[0133] Preferably, when a local or global warning is triggered, the warning information is intuitively displayed through a three-dimensional visualization interface. Specifically, it includes: Highlight identification: The abnormal deformation correlation lines and their related monitoring points are highlighted with color coding (e.g., red indicates exceeding the threshold).

[0134] Dynamic annotation: Add text or icon annotations in the three-dimensional model to clearly display the specific values and degrees of the properties exceeding the threshold.

[0135] Animation demonstration: Show the deformation propagation path and trend in the form of an animation to help users quickly understand the problem.

[0136] When the present invention triggers a warning based on the deformation monitoring of the tunnel surrounding rock, by distinguishing between local warnings and global warnings and three-dimensionally visualizing the warning results, it helps with priority management, enabling construction personnel to take targeted emergency measures according to the warning level. At the same time, the three-dimensional visualization display makes the warning information more intuitive, reduces the understanding time, and thus speeds up the decision-making and response speed.

[0137] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for tunnel construction deformation, characterized in that: Includes the following modules: Multi-point deformation monitoring module: The deformation monitoring equipment is deployed on the tunnel surrounding rock surface according to the determined regional layout density to synchronously collect deformation data of each monitoring point; Spatiotemporal data fusion module: spatially map the deformation data of the monitoring points collected by the deformation monitoring equipment and build a dynamic spatial deformation database containing time series; Spatial correlation analysis module: Based on the dynamic spatial deformation database containing time series, the dominant-slave spatial deformation correlation modeling method is used to analyze the spatial deformation correlation between monitoring points and construct the spatial deformation correlation network of the tunnel surrounding rock; Intelligent early warning module: When the spatial deformation correlation network of the tunnel surrounding rock exceeds the set safety threshold, an early warning display is displayed through the 3D visualization interface; The determined regional layout density is obtained as follows: Build a 3D geometric model of the tunnel surrounding rock based on the tunnel design drawings and geological exploration data; The three-dimensional geometric model is divided into finite element grids, and each grid cell corresponds to a specific rock area; Input historical load data of similar tunnel construction into the 3D geometric model and carry out numerical simulation of the tunnel construction process according to the set time step; The deformation gradient distribution of each finite element grid of the tunnel surrounding rock is extracted through numerical simulation results; The deformation gradient of the finite element mesh is matched according to the preset mapping relationship between the deformation gradient and the layout density, so as to determine the layout density of the deformation monitoring equipment in different areas of the tunnel surrounding rock.

2. The intelligent monitoring system for tunnel construction deformation according to claim 1, characterized in that: The spatial mapping of the deformation data of the monitoring points collected by the deformation monitoring equipment refers to the following process: Define a three-dimensional coordinate system on the three-dimensional geometric model of the tunnel surrounding rock; Locate the three-dimensional spatial coordinates of each monitoring point based on the defined three-dimensional coordinate system; Assign a unique identifier to each grid unit divided into the three-dimensional geometric model of the tunnel surrounding rock; The interpolation algorithm is used to perform spatial interpolation operations on the deformation data of the monitoring points, and then the calculated interpolation results are allocated to the corresponding grid units in the three-dimensional geometric model.

3. The intelligent monitoring system for tunnel construction deformation according to claim 2, characterized in that: The construction of a dynamic spatial deformation database containing time series is implemented as follows: During tunnel construction, deformation data of each monitoring point is collected in real time and stored in time series; The spatial framework based on the three-dimensional geometric model superimposes the time series deformation data of each monitoring point into the corresponding grid unit one by one, thus forming a dynamic spatial deformation database integrating spatial and temporal dimensions.

4. The intelligent monitoring system for tunnel construction deformation according to claim 1, characterized in that: The method of analyzing the spatial deformation association between monitoring points by using the dominant-slave spatial deformation association modeling method comprises the following steps: Traverse all monitoring points in the tunnel surrounding rock and group them into two groups to form multiple control groups; Extract the time series deformation data of each monitoring point from the dynamic spatial deformation database containing time series, perform correlation analysis on the time series deformation data of two monitoring points in each control group, and calculate the deformation correlation index; Compare the deformation correlation index calculated for each control group with the preset correlation threshold, screen out the control groups that meet the conditions, and form several associated monitoring point groups; The causal relationship analysis of the associated monitoring point group is carried out to identify the dominant monitoring points that play a leading role in deformation and the subordinate monitoring points affected by them, thereby clarifying the dominant-slave relationship of deformation propagation.

5. The intelligent monitoring system for tunnel construction deformation according to claim 4, characterized in that: The causal relationship analysis of the associated monitoring point group refers to the following process: In a coordinate system with time as the horizontal axis and deformation as the vertical axis, the time series deformation data of two monitoring points in the associated monitoring point group are respectively plotted as two deformation change curves; Identify and mark the inflection points on the two deformation change curves, and map the inflection points on the two curves one by one to form a number of inflection point groups; For each inflection point group, the collection time of the corresponding inflection points in the two deformation change curves is extracted, and the proportion of each monitoring point with an earlier collection time in the inflection point group is counted. The monitoring point with the largest proportion is selected and identified as the dominant monitoring point of the associated monitoring point group, while the other monitoring point is identified as a subordinate monitoring point. Compare and analyze the causal relationship results of all associated monitoring point groups. If it is found that the subordinate monitoring point of a certain associated monitoring point group is the dominant monitoring point in another associated monitoring point group, then merge the two associated monitoring point groups to form a new associated monitoring point group. In the new group of associated monitoring points, only the first dominant monitoring point is retained as the dominant monitoring point, and all other monitoring points are identified as subordinate monitoring points.

6. The intelligent monitoring system for tunnel construction deformation according to claim 5, characterized in that: The process of constructing the spatial deformation association network of the tunnel surrounding rock is as follows: Conduct time lag relationship analysis on the subordinate monitoring points in the associated monitoring point group, and based on this, establish a deformation association line from the dominant monitoring point to the subordinate associated monitoring point; Assign attribute information to each deformation association line; Integrate all deformation association lines to form a global spatial deformation association network; Use visualization tools to display spatial deformation association networks.

7. The intelligent monitoring system for tunnel construction deformation according to claim 6, characterized in that: The time lag relationship analysis of the subordinate monitoring points in the associated monitoring point group is performed as follows: For each associated monitoring point group, count the number of subordinate monitoring points it contains; If there are multiple subordinate monitoring points in a certain associated monitoring point group, the correlation coefficient between each subordinate monitoring point and the deformation data of the time series of the dominant monitoring point at different lag orders is calculated respectively; Find the lag order that makes the correlation coefficient reach the maximum value under different lag orders, and use the lag order as the time lag value of the subordinate monitoring point relative to the dominant monitoring point; The time lag values ​​between each subordinate monitoring point and the dominant monitoring point are arranged in order from small to large, thereby forming a time lag relationship sequence of the subordinate monitoring points.

8. The intelligent monitoring system for tunnel construction deformation according to claim 6, characterized in that: The attribute information includes deformation propagation direction, deformation correlation degree and deformation propagation speed.

9. The intelligent monitoring system for tunnel construction deformation according to claim 8, characterized in that: When the spatial deformation correlation network of the tunnel surrounding rock exceeds the set safety threshold, the warning display is performed through the three-dimensional visualization interface. See the following process: According to the attribute information of the deformation association line, a corresponding safety threshold range is set for each attribute; During the tunnel construction process, the attribute information of each deformation association line in the spatial deformation association network of the tunnel surrounding rock is monitored in real time; If the attribute information of a certain deformation association line exceeds the preset safety threshold range, a local warning is triggered; If the attribute information of multiple deformation association lines exceeds the safety threshold at the same time, a global warning is triggered; When a local or global warning is triggered, the warning information is displayed through a three-dimensional visualization interface.

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