Underground engineering surrounding rock grouting reinforcement process simulation analysis method and system

Abstract

The invention provides an underground engineering surrounding rock grouting reinforcement process simulation analysis method and system, and relates to the technical field of engineering process simulation analysis. According to the method, an original data set is formed by synchronously collecting and fusing microseism, distributed sound wave and other multi-source observation data, on this basis, a grout flow control relation and a surrounding rock mechanical relation are introduced to serve as physical constraints for data inversion, and a state parameter set for uniformly representing the grouting process state is generated; time sequence deduction is carried out, and a state sequence reflecting slurry diffusion and surrounding rock response continuous change is formed; and finally, based on the sequence, a grouting regulation and control parameter set matched with the dynamic state of the surrounding rock is screened out. The system comprises functional units for implementing the steps. According to the method, analysis from state sensing and dynamic deduction to optimization decision making in the grouting process is achieved, and construction controllability and safety are improved.

Description

Technical Field

[0001] This invention relates to the field of engineering process simulation and analysis technology, specifically to a simulation and analysis method and system for the grouting reinforcement process of underground engineering surrounding rock. Background Technology

[0002] In underground engineering rock grouting reinforcement, a long-standing challenge has been how to monitor the actual diffusion pattern of grout within complex fracture networks and the corresponding changes in the mechanical state of the surrounding rock in real time during construction. Currently, on-site operations mainly rely on empirical control of a few process parameters such as grouting pressure and flow rate, supplemented by point displacement or pressure monitoring for post-construction verification. While these discrete and limited observational information can reflect local phenomena, they are insufficient to reveal the global dynamic process of grout-rock interaction, let alone quantitatively describe the coupled evolution of the seepage field and stress field within the surrounding rock.

[0003] Because it is impossible to accurately determine the actual diffusion range of the grout and the true stress state of the surrounding rock, adjustments to construction parameters lack a reliable basis, often resulting in a conservative or excessive grouting strategy. This can not only lead to material waste and economic losses, but also potentially induce localized damage accumulation or even instability of the surrounding rock due to improper disturbance. Summary of the Invention

[0004] To achieve the above objectives, this invention proposes a simulation analysis method and system for the grouting reinforcement process of surrounding rock in underground engineering.

[0005] On one hand, the present invention includes a simulation analysis method for the grouting reinforcement process of surrounding rock in underground engineering, comprising: S1. During the grouting process, multi-source observation data reflecting the changes in the response of the surrounding rock under grouting are acquired using a unified time reference. The multi-source observation data are then aligned and merged according to the observation location and acquisition time to form the original observation dataset. S2. Perform physically constrained data inversion processing based on the original observation dataset. In this process, the control relationship describing the flow behavior of grout in fractured media and the mechanical relationship describing the evolution of the mechanical properties of surrounding rock materials under grouting disturbance are introduced. The rationality of different observation data within the same time period is jointly checked, and data results that do not conform to the control relationship and mechanical relationship are eliminated, thereby generating a set of state parameters for uniformly characterizing the state of surrounding rock and grout in the current grouting stage. S3. Using the set of state parameters as initial conditions, perform time-series deduction processing of the grouting process. Based on the changing trend of state parameters in adjacent time periods, gradually update the grout pressure distribution range, grout diffusion boundary and stress change state inside the surrounding rock to form a state change sequence that reflects the continuous evolution characteristics of the grouting process. S4. Based on the state change sequence, perform screening and calculation of grouting control parameters, and use the grout filling degree, surrounding rock disturbance degree and material consumption level represented by the state change sequence as evaluation criteria. Compare and calculate different combinations of grouting control parameters to determine the grouting control parameter set that is compatible with the current surrounding rock state. S5. The grouting control parameter set and the state change sequence are used together as the simulation analysis output to characterize the changes in the filling effect of surrounding rock fractures and the potential damage evolution trend under different grouting control conditions.

[0006] As a further technical solution, during the surrounding rock grouting construction process, multi-source observation data reflecting the changes in the surrounding rock response under grouting is acquired using a unified time reference. This multi-source observation data includes at least microseismic observation data of local fracturing activity within the surrounding rock and distributed acoustic observation data of grout flow state changes. The multi-source observation data is aligned and merged according to the observation location and acquisition time to form an original observation dataset. The unified time reference is achieved through a high-precision clock synchronization protocol, ensuring consistent timestamps from all sensors. Microseismic observation data is captured by a sensor array deployed within the surrounding rock, recording event occurrence time, spatial three-dimensional coordinates, and energy information. Distributed acoustic observation data is measured by deployed optical fibers, recording the changes in acoustic signal intensity along the fiber path over time and space. The alignment and merging operations are completed by a data processing program. This program establishes a unified construction coordinate system, maps all observation data to specific spatial locations within this coordinate system, and segments and packages the data at the same acquisition time interval, ultimately generating an original observation dataset for each time slice containing spatial location information, a set of microseismic events, and acoustic signal characteristics.

[0007] As a further technical solution, a physically constrained data inversion process is performed based on the original observation dataset. This inversion process is implemented through an iterative optimization algorithm. Two physical relationships are defined in the iterative optimization algorithm: one is a control relationship describing the relationship between slurry pressure, viscosity, and flow velocity in the fracture network; the other is a mechanical relationship describing the relationship between surrounding rock stress, strain, and damage variables. At the start of the inversion, initial values ​​are assigned to the state parameter set, which includes slurry pressure, slurry saturation, fracture aperture, surrounding rock stress components, plastic strain, and damage factor at each point in space. In each iteration, the iterative optimization algorithm uses the current state parameter set to forward calculate the predicted microseismic activity distribution and acoustic signal response based on the control and mechanical relationships. This predicted result is then compared with the actual observation data. By adjusting the values ​​of the state parameters, the difference between the predicted and observed values ​​is minimized, and it is ensured that the adjusted parameter values ​​must satisfy the fundamental physical laws defined by the control and mechanical relationships. Through this joint verification and iteration, a state parameter set that can fit multi-source observation data and conforms to physical laws is finally output.

[0008] As a further technical solution, during implementation, the entire monitoring area is divided into segments of a fixed length along the grouting hole axis. The data inversion processing program processes each segment sequentially. For the current spatial segment, the data inversion processing program only uses the observation data falling within the range of that segment to perform inversion calculations, obtaining the local state parameter set for that segment. When processing the next adjacent spatial segment, the state parameter values ​​at the boundary between the two segments are checked, such as grout pressure and surrounding rock stress. If the difference between the boundary value extrapolated from the previous segment's result and the boundary value directly calculated from the current segment's inversion is less than the preset tolerance, the continuity condition is considered met, and the inversion result of the current segment is accepted and merged into the overall state parameter set. If the difference exceeds the tolerance, the data for that segment is marked as potentially abnormal, and the state parameters for that segment are generated using interpolation based on the results of adjacent reliable segments, thereby ensuring that the final generated overall state parameter set is spatially smooth and continuous.

[0009] As a further technical solution, the data inversion processing program will run continuously, calculating the state parameters of a point in space during multiple consecutive acquisition time periods. Only when the value of the same type of state parameter at that point changes smoothly and remains within a reasonable range calculated based on the control and mechanical relationships within a preset number of consecutive time periods, will the data inversion processing program determine the parameter value as a valid state parameter and record it in the final state parameter set. This process automatically identifies and eliminates parameter mutation points caused by measurement noise or instantaneous external interference, enhancing the temporal reliability of the state parameter set.

[0010] As a further technical solution, a time-series extrapolation of the grouting process is performed using a set of state parameters as initial conditions. Based on the changing trends of the state parameters within adjacent time periods, the grout pressure distribution range, grout diffusion boundary, and internal stress change state of the surrounding rock are progressively updated, forming a state change sequence reflecting the continuous evolution characteristics of the grouting process. This time-series extrapolation is implemented through a numerical simulation loop. In each simulation step, the full-field state parameter set obtained at the end of the previous step is used as the starting point. Then, based on the control relationship describing the grout flow behavior, the redistribution of grout pressure and the change in grout saturation under the new time increment are calculated, thereby updating the grout diffusion boundary. Simultaneously, based on the mechanical relationship describing the mechanical properties of the surrounding rock, the stress-strain response of the surrounding rock under the updated grout pressure is calculated, updating the internal stress state of the surrounding rock. After the calculation is completed, time is advanced by one step, and all updated state parameters are used as the initial conditions for the next step. This process is iterated repeatedly to generate a series of state snapshots arranged in chronological order, i.e., the state change sequence.

[0011] As a further technical solution, after each simulation step, the change in slurry saturation in each spatial region is analyzed to obtain the rate of change of slurry diffusion range in that spatial region. If the rate of change in a spatial region exceeds a preset first rate threshold, it is determined that the region is changing drastically. In the next simulation step, a shorter first preset time step is used to perform fine calculations on that region and its adjacent spatial regions. If the rate of change in a spatial region does not exceed the first rate threshold, it is determined that the region is changing gently. In the next simulation step, a longer second preset time step is used for calculations, wherein the first preset time step is smaller than the second preset time step.

[0012] As a further technical solution, the state change sequence formed in the time-series extrapolation process must satisfy the constraint that the stress change inside the surrounding rock does not exceed a preset safety threshold at each time step. The execution of this constraint is handled by a decision logic. After each time step's extrapolation calculation is completed, the decision logic immediately checks whether the stress change inside the surrounding rock exceeds the preset safety threshold set based on engineering safety standards. If the threshold is exceeded, the result of that step is deemed invalid. At this time, the time-series extrapolation process will automatically revert to the previous valid time step, and then re-execute the extrapolation calculation of that time step by adjusting the update magnitude of the state parameters used in the calculation of that time step, such as limiting the growth rate of grout pressure, until all the obtained surrounding rock stress values ​​are lower than the safety threshold before the sequence is allowed to continue evolving to the next time step.

[0013] On the other hand, the present invention also includes a simulation and analysis system for the grouting reinforcement process of underground engineering surrounding rock, comprising: The data acquisition and alignment unit is used to acquire multi-source observation data reflecting the changes in the response of the surrounding rock under grouting action during the surrounding rock grouting construction process, based on a unified time reference, and to align and merge the multi-source observation data according to the observation location and acquisition time to form an original observation dataset; the multi-source observation data includes microseismic observation data of local fracturing activity inside the surrounding rock and distributed acoustic wave observation data reflecting changes in the grout flow state. The data inversion processing unit, connected to the data acquisition and alignment unit, performs physically constrained data inversion processing based on the original observation dataset. In this process, the control relationship describing the flow behavior of grout in fractured media and the mechanical relationship describing the evolution of the mechanical properties of surrounding rock materials under grouting disturbance are introduced. The rationality of different observation data within the same time period is jointly checked, and data results that do not conform to the control relationship and mechanical relationship are eliminated, thereby generating a set of state parameters for uniformly characterizing the state of surrounding rock and grout in the current grouting stage. The time-series extrapolation processing unit is connected to the data inversion processing unit and is used to perform time-series extrapolation processing of the grouting process with the state parameter set as the initial condition. According to the changing trend of the state parameters in adjacent time periods, the grout pressure distribution range, grout diffusion boundary and stress change state inside the surrounding rock are gradually updated to form a state change sequence that reflects the continuous evolution characteristics of the grouting process. The grouting control parameter screening and calculation unit is connected to the time series simulation processing unit. It is used to perform screening and calculation processing of grouting control parameters based on the state change sequence. The degree of grout filling, the degree of disturbance of surrounding rock and the level of material consumption represented by the state change sequence are used as evaluation criteria. Different combinations of grouting control parameters are compared and calculated to determine the grouting control parameter set that is compatible with the current surrounding rock state. The simulation analysis result output unit is connected to the grouting control parameter screening and calculation unit. It is used to output the grouting control parameter set and the state change sequence together as the simulation analysis result of the surrounding rock grouting reinforcement process, so as to characterize the surrounding rock fracture filling effect and potential damage evolution trend under different grouting control conditions.

[0014] As a further technical solution, the grouting control parameters are screened and calculated based on the state change sequence. The degree of grout filling, the degree of surrounding rock disturbance, and the material consumption level, represented by the state change sequence, are used as evaluation criteria. Different combinations of grouting control parameters are compared and calculated to determine the set of grouting control parameters suitable for the current surrounding rock state. The screening and calculation process is implemented through parallel simulation and comparison. The system first defines multiple sets of candidate grouting control parameter combinations, each containing specific parameters such as grouting pressure and grouting pumping rate. Then, the system uses the latest set of state parameters as a common initial state. For each set of control parameters, an independent time-series simulation is initiated to simulate the sequence of state changes that will occur when grouting is performed using that set of parameters over a future period. After the simulation is completed, the system program extracts evaluation indicators from each sequence: the degree of grout filling is measured by the proportion of the fracture volume that is finally filled by grout, the degree of disturbance to the surrounding rock is measured by the cumulative amount of surrounding rock damage variables during the simulation, and the material consumption level is measured by the cumulative total volume of grout injected. By comparing these indicators of each sequence, the system selects the grouting control parameter set that performs best in terms of filling effect, disturbance control, and material economy.

[0015] As a further technical solution, for the state change sequence corresponding to a candidate grouting control parameter combination, the screening program not only checks its final filling effect, but also its dynamic performance over multiple consecutive simulation time steps; the system sets a target filling range and a surrounding rock abnormal response threshold; only when the grout filling degree index of the state change sequence generated by the parameter combination remains within the target range over multiple consecutive time steps, and the surrounding rock disturbance degree index remains below the abnormal response threshold, does the system determine that the parameter combination is a stable and effective candidate result.

[0016] As a further technical solution, the output simulation analysis results further include the results of comparing and ranking the state change sequences corresponding to different grouting control parameter sets; the core criterion for ranking is to rank all simulation sequences that meet the constraint that the stress change inside the surrounding rock does not exceed the preset safety threshold, based on the time required to achieve the preset grout diffusion coverage target, with the parameter combination that requires the shortest time ranking higher.

[0017] This invention provides a method and system for simulating and analyzing the grouting reinforcement process of surrounding rock in underground engineering, which has the following beneficial effects: 1. This invention solves the problem of fragmented and difficult-to-coordinated analysis of surrounding rock response and grout flow state monitoring information during grouting by synchronously acquiring and integrating multi-source observation data such as microseismic and distributed acoustic waves, thereby improving the integrity and spatiotemporal consistency of integrated perception of the dynamic process of concealed engineering.

[0018] 2. This invention improves the ability to provide a physically consistent and highly accurate description of the continuous evolution of the grouting process by introducing the grout flow control relationship and the surrounding rock mechanics relationship as physical constraints for data inversion and time-series deduction.

[0019] 3. This invention improves the dynamic adaptability of grouting construction decisions to the real-time state of the surrounding rock by quantitatively screening and optimizing grouting control parameters based on the state change sequence obtained by deduction, thereby achieving an improvement from accurate perception and reliable prediction to scientific decision-making. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

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

[0022] refer to Figure 1 In this embodiment, the application scenario is the grouting reinforcement of the surrounding rock of the tunnel arch. The system hardware on which the implementation depends includes a micro-seismic sensor array and distributed acoustic wave sensing fiber deployed inside the tunnel surrounding rock, a field server for data acquisition and synchronization, a high-performance computing workstation for executing the core algorithm, and an engineering terminal for displaying the results.

[0023] The implementation process begins with the synchronous acquisition of on-site data. Before grouting begins, microseismic sensors are arranged in a three-dimensional spatial grid around the predetermined grouting area on the tunnel arch. Simultaneously, distributed acoustic sensing fibers are deployed within the grouting boreholes and adjacent rock masses. All sensors are connected to the on-site server via a clock synchronization protocol to ensure their timestamps are consistent to millisecond-level accuracy. After grouting begins, the system runs continuously. The microseismic sensor array captures micro-fracture events within the surrounding rock in real time, and each event is recorded as a data packet containing millisecond-level time, three-dimensional spatial coordinates, and energy values. The distributed acoustic sensing fibers continuously measure the propagation characteristics of acoustic signals along the fiber path, generating signal strength and phase sequences indexed by time and spatial location. The data acquisition and alignment unit uses a 1-second basic time window for real-time... The system receives these raw data streams. For each time window, all microseismic events are first mapped to the tunnel construction coordinate system with the grouting orifice as the origin, based on their coordinates. Simultaneously, the acoustic signal sequence is mapped to continuous spatial segments in the same coordinate system based on the physical location of the optical fiber. Subsequently, microseismic events that are spatially adjacent within the same second are aggregated, and the event frequency and cumulative energy of the spatial cluster are calculated. For acoustic data, the average rate of change of the signal on each spatial segment within the same time period is calculated. Finally, a structured data record is generated for each second of the time window. This record contains a timestamp, a series of spatial location points, and their corresponding microseismic activity characteristics and acoustic change characteristics. These records, arranged in chronological order, constitute the raw observation dataset for subsequent analysis.

[0024] After acquiring the original observation dataset, the system initiates physically constrained data inversion processing to generate a set of state parameters. In this embodiment, the control relationship describing the flow behavior of grout in fractured media adopts the fracture flow equation applicable to Bingham fluids, which correlates grout pressure gradient, viscosity, yield stress with fracture aperture and flow velocity. The mechanical relationship describing the evolution of the mechanical properties of surrounding rock materials adopts an elastoplastic constitutive model considering plastic softening and damage, which correlates stress, strain, and damage variables. The inversion processing unit loads the original observation dataset from a continuous time period prior to the current moment, such as data from the last 30 seconds. The inversion program first proceeds along the axial direction of the grouting borehole at 0.5-meter intervals. The monitoring area is divided into multiple continuous spatial segments; the inversion begins with the first segment closest to the borehole opening; the program reads all observation data falling within this segment, including the distribution of microseismic events and changes in acoustic signals; the system assigns initial values ​​to the state parameters to be inverted, including slurry pressure, slurry saturation, equivalent fracture aperture, six stress components of the surrounding rock, plastic strain tensor, and damage factor at each three-dimensional grid point within the segment; subsequently, the system enters an iterative optimization loop; in each iteration, based on the current state parameter values, the theoretical distribution of acoustic signals within the segment is calculated using the aforementioned slurry flow control relationship in a forward simulation, while simultaneously utilizing the surrounding rock mechanics relationship combined with microseismic activity and... The statistical correlation model of damage calculates the theoretical energy and spatial distribution of microseismic events. The system calculates the overall difference between these theoretical predictions and actual observations. The state parameters are adjusted using a gradient descent algorithm to minimize the difference, and each parameter adjustment must satisfy the constraints formed by the two physical relationships mentioned above. When the difference is less than a preset threshold or the iteration reaches its upper limit, the loop stops, and the inversion result of that spatial segment, i.e., the state parameter set of that segment, is output. Then, the next adjacent spatial segment is processed. At this point, the results at the boundary of the already inverted segment, such as grout pressure and normal stress, are introduced as boundary conditions for the current segment. After completing the inversion of the current segment, the boundary is verified. The consistency of state parameters on both sides is ensured. For example, the difference in slurry pressure at the interface is checked. If the difference is less than 0.1 MPa, the continuity condition is considered met, the current segment result is accepted, and it is integrated with the previous result. If the difference exceeds this tolerance, it is determined that there may be transient anomalies in the current segment's observation data. Instead, a linear interpolation method based on the results of reliable segments before and after is used to generate the parameters for that segment, thereby ensuring that the final set of state parameters for the entire field is spatially smooth and continuous. In addition, to ensure the temporal reliability of the state parameters, the stability of the parameters at each spatial point is assessed. For example, for the slurry pressure parameter at a certain point, its value in the inversion results of five consecutive 1-second time windows is checked; only if all five values ​​are within 1...Within the theoretically reasonable range of 0 MPa to 5 MPa, and with a gentle fluctuation trend and no significant jumps, the system confirms the latest value of this parameter as a valid state parameter and includes it in the final set of state parameters used for deduction. Through the above-mentioned spatial segmented inversion, continuity verification, and temporal stability judgment, the system finally outputs a set of state parameters characterizing the true state of the surrounding rock and grout at the current grouting stage.

[0025] After obtaining the state parameter set, the time-series simulation processing unit uses it as the initial field to predict the future evolution of the grouting process. The simulation is conducted in a three-dimensional numerical grid model that covers the grouting influence area. The core of the simulation is the coupled solution of the grout flow equation and the surrounding rock mechanics equation. The simulation starts from the current moment and progresses forward with an initial time step. In the first simulation step, the system uses the state parameter set as the starting point and first calculates the pressure redistribution and saturation change of the grout in the fracture network within a given time increment based on the grout flow control relationship, thereby updating the boundary position of grout diffusion. Subsequently... Based on the updated grout pressure field and the surrounding rock mechanics relationship, the stress-strain response of the surrounding rock is calculated, and the stress state and damage variables of each grid point are updated. After completing the calculation of one time step, the system advances the time non-uniformly; it analyzes the changes in grout saturation in each spatial region to calculate the rate of change of grout diffusion range; for example, if the system finds that the saturation of certain spatial regions near the grouting source increases by 0.1 within 1 second, and the rate of change exceeds the preset first rate threshold of 0.05 per second, then these regions are determined to have active grout flow; in the next simulation step, the system takes measures for these active regions and their adjacent affected regions. The calculation is performed using a first preset time step of 0.1 seconds; for other regions where the saturation change rate is lower than this threshold, a second preset time step of 1 second is used for updates. This adaptive time step mechanism improves overall efficiency while ensuring the simulation accuracy of critical areas. More importantly, after each time step is completed, the system immediately performs a safety constraint judgment. It checks the maximum principal stress of all surrounding rock grid points. If the stress value of any point exceeds the safety threshold set based on the uniaxial compressive strength of the surrounding rock (e.g., the threshold is 70% of the compressive strength), the result of that step is judged to violate the safety evolution law. The simulation program automatically reverts to the previous valid time step state, and then, through internal adjustments, such as limiting the maximum allowable increase in grout pressure within that step to 80% of the original value, re-executes the calculation for that time step until all stress results are below the safety threshold before allowing the sequence to continue evolving to the next time step. Through this iterative advancement, adaptive variable step size, and safety constraint revert mechanism, the system ultimately generates a state change sequence that reflects the continuous and safe evolution characteristics of the grouting process over the next few minutes. This sequence consists of a series of data frames arranged in chronological order, containing snapshots of the grout and surrounding rock conditions across the entire field.

[0026] Based on the predicted state change sequence, the grouting control parameter screening calculation unit is activated to find the optimal construction parameters. The system maintains a database containing multiple sets of candidate grouting control parameter combinations. Each set of parameters includes the target grouting pressure, grouting pump flow rate curve, and grout water-cement ratio. For example, candidate combination A is a pressure of 3 MPa, a flow rate of 30 liters per minute, and a water-cement ratio of 0.8; combination B is a pressure of 4 MPa, a flow rate of 40 liters per minute, and a water-cement ratio of 0.7. Using the latest inverted set of state parameters as a unified initial state, an independent time-series simulation process is initiated for each set of candidate parameters, simulating the grouting process for the next 10 minutes in parallel, thereby generating a corresponding predicted state change sequence for each parameter combination. After the simulation is completed, the system extracts key evaluation indicators from each sequence for quantitative comparison. The degree of grout filling is measured by the proportion of the grid volume with a grout saturation greater than 0.8 at the end of the simulation to the total target fracture volume. The disturbance degree of the surrounding rock is also considered. The degree of grouting is measured by the sum of the cumulative increments of damage variables at all grid points during the simulation period; the material consumption level is measured by the cumulative total volume of injected grout; the system assigns weights to these three indicators according to engineering requirements, such as a 50% weight for filling degree, a 30% weight for disturbance degree, and a 20% weight for consumption level, and calculates the comprehensive score for each parameter combination; however, the final score alone is insufficient to guarantee the continued effectiveness of the parameters; the system also verifies the trend stability of high-scoring combinations; it checks the performance of the state change sequence corresponding to the combination over multiple consecutive time steps, such as 20 consecutive time steps; during this period, the grout filling degree index must always be maintained within the target range of 75%-90%, and the cumulative rate of surrounding rock damage must always be lower than the abnormal response threshold of 0.05 parts per second; only those parameter combinations that can simultaneously meet these two conditions over consecutive time periods will be finally determined as the recommended grouting control parameter set adapted to the current surrounding rock state.

[0027] Finally, the simulation analysis output unit integrates and visualizes all the above results. It outputs a recommended set of optimal grouting control parameters, such as a pressure of 3.5 MPa, a flow rate of 35 liters per minute, and a water-cement ratio of 0.75 for the next stage of construction. Simultaneously, it outputs predicted data for the complete state change sequence corresponding to these recommended parameters, as well as a ranking report comparing them with other candidate parameters. The ranking report is clearly presented in the form of a bar chart. Among all simulation results that meet the surrounding rock stress safety constraints, the time required to achieve 85% grout diffusion coverage is the primary ranking criterion, with the shortest time ranking first. This report provides engineers with intuitive and quantitative decision support, enabling them to select and implement the most suitable grouting control strategy for the current surrounding rock dynamics based on comprehensive considerations such as safety, efficiency, and economy, thereby achieving accurate perception, reliable prediction, and scientific decision-making regarding the concealed grouting process.

[0028] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A simulation analysis method for the grouting reinforcement process of surrounding rock in underground engineering, characterized in that, Includes the following steps: S1. During the grouting process, multi-source observation data reflecting the changes in the response of the surrounding rock under grouting are acquired using a unified time reference. The multi-source observation data are then aligned and merged according to the observation location and acquisition time to form the original observation dataset. S2. Perform physically constrained data inversion processing based on the original observation dataset. In this process, the control relationship describing the flow behavior of grout in fractured media and the mechanical relationship describing the evolution of the mechanical properties of surrounding rock materials under grouting disturbance are introduced. The rationality of different observation data within the same time period is jointly checked, and data results that do not conform to the control relationship and mechanical relationship are eliminated, thereby generating a set of state parameters for uniformly characterizing the state of surrounding rock and grout in the current grouting stage. S3. Using the set of state parameters as initial conditions, perform time-series deduction processing of the grouting process. Based on the changing trend of state parameters in adjacent time periods, gradually update the grout pressure distribution range, grout diffusion boundary and stress change state inside the surrounding rock to form a state change sequence that reflects the continuous evolution characteristics of the grouting process. S4. Based on the state change sequence, perform screening and calculation of grouting control parameters, and use the grout filling degree, surrounding rock disturbance degree and material consumption level represented by the state change sequence as evaluation criteria. Compare and calculate different combinations of grouting control parameters to determine the grouting control parameter set that is compatible with the current surrounding rock state. S5. The grouting control parameter set and the state change sequence are used together as the simulation analysis output to characterize the changes in the filling effect of surrounding rock fractures and the potential damage evolution trend under different grouting control conditions.

2. The simulation analysis method for grouting reinforcement process of underground engineering surrounding rock according to claim 1, characterized in that: The physically constrained data inversion process in S2 first divides the original observation dataset into multiple continuous spatial segments according to the grouting direction. In each spatial segment, data screening and inversion based on the control and mechanical relationships are performed. Only when the state parameters obtained by inversion from adjacent spatial segments are consistent under the continuity condition are the corresponding results included in the final state parameter set, so as to avoid the influence of local abnormal observations on the overall state judgment.

3. The simulation analysis method for grouting reinforcement process of underground engineering surrounding rock according to claim 2, characterized in that: The set of state parameters generated in S2 is obtained by performing stability judgment on the inversion results of the same spatial location in multiple consecutive time periods. Specifically, the same state parameter is determined as a valid state parameter only when the same state parameter satisfies the control relationship and mechanical relationship constraints in a preset number of consecutive time periods, thereby eliminating non-real state changes caused by instantaneous disturbances.

4. The simulation analysis method for grouting reinforcement process of underground engineering surrounding rock according to claim 1, characterized in that: The time-series extrapolation processing in S3 sets corresponding time advance steps for different spatial regions based on the different rates of change of the slurry diffusion range in the state parameter set. If the rate of change of the slurry diffusion range in a certain spatial region in the previous time step exceeds a preset first rate threshold, then a first preset time step is set for that region to update its state; if the rate of change of the slurry diffusion range in a certain spatial region in the previous time step does not exceed the first rate threshold, then a second preset time step is set for that region to update its state; wherein, the first preset time step is smaller than the second preset time step.

5. The simulation analysis method for grouting reinforcement process of underground engineering surrounding rock according to claim 1, characterized in that: In the state change sequence formed in S3, the deduction result of each time step must meet the constraint that the stress change inside the surrounding rock does not exceed the preset safety threshold. If the deduction result of a certain time step violates the constraint, the deduction will be rolled back to the previous time step and the corresponding state parameter change amplitude will be readjusted before the deduction is performed again, so as to ensure that the state change sequence conforms to the safe evolution law in the grouting process.

6. The simulation analysis method for grouting reinforcement process of underground engineering surrounding rock according to claim 1, characterized in that: The grouting control parameter screening calculation in S4 is based on a comprehensive judgment of the change trend of the state change sequence in multiple consecutive time steps. Only when a certain combination of grouting control parameters keeps the grout filling degree in the target range in multiple consecutive time steps and does not trigger an abnormal response in the surrounding rock, is the combination of grouting control parameters determined as a candidate result.

7. The simulation analysis method for grouting reinforcement process of underground engineering surrounding rock according to claim 1, characterized in that: The simulation analysis results output by S5 further include the results of comparing and sorting the state change sequences corresponding to different grouting control parameter sets. The sorting is based on the time required for the grout diffusion coverage to reach the preset target under the premise of meeting the surrounding rock safety conditions, thereby providing a priority basis for parameter adjustment during the grouting process.

8. A simulation and analysis system for the grouting reinforcement process of underground engineering surrounding rock, used to implement the simulation and analysis method for the grouting reinforcement process of underground engineering surrounding rock as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition and alignment unit is used to acquire multi-source observation data reflecting the changes in the response of the surrounding rock under grouting action during the surrounding rock grouting construction process, based on a unified time reference, and to align and merge the multi-source observation data according to the observation location and acquisition time to form an original observation dataset; the multi-source observation data includes microseismic observation data of local fracturing activity inside the surrounding rock and distributed acoustic wave observation data reflecting changes in the grout flow state. The data inversion processing unit, connected to the data acquisition and alignment unit, performs physically constrained data inversion processing based on the original observation dataset. In this process, the control relationship describing the flow behavior of grout in fractured media and the mechanical relationship describing the evolution of the mechanical properties of surrounding rock materials under grouting disturbance are introduced. The rationality of different observation data within the same time period is jointly checked, and data results that do not conform to the control relationship and mechanical relationship are eliminated, thereby generating a set of state parameters for uniformly characterizing the state of surrounding rock and grout in the current grouting stage. The time-series extrapolation processing unit is connected to the data inversion processing unit and is used to perform time-series extrapolation processing of the grouting process with the state parameter set as the initial condition. According to the changing trend of the state parameters in adjacent time periods, the grout pressure distribution range, grout diffusion boundary and stress change state inside the surrounding rock are gradually updated to form a state change sequence that reflects the continuous evolution characteristics of the grouting process. The grouting control parameter screening and calculation unit is connected to the time series simulation processing unit. It is used to perform screening and calculation processing of grouting control parameters based on the state change sequence. The degree of grout filling, the degree of disturbance of surrounding rock and the level of material consumption represented by the state change sequence are used as evaluation criteria. Different combinations of grouting control parameters are compared and calculated to determine the grouting control parameter set that is compatible with the current surrounding rock state. The simulation analysis result output unit is connected to the grouting control parameter screening and calculation unit. It is used to output the grouting control parameter set and the state change sequence together as the simulation analysis result of the surrounding rock grouting reinforcement process, so as to characterize the surrounding rock fracture filling effect and potential damage evolution trend under different grouting control conditions.

9. The simulation analysis of the grouting reinforcement process of underground engineering surrounding rock according to claim 8, characterized in that, The data inversion processing unit is configured to segment the original observation dataset according to the grouting direction, and perform inversion and consistency verification in each segment. The corresponding state parameter set is output only when the inversion results of adjacent segments meet the continuity condition.

10. The simulation analysis of the grouting reinforcement process of underground engineering surrounding rock according to claim 8, characterized in that, The time-series simulation processing unit is configured to update the state parameters using different time-progression step sizes based on the differences in the rate of state change in different spatial regions, and to perform safety constraint judgment on the change of surrounding rock stress during each time step update process, so as to form a state change sequence that conforms to the actual grouting evolution law.

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