A method for grouting reinforcement of surrounding rock in tunnels with complex geological conditions

Abstract

This invention discloses a grouting reinforcement method for roadways under complex geological conditions, belonging to the field of roadway grouting reinforcement technology. The method includes the following steps: utilizing various geological exploration technologies to obtain the non-uniform characteristics and structural parameters of the geological conditions surrounding the roadway, and using sensors to monitor potential dynamic changes during the grouting process in real time, providing basic data for subsequent grouting control. By introducing high-response pressure sensors, fast-response control valves, and adaptive modules, this invention enables the grouting system to achieve real-time monitoring, dynamic pressure adjustment, and adaptive material optimization, improving the grouting filling effect, reducing the risk of surrounding rock fracturing and grout leakage, ensuring construction safety and surrounding rock stability, and effectively reducing manual intervention, shortening the construction cycle, and lowering project costs, making roadway reinforcement under complex geological conditions more efficient, economical, and reliable.

Description

Technical Field

[0001] This invention relates to the field of surrounding rock grouting reinforcement technology, and specifically to a method for grouting reinforcement of surrounding rock in tunnels with complex geological conditions. Background Technology

[0002] Grouting reinforcement of roadways with complex geological conditions refers to an engineering method that involves injecting grout around the rock or soil areas surrounding the roadway (i.e., the surrounding rock) to enhance its stability and bearing capacity. Complex geological conditions may include faults, fracture zones, high groundwater pressure, weak layers, or easily collapsible surrounding rock. By injecting specific grouts (such as cement grout, polyurethane grout, etc.) into the fissures or pores of the surrounding rock, it solidifies to form a denser and stronger structure, reducing deformation and inhibiting water seepage, thereby ensuring the safety and stability of the roadway during construction and subsequent use.

[0003] The existing technology has the following shortcomings:

[0004] During grouting reinforcement of surrounding rock in tunnels, insufficient response speed of the control system can lead to instantaneous loss of control over grouting pressure, resulting in serious consequences. Under complex geological conditions, the non-uniformity of the geological structure and the dynamic changes in the fluidity of the grouting material require the control system to adjust pressure rapidly. However, when the response speed of the control system cannot meet the real-time adjustment requirements, sudden pressure fluctuations may not be controlled in time, causing the grouting pressure to momentarily exceed the bearing capacity of the surrounding rock. This can result in serious problems such as surrounding rock fracturing, grout leakage, and even inducing local collapse, endangering not only the stability of the tunnel but also posing a direct threat to construction safety. Improving the response speed of the control system and its adaptability to sudden changes is key to solving this hidden danger.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a grouting reinforcement method for roadway surrounding rock suitable for complex geological conditions. By introducing a high-response pressure sensor, a fast-response control valve, and an adaptive module, the grouting system achieves real-time pressure monitoring and dynamic adjustment, avoiding problems such as surrounding rock fracturing and grout leakage caused by overpressure, thus improving the safety and long-term stability of roadway reinforcement. Simultaneously, through adaptive optimization of the rheological properties of the grouting material, the system can dynamically adjust the grout performance to adapt to complex geological conditions, reducing material waste and manual intervention, significantly improving construction efficiency, shortening the construction period, and reducing project costs. This achieves an efficient and economical roadway surrounding rock reinforcement solution, addressing the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for grouting and reinforcing the surrounding rock of roadways suitable for complex geological conditions, comprising the following steps:

[0008] Multiple geological exploration technologies are used to obtain the non-uniform characteristics and structural parameters of the geological conditions around the tunnel, and sensors are deployed to monitor potential dynamic changes in real time during the grouting process, providing basic data for subsequent grouting control;

[0009] Based on the geological condition analysis results, grouting materials suitable for complex geological structures are selected, and their rheological properties are adjusted to ensure that the grout can effectively fill the surrounding rock fissures under grouting pressure without causing excessive leakage or solidification.

[0010] Install high-response pressure sensors and fast-response control valves in the grouting system to build a hardware control platform for real-time adjustment of grouting pressure, ensuring that pressure changes can be quickly transmitted and adjusted to the target value;

[0011] The grouting control system is initialized and configured. Geological response characteristics are recorded through grouting experiments. Initial response threshold and pressure adjustment range are set. An adaptive module with dynamic correction function is embedded in the grouting control system.

[0012] A pressure adjustment algorithm based on geological dynamic feedback is designed to analyze monitoring data in real time and optimize the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure.

[0013] By employing intelligent control algorithms, the system autonomously determines grouting pressure and flow adjustment strategies based on sudden changes in monitoring data. This ensures a rapid response to sudden pressure fluctuations, minimizing pressure adjustment time and reducing the risk of surrounding rock fracture and grouting failure.

[0014] Preferably, the specific steps for obtaining the non-uniform characteristics and structural parameters of the geological conditions around the tunnel using multiple geological exploration technologies, and for real-time monitoring of potential dynamic changes during the grouting process through sensor deployment to provide basic data for subsequent grouting control are as follows:

[0015] By using comprehensive geological exploration methods, we can fully obtain the heterogeneous characteristics and key geological parameters of the surrounding rock of the tunnel.

[0016] Deploy and calibrate multiple sensors in key geological areas to build a precise real-time monitoring network;

[0017] Collect real-time data on the surrounding rock and generate a three-dimensional geological model to dynamically analyze potential risk areas;

[0018] Establish a real-time feedback mechanism to optimize grouting control and provide reference for future construction.

[0019] Preferably, based on the geological condition analysis results, the following steps are taken to select a grouting material that conforms to the complex geological structure and adjust its rheological properties to ensure that the grout can effectively fill the surrounding rock fissures under grouting pressure without causing excessive leakage and solidification:

[0020] Select grouting materials based on geological survey results to ensure their performance is suitable for complex geological conditions;

[0021] By adjusting the viscosity, fluidity, and setting time of the slurry, the rheological properties of the material are optimized to meet construction requirements;

[0022] The material properties are verified through simulation tests to ensure a high degree of compatibility with geological conditions;

[0023] During construction, the material ratio and grouting parameters are adjusted in real time to dynamically adapt to changes in geological conditions and ensure the reinforcement effect.

[0024] Preferably, the specific steps for achieving real-time, high-precision monitoring of grouting pressure through the appropriate selection, precise arrangement, and calibration of high-response pressure sensors are as follows:

[0025] Geological analysis was used to determine key monitoring areas for deploying pressure sensors and optimize the spatial coverage of data acquisition.

[0026] Select sensors with high precision, fast response and high pressure resistance to adapt to complex geological conditions;

[0027] The sensors are calibrated and dynamically tested to ensure measurement accuracy and stability in complex environments;

[0028] By integrating sensors into the hardware control platform, a real-time monitoring and early warning system can be built to achieve a closed loop of data acquisition and analysis.

[0029] Preferably, the specific steps for constructing an efficient dynamic pressure regulation mechanism to adapt to complex geological conditions through the installation and integration of rapid response control valves are as follows:

[0030] The control valve installation scheme is designed based on the characteristics of the grouting material and the monitoring layout to ensure maximum regulation efficiency.

[0031] Select control valves that are fast-responding, corrosion-resistant, and have low flow resistance to meet dynamic pressure regulation requirements;

[0032] During the commissioning phase, test the valve's response capability and adjustment accuracy, and optimize its performance parameters;

[0033] A linkage mechanism between control valves and pressure sensors is established to form a dynamic closed-loop regulation system to adapt to real-time changes.

[0034] Preferably, the specific steps for initializing the grouting control system, recording geological response characteristics through grouting experiments, setting the initial response threshold and pressure adjustment range, and embedding an adaptive module with dynamic correction function into the grouting control system are as follows:

[0035] By recording the pressure response characteristics of the surrounding rock through experimental grouting, the initial pressure range and adjustment parameters are set to ensure safety in the early stage of construction.

[0036] A nonlinear prediction model is constructed to automatically optimize the pressure regulation path based on real-time data to avoid overpressure.

[0037] An integrated adaptive module dynamically adjusts the initial response parameters to adapt to the changing needs of complex geological conditions.

[0038] The adaptive module works in conjunction with the hardware platform to dynamically optimize the pressure control strategy and improve grouting stability and efficiency.

[0039] Preferably, the specific steps for designing a pressure adjustment algorithm based on geological dynamic feedback, analyzing monitoring data in real time, and optimizing the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure are as follows:

[0040] Based on monitoring data, a dynamic model describing the geological response was established to capture real-time change characteristics. The expression of the dynamic model is as follows:

[0041] X(t+1)=A·X(t)+B·U(t)+W(t), where X(t) is the state vector, including the current surrounding rock stress σ(t), fracture aperture δ(t), and slurry flow velocity v(t), X(t+1) is the state vector at the next time step, U(t) is the control input vector, W(t) is the process noise, a is the state transition matrix, and B is the control matrix;

[0042] Based on the dynamic model output, the grouting pressure change rate and its deviation from the surrounding rock bearing capacity threshold are calculated. The calculation expression is as follows:

[0043] In the formula, ΔP(t) is the grouting pressure adjustment amount, σ max σ(t) is the bearing capacity limit of the surrounding rock, k1 is the stress adjustment coefficient of the surrounding rock, δ(t) is the current fracture aperture, and δ crit It is the critical aperture for fracture propagation, k2 is the fracture aperture adjustment coefficient, v(t) is the current slurry flow rate, v crit K is the target slurry flow rate, and k3 is the slurry flow rate adjustment coefficient.

[0044] Preferably, by combining real-time pressure adjustment, the pressure distribution along the grouting path is optimized to ensure uniform and stable pressure. The optimization formula is as follows:

[0045] P opt (x, t) = P0·exp(-α·x) + β·ΔP(t), where P opt (x, t) is the optimized pressure at position x on the path, P0 is the initial grouting pressure, α is the attenuation coefficient, and β is the amplification coefficient;

[0046] By combining the optimized path pressure with geological feedback, a closed-loop pressure control system is constructed to achieve real-time adjustment, as shown in the following formula:

[0047] P(t+1)=P opt (x, t) + γ[σ] max -σ(t)]-η[δ(t)-δ crit ]-λ[v crit -v(t)], where γ is the surrounding rock stress feedback gain coefficient, reflecting the influence of surrounding rock stress on pressure regulation, η is the fracture aperture feedback gain coefficient, λ is the flow velocity feedback gain coefficient, and P(t+1) is the grouting pressure in the next time step.

[0048] Preferably, an intelligent control algorithm is used to autonomously determine the grouting pressure and flow adjustment strategy based on sudden changes in monitoring data, ensuring a rapid response when pressure fluctuates suddenly and minimizing pressure adjustment time, thereby reducing the risk of surrounding rock fracture and grouting failure. The specific steps are as follows:

[0049] To ensure timely response to sudden pressure changes during the grouting control process, the monitoring data was first preprocessed and feature extracted. To eliminate the influence of noise and outliers, a weighted moving average method was used to smooth the data. Simultaneously, the pressure fluctuation amplitude within each monitoring cycle was extracted, and the calculation expression is as follows:

[0050] In the formula, ΔP is the pressure fluctuation amplitude, N is the total number of sampling points, and w i It is the weighting coefficient, P i It is the pressure value at the i-th sampling point. It is the average pressure value

[0051] Based on the parameters extracted from the feature extraction, a predictive model for sudden pressure changes is established using a recurrent neural network. The model predicts the pressure change in the next time period based on the current pressure fluctuation amplitude ΔP and the changing trend of the surrounding rock deformation rate. The formula is as follows:

[0052] In the formula, It is the predicted pressure value for the next time period, ΔP. t It represents the pressure fluctuation amplitude of the current time period, ΔD. t θ is the deformation rate of the surrounding rock in the current time period, θ is the set of weight parameters of the model, and f is the recurrent neural network model.

[0053] Preferably, based on the predicted pressure change value Given the current flow rate, an optimization algorithm is used to calculate the optimal pressure regulation strategy. An optimization method based on Lagrange multipliers is employed to determine the target pressure value and flow rate adjustment range during grouting, thereby minimizing the pressure regulation time. The formula is as follows:

[0054] In the formula, ΔQ is the minimum flow rate adjustment range, and P is the flow rate adjustment range. target ω is the target pressure value, and ω is the regularization parameter used to balance the adjustment speed and the magnitude of flow rate changes;

[0055] Finally, the calculated target pressure value P target The flow rate adjustment amplitude ΔQ is applied to grouting control to generate specific valve control commands. A fuzzy control algorithm is used, combined with the valve's response speed and initial opening, to dynamically adjust the valve opening and achieve precise pressure control. The formula for generating the control commands is as follows:

[0056] In the formula, θ init It is the initial valve opening, θ v Where is the valve opening angle, k is the pressure regulation coefficient, and P(t) is the current actual pressure value. It is the flow regulation coefficient, which controls the effect of flow adjustment on valve opening.

[0057] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0058] This invention, by introducing a high-response pressure sensor, a fast-response control valve, and an adaptive module, enables the grouting system to achieve real-time monitoring and dynamic pressure adjustment, effectively preventing surrounding rock fracturing and grout leakage caused by overpressure. The system can automatically optimize the pressure adjustment path based on real-time data, quickly responding to sudden changes even under complex geological conditions, ensuring the long-term stability of the surrounding rock. The continuous optimization capability of the dynamic correction algorithm allows the system to learn and adapt to different geological features, improving the safety of the construction process, reducing the risk of safety accidents such as collapses during tunnel reinforcement, and providing reliable data support and scientific basis for subsequent geological monitoring and maintenance.

[0059] This invention, through adaptive optimization of the rheological properties of grouting materials, dynamically adjusts the viscosity, fluidity, and setting time of the grout based on real-time monitoring data. This ensures the grout performance is highly matched to geological conditions, significantly improving the grouting filling effect. This dynamic adjustment avoids material waste and grouting failure, effectively reducing the risk of grout leakage, especially in highly permeable areas. Simultaneously, the intelligent control system significantly reduces manual intervention and debugging time, improving construction efficiency and lowering labor and material costs. By shortening the construction cycle, reducing repetitive operations, and minimizing material waste, overall project costs are effectively controlled, making tunnel reinforcement under complex geological conditions more efficient and economical. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0061] Figure 1 This is a flowchart of a method for grouting and reinforcing the surrounding rock of a tunnel suitable for complex geological conditions, according to the present invention. Detailed Implementation

[0062] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0063] This invention provides, for example Figure 1 The method for grouting and reinforcing the surrounding rock of roadways suitable for complex geological conditions, as shown, includes the following steps:

[0064] Various geological exploration technologies (such as seismic wave survey, geological drilling or radar imaging) are used to obtain the non-uniformity characteristics and structural parameters of the geological conditions around the tunnel, and potential dynamic changes during the grouting process are monitored in real time by the deployment of sensors, so as to provide basic data for subsequent grouting control;

[0065] The specific steps for obtaining the non-uniform characteristics and structural parameters of the geological conditions around the tunnel using various geological exploration techniques (such as seismic wave surveying, geological drilling, or radar imaging), and for providing basic data for subsequent grouting control by deploying sensors to monitor potential dynamic changes in real time during the grouting process, are as follows:

[0066] By using comprehensive geological exploration methods, we can fully obtain the heterogeneous characteristics and key geological parameters of the surrounding rock of the tunnel.

[0067] Based on the geological complexity of the tunnel, appropriate geological exploration methods are selected, such as seismic wave surveying, geological drilling, or radar imaging. First, on-site investigation is conducted to determine the potential hazardous areas and the extent of the grouting zone. Seismic wave surveying can identify the heterogeneity of geological layers and the location of fractures by analyzing the propagation velocities of reflected and refracted waves; geological drilling provides high-precision core samples to directly assess the strength, bedding distribution, and porosity of the surrounding rock; radar imaging acquires high-resolution images of the underground structure through electromagnetic wave reflection, and is particularly suitable for detecting shallow geological anomalies. The combination of these methods can comprehensively describe the physical state of the surrounding rock and identify key geological features that may affect grouting.

[0068] Deploy and calibrate multiple sensors in key geological areas to build a precise real-time monitoring network;

[0069] A monitoring network is established by deploying various sensors, including pressure sensors, displacement sensors, temperature and humidity sensors, and permeability monitoring instruments, in key geological areas. Sensor placement should be based on the detection results; for example, denser sensor deployment is recommended in fault zones, fracture zones, or high-porosity areas to ensure accurate capture of dynamic changes. After deployment, the equipment is calibrated to eliminate environmental noise interference, and the data sampling and transmission frequencies are set to ensure the timeliness and accuracy of the monitoring data.

[0070] Collect real-time data on the surrounding rock and generate a three-dimensional geological model to dynamically analyze potential risk areas;

[0071] The detection equipment and monitoring sensors are activated to collect real-time data on the surrounding rock, including key parameters such as pressure changes, displacement trends, seepage rates, and temperature and humidity variations. The data is transmitted to the central control system for preliminary analysis and comparison with historical geological data to identify the stress state and dynamic trends of the surrounding rock. For example, if a sudden increase in pressure or abnormal displacement is detected in a certain area, it can be preliminarily determined that there is a risk of fracturing in that area. Through dynamic data processing, a three-dimensional model of the geological features is generated, clearly showing the distribution of fractures, bearing capacity, and potential hazardous areas in the surrounding rock.

[0072] Establish a real-time feedback mechanism to optimize grouting control and provide reference for future construction.

[0073] By linking sensor-collected data with the control system, a real-time feedback mechanism is established. For example, when the pressure in a certain area approaches the bearing capacity of the surrounding rock, the system can alert construction personnel through an early warning function to adjust the grouting pressure and flow rate to prevent rock fracturing. Dynamic processing of real-time data supports decision-making optimization for subsequent grouting processes, including pressure regulation, material selection, and grouting path planning. Simultaneously, the monitoring data is archived to provide a reference for future tunnel construction under similar geological conditions, improving the overall application level and efficiency of geological monitoring technology.

[0074] Based on the geological condition analysis results, grouting materials suitable for complex geological structures are selected, and their rheological properties are adjusted to ensure that the grout can effectively fill the surrounding rock fissures under grouting pressure without causing excessive leakage or solidification.

[0075] Based on the geological condition analysis results, the following steps were taken to select grouting materials suitable for complex geological structures and adjust their rheological properties to ensure that the grout can effectively fill the surrounding rock fissures under grouting pressure without causing excessive leakage or solidification:

[0076] Select grouting materials based on geological survey results to ensure their performance is suitable for complex geological conditions;

[0077] Geological data obtained through geological exploration techniques (such as fracture distribution, porosity, and permeability) provides a basis for the selection of grouting materials. For different geological characteristics, grouting materials with specific properties are selected. For example, low-viscosity grouts can be used for highly permeable surrounding rock, while high-viscosity and rapidly solidifying materials are required for rock strata with large fractures. Combining real-time monitoring data, the adaptability of the grout in the actual environment is evaluated to ensure that the material performance meets the construction requirements under complex geological conditions, laying the foundation for subsequent grouting effectiveness.

[0078] By adjusting the viscosity, fluidity, and setting time of the slurry, the rheological properties of the material are optimized to meet construction requirements;

[0079] Based on the characteristics of the selected grouting material and geological conditions, the rheological properties of the material, including the viscosity, fluidity, and setting time of the grout, should be adjusted. For example, adding a water-reducing agent can lower the grout viscosity and improve its fluidity, ensuring that the grout can fully fill the cracks; or a quick-setting agent can be added to adapt to high-permeability geological conditions and prevent grout leakage. The optimized material should have good stability within the grouting pressure range, meeting construction requirements while preventing grouting failure due to insufficient rheological properties.

[0080] The material properties are verified through simulation tests to ensure a high degree of compatibility with geological conditions;

[0081] The optimized grouting material was tested for adaptability in a simulated tunnel geological environment. Grouting was performed under different pressure conditions and geological models to observe the filling effect, permeability, and setting properties of the grout. Based on the test results, the material formulation was further adjusted to ensure its reliability during construction. Simultaneously, the test data was compared and verified with data collected in real time by the geological monitoring system to optimize the material's compatibility with geological conditions, providing data support for practical applications.

[0082] During construction, material ratios and grouting parameters are adjusted in real time to dynamically adapt to changes in geological conditions and ensure reinforcement effectiveness.

[0083] During actual grouting, dynamic data of the surrounding rock and the rheological behavior of the grouting material are monitored in real time. The material ratio and grouting pressure are adjusted based on the feedback data. For example, when sensors detect grout leakage or abnormal pressure fluctuations, the grout viscosity or setting rate is immediately optimized to ensure that material properties always match changes in geological conditions. This dynamic adjustment not only enhances the uniformity and stability of the grouting but also effectively reduces material waste and construction risks, ensuring that the final reinforcement effect meets design requirements.

[0084] Install high-response pressure sensors and fast-response control valves in the grouting system to build a hardware control platform for real-time adjustment of grouting pressure, ensuring that pressure changes can be quickly transmitted and adjusted to the target value;

[0085] The specific steps for achieving real-time, high-precision monitoring of grouting pressure through the appropriate selection, precise placement, and calibration of high-response pressure sensors are as follows:

[0086] Geological analysis was used to determine key monitoring areas for deploying pressure sensors and optimize the spatial coverage of data acquisition.

[0087] Based on the geological condition analysis and grouting material optimization scheme, the installation locations of pressure sensors were determined, prioritizing areas with dense fractures, high permeability, and weak surrounding rock bearing capacity. These locations can most directly reflect the dynamic changes in grouting pressure, providing accurate data support for real-time pressure adjustment. Furthermore, multiple sensors were deployed along the grouting path to form a networked monitoring layout to capture the spatial distribution of pressure.

[0088] Select sensors with high precision, fast response and high pressure resistance to adapt to complex geological conditions;

[0089] Choose a pressure sensor with high accuracy and fast response to ensure it can capture minute fluctuations in pressure in real time. For example, sensors based on piezoelectric principles or thin-film strain gauges can be used, offering nanosecond-level response capabilities to accurately reflect dynamic grouting pressure changes. Simultaneously, the sensor must possess high pressure resistance and corrosion resistance to meet the challenges of complex geological conditions and grout properties.

[0090] The sensors are calibrated and dynamically tested to ensure measurement accuracy and stability in complex environments;

[0091] Before installation, the sensor was calibrated to ensure its measurement accuracy and data transmission stability. The calibration process included multi-point pressure testing and dynamic response testing to verify its adaptability to different pressure ranges and rapid pressure changes. Furthermore, testing was conducted in a simulated grouting environment to ensure the sensor could function properly in high-flow-rate grout and complex geological conditions, and the error range and compensation methods were recorded.

[0092] By integrating sensors into a hardware control platform, a real-time monitoring and early warning system can be built to achieve a closed loop of data acquisition and analysis.

[0093] The pressure sensor is connected to the hardware control platform, and the collected pressure data is transmitted to the central control system via a high-speed data transmission module. The system can display pressure fluctuations in real time and store the data for subsequent analysis. Simultaneously, a warning threshold is set; when the pressure exceeds the safe range, the system immediately triggers an alarm signal, providing a time buffer for the control valves to adjust.

[0094] The specific steps for constructing an efficient dynamic pressure regulation mechanism to adapt to complex geological conditions through the installation and integration of rapid response control valves are as follows:

[0095] The control valve installation scheme is designed based on the characteristics of the grouting material and the monitoring layout to ensure maximum regulation efficiency.

[0096] Based on the optimized rheological properties of the grouting material and the layout of the pressure sensors, an installation scheme for the fast-response control valve was designed. The valve needs to be located close to critical grouting areas or locations with frequent pressure fluctuations to ensure maximum efficiency in pressure regulation. Furthermore, considering the characteristics of grout flow, a valve design with low flow resistance and high pressure resistance was selected to ensure a smooth grouting process.

[0097] Select control valves that are fast-responding, corrosion-resistant, and have low flow resistance to meet dynamic pressure regulation requirements;

[0098] Control valves with millisecond-level response capabilities, such as solenoid valves or proportional control valves, are selected. These valves can quickly respond to adjustment commands issued by the central system, enabling real-time adjustment of grouting pressure. Simultaneously, the valves must be corrosion-resistant and have a long service life to adapt to complex grout environments, and precise control should be used to minimize grout waste that may occur during pressure regulation.

[0099] During the commissioning phase, test the valve's response capability and adjustment accuracy, and optimize its performance parameters;

[0100] The fast-response control valve is integrated with pressure sensors and a central control system to form a closed-loop control circuit. During the system commissioning phase, the response speed, adjustment range, and accuracy of the control valve are tested by simulating grouting conditions. For example, the opening and closing time of the valve after receiving different pressure signals is tested to ensure that the adjustment efficiency meets the dynamic requirements under complex geological conditions. At the same time, the operating parameters of the control valve are recorded to provide an optimization basis for subsequent construction.

[0101] Establish a linkage mechanism between control valves and pressure sensors to form a dynamic closed-loop regulation system to adapt to real-time changes;

[0102] A dynamic pressure regulation mechanism is established by linking sensors and valves through a central control system. When the pressure sensor detects fluctuations in grouting pressure, the central system calculates and generates optimal adjustment parameters, controlling the valves to respond instantly and adjust the pressure to the target range. Combined with the optimized rheological properties of the grouting material, this mechanism can quickly adapt to changes in geological conditions, avoiding rock fracturing and grout leakage caused by excessive pressure, and ensuring the stability and uniformity of the grouting process.

[0103] The grouting control system is initialized and configured. Geological response characteristics are recorded through grouting experiments. Initial response threshold and pressure adjustment range are set. An adaptive module with dynamic correction function is embedded in the grouting control system.

[0104] The specific steps for initializing and configuring the grouting control system, recording geological response characteristics through grouting experiments, setting the initial response threshold and pressure adjustment range, and embedding an adaptive module with dynamic correction function into the grouting control system are as follows:

[0105] By recording the pressure response characteristics of the surrounding rock through experimental grouting, the initial pressure range and adjustment parameters are set to ensure safety in the early stage of construction.

[0106] After hardware installation, leveraging the data acquisition capabilities of high-response pressure sensors and fast-response control valves, experimental grouting was conducted to simulate actual geological conditions and record the pressure response characteristics of the surrounding rock. Specific operations included testing the stress distribution, crack propagation, and grout flow path of the surrounding rock under different grouting pressures to determine the initial threshold of the surrounding rock's bearing capacity. Based on the test results, initial pressure ranges and adjustment parameters, including pressure adjustment rates and upper limit alarm thresholds, were set in the control system to ensure that the grouting system could effectively mitigate the risk of surrounding rock fracturing during the initial stages of construction.

[0107] A nonlinear prediction model is constructed to automatically optimize the pressure regulation path based on real-time data to avoid overpressure.

[0108] By analyzing experimental grouting data and real-time feedback from the monitoring system, a dynamic correction algorithm model is established to identify changes in surrounding rock properties over time. For example, when the surrounding rock pressure is detected to be approaching its bearing limit, the algorithm can automatically optimize the pressure adjustment path based on historical data and real-time conditions to avoid overpressure. The dynamic correction algorithm needs to employ nonlinear prediction methods (such as Kalman filtering or Bayesian models) to adapt to the nonlinear changes of multivariate influencing factors in complex geological environments, providing a precise adjustment basis for grouting control.

[0109] An integrated adaptive module dynamically adjusts the initial response parameters to adapt to the changing needs of complex geological conditions.

[0110] An adaptive module is embedded in the control system, enabling it to automatically optimize initial response parameters based on real-time monitoring data and dynamic correction algorithms. This module continuously learns the geological response characteristics of the surrounding rock and the pressure variation patterns during grouting, gradually adjusting the pressure regulation range, sensor sampling frequency, and valve response speed. Through this adaptive adjustment mechanism, the control system can quickly adapt to changing demands under complex geological conditions, achieving long-term stable and efficient pressure regulation.

[0111] The adaptive module and hardware platform work together to dynamically optimize the pressure control strategy and improve grouting stability and efficiency.

[0112] By linking the adaptive module with the hardware control platform, closed-loop pressure control is achieved. The system collects sensor data in real time, adjusts initial response parameters through the adaptive module, and dynamically optimizes the opening and closing strategies of control valves. For example, when grout leakage or sudden pressure changes are detected, the system can immediately adjust the pressure range and optimize valve actions to ensure the stability and uniformity of the grouting process. This linkage mechanism not only improves grouting efficiency but also significantly reduces the risk of surrounding rock fracturing and grout waste, providing intelligent protection for tunnel reinforcement under complex geological conditions.

[0113] A pressure adjustment algorithm based on geological dynamic feedback is designed to analyze and monitor data in real time (including pressure fluctuations, grout flow rate and changes in surrounding rock stress). The grouting pressure adjustment path is optimized through fitting models and prediction functions to avoid overpressure.

[0114] The following are the specific steps for designing a pressure adjustment algorithm based on geological dynamic feedback, which analyzes and monitors data in real time (including pressure fluctuations, grout flow rate, and changes in surrounding rock stress), and optimizes the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure:

[0115] Based on monitoring data (including surrounding rock stress, grouting pressure, and grout flow rate), a dynamic model describing the geological response is established to capture real-time changes. The expression of the dynamic model is as follows:

[0116] X(t+1)=A·X(t)+B·U(t)+W(t), where X(t) is the state vector, including the current surrounding rock stress σ(t), fracture aperture δ(t), and slurry flow velocity v(t), X(t+1) is the state vector at the next time step, U(t) is the control input vector, W(t) is the process noise, representing random disturbances in the geological environment, A is the state transition matrix, describing the dynamic relationship between variables within the system, and B is the control matrix, describing the degree of influence of input variables on the state.

[0117] By collecting real-time data and fitting it to a model, initial values ​​for key parameters, such as the surrounding rock stress threshold σ, can be obtained. maxTarget flow velocity vcrit and critical point for fracture propagation δ crit These parameters will be used in subsequent pressure regulation calculations.

[0118] Based on the dynamic model output, the grouting pressure change rate and its deviation from the surrounding rock bearing capacity threshold are calculated. The calculation expression is as follows:

[0119] In the formula, ΔP(t) is the grouting pressure adjustment amount, representing the increase or decrease adjustment value of the grouting pressure that needs to be made at time t, and σ max σ(t) is the current surrounding rock bearing capacity, σ(t) is the current surrounding rock stress, k1 is the surrounding rock stress adjustment coefficient, representing the weight of the influence of surrounding rock stress on the grouting pressure adjustment, and δ(t) is the current fracture aperture, representing the actual width of the fracture at time t. crit K1 is the critical fissure opening, representing the maximum allowable fissure opening under steady-state conditions. K2 is the fissure opening adjustment coefficient, representing the weight of the fissure opening on the adjustment of grouting pressure. V(t) is the current grout flow velocity, representing the flow velocity of grout in the fissure at time t. crit is the target grout flow rate, which represents the optimal grout flow rate expected under stable grouting conditions. k3 is the grout flow rate adjustment coefficient, which represents the weight of the influence of grout flow rate on the adjustment amount of grouting pressure.

[0120] This step is used to calculate the adjustment amount of real-time grouting pressure to ensure operation within the safe range of surrounding rock pressure and fracture propagation.

[0121] By combining real-time pressure adjustment, the pressure distribution along the grouting path is optimized to ensure uniform and stable pressure. The optimization formula is as follows:

[0122] P opt (x, t) = P0·exp(-α·x) + β·ΔP(t), where P opt (x, t) is the optimized pressure at position x on the path, P0 is the initial grouting pressure, α is the attenuation coefficient, which describes the attenuation characteristics of the pressure with the path position, and β is the amplification coefficient, which is used to adjust the influence of ΔP(t) on the path pressure distribution.

[0123] By optimizing the process and combining grouting pressure adjustment with path distribution, the risk of surrounding rock fracturing caused by excessive local pressure can be avoided.

[0124] By combining the optimized path pressure with geological feedback, a closed-loop pressure control system is constructed to achieve real-time adjustment, as shown in the following formula:

[0125] P(t+1)=P opt (x, t) + γ[σ] max -σ(t)]-η[δ(t)-δ crit]-λ[v crit -v(t)], where γ is the surrounding rock stress feedback gain coefficient, reflecting the influence of surrounding rock stress on pressure regulation, η is the fracture aperture feedback gain coefficient, describing the influence of fracture aperture change on pressure regulation, λ is the flow velocity feedback gain coefficient, reflecting the influence of grout flow velocity change on pressure regulation, and P(t+1) is the grouting pressure of the next time step.

[0126] This closed-loop control step dynamically adjusts the grouting pressure based on geological monitoring data, achieving a synchronous response to changes in surrounding rock pressure and fissures, and avoiding overpressure or leakage problems caused by untimely adjustments.

[0127] The system employs intelligent control algorithms to autonomously determine grouting pressure and flow adjustment strategies based on sudden changes in monitoring data. This ensures a rapid response to sudden pressure fluctuations, minimizing pressure adjustment time and reducing the risk of surrounding rock fracture and grouting failure.

[0128] The intelligent control algorithm autonomously determines grouting pressure and flow adjustment strategies based on sudden changes in monitoring data, ensuring rapid response to sudden pressure fluctuations and minimizing pressure adjustment time, thereby reducing the risk of surrounding rock fracture and grouting failure. The specific steps are as follows:

[0129] To ensure timely response to sudden pressure changes during the grouting control process, the monitoring data is first preprocessed and features are extracted. The raw data collected by the sensors in real time includes pressure values, flow rates, displacement changes, and temperature changes. To eliminate the influence of noise and outliers, a weighted moving average method is used to smooth the data. At the same time, the pressure fluctuation amplitude within each monitoring cycle is extracted, and the calculation expression is as follows:

[0130] In the formula, ΔP is the pressure fluctuation amplitude, N is the total number of sampling points, and w i It is the weighting coefficient, P i It is the pressure value at the i-th sampling point. It is the average pressure value

[0131] The pressure fluctuation amplitude ΔP extracted by this formula will serve as an important input for the control strategy in subsequent steps.

[0132] Based on the parameters extracted from the features, a predictive model for sudden pressure changes is established using a recurrent neural network (RNN). The model predicts the pressure change in the next time period based on the current pressure fluctuation amplitude ΔP and the changing trend of the surrounding rock deformation rate. Historical monitoring data is used during model training, and the weight parameters are optimized by minimizing the prediction error, as shown in the following formula:

[0133] In the formula, It is the predicted pressure value for the next time period, ΔP.t It represents the pressure fluctuation amplitude of the current time period, ΔD. t θ is the deformation rate of the surrounding rock in the current time period, θ is the set of weight parameters of the model, and f is the recurrent neural network model;

[0134] Calculated by the prediction model These parameters will be used in the decision-making process for subsequent pressure regulation strategies.

[0135] Based on the predicted pressure change value Given the current flow rate, an optimization algorithm is used to calculate the optimal pressure regulation strategy. An optimization method based on Lagrange multipliers is employed to determine the target pressure value and flow rate adjustment range during grouting, thereby minimizing the pressure regulation time. The formula is as follows:

[0136] In the formula, ΔQ is the minimum flow rate adjustment range, and P is the flow rate adjustment range. target ω is the target pressure value, and ω is the regularization parameter used to balance the adjustment speed and the magnitude of flow rate changes;

[0137] This step optimizes the pressure conditioning time, ensuring the system can respond quickly to pressure fluctuations and reducing the risk of surrounding rock fracturing.

[0138] Finally, the calculated target pressure value P target The flow rate adjustment amplitude ΔQ is applied to grouting control to generate specific valve control commands. A fuzzy control algorithm is used, combined with the valve's response speed and initial opening, to dynamically adjust the valve opening and achieve precise pressure control. The formula for generating the control commands is as follows:

[0139] In the formula, θ init It is the initial valve opening, θ v The valve opening angle determines the grouting flow rate; k is the pressure adjustment coefficient used to control the valve's opening and closing amplitude; and P(t) is the current actual pressure value, obtained through real-time monitoring. It is the flow regulation coefficient, which controls the effect of flow adjustment on valve opening.

[0140] Through this control step, the grouting system can respond quickly to sudden pressure changes, minimizing the pressure regulation time, thereby reducing the risk of surrounding rock fracture and grouting failure, and ensuring the stability and uniformity of the grouting process.

[0141] Implementation Method 1: In the process of reinforcing the surrounding rock of tunnels under complex geological conditions, the control of grouting pressure is crucial. Since the distribution of fractures and changes in porosity in the surrounding rock cannot be fully predicted through simple static analysis, traditional fixed-pressure grouting methods often cannot cope with sudden changes in geological conditions, potentially leading to problems such as overpressure, surrounding rock fracturing, or grout leakage. To achieve more precise and dynamic pressure control, this implementation method proposes an intelligent grouting system based on dynamic pressure monitoring. Through the cooperation of high-response pressure sensors and fast-response control valves, a closed-loop real-time adjustment platform is constructed to achieve intelligent control of the grouting process.

[0142] First, the system collects real-time pressure change data of the surrounding rock by installing high-precision pressure sensors at key locations within the roadway. These sensors, employing piezoelectric or strain gauge designs, possess millisecond-level response speeds, enabling them to capture minute pressure fluctuations in the surrounding rock during grouting. Furthermore, to ensure data accuracy and transmission stability, the system incorporates a data filtering module to eliminate environmental noise interference, ensuring high reliability of the real-time pressure change monitoring data.

[0143] Secondly, the system's rapid-response control valves can automatically adjust the grouting pressure based on feedback data from pressure sensors. When excessively high or low pressure is detected during grouting, the control valves react within milliseconds, adjusting their opening and closing states. For example, when the pressure sensor detects that the bearing capacity of the surrounding rock is approaching, the control system immediately issues a command to reduce the valve opening and lower the grouting pressure, thereby avoiding the risk of surrounding rock fracturing. Conversely, when the pressure is too low, the system increases the valve opening to ensure that the grout can fully fill the fissures in the surrounding rock, thus improving the reinforcement effect.

[0144] Finally, this intelligent grouting system features early warning and data storage capabilities. By setting upper and lower pressure thresholds in the control system, the system automatically issues an alarm signal when the grouting pressure exceeds the safe range, alerting construction personnel to take timely measures. Simultaneously, the system stores the monitored pressure data in real time, providing data support for subsequent geological analysis and construction optimization. This data can be used to assess the long-term stability of the surrounding rock, optimize grouting path design, and improve the safety and scientific rigor of subsequent construction.

[0145] The intelligent grouting system based on dynamic pressure monitoring solves the safety hazards and construction quality problems caused by inaccurate pressure control in traditional grouting methods by monitoring and adjusting the grouting pressure in real time. This system can significantly improve the reinforcement effect of the surrounding rock in tunnels while reducing engineering risks caused by overpressure, providing a safe and reliable solution for tunnel construction under complex geological conditions.

[0146] Implementation Method 2: In tunnel reinforcement projects with complex geological conditions, the rheological properties of grouting materials directly affect the grouting effect and the stability of the surrounding rock. Different geological conditions have different requirements for the fluidity, viscosity, and setting time of the grout. Therefore, in actual construction, a single-component grout often cannot meet all complex geological conditions. This implementation method proposes an adaptive optimization method for grouting materials based on geological analysis results and real-time monitoring data. By dynamically adjusting the material ratio, it ensures a high degree of matching between the grout performance and the actual geological conditions, thereby improving the grouting effect and reducing construction risks.

[0147] First, for different geological structures, the system uses geological exploration technology to obtain key parameters such as the distribution of fractures, porosity, and permeability of the surrounding rock. This data provides a scientific basis for the initial selection of materials. For example, in areas of highly permeable surrounding rock, selecting a low-viscosity grout ensures that the grout can fully penetrate into the depths of the fractures, achieving a good filling effect; while in areas with larger fractures but lower permeability, a grout with higher viscosity and rapid solidification is required to avoid grout leakage.

[0148] Secondly, during the actual grouting process, the system monitors the grout flow in real time using pressure and flow sensors. If the system detects that the grout is flowing too fast in a certain area and has not solidified effectively, it may indicate that the grout viscosity is too low, leading to an increased risk of leakage. In this case, the system will automatically adjust the material ratio, for example, by increasing the binder component in the grout to increase its viscosity, thereby slowing down the grout flow rate and ensuring effective filling of the cracks. Conversely, if the system detects that the grout is flowing slowly and solidifying too quickly, which may lead to uneven filling, the system will automatically reduce the proportion of accelerator to increase the grout fluidity and ensure that the cracks are completely filled.

[0149] To achieve adaptive adjustment of material proportions, the system incorporates an intelligent algorithm module that automatically generates optimized solutions based on geological data and real-time monitoring results. This algorithm employs machine learning technology to continuously learn the correlation between geological characteristics and grouting effects, thereby continuously optimizing the rheological properties of the grout. Through this dynamic adjustment mechanism, the system can adaptively adjust the material proportions under different geological conditions, achieving precise matching of grouting materials, thus improving grouting quality and the long-term stability of the surrounding rock.

[0150] An adaptive optimization method for the rheological properties of grouting materials, by dynamically adjusting the material ratio, ensures that the grout performance is highly matched to complex geological conditions, overcoming the limitations of traditional grouting materials with a single ratio. This method not only improves the safety and stability of the grouting process but also effectively reduces material waste and construction costs, providing a more flexible and efficient solution for tunnel reinforcement projects.

[0151] Implementation Method 3: One of the biggest challenges in tunnel grouting reinforcement projects under complex geological conditions is how to achieve precise pressure control in a dynamically changing geological environment. Traditional grouting control systems typically rely on fixed pressure thresholds and simple manual adjustments, which are insufficient to cope with real-time pressure fluctuations. This implementation method proposes an intelligent pressure control method based on adaptive modules. By embedding adaptive modules, the system can automatically adjust its pressure regulation strategy according to real-time data, achieving dynamic optimization control of grouting pressure.

[0152] First, an adaptive module is embedded in the control system. This module uses a dynamic correction algorithm to identify the pressure change trend of the surrounding rock and the grout flow state based on data collected by pressure sensors. For example, when the system detects a rapid increase in pressure approaching the bearing limit of the surrounding rock, the adaptive module automatically reduces the grouting speed or closes some valves to avoid the risk of surrounding rock fracturing due to excessive pressure. Conversely, when the pressure drops too quickly, the module automatically increases the valve opening to ensure the continuity and stability of the grouting process.

[0153] Secondly, the adaptive module can continuously learn and optimize grouting control strategies. Through the analysis of historical data, the system gradually establishes a geological response characteristic model and identifies pressure change patterns under different geological conditions. For example, the system can identify areas with certain special geological structures that are prone to sudden pressure changes and adjust control strategies in advance to avoid sudden situations. As data accumulates, the decision-making ability of the adaptive module also continuously improves, enabling the system to more accurately predict and control pressure fluctuations.

[0154] Finally, the adaptive module and control system work together to form a dynamic closed-loop adjustment system, ensuring real-time optimization of grouting pressure. During grouting, the system continuously adjusts the threshold and adjustment range based on real-time monitoring data, avoiding construction risks caused by untimely pressure control. Simultaneously, the system has an early warning function; when abnormal pressure fluctuations are detected, it can immediately alert construction personnel, improving the safety and reliability of the construction process.

[0155] The adaptive module-driven intelligent pressure control implementation method achieves precise control and optimization of grouting pressure through real-time data analysis and dynamic adjustment. This method effectively solves the problem of insufficient response in traditional control systems, ensuring the stability of the grouting process and the long-term safety of the surrounding rock, and providing an intelligent and automated solution for tunnel reinforcement projects under complex geological conditions.

[0156] This invention, by introducing a high-response pressure sensor, a fast-response control valve, and an adaptive module, enables the grouting system to achieve real-time monitoring and dynamic pressure adjustment, effectively preventing surrounding rock fracturing and grout leakage caused by overpressure. The system can automatically optimize the pressure adjustment path based on real-time data, quickly responding to sudden changes even under complex geological conditions, ensuring the long-term stability of the surrounding rock. The continuous optimization capability of the dynamic correction algorithm allows the system to learn and adapt to different geological features, improving the safety of the construction process, reducing the risk of safety accidents such as collapses during tunnel reinforcement, and providing reliable data support and scientific basis for subsequent geological monitoring and maintenance.

[0157] This invention, through adaptive optimization of the rheological properties of grouting materials, dynamically adjusts the viscosity, fluidity, and setting time of the grout based on real-time monitoring data. This ensures the grout performance is highly matched to geological conditions, significantly improving the grouting filling effect. This dynamic adjustment avoids material waste and grouting failure, effectively reducing the risk of grout leakage, especially in highly permeable areas. Simultaneously, the intelligent control system significantly reduces manual intervention and debugging time, improving construction efficiency and lowering labor and material costs. By shortening the construction cycle, reducing repetitive operations, and minimizing material waste, overall project costs are effectively controlled, making tunnel reinforcement under complex geological conditions more efficient and economical.

[0158] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0159] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0160] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0161] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0163] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0167] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for grouting and reinforcing the surrounding rock of roadways suitable for complex geological conditions, characterized in that, Includes the following steps: Multiple geological exploration technologies are used to obtain the non-uniform characteristics and structural parameters of the geological conditions around the tunnel, and sensors are deployed to monitor potential dynamic changes in real time during the grouting process, providing basic data for subsequent grouting control; Based on the geological condition analysis results, grouting materials suitable for complex geological structures are selected, and their rheological properties are adjusted to ensure that the grout can effectively fill the surrounding rock fissures under grouting pressure without causing excessive leakage or solidification. Install high-response pressure sensors and fast-response control valves in the grouting system to build a hardware control platform for real-time adjustment of grouting pressure, ensuring that pressure changes can be quickly transmitted and adjusted to the target value; The grouting control system is initialized and configured. Geological response characteristics are recorded through grouting experiments. Initial response threshold and pressure adjustment range are set. An adaptive module with dynamic correction function is embedded in the grouting control system. A pressure adjustment algorithm based on geological dynamic feedback is designed to analyze monitoring data in real time and optimize the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure. The system employs intelligent control algorithms to autonomously determine grouting pressure and flow adjustment strategies based on sudden changes in monitoring data. This ensures a rapid response to sudden pressure fluctuations, minimizing pressure adjustment time and reducing the risk of surrounding rock fracture and grouting failure. Based on the geological condition analysis results, the following steps were taken to select grouting materials suitable for complex geological structures and adjust their rheological properties to ensure that the grout can effectively fill the surrounding rock fissures under grouting pressure without causing excessive leakage or solidification: Select grouting materials based on geological survey results to ensure their performance is suitable for complex geological conditions; By adjusting the viscosity, fluidity, and setting time of the slurry, the rheological properties of the material are optimized to meet construction requirements; The material properties are verified through simulation tests to ensure a high degree of compatibility with geological conditions; During construction, material ratios and grouting parameters are adjusted in real time to dynamically adapt to changes in geological conditions and ensure reinforcement effectiveness. The specific steps for initializing and configuring the grouting control system, recording geological response characteristics through grouting experiments, setting the initial response threshold and pressure adjustment range, and embedding an adaptive module with dynamic correction function into the grouting control system are as follows: By recording the pressure response characteristics of the surrounding rock through experimental grouting, the initial pressure range and adjustment parameters are set to ensure safety in the early stage of construction. A nonlinear prediction model is constructed to automatically optimize the pressure regulation path based on real-time data to avoid overpressure. An integrated adaptive module dynamically adjusts the initial response parameters to adapt to the changing needs of complex geological conditions. The adaptive module and hardware platform work together to dynamically optimize the pressure control strategy and improve grouting stability and efficiency. The specific steps for designing a pressure adjustment algorithm based on geological dynamic feedback, analyzing monitoring data in real time, and optimizing the grouting pressure adjustment path through model fitting and prediction functions to avoid overpressure are as follows: Based on monitoring data, a dynamic model describing the geological response was established to capture real-time change characteristics. The expression of the dynamic model is as follows: In the formula, It is a state vector, including the current surrounding rock stress. , fracture aperture and slurry flow rate , It is the state vector for the next time step. It is the control input vector. It's process noise. It is the state transition matrix. It is a control matrix; Based on the dynamic model output, the grouting pressure change rate and its deviation from the surrounding rock bearing capacity threshold are calculated. The calculation expression is as follows: In the formula, It is the grouting pressure adjustment amount. It is the bearing capacity limit of the surrounding rock. It is the current surrounding rock stress. It is the surrounding rock stress adjustment coefficient. Critical aperture for crack propagation It is the crack aperture adjustment coefficient. This is the current slurry flow rate. It is the target slurry flow rate. It is the slurry flow rate adjustment coefficient; The intelligent control algorithm autonomously determines grouting pressure and flow adjustment strategies based on sudden changes in monitoring data, ensuring rapid response to sudden pressure fluctuations and minimizing pressure adjustment time, thereby reducing the risk of surrounding rock fracture and grouting failure. The specific steps are as follows: To ensure timely response to sudden pressure changes during the grouting control process, the monitoring data was first preprocessed and features extracted. To eliminate the influence of noise and outliers, a weighted moving average method was used to smooth the data. Simultaneously, the pressure fluctuation amplitude within each monitoring cycle was extracted, and the calculation expression is as follows: In the formula, It is the amplitude of pressure fluctuation. This is the total number of sampling points. These are weighting coefficients. It is the first Pressure values ​​at each sampling point It is the average pressure value Based on the parameters extracted from the features, a predictive model for sudden pressure changes is established using a recurrent neural network. The model is based on the current pressure fluctuation amplitude. Based on the changing trend of the surrounding rock deformation rate, the pressure change in the next time period can be predicted using the following formula: In the formula, It is the predicted pressure value for the next time period. yes , It is the deformation rate of the surrounding rock in the current time period. It is the set of weight parameters of the model. It is a recurrent neural network model; Based on the predicted pressure change value Given the current flow rate, an optimization algorithm is used to calculate the optimal pressure regulation strategy. An optimization method based on Lagrange multipliers is employed to determine the target pressure value and flow rate adjustment range during grouting, thereby minimizing the pressure regulation time. The formula is as follows: In the formula, This is the minimum flow rate adjustment range. It refers to the range of flow adjustment. It is the target pressure value. It is a regularization parameter used to balance the adjustment speed and the magnitude of flow rate changes; Finally, the calculated target pressure value will be... and flow adjustment range Applied to grouting control, it generates specific valve control commands. Employing a fuzzy control algorithm, it dynamically adjusts the valve opening based on the valve's response speed and initial opening, achieving precise pressure control. The formula for generating the control commands is as follows: In the formula, This is the initial valve opening. It is the valve opening angle. Pressure regulation coefficient, This is the current actual pressure value. It is the flow regulation coefficient, which controls the effect of flow adjustment on valve opening.

2. The method for grouting and reinforcing surrounding rock of tunnels suitable for complex geological conditions according to claim 1, characterized in that, The specific steps for obtaining the non-uniform characteristics and structural parameters of the geological conditions around the tunnel using various geological exploration technologies, and for monitoring potential dynamic changes during grouting in real time through sensor deployment to provide basic data for subsequent grouting control are as follows: By using comprehensive geological exploration methods, we can fully obtain the heterogeneous characteristics and key geological parameters of the surrounding rock of the tunnel. Deploy and calibrate multiple sensors in key geological areas to build a precise real-time monitoring network; Collect real-time data on the surrounding rock and generate a three-dimensional geological model to dynamically analyze potential risk areas; Establish a real-time feedback mechanism to optimize grouting control and provide reference for future construction.

3. The method for grouting and reinforcing surrounding rock of tunnels suitable for complex geological conditions according to claim 1, characterized in that, The specific steps for achieving real-time, high-precision monitoring of grouting pressure through the appropriate selection, precise placement, and calibration of high-response pressure sensors are as follows: Geological analysis was used to determine key monitoring areas for deploying pressure sensors and optimize the spatial coverage of data acquisition. Select sensors with high precision, fast response and high pressure resistance to adapt to complex geological conditions; The sensors are calibrated and dynamically tested to ensure measurement accuracy and stability in complex environments; By integrating sensors into the hardware control platform, a real-time monitoring and early warning system can be built to achieve a closed loop of data acquisition and analysis.

4. The method for grouting and reinforcing surrounding rock of tunnels suitable for complex geological conditions according to claim 3, characterized in that, The specific steps for constructing an efficient dynamic pressure regulation mechanism to adapt to complex geological conditions through the installation and integration of rapid response control valves are as follows: The control valve installation scheme is designed based on the characteristics of the grouting material and the monitoring layout to ensure maximum regulation efficiency. Select control valves that are fast-responding, corrosion-resistant, and have low flow resistance to meet dynamic pressure regulation requirements; During the commissioning phase, test the valve's response capability and adjustment accuracy, and optimize its performance parameters; A linkage mechanism between control valves and pressure sensors is established to form a dynamic closed-loop regulation system to adapt to real-time changes.

5. The method for grouting and reinforcing surrounding rock of tunnels suitable for complex geological conditions according to claim 1, characterized in that, By combining real-time pressure adjustment, the pressure distribution along the grouting path is optimized to ensure uniform and stable pressure. The optimization formula is as follows: In the formula, Location on the path Optimization pressure at the location It is the initial grouting pressure. It is the attenuation coefficient. It is the magnification factor; By combining the optimized path pressure with geological feedback, a closed-loop pressure control system is constructed to achieve real-time adjustment, as shown in the following formula: In the formula, It is the surrounding rock stress feedback gain coefficient, reflecting the influence of surrounding rock stress on pressure regulation. It is the crack aperture feedback gain coefficient. It is the flow rate feedback gain coefficient. It is the grouting pressure for the next time step.

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