Roadway surrounding rock grouting reinforcement method suitable for complex geological conditions

Abstract

The invention discloses a roadway surrounding rock grouting reinforcement method suitable for complex geological conditions, and relates to the technical field of surrounding rock grouting reinforcement. Potential dynamic changes in the grouting process are monitored in real time through sensor arrangement, and basic data are provided for follow-up grouting control. By introducing the high-response pressure sensor, the quick-response control valve and the self-adaptive module, the grouting system achieves real-time monitoring, dynamic pressure adjustment and self-adaptive material optimization, the grouting filling effect is improved, the surrounding rock fracture and grout leakage risks are reduced, the construction safety and the surrounding rock stability are ensured, and the grouting efficiency is improved. And meanwhile, manual intervention is effectively reduced, the construction period is shortened, the engineering cost is reduced, and roadway reinforcement under the complex geological condition is more efficient, economical and reliable.

Description

Technical Field

[0001] The invention relates to the technical field of surrounding rock grouting reinforcement, and in particular to a tunnel surrounding rock grouting reinforcement method suitable for complex geological conditions. Background Art

[0002] Grouting reinforcement of tunnel surrounding rock suitable for complex geological conditions refers to an engineering method that implements grouting technology around the rock or soil area (i.e., surrounding rock) surrounding the tunnel in complex geological conditions to enhance its stability and bearing capacity. Complex geological conditions may include faults, fracture zones, high groundwater pressure, weak layers, or surrounding rock prone to collapse. By injecting specific slurries (such as cement slurry, polyurethane slurry, etc.) into the cracks or pores of the surrounding rock, it solidifies to form a more compact and stronger structure, reducing surrounding rock deformation and inhibiting water infiltration, thereby ensuring the safety and stability of the tunnel construction and subsequent use.

[0003] The existing technology has the following deficiencies:

[0004] During the grouting reinforcement process of tunnel surrounding rock, insufficient control system response speed may lead to instantaneous loss of control of grouting pressure, leading to serious consequences. Under complex geological conditions, the heterogeneity of geological structures and the dynamic changes in the fluidity of grouting materials require the control system to be able to quickly adjust the pressure. However, when the control system response speed cannot meet the real-time adjustment needs, sudden fluctuations in pressure may not be controlled in time, causing the grouting pressure to instantly exceed the bearing limit of the surrounding rock. The result may be serious problems such as surrounding rock rupture, slurry leakage, and even inducing local collapse, which not only endangers the stability of the tunnel but also poses a direct threat to construction safety. Improving the response speed of the control system and its ability to adapt to sudden changes are the key to resolving this hidden danger.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a tunnel surrounding rock grouting reinforcement method 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 the problems of surrounding rock rupture and slurry leakage caused by overpressure, and improving the safety and long-term stability of tunnel reinforcement. At the same time, through adaptive optimization of the rheological properties of the grouting material, the system can dynamically adjust the slurry properties to adapt to complex geological conditions, reduce material waste and manual intervention, significantly improve construction efficiency, shorten construction period, and reduce project costs, thereby realizing an efficient and economical tunnel surrounding rock reinforcement solution to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a tunnel surrounding rock grouting reinforcement method suitable for complex geological conditions, comprising the following steps:

[0008] Utilize a variety of geological exploration technologies to obtain the heterogeneous characteristics and structural parameters of the geological conditions around the roadway, and use sensors to monitor potential dynamic changes during the grouting process in real time, providing basic data for subsequent grouting control;

[0009] Based on the results of geological condition analysis, select grouting materials that are suitable for complex geological structures and adjust their rheological properties to ensure that the grout can effectively fill the surrounding rock cracks under the grouting pressure without causing excessive leakage and 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] Initialize and configure the grouting control system, record geological response characteristics through grouting experiments, set the initial response threshold and pressure adjustment range, and embed an adaptive module with dynamic correction function into the grouting control system;

[0012] Design a pressure adjustment algorithm based on geological dynamic feedback, analyze monitoring data in real time, and optimize the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure;

[0013] Adopting intelligent control algorithms, the system can independently determine the grouting pressure and flow adjustment strategies based on sudden changes in monitoring data, ensuring a quick response to sudden pressure fluctuations and minimizing the pressure adjustment time, thereby reducing the risk of surrounding rock rupture and grouting failure.

[0014] Preferably, the specific steps of using a variety of geological exploration technologies to obtain the heterogeneous characteristics and structural parameters of the geological conditions around the tunnel, and using sensors to monitor the potential dynamic changes during the grouting process in real time to provide basic data for subsequent grouting control are as follows:

[0015] Comprehensive geological exploration methods are used to comprehensively obtain the heterogeneous characteristics and key geological parameters of the tunnel surrounding rock;

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

[0017] Collect real-time data of surrounding rocks and generate 3D geological models to dynamically analyze potential risk areas;

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

[0019] Preferably, based on the results of geological condition analysis, the grouting material that is suitable for the complex geological structure is selected and its rheological properties are adjusted to ensure that the slurry can effectively fill the surrounding rock cracks under the grouting pressure without causing excessive leakage and solidification. The specific steps are as follows:

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

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

[0022] Verify material properties through simulation tests to ensure their high compatibility with geological conditions;

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

[0024] Preferably, the specific steps for achieving real-time high-precision monitoring of grouting pressure by rationally selecting, accurately arranging and calibrating high-response pressure sensors are as follows:

[0025] Determine the placement of pressure sensors in key monitoring areas through geological analysis to optimize the spatial coverage of data collection;

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

[0027] Calibrate and dynamically test sensors to ensure measurement accuracy and stability in complex environments;

[0028] Integrate sensors into the hardware control platform to build a real-time monitoring and early warning system to achieve a closed loop of data collection and analysis.

[0029] Preferably, the specific steps for constructing an efficient dynamic pressure regulation mechanism to adapt to complex geological conditions by installing and integrating a fast-response control valve are as follows:

[0030] Design control valve installation scheme based on grouting material characteristics and monitoring layout to ensure maximum regulation efficiency;

[0031] Select control valves with fast response, corrosion resistance and low flow resistance to meet dynamic pressure regulation requirements;

[0032] During the commissioning phase, the valve response capability and adjustment accuracy are tested to optimize its performance parameters;

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

[0034] Preferably, the specific steps of 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 in the grouting control system are as follows:

[0035] Through experimental grouting, the pressure response characteristics of the surrounding rock are recorded, and the initial pressure range and adjustment parameters are set to ensure safety in the initial stage of construction;

[0036] Build a nonlinear prediction model to automatically optimize the pressure regulation path based on real-time data to avoid overpressure;

[0037] Integrated adaptive module to dynamically adjust initial response parameters to adapt to changing requirements of complex geological conditions;

[0038] Realize the linkage between the adaptive module and the hardware platform, dynamically optimize the pressure control strategy, and improve the stability and efficiency of grouting.

[0039] Preferably, a pressure adjustment algorithm based on geological dynamic feedback is designed, monitoring data is analyzed in real time, and the grouting pressure adjustment path is optimized through fitting models and prediction functions to avoid the occurrence of overpressure. The specific steps are as follows:

[0040] Based on the monitoring data, a dynamic model describing the geological response is established to capture the real-time change characteristics. The dynamic model expression 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 rate v(t), X(t+1) is the state vector at the next time, U(t) is the control input vector, W(t) is the process noise, a is the state transfer 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] Where ΔP(t) is the grouting pressure adjustment, σ max is the bearing limit of surrounding rock, σ(t) is the current surrounding rock stress, k1 is the surrounding rock stress adjustment coefficient, δ(t) is the current crack opening, δ crit is the critical opening of crack expansion, k2 is the crack opening adjustment coefficient, v(t) is the current slurry flow rate, v crit is the target slurry flow rate, and k3 is the slurry flow rate adjustment coefficient.

[0044] Preferably, the pressure distribution of the grouting path is optimized in combination with the real-time pressure adjustment amount to make the pressure uniform and stable. 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] The optimized path pressure is combined with geological feedback to build a closed-loop pressure control to achieve real-time adjustment. The formula is as follows:

[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 to sudden pressure fluctuations and minimizing the pressure adjustment time, thereby reducing the risk of surrounding rock fracture and grouting failure. The specific steps are as follows:

[0049] To ensure that the grouting control process responds to sudden pressure changes in a timely manner, the monitoring data is first preprocessed and features are extracted. In order to eliminate the influence of noise and outliers, the weighted moving average method is used to smooth the data. At the same time, the pressure fluctuation amplitude within each monitoring cycle is extracted. The calculation expression is as follows:

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

[0051] Based on the feature extraction parameters, a recursive neural network is used to establish a prediction model for sudden pressure changes. 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] Where, is the predicted pressure value for the next time period, ΔP t is the pressure fluctuation amplitude of the current time period, ΔD t 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 recursive neural network model.

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

[0054] Where, is the minimum flow adjustment range, ΔQ is the flow adjustment range, P target is the target pressure value, ω is the regularization parameter used to balance the adjustment speed and flow rate variation;

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

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

[0057] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0058] By introducing high-response pressure sensors, fast-response control valves, and adaptive modules, the present invention enables the grouting system to achieve real-time monitoring and dynamic pressure adjustment, effectively avoiding the problems of surrounding rock rupture and slurry leakage caused by overpressure. The system can automatically optimize the pressure adjustment path based on real-time data, and can quickly respond to sudden changes even under complex geological conditions to ensure the long-term stability of the surrounding rock. The continuous optimization capability of the dynamic correction algorithm enables the system to learn and adapt to different geological characteristics, improve the safety of the construction process, reduce the risk of safety accidents such as collapse during tunnel reinforcement, and provide reliable data support and scientific basis for subsequent geological monitoring and maintenance.

[0059] This invention adaptively optimizes the rheological properties of the grouting material. The system dynamically adjusts the viscosity, fluidity, and setting time of the slurry based on real-time monitoring data, ensuring a high degree of match between the slurry properties and the geological conditions, significantly improving the grouting filling effect. This dynamic adjustment avoids material waste and grouting failures, effectively reducing the risk of slurry leakage in highly permeable areas. At the same time, the intelligent control system significantly reduces manual intervention and debugging time, improves construction efficiency, and reduces labor and material costs. By shortening the construction period, reducing repetitive operations, and reducing material waste, the overall project cost is effectively controlled, making tunnel reinforcement in complex geological conditions more efficient and economical. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 The present invention is a flow chart of a method for grouting reinforcement of tunnel surrounding rocks suitable for complex geological conditions. DETAILED DESCRIPTION

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

[0063] The present invention provides Figure 1 A tunnel surrounding rock grouting reinforcement method suitable for complex geological conditions includes the following steps:

[0064] Utilize a variety of geological exploration technologies (such as seismic wave surveys, geological drilling, or radar imaging) to obtain the heterogeneous characteristics and structural parameters of the geological conditions around the roadway, and use sensors to monitor potential dynamic changes during the grouting process in real time, providing basic data for subsequent grouting control;

[0065] The specific steps for using various geological exploration technologies (such as seismic wave surveys, geological drilling, or radar imaging) to obtain the heterogeneous characteristics and structural parameters of the geological conditions around the roadway, and to monitor potential dynamic changes during the grouting process in real time through sensor deployment, to provide basic data for subsequent grouting control, are as follows:

[0066] Comprehensive geological exploration methods are used to comprehensively obtain the heterogeneous characteristics and key geological parameters of the tunnel surrounding rock;

[0067] Based on the geological complexity of the tunnel, appropriate geological exploration methods are selected, such as seismic surveys, geological drilling, or radar imaging. First, potential hazardous areas in the tunnel and the scope of the grouting area are determined through on-site investigation. Seismic surveys can identify the heterogeneity and fracture locations of geological layers by analyzing the propagation speeds of reflected and refracted waves. Geological drilling can provide high-precision core samples to directly assess the strength, bedding distribution, and porosity of the surrounding rock. Radar imaging obtains high-resolution images of underground structures 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 an accurate real-time monitoring network;

[0069] A monitoring network is established by deploying a variety of sensors in key geological areas, including pressure sensors, displacement sensors, temperature and humidity sensors, and permeability monitoring instruments. Sensor placement should be selected based on detection results. For example, denser sensor placement should be applied near faults, zones with dense fractures, or areas with high porosity to accurately capture dynamic changes. After deployment, the equipment is calibrated to eliminate interference from environmental noise, and the data sampling and transmission frequencies are set to ensure the timeliness and accuracy of the monitoring data.

[0070] Collect real-time data of surrounding rocks and generate 3D geological models to dynamically analyze potential risk areas;

[0071] 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, permeability, and temperature and humidity variations. This data is transmitted to a 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 specific area, a preliminary assessment can be made of the risk of rupture. Through dynamic data processing, a three-dimensional model of the geological features is generated, clearly showing the distribution of fractures in the surrounding rock, its bearing capacity, and potential hazardous areas.

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

[0073] The data collected by the sensors is linked to the control system to establish a real-time feedback mechanism. For example, when the pressure in a certain area approaches the bearing capacity of the surrounding rock, the system can use early warning functions to alert construction personnel and adjust the grouting pressure and flow rate to prevent surrounding rock fracture. Dynamic processing of real-time data supports decision-making optimization during the subsequent grouting process, including pressure regulation, material selection, and grouting path planning. Furthermore, monitoring data is archived to provide a reference for future tunnel construction under similar geological conditions, thereby improving the application level and efficiency of geological monitoring technology.

[0074] Based on the results of geological condition analysis, select grouting materials that are suitable for complex geological structures and adjust their rheological properties to ensure that the grout can effectively fill the surrounding rock cracks under the grouting pressure without causing excessive leakage and solidification;

[0075] Based on the geological analysis results, the grouting materials that are suitable for the complex geological structure are selected and their rheological properties are adjusted to ensure that the grout can effectively fill the surrounding rock cracks under the grouting pressure without causing excessive leakage and solidification. The specific steps are as follows:

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

[0077] Geological data acquired through geological exploration (such as fracture distribution, porosity, and permeability) provides the basis for grouting material selection. Grouting materials with specific properties are selected for different geological characteristics. For example, low-viscosity grouting can be used for high-permeability surrounding rock, while high-viscosity, fast-setting materials are required for rock formations with large fractures. Combined with real-time monitoring data, the grouting fluid's adaptability in the actual environment is evaluated to ensure that the material properties meet the construction requirements under complex geological conditions, laying the foundation for subsequent grouting results.

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

[0079] Based on the selected grouting material's characteristics and geological conditions, the material's rheological properties, including viscosity, fluidity, and setting time, should be adjusted. For example, adding a water-reducing agent can reduce grout viscosity and improve fluidity, ensuring it can fully fill cracks. Alternatively, adding an accelerator can adapt to highly permeable geological conditions and prevent grout leakage. The optimized material should exhibit good stability within the grouting pressure range, meeting construction requirements while preventing grouting failures due to insufficient rheological properties.

[0080] Verify material properties through simulation tests to ensure their high compatibility with geological conditions;

[0081] The optimized grouting material was tested for adaptability in a test environment simulating tunnel geological conditions. Grouting was conducted under varying pressures and geological models to observe the grout's filling efficiency, permeability, and setting properties. Based on these test results, the material formulation was further adjusted to ensure its reliability during construction. Furthermore, the test data was compared and verified with real-time data collected by the geological monitoring system to optimize the compatibility of the material with the geological conditions, providing data support for practical application.

[0082] Real-time adjustment of material ratios and grouting parameters during construction to dynamically adapt to changes in geological conditions to ensure reinforcement effectiveness;

[0083] During the actual grouting process, dynamic data of the surrounding rock and the rheological behavior of the grouting material are monitored in real time, and the material ratio and grouting pressure are adjusted based on this feedback. For example, if sensors detect slurry leakage or abnormal pressure fluctuations, the slurry viscosity or solidification rate is immediately optimized to ensure that material properties consistently match the changing geological conditions. This dynamic adjustment not only enhances the uniformity and stability of 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 to achieve real-time and high-precision monitoring of grouting pressure through reasonable selection, precise arrangement and calibration of high-response pressure sensors are as follows:

[0086] Determine the placement of pressure sensors in key monitoring areas through geological analysis to optimize the spatial coverage of data collection;

[0087] Based on geological analysis and grouting material optimization, pressure sensor installation locations 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 arranged 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] Select a pressure sensor with high precision and fast response to ensure it can capture even tiny fluctuations in pressure in real time. For example, sensors based on piezoelectric principles or thin-film strain gauges can offer nanosecond response capabilities, accurately reflecting dynamic grouting pressure changes. Furthermore, the sensor must be resistant to high pressure and corrosion to accommodate complex geological conditions and grouting fluid characteristics.

[0090] Calibrate and dynamically test sensors to ensure measurement accuracy and stability in complex environments;

[0091] The sensor is calibrated before installation to ensure measurement accuracy and data transmission stability. This calibration process includes multi-point pressure testing and dynamic response testing to verify its adaptability to varying pressure ranges and rapid pressure changes. Furthermore, testing is performed in a simulated grouting environment to ensure the sensor's ability to operate properly in high-flow slurry and complex geological environments. Error limits and compensation methods are documented.

[0092] Integrate sensors into the hardware control platform to build a real-time monitoring and early warning system, and achieve a closed loop of data collection 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. Furthermore, a warning threshold is set. When the pressure exceeds the safe range, the system immediately triggers an alarm signal, providing a buffer for valve adjustment.

[0094] The specific steps to build an efficient dynamic pressure regulation mechanism to adapt to complex geological conditions by installing and integrating fast-response control valves are as follows:

[0095] Design control valve installation scheme based on grouting material characteristics and 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, a fast-response control valve installation plan was designed. The valves should be located close to critical grouting areas or locations with frequent pressure fluctuations to maximize pressure regulation efficiency. Furthermore, considering the characteristics of slurry flow, a valve design with low flow resistance and high-pressure resistance was selected to ensure a smooth grouting process.

[0097] Select control valves with fast response, corrosion resistance and low flow resistance to meet dynamic pressure regulation requirements;

[0098] Select control valves with millisecond-level response capabilities, such as solenoid valves or proportional control valves. These valves can quickly respond to control commands from the central system, enabling instant adjustment of grouting pressure. Furthermore, the valves must be corrosion-resistant and long-lasting, adaptable to complex slurry environments, and minimize slurry waste during pressure regulation through precise control.

[0099] During the commissioning phase, the valve response capability and adjustment accuracy are tested to optimize its performance parameters;

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

[0101] Build 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 central control system links sensors and valves to create a dynamic pressure regulation mechanism. When pressure sensors detect fluctuations in grouting pressure, the central system calculates and generates optimal adjustment parameters, controlling the valves to instantly adjust the pressure to the target range. Combined with the optimized rheological properties of the grouting material, this mechanism rapidly adapts to changing geological conditions, preventing surrounding rock fractures and slurry leakage caused by excessive pressure, and ensuring the stability and uniformity of the grouting process.

[0103] Initialize and configure the grouting control system, record geological response characteristics through grouting experiments, set the initial response threshold and pressure adjustment range, and embed an adaptive module with dynamic correction function into 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 in the grouting control system are as follows:

[0105] Through experimental grouting, the pressure response characteristics of the surrounding rock are recorded, and the initial pressure range and adjustment parameters are set to ensure safety in the initial stage of construction;

[0106] After hardware installation, leveraging the data acquisition capabilities of high-response pressure sensors and fast-response control valves, experimental grouting simulated actual geological conditions and recorded the pressure response characteristics of the surrounding rock. This involved testing the stress distribution, crack propagation, and slurry flow path of the surrounding rock at various grouting pressures to determine the initial threshold of the surrounding rock's bearing capacity. Based on these test results, the control system set the initial pressure range and adjustment parameters, including the pressure adjustment rate and upper alarm threshold, to ensure the grouting system effectively mitigated the risk of surrounding rock fracture during the initial construction phase.

[0107] Build a nonlinear prediction model 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 was established to identify changes in surrounding rock characteristics over time. For example, when the surrounding rock pressure is monitored to be approaching its bearing limit, the algorithm can automatically optimize the pressure regulation path based on historical data and real-time status to avoid overpressure. The dynamic correction algorithm requires the use of nonlinear prediction methods (such as Kalman filtering or Bayesian modeling) to adapt to the nonlinear changes of multivariate influencing factors in complex geological environments and provide a precise adjustment basis for grouting control.

[0109] Integrated adaptive module to dynamically adjust initial response parameters to adapt to changing requirements 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 a dynamic correction algorithm. This module continuously learns the geological response characteristics of the surrounding rock and the pressure variations during the grouting process, gradually adjusting the pressure regulation range, sensor sampling frequency, and valve response speed. This adaptive adjustment mechanism enables the control system to quickly adapt to changing requirements under complex geological conditions, achieving long-term stable and efficient pressure regulation.

[0111] Realize the linkage between the adaptive module and the hardware platform, dynamically optimize the pressure control strategy, and improve the stability and efficiency of grouting;

[0112] The adaptive module is linked to the hardware control platform to achieve closed-loop pressure control. The system collects sensor data in real time, adjusts initial response parameters through the adaptive module, and dynamically optimizes the valve opening and closing strategy. For example, if slurry leakage or a sudden pressure change is detected, the system can immediately adjust the pressure range and optimize valve operation 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 fracture and slurry waste, providing intelligent support for roadway reinforcement in complex geological conditions.

[0113] Design a pressure adjustment algorithm based on geological dynamic feedback, analyze monitoring data in real time (including pressure fluctuations, slurry flow rate, and surrounding rock stress changes), and optimize the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure.

[0114] Design a pressure adjustment algorithm based on geological dynamic feedback, analyze monitoring data in real time (including pressure fluctuations, slurry flow rate, and surrounding rock stress changes), and optimize the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure. The specific steps are as follows:

[0115] Based on the monitoring data (including surrounding rock stress, grouting pressure and slurry flow rate), a dynamic model describing the geological response is established to capture the real-time change characteristics. The dynamic model expression 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 rate v(t); X(t+1) is the state vector at the next time; U(t) is the control input vector; W(t) is the process noise, representing the random disturbance in the geological environment; A is the state transfer matrix, describing the dynamic relationship between the internal variables of the system; and B is the control matrix, describing the influence of the input variables on the state.

[0117] By collecting real-time data and performing model fitting, the initial values of key parameters can be obtained, such as the surrounding rock stress threshold σ max, target flow velocity vcrit and critical point of crack expansion 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] Where ΔP(t) is the grouting pressure adjustment, which means the increase or decrease adjustment value of the grouting pressure at time t, σ max is the bearing limit of the surrounding rock, σ(t) is the current surrounding rock stress, the actual pressure borne by the surrounding rock at time t, k1 is the surrounding rock stress adjustment coefficient, which represents the influence weight of the surrounding rock stress on the grouting pressure adjustment, δ(t) is the current crack opening, which represents the actual width of the crack at time t, δ crit is the critical opening of the crack expansion, which indicates the maximum opening allowed for the crack under stable conditions; k2 is the crack opening adjustment coefficient, which indicates the weight of the influence of the crack opening on the grouting pressure adjustment; v(t) is the current slurry flow rate, which indicates the flow rate of the slurry in the crack at time t; v crit is the target slurry flow rate, which indicates the optimal slurry flow rate expected under stable grouting conditions; k3 is the slurry flow rate adjustment coefficient, which indicates the weight of the influence of slurry flow rate on the grouting pressure adjustment amount;

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

[0121] Combined with the real-time pressure adjustment, the pressure distribution along the grouting path is optimized to make the pressure uniform and stable. 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 pressure with path position, and β is the amplification coefficient, which is used to adjust the influence of ΔP(t) on the path pressure distribution;

[0123] Through the optimization steps, the grouting pressure adjustment is combined with the path distribution to avoid the risk of surrounding rock rupture caused by excessive local pressure.

[0124] The optimized path pressure is combined with geological feedback to build a closed-loop pressure control to achieve real-time adjustment. The formula is as follows:

[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, which reflects the influence of surrounding rock stress on pressure regulation; η is the fracture aperture feedback gain coefficient, which describes the influence of fracture aperture change on pressure regulation; λ is the flow velocity feedback gain coefficient, which reflects the influence of slurry flow velocity change on pressure regulation; and P(t+1) is the grouting pressure in the next time step.

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

[0127] Adopting intelligent control algorithms, the system can independently determine the grouting pressure and flow adjustment strategies based on sudden changes in monitoring data, ensuring a quick response to sudden pressure fluctuations and minimizing the pressure adjustment time, thereby reducing the risk of surrounding rock fracture and grouting failure.

[0128] Adopting intelligent control algorithms, we can autonomously determine 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. Specific steps are as follows:

[0129] To ensure that the grouting control process responds to sudden pressure changes in a timely manner, the monitoring data is first preprocessed and feature extracted. The raw data collected by the sensor in real time includes pressure values, flow values, displacement changes, and temperature changes. In order to eliminate the influence of noise and outliers, the weighted sliding average method is used to smooth the data. At the same time, the pressure fluctuation amplitude within each monitoring cycle is extracted. The calculation expression is as follows:

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

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

[0132] Based on the parameters extracted from the features, a recurrent neural network (RNN) is used to establish a prediction model for sudden pressure changes. The model predicts the pressure changes 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. The formula is as follows:

[0133] Where, is the predicted pressure value for the next time period, ΔPt is the pressure fluctuation amplitude of the current time period, ΔD t 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 recursive neural network model;

[0134] Calculated by the prediction model Parameters will be used in the subsequent decision-making process of pressure regulation strategy.

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

[0136] Where, is the minimum flow adjustment range, ΔQ is the flow adjustment range, P target is the target pressure value, ω is the regularization parameter used to balance the adjustment speed and flow rate variation;

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

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

[0139] Where θ init is the initial valve opening, θ v is the valve opening angle, which determines the size of the grouting flow rate, k is the pressure adjustment coefficient, which is used to control the opening and closing amplitude of the valve, and P(t) is the current actual pressure value, which is obtained through real-time monitoring. 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, minimize the pressure adjustment time, thereby reducing the risk of surrounding rock rupture and grouting failure, and ensuring the stability and uniformity of the grouting process.

[0141] Implementation method 1: In the process of tunnel surrounding rock reinforcement under complex geological conditions, the control of grouting pressure is crucial. Since the distribution of surrounding rock cracks and changes in porosity cannot be fully predicted through simple static analysis, the traditional fixed pressure grouting method is often unable to cope with sudden changes in geological conditions, which may lead to problems such as overpressure, surrounding rock rupture or slurry leakage. In order to achieve more accurate and dynamic pressure control, this implementation method proposes an intelligent grouting system based on dynamic pressure monitoring. By combining 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 installs high-precision pressure sensors at key locations within the roadway to collect real-time data on surrounding rock pressure changes. These sensors utilize piezoelectric or strain gauge designs, offering millisecond-level response speeds and are capable of capturing subtle pressure fluctuations during the grouting process. Furthermore, to ensure data accuracy and transmission stability, the system incorporates a built-in data filtering module to eliminate environmental noise interference and ensure high reliability of the real-time monitoring data on pressure changes.

[0143] Secondly, the system's fast-response control valve automatically adjusts grouting pressure based on feedback from pressure sensors. If the pressure during grouting is detected as excessively high or low, the control valve reacts within milliseconds, adjusting its opening and closing status. For example, if the pressure sensor detects that the surrounding rock is approaching its bearing capacity, the control system immediately issues a command to reduce the valve opening, lowering the grouting pressure and thus preventing the risk of surrounding rock fracture. Conversely, if the pressure is too low, the system increases the valve opening to ensure that the slurry fully fills the surrounding rock cracks, thereby improving the reinforcement effect.

[0144] Finally, the 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 when grouting pressure exceeds a safe range, prompting construction personnel to take timely action. The system also stores 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 enhance the safety and scientific nature of subsequent construction.

[0145] The intelligent grouting system, based on dynamic pressure monitoring, addresses safety hazards and construction quality issues associated with inaccurate pressure control in traditional grouting methods by monitoring and adjusting grouting pressure in real time. This system significantly improves the reinforcement of surrounding rock in tunnels while reducing engineering risks associated with overpressure, providing a safe and reliable solution for tunnel construction in 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 surrounding rock stability. Different geological conditions have different requirements for the fluidity, viscosity, and setting time of the slurry. Therefore, in actual construction, a single slurry ratio is often difficult to take into account 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 that the slurry performance is highly matched with the actual geological conditions, thereby improving the grouting effect and reducing construction risks.

[0147] First, based on different geological structures, the system uses geological exploration technology to obtain key parameters of the surrounding rock, such as fracture distribution, porosity, and permeability. This data provides a scientific basis for preliminary material selection. For example, in areas with high permeability, choosing a low-viscosity slurry ensures that the slurry can fully penetrate deep into the fractures and achieve a good filling effect. In areas with larger fractures but lower permeability, a higher-viscosity, faster-setting slurry is required to prevent leakage.

[0148] Secondly, during the actual grouting process, the system monitors the flow of the slurry in real time through pressure sensors and flow sensors. If the system detects that the slurry is flowing too quickly in a certain area and failing to solidify effectively, it may indicate that the slurry viscosity is too low, leading to an increased risk of leakage. At this time, the system automatically adjusts the material ratio, such as increasing the binder component in the slurry to increase the slurry viscosity, thereby slowing the slurry flow rate and ensuring effective filling of the cracks. Conversely, if the system detects that the slurry 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 slurry's fluidity and ensure that the cracks are completely filled.

[0149] To achieve adaptive adjustment of material ratios, the system incorporates an intelligent algorithm module that automatically generates optimization plans based on geological data and real-time monitoring results. This algorithm leverages machine learning techniques to continuously learn the correlation between geological characteristics and grouting results, continuously optimizing the slurry's rheological properties. This dynamic adjustment mechanism allows the system to adaptively adjust material ratios under varying geological conditions, achieving precise matching of grouting materials, thereby improving grouting quality and the long-term stability of the surrounding rock.

[0150] This adaptive optimization of the rheological properties of grouting materials dynamically adjusts the material ratio to precisely match grouting properties to complex geological conditions, overcoming the limitations of traditional grouting materials, which rely on a single ratio. This approach 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 roadway reinforcement projects.

[0151] Implementation 3: One of the greatest challenges facing tunnel grouting reinforcement projects under complex geological conditions is achieving precise pressure control within a dynamically changing geological environment. Traditional grouting control systems typically rely on fixed pressure thresholds and simple manual adjustments, making them difficult to cope with real-time pressure fluctuations. This implementation proposes an intelligent pressure control method driven by an adaptive module. By embedding an adaptive module, the system automatically adjusts the pressure regulation strategy based on 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 pressure trends and slurry flow patterns in the surrounding rock based on data collected by pressure sensors. For example, if the system detects a rapid pressure increase approaching the rock's bearing capacity, the adaptive module automatically reduces the grouting rate or closes some valves to prevent the risk of rock fracture caused by excessive pressure. Conversely, if the pressure drops too quickly, the module automatically increases the valve opening to ensure continuity and stability of the grouting process.

[0153] Secondly, the adaptive module continuously learns and optimizes grouting control strategies. By analyzing historical data, the system gradually builds a geological response characteristic model and identifies patterns of pressure variation under different geological conditions. For example, the system can identify areas with unique geological structures prone to sudden pressure changes and proactively adjust control strategies to avoid unexpected situations. As data continues to accumulate, the adaptive module's decision-making capabilities continue to improve, 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 regulation system, ensuring real-time optimization of grouting pressure. During the grouting process, the system continuously adjusts the threshold and adjustment range based on real-time monitoring data, avoiding construction risks caused by untimely pressure control. Furthermore, the system has an early warning function that immediately alerts construction personnel when abnormal pressure fluctuations are detected, improving construction safety and reliability.

[0155] This adaptive module-driven intelligent pressure control implementation achieves precise control and optimization of grouting pressure through real-time data analysis and dynamic adjustment. This approach effectively addresses the responsiveness issues of traditional control systems, ensuring the stability of the grouting process and the long-term safety of the surrounding rock. It provides an intelligent, automated solution for tunnel reinforcement projects in complex geological conditions.

[0156] By introducing high-response pressure sensors, fast-response control valves, and adaptive modules, the present invention enables the grouting system to achieve real-time monitoring and dynamic pressure adjustment, effectively avoiding the problems of surrounding rock rupture and slurry leakage caused by overpressure. The system can automatically optimize the pressure adjustment path based on real-time data, and can quickly respond to sudden changes even under complex geological conditions to ensure the long-term stability of the surrounding rock. The continuous optimization capability of the dynamic correction algorithm enables the system to learn and adapt to different geological characteristics, improve the safety of the construction process, reduce the risk of safety accidents such as collapse during tunnel reinforcement, and provide reliable data support and scientific basis for subsequent geological monitoring and maintenance.

[0157] This invention adaptively optimizes the rheological properties of the grouting material. The system dynamically adjusts the viscosity, fluidity, and setting time of the slurry based on real-time monitoring data, ensuring a high degree of match between the slurry properties and the geological conditions, significantly improving the grouting filling effect. This dynamic adjustment avoids material waste and grouting failures, effectively reducing the risk of slurry leakage in highly permeable areas. At the same time, the intelligent control system significantly reduces manual intervention and debugging time, improves construction efficiency, and reduces labor and material costs. By shortening the construction period, reducing repetitive operations, and reducing material waste, the overall project cost is effectively controlled, making tunnel reinforcement in complex geological conditions more efficient and economical.

[0158] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0159] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0160] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0161] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0162] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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 clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

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

[0167] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A tunnel surrounding rock grouting reinforcement method suitable for complex geological conditions, characterized in that: The following steps are involved: Utilize a variety of geological exploration technologies to obtain the heterogeneous characteristics and structural parameters of the geological conditions around the roadway, and use sensors to monitor potential dynamic changes during the grouting process in real time, providing basic data for subsequent grouting control; Based on the results of geological condition analysis, select grouting materials that are suitable for complex geological structures and adjust their rheological properties to ensure that the grout can effectively fill the surrounding rock cracks under the grouting pressure without causing excessive leakage and 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; Initialize and configure the grouting control system, record geological response characteristics through grouting experiments, set the initial response threshold and pressure adjustment range, and embed an adaptive module with dynamic correction function into the grouting control system; Design a pressure adjustment algorithm based on geological dynamic feedback, analyze monitoring data in real time, and optimize the grouting pressure adjustment path through fitting models and prediction functions to avoid overpressure; Adopting intelligent control algorithms, the system can independently determine the grouting pressure and flow adjustment strategies based on sudden changes in monitoring data, ensuring a quick response to sudden pressure fluctuations and minimizing the pressure adjustment time, thereby reducing the risk of surrounding rock rupture and grouting failure.

2. The method for grouting reinforcement of tunnel surrounding rocks suitable for complex geological conditions according to claim 1, characterized in that: The specific steps for using various geological exploration technologies to obtain the heterogeneous characteristics and structural parameters of the geological conditions around the roadway and using sensor arrangements to monitor potential dynamic changes during the grouting process in real time to provide basic data for subsequent grouting control are as follows: Comprehensive geological exploration methods are used to comprehensively obtain the heterogeneous characteristics and key geological parameters of the tunnel surrounding rock; Deploy and calibrate multiple sensors in key geological areas to build an accurate real-time monitoring network; Collect real-time data of surrounding rocks and generate 3D geological models to dynamically analyze potential risk areas; Build a real-time feedback mechanism to optimize grouting control and provide reference for future construction.

3. The method for grouting reinforcement of tunnel surrounding rocks suitable for complex geological conditions according to claim 1, characterized in that: Based on the geological analysis results, the grouting materials that are suitable for the complex geological structure are selected and their rheological properties are adjusted to ensure that the grout can effectively fill the surrounding rock cracks under the grouting pressure without causing excessive leakage and solidification. The specific steps are as follows: Select grouting materials based on geological survey results to ensure their performance is suitable for complex geological conditions; By adjusting the slurry viscosity, fluidity and setting time, the rheological properties of the material can be optimized to meet construction requirements; Verify material properties through simulation tests to ensure their high compatibility with geological conditions; During construction, material ratios and grouting parameters are adjusted in real time to dynamically adapt to changes in geological conditions to ensure reinforcement effectiveness.

4. The method for grouting reinforcement of tunnel surrounding rocks suitable for complex geological conditions according to claim 1, characterized in that: The specific steps to achieve real-time and high-precision monitoring of grouting pressure through reasonable selection, precise arrangement and calibration of high-response pressure sensors are as follows: Determine the placement of pressure sensors in key monitoring areas through geological analysis to optimize the spatial coverage of data collection; Select sensors with high precision, fast response and high pressure resistance to adapt to complex geological conditions; Calibrate and dynamically test sensors to ensure measurement accuracy and stability in complex environments; Integrate sensors into the hardware control platform to build a real-time monitoring and early warning system to achieve a closed loop of data collection and analysis.

5. The tunnel surrounding rock grouting reinforcement method applicable to complex geological conditions according to claim 4, characterized in that: The specific steps to build an efficient dynamic pressure regulation mechanism to adapt to complex geological conditions by installing and integrating fast-response control valves are as follows: Design control valve installation scheme based on grouting material characteristics and monitoring layout to ensure maximum regulation efficiency; Select control valves with fast response, corrosion resistance and low flow resistance to meet dynamic pressure regulation requirements; During the commissioning phase, the valve response capability and adjustment accuracy are tested to optimize its performance parameters; Build a linkage mechanism between control valves and pressure sensors to form a dynamic closed-loop adjustment system to adapt to real-time changes.

6. The tunnel surrounding rock grouting reinforcement method applicable to complex geological conditions according to claim 1, characterized in that: 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 in the grouting control system are as follows: Through experimental grouting, the pressure response characteristics of the surrounding rock are recorded, and the initial pressure range and adjustment parameters are set to ensure safety in the initial stage of construction; Build a nonlinear prediction model to automatically optimize the pressure regulation path based on real-time data to avoid overpressure; Integrated adaptive module to dynamically adjust initial response parameters to adapt to changing requirements of complex geological conditions; Realize the linkage between the adaptive module and the hardware platform, dynamically optimize the pressure control strategy, and improve the stability and efficiency of grouting.

7. The method for grouting reinforcement of tunnel surrounding rocks suitable for complex geological conditions according to claim 1, characterized in that: 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: Based on the monitoring data, a dynamic model describing the geological response is established to capture the real-time change characteristics. The dynamic model expression is as follows: 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 rate v(t), X(t+1) is the state vector at the next time, U(t) is the control input vector, W(t) is the process noise, A is the state transfer matrix, and B is the 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: Where ΔP(t) is the grouting pressure adjustment, σ max is the bearing limit of surrounding rock, σ(t) is the current surrounding rock stress, k1 is the surrounding rock stress adjustment coefficient, δ(t) is the current crack opening, δ crit is the critical opening of crack expansion, k2 is the crack opening adjustment coefficient, v(t) is the current slurry flow rate, v crit is the target slurry flow rate, and k3 is the slurry flow rate adjustment coefficient.

8. The method for grouting reinforcement of tunnel surrounding rocks suitable for complex geological conditions according to claim 7, characterized in that: Combined with the real-time pressure adjustment, the pressure distribution along the grouting path is optimized to make the pressure uniform and stable. The optimization formula is as follows: 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; The optimized path pressure is combined with geological feedback to build a closed-loop pressure control to achieve real-time adjustment. The formula is as follows: 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.

9. The tunnel surrounding rock grouting reinforcement method applicable to complex geological conditions according to claim 1, characterized in that: Adopting intelligent control algorithms, we can autonomously determine 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. Specific steps are as follows: To ensure that the grouting control process responds to sudden pressure changes in a timely manner, the monitoring data is first preprocessed and features are extracted. In order to eliminate the influence of noise and outliers, the weighted moving average method is used to smooth the data. At the same time, the pressure fluctuation amplitude within each monitoring cycle is extracted. The calculation expression is as follows: Where ΔP is the pressure fluctuation amplitude, N is the total number of sampling points, and w i is the weighting coefficient, P i is the pressure value of the i-th sampling point, is the average pressure value Based on the feature extraction parameters, a recursive neural network is used to establish a prediction model for sudden pressure changes. 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: Where, is the predicted pressure value for the next time period, ΔP t is the pressure fluctuation amplitude of the current time period, ΔD t 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 recursive neural network model.

10. The tunnel surrounding rock grouting reinforcement method applicable to complex geological conditions according to claim 9, characterized in that: According to the predicted pressure change value The optimization algorithm is used to calculate the optimal pressure regulation strategy based on the current flow rate value. The Lagrange multiplier-based optimization method is used to determine the target pressure value and flow adjustment range during the grouting process, thereby minimizing the pressure regulation time. The formula is as follows: Where, is the minimum flow adjustment range, ΔQ is the flow adjustment range, P target is the target pressure value, ω is the regularization parameter used to balance the adjustment speed and flow rate variation; Finally, the calculated target pressure value P target The flow adjustment amplitude ΔQ is applied to grouting control to generate specific valve control instructions. The fuzzy control algorithm is used to dynamically adjust the valve opening in combination with the valve response speed and initial opening to achieve precise pressure control. The formula for generating the control instruction is as follows: Where θ init is the initial valve opening, θ v is the valve opening angle, k is the pressure adjustment coefficient, P(t) is the current actual pressure value, is the flow regulation coefficient, which controls the effect of flow adjustment on valve opening.

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