Tunnel support adaptive adjustment method and system based on rock mass-support feedback

Abstract

The present application provides a tunnel support adaptive adjustment method and system based on rock mass-support feedback, relating to the technical field of tunnel engineering support. The method collects surrounding rock strain data and stress data through a sensor network arranged in the surrounding rock of the tunnel to generate original monitoring data; calculates the strain energy density of the surrounding rock using a data processing unit; matches the strain energy density with the preset energy stage characteristics using an adaptive control algorithm to generate an energy stage matching result; determines the energy state stage and generates an energy stage identifier based on a stage confirmation module; generates a support mode switching instruction through a support mode mapping query; and finally adjusts the support structure working parameters by a support control unit, realizing real-time monitoring of the energy state of the surrounding rock and dynamic matching of the support parameters, effectively improving the adaptability and safety of the tunnel support, and reducing the lag of manual intervention.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering support, and in particular to a tunnel support adaptive adjustment method and system based on rock mass-support feedback. BACKGROUND

[0002] In the field of tunnel engineering, the stability of surrounding rock directly affects the safety and service life of the project. Traditional support methods usually rely on static design parameters and periodic manual monitoring, lacking real-time and dynamic feedback adjustment mechanisms. When the stress state of surrounding rock changes, such as strain energy accumulation due to geological condition fluctuations or construction disturbances, traditional methods cannot timely perceive and adjust support parameters, easily causing support structure lag response, local failure, and even tunnel collapse. In addition, fixed support mode is difficult to adapt to the stage changes of surrounding rock energy state, which may lead to resource waste or safety risks.

[0003] Therefore, there is an urgent need for an adaptive adjustment method based on real-time rock mass-support feedback, which can automatically monitor, analyze and respond to the energy state of surrounding rock to dynamically optimize support parameters, thereby improving the safety and efficiency of tunnel engineering. SUMMARY

[0004] To solve the above problems existing in the prior art, the present application provides a tunnel support adaptive adjustment method based on rock mass-support feedback, comprising:

[0005] S1: based on the safety monitoring results, collecting surrounding rock strain data and stress data through the sensor network arranged in the surrounding rock of the tunnel, and generating original monitoring data;

[0006] S2: based on the original monitoring data, calculating the strain energy density of the surrounding rock through the data processing unit arranged in the central control room, the data processing unit being connected with the sensor network;

[0007] S3: through the adaptive control algorithm deployed on the main processor of the control system, the adaptive control algorithm being connected with the data processing unit, matching the strain energy density with a plurality of preset energy stage characteristics to generate an energy stage matching result;

[0008] S4: based on the energy stage matching result, determining the energy state stage of the surrounding rock through the stage confirmation module integrated in the adaptive control algorithm, the stage confirmation module being connected with the main logic unit of the adaptive control algorithm, and generating an energy stage identifier;

[0009] S5: based on the energy stage identifier, generating a support mode switching instruction corresponding to the current energy state stage through the support mode mapping queryer connected in the adaptive control algorithm, the support mode mapping queryer being connected with the preset support mode mapping relationship database;

[0010] S6: Based on the support mode switching instruction, the support control unit arranged in the support control cabinet is used to adjust the working parameters of the support structure, and an updated support state is generated.

[0011] Compared with the prior art, the present application has the following advantages:

[0012] Firstly, in S1, the sensor network arranged in the surrounding rock of the tunnel is used to collect surrounding rock strain data and stress data to generate original monitoring data, which provides real-time and comprehensive surrounding rock state information for the system and lays the foundation for data-driven decision-making. Then, in S2, based on the original monitoring data, the data processing unit arranged in the central control room is used to calculate the strain energy density of the surrounding rock, which converts the original data into a quantifiable energy index, facilitating subsequent stage identification. Then, in S3, the adaptive control algorithm deployed on the main processor of the control system is used to match the strain energy density with the preset multiple energy stage characteristics to generate an energy stage matching result, which realizes the preliminary classification of the energy state of the surrounding rock and provides a basis for accurate adjustment. In S4, based on the energy stage matching result, the stage confirmation module integrated in the adaptive control algorithm is used to determine the current energy state stage of the surrounding rock to generate an energy stage identifier, which further refines the state judgment and ensures the accuracy of the decision. In S5, based on the energy stage identifier, the support mode mapping queryer connected in the adaptive control algorithm is used to generate a support mode switching instruction corresponding to the current energy state stage, so as to map the energy state to a specific support action and realize mode matching. Finally, in S6, based on the support mode switching instruction, the support control unit arranged in the support control cabinet is used to adjust the working parameters of the support structure to generate an updated support state, completing the closed-loop control from analysis to execution.

[0013] In the whole process, the sensor network, the data processing unit, the adaptive control algorithm and the support control unit are connected to each other to form a real-time feedback loop, which can dynamically respond to the changes of the surrounding rock, avoid support lag or over-reaction, significantly improve the adaptability and reliability of the tunnel support, and reduce manual intervention and resource waste. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0015] Figure 1Fig. 1 shows a flowchart of a tunnel support adaptive adjustment method based on rock mass-support feedback according to an embodiment of the present application.

[0016] Figure 2 Fig. 2 shows a structural diagram of a tunnel support adaptive adjustment system based on rock mass-support feedback according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0018] The specific embodiments of the present application will be described below.

[0019] Embodiment 1

[0020] As shown in Fig. 1, the present application provides a tunnel support adaptive adjustment method based on rock mass-support feedback, which comprises: Figure 1

[0021] S1: based on the safety monitoring result, collecting the surrounding rock strain data and stress data through the sensor network arranged in the surrounding rock of the tunnel, and generating the original monitoring data;

[0022] S2: based on the original monitoring data, calculating the strain energy density of the surrounding rock through the data processing unit arranged in the central control room, the data processing unit being connected with the sensor network;

[0023] S3: through the adaptive control algorithm deployed on the main processor of the control system, the adaptive control algorithm being connected with the data processing unit, matching the strain energy density with the preset multiple energy stage characteristics, and generating the energy stage matching result;

[0024] S4: based on the energy stage matching result, determining the energy state stage in which the surrounding rock is currently located through the stage confirmation module integrated in the adaptive control algorithm, the stage confirmation module being connected with the main logic unit of the adaptive control algorithm, and generating the energy stage identifier;

[0025] S5: based on the energy stage identifier, generating the support mode switching instruction corresponding to the current energy state stage through the support mode mapping queryer connected in the adaptive control algorithm, the support mode mapping queryer being connected with the preset support mode mapping relationship database;

[0026] ​S6: Based on the support mode switching instruction, the support control unit arranged in the support control cabinet is connected with the adaptive control algorithm, the working parameters of the support structure are adjusted, and the updated support state is generated.

[0027] In the implementation process of the tunnel support adaptive adjustment method based on rock mass-support feedback, the sensor network arranged in the tunnel surrounding rock is composed of various types of sensors, including strain gauges and stress gauges. These devices are embedded in the interior and surface of the surrounding rock in a grid-like distribution, forming a monitoring system covering the key areas of the tunnel. The sensor network continuously collects deformation data and internal stress change data generated by the surrounding rock during the stress process. These data are collected to the central control room through wired or wireless transmission, forming raw monitoring data. The raw monitoring data contains the mechanical state information of the surrounding rock at different positions and different time points, providing basic materials for subsequent analysis.

[0028] The data processing unit is arranged in the central control room, which is a specially configured computer system, and is physically connected with the sensor network through a data interface. After receiving the raw monitoring data, the data processing unit first performs data cleaning and format standardization processing, and then calculates the strain energy density of the surrounding rock based on the theory of elasticity. Strain energy density refers to the energy stored in unit volume of rock mass during deformation. Its calculation needs to integrate stress data and strain data, and is derived through a specific mathematical relationship. This calculation process converts scattered monitoring data into a unified energy index, enabling the surrounding rock state to be characterized in a quantitative energy form.

[0029] The adaptive control algorithm is deployed on the main processor of the control system, which is a specially programmed software module, and is logically connected with the data processing unit through a data bus. The adaptive control algorithm has multiple energy stage characteristics preset inside, which are typical energy state patterns summarized from a large number of engineering practices. The algorithm matches and analyzes the calculated strain energy density with these preset characteristics, and the matching process uses pattern recognition techniques, including but not limited to clustering analysis or neural network algorithms, to finally generate an energy stage matching result. This result preliminarily judges the current energy state level of the surrounding rock.

[0030] The stage confirmation module is a sub-module in the adaptive control algorithm, connected with the main logic unit through internal data paths. This module verifies and confirms the energy stage matching result, excludes possible mismatching conditions through comparison of historical data and trend analysis, finally determines the accurate energy state stage of the surrounding rock, and generates the corresponding energy stage identifier. The energy stage identifier adopts a standardized coding form, clearly indicating the specific energy state level of the surrounding rock.

[0031] The support mode mapping queryer is another functional module in the adaptive control algorithm, which establishes a data connection with the preset support mode mapping relationship database. This database stores the corresponding relationship between different energy state stages and the corresponding support mode. Based on the energy stage identifier, the queryer retrieves the matching support mode in the database and generates specific support mode switching instructions. These instructions detail the support parameters that need to be adjusted and the target working state.

[0032] The support control unit is installed in the support control cabinet, which is a dedicated control device connected to the adaptive control algorithm through control lines. After receiving the support mode switching instructions, the support control unit analyzes the instruction content and then drives the corresponding actuator to adjust the working parameters of the support structure. These parameters may include the prestress, stiffness, or working mode of the support elements. After the adjustment is completed, the system generates an updated support state feedback, forming a complete control loop.

[0033] The entire implementation process realizes an automated process from data acquisition to support adjustment through the coordinated work of multiple components. The sensor network provides real-time monitoring data, the data processing unit performs energy conversion calculation, the adaptive control algorithm completes state recognition and decision generation, and finally the support control unit realizes execution adjustment. This integrated implementation can timely respond to changes in surrounding rock state, dynamically optimize support parameters, and significantly improve the safety and adaptability of tunnel engineering. Through the real-time feedback adjustment mechanism, the system can effectively prevent the risk of surrounding rock instability, prolong the service life of the support structure, and reduce the need for manual intervention, improving engineering management efficiency.

[0034] In some implementations, the data processing unit is integrated with a stress-strain relationship calculation module, an energy density integrator, and a spatial distribution analyzer connected in sequence, and S2 includes:

[0035] S2.1: Based on the original monitoring data, calculate the stress distribution and strain distribution of the surrounding rock through the stress-strain relationship calculation module to generate stress-strain data, wherein the stress-strain relationship calculation module is connected to the main processor of the data processing unit;

[0036] S2.2: Based on the stress-strain data, calculate the strain energy density of the unit volume of surrounding rock through the energy density integrator to generate preliminary energy density;

[0037] S2.3: Based on the preliminary energy density, calculate the spatial distribution characteristics of the strain energy density of the surrounding rock through the spatial distribution analyzer;

[0038] S2.4: Based on the spatial distribution characteristics, identify the energy concentration area to generate strain energy density.

[0039] In the implementation details of the data processing unit, the stress-strain relationship calculation module is the first processing link, which establishes the corresponding relationship between stress and strain through a specific mathematical calculation model. After receiving the original monitoring data, the module first classifies the data, extracts the strain data and stress data respectively, and then establishes the stress-strain relationship curve based on the principles of material mechanics. In the calculation process, the anisotropic properties of the surrounding rock are considered, and appropriate constitutive models are used for relationship derivation, finally generating stress-strain data that accurately reflect the mechanical state of the surrounding rock. These data provide necessary input parameters for subsequent energy calculation.

[0040] The energy density integrator is the core calculation module in the data processing unit, which is responsible for the quantitative calculation of strain energy density. Based on the stress-strain data, the module uses integral operation method to calculate the strain energy stored in unit volume of surrounding rock. In the calculation process, the module integrates the relevant components of stress and strain, and comprehensively considers the deformation energy and distortion energy of the surrounding rock, finally generates the preliminary energy density value. This calculation process strictly follows the law of conservation of energy, ensuring that the physical meaning of the calculation result is clear.

[0041] The spatial distribution analyzer follows the output results of the energy density integrator, and analyzes the spatial characteristics of the preliminary energy density. The analyzer uses spatial interpolation algorithm and gridding processing method to convert the energy density values of discrete points into continuous spatial distribution map. During the analysis process, special attention is paid to the gradient change and concentrated area characteristics of energy density, and through techniques such as contour analysis or heat map generation, the distribution rule of strain energy density in the surrounding rock space is clearly displayed. This spatial analysis helps to identify potential dangerous areas and energy concentration sites.

[0042] Based on the spatial distribution characteristics, the system further identifies the energy concentration area, which is realized by setting a reasonable threshold range. The analyzer automatically detects the areas where the energy density exceeds the preset threshold, marks these areas as energy concentration areas, and records their spatial position and energy intensity information. In the identification process, clustering algorithm is used to classify and analyze high-energy areas, distinguish different types of energy concentration modes, and finally generate complete strain energy density distribution information.

[0043] These processing modules are connected in sequence to form a complete data processing chain. The stress-strain relationship calculation module provides accurate mechanical relationship basis for the system, the energy density integrator realizes the quantitative evaluation of energy, and the spatial distribution analyzer expands the analysis dimension. Through this modular design, the data processing unit can comprehensively evaluate the energy state of the surrounding rock from multiple angles, providing reliable data support for subsequent adaptive control. In the implementation process, the modules are connected through standard data interfaces for data transmission, ensuring smooth and efficient processing flow.

[0044] In some implementations, S3 includes:

[0045] S3.1: Calculate the trend of strain energy density by an adaptive control algorithm, generate energy trend data;

[0046] S3.2: Compare the current strain energy density with multiple preset energy thresholds based on energy trend data, generate threshold comparison results;

[0047] S3.3: Match corresponding energy stage characteristics based on threshold comparison results, generate preliminary matching results;

[0048] S3.4: Verify the rationality of preliminary matching results, generate energy stage matching results.

[0049] In the implementation of the adaptive control algorithm, the calculation of the trend of strain energy density is achieved by analyzing historical energy data sequences. The algorithm collects strain energy density data for consecutive time periods and uses time series analysis methods, including moving average or exponential smoothing techniques, to extract energy change trend characteristics. Special attention is paid to the rate and direction of change during the trend calculation process to identify whether energy accumulation is in an upward, downward, or stable phase. These trend data provide dynamic reference for subsequent stage matching.

[0050] Based on energy trend data, the system compares the current strain energy density with multiple preset energy thresholds. These preset thresholds are determined based on engineering experience and theoretical calculations and correspond to different energy state levels. The comparison process uses a hierarchical judgment method, first determines the threshold interval in which the current energy value is located, and then analyzes its relationship with adjacent thresholds to generate detailed threshold comparison results. This comparison process not only considers absolute values, but also combines trend data for comprehensive judgment.

[0051] Threshold comparison results are then used to match corresponding energy stage characteristics. The adaptive control algorithm internally stores standard characteristic patterns for various energy stages, including energy value ranges, trend requirements, and duration parameters. The matching process uses a feature matching algorithm to calculate the similarity between the current energy state and the standard characteristics, selecting the characteristic with the highest matching degree as the preliminary matching result. A confidence assessment is set during the matching process to ensure the reliability of the matching result.

[0052] The preliminary matching result also needs to go through a rationality verification link. The verification process includes cross-validation and historical data comparison to check the consistency of the current matching result with recent energy change patterns, while considering the special influence of geological conditions and construction factors. Verification is carried out through logical judgment rules and expert knowledge base to exclude obviously inconsistent matching results, and finally generate confirmed energy stage matching results.

[0053] This series of processing steps ensures the accuracy and reliability of energy phase identification. Trend analysis enables the system to anticipate the direction of energy state development, threshold comparison provides quantitative judgment criteria, feature matching achieves precise classification of states, and reasonableness verification serves as a quality assurance. Through this multi-level processing mechanism, the adaptive control algorithm can adapt to various complex working conditions and make judgments and decisions that meet actual engineering needs.

[0054] In some implementations, S3.2 includes:

[0055] S3.2.1: Based on the energy trend data, calculate the accumulation rate of strain energy density;

[0056] S3.2.2: Based on the accumulation rate, dynamically adjust multiple preset energy thresholds to generate dynamic thresholds;

[0057] S3.2.3: Based on the dynamic thresholds, compare the current strain energy density with the dynamic thresholds in real time to generate threshold comparison results.

[0058] In further refinement of the threshold comparison process, the accumulation rate of strain energy density is calculated by analyzing the amount of energy density change per unit time. The system collects continuous time series of strain energy density data and uses differential calculation or difference operation to accurately calculate the instantaneous change rate of energy density. The calculation of the accumulation rate pays special attention to the degree of change trend, distinguishing between rapid accumulation, slow accumulation and stable state, etc., providing a scientific basis for dynamic threshold adjustment.

[0059] Based on the accumulation rate, the system dynamically adjusts multiple preset energy thresholds. The adjustment process follows preset adjustment rules: when rapid energy accumulation is detected, the threshold is appropriately lowered to provide early warning; when the energy change is smooth, the threshold is maintained or appropriately increased to avoid false positives. Threshold adjustment uses a gradual change approach to avoid sudden and large adjustments that can destabilize the system. The generation of dynamic thresholds takes into account current energy state, historical change patterns, engineering safety requirements and other factors.

[0060] When using dynamic thresholds for real-time comparison, the system compares the current strain energy density with the adjusted thresholds. The comparison process uses a sliding window technique to continuously update the latest data, ensuring the timeliness of the comparison results. Real-time comparison not only focuses on whether the instantaneous value exceeds the threshold, but also analyzes the duration and amplitude of the sustained over-limit, generating comprehensive threshold comparison results. This process can effectively distinguish between accidental fluctuations and substantial changes.

[0061] The implementation of the dynamic threshold mechanism enables the system to better adapt to the nonlinear variation characteristics of the surrounding rock energy state. By considering the rate factor of energy accumulation, the system can respond more sensitively to sudden changes while maintaining appropriate judgment margins during stable periods. This adaptive adjustment capability significantly improves the accuracy of state recognition, providing a more reliable basis for support decision-making. During implementation, the system also establishes a record and trace mechanism for threshold adjustment, facilitating subsequent analysis and optimization.

[0062] In some implementations, the support control unit includes a connected phase change controller and a state monitor, and S6 includes:

[0063] S6.1: Based on the support mode switching instruction, analyze the target energy dissipation mode specified in the support mode switching instruction, and generate a mode analysis result;

[0064] S6.2: Based on the mode analysis result, control the stress-induced phase change of the shape memory alloy support net through the phase change controller, and generate a phase change control instruction, wherein the phase change controller is connected to the shape memory alloy support net;

[0065] S6.3: Based on the phase change control instruction, monitor the phase change state and energy absorption of the shape memory alloy support net through the state monitor, and generate an energy absorption state, wherein the state monitor is connected to the shape memory alloy support net;

[0066] S6.4: Based on the energy absorption state, confirm that the support structure has entered the energy dissipation mode, and generate an updated support state.

[0067] In the specific implementation of the support control unit, the cooperation of the phase change controller and the state monitor realizes intelligent support adjustment. Based on the support mode switching instruction, the system first analyzes the target energy dissipation mode specified in the instruction. The analysis process includes instruction decoding and parameter extraction, which clearly defines the energy dissipation target and related control parameters that need to be achieved. The analysis result details the specific requirements of the target working mode, providing a clear direction for subsequent control.

[0068] The phase change controller sends control signals to the shape memory alloy support net according to the mode analysis result. Shape memory alloy is a kind of intelligent material with special phase change characteristics, which will undergo phase change when receiving specific stimuli (such as temperature change or stress change), thereby changing its mechanical properties. The phase change controller triggers the stress-induced phase change process of the alloy by precisely controlling the stimulation parameters, causing it to transform from austenite phase to martensite phase, which can effectively absorb and dissipate energy. During the control process, the phase change controller adjusts the intensity and timing of the control signal according to the target energy dissipation requirements to ensure that the phase change process matches the energy absorption requirements.

[0069] The state monitor monitors the phase change state and energy absorption of the shape memory alloy support net in real time. The monitoring process uses special sensors to detect the phase change degree, temperature change and stress state of the alloy, while evaluating the energy dissipation effect. The monitoring data includes phase change progress indicators, energy absorption efficiency and material state parameters, etc. After processing, detailed energy absorption state reports are generated. The state monitor forms a closed-loop control with the phase change controller to ensure that the phase change process proceeds as expected.

[0070] Based on the energy absorption state, the system confirms whether the support structure has entered the expected energy dissipation mode. The confirmation process includes state verification and effect evaluation, checking the consistency of the actual energy absorption effect with the target requirements. When it is confirmed that the support structure has successfully entered the target working mode, the system generates updated support state information, completing the adjustment process. The entire implementation process embodies the organic combination of intelligent materials and automatic control technology, achieving efficient energy management through the active response characteristics of materials.

[0071] This implementation fully utilizes the unique properties of shape memory alloys, enabling them to play an intelligent adjustment role in tunnel support. When the energy of surrounding rock accumulates, the phase change process of the alloy can actively dissipate energy, preventing excessive energy accumulation from causing damage. At the same time, state monitoring ensures the controllability and reliability of the adjustment process, providing an innovative technical means for tunnel support. Through this intelligent support method, the system can more effectively cope with complex surrounding rock conditions, improving the safety and durability of the project.

[0072] In some implementations, S6.2 includes:

[0073] S6.2.1: Based on the mode analysis result, send a phase change trigger signal to the shape memory alloy support net through the phase change controller;

[0074] S6.2.2: Based on the phase change trigger signal, control the temperature change of the alloy wire, and generate a temperature control instruction;

[0075] S6.2.3: Based on the temperature control instruction, monitor the martensite phase change process, confirm that the shape memory alloy support net has entered the phase change energy absorption state, and generate a phase change control instruction.

[0076] In the control process of the shape memory alloy support net, the phase change controller needs to send a phase change trigger signal to the shape memory alloy support net. The shape memory alloy support net is a net-shaped support structure made of special metal materials with shape memory effect, which can undergo phase change when receiving specific stimuli. The phase change trigger signal can be an electrical signal, a thermal signal or a magnetic signal, and the specific form depends on the type of shape memory alloy used and the activation method. These signals are transmitted to each node of the support net through a specially designed drive circuit, ensuring that the signals can uniformly act on the entire support net structure.

[0077] Based on the phase transition trigger signal, the system needs to control the temperature change of the alloy wire. The alloy wire is the basic unit that constitutes the shape memory alloy support net, and its temperature change directly affects the progress of the phase transition process. The temperature control command is realized through the temperature regulation system, which usually includes heating elements, temperature sensors and feedback control loops. The heating element can be a resistance heating wire or an infrared heater, which generates corresponding heat according to the control command; the temperature sensor monitors the temperature change of the alloy wire in real time and feeds back the measurement data to the control system; the feedback control loop dynamically adjusts the heating power according to the difference between the set temperature and the actual temperature, ensuring that the temperature is accurately controlled within the target range.

[0078] During the temperature control process, the martensitic phase transition process needs to be monitored in real time. Martensitic phase transition is the process of shape memory alloy transforming from high-temperature austenite phase to low-temperature martensite phase, which is accompanied by crystal structure reorganization and mechanical property change. The monitoring process uses multiple detection methods, including resistance measurement method, differential scanning calorimetry and acoustic emission detection. The resistance measurement method uses the resistance change characteristics in the phase transition process to track the phase transition process; the differential scanning calorimetry identifies the thermal effects of phase transition by measuring the heat flow change; the acoustic emission detection can capture the sound signals generated by the microstructure changes in the phase transition process. These monitoring data together constitute a comprehensive tracking of the phase transition process.

[0079] By comprehensively analyzing the monitoring data, the system confirms whether the shape memory alloy support net has entered the phase transition energy absorption state. The phase transition energy absorption state refers to the special state in which the shape memory alloy can absorb and dissipate mechanical energy through the change of crystal structure during the phase transition process. The confirmation process needs to evaluate multiple indicators, including phase transition completion degree, energy absorption efficiency and mechanical response characteristics. The phase transition completion degree is judged by the progress of the phase transition process; the energy absorption efficiency is evaluated by measuring the ratio of input mechanical energy to phase transition dissipation energy; the mechanical response characteristics are confirmed by analyzing the changes of stress-strain curve. When all these indicators meet the predetermined standards, the system generates a phase transition control command, indicating that the shape memory alloy support net is ready to play its energy absorption function.

[0080] This implementation ensures that the shape memory alloy support net can operate in its best working state. Through precise temperature control and phase transition process monitoring, the system can fully utilize the super-elasticity characteristics and phase transition energy dissipation capacity of the shape memory alloy. When the surrounding rock energy accumulates, the shape memory alloy support net absorbs energy through stress-induced phase transition; when the energy is released, it can recover to its original state through reverse phase transition. This reversible energy absorption mechanism provides continuous and reliable protection for the tunnel support, effectively preventing the instability phenomenon caused by excessive accumulation of surrounding rock energy.

[0081] In some implementations, the support control unit includes a connected prestress controller and an energy release monitor, S6 includes:

[0082] S6.5: Based on the support mode switching instruction, analyze the prestress adjustment parameters specified in the support mode switching instruction to generate a parameter analysis result;

[0083] S6.6: Based on the parameter analysis result, control the prestress-adjustable system anchor cable to adjust the applied prestress through the prestress controller, and generate a prestress adjustment instruction, wherein the prestress controller is connected to the prestress-adjustable system anchor cable;

[0084] S6.7: Based on the prestress adjustment instruction, monitor the anchor cable prestress change and surrounding rock energy release through the energy release monitor, and generate an energy release state, wherein the energy release monitor is connected to the prestress-adjustable system anchor cable;

[0085] S6.8: Based on the energy release state, confirm that the support structure has entered the energy control working mode, and generate an updated support state.

[0086] In the control process of the prestress-adjustable system anchor cable, the system first needs to analyze the prestress adjustment parameters specified in the support mode switching instruction. The prestress adjustment parameters include target prestress value, adjustment rate, holding time and other key indicators. The target prestress value is the ideal prestress level calculated according to the current energy state of the surrounding rock and the support requirements; the adjustment rate determines the speed of the prestress change, which needs to be reasonably set according to the response characteristics of the surrounding rock; the holding time specifies the duration that the prestress needs to maintain after reaching the target value. The analysis process is realized through a special parameter analysis algorithm, which can identify various parameters in the instruction and verify their rationality and integrity.

[0087] Based on the parameter analysis result, the prestress controller starts to control the prestress-adjustable system anchor cable to adjust the applied prestress. The prestress-adjustable system anchor cable is an anchoring system that can adjust the prestress in real time, usually composed of anchor cable body, tensioning device and locking mechanism. The prestress controller changes the prestress state of the anchor cable by driving the work of the tensioning device, which can be hydraulic, mechanical or electric. During the control process, the controller needs to accurately control the size and action time of the tensioning force to ensure smooth transition of the prestress to the target value and avoid impact or disturbance to the surrounding rock.

[0088] Based on the prestress adjustment instructions, the energy release monitor begins to monitor the changes in anchor cable prestress and the energy release of surrounding rock. The energy release monitor contains various sensors, such as force sensors to measure anchor cable prestress changes, displacement sensors to monitor surrounding rock deformation, and acoustic emission sensors to detect acoustic signals generated by surrounding rock micro-fractures. After comprehensive processing of these sensor data, the energy release characteristics of surrounding rock during the prestress adjustment process can be reflected, including the intensity, rate, and spatial distribution of energy release.

[0089] By analyzing the monitoring data, the system generates an energy release status report. The energy release status reflects the changes in surrounding rock energy during the prestress adjustment process, including the sufficiency, uniformity, and stability of energy release. The sufficiency evaluation mainly looks at whether the energy release reaches the expected level; the uniformity evaluation focuses on the distribution of energy release in different positions; and the stability evaluation checks whether the energy release process is smooth and controllable. These evaluation indicators together constitute a comprehensive description of the energy release status.

[0090] Based on the energy release status, the system confirms whether the support structure has entered the energy control working mode. The energy control working mode refers to a specific working state of the support system in which it actively adjusts the prestress to achieve orderly release and effective control of surrounding rock energy. The confirmation process needs to verify multiple conditions, including whether the prestress is stable within the target range, whether the energy release achieves the expected effect, and whether the surrounding rock deformation is controlled within the allowed range. Only when all these conditions are met does the system confirm that the support structure has successfully entered the energy control working mode, at which point the support system can effectively manage the surrounding rock energy and maintain tunnel stability.

[0091] In some implementations, the support control unit includes a connected ductility controller and a ductility monitor, and S6 includes:

[0092] S6.9: Based on the support mode switching instructions, analyze the ductility deformation parameters specified in the support mode switching instructions to generate ductility parameters;

[0093] S6.10: Based on the ductility parameters, control the ductility system anchor rod into the ductility deformation state through the ductility controller, and generate ductility deformation control instructions, wherein the ductility controller is connected to the ductility system anchor rod;

[0094] S6.11: Based on the ductility deformation control instructions, monitor the ductility deformation process and energy dissipation of the ductility system anchor rod through the ductility monitor, and generate a ductility deformation state, wherein the ductility monitor is connected to the ductility system anchor rod;

[0095] S6.12: Based on the ductility deformation state, confirm that the support structure has entered the ductility energy dissipation working mode, and generate an updated support state.

[0096] In the control process of the ductility system anchor, the system first analyzes the specified ductility deformation parameters in the support mode switching instruction. The ductility deformation parameters include target deformation, deformation rate, energy dissipation capacity requirements and other indicators. The target deformation refers to the maximum plastic deformation allowed to occur when the anchor is under stress. This parameter needs to be reasonably determined according to the deformation characteristics of the surrounding rock and the safety requirements of the support; the deformation rate specifies the speed of ductile deformation, which needs to match the deformation rate of the surrounding rock; the energy dissipation capacity requirement specifies the minimum energy value that the anchor needs to absorb during deformation. The analysis process is completed through parameter extraction and verification algorithm to ensure the reasonableness and feasibility of the parameters.

[0097] Based on the ductility parameters, the ductility controller starts to control the ductility system anchor to enter the ductility deformation state. The ductility system anchor is a support component specially designed to maintain its load-bearing capacity under large deformation, usually made of high ductility steel or special composite materials. The ductility controller adjusts the boundary conditions of the anchor or activates specific energy dissipation mechanisms to promote it to enter the ductility working state. The control methods may include releasing certain restraint devices, adjusting the stiffness of the connection nodes or activating built-in energy dissipation elements. During the control process, it is necessary to ensure that the deformation process is smooth and controllable, and avoid sudden stiffness changes that may adversely affect the support system.

[0098] Based on the ductility deformation control instruction, the ductility monitor starts to monitor the ductility deformation process and energy dissipation of the ductility system anchor. The ductility monitor is equipped with various special sensors, including high-precision strain gauges to measure the plastic deformation of the anchor, force sensors to monitor load changes, and energy dissipation measurement devices to evaluate energy dissipation effects. The monitoring data is transmitted to the processing system in real time, and after analysis, a ductility deformation state report is generated. This report describes in detail the deformation characteristics, energy dissipation performance and overall working state of the anchor, providing a basis for the system to evaluate the support effect.

[0099] Through the analysis of monitoring data, the system confirms whether the support structure has entered the ductility energy dissipation working mode. The ductility energy dissipation working mode refers to a specific working state of the support system that absorbs and dissipates the energy of the surrounding rock through ductile deformation. The confirmation process needs to evaluate multiple aspects, including the uniformity of deformation development, the stability of energy dissipation capacity and the reliability of overall performance. The uniformity of deformation development focuses on whether the plastic deformation is reasonably distributed throughout the length of the anchor; the stability of energy dissipation capacity checks whether the energy absorption effect is sustained and effective; the reliability of overall performance evaluates whether the anchor can still maintain the necessary load-bearing capacity in the ductile deformation state. When all these evaluation indicators meet the predetermined standards, the system confirms that the support structure has successfully entered the ductility energy dissipation working mode.

[0100] This implementation enables the support system to fully exploit the advantages of flexible support. When the surrounding rock undergoes large deformation, the flexible system anchor absorbs energy through controllable plastic deformation, relieves stress concentration, and avoids brittle failure. This support method is particularly suitable for soft rock tunnels with large deformation or high stress areas, and can provide more secure and reliable support. By precisely controlling the flexible deformation process, the system can maximize the self-bearing capacity of the surrounding rock while ensuring safety, achieving the unity of safety and economy.

[0101] In some implementations, S1 includes:

[0102] S1.1: Based on the safety monitoring results, collect surrounding rock strain data through a distributed strain sensor array arranged on the surface of the surrounding rock of the tunnel;

[0103] S1.2: Based on the safety monitoring results, collect surrounding rock stress data through a stress sensor array installed inside the surrounding rock;

[0104] S1.3: Based on the strain data and stress data, perform time synchronization and spatial registration, and fuse into a unified monitoring data set to generate original monitoring data.

[0105] In the initial stage of data collection, the strain sensor array distributed on the surface of the surrounding rock of the tunnel begins to collect surrounding rock strain data. The strain sensor array is a monitoring network formed by arranging multiple strain measurement devices in a certain pattern. These devices can be resistance strain gauges, fiber optic sensor or other types of strain measurement devices. They are directly installed on the surface of the surrounding rock and can sensitively detect the deformation of the surrounding rock under stress. Each sensor measures the strain value at its location, including linear strain, shear strain and other components. These data reflect the deformation distribution and change trend of the surrounding rock surface. The arrangement density and position of the sensor array need to be carefully designed according to the tunnel geometry, geological conditions and monitoring requirements to ensure that representative deformation information can be captured.

[0106] At the same time, the stress sensor array installed inside the surrounding rock also collects surrounding rock stress data. The stress sensor array is a three-dimensional monitoring system composed of multiple stress measurement devices. These devices are buried at different depths inside the surrounding rock through drilling. Common stress sensors include piezoelectric stress gauges, vibrating wire stress gauges and other devices that can directly measure the stress state inside the surrounding rock. Stress data includes normal stress and shear stress in various directions, which reflect the stress state and stress distribution characteristics of the surrounding rock. The arrangement of stress monitoring points needs to consider the structure characteristics of the rock mass and the distribution law of the stress field to ensure that the monitoring data can truly reflect the stress state of the surrounding rock.

[0107] After obtaining the strain data and stress data, the system needs to perform time synchronization processing. Time synchronization is an important step to ensure that the data collected by different sensors are consistent in time dimension, which is achieved by applying uniform timestamps to all data. The time synchronization system usually uses a high-precision clock source to send synchronization signals to all sensors through wired or wireless methods, ensuring that even if the sensors are distributed in different locations, their data collection times can remain highly consistent. This synchronization process provides a time basis for subsequent analysis of the relationship between different parameters, avoiding data analysis errors caused by time differences.

[0108] On the basis of completing time synchronization, the system also needs to perform spatial registration processing. Spatial registration is the process of establishing the spatial position relationship of different sensors, which is achieved by assigning precise spatial coordinates to each sensor. The registration process needs to consider factors such as the installation position, orientation, and measurement range of the sensors, and through coordinate conversion and position calibration, all monitoring data is unified into the same coordinate system. This spatial registration allows monitoring data from different locations to be analyzed in a unified spatial framework, creating conditions for studying the spatial distribution of the mechanical state of surrounding rock.

[0109] Finally, the system fuses the strain data and stress data that have been time and space registered to generate a unified monitoring data set. Data fusion is the process of integrating multi-source monitoring information into a consistent data set through specific algorithms, including data association, feature extraction, and information synthesis. In the fusion process, conflicts, redundancies, and inconsistencies between different sensor data need to be resolved, and data quality is improved through data filtering, outlier processing, and reliability evaluation. The final raw monitoring data set has the characteristics of time consistency, spatial matching, and information completeness, providing a reliable data foundation for subsequent strain energy density calculation and support decision-making. This data acquisition and processing method of the system ensures the accuracy and reliability of the monitoring data, providing an important guarantee for the effective operation of the entire adaptive adjustment system.

[0110] Embodiment 2

[0111] As shown in Figure 2 the second aspect, the present application proposes a tunnel support adaptive adjustment system based on rock-mass-support feedback. The system uses the tunnel support adaptive adjustment method based on rock-mass-support feedback provided by any of the above embodiments. The system includes:

[0112] A sensor network is arranged in the surrounding rock of the tunnel, which is used to collect strain data and stress data of the surrounding rock based on safety monitoring results, and generate raw monitoring data.

[0113] A data processing unit is set in the central control room, and the data processing unit is connected with the sensor network, which is used to calculate the strain energy density of the surrounding rock based on the raw monitoring data.

[0114] An adaptive control algorithm module is arranged on the control system main processor, and is connected with the data processing unit, and is used to match the strain energy density with the preset energy stage characteristics to generate an energy stage matching result;

[0115] A stage confirmation module is integrated in the adaptive control algorithm module, and is connected with the main logic unit of the adaptive control algorithm module, and is used to determine the energy state stage in which the surrounding rock is currently located based on the energy stage matching result to generate an energy stage identifier;

[0116] A support mode mapping queryer is connected in the adaptive control algorithm module, and is connected with the preset support mode mapping relationship database, and is used to generate a support mode switching instruction corresponding to the current energy state stage based on the energy stage identifier;

[0117] A support control unit is arranged in the support control cabinet, and is connected with the adaptive control algorithm module, and is used to adjust the working parameters of the support structure based on the support mode switching instruction to generate an updated support state.

[0118] The system corresponds to the method provided in the above embodiment 1, and will not be described here one by one.

[0119] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A tunnel support adaptive adjustment method based on rock mass-support feedback, characterized in that, include: S1: Based on the safety monitoring results, raw monitoring data is generated by collecting the surrounding rock strain and stress data through a sensor network deployed in the tunnel surrounding rock. S2: Based on the raw monitoring data, the strain energy density of the surrounding rock is calculated through the data processing unit set up in the central control room, which is connected to the sensor network. S3: Through the adaptive control algorithm deployed on the main processor of the control system, the adaptive control algorithm is connected to the data processing unit to match the strain energy density with multiple preset energy stage characteristics and generate energy stage matching results. S4: Based on the energy stage matching results, the stage confirmation module integrated in the adaptive control algorithm is used to determine the current energy state stage of the surrounding rock and generate an energy stage identifier. S5: Based on the energy stage identifier, the support mode mapping queryer connected in the adaptive control algorithm is connected to the preset support mode mapping relationship database to generate a support mode switching instruction corresponding to the current energy state stage. S6: Based on the support mode switching command, the support control unit set in the support control cabinet is connected to the adaptive control algorithm to adjust the working parameters of the support structure and generate the updated support status. The support control unit includes a connected phase change controller and a condition monitor. S6 includes: S6.1: Based on the support mode switching command, parse the target energy dissipation mode specified in the support mode switching command and generate the mode parsing result; S6.2: Based on the pattern analysis results, the shape memory alloy support mesh is controlled by a phase change controller to trigger stress-induced phase change and generate phase change control commands. The phase change controller is connected to the shape memory alloy support mesh. S6.3: Based on phase change control commands, the phase change state and energy absorption of the shape memory alloy support mesh are monitored through a state monitor, and an energy absorption state is generated. The state monitor is connected to the shape memory alloy support mesh. S6.4: Based on the energy absorption state, confirm that the support structure has entered the energy dissipation mode and generate an updated support state; S6.2 includes: S6.2.1: Based on the pattern analysis results, a phase change trigger signal is sent to the shape memory alloy support network through the phase change controller; S6.2.2: Based on the phase change trigger signal, control the temperature change of the alloy wire and generate temperature control commands; S6.2.3: Based on temperature control commands, monitor the martensitic phase transformation process, confirm that the shape memory alloy support mesh has entered the phase transformation energy absorption state, and generate phase transformation control commands.

2. The tunnel support adaptive adjustment method based on rock mass-support feedback according to claim 1, characterized in that, The data processing unit integrates a stress-strain relationship calculation module, an energy density integrator, and a spatial distribution analyzer connected in sequence. S2 includes: S2.1: Based on the original monitoring data, the stress and strain distribution of the surrounding rock are calculated through the stress-strain relationship calculation module to generate stress-strain data. The stress-strain relationship calculation module is connected to the main processor of the data processing unit. S2.2: Based on stress-strain data, the strain energy density per unit volume of surrounding rock is calculated using an energy density integrator to generate a preliminary energy density. S2.3: Based on the preliminary energy density, the spatial distribution characteristics of the surrounding rock strain energy density are calculated using a spatial distribution analyzer; S2.4: Based on spatial distribution characteristics, identify energy concentration areas and generate strain energy density.

3. The tunnel support adaptive adjustment method based on rock mass-support feedback according to claim 1, characterized in that, S3 includes: S3.1: Calculate the variation trend of strain energy density through an adaptive control algorithm to generate energy trend data; S3.2: Based on energy trend data, compare the current strain energy density with multiple preset energy thresholds to generate threshold comparison results; S3.3: Based on the threshold comparison results, match the corresponding energy stage features to generate preliminary matching results; S3.4: Verify the rationality of the preliminary matching results and generate the energy stage matching results.

4. The tunnel support adaptive adjustment method based on rock mass-support feedback according to claim 3, characterized in that, S3.2 includes: S3.2.1: Calculate the cumulative rate of strain energy density based on energy trend data; S3.2.2: Based on the accumulation rate, dynamically adjust multiple preset energy thresholds to generate a dynamic threshold; S3.2.3: Based on the dynamic threshold, the current strain energy density is compared with the dynamic threshold in real time to generate the threshold comparison result.

5. The tunnel support adaptive adjustment method based on rock mass-support feedback according to claim 1, characterized in that, The support control unit includes a connected prestress controller and an energy release monitor; S6 includes: S6.5: Based on the support mode switching command, parse the prestress adjustment parameters specified in the support mode switching command and generate parameter analysis results; S6.6: Based on the parameter analysis results, the prestressing adjustment system anchor cable is controlled to adjust the applied prestress through the prestressing controller, and a prestressing adjustment command is generated. The prestressing controller is connected to the prestressing adjustment system anchor cable. S6.7: Based on the prestress adjustment command, the changes in prestress of the prestress of the prestressed adjustable system anchor cable and the energy release of the surrounding rock are monitored through the energy release monitor, and the energy release status is generated. The energy release monitor is connected to the prestressed adjustable system anchor cable. S6.8: Based on the energy release status, confirm that the support structure has entered the energy control working mode and generate the updated support status.

6. The tunnel support adaptive adjustment method based on rock mass-support feedback according to claim 1, characterized in that, The support control unit includes a connected toughness controller and a toughness monitor; S6 includes: S6.9: Based on the support mode switching command, parse the toughness deformation parameters specified in the support mode switching command and generate toughness parameters; S6.10: Based on the toughness parameters, the toughness system anchor bolt is controlled to enter the toughness deformation state through the toughness controller, and a toughness deformation control command is generated. The toughness controller is connected to the toughness system anchor bolt. S6.11: Based on the toughness deformation control command, the toughness deformation process and energy dissipation of the toughness system anchor are monitored through the toughness monitor to generate the toughness deformation state. The toughness monitor is connected to the toughness system anchor. S6.12: Based on the ductile deformation state, confirm that the support structure has entered the ductile energy dissipation working mode and generate the updated support state.

7. The tunnel support adaptive adjustment method based on rock mass-support feedback according to claim 1, characterized in that, S1 includes: S1.1: Based on the safety monitoring results, the surrounding rock strain data is collected by a distributed array of strain sensors deployed on the surface of the tunnel surrounding rock; S1.2: Based on the safety monitoring results, stress data of the surrounding rock is collected by an array of stress sensors installed inside the surrounding rock; S1.3: Based on strain data and stress data, time synchronization and spatial registration are performed, and the data are merged into a unified monitoring dataset to generate raw monitoring data.

8. A tunnel support adaptive adjustment system based on rock mass-support feedback, characterized in that, The system employs the tunnel support adaptive adjustment method based on rock mass-support feedback as described in any one of claims 1 to 7, and the system comprises: A sensor network is deployed within the surrounding rock of the tunnel to collect surrounding rock strain and stress data based on safety monitoring results, and generate raw monitoring data. The data processing unit, located in the central control room, is connected to the sensor network and is used to calculate the strain energy density of the surrounding rock based on the raw monitoring data. The adaptive control algorithm module is deployed on the main processor of the control system. The adaptive control algorithm module is connected to the data processing unit and is used to match the strain energy density with multiple preset energy stage characteristics to generate energy stage matching results. The stage confirmation module is integrated into the adaptive control algorithm module. The stage confirmation module is connected to the main logic unit of the adaptive control algorithm module. It is used to determine the current energy state stage of the surrounding rock based on the energy stage matching results and generate an energy stage identifier. The support mode mapping queryer is connected to the adaptive control algorithm module. The support mode mapping queryer is connected to the preset support mode mapping relationship database and is used to generate support mode switching instructions corresponding to the current energy state stage based on the energy stage identifier. The support control unit is located in the support control cabinet. The support control unit is connected to the adaptive control algorithm module and is used to adjust the working parameters of the support structure based on the support mode switching command to generate the updated support status.

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