High-dispersed stratum grouting toughening dynamic evaluation and tunnel intelligent excavation control system

Abstract

The application discloses a high-dispersed stratum grouting toughening dynamic evaluation and tunnel intelligent excavation control system, and particularly relates to the following: a data acquisition module acquires stratum data and construction monitoring data; a multi-source information fusion and dynamic inversion analysis module fuses and processes the above data, updates equivalent mechanical parameters of the stratum based on a Bayesian updating framework, and outputs an equivalent parameter set; a stability comprehensive evaluation and risk grading module constructs a comprehensive stability index CSI based on the equivalent mechanical parameter set, the construction monitoring data and grouting detection results; a construction decision and regulation module receives the CSI, its change characteristics and grouting spatial distribution information, and generates a parameterized control instruction; and an adaptive feedback and optimization module forms a closed-loop control process of detection-inversion-evaluation-decision-feedback. The application realizes spatialization and quantification of grouting effect in a high-dispersed stratum, identifies grouting weak areas and reinforcement non-uniform characteristics, and improves objectivity and reliability of grouting effect evaluation.

Description

Technical Field

[0001] This invention relates to the field of tunnel and underground engineering construction control technology, and more specifically, to a dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata. Background Technology

[0002] When tunnels traverse highly dispersed strata such as fault fracture zones, water-rich sand and gravel layers, and strongly weathered rock layers, the surrounding rock structure suffers from poor integrity and extremely uneven physical property distribution. These rock layers are prone to deformation and even local instability after construction disturbances, posing extremely high construction safety risks. To improve these adverse geological conditions, full-section or pre-grouting reinforcement has become the mainstream method. Grout consolidates loose particles and cements fractures, improving the overall strength and self-stabilizing capacity of the surrounding rock. However, the pore size and connectivity within highly dispersed strata vary significantly, leading to high uncertainty in the grout diffusion path, filling range, and reinforcement effect. Grouted reinforcement zones often exhibit spatial discontinuities and non-uniform strength, with their actual structural state frequently deviating from expectations. Therefore, after grouting, a dynamic and spatial systematic evaluation of the grouting effect and surrounding rock stability is necessary to accurately determine the extent of the reinforced zone, strength distribution, and potential weak points. Meanwhile, tunnel excavation methods, such as the CD method and bench excavation method, as well as key construction parameters, such as advance length, support timing, and support stiffness, should be dynamically adjusted based on real-time feedback on the geological conditions. This is to avoid conservative construction leading to decreased efficiency or loss of control due to information lag. Only by forming a closed loop between grouting effect evaluation and excavation parameter adjustment can safe and efficient tunnel construction be achieved under highly discrete geological conditions.

[0003] Currently, the evaluation of grouting effectiveness in highly discrete strata mainly relies on methods such as core sampling, test section verification, and pressure-flow rate analysis during the grouting process. Core sampling can only reflect the distribution and strength of grout at local points; test section verification is time-consuming and labor-intensive, and the information obtained is limited; pressure-flow rate analysis can only provide indirect information about the grouting process and cannot comprehensively characterize the three-dimensional distribution characteristics of the grout on a spatial scale, nor can it simultaneously reflect the changes in the overall mechanical properties of the strata after grouting. The above methods are difficult to adapt to the needs of the constantly changing strata state during construction, and the evaluation results lag behind the actual working conditions. In addition, the selection of excavation methods and construction parameters is usually based on previous geological surveys and engineering experience, with weak utilization of actual feedback on the grouting reinforcement effect. Due to the lack of systematic and real-time perception and dynamic evaluation of strata state, construction decisions cannot reflect the real changes in the strata in a timely manner, which will lead to two problems: first, the adoption of overly conservative excavation schemes to avoid risks, resulting in a significant reduction in construction efficiency; second, the inability to identify grouting weak areas or the trend of surrounding rock deterioration in a timely manner, resulting in insufficient stability and increasing the probability of accidents such as collapse and water inrush. Therefore, the existing evaluation and control system is insufficient to meet the comprehensive requirements of safety, efficiency and precise control in tunnel construction in highly discrete strata. Summary of the Invention

[0004] This invention aims to overcome at least one defect in the prior art and provide a dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata. It is used to solve the problems in the prior art that it is difficult to continuously, objectively and spatially evaluate the grouting effect during construction, rely on experience to select excavation methods and construction parameters, and cannot be dynamically adjusted, resulting in delayed construction decisions, insufficient risk control, and difficulty in unifying construction safety and efficiency.

[0005] The technical solution adopted in this invention is a dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata, comprising: The data acquisition module is used to acquire cross-hole tomography data, in-situ mechanical test data, borehole core sampling data, and construction monitoring data on surrounding rock deformation, support stress, and pore water pressure during tunnel excavation and after grouting construction, and to uniformly identify and associate the data according to spatial location and construction stage. The multi-source information fusion and dynamic inversion analysis module is used to perform unified fusion processing on the multi-source information acquired by the data acquisition module, and to perform phased updates on the equivalent mechanical parameters of the formation based on the Bayesian update framework, outputting a set of equivalent parameters that reflect the mechanical state of the formation at the current construction stage. The stability comprehensive evaluation and risk classification module is used to construct a comprehensive stability index (CSI) based on the aforementioned set of equivalent mechanical parameters, combined with construction monitoring data and grouting test results; and to classify the risk level of the surrounding rock according to different comprehensive stability indices. The construction decision and control module is used to receive the comprehensive stability index (CSI) and its variation characteristics, and, in combination with the grouting spatial distribution information, match and generate parameterized control instructions in the pre-established excavation set and construction parameter mapping rule library. The adaptive feedback and optimization module is used to monitor new data in real time during the construction process, analyze and adaptively adjust the parameters of the above modules, forming a closed-loop control process of detection-inversion-evaluation-decision-feedback.

[0006] The modules work collaboratively to ensure consistency and real-time performance in geological mechanics understanding, grouting effect evaluation, and construction control, thereby improving the safety and adaptability of tunnel construction in highly discrete strata. In the current system architecture, the multi-source information fusion and dynamic inversion analysis module, the stability comprehensive evaluation and risk classification module, and the construction decision-making and control module work sequentially in the order of data processing.

[0007] In order to evaluate the grouting effect, perceive the surrounding rock condition, optimize construction parameters and control risks, the cross-hole tomographic detection data is obtained based on cross-hole resistivity tomography and / or cross-hole acoustic tomography. Multiple sets of transmitting and receiving units are deployed in the borehole, and physical property parameters reflecting the grout diffusion range, spatial connectivity and filling uniformity are obtained through excitation and response acquisition under the combination of borehole position and depth. The in-situ mechanical test data were obtained through pressure meter tests, borehole load tests, borehole shear tests, and water pressure tests, and were used to reflect the changes in strength, stiffness, and permeability of the formation under in-situ conditions before and after grouting. The core sampling data includes test results of compressive strength, splitting tensile strength, elastic modulus, and density of the extracted core samples. These test results are used to compare and verify the equivalent mechanical parameters obtained from geophysical inversion, and to correct and optimize the deviation parameters.

[0008] Cross-hole tomography data can intuitively obtain the diffusion range, filling continuity, weak areas, and enrichment areas of the grout in three-dimensional space after grouting, and identify the deviation between the actual reinforced area and the design expectation, providing a direct basis for judging whether the grouting has formed a continuous and uniform pre-supported arch. In-situ mechanical test data reflects the actual reinforcement effect of grouting on the surrounding rock. By monitoring the real-time changes in the mechanical properties of the strata through in-situ mechanical test data, the strength attenuation or local instability trend caused by construction can be identified, providing a basis for dynamically adjusting construction parameters. Borehole core sampling monitoring data can determine whether the grouting has achieved particle cementation and filled the cracks. In-situ test parameters can also be calibrated to improve the reliability and accuracy of the overall evaluation.

[0009] To provide a unified data foundation for the dynamic inversion and updating of stratigraphic equivalent parameters, the multi-source information fusion and dynamic inversion analysis module performs unified fusion processing on the multi-source information acquired by the data acquisition module. Specifically, The cross-hole tomography data, the construction monitoring data, and the measured mechanical parameters obtained from borehole core sampling are uniformly integrated and processed to construct a comprehensive parameter model, and form an equivalent parameter set that reflects the geological structure characteristics and mechanical state of the grouting reinforcement zone.

[0010] By integrating multi-source information acquired by the data acquisition module, a comprehensive analytical foundation capable of simultaneously reflecting spatial distribution and mechanical behavior is constructed, providing data support for the dynamic inversion of stratigraphic equivalent mechanical parameters.

[0011] To perform dynamic inversion analysis, the multi-source information fusion and dynamic inversion analysis module updates the equivalent mechanical parameters of the formation in stages, outputting a set of equivalent parameters reflecting the mechanical state of the formation at the current construction stage. Specifically, Using the equivalent mechanical parameters output by the comprehensive parameter model as prior knowledge, and the newly added monitoring data of surrounding rock deformation and support stress during construction as observation constraints, the probability distribution of formation parameters is continuously updated through Bayesian inference. The core relationship is expressed as follows:

[0012] This is a vector of formation equivalent mechanical parameters; The prior probability distribution established for the geophysical inversion results; It is a set of observation data consisting of construction monitoring data and borehole core sampling data; Let the likelihood function of the observed data be given the parameters. This represents the updated posterior parameter distribution.

[0013] Based on the fusion of multi-source information, a Bayesian inversion method is introduced to dynamically update the formation equivalent mechanical parameters. The formation equivalent mechanical parameters are continuously corrected through the continuous fusion of multi-source information and Bayesian updates, gradually approaching the real state as construction progresses. This keeps the grouting body distribution model and surrounding rock parameters synchronized with the field measured data, providing an effective parameter basis for the comprehensive evaluation of surrounding rock stability and the control of construction parameters.

[0014] To characterize the overall stability state of the surrounding rock at the current construction stage, the stability comprehensive evaluation and risk classification module constructs a comprehensive stability index (CSI) based on an equivalent set of mechanical parameters, combined with construction monitoring data and grouting test results. Specifically, Based primarily on the set of equivalent mechanical parameters and construction monitoring data, and using the grouting column detection results obtained from borehole core sampling as auxiliary verification information, a comprehensive stability index (CSI) is constructed to characterize the overall stability of the surrounding rock at the current construction stage through comprehensive identification of grouting effect characteristics, formation mechanical state, and surrounding rock deformation response.

[0015] The stability comprehensive evaluation and risk classification module is based on the results of multi-source information fusion and dynamic inversion analysis. It combines the surrounding rock deformation monitoring data during construction with the physical detection results of the grouting column obtained by borehole core sampling into the evaluation process. By comprehensively processing multiple evaluation results such as the spatial distribution of grouting body, changes in stratum isomechanical parameters, surrounding rock deformation, support stress, measured strength parameters of borehole core sampling, and construction monitoring response, a comprehensive stability index (CSI) is formed to quantitatively characterize the overall stability level of the surrounding rock at the current construction stage.

[0016] In order to enable tunnel construction in different geological environments, the excavation methods include full-face excavation, bench excavation, CD method, CRD method and double-sidewall pilot tunnel method.

[0017] In order to directly guide the construction, the parameterized control instructions include excavation method, single-cycle advance, support parameters and construction sequence.

[0018] The construction decision-making and control module matches and generates parameterized control commands, specifically, The construction decision-making and control module classifies and matches tunnel excavation methods based on the stability range of the Comprehensive Stability Index (CSI). When the CSI is within the safe range and the grouting effect is uniform, the full-section method or the large-scale step method should be used. When the CSI is within a controllable range and there is uneven grouting or local risk, the small-step step method or CD method should be matched, and the support measures should be strengthened. When the CSI is located in the warning zone or danger zone, the CRD method or the double-sided wall pilot tunnel method should be used, and step-by-step excavation, rapid closure and enhanced support control measures should be adopted.

[0019] Using CSI as the core decision variable, a deterministic correspondence is established between the excavation method and the range of CSI values ​​by setting a stability grading threshold.

[0020] The initial values ​​of the safe zone, controllable zone, warning zone, and danger zone of the Comprehensive Stability Index (CSI) are determined based on the principles of surrounding rock stability classification, construction monitoring response characteristics, numerical simulation analysis results, and existing tunnel engineering construction experience.

[0021] The aforementioned grading thresholds are determined by comprehensively comparing engineering monitoring data, numerical simulation results, and construction experience, and can be corrected and calibrated according to the specific geological conditions, burial depth, groundwater, and support system type of the project.

[0022] To continuously improve the system's cognitive and decision-making capabilities, the adaptive feedback and optimization module forms a closed-loop control process of detection-inversion-evaluation-decision-feedback, specifically as follows: The newly added monitoring data and core sampling results during construction are used as feedback inputs to the multi-source information fusion and dynamic inversion analysis module and the construction decision and control module. Based on the difference analysis between the actual construction response and the predicted control effect, the mapping rules of parameters and excavation methods are adaptively corrected to form a closed-loop control process of detection-inversion-evaluation-decision-feedback.

[0023] The adaptive feedback and optimization module uses newly added monitoring data and, when necessary, supplementary core sampling results as feedback information to re-input into the multi-source information fusion and dynamic inversion analysis module and the construction decision-making and control module. By analyzing the differences between the actual construction response and the predicted control effect, the numerical model parameters and excavation method mapping rules are corrected, thereby continuously improving the system's cognitive and decision-making capabilities.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: This system enables spatial and quantitative dynamic evaluation of grouting effects in highly discrete strata, identifying weak grouting zones and non-uniform reinforcement characteristics, thus improving the objectivity and reliability of grouting effect evaluation. By dynamically updating the equivalent mechanical parameters of the strata, the analysis model can evolve synchronously with construction progress, improving the accuracy of surrounding rock stability assessment. It also achieves dynamic quantification and risk classification of the surrounding rock stability state, allowing for early identification and graded control of construction risks, enhancing construction safety. Furthermore, it enables intelligent selection and dynamic adjustment of tunnel excavation methods and key construction parameters, improving the matching degree between construction decisions and actual geological conditions. A closed-loop feedback and adaptive optimization mechanism based on monitoring data is established, enhancing the system's adaptability and controllability under complex, highly discrete strata conditions. Overall, it improves the scientific rigor, safety, and informatization level of the tunnel construction process, demonstrating significant engineering application value.

[0025] This invention realizes the transformation of grouting effect evaluation from qualitative experience to quantitative objective cognition, enables the transition of surrounding rock mechanical parameters from static assumptions to dynamic updates, and allows tunnel excavation methods and construction parameters to be intelligently adjusted according to changes in the actual state of the strata, significantly improving the safety, adaptability and engineering controllability of tunnel construction in highly discrete strata. Attached Figure Description

[0026] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0027] Figure 1 This is a system module architecture diagram of a dynamic evaluation and intelligent tunnel excavation control system for highly discrete strata grouting toughening.

[0028] Figure 2 This is a schematic diagram illustrating the Bayesian dynamic inversion and formation parameter update principle of a dynamic evaluation and intelligent tunnel excavation control system for highly discrete formation grouting toughening.

[0029] Figure 3 This is a schematic diagram illustrating the multi-dimensional stability evaluation and risk classification of a dynamic evaluation system for grouting toughening of highly discrete strata and a smart tunnel excavation control system according to the present invention.

[0030] Figure 4This is a schematic diagram illustrating the principle of intelligent matching and parameter control of excavation mode in a dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to the present invention.

[0031] Figure 5 This is a comparison chart of the risk spatial distribution and monitoring index trends of a dynamic evaluation and intelligent tunnel excavation control system for highly discrete strata grouting toughening. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0034] Example 1 like Figure 1 As shown in the figure, this embodiment provides a dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata, including: The system comprises a data acquisition module, a multi-source information fusion and dynamic inversion analysis module, a stability comprehensive evaluation and risk classification module, a construction decision-making and control module, and an adaptive feedback and optimization module. These modules work collaboratively to ensure consistency and real-time performance in geological mechanics understanding, grouting effect evaluation, and construction control, thereby improving the safety and adaptability of tunnel construction in highly discrete strata.

[0035] The data acquisition module continuously acquires multi-source construction monitoring data after grouting and during tunnel excavation, including cross-hole tomography data, in-situ mechanical test data, core sampling data, and information on surrounding rock deformation, support stress, and pore water pressure. The cross-hole tomography detection device is deployed based on the grouting holes. Measuring points are arranged along the borehole profile where the grout body is located. Spatial distribution information of the grout in the region is obtained by measuring segment by segment from adjacent boreholes. The relative resistivity change at each measuring point is expressed as:

[0036] and These are the first and second days before and after grouting. Resistivity at each measuring point; Trans-hole acoustic tomography is used to reflect changes in formation wave velocity and integrity. The wave velocity changes at the measurement points can be expressed as follows:

[0037] and These are the first and second days before and after grouting. The wave velocity at each measuring point, passing through each measuring point The spatial distribution of resistivity and wave velocity is used to assess the impact of grouting on the formation's mechanical state. These resistivity and wave velocity variation indices form a continuous spatial parameter field, providing continuous spatial constraints for dynamic inversion.

[0038] In-situ mechanical test data are obtained directly under in-situ conditions of the formation by setting up test holes in the grouting reinforcement zone and using methods such as pressure meter test, in-hole load test, in-hole shear test and water pressure test to obtain the deformation modulus, strength parameters and permeability characteristics of the formation. This data is used to characterize the real changes in the mechanical and hydraulic properties of the formation before and after grouting.

[0039] Before excavation, core sampling is prioritized in geophysical inversion anomaly zones, selecting areas with significant differences in grouting effects or potential risks. Core samples are used to conduct tests on compressive strength, splitting tensile strength, elastic modulus, and density. The measured mechanical parameters are compared and verified with the cross-hole tomography inversion results to correct deviations in the geophysical inversion, achieving effective integration of spatial continuity perception and on-site measured data, and ensuring that the evaluation of grouting effect has a valid basis.

[0040] The surrounding rock deformation data is obtained by setting up several monitoring sections along the tunnel axis in the excavated section. Multiple displacement gauges are installed at each monitoring section, and the crown settlement and surrounding convergence are measured periodically using an automatic total station. The cumulative deformation value and deformation rate per unit time are calculated. For deep surrounding rock deformation, multiple displacement gauges can be installed in the borehole to obtain the radial displacement changes at different depths, thereby forming a data sequence of surrounding rock deformation evolution over time, which is used to reflect the degree of influence of excavation disturbance on the stability of the surrounding rock.

[0041] Support stress data is acquired by deploying stress monitoring devices in the initial support structure. The stress response and temporal variation characteristics of the support components are collected to characterize the coordinated stress state between the support system and the surrounding rock. Support stress data and surrounding rock deformation data are recorded simultaneously to form a rock-support coordinated stress response curve.

[0042] Pore ​​water pressure data were obtained by deploying vibrating wire pore water pressure gauges in the grouting reinforcement zone and ahead of the tunnel face. The gauges were installed at different depths to monitor the dynamic response of groundwater pressure as construction progressed and the grouting sealing effect changed. By analyzing the amplitude and rate of change of pore water pressure, it was possible to determine whether the formation seepage channels were effectively sealed and the degree of influence of water pressure on the surrounding rock stability.

[0043] like Figure 2 As shown, the multi-source information fusion and dynamic inversion analysis module processes data from different sources uniformly based on the multi-source information acquired by the data acquisition module. Using the tunnel axis and grouting hole coordinates as a unified spatial reference, the grouting reinforcement zone is divided into three-dimensional spatial units, forming several spatial unit structures for data mapping and parameter updating. The trans-hole resistivity change field and wave velocity change field are mapped to the corresponding spatial units. The in-situ mechanical test results and the strength and modulus parameters obtained from borehole core sampling tests are located at the corresponding spatial positions. Furthermore, construction response data such as surrounding rock deformation, support stress, and pore water pressure are associated with the corresponding spatial units according to the monitoring section location, constructing a unified spatial and temporal data framework.

[0044] Within a unified spatial framework, various physical quantities are standardized, and resistivity change rate, wave velocity change rate, surrounding rock deformation rate, support stress growth rate, and in-situ modulus and strength parameters are converted into comparable index quantities, and classified into structural characterization indexes and mechanical behavior indexes according to their physical properties.

[0045] Based on the above classification results, structural characterization indicators and mechanical behavior indicators are jointly combined within each spatial unit to form unit-level equivalent set parameters. These equivalent set parameters simultaneously include information on the geological structure state and mechanical response characteristics, and are used to characterize the structural integrity and bearing capacity level of the spatial unit at the current construction stage.

[0046] Within the grouting reinforcement zone, the equivalent ensemble parameters of each spatial unit are summarized and consistently corrected to establish a comprehensive parameter expression system for the grouting reinforcement zone. This parameter system uniformly describes the spatial distribution characteristics of the grout body and the formation mechanical state, and serves as the input basis for the dynamic inversion and updating of the formation's equivalent mechanical parameters.

[0047] During the construction process, new monitoring data are continuously collected and incorporated into the fusion process. The equivalent set parameters of the corresponding spatial units are corrected and updated so that the comprehensive parameter expression results are adjusted synchronously with the construction stage and remain consistent with the actual site conditions.

[0048] Based on multi-source information fusion, a Bayesian inversion method is introduced to dynamically update the equivalent mechanical parameters of the formation. The system uses the formation structure zoning results and corresponding physical property parameters obtained from cross-hole tomography inversion as a basis, combined with the spatial distribution characteristics of the grouting body and the reinforcement influence range, to perform equivalent processing on the mechanical parameters at different spatial locations, forming a set of equivalent mechanical parameters that can characterize the overall mechanical behavior of the formation after grouting reinforcement. Based on this, a formation equivalent mechanical parameter system is established. The probability description serves as prior information for the inversion calculation.

[0049] As construction progresses, data from surrounding rock deformation monitoring, support stress monitoring, and measured mechanical parameters obtained from borehole core sampling are continuously input into the system and uniformly incorporated into the observation dataset D. The system establishes a response relationship between the observation data and the formation's equivalent mechanical parameters, enabling monitoring information to serve as constraints on these parameters in inversion calculations, forming the observation likelihood function. Based on the consistency between prior information and observational information, the probability distribution of the formation's equivalent mechanical parameters is updated according to the Bayesian inference criterion, expressed as:

[0050] This is a vector of formation equivalent mechanical parameters; The prior probability distribution established for the geophysical inversion results; It is a set of observation data consisting of construction monitoring data and borehole core sampling data; Let the likelihood function of the observed data be given the parameters. This is the updated posterior parameter distribution; to achieve the correction and update of the formation equivalent mechanical parameters from the prior distribution to the posterior distribution.

[0051] This process is repeated in stages as construction progresses. After each construction stage is completed, new observation datasets are constructed using newly added construction monitoring data and, if necessary, supplementary core sampling test results. The existing parameters are then further refined, and the posterior distribution obtained in the current stage is used as the prior distribution for the next stage. ,Right now

[0052] For the current stage of observation data D k Given the posterior probability distribution of parameter θ.

[0053] Through the above process, the stratigraphic equivalent parameters Through continuous integration of multi-source information and constant correction via Bayesian updates, the model gradually approaches the actual state as construction progresses, ensuring that the grout distribution model and surrounding rock parameters remain synchronized with on-site measured data, thus providing an effective parameter basis for comprehensive evaluation of surrounding rock stability and control of construction parameters.

[0054] like Figure 3-4 As shown, the stability comprehensive evaluation and risk classification module is based on the set of equivalent mechanical parameters and combines the surrounding rock deformation monitoring data during construction. It uses the grouting column entity detection results obtained from borehole core sampling as auxiliary verification information in the evaluation. Specifically, the system comprehensively processes multiple evaluation results, including the spatial distribution of the grouting body, changes in the formation's equivalent mechanical parameters, surrounding rock deformation, support stress, measured strength parameters from borehole core sampling, and construction monitoring response, to form the Comprehensive Stability Index (CSI). This CSI is used to quantitatively characterize the overall stability level of the surrounding rock at the current construction stage and can be expressed as:

[0055] Indicates the first The stability evaluation result can be determined by the corresponding monitoring data and inversion parameters. It is obtained by weighted fusion of measured response information and inversion analysis results, with a value range of 0 to 1. The monitoring data includes grouting pressure, flow rate, depth information, as well as the grouting volume and grouting uniformity index of the section calculated from it. It also includes parameters such as crown settlement, perimeter convergence and its rate of change collected by the surrounding rock deformation monitoring system. As a comprehensive function, a weighted summation is adopted, and the weights of each evaluation index are dynamically adjusted according to their influence on surrounding rock stability and their deviation from the safe control range. When the deviation of a certain index from the safe range increases, its corresponding weight increases accordingly, thereby enhancing the dominant role of that index in the CSI and reflecting the engineering characteristics of surrounding rock stability being influenced by multiple factors and dominated by local adverse factors.

[0056] The CSI (Combined Stability Index) is based on the degree of deviation of each evaluation index from its corresponding safe control range. When grouting uniformity decreases, formation mechanical parameters deteriorate, the deformation rate of the surrounding rock increases, or the response to construction disturbance intensifies, the deviation of the corresponding index from the safe control range increases accordingly, and the CSI changes synchronously, thus achieving a synchronous characterization of the stability state of the surrounding rock. As construction progresses, changes in the CSI can intuitively reflect the gradual evolution of the surrounding rock stability from a safe condition to a critical condition and even an unstable condition.

[0057] After obtaining the CSI (Construction Risk Index), the system further performs a construction risk level assessment. This assessment process includes four stages: spatial unit identification, section comprehensive analysis, time trend determination, and graded output.

[0058] At the spatial level, spatial units defined by the multi-source information fusion stage are used as the basic evaluation units. The CSI (Comprehensive Stability Index) of each unit is calculated to identify areas with uneven grout distribution and weak local mechanical properties. If a unit exhibits significantly insufficient grout uniformity, or its equivalent strength or modulus parameters are significantly lower than the design targets, or the rate of deformation of the surrounding rock and the rate of increase in support stress exceed control values, the CSI of that unit will decrease preferentially and it will be identified as a potential risk unit. Based on this, the CSIs of multiple spatial units within a certain influence range ahead of the tunnel face are weighted and integrated to form the section comprehensive stability index (CSI). seg During the weight allocation process, the weight of units close to the working face or those sensitive to monitoring response should be appropriately increased to avoid local risks being masked by the overall average effect, so that the segment index can truly reflect the stability of key stress areas.

[0059] In terms of time dimension, CSI for continuous construction phases seg Perform comparative analysis to calculate the inter-stage differences and rates of change. If CSI... seg If the surrounding rock stability is continuously decreasing even within the same classification range, or if the rate of decrease exceeds a preset threshold per unit time, it is determined that the stability is in a deteriorating trend and is upgraded in the risk level assessment. This trend discrimination mechanism allows risk identification to no longer be limited to absolute values ​​at a single moment, but to simultaneously reflect the evolution direction and intensity of the stable state.

[0060] After completing the spatial and temporal integrated analysis, CSI will be... seg The system is matched with a preset stability grading range to form the final construction risk level. When the index approaches or exceeds the control threshold, it triggers the corresponding level of early warning information, providing a continuous and quantitative basis for subsequent construction decisions and control.

[0061] The stability grading range is determined comprehensively based on the principles of surrounding rock stability grading, construction monitoring response characteristics, numerical simulation analysis results, and existing tunnel engineering construction experience. According to the engineering grading standards for surrounding rock stability, the surrounding rock state is divided into stable, basically stable, controllable unstable, and unstable levels. Statistical analysis is performed on the monitoring data under each level of working condition to extract the deviation of key indicators such as surrounding rock deformation, deformation rate, support stress level, and grouting uniformity from the safe control range, and these are uniformly categorized into the 0-1 range. Combined with numerical simulation results, the critical change characteristics of the surrounding rock response under different stability levels are compared and verified, thereby determining the typical value range of CSI at each stability level.

[0062] The specific grading standards are as follows: CSI ≥ 0.8 indicates that the surrounding rock is generally stable and the response to construction disturbance is small; 0.7 ≤ CSI < 0.8 indicates that the stability of the surrounding rock is controllable but there is some heterogeneity; 0.5 ≤ CSI < 0.7 indicates that the stability of the surrounding rock is close to the control boundary; 0.4 ≤ CSI < 0.5 indicates that the stability of the surrounding rock is poor and requires sectional excavation and reinforced support measures; CSI < 0.4 indicates that the surrounding rock is in a high-risk or unstable state. The above grading thresholds are determined comprehensively based on engineering monitoring data, numerical simulation results, and construction experience, and can be modified and calibrated according to specific engineering geological conditions, burial depth, groundwater conditions, and support system type.

[0063] Through a continuous judgment process from indicator construction, spatial integration, trend identification to interval matching, the dynamic output of construction risk level is realized, so that the risk identification results can reflect the spatial differences and temporal evolution characteristics of the surrounding rock stability state under highly discrete strata conditions, and enhance the consistency between risk classification results and actual construction conditions.

[0064] The construction decision-making and control module uses CSI and its changing trends as core inputs. It also incorporates the spatial distribution characteristics of the grouting body and monitored and identified risk scenarios. From a pre-established set of excavation methods and a rule base mapping construction parameters, it intelligently selects excavation methods and parameter combinations that match the stability of the surrounding rock. The set of excavation methods includes at least the full-face method, bench method, CD method, CRD method, and double-sidewall pilot tunnel method. The system uses CSI as the core decision variable and establishes a deterministic correspondence between excavation methods and CSI value ranges by setting stability grading thresholds. The decision-making process is as follows:

[0065] M represents the excavation method output by the construction decision and control module.

[0066] The construction decision and control module outputs a parametric control suggestion for the construction plan, which includes at least single-cycle advance, step excavation sequence, support type, and support timing. This can serve as an operational standard for on-site construction adjustments.

[0067] Among them, the multi-source information fusion and dynamic inversion analysis module, the stability comprehensive evaluation and risk classification module, and the construction decision and control module work in sequence according to the data processing order.

[0068] The multi-source information fusion and dynamic inversion analysis module is based on the Bayesian update framework. It updates the equivalent mechanical parameters of the formation in stages, integrates the parameter model, and outputs a set of equivalent parameters that reflects the mechanical state of the formation at the current construction stage. The set of equivalent parameters is passed as input data to the stability comprehensive evaluation and risk classification module for calculation and updating of relevant stability evaluation indicators.

[0069] The stability comprehensive evaluation and risk classification module is based on the set of equivalent mechanical parameters and combines the spatial distribution characteristics of the grouting body, the surrounding rock deformation monitoring data and the support stress monitoring data to construct a comprehensive stability index (CSI) to characterize the overall stability state of the surrounding rock at the current construction stage.

[0070] The construction decision and control module receives the Comprehensive Stability Index (CSI) and its variation characteristics, and combines it with the grouting spatial distribution information to generate construction control instructions corresponding to the current surrounding rock stability state by matching them with the pre-established excavation method and construction parameter mapping rule library.

[0071] Through the continuous and coordinated work of multi-source information fusion, dynamic inversion, comprehensive stability evaluation, and construction decision control, the system can form a construction control mechanism corresponding to the evolution of the formation state during the construction process.

[0072] The multi-source information fusion and Bayesian dynamic inversion module provides updated geological equivalent mechanical parameters as construction progresses. Based on this, the stability comprehensive evaluation and risk classification module constructs a comprehensive stability index (CSI) to uniformly characterize the surrounding rock stability. The construction decision-making and control module adjusts the excavation method and construction parameters according to the CSI's value range and its changing trend. The construction control results are simultaneously constrained by the evolution characteristics of the geological equivalent mechanical parameters and the construction monitoring response, enabling the construction plan to be dynamically adjusted according to changes in the surrounding rock stability and maintaining continuity and consistency between construction stages.

[0073] The adaptive feedback and optimization module uses newly added monitoring data and, when necessary, supplementary core sampling results as feedback information, re-inputting them into the multi-source information fusion and dynamic inversion analysis module to update the formation's equivalent mechanical parameters. The updated set of equivalent mechanical parameters is then passed as new input data to the stability comprehensive evaluation and risk classification module, recalculating the Comprehensive Stability Index (CSI) and determining the risk level. Subsequently, the construction decision-making and control module modifies the mapping rules between excavation methods and construction parameters based on the updated CSI and its changing trends. By analyzing the differences between actual construction response and predicted control effects, the module continuously optimizes the numerical model parameters and excavation method matching strategies, forming a closed-loop control mechanism that interconnects parameter updates, risk reassessment, and decision-making strategy correction, thereby achieving continuous optimization of the system's cognitive and construction control capabilities.

[0074] Through the above technical solutions, this invention realizes the transformation of grouting effect evaluation from qualitative experience to quantitative objective cognition, enables the transition of surrounding rock mechanical parameters from static assumptions to dynamic updates, and allows tunnel excavation methods and construction parameters to be intelligently adjusted according to changes in the actual state of the strata, significantly improving the safety, adaptability and engineering controllability of tunnel construction in highly discrete strata.

[0075] Example 2 like Figure 5 As shown, the application of a dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata is realized based on the dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata described in Example 1.

[0076] Taking a section of a shallow-buried urban tunnel that passes through a highly discrete water-rich sand layer interbedded with strongly weathered rock as the engineering background, the surrounding rock structure in this section is loose and the risk of uneven grouting diffusion is high. Before construction, advanced full-section grouting was used for reinforcement treatment.

[0077] Before construction, cross-hole resistivity and cross-hole acoustic tomography probes were laid out in front of the tunnel face to form a three-dimensional probe profile of the grouting area. Core drilling holes were also reserved at key locations. After grouting, the data acquisition module was first activated to obtain cross-hole tomography data and calculate the resistivity change at each measuring point. and wave speed change This provides an initial indication of the slurry diffusion range and the spatial continuity of the reinforced area.

[0078] The tomography results revealed that the resistivity variation was small and the wave velocity increase was not significant in some areas. The system identified this section as a "suspected weak grouting zone," and core sampling was performed at the corresponding location. The core sampling results showed that the grout bonding in this area was insufficient, and the compressive strength and elastic modulus were significantly lower than the design target values. The core sampling test data was input into the multi-source information fusion and dynamic inversion analysis module, and together with the cross-hole tomography results and in-situ test data, it served as observation constraints. A Bayesian update process was introduced to correct the formation equivalent mechanical parameters, making the inversion model closer to the actual grouting reinforcement state.

[0079] Subsequently, during tunnel excavation, data on surrounding rock deformation, support stress, and pore water pressure were continuously collected and updated synchronously with the dynamic inversion results. The comprehensive stability evaluation and risk classification module integrates the spatial distribution of grouting body, geological parameters, and surrounding rock deformation response to calculate the comprehensive stability index (CSI).

[0080] In this embodiment, during the initial stage of the first excavation, the CSI was calculated to be approximately 0.62 based on initial monitoring and inversion data, indicating a moderately stable range. The system determined that the overall stability of the surrounding rock was controllable, but there was a risk of localized weak points. Based on this, the construction decision-making and control module matched the small-scale step method and provided construction parameter suggestions, including controlling the single-cycle advance to 1.2–1.5 m, strengthening the advanced small-diameter pipe support, and shortening the initial support closure time.

[0081] As construction progresses, monitoring data of the surrounding rock indicates that the settlement rate of the arch crown is gradually decreasing, and the deformation of the surrounding rock before the secondary lining is stabilizing. After dynamic inversion and updating, the CSI has improved to 0.75. The construction plan has been adjusted, and it is recommended to transition to the larger advance step method to improve construction efficiency.

[0082] Upon entering the next highly discrete section, monitoring showed an increase in local deformation rate, significant fluctuations in pore water pressure, and a drop in CSI to 0.48. The system immediately switched the excavation mode to the CRD method and output control parameters for sectional excavation, rapid closure, and reinforced support. Based on this, the construction site adjusted the construction methods in a timely manner, effectively avoiding the risk of surrounding rock instability.

[0083] Throughout the construction process, the adaptive feedback and optimization module continuously feeds back newly acquired monitoring data and core sampling supplementary test results to the inversion and decision module, dynamically correcting the formation parameter model and excavation method matching rules, so that the system's understanding of the surrounding rock condition is constantly closer to the real working conditions.

[0084] It can be seen that the present invention can organically integrate cross-hole detection, core sampling and construction monitoring information, construct the surrounding rock stability evaluation results in real time, and use CSI as a unified control index to realize the closed-loop control of "dynamic evaluation of grouting effect - quantitative identification of surrounding rock stability - intelligent matching of excavation method - real-time adjustment of construction parameters". It realizes the intelligent control closed loop driven by the actual state of the strata, which significantly improves the safety and controllability of tunnel construction under highly discrete strata conditions.

[0085] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementation methods. Clearly, many modifications and variations can be made based on the content of this specification. The selection and specific description of these embodiments in this specification are intended to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata, characterized in that, include: The data acquisition module is used to acquire cross-hole tomography data, in-situ mechanical test data, borehole core sampling data, and construction monitoring data on surrounding rock deformation, support stress, and pore water pressure during tunnel excavation and after grouting construction, and to uniformly identify and associate the data according to spatial location and construction stage. The multi-source information fusion and dynamic inversion analysis module is used to perform unified fusion processing on the multi-source information acquired by the data acquisition module, and to perform phased updates on the equivalent mechanical parameters of the formation based on the Bayesian update framework, outputting a set of equivalent parameters that reflect the mechanical state of the formation at the current construction stage. The stability comprehensive evaluation and risk classification module is used to construct a comprehensive stability index (CSI) based on the equivalent mechanical parameter set, combined with construction monitoring data and grouting test results; and to determine the construction risk level by comparing the CSI with a preset stability classification interval. The construction decision and control module is used to receive the comprehensive stability index (CSI) and its variation characteristics, and, in combination with the grouting spatial distribution information, match and generate parameterized control instructions in the pre-established excavation set and construction parameter mapping rule library. The adaptive feedback and optimization module is used to monitor new data in real time during the construction process, analyze and adaptively adjust the parameters of the above modules, forming a closed-loop control process of detection-inversion-evaluation-decision-feedback.

2. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 1, characterized in that, The cross-hole tomographic detection data is obtained based on cross-hole resistivity tomography and / or cross-hole acoustic tomography. Multiple sets of transmitting and receiving units are deployed in the borehole. Through excitation and response acquisition under the combination of borehole position and depth, physical property parameters reflecting the slurry diffusion range, spatial connectivity and filling uniformity are obtained. The in-situ mechanical test data were obtained through pressure meter tests, borehole load tests, borehole shear tests, and water pressure tests, and were used to reflect the changes in strength, stiffness, and permeability of the formation under in-situ conditions before and after grouting. The core sampling data includes test results of compressive strength, splitting tensile strength, elastic modulus, and density of the extracted core samples. These test results are used to compare and verify the equivalent mechanical parameters obtained from geophysical inversion, and to correct and optimize the deviation parameters.

3. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 1, characterized in that, The multi-source information fusion and dynamic inversion analysis module performs unified fusion processing on the multi-source information acquired by the data acquisition module. Specifically, The cross-hole tomography data, the construction monitoring data, and the measured mechanical parameters obtained from borehole core sampling are uniformly integrated and processed to construct a comprehensive parameter model, and form an equivalent parameter set that reflects the geological structure characteristics and mechanical state of the grouting reinforcement zone.

4. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 3, characterized in that, The multi-source information fusion and dynamic inversion analysis module updates the equivalent mechanical parameters of the formation in stages, and outputs a set of equivalent parameters reflecting the mechanical state of the formation at the current construction stage. Specifically, Using the equivalent mechanical parameters output by the comprehensive parameter model as prior knowledge, and the newly added monitoring data of surrounding rock deformation and support stress during construction as observation constraints, the probability distribution of formation parameters is continuously updated through Bayesian inference. The core relationship is expressed as follows: This is a vector of formation equivalent mechanical parameters; The prior probability distribution established for the geophysical inversion results; It is a set of observation data consisting of construction monitoring data and borehole core sampling data; Let the likelihood function of the observed data be given the parameters. This represents the updated posterior parameter distribution.

5. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 1, characterized in that, The stability comprehensive evaluation and risk classification module, based on an equivalent set of mechanical parameters and combined with construction monitoring data and grouting test results, constructs a comprehensive stability index (CSI), specifically as follows: Based primarily on the set of equivalent mechanical parameters and construction monitoring data, and using the grouting column detection results obtained from borehole core sampling as auxiliary verification information, a comprehensive stability index (CSI) is constructed to characterize the overall stability of the surrounding rock at the current construction stage through comprehensive identification of grouting effect characteristics, formation mechanical state, and surrounding rock deformation response.

6. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 1, characterized in that, The excavation methods include full-section excavation, bench excavation, CD method, CRD method, and double-sidewall pilot tunnel method.

7. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 1, characterized in that, The parameterized control commands include excavation methods, single-cycle advance, support parameters, and construction sequence.

8. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 7, characterized in that, The construction decision-making and control module matches and generates parameterized control commands, specifically, The construction decision-making and control module classifies and matches tunnel excavation methods based on the stability range of the Comprehensive Stability Index (CSI). When the CSI is within the safe range and the grouting effect is uniform, the full-section method or the large-scale step method should be used. When the CSI is within a controllable range and there is uneven grouting or local risk, the small-step step method or CD method should be matched, and the support measures should be strengthened. When the CSI is located in the warning zone or danger zone, the CRD method or the double-sided wall pilot tunnel method should be used, and step-by-step excavation, rapid closure and enhanced support control measures should be adopted.

9. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 8, characterized in that, The initial values ​​of the safe zone, controllable zone, warning zone, and danger zone of the Comprehensive Stability Index (CSI) are determined based on the principles of surrounding rock stability classification, construction monitoring response characteristics, numerical simulation analysis results, and existing tunnel engineering construction experience.

10. The dynamic evaluation and intelligent tunnel excavation control system for grouting toughening of highly discrete strata according to claim 1, characterized in that, The adaptive feedback and optimization module forms a closed-loop control process of detection-inversion-evaluation-decision-feedback, specifically as follows: The newly added monitoring data and core sampling results during construction are used as feedback inputs to the multi-source information fusion and dynamic inversion analysis module and the construction decision and control module. Based on the difference analysis between the actual construction response and the predicted control effect, the mapping rules of parameters and excavation methods are adaptively corrected to form a closed-loop control process of detection-inversion-evaluation-decision-feedback.

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