Method and device for dynamically optimizing support parameters of TBM (tunnel boring machine) penetrating through soft and broken stratum

Abstract

The invention provides a dynamic optimization method and device for support parameters of a TBM (tunnel boring machine) crossing a weak and broken stratum, and the method comprises the steps: carrying out a rebound test on field surrounding rock through a rebound strength meter, and obtaining a rebound strength value; constructing a correlation model of the rebound strength of the field surrounding rock and the surrounding rock strength parameters; drawing up and executing a support scheme based on the correlation model; and monitoring the rebound strength, the actual support parameters and the actually measured deformation data of the field surrounding rock, calibrating and optimizing the correlation model, and updating the support scheme. According to the method, the accuracy and the adaptability of support design are remarkably improved, the problem of contradiction between support and tunneling efficiency caused by high variability of surrounding rock in a soft and broken stratum in the prior art is solved, accurate dynamic optimization of TBM support parameters is realized, the tunneling efficiency is remarkably improved on the premise of ensuring construction safety, and the construction cost is reduced. And the engineering cost is reduced, and an innovative solution is provided for TBM construction in the soft and broken stratum.

Description

Technical Field

[0001] This invention relates to the field of tunnel and underground engineering design technology, and in particular to a method and apparatus for dynamic optimization of support parameters for TBMs traversing weak and fractured strata. Background Technology

[0002] Open-face tunnel boring machines (TBMs) are widely used in long tunnel projects due to their high excavation efficiency and good tunnel quality. However, traditional TBM construction faces severe challenges when traversing weak, fractured, and highly variable strata (such as argillaceous sandstone, carbonaceous shale, and fault fracture zones). Due to the high spatial variability of the surrounding rock's mechanical properties in such strata, even within the same lithology, differences in the content and cementation of minerals such as argillaceous and sandy materials can lead to significant variations in rock strength, deformation modulus, and self-stabilizing capacity over short distances. Currently, open-face TBM construction often employs pre-designed, relatively fixed support schemes (generally configuring steel arch spacing and the number of anchor bolts based on the surrounding rock classification and tunnel depth). This relatively fixed design mode has the following drawbacks: 1. When the surrounding rock conditions are better than expected, high-strength support is still used, which leads to material waste. In addition, the support operations such as steel arch frame installation and anchor bolt (cable) drilling seriously encroach on the tunneling time and reduce the tunneling efficiency of TBM.

[0003] 2. When the surrounding rock conditions suddenly become worse than expected, the preset support strength may not be sufficient to resist the surrounding rock pressure, which may lead to excessive deformation of the surrounding rock and even cause geological disasters such as machine jamming and collapse. The downtime caused by dealing with these adverse geological problems is longer.

[0004] Existing methods for optimizing support parameters generally employ empirical analogy and numerical simulation. The former is highly subjective and cannot adapt to rapid fluctuations in the strength of the surrounding rock, easily leading to over-support or under-support. The latter relies on preset surrounding rock parameters, which are difficult to solve the problem of rapid changes in the mechanical parameters of the surrounding rock in highly variable strata. The calculation results usually deviate greatly from the actual working conditions on site, resulting in poor practicality of the optimization results.

[0005] There is currently no effective solution to the problem of conflicting support and tunneling efficiency caused by the high variability of surrounding rock in soft and fractured strata in existing related technologies. Summary of the Invention

[0006] This invention provides a method and apparatus for dynamically optimizing support parameters for TBMs traversing weak and fractured strata, in order to address the shortcomings of existing related technologies where the high variability of surrounding rock in weak and fractured strata leads to a contradiction between support and tunneling efficiency.

[0007] In a first aspect, the present invention provides a method for dynamically optimizing support parameters for a TBM traversing weak and fractured strata, comprising: The rebound strength value was obtained by conducting a rebound test on the surrounding rock at the site using a rebound strength tester. Construct a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock in the field; Based on the aforementioned correlation model, a support plan is formulated and implemented; Monitor the rebound strength of the surrounding rock, actual support parameters, and measured deformation data at the site, and calibrate and optimize the associated model to update the support scheme.

[0008] According to the present invention, a method for dynamic optimization of support parameters for TBM traversing weak and fractured strata includes obtaining rebound strength values ​​by conducting rebound tests on the surrounding rock in the field using a rebound strength tester, comprising: Determine the sampling section for conducting rebound tests on the surrounding rock at the site, and set up the measurement area and measurement points on the sampling section; A rebound test was conducted on the surrounding rock at the site to obtain experimental data. Outliers were removed from the experimental data to determine the representative value of the rebound strength of the current test area. The rebound strength value of the current acquisition section is determined based on the average of the representative rebound strength values ​​of different measurement areas on the same acquisition section.

[0009] According to the present invention, a dynamic optimization method for support parameters of a TBM traversing weak and fractured strata is provided, which constructs a correlation model between the rebound strength and the strength of the surrounding rock in the field, including: Within the same lithological strata, select tunnel sections that have been supported and whose support deformation has stabilized, and establish a sequence SDS; The rock mass mechanical parameters of the surrounding rock at the site are determined according to the SDS sequence; the rock mass mechanical parameters include rock mass shear modulus and rock mass shear strength parameters, and the rock mass shear strength parameters include cohesion and internal friction angle; Based on the aforementioned rock mechanics parameters, a correlation model between the rebound strength and cohesion of the surrounding rock in the field is established. Based on the aforementioned correlation model, an expression for the deformation of surrounding rock expressed using rebound strength is constructed.

[0010] According to the present invention, a method for dynamic optimization of support parameters for TBM traversing weak and fractured strata is provided, which obtains the rock mass shear modulus of the surrounding rock in the field, including: The basic shear modulus was obtained based on the geological survey data of the surrounding rock at the site, and the dynamic shear modulus value was calibrated by reducing it using the elastic wave method. The dynamic shear modulus value is reduced and converted to obtain the reference range of static shear modulus value; If the basic shear modulus falls within the reference range of the static shear modulus, the basic shear modulus obtained from the geological survey data is directly used as the rock mass shear modulus of the surrounding rock at the site. Otherwise, an in-situ load test is conducted on the surrounding rock at the site to obtain the rock mass shear modulus corresponding to the current lithology.

[0011] According to the present invention, a method for dynamic optimization of support parameters for TBM traversing weak and fractured strata is provided to obtain the cohesion of the surrounding rock in the field, including: Calculate the support resistance of the support structure to the surrounding rock based on the on-site support parameters; By combining the SDS sequence with the Castner solution and the support resistance, the cohesion of the surrounding rock in the field is calculated, and a correlation data sequence between the rebound strength value and the cohesion is established.

[0012] According to the present invention, a dynamic optimization method for support parameters of a TBM traversing weak and fractured strata is provided. Based on the rock mass mechanical parameters, a correlation model between the rebound strength and cohesion of the surrounding rock in the field is established, including: For relatively intact rock masses, linear correlation is used to perform regression analysis on the associated data sequences to obtain the correlation function; For moderately intact rock masses, regression analysis of the associated data sequences is performed using an exponential function correlation to obtain the correlation function. For weak and fractured surrounding rock, machine learning is used to obtain the correlation between rebound strength and cohesion, and the correlation function is obtained.

[0013] According to the present invention, a dynamic optimization method for support parameters of a TBM traversing weak and fractured strata is provided. Based on the aforementioned correlation model, an expression for the deformation of the surrounding rock expressed using rebound strength is constructed, including: Substituting the correlation function into the Castner solution, we obtain the expression for the support resistance and surrounding rock deformation expressed in terms of rebound strength.

[0014] According to the present invention, a dynamic optimization method for support parameters of a TBM traversing weak and fractured strata is provided, which formulates a support scheme based on the correlation model, including: Based on the expressions for the support resistance and deformation of the surrounding rock in the field, the maximum allowable deformation is determined, and the relationship between the rebound strength value of the surrounding rock in the field and the support scheme is established. Determine the relationship between support parameters based on conventional support schemes; Multiple rebound strength ranges are preset, and multiple support schemes are formulated based on the relationship of the support parameters.

[0015] According to the present invention, a method for dynamic optimization of support parameters for TBM traversing weak and fractured strata is provided, which monitors the rebound strength of the surrounding rock, actual support parameters, and measured deformation data, calibrates and optimizes the associated model, and updates the support scheme, including: If the deviation between the measured deformation data after support and the maximum allowable deformation exceeds the preset standard, the correlation model between the rebound strength value and the cohesion is further inverted based on the measured deformation data, and the support scheme and the correspondence between the support scheme and the rebound strength are updated.

[0016] Secondly, the present invention also provides a device for dynamically optimizing support parameters for TBMs traversing weak and fractured strata, comprising: The test module is used to conduct rebound tests on the surrounding rock in the field using a rebound strength tester to obtain the rebound strength value; The module is used to build a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock in the field. The formulation module is used to formulate and execute support schemes based on the association model; The optimization module is used to monitor the rebound strength of the surrounding rock, actual support parameters, and measured deformation data, and to calibrate and optimize the associated model to update the support scheme.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Real-time and precise matching of support parameters with formation variations: Traditional TBM support parameters are mostly pre-set based on preliminary geological surveys and surrounding rock classifications, making it difficult to adapt to the strength variations of weak and fractured strata over short distances. This invention, through on-site high-frequency rebound testing, can quickly obtain the surface strength index of the surrounding rock and establish a correlation model with key mechanical parameters such as rock mass cohesion, enabling support parameters to be dynamically adjusted according to spatial changes in surrounding rock strength. This method solves the problem of over-support or under-support caused by the "unchanging" design of traditional methods, achieving real-time and precise matching of support strength with formation conditions.

[0018] 2. Significantly improves TBM tunneling efficiency and construction economy: In soft and fractured strata, support work often accounts for 50%-80% of the TBM construction cycle. This invention rapidly determines the surrounding rock conditions through rebound strength, precisely controlling the support strength within the necessary range and avoiding unnecessary strong support work. When the surrounding rock conditions are good, the support strength can be appropriately reduced, decreasing the time spent on steel arch installation and anchor drilling; when the surrounding rock conditions are poor, the support can be strengthened in a timely manner to prevent excessive deformation leading to rock deformation encroachment and TBM jamming.

[0019] 3. Enhanced proactive risk control in construction of weak and fractured strata: This invention establishes an expression for surrounding rock deformation based on the Kästner solution, enabling the prediction of surrounding rock deformation trends under different support parameters based on rebound strength. This allows for assessment of the effectiveness of support before construction. Combined with pre-defined deformation requirements, it allows for proactive development of support parameter adjustment strategies, controlling surrounding rock deformation within a safe range and effectively preventing engineering accidents such as machine jamming and collapse. This predictive control based on mechanical principles changes the traditional passive "deform first, then reinforce" approach, significantly improving construction safety.

[0020] 4. Achieved intelligent data-driven support decision-making: This invention not only provides a rebound strength testing method but also constructs a complete technical system for data acquisition, model building, parameter optimization, and effect verification. By continuously accumulating field data (rebound value, support parameters, deformation), the relational model can be periodically calibrated and optimized, enabling the decision-making system to possess self-learning and adaptive intelligent characteristics. This data-driven approach reduces the subjectivity of relying on engineering experience, improves the scientificity and reliability of support decisions, and lays the foundation for intelligent TBM construction in complex strata.

[0021] 5. Excellent field applicability and engineering operability: The rebound hammer used in this invention is a common engineering device. The testing method is simple and easy to implement, requiring no complex operation, making it suitable for rapid implementation within the limited construction space of a TBM. The rebound test is organically combined with conventional support operations, and the test section layout is flexible (usually one section for every 2-4 arch frames), ensuring continuous data acquisition without affecting the construction progress. The support parameter adjustment scheme is presented in a clear correspondence table, allowing on-site technicians to quickly understand and implement it, demonstrating good value for engineering promotion and application. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the dynamic optimization method for support parameters of TBM traversing weak and fractured strata provided by the present invention; Figure 2 This is a schematic diagram illustrating the process of dynamically optimizing support parameters in an embodiment of the present invention; Figure 3 This is a schematic diagram of the longitudinal section of the rebound test arrangement in an embodiment of the present invention; Figure 4This is a schematic diagram of the cross-sectional arrangement of the rebound test in an embodiment of the present invention; Figure 5 This is a diagram showing the layout of measuring points within a single measuring area in an embodiment of the present invention; Figure 6 This is a graph showing the relationship between support resistance and surrounding rock displacement under different rebound strengths in this embodiment of the invention. Figure 7 This is a graph showing the relationship between support resistance and arch spacing in an embodiment of the present invention (other support parameters have a corresponding relationship with arch spacing). Figure 8 This is a schematic diagram of classic tunnel support in an embodiment of the present invention; Figure 9 This is a graph showing the regression analysis results of cohesion and resilience in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] This invention provides a method for dynamically optimizing support parameters for TBMs traversing weak and fractured strata. Figure 1 This is a flowchart of the dynamic optimization method for support parameters of TBM traversing weak and fractured strata provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step S101: Conduct a rebound test on the surrounding rock at the site using a rebound strength tester to obtain the rebound strength value; Step S102: Construct a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock in the field; Step S103: Formulate and implement a support plan based on the correlation model; Step S104: Monitor the rebound strength of the surrounding rock, actual support parameters and measured deformation data, and calibrate and optimize the associated model to update the support scheme.

[0026] In this method, firstly, the rebound strength value of the surrounding rock is collected at a high frequency at the construction site. Then, a correlation model between the rebound strength and strength parameters of the surrounding rock is constructed. Next, multiple support schemes are formulated and implemented based on this correlation model. Finally, the rebound strength, actual support parameters, and measured deformation data of the surrounding rock are continuously monitored, and the correlation model is optimized based on the monitoring results, thereby updating the support scheme. In the above process, an innovative dynamic optimization approach is introduced to address the shortcomings of traditional TBM support design in soft and fractured strata. This method establishes a real-time correlation between the rebound strength of the surrounding rock surface and support parameters through the rapidly measurable rebound strength index, significantly improving the accuracy and adaptability of support design. It solves the problem of the contradiction between support and tunneling efficiency caused by the high variability of surrounding rock in soft and fractured strata in existing related technologies, achieving precise dynamic optimization of TBM support parameters. While ensuring construction safety, it significantly improves tunneling efficiency and reduces engineering costs, providing an innovative solution for TBM construction in soft and fractured strata.

[0027] Figure 2 This is a schematic diagram illustrating the process of dynamically optimizing support parameters in an embodiment of the present invention, as shown below. Figure 2 As shown, in some embodiments, step S101, which involves conducting a rebound test on the surrounding rock at the site using a rebound strength tester to obtain the rebound strength value, includes: determining the sampling section for conducting the rebound test on the surrounding rock at the site, and setting up a test area and test point on the sampling section; conducting a rebound test on the surrounding rock at the site, obtaining experimental data, removing outliers from the experimental data, and determining the representative value of the rebound strength of the current test area; and determining the rebound strength value of the current sampling section based on the average of the representative values ​​of the rebound strength of different test areas on the same sampling section.

[0028] In this embodiment, since the rebound hammer is originally used for concrete strength testing, for relatively weak surrounding rock, a rebound hammer with a wider range should be selected, or the equipment should be specially modified to avoid the surrounding rock strength being too low to measure the rebound value.

[0029] To accurately establish the relationship between surrounding rock strength parameters and rebound strength, rebound testing should be conducted continuously and in conjunction with support construction. Generally, a sampling section can be taken every four arch frames. Figure 3 As shown, Figure 3 This is a schematic diagram of the longitudinal section of the rebound test arrangement in an embodiment of the invention. When the properties of the surrounding rock change drastically, the sampling section is densified to one section for every two arch frames. Rebound tests should be conducted on-site as soon as possible after the surrounding rock is exposed. The rebound tests should cover the initial support deformation monitoring range, generally four parts: the arch crown, arch bottom, and left and right arch waists. One testing area is set up for each part, with a measuring point spaced 5 cm apart. Five rows are set up along the tunnel axis and five rows along the tunnel circumference, for a total of 25 measuring points. Figure 4As shown, Figure 4 This is a schematic diagram of the cross-section of the rebound test setup in an embodiment of the invention. After support is installed, the deformation of the cross-section where the field rebound test has been conducted should be monitored more intensively to form a rich sequence of rebound strength and deformation for subsequent use in establishing relevant relationship models.

[0030] like Figure 5 As shown, Figure 5 This is a diagram showing the layout of test points within a single test area in this embodiment of the invention. During the rebound test, it is important to remove outlier data points and use the average value after removing outliers as the representative value of the rebound strength for that test area. The specific formula is as follows:

[0031] in, This represents the representative value of rebound strength. This represents the rebound value of each measuring point within the current measuring area after removing anomalies. This indicates the number of valid measurement points after removing outliers. i This indicates the measurement point number. Furthermore, for the same cross-section... The average of the representative rebound strength values ​​for each test area is used to obtain the rebound strength value for that cross-section. The specific formula is as follows:

[0032] in, Indicates the rebound strength value. This indicates the number of survey areas for that cross section. This indicates the survey area number.

[0033] In some embodiments, step S102, constructing a correlation model between the rebound strength and the strength of the surrounding rock at the site, includes: selecting tunnel sections that have been supported and whose support deformation has stabilized within the same lithological strata, and establishing a sequence SDS; determining the rock mass mechanical parameters of the surrounding rock at the site based on the sequence SDS; the rock mass mechanical parameters include the rock mass shear modulus and the rock mass shear strength parameters, which include cohesion and internal friction angle; establishing a correlation model between the rebound strength and cohesion of the surrounding rock at the site based on the rock mass mechanical parameters; and constructing an expression for the deformation of the surrounding rock expressed by the rebound strength, in conjunction with the correlation model.

[0034] The rebound strength value measured by the rebound hammer reflects the surface hardness and elasticity of the surrounding rock, and is a composite index related to both the strength and elastic modulus of the surrounding rock. In the same lithological strata, a tunnel section with completed support and stable support deformation (crown settlement rate <0.1 mm / d, horizontal convergence deformation rate <0.2 mm / d) is selected to establish a sequence SDS (Strength-Design-Response) [historical rebound strength value]. The parameters used for the support (steel arch frame type and spacing, length and spacing of system anchor bolts (cables), grade and thickness of shotcrete, etc.) and the measured deformation (average cross-sectional deformation) data after support were used. With the above data sequence, the rock mass mechanical parameters can be further deduced: Obtaining the rock mass shear modulus of the surrounding rock at the site includes: obtaining the basic shear modulus based on the geological survey data of the surrounding rock at the site, and calibrating it using the elastic wave method to obtain the dynamic shear modulus value; reducing and converting the dynamic shear modulus value to obtain the reference range of the static shear modulus value; if the basic shear modulus falls within the reference range of the static shear modulus value, the basic shear modulus obtained from the geological survey data is directly used as the rock mass shear modulus of the surrounding rock at the site; otherwise, in-situ load tests are further conducted on the surrounding rock at the site to obtain the rock mass shear modulus corresponding to the current lithology.

[0035] For example, rock mass shear modulus The shear modulus reflects the rock mass's resistance to shear deformation and is related to its lithology, but also exhibits some variability due to the rock mass's structure. To quickly and accurately obtain the shear modulus value of the rock mass exposed in the field, a basic shear modulus can be obtained based on previous geological survey data. The elastic wave method is used for calibration, and the specific formula is as follows:

[0036] in, This represents the dynamic shear modulus of the rock mass. Indicates the density of the rock mass. This represents the transverse wave velocity. The shear modulus measured by the elastic wave method is the dynamic shear modulus value. Under weak geological conditions, the dynamic shear modulus value will be higher than the static shear modulus value, requiring a reduction conversion. Empirically, a reduction of 3 to 5 times is recommended. If the shear modulus is obtained from geological survey data... It can fall within the dynamic shear modulus measured by the elastic wave method. Within the range of static shear modulus values ​​obtained after reduction (i.e.) lie in If the values ​​are within the range of lithology, geological survey data can be used directly. If they are not within this range, in-situ load tests should be conducted to supplement the shear modulus values ​​corresponding to this type of lithology.

[0037] The main parameters of rock mass shear strength include cohesion. and internal friction angle The internal friction angle Primarily determined by the properties of the rock mass itself, it is relatively stable and can be directly obtained by referring to geological exploration data, while cohesion... It is highly susceptible to changes in lithology and excavation disturbance, exhibiting significant uncertainty and variability. Obtaining the cohesion of the surrounding rock at the site includes: calculating the support resistance of the support structure to the surrounding rock based on the on-site support parameters; using sequential SDS combined with the Castner solution and support resistance to back-calculate the cohesion of the surrounding rock at the site, and establishing a correlation data sequence between the rebound strength value and the cohesion.

[0038] For example, the method of the present invention employs a data sequence. Inverse calculation of cohesion using Kästner's solution Values ​​are used to establish a data sequence. , [Historical rebound strength] Cohesion The specific formula is as follows:

[0039] in, Indicates the amount of deformation. Indicates the original rock stress. Indicates the angle of internal friction. This represents the cohesion of the rock mass to be determined. Indicates the radius of the tunnel. Indicates the rock mass shear modulus. The formula for the reaction force of the support structure to the surrounding rock is as follows:

[0040] in, Indicates the supporting force of the steel arch frame. Indicates the support force of shotcrete. This indicates the support force of the prestressed anchor cable.

[0041] The formula for calculating the support force of the steel arch frame is as follows:

[0042] in, This represents the cross-sectional area of ​​the steel arch frame. Indicates the yield strength of the steel arch frame. This indicates the spacing of the steel arch frames along the tunnel axis. This indicates the radius of the centerline of the steel arch frame.

[0043] The formula for calculating the support force of shotcrete is as follows:

[0044] in, This indicates the uniaxial compressive strength of shotcrete. Indicates the radius of the tunnel. This indicates the thickness of the sprayed concrete.

[0045] The formula for calculating the support force of prestressed anchor cables is as follows:

[0046] in, Indicates the design anchoring force of the prestressed anchor cable. Indicates the spacing of prestressed anchor cables. This indicates the spacing between prestressed anchor cables.

[0047] Based on rock mechanics parameters, a correlation model between the rebound strength and cohesion of the surrounding rock in the field was established, including: for relatively intact rock masses, regression analysis of the associated data sequence was performed using linear correlation to obtain the correlation function; for moderately intact rock masses, regression analysis of the associated data sequence was performed using exponential correlation to obtain the correlation function; for weak and fractured surrounding rock, machine learning was used to obtain the correlation between rebound strength and cohesion, and the correlation function was obtained.

[0048] In the above process, the data sequence has been obtained ( , [Historical rebound strength] Cohesion Based on this, regression analysis is performed to establish the rebound strength. With cohesion A correlation model between them. For example, in relatively intact rock masses, a model like this can be used. * + b Regression analysis was performed to determine the linear correlation between the two; in moderately intact rock masses, a regression analysis could be conducted using methods such as... Regression analysis was performed on the exponential function correlation; however, in weak and fractured surrounding rock, the correlation is more complex, and machine learning methods can be used to obtain the rebound strength. With cohesion The correlation between them. Through the above regression analysis, the correlation function can be obtained. .

[0049] Based on the correlation model, an expression for the deformation of surrounding rock expressed by rebound strength is constructed, including: substituting the correlation function into the Castner solution to obtain an expression for the support resistance and the deformation of surrounding rock expressed by rebound strength.

[0050] For example, by substituting the correlation function into the Kastner solution, we can obtain the expression for the support resistance and surrounding rock deformation (plastic zone) expressed in terms of rebound strength, as shown in the following formula:

[0051] The above formula can be further simplified to:

[0052] in, Indicates the original rock stress. Indicates the angle of internal friction. Indicates the radius of the tunnel. Indicates the rock mass shear modulus. This indicates the reaction force of the support structure on the surrounding rock.

[0053] In some embodiments, step S103, which involves formulating a support scheme based on an association model, includes: determining the maximum allowable deformation based on the expression of the support resistance and deformation of the surrounding rock at the site, and establishing the relationship between the rebound strength value of the surrounding rock at the site and the support scheme; determining the relationship of support parameters according to conventional support schemes; and pre-setting multiple rebound strength ranges and formulating multiple support schemes in combination with the relationship of support parameters.

[0054] For example, in the expression for support resistance and surrounding rock deformation expressed in terms of rebound strength, the parameter combination... If the deformation does not change significantly within a certain section of the tunnel, then the amount of deformation... Only with Relevant. Based on the construction requirements of the secondary lining structure and the allowable deformation, the maximum allowable deformation can be determined. Then, at this time... and They are negatively correlated, given the allowable deformation ( u max ) and one Within the range, a... The range, and This is related to the specific support scheme (parameters). This allows us to establish the on-site rebound value. The relationship with the support scheme, such as Figure 6 As shown, Figure 6 This is a graph showing the relationship between the reaction force of the support structure on the surrounding rock and the rebound strength value in an embodiment of the present invention.

[0055] According to conventional support schemes, there is a correlation between the parameters of steel arch frames, shotcrete, and prestressed anchor cables: 1. The spacing between anchor bolts (cables) should be 1 or 2 times the spacing between steel arch frames; 2. The thickness of the shotcrete should be flush with the steel arch frame, meaning the shotcrete thickness equals the cross-sectional height of the steel arch frame. Furthermore, the prestressed anchor cables should penetrate the loosened rock zone and be anchored 2-3 meters into the stable rock layer; that is, the anchor cable length should be equal to the radius of the plastic zone. According to Kästner's solution, the formula for calculating the radius of the plastic zone is:

[0056] in, Indicates the radius of the plastic zone.

[0057] Multiple rebound strength ranges are preset, corresponding to different steel arch frame installation spacings. < < < Based on the above support parameter relationships, a series of support schemes can be formulated. Given an acceptable deformation amount, calculations can be performed. and corresponding ,like Figure 7 As shown, Figure 7 This is a graph showing the relationship between the arch frame spacing and the support force in an embodiment of the present invention. (Based on actual field measurements...) exist[ When the area is within the specified range, support scheme 1 shall be adopted; when the actual on-site measurement is... exist[ When the area is within the specified range, support scheme 2 shall be adopted; when the actual on-site measurement is... exist[ When the value is within the specified range, support scheme 3 is adopted; similarly, a series of corresponding combinations of field-measured rebound strength values ​​and support schemes can be established to achieve dynamic adjustment of support schemes based on field rebound strength, thereby accelerating the construction progress while ensuring safety and stability.

[0058] In some embodiments, step S104 involves monitoring the rebound strength of the surrounding rock, actual support parameters, and measured deformation data, calibrating and optimizing the correlation model, and updating the support scheme. This includes: if the deviation between the measured deformation data after support and the maximum allowable deformation exceeds a preset standard, then the correlation model between the rebound strength value and cohesion is further inverted based on the measured deformation data, and the support scheme and the correspondence between the support scheme and the rebound strength are updated.

[0059] In practical applications, the rebound strength, actual support parameters, and measured deformation data are continuously monitored, as shown in Table 1. The established correlation model is periodically calibrated and optimized based on the deformation conditions. If the measured deformation value after support deviates by more than 20% from the allowable deformation value used in the calculation, the rebound strength value is further inverted based on the measured deformation value. and cohesion The relationship model is used to update the support scheme and its relationship with the rebound value. The correspondence.

[0060] Table 1 Rebound Strength - Support Parameters - Measured Deformation Record

[0061] Next, we will elaborate on the above method using a water diversion tunnel project as an example. The water diversion tunnel project adopts open-face TBM excavation with an excavation diameter of 10m. Typical support parameters are as follows: Figure 8 As shown, Figure 8 This is a schematic diagram of classic tunnel support in an embodiment of the present invention. The inner diameter of the tunnel lining is 8.4m, and the lining is made of C40 reinforced concrete with a thickness of 40cm. The initial support uses H175 box-type arch frames with a frame spacing of 75cm. The entire cross-section uses 15m long prestressed anchor cables with a designed anchoring force of 350kN and a spacing of 1.5m between rows. The side arches are sprayed with C25 polypropylene coarse fiber concrete with a thickness of 17.5cm. In soft rock strata, the TBM side excavation function is activated, and the side excavation is increased by 6.25cm. To prevent damage to the initial support, the maximum allowable incremental deformation after the steel arch frame is constructed is 11cm. Considering that the 6.25cm side excavation in the shield area has already been completed before the steel arch frame is constructed, the maximum allowable deformation of the tunnel surrounding rock is 17.25cm. Once this value is exceeded, there will be a significant risk of initial support failure and arch replacement.

[0062] During the actual excavation, the tunnel traversed soft rock strata and fault fracture zones, with drastic changes in lithology along the route. When traversing a section of soft rock with high ground stress, the surrounding rock conditions suddenly deteriorated, and the on-site support plan failed to be strengthened in time, causing large deformation of the surrounding rock. This resulted in the equipment getting stuck, and it took several months for the TBM to be freed. Furthermore, it was necessary to replace the arches of the relevant tunnel sections.

[0063] Subsequently, this method was used to conduct intensive field tests of rebound strength and elastic wave tests on the exposed rock mass behind the open-face TBM shield. Combined with geological survey data, the correlation between rebound strength and surrounding rock strength parameters was established. Furthermore, the correlation between rebound strength and support scheme was derived, specifically: 1. Acquisition of on-site monitoring data After the TBM shield is exposed in the surrounding rock, a monitoring section is set up every 4 times the arch frame spacing (3m). A total of 100 sections are set up in the test section. The rebound value is measured at the top arch, arch waist and arch bottom of the monitoring section using a rebound hammer. At the same time, the dynamic shear modulus of the rock mass of the monitoring section is obtained by elastic wave method.

[0064] After summarizing, the elastic wave shear wave velocities within the test section ranged from 1000 m / s to 2000 m / s, with approximately 80% falling between 1100 m / s and 1500 m / s. This demonstrates that the deformation parameters of the rock mass exhibit relatively low variability within the same lithological strata. The median value was then taken. The velocity is 1300 m / s. The dynamic shear modulus in this test section is calculated to be 4.5 GPa. After empirical reduction, the reasonable range of the static shear modulus is (0.9 GPa~1.5 GPa). On-site geological confirmation indicates that the lithology in this section is carbonaceous shale. Previous geological data shows that the static shear modulus for this type of surrounding rock is 1.2 GPa. Since this value is within the reasonable range for dynamic shear modulus verification, it is adopted.

[0065] The measured values ​​of rebound strength in the field varied considerably and were directly proportional to the average deformation of the monitored section. After removing abnormal monitoring values, the SDS sequence [rebound strength value, support parameters, deformation] for this test section was obtained, as shown in Table 2.

[0066] Table 2 Typical sections of rebound hammer test values ​​and deformation values

[0067] 2. Calculate rock mass strength parameters According to geological exploration results, the burial depth here is 1260m and the ground stress level is 32MPa.

[0068] The steel arch frame is an H175 box-type arch frame with a spacing of 75cm between arch frames. Calculations show that the steel cross-section of the steel arch frame provides support force. The mortar section of the steel arch frame provides support force of 0.4663 MPa. It is 0.2539 MPa.

[0069] The shotcrete is C25 concrete. Since deformation mainly occurs in the short period after excavation, when the shotcrete strength is still relatively low, the short-age strength of the shotcrete is taken as 10 MPa. The calculated support force provided by the shotcrete can be obtained. It is 0.3508 MPa.

[0070] The prestressed anchor cable has a pre-anchoring force of 350kN, and the spacing between rows is 1.5m. Calculations show that the prestressed anchor cable provides support force. It is 0.1587 MPa.

[0071] In summary, the total support capacity that the support system can provide is MPa.

[0072] Based on the preliminary geological data, the internal friction angle of carbonaceous shale is 27°. With the internal friction angle fixed, the cohesion of the rock mass is calculated using the Castner solution based on the above calculation conditions. The calculation results are shown in Table 3.

[0073] Table 3 Results of Back-Calculated Rock Mass Cohesion

[0074] 3. Establish the relationship between rock mass cohesion and rebound strength. Based on the above calculation results, an exponential function is used. Regression analysis was performed on the representative values ​​of cohesion and resilience in the form of [formula missing], and the results are as follows: Figure 9 As shown, Figure 9 This is a regression analysis result diagram of cohesion and resilience in an embodiment of the present invention, and the fitting formula is: The fitting results are good. =0.993119, RMSE=0.269692).

[0075] 4. Develop a support plan After the support is installed, the maximum deformation that meets the requirements of no damage to the initial support and no encroachment by the surrounding rock is 17.25cm. That is, the maximum allowable deformation under each support scheme cannot exceed 17.25cm. Based on this requirement, a matching scheme table for support parameters and rebound strength is formulated.

[0076] Based on this fitting formula, support schemes with different rebound strength ranges can be formulated, as shown in Table 4.

[0077] Table 4. Matching Table of Support Scheme and Backfill Strength and Design Deformation Amount

[0078] The above-mentioned scheme was applied to the subsequent tunnel excavation. The support scheme was adjusted in a timely manner according to the rebound value on site, which ultimately achieved efficient tunneling and safe support of the TBM. The large deformation phenomenon in the subsequent tunnel excavation section was greatly reduced, and the project time and economic benefits were obvious.

[0079] This invention also provides a device for dynamically optimizing support parameters for a TBM traversing weak and fractured strata. The following describes the device, which can be referred to in conjunction with the method described above. The device includes: The test module is used to conduct rebound tests on the surrounding rock in the field using a rebound strength tester to obtain the rebound strength value; The module is used to build a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock in the field. The formulation module is used to formulate and execute support plans based on the correlation model; The optimization module is used to monitor the rebound strength of the surrounding rock, actual support parameters, and measured deformation data, and to calibrate and optimize the associated model to update the support scheme.

[0080] In operation, this device first uses a testing module to collect high-frequency data on the rebound strength of the surrounding rock at the construction site. Then, a construction module builds a correlation model between the rebound strength and the overall strength of the surrounding rock. A planning module then formulates and implements multiple support schemes based on this correlation model. Finally, an optimization module continuously monitors the rebound strength, actual support parameters, and measured deformation data of the surrounding rock, optimizing the correlation model based on the monitoring results to update the support scheme. This process addresses the shortcomings of traditional TBM support design in soft and fractured strata by introducing an innovative dynamic optimization approach. This device establishes a real-time correlation between the rebound strength of the surrounding rock surface and support parameters through the rapidly measurable index of rebound strength. This significantly improves the accuracy and adaptability of support design and solves the problem of the contradiction between support and tunneling efficiency caused by the high variability of surrounding rock in soft and fractured strata in existing related technologies. It realizes the precise dynamic optimization of TBM support parameters, significantly improves tunneling efficiency and reduces engineering costs while ensuring construction safety, and provides an innovative solution for TBM construction in soft and fractured strata.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic optimization of support parameters for TBMs traversing weak and fractured strata, characterized in that, include: The rebound strength value was obtained by conducting a rebound test on the surrounding rock at the site using a rebound strength tester. Construct a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock in the field; Based on the aforementioned correlation model, a support plan is formulated and implemented; Monitor the rebound strength of the surrounding rock, actual support parameters, and measured deformation data at the site, and calibrate and optimize the associated model to update the support scheme.

2. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 1, characterized in that, Rebound strength values ​​were obtained by conducting rebound tests on the surrounding rock at the site using a rebound strength tester, including: Determine the sampling section for conducting rebound tests on the surrounding rock at the site, and set up the measurement area and measurement points on the sampling section; A rebound test was conducted on the surrounding rock at the site to obtain experimental data. Outliers were removed from the experimental data to determine the representative value of the rebound strength of the current test area. The rebound strength value of the current acquisition section is determined based on the average of the representative rebound strength values ​​of different measurement areas on the same acquisition section.

3. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 1, characterized in that, Construct a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock, including: Within the same lithological strata, select tunnel sections that have been supported and whose support deformation has stabilized, and establish a sequence SDS; The rock mass mechanical parameters of the surrounding rock at the site are determined according to the SDS sequence; the rock mass mechanical parameters include rock mass shear modulus and rock mass shear strength parameters, and the rock mass shear strength parameters include cohesion and internal friction angle; Based on the aforementioned rock mechanics parameters, a correlation model between the rebound strength and cohesion of the surrounding rock in the field is established. Based on the aforementioned correlation model, an expression for the deformation of surrounding rock expressed using rebound strength is constructed.

4. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 3, characterized in that, Obtaining the shear modulus of the surrounding rock at the site includes: The basic shear modulus was obtained based on the geological survey data of the surrounding rock at the site, and the dynamic shear modulus value was obtained by elastic wave method and then calibrated by reduction. The dynamic shear modulus value is reduced and converted to obtain the reference range of static shear modulus value; If the basic shear modulus falls within the reference range of the static shear modulus, the basic shear modulus obtained from the geological survey data is directly used as the rock mass shear modulus of the surrounding rock at the site. Otherwise, an in-situ load test is conducted on the surrounding rock at the site to obtain the rock mass shear modulus corresponding to the current lithology.

5. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 3, characterized in that, Obtaining the cohesion of the surrounding rock at the site includes: Calculate the support resistance of the support structure to the surrounding rock based on the on-site support parameters; By combining the SDS sequence with the Castner solution and the support resistance, the cohesion of the surrounding rock in the field is calculated, and a correlation data sequence between the rebound strength value and the cohesion is established.

6. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 5, characterized in that, Based on the aforementioned rock mechanics parameters, a correlation model between the rebound strength and cohesion of the surrounding rock in the field is established, including: For relatively intact rock masses, linear correlation is used to perform regression analysis on the associated data sequences to obtain the correlation function; For moderately intact rock masses, regression analysis of the associated data sequences is performed using an exponential function correlation to obtain the correlation function. For weak and fractured surrounding rock, machine learning is used to obtain the correlation between rebound strength and cohesion, and the correlation function is obtained.

7. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 6, characterized in that, Based on the aforementioned correlation model, an expression for the deformation of surrounding rock expressed using rebound strength is constructed, including: Substituting the correlation function into the Castner solution, we obtain the expression for the support resistance and surrounding rock deformation expressed in terms of rebound strength.

8. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 7, characterized in that, Based on the aforementioned correlation model, a support scheme is proposed, including: Based on the expressions for the support resistance and deformation of the surrounding rock in the field, the maximum allowable deformation is determined, and the relationship between the rebound strength value of the surrounding rock in the field and the support scheme is established. Determine the relationship between support parameters based on conventional support schemes; Multiple rebound strength ranges are preset, and multiple support schemes are formulated based on the relationship of the support parameters.

9. The method for dynamic optimization of support parameters for TBM traversing weak and fractured strata according to claim 8, characterized in that, Monitor the rebound strength of the surrounding rock, actual support parameters, and measured deformation data at the site, and calibrate and optimize the associated model to update the support scheme, including: If the deviation between the measured deformation data after support and the maximum allowable deformation exceeds the preset standard, the correlation model between the rebound strength value and the cohesion is further inverted based on the measured deformation data, and the support scheme and the correspondence between the support scheme and the rebound strength are updated.

10. A device for dynamically optimizing support parameters for TBMs traversing weak and fractured strata, characterized in that, include: The test module is used to conduct rebound tests on the surrounding rock in the field using a rebound strength tester to obtain the rebound strength value; The module is used to build a correlation model between the rebound strength of the surrounding rock and the strength parameters of the surrounding rock in the field. The formulation module is used to formulate and execute support schemes based on the association model; The optimization module is used to monitor the rebound strength of the surrounding rock, actual support parameters, and measured deformation data, and to calibrate and optimize the associated model to update the support scheme.

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