Intelligent grouting hole plugging pressure measurement and auxiliary decision system based on permeability coefficient evaluation

Abstract

The application provides an intelligent grouting hole plugging pressure measurement and auxiliary decision system based on permeability coefficient evaluation, and belongs to the technical field of subway tunnel engineering. The data acquisition module is used for real-time monitoring of grouting pressure and grouting flow. The formation permeability coefficient evaluation module is used for calculating the formation permeability coefficient according to the data collected by the data acquisition module and transmitting the calculation result to the auxiliary decision module. The pressure measurement system module is used for recording the time data of slurry pressure recovery after grouting and feeding back the time data to the auxiliary decision module. The auxiliary decision module is used for comprehensively considering the slurry pressure recovery time and the formation permeability coefficient and providing a decision suggestion on whether to supplement irrigation. The visual interface module is used for displaying real-time monitoring data and the decision suggestion generated by the auxiliary decision module, and is convenient for user operation. The application effectively solves the application limitation of the existing subway tunnel engineering grouting technology in complex strata and has significant engineering application value.

Description

Technical Field

[0001] This invention relates to the field of subway tunnel engineering technology, and in particular to an intelligent grouting hole sealing pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation. Background Technology

[0002] In subway tunnel construction, grouting and sealing are key technologies for controlling groundwater seepage and ensuring geological stability. Traditional grouting hole sealing operations mainly rely on engineering experience, evaluating sealing effectiveness through manual monitoring of single parameters such as grouting pressure and flow rate, lacking precise quantitative analysis of geological permeability characteristics and intelligent decision support. As subway tunnels extend into deeper burial areas and complex geological conditions (such as water-rich sand layers, lenticular interlayers, alluvial fans, and other interbedded strata), the limitations of traditional methods become increasingly apparent, mainly in the following aspects:

[0003] Existing technologies mostly employ single-point pressure or flow sensors, which can only acquire local pressure or flow data from grouting holes and cannot comprehensively reflect the seepage field distribution and dynamic changes in the permeability coefficient of the strata surrounding the tunnel. Furthermore, subway tunnel construction faces challenges such as shield vibration, high humidity, and signal attenuation in confined spaces. Traditional sensors are susceptible to noise interference, resulting in low data reliability. For example, shield construction vibration causes fluctuations in pressure sensor data, and existing filtering algorithms are not optimized for this type of noise, requiring manual removal of outlier data, leading to significant decision-making lag.

[0004] Furthermore, the difficulty in accurately predicting the permeability coefficient of the strata directly leads to poor grouting results. Due to the limitations of drilling and sampling during geological exploration, it is impossible to accurately grasp the physical and mechanical properties of the strata surrounding the subway line. The permeability coefficient of the strata directly determines the groutability and the grouting effect. The density of the strata directly reflects its deformation modulus and thus its potential deformation. Large deformation will directly result in a large deformation space between the lining structure and the soil. Grouting in subway tunnel engineering serves two purposes: firstly, to seal off external water intrusion and prevent tunnel leakage; and secondly, to increase soil density and fill the potential deformation space of the lining. Currently, subway tunnel grouting mainly uses fixed grouting pressure and fixed grouting volume as grouting control conditions. When encountering complex and variable geological conditions, phenomena such as incomplete grouting and over-grouting are very likely to occur.

[0005] Furthermore, existing systems lack quantitative analysis of the grout pressure recovery process, and replenishment decisions rely on fixed pressure thresholds or manual experience, failing to adapt to the differences in permeability characteristics across different formation levels. For example, in clay formations with low permeability, judging replenishment needs solely based on pressure recovery time can easily lead to over-grouting; while in high-permeability sand layers, the lack of coupled analysis of formation permeability coefficient and pressure decay index may overlook local leakage risks, potentially triggering water inrush accidents. According to industry statistics, the grouting qualification rate of traditional methods in complex formations is only 65%-75%, with a leakage repair rate as high as 20%.

[0006] Therefore, the need for an intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation is of great practical significance. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent grouting hole sealing pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation. Through intelligent and precise grouting effect evaluation and decision support, it effectively solves the application limitations of existing subway tunnel grouting technology in complex strata, improves the safety and economy of subway tunnel grouting projects, and has significant engineering application value.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A smart grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation includes:

[0010] The data acquisition module is used to monitor grouting pressure and grouting flow rate in real time;

[0011] The formation permeability coefficient assessment module is used to calculate the formation permeability coefficient based on the data collected by the data acquisition module and transmit the calculation results to the auxiliary decision-making module.

[0012] The pressure measurement system module is used to record the time data of grout pressure recovery after grouting and to feed the time data back to the auxiliary decision-making module.

[0013] The auxiliary decision-making module is used to provide decision-making suggestions on whether re-grouting is needed, taking into account the grout pressure recovery time and formation permeability coefficient.

[0014] The visualization interface module is used to display real-time monitoring data and decision suggestions generated by the decision support module, making it easy for users to operate.

[0015] Preferably, the data acquisition module includes a pressure sensor, an electromagnetic flow sensor, and a wireless transmission unit, used to acquire multi-dimensional data of grouting pressure P(t) and grouting flow rate Q(t) in the grouting hole of the subway tunnel stratum in real time.

[0016] Preferably, the formation permeability coefficient assessment module has a built-in Darcy's law correction model, which calculates the real-time formation permeability coefficient of the subway tunnel formation based on the Darcy's law correction model, and dynamically corrects it in combination with the geological survey data of the subway tunnel formation.

[0017] Preferably, the formula for the Darcy's law correction model is:

[0018]

[0019] Where, k(t) is the real-time formation permeability coefficient; L is the radius of influence of the grouting hole, initialized using geological exploration data; A is the cross-sectional area of ​​the water passage; ΔP(t) is the grout pressure difference between the grouting hole and the observation hole; μ(T) is the groundwater dynamic viscosity coefficient corrected for temperature T, calculated using the formula:

[0020]

[0021] Where μ0 is the viscosity coefficient at standard temperature T0, and β is the temperature correction factor.

[0022] Preferably, the formation permeability coefficient assessment module further includes a subway tunnel vibration and noise filtering unit and a formation fracture data storage unit. The subway tunnel vibration and noise filtering unit adopts the Kalman filtering algorithm to optimize parameters based on the vibration and noise of subway tunnel shield construction. The formation fracture data storage unit contains the gradation and density of the subway tunnel formation, which is used to infer the in-situ permeability coefficient of the formation based on the calculation results of the Darcy's law correction model.

[0023] Preferably, the depth gradient measuring point arrangement density of the pressure measuring system module is 1-5 m / point, used to acquire grout pressure distribution data at different depths within the grouting hole range of 0-50 m, and obtain the grout pressure recovery curve after grouting; wherein, the formula for calculating the pressure recovery rate is:

[0024]

[0025] Among them, v p P is the pressure recovery rate. max P is the peak pressure at the end of grouting. stable To stabilize the slurry pressure value, t s The grout pressure recovery time is defined as the minimum duration during which the grout pressure value remains continuously stable within 95% ± 5% of the peak pressure at the end of grouting.

[0026] Preferably, the auxiliary decision-making module includes a discrimination rule base, which contains three levels of decision rules for subway tunnel grouting:

[0027] Preset safety threshold k for the permeability coefficient of subway tunnel strata thresholdand the lower limit of slurry pressure recovery time t min When the real-time formation permeability coefficient k(t) > k threshold And the slurry pressure recovery time t s <t min When the auxiliary decision-making module determines that the formation permeability is not up to standard, it triggers a re-irrigation command.

[0028] Preset pressure decay index critical value α critical When k(t)≤k threshold However, the pressure decay index α > α critical When the auxiliary decision-making module determines that there is a risk of local leakage in the formation, it generates a leakage early warning signal.

[0029] The upper limit of the preset slurry pressure recovery time is t. max and the standard value of stable slurry pressure P standard When the slurry pressure recovery time t s Greater than t max And the slurry pressure value P after stabilization stable Greater than P standard When the auxiliary decision-making module determines that the grouting and sealing of the subway tunnel has failed, it issues a sealing failure alarm.

[0030] Wherein, the safety threshold k of the permeability coefficient of the subway tunnel strata threshold Lower limit of slurry pressure recovery time t min α, the critical value of the pressure decay index critical The upper limit of the slurry pressure recovery time is t. max and the standard value of stable slurry pressure P standard The system supports custom configuration of subway tunnel construction units based on the permeability coefficient level of the subway tunnel strata through a human-machine interface, in order to adapt to the grouting decision-making needs of strata with different permeability characteristics.

[0031] Preferably, the critical value of the pressure decay index α critical The calculation formula is:

[0032]

[0033] Where P′(t) is the grout pressure at time t after grouting, and P′(0) is the initial grout pressure at the end of grouting.

[0034] Preferably, the auxiliary decision-making module further includes a subway tunnel grouting execution control module, used to receive the grouting command from the auxiliary decision-making module and calculate the target grouting flow rate based on the dynamically modified Darcy's law correction model formula:

[0035]

[0036] Among them, Q injΔP is the target flow rate for grouting. inj The rated working pressure difference for subway tunnel grouting equipment;

[0037] The industrial bus sends control commands to the grouting pump, including grouting pressure, flow rate, and duration. The commands must be confirmed by the on-site grouting engineer in the visualization interface module before execution. Manual intervention to adjust grouting parameters is also supported to ensure construction safety.

[0038] Preferably, the visualization interface module integrates a real-time monitoring unit for subway tunnel grouting, a ground seepage decision analysis unit, and a construction report generation unit. The real-time monitoring unit dynamically displays tunnel ground pressure, grouting hole seepage flow, ground permeability coefficient curves, and grout pressure recovery process diagrams. The ground seepage decision analysis unit, combined with the tunnel BIM model, visualizes the coupling relationship between the grouting decision logic tree and the ground seepage field distribution. The construction report generation unit automatically generates a specialized PDF report on subway tunnel grouting, including trends in ground permeability coefficient changes and grout pressure recovery characteristics analysis, supporting data exchange with the subway tunnel construction management platform.

[0039] The visualization interface module also supports access via a tunnel mobile terminal APP, compatible with explosion-proof iOS / Android terminals for tunnel construction, and is used to view tunnel grouting hole data in real time, receive leakage early warning information, and make grouting decisions and confirmations.

[0040] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0041] (1) This invention integrates sensors such as pressure and flow rate to construct a three-dimensional monitoring system covering the tunnel strata, acquiring multi-dimensional data such as grouting pressure and flow rate in real time, breaking through the limitations of traditional single-parameter monitoring. The stratum permeability coefficient assessment module combines geological exploration data with a modified Darcy's law model to dynamically calculate the stratum permeability coefficient, realizing accurate quantitative analysis of stratum permeability characteristics, providing a scientific numerical basis for grouting effect evaluation, solving the problem of rough assessment of permeability characteristics under complex geological conditions and reliance on experience judgment, enabling engineers to accurately grasp the stratum seepage state based on real-time data.

[0042] (2) The auxiliary decision-making module provided by this invention extracts characteristic parameters such as grout pressure recovery time and pressure decay index to construct multi-parameter coupled fuzzy logic discrimination rules, supporting the customization of safety thresholds according to tunnel strata level, and realizing adaptive grouting decisions under different geological conditions. This mechanism changes the traditional decision-making mode dominated by fixed thresholds or human experience, and can accurately identify local leakage risks and judge whether the permeability meets the standards, avoiding over-grouting or insufficient grouting. It significantly improves the adaptability of grouting operations in complex conditions such as water-rich sand layers and interlayers, provides intelligent decision support for construction safety, and transforms grouting operations from "experience-driven" to "data-driven".

[0043] (3) The visualization interface module provided by this invention integrates the tunnel BIM model, dynamically displaying the seepage field distribution, decision-making logic, and monitoring data in real time. Engineers can intuitively grasp the grouting effect and risk status through multiple terminals, solving the problems of single information display and opaque decision-making logic in traditional monitoring systems. At the same time, the system is linked with the grouting equipment through an industrial bus to realize the automatic generation, manual confirmation, and execution of grouting instructions, forming a closed-loop control of the entire process of "monitoring-analysis-decision-execution". This improves the efficiency and response speed of construction management, providing an integrated intelligent solution for subway tunnel grouting projects from data acquisition to on-site execution, and promoting the upgrading of subway tunnel construction management towards intelligence. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a module structure diagram of an intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to the present invention.

[0046] Figure 2 This is a schematic diagram of the overall structure of the device provided by the intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to the present invention.

[0047] Figure 3 This is a side view of the device structure provided by the intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to the present invention.

[0048] Figure 4 This is a schematic diagram of the monitoring data acquisition and observation device provided in an embodiment of the present invention.

[0049] Explanation of reference numerals in the attached figures:

[0050] 1. Pressure sensor; 2. Electromagnetic flow sensor; 3. Drain valve; 4. Slurry pressure gauge; 5. Piezometer; 6. Data acquisition device; 7. Mobile monitoring device; 8. PC monitoring device. Detailed Implementation

[0051] 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.

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] like Figure 1 As shown, this invention provides an intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation, comprising:

[0054] The data acquisition module is used to monitor grouting pressure and grouting flow rate in real time;

[0055] The formation permeability coefficient assessment module is used to calculate the formation permeability coefficient based on the data collected by the data acquisition module and transmit the calculation results to the auxiliary decision-making module.

[0056] The pressure measurement system module is used to record the time data of grout pressure recovery after grouting and to feed the time data back to the auxiliary decision-making module.

[0057] The auxiliary decision-making module is used to provide decision-making suggestions on whether re-grouting is needed, taking into account the grout pressure recovery time and formation permeability coefficient.

[0058] The visualization interface module is used to display real-time monitoring data and decision suggestions generated by the decision support module, making it easy for users to operate.

[0059] The data acquisition module includes a pressure sensor, an electromagnetic flow sensor, and a wireless transmission unit, used to acquire multi-dimensional data on grouting pressure P(t) and grouting flow rate Q(t) in the grouting holes of the subway tunnel strata in real time. The pressure sensor, model PT1000-T, uses a 316L stainless steel anti-corrosion housing, has an IP68 protection rating, and a measurement accuracy of ±0.1%FS, acquiring real-time grouting pressure P(t) in kPa. The electromagnetic flow sensor, model FM200-T, is installed on the outlet pipe of the grouting hole, with a measurement accuracy of ±0.5%, monitoring seepage flow rate Q(t) in m³. 3 / h;

[0060] Reference Figures 2 to 3 Pressure sensor 1 is installed in the inlet pipe of the grouting hole via a threaded interface, forming a monitoring link with electromagnetic flow sensor 2 to collect grouting pressure and flow rate in real time. From the tunnel cross-section perspective, this diagram reveals the monitoring layout along the depth direction of the grouting hole. Piezometer 5 is buried along the grouting hole axis at a density of 1–5 m / point, connected in series with a waterproof cable to the data acquisition device 6 at the hole opening, acquiring real-time grout pressure distribution data at different depths for plotting the grout pressure recovery curve after grouting. Grout pressure gauge 4 is installed at the front end of the grouting hole outlet, connected to the pipeline via a metal flexible hose, visually displaying the real-time grout pressure value and providing dual verification with the piezometer data. Sensor cables are uniformly routed into a waterproof cable tray, laid along the tunnel wall to the data acquisition device 6, powered by a 20Ah lithium battery via a DC-DC converter, supporting 72 hours of offline operation. This arrangement achieves three-dimensional monitoring of the formation permeability field, providing depth gradient data support for the formation permeability coefficient correction model.

[0061] Simultaneously, the two sensors transmit data to the data acquisition device 6 via a wireless transmission unit. The data acquisition device 6, with its built-in LoRa tunnel-enhanced transceiver and 4G DTU module, uploads the data to the cloud server at a frequency of 10Hz according to the NB-IoT private network protocol. The drain valve 3 is connected in parallel to the grouting pipeline and communicates with the data acquisition device 6 via an electric actuator. It can automatically adjust its opening degree to balance the pipeline pressure based on monitoring data.

[0062] Reference Figure 4 The data acquisition device 6 communicates with the mobile monitoring device 7 via a LoRa wireless link, allowing on-site engineers to view the formation permeability coefficient curve, grout pressure recovery process diagram, and grouting decision suggestions in real time. Simultaneously, data is transmitted to the PC monitoring device 8 via a 4G DTU module, whose integrated tunnel BIM model interface visualizes the seepage field distribution and decision logic tree. When the auxiliary decision module triggers a grouting command, the data acquisition device 6 sends control parameters to the grouting pump via an industrial bus. The command requires secondary confirmation from the PC or mobile terminal before execution, thus ensuring construction safety through multi-terminal interaction and supporting intelligent grouting control driven by a dynamically corrected Darcy's law model.

[0063] In addition, sensor data is transmitted to the data processing unit at a frequency of 10Hz via a combination of LoRa tunnel enhanced transceiver and 4G DTU according to the NB-IoT private network protocol. The power supply system uses a 20Ah lithium battery, which supports continuous operation for 72 hours without external power supply and is suitable for high humidity and no mains power environment in the tunnel excavation section.

[0064] The raw signal output by the data acquisition module first enters the formation permeability coefficient assessment module. The formation permeability coefficient assessment module has a built-in Darcy's law correction model. The real-time formation permeability coefficient of the subway tunnel formation is calculated according to the Darcy's law correction model, and dynamically corrected in combination with the geological survey data of the subway tunnel formation.

[0065] The formula for the modified Darcy's law model is as follows:

[0066]

[0067] Where, k(t) is the real-time formation permeability coefficient; L is the radius of influence of the grouting hole, initialized using geological exploration data; A is the cross-sectional area of ​​the water passage; ΔP(t) is the grout pressure difference between the grouting hole and the observation hole; μ(T) is the groundwater dynamic viscosity coefficient corrected for temperature T, calculated using the formula:

[0068]

[0069] Where μ0 is the viscosity coefficient at standard temperature T0, and β is the temperature correction factor.

[0070] In addition, the formation permeability coefficient assessment module also includes a subway tunnel vibration and noise filtering unit and a formation fracture data storage unit. The subway tunnel vibration and noise filtering unit uses the Kalman filtering algorithm to optimize parameters based on the vibration and noise of subway tunnel shield construction; specifically:

[0071] For the vibration fluctuations of 10-50Hz and ±20kPa during tunnel boring machine (TBM) construction, a Kalman filter algorithm was used for optimization. A state-space model was employed to denoise the pressure data, and the filtered error was controlled within ±5%. The formula is as follows:

[0072] x(t) = ax(t-1) + bu(t) + ω(t);

[0073] The geological fracture data storage unit contains the gradation and density of the subway tunnel strata, which is used to infer the in-situ permeability coefficient of the strata based on the calculation results of the Darcy's Law modified model.

[0074] Furthermore, the depth gradient measuring point arrangement density of the pressure measuring system module is 1-5 m / point, used to acquire grout pressure distribution data at different depths within the grouting hole range of 0-50 m, and obtain the grout pressure recovery curve after grouting; wherein, the formula for calculating the pressure recovery rate is:

[0075]

[0076] Among them, v p P is the pressure recovery rate. max P is the peak pressure at the end of grouting. stableTo stabilize the slurry pressure value, t s The grout pressure recovery time is defined as the minimum duration during which the grout pressure value remains continuously stable within 95% ± 5% of the peak pressure at the end of grouting.

[0077] In addition, the auxiliary decision-making module includes a discrimination rule base, which contains three-level decision rules for subway tunnel grouting:

[0078] Preset safety threshold k for the permeability coefficient of subway tunnel strata threshold and the lower limit of slurry pressure recovery time t min When the real-time formation permeability coefficient k(t) > k threshold And the slurry pressure recovery time t s <t min When the auxiliary decision-making module determines that the formation permeability is not up to standard, it triggers a re-irrigation command.

[0079] Preset pressure decay index critical value α critical When k(t)≤k threshold However, the pressure decay index α > α critical When the auxiliary decision-making module determines that there is a risk of local leakage in the formation, it generates a leakage early warning signal.

[0080] The upper limit of the preset slurry pressure recovery time is t. max and the standard value of stable slurry pressure P standard When the slurry pressure recovery time t s Greater than t max And the slurry pressure value P after stabilization stable Greater than P standard When the auxiliary decision-making module determines that the grouting and sealing of the subway tunnel has failed, it issues a sealing failure alarm.

[0081] Wherein, the safety threshold k of the permeability coefficient of the subway tunnel strata threshold Lower limit of slurry pressure recovery time t min α, the critical value of the pressure decay index critical The upper limit of the slurry pressure recovery time is t. max and the standard value P in the slurry pressure tower standard The system supports custom configuration of subway tunnel construction units based on the permeability coefficient level of the subway tunnel strata, such as extremely permeable layer, weak permeable layer, medium permeable layer and strong permeable layer, through the human-machine interface, to adapt to the grouting decision requirements of strata with different permeability characteristics, which is especially suitable for strata tunnels that need to be lined after shield tunneling.

[0082] In the above content, the critical value α of the pressure decay index... critical The calculation formula is:

[0083]

[0084] Where P′(t) is the grout pressure at time t after grouting, and P′(0) is the initial grout pressure at the end of grouting.

[0085] In addition, the auxiliary decision-making module also includes a subway tunnel grouting execution control module, which receives the grouting command from the auxiliary decision-making module and calculates the target grouting flow rate based on the dynamically modified Darcy's law correction model formula.

[0086]

[0087] Among them, Q inj ΔP is the target flow rate for grouting. inj The rated working pressure difference for subway tunnel grouting equipment;

[0088] Control commands containing grouting pressure, flow rate, and duration are sent to the grouting pump via an industrial bus. These commands require secondary confirmation by the on-site grouting engineer through a visual interface module before execution. Manual intervention to adjust grouting parameters is also supported to ensure construction safety. For example, upon receiving a replenishment command, the execution control module calculates the target grouting flow rate based on a modified Darcy's law model and sends a command containing a pressure of 3-5 MPa and a flow rate of 2-5 m³ / h to the grouting pump via the Modbus RTU protocol. 3 Control commands are issued in / h format and last for 30-60 minutes. Commands must be confirmed by the on-site engineer on the visual interface before execution. Manual adjustment of ±10% is also supported.

[0089] Furthermore, the visualization interface module integrates a real-time monitoring unit for subway tunnel grouting, a ground seepage decision analysis unit, and a construction report generation unit. The real-time monitoring unit dynamically displays tunnel ground pressure, grouting hole seepage flow, ground permeability coefficient curves, and grout pressure recovery process diagrams. The ground seepage decision analysis unit, combined with the tunnel BIM model, visualizes the coupling relationship between the grouting decision logic tree and the ground seepage field distribution, and the BIM model highlights seepage risk areas with different colors.

[0090] The construction report generation unit automatically generates a special PDF report on grouting in subway tunnels, which includes the trend of changes in the formation permeability coefficient and the analysis of grout pressure recovery characteristics. It supports data exchange with the subway tunnel construction management platform. For example, the construction report generation unit automatically generates PDF reports on a daily / weekly / monthly basis, which include the calculation process of the formation permeability coefficient, the analysis of grout pressure recovery characteristics, decision records of the number of grouting sessions, and early warning processing results. It also exchanges data with the subway engineering management platform through a JSON interface to achieve full-process traceability of construction data.

[0091] The visualization interface module also supports access via a tunnel mobile terminal APP, compatible with explosion-proof iOS / Android terminals for tunnel construction, and is used to view tunnel grouting hole data in real time, receive leakage early warning information, and make grouting decisions and confirmations.

[0092] The following example uses grouting in a subway tunnel project. The data collected by the sensor includes: Q(t) = 3m 3 / h, T=25℃, fracture aperture 0.8mm, connectivity 70%, after correction, k(t) = 2.38m / d (exceeding k threshold =1.5m / d);

[0093] Grout pressure recovery time t s =3h <t min =4h, the auxiliary decision-making module triggers the replenishment, and the execution control module calculates Q. inj =4m 3 / h, the grouting pump will be started after the operator confirms the operation;

[0094] The visualization interface displays the grouting pressure curve in real time as it stabilizes. After 2 hours, k(t) drops to 1.2 m / d, and the system automatically stops grouting and generates an analysis report containing the grouting parameters and effects.

[0095] During the monitoring process, when α = 0.12h -1 >αcritical=0.08h -1 At that time, k(t)=1.9m / d≤k threshold =2.0m / d. The system still uses the BIM model to locate abnormal areas at a depth of 15-20m, prompting workers to check for highly permeable strata and take sealing measures to avoid water inrush accidents.

[0096] Therefore, the above-mentioned intelligent grouting hole sealing pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation effectively solves the limitations of existing subway tunnel grouting technology in complex strata through intelligent and precise grouting effect evaluation and decision support, improves the safety and economy of subway tunnel grouting projects, and has significant engineering application value.

[0097] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A smart grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation, characterized in that, include: The data acquisition module is used to monitor grouting pressure and grouting flow rate in real time; The formation permeability coefficient assessment module is used to calculate the formation permeability coefficient based on the data collected by the data acquisition module and transmit the calculation results to the auxiliary decision-making module. The pressure measurement system module is used to record the time data of grout pressure recovery after grouting and to feed the time data back to the auxiliary decision-making module. The pressure measurement system module has a depth gradient measuring point arrangement density of 1~5m / point, used to acquire grout pressure distribution data at different depths within the grouting hole range of 0~50m, and obtain the grout pressure recovery curve after grouting; wherein, the formula for calculating the pressure recovery rate is: ； in, For pressure recovery rate, This represents the peak pressure at the end of grouting. To stabilize the slurry pressure value, The grout pressure recovery time is defined as the minimum duration during which the grout pressure value remains continuously stable within the range of 95% ± 5% of the peak pressure at the end of grouting. The auxiliary decision-making module is used to provide decision-making suggestions on whether re-grouting is needed, taking into account the grout pressure recovery time and formation permeability coefficient. The auxiliary decision-making module includes a discrimination rule base, which contains three levels of decision rules for subway tunnel grouting: Preset safety threshold for the permeability coefficient of subway tunnel strata and the lower limit of slurry pressure recovery time When the real-time formation permeability coefficient And the slurry pressure recovery time When the auxiliary decision-making module determines that the formation permeability is not up to standard, it triggers a re-irrigation command. Preset pressure decay index threshold ,when But the pressure decay index When the auxiliary decision-making module determines that there is a risk of local leakage in the formation, it generates a leakage early warning signal. Upper limit of preset slurry pressure recovery time and the standard value of stable grout pressure When the slurry pressure recovers Greater than And the slurry pressure value after stabilization Greater than When the auxiliary decision-making module determines that the grouting and sealing of the subway tunnel has failed, it issues a sealing failure alarm. Among them, the safety threshold of the permeability coefficient of the subway tunnel strata Lower limit of slurry pressure recovery time Pressure decay index critical value Upper limit of slurry pressure recovery time and the standard value of stable grout pressure It supports the subway tunnel construction unit to customize the configuration through the human-machine interface according to the permeability coefficient level of the subway tunnel strata, so as to adapt to the grouting decision-making needs of strata with different permeability characteristics. The visualization interface module is used to display real-time monitoring data and decision suggestions generated by the decision support module, making it easy for users to operate.

2. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 1, characterized in that, The data acquisition module includes a pressure sensor, an electromagnetic flow sensor, and a wireless transmission unit, used to collect the grouting pressure inside the grouting holes in the subway tunnel strata in real time. Grouting flow rate Multidimensional data.

3. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 2, characterized in that, The formation permeability coefficient assessment module has a built-in Darcy's law correction model. It calculates the real-time formation permeability coefficient of the subway tunnel formation based on the Darcy's law correction model and makes dynamic corrections based on the geological survey data of the subway tunnel formation.

4. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 3, characterized in that, The formula for the modified Darcy's law model is as follows: ； in, Real-time formation permeability coefficient; L The radius of influence of the grouting holes is initialized using geological survey data; A This refers to the cross-sectional area of ​​the water passage. The pressure difference between the grouting hole and the observation hole; As temperature T The corrected dynamic viscosity coefficient of groundwater is calculated using the following formula: ； in, Standard temperature The viscosity coefficient below, This is the temperature correction factor.

5. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 3, characterized in that, The formation permeability coefficient assessment module also includes a subway tunnel vibration and noise filtering unit and a formation fracture data storage unit. The subway tunnel vibration and noise filtering unit adopts the Kalman filtering algorithm to optimize parameters based on the vibration and noise of subway tunnel shield construction. The formation fracture data storage unit has built-in gradation and density of the subway tunnel formation, which is used to infer the in-situ permeability coefficient of the formation based on the calculation results of the Darcy's law correction model.

6. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 1, characterized in that, The critical value of the pressure decay index The calculation formula is: ； in, This represents the grout pressure at time t after grouting. This represents the initial grout pressure value at the end of the grouting process.

7. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 1, characterized in that, The auxiliary decision-making module also includes a subway tunnel grouting execution control module, which receives the grouting command from the auxiliary decision-making module and calculates the target grouting flow rate based on the dynamically modified Darcy's law correction model formula. ； in, The target flow rate for grouting, The rated working pressure difference for subway tunnel grouting equipment; Real-time formation permeability coefficient; A This refers to the cross-sectional area of ​​the water passage. As temperature T Corrected dynamic viscosity coefficient of groundwater; The industrial bus sends control commands to the grouting pump, including grouting pressure, flow rate, and duration. The commands must be confirmed by the on-site grouting engineer in the visualization interface module before execution. Manual intervention to adjust grouting parameters is also supported to ensure construction safety.

8. The intelligent grouting hole plugging pressure measurement and auxiliary decision-making system based on permeability coefficient evaluation according to claim 1, characterized in that, The visualization interface module integrates a real-time monitoring unit for subway tunnel grouting, a ground seepage decision analysis unit, and a construction report generation unit. The real-time monitoring unit dynamically displays tunnel ground pressure, grouting hole seepage flow, ground permeability coefficient curves, and grout pressure recovery process diagrams. The ground seepage decision analysis unit, combined with the tunnel BIM model, visualizes the coupling relationship between the grouting decision logic tree and the ground seepage field distribution. The construction report generation unit automatically generates a specialized PDF report on subway tunnel grouting, including trends in ground permeability coefficient changes and grout pressure recovery characteristics analysis, and supports data exchange with the subway tunnel construction management platform. The visualization interface module also supports access via a tunnel mobile terminal APP, compatible with explosion-proof iOS / Android terminals for tunnel construction, and is used to view tunnel grouting hole data in real time, receive leakage early warning information, and make grouting decisions and confirmations.

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