A method for anti-buoyancy reinforcement of shield tunnels spanning utility tunnels in water-rich silty sand strata

Abstract

This invention discloses a method for anti-buoyancy reinforcement of shield tunnels spanning utility tunnels in water-rich silty sand strata, comprising: obtaining a stratum distribution model based on three-dimensional geological data of the water-rich silty sand strata; obtaining disaster sign activation signals based on the stratum distribution model; obtaining preliminary layout parameters of the defense barrier based on the disaster sign activation signals; obtaining a drainage joint scheme based on the preliminary layout parameters of the defense barrier; obtaining a spatial composition network based on the drainage joint scheme; obtaining optimization iteration results based on the spatial composition network; obtaining construction sequence data based on the optimization iteration results; and obtaining the final stability mitigation index based on the construction sequence data. This invention can significantly improve the anti-buoyancy stability and construction safety of utility tunnels in water-rich silty sand strata, providing efficient and replicable technical support for disaster prevention and control in underground engineering under complex geological conditions.

Description

Technical Field

[0001] This invention belongs to the field of shield tunneling construction, and in particular relates to a method for anti-buoyancy reinforcement of shield tunnels spanning utility tunnels in water-rich silty sand strata. Background Technology

[0002] In urban underground space development, anti-buoyancy reinforcement technology for tunnel boring machines (TBMs) spanning utility tunnels is crucial for ensuring infrastructure safety, especially in complex hydrogeological environments such as water-rich silty sand strata. These strata, due to their high water content and low density, are prone to engineering risks such as groundwater buoyancy and soil deformation, directly threatening the stability of the utility tunnel structure. Current methods often rely on single reinforcement techniques or static geological analysis, making it difficult to adapt to the dynamic changes and complex hydrological conditions of water-rich silty sand strata. For example, traditional grouting reinforcement struggles to accurately address permeability differences in different areas within the stratum, while conventional drainage systems may fail due to rapid groundwater recharge. These limitations prevent an effective balance between anti-buoyancy requirements and the dynamic response of the stratum during construction, increasing engineering risks.

[0003] The core challenge of water-rich silty sand strata lies in the dynamic recharge of groundwater and the spatial heterogeneity of the strata's bearing capacity. The high fluidity of groundwater causes the water pressure distribution around the utility tunnel to constantly change over time and space, making conventional anti-buoyancy measures difficult to respond quickly. For example, during tunnel boring machine (TBM) excavation, ground disturbance may trigger a sudden increase in local water pressure, exacerbating the risk of the utility tunnel floating. This dynamic water pressure variation further complicates the placement of anti-buoyancy anchor points, as these points need to be precisely adapted to the spatial characteristics of the strata, and traditional methods often cannot accurately determine the optimal location of the anchor points in three-dimensional space. For instance, in one TBM construction project, the failure to adjust the anti-buoyancy anchor point positions in a timely manner led to localized floating of the utility tunnel, causing ground subsidence and affecting the project's progress.

[0004] Therefore, how to accurately construct a highly adaptable anti-buoyancy reinforcement system under the dynamic hydrological conditions and spatial heterogeneity of water-rich silty sand strata, and optimize the spatial layout of anchor points, has become a key issue to ensure the safety and stability of the shield tunnel crossing the utility tunnel. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a method for anti-buoyancy reinforcement of shield tunnels spanning utility tunnels in water-rich silty sand strata, comprising:

[0006] Three-dimensional geological data of water-rich silty sand strata were obtained, and the bearing capacity distribution at different depths was calculated based on the permeability coefficient and void ratio to obtain a stratum distribution model.

[0007] The groundwater level changes and soil deformation are monitored according to the geological distribution model. When the pore water pressure exceeds the preset threshold, a disaster sign activation signal is output.

[0008] Based on the disaster symptom activation signal, the anti-buoyancy system is activated, and emergency plan data is generated by grouting reinforcement and structural strengthening to obtain the preliminary layout parameters of the defense barrier.

[0009] The location of the permeable waterproof curtain is extracted from the preliminary layout parameters of the defense barrier, and the groundwater flow path is simulated using a ring drainage system to determine the combined drainage scheme.

[0010] The reduced buoyancy value in the drainage scheme is obtained, and the three-dimensional coordinates of the anchoring points are calculated in combination with the shield tunneling trajectory to obtain the spatial network for the arrangement of anti-buoyancy anchors.

[0011] By integrating the load distribution and disaster symptom data through the spatial network, the stability response of the utility tunnel is simulated using the finite element analysis algorithm. When the response value exceeds the safe range, the anchoring points are adjusted to generate optimization iteration results.

[0012] Based on the optimization iteration results, the adjusted anchorage points are extracted, the parameters of the shield tunneling machine's drilling equipment are obtained to generate construction sequence data, and a three-dimensional anti-buoyancy network is obtained.

[0013] Based on the construction sequence data, the ground feedback is monitored in real time, and the feedback deviation is processed using a Bayesian update algorithm to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel.

[0014] Preferably, the process of obtaining three-dimensional geological data of water-rich silty sand strata, calculating the bearing capacity distribution at different depths based on permeability coefficient and void ratio, and obtaining a stratigraphic distribution model includes:

[0015] The original geological data of water-rich silty sand strata were collected through ground radar detection and borehole sampling.

[0016] Based on the original geological data, a three-dimensional geological model is constructed;

[0017] Based on the three-dimensional geological model, the permeability coefficient and porosity at each depth point are extracted;

[0018] Based on the permeability coefficient and void ratio, soil mechanical parameters are calculated to obtain the mechanical properties at different depths;

[0019] Based on the aforementioned mechanical properties, load-bearing capacity distribution interpolation is performed to obtain a continuous load-bearing capacity distribution field;

[0020] Based on the continuous bearing capacity distribution field, the spatial structure of the stratigraphic distribution model is constructed.

[0021] Preferably, the process of monitoring groundwater level changes and soil deformation according to the geological distribution model, and outputting a disaster hazard activation signal when the pore water pressure exceeds a preset threshold, includes:

[0022] The distribution of monitoring points is set according to the geological distribution model, and groundwater level data and soil deformation data are collected in real time through the monitoring points.

[0023] Calculate the pore water pressure based on the groundwater level data and soil deformation data;

[0024] If the pore water pressure exceeds a preset threshold, a disaster symptom activation signal is generated.

[0025] Based on the aforementioned disaster symptom activation signals, and in conjunction with the formation distribution model, the formation stress distribution is calculated;

[0026] Based on the stress distribution in the formation, abnormal stress areas are identified, and the extent of potential disaster areas is determined.

[0027] Based on the extent of the potential disaster area, the distribution of monitoring points is adjusted, and the stratigraphic distribution model is updated.

[0028] Preferably, the process of activating the anti-buoyancy system based on the disaster symptom activation signal, generating emergency plan data through grouting reinforcement and structural strengthening, and obtaining preliminary layout parameters of the defense barrier includes:

[0029] Receive the disaster symptom activation signal, and collect foundation settlement data and water pressure data based on the disaster symptom activation signal;

[0030] Based on the foundation settlement data and water pressure data, determine whether the triggering conditions of the anti-buoyancy system are met;

[0031] If the triggering conditions are met, the proportion of grouting material and the injection points are calculated to generate a grouting reinforcement scheme.

[0032] Based on the grouting reinforcement scheme, simulate the stress changes of the foundation to generate structural reinforcement requirement data;

[0033] Based on the structural reinforcement requirements data, reinforcement materials and construction methods are matched to generate a structural reinforcement plan;

[0034] The grouting reinforcement scheme and the structural strengthening scheme are integrated to generate emergency response plan data.

[0035] Preferably, the process of extracting the location of the permeable waterproof curtain from the preliminary layout parameters of the defense barrier, simulating the groundwater flow path using a ring-shaped drainage system, and determining the combined drainage scheme includes:

[0036] Geometric location data of the permeable waterproof curtain was extracted based on emergency plan data;

[0037] Based on the geometric location data, a three-dimensional geometric model of the ring-shaped drainage system is constructed.

[0038] Based on the aforementioned three-dimensional geometric model, the groundwater flow path is simulated to obtain water pressure distribution data;

[0039] Adjust the pipe layout of the ring drainage system based on the water pressure distribution data;

[0040] Based on the adjusted pipeline layout, calculate the trend of buoyancy value changes and generate optimized drainage system parameters;

[0041] Based on the optimized drainage system parameters, a combined drainage scheme is determined.

[0042] Preferably, the process of obtaining the reduced buoyancy value in the combined drainage scheme and calculating the three-dimensional coordinates of the anchorage point in conjunction with the shield tunneling trajectory includes:

[0043] The reduced buoyancy data is obtained from the drainage combined scheme, and the buoyancy data is standardized to obtain a standardized buoyancy dataset.

[0044] Based on the standardized buoyancy dataset and the shield tunneling trajectory, the initial three-dimensional coordinate set of the anchorage point is calculated.

[0045] Based on the initial three-dimensional coordinate set, construct a spatial network structure;

[0046] If the node spacing of the spatial network structure exceeds a preset threshold, the anchoring points are adjusted to obtain an optimized spatial distribution framework.

[0047] Based on the optimized spatial distribution framework, the final three-dimensional coordinates of the anchorage points are determined.

[0048] Preferably, the process of generating optimization iteration results includes:

[0049] Acquire spatial network data, carrier distribution data, and disaster symptom information to form an initial dataset;

[0050] The initial dataset is cleaned and normalized to obtain a comprehensive dataset;

[0051] Based on the comprehensive dataset, the stability response of the utility tunnel was simulated using the finite element analysis algorithm to obtain the stability response value;

[0052] If the stability response value exceeds the preset threshold, the anchoring point is adjusted and a candidate point scheme is generated.

[0053] The stability of the candidate point location schemes is verified to obtain the optimized point location distribution;

[0054] Based on the optimized point distribution, the spatial composition network data is updated.

[0055] Preferably, the process of extracting the adjusted anchorage points based on the optimization iteration results, obtaining the parameters of the tunnel boring machine's drilling equipment to generate construction sequence data, and obtaining the three-dimensional anti-buoyancy network includes:

[0056] Extract the adjusted anchorage point data from the optimization iteration results;

[0057] Based on the adjusted anchorage point data and the preset anti-buoyancy network topology model, an initial three-dimensional anti-buoyancy network is generated.

[0058] Obtain the real-time operating status of the tunnel boring machine's onboard equipment and generate drilling equipment parameters;

[0059] Based on the drilling equipment parameters, a construction parameter configuration is generated;

[0060] Based on the aforementioned construction parameter configuration and combined with the construction schedule planning algorithm, construction sequence data is generated.

[0061] If the construction sequence data does not meet the preset topology constraints, the topology of the three-dimensional anti-buoyancy network is adjusted to obtain the optimized three-dimensional anti-buoyancy network.

[0062] Based on the optimized three-dimensional anti-buoyancy network, the final three-dimensional anti-buoyancy network data is generated.

[0063] Preferably, the process of real-time monitoring of ground feedback based on the construction sequence data and using a Bayesian update algorithm to process feedback deviations to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel includes:

[0064] Based on the construction sequence data, the formation feedback signal is collected in real time to obtain the original feedback dataset;

[0065] The Bayesian update algorithm is used to iteratively update the deviation in the original feedback dataset to obtain the corrected formation parameters;

[0066] Based on the corrected formation parameters, the stability mitigation index is calculated;

[0067] If the stability resolution index is lower than the preset threshold, the construction sequence parameters are adjusted to generate an optimized construction sequence dataset.

[0068] Based on the optimized construction sequence dataset, the formation feedback signal was re-acquired to obtain the updated feedback dataset;

[0069] Based on the updated feedback dataset, the Bayesian update process is repeated to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel.

[0070] Compared with the prior art, the present invention has the following advantages and technical effects:

[0071] This invention, through the joint calculation of three-dimensional geological data, permeability coefficient and porosity ratio, has for the first time achieved continuous and refined modeling of the bearing capacity distribution of water-rich silty sand strata, providing a highly reliable stratum distribution model for subsequent anti-buoyancy design.

[0072] This invention, based on a geological distribution model, monitors real-time groundwater levels and soil deformation. It can trigger a disaster sign activation signal the instant the pore water pressure exceeds a threshold, significantly shortening the risk identification and emergency response time.

[0073] This invention activates a signal-driven anti-buoyancy system linkage mechanism, integrating grouting reinforcement, structural strengthening, permeable waterproof curtain and ring drainage system into a unified emergency plan, forming a multi-layered defense barrier and synergistically reducing buoyancy values.

[0074] This invention uses the reduced buoyancy value as input and calculates the three-dimensional coordinates of the anchoring points in real time in conjunction with the tunnel boring machine's trajectory. It then constructs a dynamically adjustable spatial network to achieve seamless matching between the anchor cable arrangement and the construction trajectory.

[0075] This invention integrates spatial network data on load distribution and disaster signs, and uses finite element analysis to iteratively optimize anchorage points, ensuring that the stability response of the utility tunnel remains within a safe range and improving the overall structural safety margin.

[0076] The optimized anchoring points of this invention directly drive the parameters of the tunnel boring machine's drilling equipment to generate construction sequence data, forming a three-dimensional anti-buoyancy network, effectively reducing on-site measurement and human intervention, and improving construction efficiency and accuracy.

[0077] During the construction process, this invention collects formation feedback signals in real time and uses a Bayesian update algorithm to continuously correct formation parameters and stability indicators, thereby achieving online verification and dynamic optimization of the anti-buoyancy effect.

[0078] The multi-source data fusion and dynamic optimization strategy of this invention significantly improves the anti-buoyancy stability and construction safety of pipe corridors in water-rich silty sand strata, providing efficient and replicable technical support for disaster prevention and control in underground engineering under complex geological conditions. Attached Figure Description

[0079] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0080] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0081] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0082] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0083] like Figure 1 As shown, this embodiment provides a method for anti-buoyancy reinforcement of a shield tunnel overpass in water-rich silty sand strata, including:

[0084] Three-dimensional geological data of water-rich silty sand strata were obtained, and the bearing capacity distribution at different depths was calculated based on the permeability coefficient and void ratio to obtain a stratum distribution model.

[0085] The groundwater level changes and soil deformation are monitored based on the stratigraphic distribution model. When the pore water pressure exceeds the preset threshold, a disaster sign activation signal is output.

[0086] The anti-buoyancy system is activated based on disaster signs and signals. Grouting reinforcement and structural strengthening are used to generate emergency plan data and obtain preliminary layout parameters for the defense barrier.

[0087] The location of the permeable waterproof curtain is extracted from the preliminary layout parameters of the defense barrier, and the groundwater flow path is simulated using a ring drainage system to determine the combined drainage scheme.

[0088] The reduced buoyancy value in the combined drainage scheme is obtained, and the three-dimensional coordinates of the anchoring points are calculated in combination with the shield tunneling trajectory to obtain the spatial network for the arrangement of anti-buoyancy anchors.

[0089] By integrating load distribution and disaster symptom data through spatial network construction, the stability response of the utility tunnel is simulated using finite element analysis algorithm. When the response value exceeds the safe range, the anchoring points are adjusted to generate optimization iteration results.

[0090] Based on the optimization iteration results, the adjusted anchorage points are extracted, the parameters of the shield tunneling machine's drilling equipment are obtained to generate construction sequence data, and a three-dimensional anti-buoyancy network is obtained.

[0091] Real-time monitoring of ground feedback based on construction sequence data, and the use of a Bayesian update algorithm to process feedback deviations, achieves anti-buoyancy reinforcement of the tunnel over the utility tunnel.

[0092] Furthermore, the process of obtaining three-dimensional geological data of water-rich silty sand strata, calculating the bearing capacity distribution at different depths based on permeability coefficient and void ratio, and obtaining a stratigraphic distribution model includes:

[0093] The original geological data of water-rich silty sand strata were collected through ground radar detection and borehole sampling.

[0094] A three-dimensional geological model is constructed based on the original geological data;

[0095] Based on the three-dimensional geological model, the permeability coefficient and porosity at each depth point are extracted;

[0096] Based on the permeability coefficient and void ratio, soil mechanical parameters are calculated to obtain the mechanical properties at different depths;

[0097] Based on the mechanical properties, the bearing capacity distribution is interpolated to obtain a continuous bearing capacity distribution field;

[0098] Based on the continuous bearing capacity distribution field, the spatial structure of the stratigraphic distribution model is constructed.

[0099] Specifically, this embodiment acquires three-dimensional geological data of water-rich silty sand strata through ground-based radar detection and borehole sampling techniques. For example, ground-based radar detection, by emitting electromagnetic waves and receiving reflected signals, detects changes in the underground medium, can quickly cover a large area, and is suitable for the preliminary construction of a stratigraphic framework.

[0100] In one embodiment, a ground-penetrating radar with a frequency of 100MHz is used, with a scanning depth of up to 20m and a resolution of approximately 0.5m, to acquire information on the thickness of the silt layer and the distribution of groundwater. Borehole sampling is performed to obtain physical samples through a drilling rig to supplement the accuracy of the radar data.

[0101] For example, at 5km 2 Ten boreholes, each 30m deep, were drilled within the area to obtain soil samples for particle size analysis, determining the particle composition and water content of the silt layer. These data were then combined to generate a high-resolution three-dimensional geological model, clearly showing the spatial distribution of the silt layer, clay layer, and groundwater level. The permeability coefficient and void ratio were then extracted from the three-dimensional geological model.

[0102] For example, the model shows that the permeability coefficient of the silt layer at a depth of 0-5m is approximately 10. -5 m / s, porosity of 0.45; at a depth of 5-10m, the permeability coefficient decreases to 10. -6 The soil density is m / s and the void ratio is 0.40. These parameters are input into finite element analysis software to calculate soil mechanical parameters such as cohesion and internal friction angle.

[0103] In one possible implementation, the finite element method is used to simulate the foundation loading conditions. The shear strength at a depth of 0-5m is found to be 50 kPa, satisfying the preset bearing capacity threshold of 40 kPa. However, the shear strength at a depth of 10-15m is only 30 kPa, requiring further analysis. For points that meet the bearing capacity threshold, the Kriging interpolation algorithm is used to calculate the continuous bearing capacity distribution field.

[0104] For example, in the 0-5m depth region, based on bearing capacity data from 10 points, interpolation generates a bearing capacity distribution map covering the entire area, showing a smooth transition of bearing capacity from 40kPa to 60kPa. This continuous distribution field provides the basis for stratigraphic distribution models. Stereoscopic methods are used to divide stratigraphic units, combining factors such as groundwater saturation to divide the strata into high-bearing-capacity zones and low-bearing-capacity zones.

[0105] For example, the 0-5m depth region is classified as a stable unit due to its high permeability and low porosity. The random forest algorithm predicts potential sliding surfaces and, combined with groundwater influence factors (such as a water level height of 10m), assesses the probability of the sliding surface occurring.

[0106] For example, the model predicts a slip risk of 0.7 in a region with a depth of 5-10m, exceeding the preset threshold of 0.6, indicating a high risk. The high-risk area is then meshed, with the mesh size reduced from 1m to 0.5m. The bearing capacity distribution is recalculated, generating a refined stratigraphic distribution model that accurately reflects local stratigraphic changes. Volume rendering technology transforms the refined model into 3D visualized data, outputting the disaster risk assessment results.

[0107] For example, high-risk areas are highlighted in red, which visually presents the spatial risk distribution and facilitates engineering decision-making.

[0108] Preferably, such visualization results can guide foundation reinforcement design, reduce landslide risk, and improve engineering safety.

[0109] Furthermore, based on the stratigraphic distribution model, the process of monitoring groundwater level changes and soil deformation, and outputting a disaster hazard activation signal when pore water pressure exceeds a preset threshold, includes:

[0110] The distribution of monitoring points is set according to the stratigraphic distribution model, and groundwater level data and soil deformation data are collected in real time through the monitoring points.

[0111] Calculate pore water pressure based on groundwater level data and soil deformation data;

[0112] If the pore water pressure exceeds a preset threshold, a disaster symptom activation signal will be generated.

[0113] Based on the activation signals of disaster signs and combined with the stratigraphic distribution model, the stress distribution in the stratigraphic formation is calculated.

[0114] Based on the distribution of formation stress, identify areas of abnormal stress and determine the extent of potential disaster areas;

[0115] Based on the extent of the potential disaster area, the distribution of monitoring points was adjusted, and the stratigraphic distribution model was updated.

[0116] In one possible implementation, real-time data acquisition from monitoring point distribution is the core component of monitoring groundwater level changes and soil deformation. Monitoring points are typically located in key areas of water-rich silty sand strata, such as areas with significant groundwater level fluctuations or complex geological structures. This embodiment utilizes high-precision pressure sensors and displacement gauges installed at the monitoring points to acquire real-time data on groundwater level changes and soil deformation.

[0117] For example, in a certain engineering project, a sensor was installed every 50 meters, covering an area of ​​500×500 meters. The sensors collected data once per hour to ensure the capture of dynamic changes in groundwater levels and minute soil deformations. This high-frequency data collection can reflect the state of the strata in a timely manner, providing a reliable basis for subsequent disaster early warning.

[0118] For example, when pressure sensors are used to measure pore water pressure, data can be acquired by sensors buried at different depths. Suppose a monitoring point measures a pore water pressure of 150 kPa at a depth of 5 meters, exceeding a preset threshold of 100 kPa, the system will automatically trigger a disaster warning signal. This signal triggering mechanism can send an alarm to the monitoring center via a wireless communication module, indicating potential formation instability. The advantage of this mechanism is its rapid response, avoiding the delays of manual inspection and ensuring that disaster warning signs are identified promptly.

[0119] In one embodiment, when calculating the formation stress distribution using a formation distribution model, a three-dimensional stress field is constructed using the collected pore water pressure and soil deformation data.

[0120] For example, in a water-rich silty sand formation, monitoring data shows that rising groundwater levels lead to increased pore water pressure, which in turn causes localized stress concentration in the formation. Using finite element analysis software, engineers input the monitoring data into the model. It is understood that this embodiment, by using the support vector machine algorithm to classify the formation stress distribution, can effectively distinguish between normal and abnormal stress areas.

[0121] For example, stress data in a certain area indicates an abnormally high stress zone at a depth of 10 meters. The algorithm, trained using historical data, identifies this area as potentially at risk of landslides due to rapid changes in groundwater levels. The classification results are displayed in a 3D model using color coding, with abnormal areas highlighted in red for easy visual assessment of potential risks. This classification method improves the accuracy of disaster prediction.

[0122] For example, when dynamically updating abnormal stress areas, the system updates monitoring data every 6 hours through real-time data transmission. If a sustained increase in pore water pressure is detected in a certain area, the system will mark it as a potential disaster area and automatically adjust the layout of monitoring points, increasing the sensor density in that area, for example, from a spacing of 50 meters to 20 meters. This optimized monitoring scheme can more accurately capture local changes and improve the reliability of model predictions.

[0123] In one possible implementation, when the optimized monitoring scheme is used to update the stratigraphic distribution model, the predicted values ​​of groundwater level and soil deformation are recalculated by adding new data points.

[0124] For example, in one project, by increasing the number of monitoring points, it was discovered that the groundwater level was rising at a rate of 0.2 meters per day in a specific area, predicting a potential risk of landslides within the next 48 hours. The updated model can generate high-precision predictive data, providing a scientific basis for disaster prevention and control. This approach significantly improves the targeting of monitoring and the accuracy of predictions.

[0125] Furthermore, the process of activating the anti-buoyancy system based on disaster warning signals, generating emergency response plan data through grouting reinforcement and structural strengthening, and obtaining preliminary layout parameters for the defense barrier includes:

[0126] Receive disaster sign activation signals, and collect foundation settlement data and water pressure data based on the disaster sign activation signals;

[0127] Based on the foundation settlement data and water pressure data, determine whether the triggering conditions of the anti-buoyancy system are met;

[0128] If the triggering conditions are met, the proportion of grouting material and the injection points are calculated to generate a grouting reinforcement scheme.

[0129] Based on the grouting reinforcement scheme, simulate the changes in foundation stress to generate structural reinforcement requirements data;

[0130] Based on the structural reinforcement requirements data, reinforcement materials and construction methods are matched to generate a structural reinforcement plan;

[0131] Integrate grouting reinforcement schemes and structural strengthening schemes to generate emergency response plan data.

[0132] For example, in determining the triggering conditions of the anti-buoyancy system, the sensor network is a core component, used to collect real-time data on foundation settlement and water pressure. In this embodiment, the sensor network is deployed in key areas of the anti-buoyancy design, such as the bottom of the basement or areas near groundwater flow.

[0133] Preferably, the sensor includes a high-precision displacement gauge and a water pressure sensor. The displacement gauge monitors the vertical settlement of the foundation with an accuracy of 0.1 mm; the water pressure sensor measures the pore water pressure with an accuracy of 1 kPa.

[0134] For example, in the foundation monitoring of a high-rise building, a sensor network covers an area of ​​1000×1000 meters, with a monitoring point set up every 100 meters, and data is collected every 2 hours. When a monitoring point detects a foundation settlement exceeding 5 mm or a pore water pressure exceeding 120 kPa, the system determines that the anti-buoyancy trigger condition is met and automatically starts the subsequent process. This high-frequency acquisition ensures the real-time nature and accuracy of the data.

[0135] In one embodiment, a grouting reinforcement model is used to calculate the mix proportions of the grouting material and the injection points. The model determines the mix proportions of cement grout or chemical grout based on foundation soil properties, such as clay content or permeability coefficient.

[0136] For example, in a project with highly permeable sandy soil, the model recommended using a cement-water glass grout with a ratio of 1:0.5. Injection points were selected in areas of maximum foundation settlement, spaced 2 meters apart. The injection points were determined using a 3D geological model to ensure uniform grout diffusion. This method effectively improves the bearing capacity of the foundation.

[0137] For example, the finite element method is used to simulate the stress changes in the foundation after grouting. Engineers input settlement and water pressure data collected by sensors into finite element software to generate a three-dimensional stress distribution map of the foundation.

[0138] For example, after grouting in a certain area, the stress distribution shows a 20% reduction in stress in the settlement area, indicating that grouting effectively dispersed the load. The analysis results generate structural reinforcement requirement data, such as the area requiring reinforcement and the reinforcement strength.

[0139] In one embodiment, the structural reinforcement scheme is generated by matching a preset reinforcement scheme library. The scheme library contains various materials and construction methods, such as reinforced concrete reinforcement or carbon fiber reinforcement.

[0140] For example, a project selected carbon fiber fabric reinforcement based on demand data to cover the settlement area, using 200 grams of carbon fiber fabric per square meter, and employing a wet bonding process during construction. This approach can quickly improve structural strength.

[0141] For example, the emergency response plan generation algorithm integrates grouting reinforcement and structural strengthening schemes to generate emergency response plan data. The algorithm comprehensively analyzes the foundation settlement trend and stress distribution to generate a plan that includes construction sequence and emergency measures.

[0142] For example, one plan requires grouting reinforcement to be completed first, followed by carbon fiber reinforcement, with a construction period of 7 days to ensure foundation stability.

[0143] In one embodiment, the geometric parameters and deployment location of the defensive barrier are calculated based on emergency response plan data. The barrier is typically a reinforced concrete retaining wall, with its height and thickness determined according to the stress on the foundation.

[0144] For example, a project calculated that the retaining wall would be 3 meters high and 0.5 meters thick, and would be deployed around the settlement area. This layout can effectively resist foundation sliding.

[0145] For example, digital twin technology is used to simulate the stability of barrier deployments. Digital twin models build virtual models of the foundation and barrier based on real-time data, simulating different operating conditions such as heavy rain or earthquakes.

[0146] For example, simulations show that the barrier remains stable even under a magnitude 7 earthquake, proving the reliability of the layout. This technology provides a scientific basis for optimizing barrier design.

[0147] Furthermore, the process of extracting the location of the permeable waterproof curtain from the preliminary layout parameters of the defense barrier, simulating the groundwater flow path using a ring-shaped drainage system, and determining the combined drainage scheme includes:

[0148] Geometric location data of the permeable waterproof curtain was extracted based on emergency plan data;

[0149] Based on the geometric location data, construct a three-dimensional geometric model of the ring-shaped drainage system;

[0150] Based on the three-dimensional geometric model, the groundwater flow path is simulated to obtain water pressure distribution data;

[0151] Adjust the pipe layout of the ring drainage system based on water pressure distribution data;

[0152] Based on the adjusted pipeline layout, calculate the trend of buoyancy value changes and generate optimized drainage system parameters;

[0153] Based on the optimized drainage system parameters, a combined drainage scheme is determined.

[0154] For example, when acquiring geometric location data for a permeable waterproof curtain, topographic data of the barrier area is collected using three-dimensional laser scanning technology, and combined with a geological exploration report, the depth and width of the curtain are determined.

[0155] In one implementation method, assuming a foundation pit project requires a waterproof curtain to cover a 20-meter-deep layer of sand, with a width of 1.5 meters, the coordinates are precisely marked using CAD software as a ring-shaped area extending 0.5 meters outward from the edge of the foundation pit. This method ensures accurate curtain positioning and reduces the risk of groundwater leakage. When constructing the three-dimensional geometric model of the ring-shaped drainage system, BIM technology is used to generate a digital model of the drainage pipes.

[0156] For example, for the aforementioned foundation pit, the ring-shaped drainage system is designed with 0.8-meter diameter PVC pipes arranged along the outer side of the curtain, with a pipe spacing of 2 meters. The model needs to consider the soil permeability coefficient; for example, the permeability coefficient of sandy soil is 10. -4m / s is used to ensure a reasonable drainage path. When simulating groundwater flow paths using the finite element method, software such as MODFLOW is used to analyze the water flow distribution.

[0157] For example, in the simulation, the initial groundwater level is assumed to be 2 meters below the surface, and the water pressure reaches 0.15 MPa near the curtain. The simulation results show that the water flow mainly flows along the outside of the curtain, avoiding the interior of the pit. This analysis clarifies the key areas of water pressure distribution, providing a basis for subsequent adjustments. If the water pressure distribution data exceeds a preset threshold (e.g., 0.12 MPa), the layout of the ring drainage system needs to be adjusted.

[0158] In one embodiment, the density of drainage pipes is increased, with the spacing adjusted from 2 meters to 1.5 meters, and additional water pumps with a power of 5kW and a pumping capacity of 50m³ are installed in high-pressure areas. 3 / h. After resimulation, the water pressure dropped to 0.10MPa, meeting safety requirements. This adjustment effectively dispersed the water pressure, ensuring the stability of the foundation pit. When calculating the buoyancy value change trend based on the adjusted groundwater flow path, data from monitoring points was analyzed.

[0159] For example, the buoyancy value before adjustment was 500 kN / m. 2 After adjustment, it decreased to 400 kN / m 2 The trend shows that buoyancy is gradually stabilizing.

[0160] Preferably, the optimized drainage system parameters include a pipe diameter of 0.8 meters and a water pump power of 5 kW to ensure drainage efficiency. When generating a combined drainage scheme, a comprehensive control strategy is formed by integrating the curtain wall and the drainage system.

[0161] For example, the curtain is made of high-density polyethylene material, 0.5 meters thick, and the drainage system is equipped with automatic control valves that adjust the drainage volume based on water pressure sensor data. This solution is highly integrated, reducing construction complexity. Detailed construction drawings and control parameters are generated when integrating the drainage system with the waterproof curtain.

[0162] For example, control parameters include a drainage pump start-up water pressure threshold of 0.08 MPa and a curtain permeability coefficient controlled at 10. -6 Below m / s. This configuration ensures that the buoyancy value is reduced to a safe range, while also facilitating on-site implementation.

[0163] Furthermore, the process of obtaining the reduced buoyancy value in the combined drainage scheme and calculating the three-dimensional coordinates of the anchorage point based on the shield tunneling trajectory includes:

[0164] Reduced buoyancy data is obtained from the combined drainage scheme, and the buoyancy data is standardized to obtain a standardized buoyancy dataset.

[0165] Based on the standardized buoyancy dataset and the shield tunneling trajectory, the initial three-dimensional coordinate set of the anchorage point is calculated.

[0166] Construct a spatial network structure based on the initial three-dimensional coordinate set;

[0167] If the node spacing of the spatial network structure exceeds the preset threshold, the anchoring points are adjusted to obtain the optimized spatial distribution framework.

[0168] Based on the optimized spatial distribution framework, the final three-dimensional coordinates of the anchorage points are determined.

[0169] For example, when obtaining reduced buoyancy data from a combined drainage scheme, groundwater level and buoyancy value data are collected through on-site monitoring equipment. Suppose that in a foundation pit project, the initial buoyancy value is 500 kN, and after the implementation of the combined drainage scheme, the buoyancy value is reduced to 300 kN. Data cleaning processing includes removing outliers, filling in missing data, and standardizing the format.

[0170] For example, abnormally high data points caused by sensor malfunctions are removed to ensure the dataset reflects the true state of groundwater. After standardization, buoyancy data is stored in a uniform unit (kN) for easier subsequent analysis. This cleaning method ensures data reliability and provides accurate input for subsequent coordinate calculations.

[0171] In one approach, based on a standardized buoyancy dataset and the tunnel boring machine's (TBM) directional tunneling trajectory, the initial coordinate set of anchorage points is determined using a three-dimensional coordinate calculation method. Assuming the TBM tunneling trajectory is a curve extending along the X-axis, with trajectory point coordinates ranging from (100, 50, 10) to (200, 50, 10), the initial anchorage points are uniformly distributed along the trajectory based on buoyancy distribution, with a spacing of 10m. By calculating the spatial correspondence between trajectory points and buoyancy data, initial coordinate sets are generated, such as (110, 50, 10) and (120, 50, 10). This method ensures that the anchorage points are consistent with the tunneling trajectory, improving the targeted nature of the anti-buoyancy design.

[0172] For example, when constructing a spatial network structure using spatial geometry algorithms, anchor points are considered nodes, and connecting lines are considered edges, forming a three-dimensional mesh. Assume the initial coordinate set contains 10 anchor points with an average node spacing of 10 meters. If the preset threshold is 12 meters, it's necessary to check if the node spacing exceeds this limit. If it does, adjust the positions of some anchor points, such as moving (120, 50, 10) to (118, 50, 10), and recalculate the mesh. This adjustment optimizes the uniformity of the spatial network and improves the stability of the anti-buoyancy anchor cables.

[0173] In one implementation method, when using a linear programming algorithm to determine the final anchorage point coordinates, the objective function is set to minimize the anchor cable length and material cost. It is assumed that the anchor cable layout requires at least two anchor cables to be connected at each point, with a length not exceeding 15m. Through algorithm optimization, the final coordinate set is obtained, such as (110, 50, 10) and (118, 50, 10). This method balances construction cost and anti-buoyancy effect, ensuring that the anchor cable layout is economical and efficient.

[0174] For example, when generating a spatial arrangement scheme for anti-buoyancy anchor cables, the length and angle of the anchor cables are designed based on the final coordinate set. Assuming the anchor cable length is 12m and the inclination angle is 30 degrees, the generated dataset includes the start point, end point, and angle information for each anchor cable. This scheme is easy for construction teams to apply directly, improving construction efficiency.

[0175] In one implementation method, when extracting key point information from the anchor cable layout dataset to generate a spatial network visualization model, 3D modeling software is used to draw the anchor cable distribution map. The model assumes that the anchor cables are radially distributed around the foundation pit, with nodes highlighted in red and anchor cables represented by blue lines. This visualization model intuitively presents the spatial relationship of the anti-buoyancy anchor cables, facilitating construction management and scheme verification.

[0176] Furthermore, the process of generating the optimization iteration results includes:

[0177] Acquire spatial network data, carrier distribution data, and disaster symptom information to form an initial dataset;

[0178] The initial dataset is cleaned and normalized to obtain a comprehensive dataset;

[0179] Based on the comprehensive dataset, the stability response of the utility tunnel was simulated using the finite element analysis algorithm, and the stability response value was obtained.

[0180] If the stability response value exceeds the preset threshold, the anchoring point will be adjusted and candidate point schemes will be generated.

[0181] The stability of the candidate site selection schemes is verified to obtain the optimized site distribution.

[0182] Based on the optimized point distribution, update the spatial composition network data.

[0183] In one implementation method, this embodiment acquires spatial network data using three-dimensional laser scanning technology.

[0184] For example, in underground utility tunnel projects, a high-precision laser scanner is used to scan the internal structure of the tunnel, generating point cloud data that includes spatial location information of the tunnel walls, supporting structures, and anchorage points. This point cloud data forms an initial spatial network, recording the spatial geometric features of the tunnel and providing a foundation for subsequent analysis.

[0185] For example, in a certain urban integrated utility tunnel project, the spatial network data generated by scanning included the coordinates of the tunnel's centerline and the relative positions of key nodes, with an accuracy of millimeters, laying the foundation for load-bearing distribution analysis.

[0186] For example, sensors can collect distributed data and disaster symptom information through distributed fiber optic sensing technology.

[0187] Preferably, fiber optic sensors are deployed in key areas of the utility tunnel to monitor stress changes and minute displacements on the tunnel walls in real time, detecting potential cracks or signs of settlement. For example, if a sensor in a certain section of the tunnel records a local stress value of 2.5 MPa, exceeding the normal range by 0.5 MPa, it indicates a potential risk of soil settlement. These data are combined with spatial network data to form an initial dataset containing stress, displacement, and geological hazard information.

[0188] Preferably, the comprehensive dataset is imported into the finite element model, and the material properties of the pipe gallery, such as the concrete strength being C30, are set to simulate the stress distribution under external loads, such as earth pressure of 100 kPa. The simulation results show that the maximum stress at a certain node is 3.2 MPa, exceeding the preset threshold of 2.8 MPa, indicating that the anchorage point needs to be optimized. This simulation provides a quantitative basis for stability assessment.

[0189] In one implementation method, the anchoring point position is adjusted through iterative optimization based on the gradient descent algorithm.

[0190] Specifically, for nodes exceeding the threshold, the anchor point coordinates are adjusted to gradually reduce stress concentration.

[0191] For example, after moving an anchoring point 0.5 meters along the axis of the utility tunnel, the stress value drops to 2.6 MPa, meeting the threshold requirement. Candidate point locations are then generated, containing the adjusted coordinate set.

[0192] For example, the stability verification of candidate site schemes is achieved through multi-condition simulation using simulation analysis technology.

[0193] Preferably, different load conditions, such as a seismic load of 0.2g and a groundwater pressure of 50kPa, were simulated to verify the stability of the candidate sites. The results showed that the adjusted site location scheme met the stability requirements under all working conditions, and the optimized site distribution was confirmed.

[0194] In one embodiment, iterative calculations to simulate the optimal point distribution multiple times are achieved using the Monte Carlo method.

[0195] For example, for the optimized anchorage location, 100 random load combinations were simulated to confirm that the fluctuation range of the stability response value was within 5%, thus determining the final anchorage location scheme. This method improves the robustness of the scheme.

[0196] For example, updating spatial network data and generating utility tunnel stability optimization results can be achieved through a BIM platform.

[0197] Specifically, the final anchorage point scheme is imported into the BIM model, the spatial network of the utility tunnel is updated, and a three-dimensional visualization result is generated to show the optimized anchorage point distribution and stress state.

[0198] For example, the optimization results generated by a certain project show that the overall stability of the utility tunnel has improved, and the stress value in the key area has been reduced to a safe range.

[0199] Furthermore, the process of extracting adjusted anchorage points based on optimization iteration results, obtaining parameters of the tunnel boring machine's drilling equipment to generate construction sequence data, and obtaining the three-dimensional anti-buoyancy network includes:

[0200] Extract the adjusted anchorage point data from the optimization iteration results;

[0201] Based on the adjusted anchorage point data and the preset anti-buoyancy network topology model, an initial three-dimensional anti-buoyancy network is generated.

[0202] Obtain the real-time operating status of the tunnel boring machine's onboard equipment and generate drilling equipment parameters;

[0203] Generate construction parameter configuration based on drilling equipment parameters;

[0204] Based on the configuration of construction parameters and combined with the construction schedule planning algorithm, construction sequence data is generated;

[0205] If the construction sequence data does not meet the preset topology constraints, the topology of the three-dimensional anti-buoyancy network is adjusted to obtain the optimized three-dimensional anti-buoyancy network.

[0206] Based on the optimized three-dimensional anti-buoyancy network, the final three-dimensional anti-buoyancy network data is generated.

[0207] For example, the anchorage point data involved in this embodiment includes at least information such as spatial coordinates, stress distribution, and geological constraints.

[0208] In one implementation method, this embodiment uses spatial point cloud analysis technology to select point data that meet the stability conditions from the optimization iteration results.

[0209] For example, suppose the anchorage point data for a certain section of a utility tunnel contains 100 points, each with three-dimensional coordinates and stress values. Cluster analysis is used to group these points according to geological characteristics, generating an initial anchorage point distribution. This method ensures that the point distribution matches the geological conditions, improving the stability of the subsequent anti-buoyancy network. When generating the anchorage point distribution, the point optimization algorithm employs a weighted distance-based optimization method.

[0210] Specifically, the minimum spacing between the points is set to 2 meters, and the maximum bearing capacity is 500 kN. The positions of the points are adjusted through iterative calculations.

[0211] For example, in a utility tunnel project, the initial distribution of anchor points could lead to localized stress concentration. After optimization, the spacing between anchor points became more uniform, and the stress distribution became more reasonable. This optimization method can effectively reduce the risk of stress concentration during construction. When generating the initial three-dimensional anti-buoyancy network based on a pre-set anti-buoyancy network topology model, topology generation technology is used to connect the anchoring points into a grid structure.

[0212] For example, in a 10m × 10m × 5m tunnel section, a triangular mesh topology is generated based on point data, with the side length of each mesh unit controlled within 1.5 meters. This topology effectively disperses buoyancy resistance, ensuring the overall stability of the tunnel. When acquiring the real-time operating status of the tunnel boring machine's onboard equipment, sensors collect data on the equipment's vibration frequency, propulsion speed, and torque.

[0213] For example, when a tunnel boring machine is running, its vibration frequency is 5 Hz and its propulsion speed is 0.1 m / s. By analyzing these data, drilling equipment parameters are generated, such as a drilling depth of 3 meters and a drilling diameter of 0.5 meters. This parameter generation method provides a precise basis for subsequent construction. In the construction parameter configuration, construction parameters are set based on the drilling equipment parameters and geological conditions.

[0214] For example, in soft soil geology, the drilling speed is set to 0.05 m / s and the grouting pressure to 2 MPa. This configuration ensures that the construction process is adapted to the geological conditions and improves construction efficiency. The generation of construction sequence data is achieved through a schedule planning algorithm.

[0215] For example, within a one-month construction period, the project is divided into three phases: foundation drilling, anchor point installation, and anti-buoyancy network construction, with each phase allocated 10 days. The construction sequence is optimized using algorithms to reduce process conflicts and improve construction efficiency. If the construction sequence data meets the topology constraints of the anti-buoyancy network, the network structure is adjusted using a topology optimization algorithm.

[0216] For example, in the initial topology, some grid cells are subjected to uneven forces. By adjusting the grid connection method, the anti-buoyancy distribution can be made more uniform.

[0217] Preferably, this adjustment can effectively improve the anti-buoyancy capability of the utility tunnel. The final three-dimensional anti-buoyancy network is generated through a mesh generation algorithm.

[0218] For example, the optimized topology data is divided into 1000 grid cells, with each cell having a side length controlled at 0.8 meters, generating the final anti-buoyancy network data. This fine division ensures the stability of the network structure and provides a reliable basis for the construction of the utility tunnel.

[0219] It is understood that the implementation methods of each of the above steps, through data analysis, algorithm optimization, and topology adjustment, are closely integrated with the business requirements of the utility tunnel's anti-buoyancy network, forming a complete solution from site optimization to network generation. This solution, through the mutual support of multiple stages, ensures the stability and efficiency of utility tunnel construction.

[0220] Furthermore, based on real-time monitoring of ground feedback using construction sequence data, and employing a Bayesian update algorithm to handle feedback deviations, the process of achieving anti-buoyancy reinforcement of the tunnel over the utility tunnel includes:

[0221] Based on the construction sequence data, the formation feedback signal is collected in real time to obtain the raw feedback dataset;

[0222] The Bayesian update algorithm is used to iteratively update the bias in the original feedback dataset to obtain the corrected formation parameters;

[0223] Calculate the stability mitigation index based on the corrected formation parameters;

[0224] If the stability resolution index is lower than the preset threshold, the construction sequence parameters are adjusted to generate an optimized construction sequence dataset.

[0225] Based on the optimized construction sequence dataset, the formation feedback signal was re-acquired to obtain the updated feedback dataset;

[0226] Based on the updated feedback dataset, the Bayesian update process is repeated to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel.

[0227] For example, when acquiring construction sequence data, sensors on the tunnel boring machine (TBM) collect ground feedback signals in real time, generating a raw feedback dataset. These sensors include acoustic and pressure sensors to monitor ground density and stress changes. Assuming that during tunnel construction, the sensors collect data once per second, recording ground stress values, pore water pressure, etc., forming a raw feedback dataset containing thousands of records. This method ensures the real-time nature and comprehensiveness of the data, providing a reliable foundation for subsequent analysis.

[0228] In one implementation, a Bayesian update algorithm is used to handle the bias in the original feedback dataset.

[0229] For example, the initial formation parameters might assume a soil strength of 50 kPa, but sensor data could show lower strength in some areas, such as 40 kPa. The Bayesian algorithm, through iterative updates and by combining historical data and real-time feedback, corrects the distribution of formation parameters to obtain a more accurate soil strength value, such as 45 kPa. This correction improves the accuracy of formation parameters and provides more reliable data support for stability analysis.

[0230] Specifically, stability mitigation indices are calculated based on the corrected formation parameters.

[0231] For example, the buoyancy stability quantification value is calculated using the corrected soil strength and pore water pressure, assuming a threshold of 0.8. If the calculated result is 0.6, it indicates insufficient stability, requiring adjustment of the construction sequence parameters, such as reducing the tunneling speed or increasing the anchor point density. After adjustment, an optimized construction sequence dataset is generated; for example, reducing the tunneling speed from 5 m / h to 3 m / h and re-collecting the stratum feedback signal yields an updated feedback dataset.

[0232] For example, the updated feedback dataset shows that the soil strength stabilizes at 45 kPa, and the pore water pressure decreases by 10%. By repeating the Bayesian update algorithm, a new stability mitigation index, such as 0.85, is obtained. If this value meets the preset threshold of 0.8, the anti-buoyancy effect is verified. The entire process, through sensor data, algorithm iteration, and parameter adjustment, forms a closed-loop optimization, ensuring the stability of the anti-buoyancy network and construction safety. This method, through real-time data-driven decision-making, significantly improves the adaptability of the construction sequence and the reliability of the formation's anti-buoyancy effect.

[0233] In one approach to achieving this, if the quantified value of anti-buoyancy stability still does not meet the standard, the construction sequence should be further adjusted, such as by adding temporary anchor points or optimizing the drilling depth.

[0234] For example, in one construction project, the initial drilling depth was 10m. After being adjusted to 12m, the stability quantification value increased from 0.7 to 0.9. This method of multiple adjustments and verifications ensures that the anti-buoyancy network can adapt to complex geological conditions, enhancing the flexibility and stability of construction.

[0235] For example, the anti-buoyancy effect is confirmed by comparing the stability quantification values ​​of multiple iterations in the final verification results.

[0236] For example, the initial verification value was 0.6, which improved to 0.85, and eventually stabilized at 0.9, indicating that the construction sequence adjustment was effective. This multi-faceted verification method, through data-driven and iterative optimization, ensured the reliability of the anti-buoyancy network and the efficiency of construction, providing a reference implementation path for similar projects.

[0237] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for anti-buoyancy reinforcement of shield tunnels spanning utility tunnels in water-rich silty sand strata, characterized in that, include: Three-dimensional geological data of water-rich silty sand strata were obtained, and the bearing capacity distribution at different depths was calculated based on the permeability coefficient and void ratio to obtain a stratum distribution model. The groundwater level changes and soil deformation are monitored according to the geological distribution model. When the pore water pressure exceeds the preset threshold, a disaster sign activation signal is output. Based on the disaster symptom activation signal, the anti-buoyancy system is activated, and emergency plan data is generated by grouting reinforcement and structural strengthening to obtain the preliminary layout parameters of the defense barrier. The location of the permeable waterproof curtain is extracted from the preliminary layout parameters of the defense barrier, and the groundwater flow path is simulated using a ring drainage system to determine the combined drainage scheme. The reduced buoyancy value in the drainage scheme is obtained, and the three-dimensional coordinates of the anchoring points are calculated in combination with the shield tunneling trajectory to obtain the spatial network for the arrangement of anti-buoyancy anchors. The process of obtaining the reduced buoyancy value in the drainage scheme and calculating the three-dimensional coordinates of the anchorage point based on the shield tunneling trajectory includes: The reduced buoyancy data is obtained from the drainage combined scheme, and the buoyancy data is standardized to obtain a standardized buoyancy dataset. Based on the standardized buoyancy dataset and the shield tunneling trajectory, the initial three-dimensional coordinate set of the anchorage point is calculated. Based on the initial three-dimensional coordinate set, construct a spatial network structure; If the node spacing of the spatial network structure exceeds a preset threshold, the anchoring points are adjusted to obtain an optimized spatial distribution framework. Based on the optimized spatial distribution framework, the final three-dimensional coordinates of the anchorage points are determined. By integrating the load distribution and disaster symptom data through the spatial network, the stability response of the utility tunnel is simulated using the finite element analysis algorithm. When the response value exceeds the safe range, the anchoring points are adjusted to generate optimization iteration results. The process of generating optimization iteration results includes: Acquire spatial network data, carrier distribution data, and disaster symptom information to form an initial dataset; The initial dataset is cleaned and normalized to obtain a comprehensive dataset; Based on the comprehensive dataset, the stability response of the utility tunnel was simulated using the finite element analysis algorithm to obtain the stability response value; If the stability response value exceeds the preset threshold, the anchoring point is adjusted and a candidate point scheme is generated. The stability of the candidate point location schemes is verified to obtain the optimized point location distribution; Based on the optimized point distribution, update the spatial network data; Based on the optimization iteration results, the adjusted anchorage points are extracted, the parameters of the shield tunneling machine's drilling equipment are obtained to generate construction sequence data, and a three-dimensional anti-buoyancy network is obtained. The process of extracting adjusted anchorage points based on the optimization iteration results, obtaining parameters of the tunnel boring machine's drilling equipment to generate construction sequence data, and obtaining the three-dimensional anti-buoyancy network includes: Extract the adjusted anchorage point data from the optimization iteration results; Based on the adjusted anchorage point data and the preset anti-buoyancy network topology model, an initial three-dimensional anti-buoyancy network is generated. Obtain the real-time operating status of the tunnel boring machine's onboard equipment and generate drilling equipment parameters; Based on the drilling equipment parameters, a construction parameter configuration is generated; Based on the aforementioned construction parameter configuration and combined with the construction schedule planning algorithm, construction sequence data is generated. If the construction sequence data does not meet the preset topology constraints, the topology of the three-dimensional anti-buoyancy network is adjusted to obtain the optimized three-dimensional anti-buoyancy network. Based on the optimized three-dimensional anti-buoyancy network, the final three-dimensional anti-buoyancy network data is generated; Based on the construction sequence data, the ground feedback is monitored in real time, and the feedback deviation is processed using a Bayesian update algorithm to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel.

2. The method according to claim 1, characterized in that, The process of obtaining three-dimensional geological data of water-rich silty sand strata, calculating the bearing capacity distribution at different depths based on permeability coefficient and void ratio, and obtaining a stratigraphic distribution model includes: The original geological data of water-rich silty sand strata were collected through ground radar detection and borehole sampling. Based on the original geological data, a three-dimensional geological model is constructed; Based on the three-dimensional geological model, the permeability coefficient and porosity at each depth point are extracted; Based on the permeability coefficient and void ratio, soil mechanical parameters are calculated to obtain the mechanical properties at different depths; Based on the aforementioned mechanical properties, load-bearing capacity distribution interpolation is performed to obtain a continuous load-bearing capacity distribution field; Based on the continuous bearing capacity distribution field, the spatial structure of the stratigraphic distribution model is constructed.

3. The method according to claim 1, characterized in that, The process of monitoring groundwater level changes and soil deformation based on the aforementioned stratigraphic distribution model, and outputting a disaster hazard activation signal when the pore water pressure exceeds a preset threshold, includes: The distribution of monitoring points is set according to the geological distribution model, and groundwater level data and soil deformation data are collected in real time through the monitoring points. Calculate the pore water pressure based on the groundwater level data and soil deformation data; If the pore water pressure exceeds a preset threshold, a disaster symptom activation signal is generated. Based on the aforementioned disaster symptom activation signals, and in conjunction with the formation distribution model, the formation stress distribution is calculated; Based on the stress distribution in the formation, abnormal stress areas are identified, and the extent of potential disaster areas is determined. Based on the extent of the potential disaster area, the distribution of monitoring points is adjusted, and the stratigraphic distribution model is updated.

4. The method according to claim 1, characterized in that, The process of activating the anti-buoyancy system based on the disaster symptom activation signal, generating emergency plan data through grouting reinforcement and structural strengthening, and obtaining preliminary layout parameters of the defense barrier includes: Receive the disaster symptom activation signal, and collect foundation settlement data and water pressure data based on the disaster symptom activation signal; Based on the foundation settlement data and water pressure data, determine whether the triggering conditions of the anti-buoyancy system are met; If the triggering conditions are met, the proportion of grouting material and the injection points are calculated to generate a grouting reinforcement scheme. Based on the grouting reinforcement scheme, simulate the stress changes of the foundation to generate structural reinforcement requirement data; Based on the structural reinforcement requirements data, reinforcement materials and construction methods are matched to generate a structural reinforcement plan; The grouting reinforcement scheme and the structural strengthening scheme are integrated to generate emergency response plan data.

5. The method according to claim 1, characterized in that, The process of extracting the location of the permeable waterproof curtain from the preliminary layout parameters of the defense barrier, simulating the groundwater flow path using a ring-shaped drainage system, and determining the combined drainage scheme includes: Geometric location data of the permeable waterproof curtain was extracted based on emergency plan data; Based on the geometric location data, a three-dimensional geometric model of the ring-shaped drainage system is constructed. Based on the aforementioned three-dimensional geometric model, the groundwater flow path is simulated to obtain water pressure distribution data; Adjust the pipe layout of the ring drainage system based on the water pressure distribution data; Based on the adjusted pipeline layout, calculate the trend of buoyancy value changes and generate optimized drainage system parameters; Based on the optimized drainage system parameters, a combined drainage scheme is determined.

6. The method according to claim 1, characterized in that, The process of real-time monitoring of ground feedback based on the construction sequence data, and using a Bayesian update algorithm to process feedback deviations, to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel includes: Based on the construction sequence data, the formation feedback signal is collected in real time to obtain the original feedback dataset; The Bayesian update algorithm is used to iteratively update the deviation in the original feedback dataset to obtain the corrected formation parameters; Based on the corrected formation parameters, the stability mitigation index is calculated; If the stability resolution index is lower than the preset threshold, the construction sequence parameters are adjusted to generate an optimized construction sequence dataset. Based on the optimized construction sequence dataset, the formation feedback signal was re-acquired to obtain the updated feedback dataset; Based on the updated feedback dataset, the Bayesian update process is repeated to achieve anti-buoyancy reinforcement of the tunnel over the utility tunnel.

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