A method and system for monitoring segment settlement of a shield tunnel in a soft silty soil stratum

Abstract

This invention relates to the field of tunnel safety monitoring and alarm systems, specifically a method and system for monitoring the settlement of tunnel segments in undersea shield tunnels located in soft silty sand strata. It aims to address the challenges of accurately monitoring long-term settlement of tunnel segments in soft silty sand strata due to their high permeability, low bearing capacity, and strong plasticity. Existing methods suffer from insufficient real-time continuity, delayed early warning, and difficulty in long-term stable operation in the undersea environment, thus failing to effectively support the optimization of construction parameters and proactive prevention of operational risks. The method includes: deploying multi-source sensors based on geological surveys to construct an anti-interference data acquisition and transmission system; preprocessing the monitoring data and then using a finite element model to invert soil and rock parameters to calculate the settlement; triggering graded early warnings based on preset thresholds and generating a comprehensive report; and integrating the multi-source analysis results into a BIM model. This method significantly improves monitoring accuracy and real-time performance, dynamically captures settlement evolution patterns, provides early warnings of potential risks, and ensures the safe operation of undersea tunnels.

Description

Technical Field

[0001] This invention relates to the field of tunnel safety monitoring and alarm, specifically to a method and system for monitoring the settlement of segments in a submarine shield tunnel in soft silty sand strata. Background Technology

[0002] As a critical transportation infrastructure crossing waterways, undersea shield tunnels face significant engineering and technical challenges when traversing soft silty sand strata. These strata are characterized by high permeability, low bearing capacity, and significant structural plasticity, which can easily lead to a series of safety problems such as uneven settlement of the tunnel segments during tunnel excavation and long-term operation, seriously threatening the overall stability and operational safety of the tunnel.

[0003] In existing technologies, monitoring of tunnel segment settlement largely relies on periodic manual on-site inspections or low-frequency automated data acquisition. These methods inherently suffer from deficiencies in the real-time nature of data updates and the continuity of monitoring results. They struggle to dynamically capture the evolution of settlement during construction disturbances and ground consolidation, leading to delayed early warnings and hindering the provision of timely and effective data support for dynamic optimization of construction parameters and proactive control of engineering risks. Furthermore, the high water pressure and strong corrosiveness of the seabed environment, along with the complex geological behavior of silty sand strata, further increase the difficulty of implementing long-term reliable monitoring.

[0004] Currently, synchronous grouting technology is widely used in undersea shield tunnels to control ground deformation. The principle behind this technology is as follows: during the tunnel boring machine's (TBM) advancement, a specific grout is injected into the "tail gap" between the shield shell and the outer wall of the tunnel segments. This grout provides fluid support in the initial stage and solidifies later to form a shell with a certain strength. This effectively compensates for most of the instantaneous elastic settlement and ground loss caused by stress release during construction, thus effectively controlling the early settlement and deformation of the tunnel segments.

[0005] However, synchronous grouting primarily addresses the issue of instantaneous settlement. Due to the characteristics of soft silty sand strata, segment settlement continues to occur over time after tunnel segment construction, and this process cannot be effectively suppressed by conventional synchronous grouting techniques. Prolonged, insidious settlement gradually becomes a key factor affecting the structural stability and safety of the tunnel during its operational phase.

[0006] Therefore, with synchronous grouting now a standard procedure, the urgent need in engineering practice has shifted from controlling instantaneous settlement to accurately predicting and managing long-term consolidation settlement that cannot be eliminated by grouting. This field urgently requires a systematic approach that can span the entire construction and operation cycle, integrating real-time monitoring, intelligent data analysis, dynamic model updates, and proactive risk warnings, to achieve accurate monitoring of long-term settlement behavior when subsea tunnels traverse soft silty sand strata. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention proposes a method and system for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata. This method solves the problems of difficulty in accurately monitoring long-term consolidation settlement of submarine shield tunnel segments caused by the high permeability, low bearing capacity, and strong plasticity of soft silty sand strata. Existing monitoring methods suffer from poor real-time performance, insufficient continuity, and delayed early warning. Furthermore, they are affected by the high water pressure and strong corrosive environment of the seabed, making long-term reliable operation difficult and unable to provide effective data support for optimizing construction parameters and proactively controlling operational risks.

[0008] To achieve the above objectives, this invention proposes a method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata, specifically including the following steps:

[0009] (1) Install multi-source sensors to collect monitoring data;

[0010] (2) Construct a data acquisition and transmission system to collect and transmit monitoring data;

[0011] (3) Preprocess the monitoring data;

[0012] (4) Establish a three-dimensional finite element model of the tunnel, input the preprocessed data into the model, and perform parameter inversion analysis;

[0013] (5) Based on the parameters obtained from the inversion, the long-term settlement of the tunnel segments is calculated using the consolidation settlement prediction model;

[0014] (6) Based on real-time monitoring values, predicted settlement trends and preset thresholds, automatically trigger graded early warning alarms and generate comprehensive reports;

[0015] (7) The preprocessed monitoring data, inversion parameters, predicted settlement, early warning level and comprehensive report are mapped to the pre-built tunnel segment BIM model through a standardized interface to realize the visualization of structural response, risk warning labeling and interactive access to the report.

[0016] Based on the above method, this invention also provides a settlement monitoring system for submarine shield tunnel segments in soft silty sand strata, including: a multi-sensor monitoring module, an anti-interference data acquisition and transmission module, a data preprocessing module, a BIM-finite element collaborative analysis module, a settlement prediction module, and an intelligent graded early warning module.

[0017] This invention constructs a closed-loop monitoring method and system that integrates multi-source in-situ sensing, anti-interference data chain, BIM-finite element collaborative inversion, and consolidation settlement prediction. This enables high-precision, continuous, predictable, and early warning intelligent monitoring of long-term settlement of submarine shield tunnel segments in soft silty sand strata, effectively supporting dynamic optimization of construction parameters and proactive risk prevention and control during operation.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] This invention addresses the challenge of accurately monitoring long-term consolidation settlement of tunnel lining segments due to the high permeability, low bearing capacity, and strong plasticity of soft silty sand strata. A multi-source sensor deployment scheme is designed, simultaneously deploying displacement sensors, strain sensors, and pore water pressure gauges at key locations of the tunnel lining segments, joints, and the outer silty sand layer. This achieves coupled sensing of structural deformation and formation hydraulic response, significantly improving the accuracy and timeliness of settlement monitoring and effectively overcoming the data lag limitations of traditional methods.

[0020] This invention addresses the problems of easy failure and unstable data transmission in monitoring systems under high water pressure and strong corrosion environments on the seabed. It constructs an anti-interference transmission architecture based on the integration of RS485 bus and optical fiber, ensuring high reliability, integrity and real-time performance of monitoring data under complex marine conditions, and providing hardware support for long-term stable operation.

[0021] This invention addresses the problems of high noise, asynchrony, and missing data in raw monitoring data by introducing a preprocessing workflow that includes filtering and denoising, time synchronization, interpolation completion, standardization, and compression. This provides high-quality, structured input data for subsequent modeling and analysis, improving the robustness and accuracy of the entire system.

[0022] This invention addresses the problem that existing monitoring methods can only acquire discrete, static settlement data, making it difficult to dynamically capture the evolution of consolidation settlement in soft silty sand strata. It innovatively deeply couples a BIM visualization platform with three-dimensional finite element inversion analysis: the BIM model is used to map the deformation and strain distribution of tunnel segments in real time; measured displacement and pore water pressure data drive the finite element model, and the actual mechanical parameters of the strata are inverted through an optimized algorithm; and combined with a consolidation settlement model suitable for silty sand strata, quantitative prediction of long-term settlement trends is achieved, thus shifting from "passive recording" to "active prediction," significantly improving the forward-looking nature and engineering guidance value of the monitoring system.

[0023] This invention addresses the problems of delayed early warning and passive response by developing an intelligent hierarchical early warning system. Based on preset multi-dimensional thresholds such as settlement amount and settlement rate, it automatically triggers alarms of different levels and generates a comprehensive report that includes risk location, cause analysis and disposal suggestions. This supports engineers in making rapid decisions and taking proactive measures to effectively prevent major safety accidents. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the calculation process of the present invention;

[0025] Figure 2 This is a schematic diagram of the inversion process of the present invention;

[0026] Figure 3This is a schematic diagram of the settlement report of the present invention. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1 As shown, a method and system for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata includes the following specific implementation steps:

[0029] S1: Arrangement and Installation of Multiple Sensors

[0030] Based on the geological survey report of the undersea tunnel, the spatial distribution of the weak silty sand strata was identified. In conjunction with the shield tunnel structure, displacement and strain sensors were installed at predetermined intervals at the arch crown, shoulders, waist, foot, and bottom of the tunnel segments in the weak silty sand strata. Simultaneously, strain sensors were installed at the circumferential and longitudinal joints of adjacent segments to comprehensively monitor the settlement and deformation of the segments. Multiple pore water pressure gauges were buried at predetermined intervals and depths in the silty sand layer outside the segments to monitor changes in pore water pressure in real time.

[0031] During sensor installation, strictly ensure the integrity of the IP68-level seal of the interface, and carefully check the sealing process of the joints and cable connections to prevent seawater leakage or sand particles from entering the sensor, thus avoiding electronic component failure due to water or sand ingress.

[0032] S2: Data Acquisition and Transmission

[0033] An anti-interference data acquisition and transmission architecture centered on a data acquisition card was constructed, establishing connections with various sensors via an RS485 bus to create an underwater data acquisition channel. To avoid signal attenuation and resist interference from the complex electromagnetic environment within the tunnel, fiber optic lines were laid for data transmission, ensuring high-speed and stable transmission of large-capacity monitoring data and meeting the long-term monitoring needs of the subsea tunnel.

[0034] In response to the characteristics of high water pressure and strong salt spray corrosion in underwater tunnels, the data acquisition card adopts a reinforced protection design: the whole is encapsulated in an IP68 waterproof shell, and the internal circuit board is coated with moisture-proof and salt spray-proof coating; the sensors and bus interfaces are all underwater sealed, and the RS485 communication port uses a waterproof aviation plug to prevent seawater intrusion and short circuits.

[0035] During normal operation, the system is set to automatically collect monitoring data on displacement, joint deformation, and pore water pressure every hour; during construction, the sampling frequency is increased to once every 20 minutes. The collected data includes segment displacement values, strain values, joint deformation, pore water pressure, and their corresponding timestamps, and is simultaneously stored on a local server and a cloud database to achieve dual backup and prevent data loss.

[0036] In addition, the data acquisition unit inside the tunnel has a built-in caching function. When communication is interrupted or the network is unstable, the data can be temporarily stored locally. After communication is restored, the data will be automatically transmitted to the central server to ensure the integrity and continuity of the monitoring data.

[0037] S3: Data Preprocessing

[0038] The collected monitoring data is preprocessed, including the following operations:

[0039] (1) Noise Removal: Low-pass filtering or median filtering is used to remove short-term outliers and high-frequency noise from the collected data. Specifically, for continuous monitoring data such as displacement, strain, and pore water pressure, the actual changes are low-frequency trends, while noise is mostly high-frequency random fluctuations (such as electronic thermal noise). Low-pass filtering is used to process the data to effectively smooth the data curves, eliminating high-frequency noise while fully preserving the low-frequency evolution trend reflecting processes such as segment settlement and formation consolidation. For sudden impulse noise caused by occasional strong interference that may exist in the data, median filtering is used. This method replaces the original value with the median value of the data within the sliding window, effectively eliminating such isolated outliers with large amplitudes.

[0040] (2) Handling Missing Data: Due to the special environment of the undersea tunnel, data transmission may be lost or incomplete. Interpolation methods such as linear interpolation and cubic spline interpolation can be used to fill in the missing data points and ensure data integrity. Specifically, for monitoring parameters with generally flat trends and short missing durations, such as less than 3 sampling periods, linear interpolation is preferred. This method is computationally efficient and stable, ensuring a smooth transition between the filled value and the data before and after it. For monitoring parameters with obvious trends or periodic / curvature characteristics, or when the data before and after the missing segment exhibit nonlinear characteristics, cubic spline interpolation is used. This method can better maintain the continuity of the first and second derivatives of the data curve, thus more accurately restoring the true trajectory of data change.

[0041] In practice, the system's built-in preprocessing algorithm first automatically determines the duration of the missing data segment and the fluctuation characteristics of the data before and after it, and selects the most suitable interpolation method according to preset rules. All interpolation operations are logged for analysts to review.

[0042] (3) Data Standardization: Since different types of sensors (displacement, strain) collect data of varying magnitudes, direct comparisons or calculations may result in errors due to differing dimensions. To standardize the data, data standardization can be performed. Specifically, the mean value of historical monitoring data sequences from various sensors is calculated. ) and standard deviation ( Z-score normalization method is used (formula: This method eliminates dimensions, transforming data from different sensors into a standard distribution with a mean of 0 and a standard deviation of 1, laying the foundation for subsequent multi-source data analysis.

[0043] (4) Outlier Detection and Correction: Sensor data may contain abrupt outliers due to equipment malfunctions, external interference, or other reasons. These outliers can be detected using the z-score or IQR (interquartile range) method in statistics. For detected outliers, corrections are made based on the trend of the preceding and following data. If the outliers are too outliers to be corrected, these data are discarded.

[0044] (5) Timestamp synchronization: Since different sensors in the tunnel may have different acquisition times, especially in multi-sensor systems, it is necessary to synchronize the data with timestamps to ensure that the data of all sensors are aligned at the same time.

[0045] (6) Data Compression and Storage Optimization: Since the monitoring system for the undersea tunnel collects a large amount of real-time data, this data may occupy a significant amount of storage space and increase the bandwidth requirements for data transmission, affecting system efficiency. Therefore, data compression technology is needed to reduce the burden on storage and transmission. This invention uses Huffman coding to effectively compress data, reduce storage space, and improve transmission efficiency.

[0046] S4: Establish a three-dimensional finite element model and perform inversion analysis.

[0047] A three-dimensional finite element numerical model was established using numerical simulation software. Specifically, based on tunnel design drawings, geological survey reports, and other data, a geometric model including tunnel segments, synchronous grouting layers, and surrounding soil layers was created using ABAQUS software. The synchronous grouting layer, as a key structure for suppressing elastic settlement, was included in the model and solid-state modeled.

[0048] For the soft silty sand strata and segment structures surrounding the tunnel, a fine mesh is used, employing a high-quality mesh generation strategy with localized densification: In the area adjacent to the tunnel (especially stress concentration zones), the element size is controlled to approximately 0.1 times the segment width, with no fewer than 24 circumferential elements and longitudinal element lengths roughly equivalent to the segment width; the mesh is further refined at the circumferential and longitudinal joints to accurately capture the complex mechanical behavior of the joints; in areas far from the tunnel, a gradually coarsening mesh is used to balance computational efficiency and accuracy. Mesh quality is strictly controlled through indicators such as twist and aspect ratio to avoid distorted elements, ensuring the accuracy and stability of inversion analysis and settlement prediction. The mesh type can be selected as needed. For example, for soils requiring consideration of pore water pressure dissipation, C3D8P (eight-node hexahedral pore pressure element) is selected; for solid structures such as segments, C3D8R (eight-node reduced integral element) is selected.

[0049] In ABAQUS, the collaborative working mechanism between the tunnel structure and the surrounding strata, as well as the nonlinear mechanical response of the segment joints, are realistically simulated by defining the surface-to-surface contact behavior between different model components. Specifically, surface-to-surface contact is used between the segments and the grouting layer, and between the grouting layer and the strata, defining hard contact in the normal direction and friction in the tangential direction. The joint surfaces of adjacent segments also use surface-to-surface contact, with hard contact in the normal direction allowing separation to simulate joint opening, and friction defined in the tangential direction to simulate the shear behavior of the joint.

[0050] Material parameters are assigned values ​​based on actual engineering conditions: For tunnel segments, material properties are assigned according to the concrete strength grade. For example, for C60 concrete, the elastic modulus is taken as 36.5 GPa, Poisson's ratio as 0.167, and density as 2400 kg / m³. For grouting layers, material properties that change over time are used to simulate the solidification process of the grout, which can be achieved through the field variable function of Abaqus. For silty sand layers and other soils, the elastic modulus, Poisson's ratio, density, etc., of the soil layers are set according to field exploration data. Considering the different depths and compositions of the soil layers, different constitutive models and parameters need to be selected for different soil layers. For example, considering the characteristics of weak silty sand layers, a modified Cambridge model is selected, and the elastic modulus is obtained from the geological survey report. Poisson's ratio and initial porosity Compression index obtained through indoor consolidation tests With rebound index Cohesion obtained through direct shear or triaxial tests internal friction angle and the critical state effective stress ratio obtained by fitting the experimental data with the critical state soil mechanics theory. Compression index Rebound Index .

[0051] Reasonable boundary conditions are set according to the actual geological conditions of the undersea tunnel to realistically simulate the physical environment, including seawater load and groundwater seepage, so as to ensure that the finite element calculation results can accurately reflect the actual project.

[0052] Regarding the mechanical boundary conditions: Fixed constraints are applied to the bottom boundary of the model to restrict the vertical displacement of the nodes; based on geostress measurement data, corresponding horizontal constraints are applied to the side boundaries; considering the self-weight of the overlying seawater, equivalent surface loads or displacement boundary conditions are applied to the surface boundaries, with the seawater load magnitude determined according to... Calculate the density of seawater. 1025 kg / m³ can be taken as the value, where h is the actual seawater depth.

[0053] Regarding pore water pressure boundary conditions: A pore water pressure boundary is set at the top of the model to simulate the hydrostatic pressure environment on the seabed; a non-drainage or zero-flow boundary is set at the bottom of the model to simulate the underlying impermeable layer; the lateral boundary of the model is set as a hydrostatic pressure boundary or a zero-flow boundary according to the actual hydrogeological conditions.

[0054] Perform the initial stress equilibrium step to ensure that the initial stress field of the model matches the measured stress state in the field, thus ensuring the accuracy of subsequent simulations.

[0055] Finally, the preprocessed displacement data is input into the three-dimensional finite element model as the target value. A particle swarm optimization algorithm is used to iteratively adjust the parameters to be inverted in the model until the error between the calculated results and the measured data is minimized, ultimately yielding structural and soil parameters that conform to the actual situation. For soft silty sand formations, the parameters typically include: elastic modulus, Poisson's ratio, cohesion, internal friction angle, permeability coefficient, and compressibility index.

[0056] S5: Establish a consolidation settlement model to calculate settlement.

[0057] Based on the structural and soil parameters obtained from the inversion, and considering the engineering practice of weak silty sand strata where consolidation settlement is the dominant factor, a consolidation settlement model is used. Calculate the settlement.

[0058] The consolidation settlement model is derived based on soil compression tests and the effective stress principle:

[0059] Through one-dimensional compression tests, the void ratio of the soil was determined. With effective stress The relationship is linear on a logarithmic scale, and the expression is:

[0060] ,

[0061] in, For soil at initial effective stress Porosity under action; The compressibility index represents the compressibility of the soil. The larger the diameter, the stronger the soil compressibility; This represents the initial effective stress.

[0062] When the effective stress changes from the initial value Increase to the final value porosity change for:

[0063] ,

[0064] The negative sign indicates a decrease in the porosity.

[0065] Settlement Related to changes in void ratio, the void ratio of soil Defined as the ratio of pore volume to soil particle volume, where the soil particle volume is 1. (Particle volume is incompressible)

[0066] initial volume ,

[0067] Compressed volume ,

[0068] Volume change (in (The negative sign indicates a decrease in porosity and volume compression.)

[0069] Volumetric strain It is the ratio of the change in volume to the initial volume, that is:

[0070] ,

[0071] Settlement It is the volumetric strain in the soil layer thickness The cumulative total (taking the absolute value, since settlement is positive) is therefore: ,

[0072] The previous formula Substituting into the above equation, we get:

[0073] ,

[0074] Since the settlement value is positive, the negative sign is omitted in the formula, and the final form is:

[0075] ,

[0076] Among them, the compression index The optimized value obtained through inversion analysis is adopted. This value is based on the results of the standard consolidation test in the field and is dynamically corrected in combination with the measured settlement response. H can be obtained from the geological survey report. Initial effective stress. pass Calculated. Wherein The effective unit weight of soil, For calculating the depth of the point, the vertical stress increment caused by the additional load. This calculation addresses the increase in ground stress caused by tunnel construction and operation. Key parameters such as the ground elastic modulus, upon which the calculation relies, are based on the latest results obtained from inversion analysis. Calculations can be performed using Boussinesq elastic theory solutions or based on an updated finite element model after inversion, ensuring that the estimated stress increment is consistent with the actual ground response. The resulting settlement can be used to predict soil settlement changes during tunnel construction and operation.

[0077] S6: Graded Early Warning and Settlement Report Generation

[0078] Based on tunnel design specifications, segment structure safety limits, and historical monitoring data, the total settlement was established as the primary factor. Settling rate Maximum strain of tunnel segments and the opening of circumferential and longitudinal joints These are key monitoring indicators. The system automatically identifies risks and responds in a tiered manner based on preset three-level early warning thresholds.

[0079] (1) Setting the three-level early warning threshold

[0080] Level 1 Warning (Blue, Normal Monitoring Status): When , , When any monitored or predicted value is below 80% of its preset safety threshold, the system marks it as "normal" and executes the routine monitoring and data recording process.

[0081] Level 2 Warning (Yellow, Alert Status): When any indicator reaches 80% to 95% of its safety threshold, or when the settlement rate / joint opening rate exceeds the design allowable value for three consecutive monitoring cycles, the system will automatically trigger a yellow alarm. This will prompt engineering personnel to intervene and initiate specialized data analysis.

[0082] Level 3 Warning (Red, Alarm Status): When any indicator reaches or exceeds 95% of its safety threshold, or the settlement rate / joint opening rate instantaneously exceeds twice the design allowable value, or the prediction model shows that any indicator trend will exceed the safety threshold within the next 3 cycles, the system will immediately trigger the highest level red alarm. Emergency warnings will be issued to staff through multiple channels, including sound and light alerts, SMS messages, and application push notifications.

[0083] (2) Intelligent early warning and linkage response

[0084] When the system triggers a level 2 or higher warning, the following linked operations will be executed automatically:

[0085] Risk visualization and positioning: The precise location, number, and ring number of alarm segments or joints are highlighted in the BIM platform, and settlement cloud maps, strain distribution maps, and joint opening distribution maps are rendered based on finite element calculation results, intuitively presenting the spatial evolution of the risk area.

[0086] Multi-source data correlation diagnosis: Automatically retrieves and displays the historical data curves of the alarm point, adjacent sensor readings, and the latest formation parameters obtained through inversion, providing engineers with a data basis for comprehensive judgment. When the joint opening alarm occurs, the system will focus on displaying the differential settlement and strain data of the segments on both sides of the joint.

[0087] Expert knowledge base assists decision-making: The system's pre-built expert knowledge base intelligently pushes possible cause analyses based on the combination of alarm indicators and the current operating conditions. For example, if the joint opening... Excessive settlement accompanied by differential settlement may be caused by "uneven stress on the segments or insufficient pre-tightening force of connecting bolts".

[0088] If the joint opening and settlement rate alarms simultaneously, the possible causes are "insufficient grouting or excessive formation loss".

[0089] (3) Settlement report is automatically generated

[0090] After the warning is triggered, the system automatically generates a structured settlement report, which mainly includes:

[0091] Monitoring data summary: Lists the measured data of all relevant sensors during the warning period, with special note on the opening data of key joints.

[0092] Deformation trend analysis: Combining BIM visualization and finite element prediction data, the dynamic changes in settlement trend, strain distribution and joint opening are displayed.

[0093] Risk assessment conclusion: Clearly identify the specific segments and joint locations that exceed the safety threshold in the actual and predicted data, and assess the risk level that may lead to water leakage, uneven settlement or structural damage.

[0094] Targeted adjustment measures recommendations: Based on the warning level and diagnostic results, generate actionable decision recommendations. For example, for a joint opening alarm, recommendations could include "inspect and retighten the connecting bolts" or "perform additional grouting behind the joint."

[0095] S7: Dynamically update BIM model

[0096] Preprocessed monitoring data, geotechnical parameters obtained from inversion, predicted settlement time history curves, early warning levels, and comprehensive assessment reports are pushed in real time to a pre-built tunnel segment BIM model via a standardized data interface. This enables interactive access to structural response visualization, risk warning annotation, and reports on the BIM platform. Figure 3 As shown, the locations of pipe segments and joints in the BIM model that exceed the limits are highlighted, and potential leakage or structural damage risk areas are clearly marked with red warning lines; deformation trend analysis charts are displayed simultaneously, including settlement-time curves, dynamic changes in joint opening, and strain distribution cloud maps, which intuitively reflect the structural response process; at the same time, an automatically generated report containing a summary of monitoring data, risk assessment conclusions, and targeted control measures suggestions is generated, allowing users to view and implement decision-making interventions with one click.

[0097] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata, characterized in that, Includes the following steps: S1: Install multi-source sensors to collect monitoring data; S2: Construct a data acquisition and transmission system to collect and transmit monitoring data; S3: Preprocess the monitoring data; S4: Establish a three-dimensional finite element model of the tunnel, input the preprocessed data into the model, and perform parameter inversion analysis; S5: Based on the parameters obtained from the inversion, the long-term settlement of the tunnel segments is calculated using a consolidation settlement prediction model; S6: Based on real-time monitoring values, predicted settlement trends, and preset thresholds, automatically trigger tiered early warning alarms and generate comprehensive reports; S7: The preprocessed monitoring data, inversion parameters, predicted settlement, early warning level and comprehensive report are mapped to the pre-built tunnel segment BIM model through a standardized interface to realize the visualization of structural response, risk warning labeling and interactive access to reports.

2. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, The monitoring data collected in S1 includes: (1) Segment displacement data obtained by displacement sensors installed at key parts of shield tunnel segments; (2) Structural strain data obtained by strain sensors installed at key parts of shield tunnel segments and at circumferential and longitudinal joints of adjacent segments; (3) Formation pore water pressure data obtained by a pore water pressure gauge buried in the silt layer outside the pipe segment.

3. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, In S2, the monitoring data collection frequency is set according to the stage of the tunnel; the stage includes the normal operation stage and the construction stage.

4. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, The data preprocessing steps in S3 include: (1) Denoise the collected raw data; (2) Handling missing data; (3) Standardize the data; (4) Detect and correct outliers; (5) Synchronize the data with timestamps; (6) Optimize data compression and storage.

5. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, The inversion analysis steps in S4 include: (1) Based on the geological survey report and tunnel design drawings, a three-dimensional finite element model including the strata, tunnel segments and synchronous grouting layer was constructed using finite element software, and constitutive models and material parameters were selected for each component; (2) Mesh each model component and select the mesh type; (3) Select contact behaviors between model components; (4) Set boundary conditions based on the actual geological conditions of the undersea tunnel; (5) Input the preprocessed data into the finite element model and perform parameter inversion optimization.

6. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, The consolidation settlement prediction model in S5 is as follows: ， Where S represents the settlement amount; Compression index; Initial void ratio; H: Soil layer thickness; Initial effective stress; Additional stress.

7. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, The S6-level early warning system adopts a three-level early warning system, namely, the first-level early warning threshold, the second-level early warning threshold, and the third-level early warning threshold. The scope of the Level 1 warning is defined as any monitored or predicted value falling below 80% of its preset safety threshold; The scope of the Level II early warning is when any indicator reaches 80% to 95% of its safety threshold, or when the settlement rate / joint opening rate exceeds the design allowable value for three consecutive monitoring cycles. The scope of the three-level early warning is when any indicator reaches or exceeds 95% of its safety threshold, or when the settlement rate / joint opening rate instantaneously exceeds twice the design allowable value, or when the prediction model shows that any indicator trend will exceed the safety threshold within the next three cycles.

8. The method for monitoring the settlement of submarine shield tunnel segments in soft silty sand strata according to claim 1, characterized in that, The comprehensive report in S6 includes three core components: risk identification, cause analysis, and optimization suggestions.

9. A system applicable to the settlement monitoring method for submarine shield tunnel segments in soft silty sand strata as described in any one of claims 1-8, characterized in that, include: (1) A multi-sensor monitoring module, including a displacement sensor, a strain sensor and a pore water pressure gauge, is used to acquire data on the deformation of the pipe segment structure and the pore water pressure of the formation. (2) Data acquisition and transmission module, built on RS485 bus and fiber optic network, used to acquire and transmit sensor data; (3) Data preprocessing module, used to denoise, interpolate, standardize, synchronize and compress the raw monitoring data; (4) BIM-Finite Element Collaborative Analysis Module, which combines BIM platform and finite element software to realize settlement inversion and visualized dynamic monitoring; (5) Settlement prediction module, used to predict the settlement trend of tunnel segments based on the inversion results and the consolidation settlement model; (6) Intelligent hierarchical early warning module, which is used to automatically trigger hierarchical early warning based on monitoring results and generate a comprehensive report.

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