Self-adaptive settlement monitoring and compensation construction integrated system

Abstract

The invention discloses a self-adaptive settlement monitoring and compensation construction integrated system, and belongs to the technical field of foundation settlement monitoring and compensation integration. The system comprises a monitoring module, an intelligent control module, a compensation construction module and a digital twinning module. The monitoring module comprises a settlement sensor group, a stress sensor, a three-dimensional displacement sensor and a micro geological radar which are arranged according to a bionic root topology; the micro geological radar realizes layered scanning in an underground range by adopting a multi-band synthetic hole technology; the intelligent control module comprises an integrated settlement prediction algorithm, a multi-stage early warning unit and a seepage-stress coupling analysis model. The output end of the seepage-stress coupling analysis model and the compensation construction module form closed-loop control. The drainage efficiency is remarkably improved through intelligent association of the flow guide pipe and the pore water pressure, the digital twin module constructs a multi-source data interaction platform, dynamic mapping of a physical entity and a virtual model is achieved, the settlement control precision can be improved, and meanwhile the construction cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated foundation settlement monitoring and compensation, and in particular to an integrated adaptive settlement monitoring and compensation construction system. Background Art

[0002] Traditional settlement monitoring relies on manual observation or single-point automated equipment, resulting in data lag and insufficient accuracy. For example, early railway subgrade monitoring relied primarily on manual inspections. Although automated equipment was introduced after the 1990s, it still faces limitations such as low monitoring frequency and missing three-dimensional spatial data. Regarding control technology, traditional grouting processes are prone to secondary settlement due to uneven slurry diffusion. While piling can increase bearing capacity, it is prone to causing pile friction in soft soil due to high vibration and high costs.

[0003] Patent document CN101709968B discloses an adaptive scanning roadbed settlement remote monitoring device and method, including a settlement monitoring pile, a light source, a fixed observation pile, a position measurement unit, and an electric rotating platform. The method uses multiple light sources including a first and a second point light source; the first and second point light sources are imaged on the position measurement unit, the imaging magnification is calculated from the image point distance, and the roadbed settlement is calculated from the imaging magnification and the image point displacement; the electric rotating platform 3 drives the light source position measurement unit to rotate, aligns with the light source in turn, and measures the roadbed settlement of multiple test points. This solution accurately measures the imaging magnification and improves measurement accuracy; the light source intensity is adjustable and can be measured in all weather conditions; one detector measures multiple targets, establishes a unified benchmark, and reduces equipment costs. However, existing roadbed settlement remote monitoring devices have technical problems such as lack of simultaneous perception capabilities of multiple parameters in deep strata and control lag.

[0004] Patent document CN213389563U discloses an adaptive settlement monitoring and control system for bridge transition sections, comprising a plurality of piezoelectric sensors, an electrical signal information processor, an energy storage device, a display terminal, and a plurality of piezoelectric units; the piezoelectric units are made of electrostrictive material; the electrical signal information processor is connected to the piezoelectric sensor and the display terminal, the input end of the energy storage device is connected to the piezoelectric sensor and an external circuit, and the output end is connected to the piezoelectric unit; the piezoelectric sensor and the piezoelectric unit are arranged between the gravel and the slats in the bridge transition section. The system uses the electrostrictive effect to cause the piezoelectric unit to stretch and first bear the weight of the slats, and then the spring gradually rebounds to take over the force from the piezoelectric unit, thereby controlling the slat settlement. This system realizes automatic monitoring and remote control of the slat settlement in the bridge transition section under road traffic loads, thereby achieving adaptive settlement of the road transition section and guiding engineering construction such as grouting reinforcement. However, existing adaptive settlement monitoring devices have technical problems such as data island effects and lack of ecological control.

[0005] Patent document CN108425384A discloses a pile foundation settlement monitoring device and method, comprising a frame positioned within the pile foundation; a fiber grating (FBG) sensor mounted on and / or connected to the frame to monitor the settlement of the top of the pile foundation; and a demodulator connected to the FBG sensor. This solution addresses the inconvenience of pier settlement monitoring in existing technologies. However, existing pile foundation settlement monitoring devices suffer from technical issues such as a crude early warning system and a rigid emergency response system. Summary of the Invention

[0006] The purpose of the present invention is to provide an integrated adaptive settlement monitoring and compensation construction system. The monitoring module adopts a sensor group with a bionic root topology layout and a multi-band synthetic micro geological radar to realize the layered scanning of soil and full-dimensional real-time perception of pore water pressure and stress displacement within the underground range. The intelligent control module converts geological data into dynamic grouting parameters based on the seepage-stress coupling analysis model and AI prediction algorithm, and drives the three-dimensional drainage network of the high-permeability bionic root spiral pile structure and the hydraulic servo grouting system in the compensation construction module to work together. The drainage efficiency is significantly improved through the intelligent association of the diversion pipe and the pore water pressure. The digital twin module constructs a multi-source data interaction platform to realize the dynamic mapping of physical entities and virtual models, which is conducive to improving the settlement control accuracy and reducing construction costs at the same time, so as to solve the problems raised in the above background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an integrated adaptive settlement monitoring and compensation construction system, comprising a monitoring module, an intelligent control module, a compensation construction module and a digital twin module; the monitoring module comprises a settlement sensor group, a stress sensor, a three-dimensional displacement sensor and a micro geological radar arranged according to the bionic root system topology, the micro geological radar adopts a multi-band synthetic hole technology to realize layered scanning within the range of 5m-15m underground; the intelligent control module comprises an integrated settlement prediction algorithm, a multi-level early warning unit and a seepage-stress coupling analysis model, the output end of the seepage-stress coupling analysis model is connected to the compensation construction module. The module forms a closed-loop control; the compensation construction module includes an adjustable grouting device, a hydraulic servo system, a bionic root spiral pile structure and a dynamic control subsystem. A diversion pipe is arranged between the blades of the spiral pile structure to form a three-dimensional drainage network. The drainage efficiency of the diversion pipe is dynamically associated with the pore water pressure data of the monitoring module; the digital twin module constructs a multi-directional interactive platform that integrates physical test data, simulation models and AI predictions; the bionic root spiral pile structure adopts high-permeability concrete material, and its permeability coefficient is three orders of magnitude higher than that of traditional piles, and the drainage efficiency is dynamically associated with the pore water pressure data of the monitoring module through a feedback control unit.

[0008] Preferably, the bionic root spiral pile structure includes a variable-section pile body, spiral blades and a diversion pipe; the diameter of the variable-section pile body decreases exponentially along the depth, the decreasing gradient of the exponential function is dynamically adjusted based on the soil density detected by the geological radar, gradient drainage holes are set at the distance between adjacent blades of the spiral blades, the aperture of the drainage holes is dynamically adjusted according to the real-time data of the groundwater level sensor, the inner wall of the diversion pipe is coated with a nano-hydrophobic coating, and its inclination angle is adjusted in real time according to the soil permeability coefficient.

[0009] Preferably, the digital twin module includes a virtual-reality interaction unit, an ecological assessment unit and a virtual grouting simulation unit; the virtual-reality interaction unit adopts an improved data fusion algorithm to perform weighted fusion of physical experimental data and simulation data, the ecological assessment unit has a built-in carbon footprint calculation model to optimize the grouting material usage and drainage efficiency, and the virtual grouting simulation unit adopts a coupling algorithm to perform grouting simulation to ensure that the grouting pressure prediction error is less than 5%.

[0010] Preferably, the settlement prediction algorithm includes a root growth simulation sub-model, an LSTM neural network time series prediction model and a confidence assessment unit; the root growth simulation sub-model predicts the soil consolidation rate based on the drainage data of the screw piles, the input parameters of the LSTM neural network time series prediction model include BIM modeling data and real-time monitored settlement rate, pore water pressure and stress data, and the confidence assessment unit triggers model self-training when the prediction error exceeds a preset threshold.

[0011] Preferably, the dynamic control subsystem synchronously adjusts the grouting pressure and the drainage channel opening according to the pore water pressure data, and realizes the double closed-loop control of the seepage field and the stress field through the PID controller; the high-permeability concrete material includes coarse aggregate with a particle size of 5mm-15mm, 3% to 5% silicon carbide whiskers, and a built-in micron-level capillary network, and the distribution density of the micron-level capillary network increases along the depth direction of the pile body.

[0012] Preferably, the monitoring module also includes a sliding scanning unit and a data fusion unit; the sliding scanning unit controls the micro geological radar to periodically scan the land density within the range of 5m-15m underground at preset time intervals, and the data fusion unit uses the Kalman filtering algorithm to achieve spatiotemporal alignment of radar data and sensor data, and generate a three-dimensional geological model.

[0013] Preferably, the multi-level warning unit includes first-level warning, second-level warning and third-level warning; first-level warning: when the settlement rate is greater than 3mm / d, an audible and visual alarm is triggered, and the drainage channel pressurization mode is started; second-level warning: when the cumulative settlement is greater than 85% of the design value, a pile structure adjustment plan is automatically generated and sent to the digital twin module for verification; third-level warning: when the predicted final settlement exceeds the standard, emergency grouting is started, and the grouting pressure is increased to 1.5-2 times the normal value.

[0014] Preferably, the ecological assessment unit includes a carbon footprint tracking module, an energy consumption optimization module and a drainage-consolidation coordination module; the carbon footprint tracking module records the carbon emission data of the entire cycle from the production of grouting materials to construction, the energy consumption optimization module dynamically adjusts the timing of grouting and drainage operations according to real-time electricity prices and equipment efficiency, and the drainage-consolidation coordination module establishes a quantitative relationship function between drainage efficiency and soil consolidation rate based on Darcy's law, and optimizes the aperture parameters of the drainage holes.

[0015] Preferably, the seepage-stress coupling analysis model is constructed based on the improved Biot consolidation theory, and receives the pore water pressure data, stress data and three-dimensional displacement data of the monitoring module in real time. The coupling equations of the soil seepage field and stress field are solved synchronously through the implicit iterative algorithm. The boundary conditions of the coupling equations are dynamically updated according to the soil density scanned by the micro-geological radar, and the calculation results are fed back to the dynamic control subsystem of the compensation construction module in real time for correcting the control parameters of the grouting pressure and the drainage channel opening.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. The present invention utilizes a sensor group arranged in a bionic root topology and a multi-band synthetic micro-geo-radar in the monitoring module to achieve stratified soil scanning and full-dimensional real-time perception of pore water pressure, stress, and displacement within a range of 5-15 meters underground. The intelligent control module, based on a seepage-stress coupling analysis model and AI prediction algorithm, converts geological data into dynamic grouting parameters. This drives the coordinated operation of the three-dimensional drainage network of the high-permeability bionic root spiral pile structure and the hydraulic servo grouting system in the compensation construction module. This significantly improves drainage efficiency through the intelligent association of diversion pipes with pore water pressure. The digital twin module builds a multi-source data interaction platform, enabling dynamic mapping between physical entities and virtual models, which helps improve settlement control accuracy while reducing construction costs.

[0018] 2. This invention uses an improved data fusion algorithm in the virtual-reality interaction unit to perform multi-source weighted fusion of physical test, sensor monitoring, and simulation data to construct a real-time dynamic mapping model with low error. The ecological assessment unit then quantifies the correlation between grouting material usage, drainage efficiency, and carbon emissions based on a carbon footprint calculation model, dynamically optimizing construction parameters and thereby reducing carbon emissions while maintaining equivalent settlement control. The virtual grouting simulation unit employs a seepage-stress-temperature multi-field coupling algorithm to achieve millimeter-level simulation of grouting pressure, diffusion path, and formation response, keeping prediction errors within 5%, which helps improve the construction qualification rate.

[0019] 3. The present invention significantly improves the accuracy and timeliness of settlement control through multi-level early warning units. The first-level early warning triggers sound and light alarms and drainage pressurization with a settlement rate of 3mm / d as the threshold, realizing early intervention of risks. The second-level early warning automatically generates a pile optimization plan when the cumulative settlement reaches 85% of the design value, and conducts construction simulation verification through the digital twin platform, forming a closed-loop decision-making chain of "early warning-plan generation-virtual verification". The third-level early warning adopts an emergency grouting mechanism based on a predictive model. When the risk of settlement exceeding the standard occurs, the grouting pressure is dynamically increased to 1.5-2 times the conventional value, and the high-transparency structure of the bionic root spiral pile structure is combined to achieve rapid foundation reinforcement. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the overall structure of the system of the present invention;

[0021] Figure 2 This is a schematic diagram of the bionic root spiral pile structure of the present invention;

[0022] Figure 3 Schematic diagram of the settlement prediction algorithm structure of the present invention;

[0023] Figure 4 Schematic diagram of the virtual-reality interaction unit structure of the present invention;

[0024] Figure 5 Schematic diagram of the structure of the seepage-stress coupling analysis model of the present invention;

[0025] Figure 6 Schematic diagram of the structure of the high permeability concrete material of the present invention;

[0026] Figure 7 This is a schematic diagram of the multi-level warning unit structure of the present invention.

[0027] In the figure: 1. Monitoring module; 2. Intelligent control module; 3. Compensation construction module; 4. Digital twin module; 5. Settlement sensor group; 6. Stress sensor; 7. Three-dimensional displacement sensor; 8. Micro geological radar; 9. Settlement prediction algorithm; 10. Multi-level early warning unit; 11. Seepage-stress coupling analysis model; 12. Adjustable grouting device; 13. Hydraulic servo system; 14. Bionic root spiral pile structure; 15. Diversion pipe; 16. Variable-section pile body; 17. Spiral blade; 18. Drainage hole; 19. Virtual-reality interaction unit; 20. Ecological assessment unit; 21. Virtual grouting simulation unit; 22. Root growth simulation sub-model; 23. LSTM neural network time series prediction model; 24. Confidence assessment unit; 25. Dynamic control subsystem; 27. Sliding scanning unit; 28. Data fusion unit; 29. ​​Carbon footprint tracking module; 30. Energy consumption optimization module; 31. Drainage-consolidation collaborative module. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] See also Figure 1-7 This embodiment provides a technical solution: an integrated adaptive settlement monitoring and compensation construction system, including a monitoring module 1, an intelligent control module 2, a compensation construction module 3 and a digital twin module 4; the monitoring module 1 includes a settlement sensor group 5, a stress sensor 6, a three-dimensional displacement sensor 7 and a micro-geological radar 8 arranged according to the bionic root system topology, and the micro-geological radar 8 uses a multi-band synthetic hole technology to achieve layered scanning within the range of 5m-15m underground; the intelligent control module 2 includes an integrated settlement prediction algorithm 9, a multi-level early warning unit 10 and a seepage-stress coupling analysis model 11, and the output end of the seepage-stress coupling analysis model 11 is connected to the compensation construction module 3 A closed-loop control is formed; the compensation construction module 3 includes an adjustable grouting device 12, a hydraulic servo system 13, a bionic root spiral pile structure 14 and a dynamic control subsystem 25. A guide pipe 15 is arranged between the blades of the spiral pile structure 14 to form a three-dimensional drainage network. The drainage efficiency of the guide pipe 15 is dynamically correlated with the pore water pressure data of the monitoring module 1; the digital twin module 4 constructs a multi-directional interactive platform that integrates physical test data, simulation models and AI predictions; the bionic root spiral pile structure 14 adopts high-permeability concrete material, and its permeability coefficient is three orders of magnitude higher than that of traditional piles, and the drainage efficiency is dynamically correlated with the pore water pressure data of the monitoring module 1 through a feedback control unit.

[0030] The monitoring module 1 uses a sensor group arranged in a bionic root topology and a multi-band synthetic micro-geological radar 8 to achieve full-dimensional real-time perception of soil layer scanning and pore water pressure, stress and displacement within the underground range of 5m-15m. The intelligent control module 2 converts geological data into dynamic grouting parameters based on the seepage-stress coupling analysis model 11 and AI prediction algorithm, driving the three-dimensional drainage network of the high-permeability bionic root spiral pile structure 14 in the compensation construction module 3 and the hydraulic servo grouting system to work together. The drainage efficiency is significantly improved through the intelligent association of the diversion pipe 15 with the pore water pressure. The digital twin module 4 constructs a multi-source data interaction platform to achieve dynamic mapping between physical entities and virtual models. The system forms a "perception-analysis-control-verification" closed loop, which is conducive to improving settlement control accuracy while reducing construction costs. It provides full-cycle dynamic protection for engineering safety under complex geological conditions, especially showing significant technical advantages in areas with fluctuating groundwater levels.

[0031] The bionic root-based spiral pile structure 14 includes a variable-section pile body 16, spiral blades 17, and a diversion tube 15. The diameter of the variable-section pile body 16 decreases exponentially with depth, and the decreasing gradient of the exponential function is dynamically adjusted based on the soil density detected by geological radar. Gradient drainage holes 18 are provided at intervals between adjacent spiral blades 17. The aperture of the drainage holes 18 is dynamically adjusted based on real-time data from a groundwater level sensor. The inner wall of the diversion tube 15 is coated with a nano-hydrophobic coating, and its inclination angle is adjusted in real time based on the soil permeability coefficient.

[0032] By adopting an exponentially decreasing diameter gradient of the variable-section pile body 16 in the bionic root spiral pile structure 14, and combining the soil density detected by geological radar to optimize the cross-sectional parameters in real time, the stress distribution of the pile body is more in line with the mechanical properties of the stratum, thereby improving the bearing capacity. The gradient-arranged drainage holes 18 on the spiral blades 17 dynamically adjust the aperture based on the groundwater level sensor data to achieve a precise match between the pore water pressure and the drainage rate, avoiding the problem of easy blockage or excessive drainage of traditional drainage structures. The nano-hydrophobic coating on the inner wall of the diversion tube 15 is conducive to reducing drainage resistance. Combined with the inclination angle adjusted in real time by the soil permeability coefficient, a self-cleaning and efficient drainage channel is formed, which is conducive to improving the service life. The three work together to break through the contradiction between traditional pile foundation drainage and bearing capacity, reduce the amount of settlement compensation construction in complex geology such as soft soil and high water level, reduce operation and maintenance costs, and achieve a dynamic balance between structural stability and hydrological regulation.

[0033] The digital twin module 4 includes a virtual-reality interaction unit 19, an ecological assessment unit 20, and a virtual grouting simulation unit 21. The virtual-reality interaction unit 19 uses an improved data fusion algorithm to perform weighted fusion of physical experimental data and simulation data. The ecological assessment unit 20 has a built-in carbon footprint calculation model to optimize grouting material usage and drainage efficiency. The virtual grouting simulation unit 21 uses a coupling algorithm to simulate grouting, ensuring that the grouting pressure prediction error is less than 5%.

[0034] Through the improved data fusion algorithm of the virtual-reality interaction unit 19, multi-source weighted fusion of physical test, sensor monitoring and simulation data is carried out to construct a real-time dynamic mapping model with small error. Then, through the ecological assessment unit 20, the correlation between grouting material consumption, drainage efficiency and carbon emissions is quantified according to the carbon footprint calculation model, and the construction parameters are dynamically optimized, thereby reducing carbon emissions under the same settlement control effect. The virtual grouting simulation unit 21 adopts the seepage-stress-temperature multi-field coupling algorithm to realize millimeter-level simulation of grouting pressure, diffusion path and formation response, and the prediction error is controlled within 5%, which is conducive to improving the construction qualification rate. At the same time, the three work together to form a "perception-simulation-optimization" closed loop, which is conducive to improving the resource allocation efficiency of settlement compensation construction and shortening the construction period.

[0035] The settlement prediction algorithm 9 includes a root growth simulation sub-model 22, an LSTM neural network time series prediction model 23, and a confidence assessment unit 24. The root growth simulation sub-model 22 predicts the soil consolidation rate based on the drainage data of the screw piles. The input parameters of the LSTM neural network time series prediction model 23 include BIM modeling data and real-time monitored settlement rate, pore water pressure, and stress data. The confidence assessment unit 24 triggers model self-training when the prediction error exceeds a preset threshold.

[0036] A bionic soil consolidation rate prediction framework is constructed based on the spiral pile drainage data through the root growth simulation sub-model 22, which is conducive to improving the prediction accuracy. The LSTM neural network time series prediction model 23 is used to integrate BIM modeling parameters with multi-source heterogeneous data such as real-time settlement rate, pore water pressure and stress. The nonlinear coupling relationship of geological parameters is captured through a bidirectional gating mechanism, which can reduce the time series prediction error. The confidence assessment unit 24 uses a sliding window mechanism to dynamically track the prediction deviation. When the error is detected to exceed the threshold, the incremental learning algorithm is automatically triggered to update the model weight. Under the collaborative framework of "mechanism driven-data driven-dynamic verification", the three can achieve millimeter-level prediction of settlement trends under complex working conditions.

[0037] The dynamic control subsystem 25 synchronously adjusts the grouting pressure and drainage channel opening based on pore water pressure data, and implements dual closed-loop control of the seepage field and stress field through a PID controller. The high-permeability concrete material includes coarse aggregate with a particle size of 5mm-15mm, 3%-5% silicon carbide whiskers, and a built-in micron-scale capillary network. The distribution density of the micron-scale capillary network increases along the depth of the pile.

[0038] Through the dynamic control subsystem 25, the grouting pressure and the drainage channel opening are synchronously decoupled based on the pore water pressure data, and a dual closed-loop control system of the seepage field and the stress field is constructed through the PID controller, which is conducive to shortening the response time of the compensation construction. The high-permeability concrete material is optimized by 5mm-15mm coarse aggregate grading and the incorporation of 3% to 5% silicon carbide whiskers, which is conducive to improving the compressive strength. Its built-in micron-level capillary network is distributed incrementally along the depth of the pile. Combined with the hydrophobic coating, the drainage efficiency is positively correlated with the depth, which can improve the drainage flux under the condition of sudden rise in groundwater level. The two are linked through the digital twin module to form a "material-structure-control" three-in-one optimization mechanism, which is conducive to reducing the energy consumption of soft soil foundation settlement compensation construction and improving the service life of the structure, providing an adaptive solution for extreme hydrogeological engineering.

[0039] The monitoring module 1 also includes a sliding scanning unit 27 and a data fusion unit 28; the sliding scanning unit 27 controls the micro geological radar 8 to periodically scan the ground density within a range of 5m-15m underground at preset time intervals, and the data fusion unit 28 uses the Kalman filter algorithm to achieve spatiotemporal registration of radar data and sensor data and generate a three-dimensional geological model;

[0040] The sliding scanning unit 27 drives the micro geological radar 8 to periodically scan the underground soil 5m-15m at intervals of minutes, which is conducive to accurately capturing the transient changes of soil parameters caused by construction disturbances. The data fusion unit 28 adopts an improved Kalman filter algorithm to perform spatiotemporal domain alignment on the radar scanning data and the pore water pressure / stress / displacement signals of the sensor group, and reconstruct the three-dimensional geological model in real time. By linking with the digital twin module 4, a "scanning-fusion-verification-optimization" closed loop is formed, which is conducive to improving the efficiency of complex stratum modeling, shortening the early warning response time, and reducing the misjudgment rate in subway construction in soft soil areas.

[0041] The multi-level warning unit 10 includes level one, level two, and level three warnings. Level one warning: When the settlement rate is greater than 3 mm / d, an audible and visual alarm is triggered, and the drainage channel pressurization mode is activated. Level two warning: When the cumulative settlement is greater than 85% of the design value, a pile structure adjustment plan is automatically generated and sent to the digital twin module 4 for verification. Level three warning: When the predicted final settlement exceeds the standard, emergency grouting is activated, and the grouting pressure is increased to 1.5-2 times the normal value.

[0042] The accuracy and timeliness of settlement control have been significantly improved through the multi-level early warning unit 10. The first-level early warning triggers an audible and visual alarm and drainage pressurization with a settlement rate of 3mm / d as the threshold, achieving early risk intervention. The second-level early warning automatically generates a pile optimization plan when the cumulative settlement reaches 85% of the design value, and conducts construction simulation verification through the digital twin platform, forming a closed-loop decision-making chain of "early warning-plan generation-virtual verification". The third-level early warning adopts an emergency grouting mechanism based on a predictive model. When the risk of settlement exceeding the standard occurs, the grouting pressure is dynamically increased to 1.5-2 times the normal value, and the high-transparency structure of the bionic root spiral pile structure 14 is combined to achieve rapid foundation reinforcement. This three-level progressive early warning system shortens the settlement control response time through the deep coupling of physical monitoring, intelligent prediction and engineering compensation. At the same time, the trial and error cost of the plan is reduced through digital twin technology 4, and an intelligent prevention and control network covering the entire settlement cycle is built.

[0043] The ecological assessment unit 20 includes a carbon footprint tracking module 29, an energy consumption optimization module 30, and a drainage-consolidation coordination module 31. The carbon footprint tracking module 29 records carbon emission data from the production of grouting materials to the entire construction cycle. The energy consumption optimization module 30 dynamically adjusts the timing of grouting and drainage operations based on real-time electricity prices and equipment efficiency. The drainage-consolidation coordination module 31 establishes a quantitative relationship function between drainage efficiency and soil consolidation rate based on Darcy's law and optimizes the aperture parameters of the drainage holes 18.

[0044] The seepage-stress coupling analysis model 11 is constructed based on the improved Biot consolidation theory. It receives pore water pressure data, stress data, and three-dimensional displacement data from the monitoring module 1 in real time. It synchronously solves the coupling equations of the soil seepage field and stress field through an implicit iterative algorithm. The boundary conditions of the coupling equations are dynamically updated according to the soil density scanned by the micro-geological radar 8. The calculation results are fed back in real time to the dynamic control subsystem 25 of the compensation construction module 3 for modifying the control parameters of the grouting pressure and the drainage channel opening.

[0045] The carbon footprint tracking module 29 is combined with the peak-valley electricity price response mechanism of the energy consumption optimization module 30, which is conducive to reducing construction energy consumption costs. The drainage efficiency-consolidation rate quantification model constructed by the drainage-consolidation coordination module 31 based on Darcy's law can optimize the parameters of the drainage hole 18 and shorten the soil consolidation period. The seepage-stress coupling analysis model 11 incorporates pore water pressure, stress and displacement data into implicit iterative calculations through the improved Biot theory. Combined with the soil density boundary conditions dynamically updated by the micro-geological radar 8, the soil deformation prediction accuracy is improved. The calculation results are used to calibrate the grouting pressure and drainage channel opening parameters in real time, forming an intelligent closed loop of "monitoring-analysis-control". Through the deep integration of ecological indicators and engineering parameters, this system achieves a reduction in construction carbon emissions while ensuring the accuracy of settlement control.

[0046] The above is a schematic description of the present invention and its embodiments, which is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs a structure and embodiment similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. An integrated adaptive settlement monitoring and compensation construction system, characterized by: The system comprises a monitoring module, an intelligent control module, a compensation construction module and a digital twin module. The monitoring module comprises a settlement sensor group, a stress sensor, a three-dimensional displacement sensor and a micro-geological radar arranged according to the bionic root system topology. The micro-geological radar uses multi-band synthetic hole technology to achieve layered scanning within the range of 5m-15m underground. The intelligent control module comprises an integrated settlement prediction algorithm, a multi-level early warning unit and a seepage-stress coupling analysis model. The output end of the seepage-stress coupling analysis model forms a closed-loop control with the compensation construction module. The compensation construction module comprises an adjustable grouting device, a hydraulic servo system, a bionic root system spiral pile structure and a dynamic control subsystem. Diversion pipes are arranged between the blades of the spiral pile structure to form a three-dimensional drainage network. The drainage efficiency of the diversion pipes is dynamically correlated with the pore water pressure data of the monitoring module. The digital twin module constructs a multi-directional interactive platform that integrates physical test data, simulation models and AI predictions. The bionic root system spiral pile structure uses high-permeability concrete material, whose permeability coefficient is three orders of magnitude higher than that of traditional piles, and the drainage efficiency is dynamically correlated with the pore water pressure data of the monitoring module through a feedback control unit.

2. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The bionic root spiral pile structure includes a variable-section pile body, spiral blades and a diversion pipe; the diameter of the variable-section pile body decreases exponentially along the depth, and the decreasing gradient of the exponential function is dynamically adjusted based on the soil density detected by the geological radar. Gradient drainage holes are set at the distance between adjacent spiral blades, and the aperture of the drainage holes is dynamically adjusted according to the real-time data of the groundwater level sensor. The inner wall of the diversion pipe is coated with a nano-hydrophobic coating, and its inclination angle is adjusted in real time according to the soil permeability coefficient.

3. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The digital twin module includes a virtual-reality interaction unit, an ecological assessment unit, and a virtual grouting simulation unit; the virtual-reality interaction unit uses an improved data fusion algorithm to perform weighted fusion of physical experimental data and simulation data; the ecological assessment unit has a built-in carbon footprint calculation model to optimize the grouting material usage and drainage efficiency; the virtual grouting simulation unit uses a coupling algorithm to perform grouting simulation to ensure that the grouting pressure prediction error is less than 5%.

4. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The settlement prediction algorithm includes a root growth simulation sub-model, an LSTM neural network time series prediction model and a confidence assessment unit; the root growth simulation sub-model predicts the soil consolidation rate based on the drainage data of the screw piles, the input parameters of the LSTM neural network time series prediction model include BIM modeling data and real-time monitored settlement rate, pore water pressure and stress data, and the confidence assessment unit triggers model self-training when the prediction error exceeds a preset threshold.

5. The adaptive settlement monitoring and compensation construction integrated system according to claim 1 is characterized by: The dynamic control subsystem synchronously adjusts the grouting pressure and the drainage channel opening according to the pore water pressure data, and realizes dual closed-loop control of the seepage field and the stress field through the PID controller; the high-permeability concrete material includes coarse aggregate with a particle size of 5mm-15mm, 3% to 5% silicon carbide whiskers, and a built-in micron-level capillary network, and the distribution density of the micron-level capillary network increases along the depth direction of the pile body.

6. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The monitoring module also includes a sliding scanning unit and a data fusion unit; the sliding scanning unit controls the micro geological radar to periodically scan the land density within the range of 5m-15m underground at preset time intervals; the data fusion unit uses the Kalman filter algorithm to achieve spatiotemporal alignment of radar data and sensor data, and generate a three-dimensional geological model.

7. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The multi-level warning unit includes level one, level two and level three warnings; level one warning: when the settlement rate is greater than 3 mm / d, an audible and visual alarm is triggered, and the drainage channel pressurization mode is started; level two warning: when the cumulative settlement is greater than 85% of the design value, a pile structure adjustment plan is automatically generated and sent to the digital twin module for verification; level three warning: when the predicted final settlement exceeds the standard, emergency grouting is started, and the grouting pressure is increased to 1.5-2 times the normal value.

8. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The ecological assessment unit includes a carbon footprint tracking module, an energy consumption optimization module and a drainage-consolidation coordination module; the carbon footprint tracking module records carbon emission data from the production of grouting materials to the entire construction cycle; the energy consumption optimization module dynamically adjusts the timing of grouting and drainage operations according to real-time electricity prices and equipment efficiency; the drainage-consolidation coordination module establishes a quantitative relationship function between drainage efficiency and soil consolidation rate based on Darcy's law, and optimizes the aperture parameters of the drainage holes.

9. The adaptive settlement monitoring and compensation construction integrated system according to claim 1, characterized in that: The seepage-stress coupling analysis model is constructed based on the improved Biot consolidation theory. It receives pore water pressure data, stress data, and three-dimensional displacement data from the monitoring module in real time. The coupled equations of the soil seepage field and stress field are solved synchronously through an implicit iterative algorithm. The boundary conditions of the coupled equations are dynamically updated according to the soil density scanned by the micro-geological radar. The calculation results are fed back to the dynamic control subsystem of the compensation construction module in real time to correct the control parameters of the grouting pressure and the drainage channel opening.

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