An intelligent monitoring system for deep foundation pit construction dewatering and collapse prevention

Abstract

The application discloses a kind of deep foundation pit construction dewatering anti-collapse intelligent monitoring systems, belong to deep foundation pit construction safety monitoring technical field, to solve the problems such as low efficiency, data lag, poor multi-parameter synergy of traditional monitoring system, first, the intelligent monitoring system including multi-parameter monitoring module, data transmission module, intelligent analysis processing module, early warning module and emergency control module is built;Multi-parameter monitoring module synchronously acquires various parameters such as underground water level, soil deformation, sensors are arranged according to specification to ensure data accuracy;Subsequently, through parameter calibration and threshold setting, real-time multi-parameter collaborative monitoring, data preprocessing and coupling analysis, intelligent risk prediction, hierarchical early warning and emergency control, complete whole-process monitoring and control, finally, through data archiving and review optimization, improve monitoring system, realize multi-parameter collaborative monitoring and closed-loop management, risk prediction is accurate, emergency control linkage is strong, applicability and expansibility are wide.

Description

Technical Field

[0001] An intelligent monitoring system for preventing collapse during dewatering in deep foundation pit construction is disclosed. This invention belongs to the field of deep foundation pit construction safety monitoring technology, specifically relating to the technical field of intelligent monitoring systems for preventing collapse during dewatering in deep foundation pit construction. Background Technology

[0002] Deep foundation pit construction is a crucial process in building construction and municipal engineering. Dewatering, as a core component of deep foundation pit construction, primarily aims to drain water from the pit, lower the groundwater level, and increase the strength of the slopes and bottom soil. This provides a dry working environment for earthwork excavation and foundation construction, effectively preventing seepage deformation and damage such as quicksand, piping, and sudden bottom seepage, ensuring the stability of the foundation pit and the safety of the surrounding environment. However, during deep foundation pit dewatering, factors such as complex geological conditions, unreasonable dewatering parameters, construction disturbances, and untimely monitoring can easily lead to problems such as sudden rises and falls in the groundwater level, uneven soil settlement, and excessive slope displacement. These issues can then trigger foundation pit collapse accidents, causing casualties, property damage, and construction delays.

[0003] Currently, deep foundation pit construction dewatering monitoring mostly adopts traditional manual monitoring methods, which involve periodically measuring parameters such as groundwater level and slope displacement. This method suffers from drawbacks such as low monitoring efficiency, data lag, high labor costs, and insufficient monitoring accuracy. It cannot achieve real-time dynamic monitoring of the dewatering process and foundation pit stability, making it difficult to predict collapse risks in advance. With the development of intelligent technology, some automated monitoring equipment has emerged. However, these devices mostly monitor single parameters and lack coordinated monitoring of multiple parameters such as the operational status of the dewatering system, soil mechanical properties, and groundwater level changes. Furthermore, the monitoring data is disconnected from emergency control measures, failing to achieve closed-loop management of "monitoring-analysis-prediction-control," resulting in poor timeliness and effectiveness of monitoring and early warning.

[0004] Furthermore, existing monitoring systems do not fully consider the differences in dewatering parameters under different geological conditions and lack targeted risk prediction models, resulting in low accuracy in risk prediction and failing to meet the actual needs of monitoring deep foundation pit dewatering and preventing collapse under complex geological conditions. Simultaneously, some automated monitoring systems lack the ability to deeply analyze and dynamically update monitoring data, making it impossible to optimize monitoring parameters and prediction models based on field measurements, further reducing monitoring reliability. Therefore, developing an intelligent monitoring system for deep foundation pit construction dewatering and preventing collapse that can achieve multi-parameter collaborative monitoring, intelligent risk prediction, and closed-loop emergency control has become an urgent technical problem to be solved in this field. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the existing defects and provide an intelligent monitoring system for dewatering and preventing collapse during deep foundation pit construction. This system solves the problems of low efficiency, data lag, poor coordination of multiple parameters, inaccurate risk prediction, and disconnect between monitoring and control in traditional monitoring systems.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A smart monitoring system for preventing collapse during dewatering in deep foundation pit construction includes the following steps:

[0008] Step 1: Establish an intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction. This system includes a multi-parameter monitoring module, a data transmission module, an intelligent analysis and processing module, an early warning module, and an emergency control module. The multi-parameter monitoring module collects groundwater level parameters, soil deformation parameters, dewatering system operating parameters, and environmental parameters. The data transmission module transmits monitoring data and interacts with other data in real time. The intelligent analysis and processing module processes and analyzes the monitoring data and predicts the risk of foundation pit collapse. The early warning module issues tiered early warning information. The emergency control module works in conjunction with the dewatering system and the support system to output emergency control commands.

[0009] Step 2: Based on the geological survey report of the deep foundation pit, the dewatering construction plan and design specifications, and combined with the hydrogeological parameters determined by the on-site pumping test, set the calibration range, safety threshold, early warning threshold and danger threshold of various monitoring parameters, calibrate the sensors through on-site tests and correct measurement errors;

[0010] Step 3: Start the monitoring system. The multi-parameter monitoring module collects various monitoring parameters in real time at a preset frequency. The data transmission module encrypts the monitoring data and transmits it to the intelligent analysis and processing module in real time.

[0011] Step 4: The intelligent analysis and processing module performs preprocessing on the monitoring data, including filtering, noise reduction, and completion. Then, it performs coupled analysis on the various types of preprocessed data to establish the correlation between multiple parameters and verify the rationality of the precipitation parameters in conjunction with the pit water inflow prediction model.

[0012] Step 5: The intelligent analysis and processing module uses the optimized risk prediction model to predict the risk of foundation pit collapse in stages based on the coupling analysis results and preset thresholds. The model self-optimization unit regularly combines historical data, actual construction conditions and inverse analysis optimization algorithms to optimize model parameters and improve prediction accuracy.

[0013] Step 6: The early warning module issues early warning information of the corresponding level and pushes it through multiple channels based on the risk prediction results; the emergency control module automatically outputs control instructions based on the early warning level and parameter coupling analysis results, and links the precipitation system and support system for emergency control, supporting manual correction of control instructions;

[0014] Step 7: Archive the monitoring data, risk prediction results, early warning information and emergency control records, regularly check the monitoring system and calibrate the sensors, and review and optimize the monitoring system and model parameters after construction is completed.

[0015] As a preferred technical solution of the present invention, in step 1, the water level sensors of the multi-parameter monitoring module are arranged in a ring inside and outside the foundation pit, with a spacing of 5-8m inside the foundation pit and 8-12m outside the foundation pit, and are inserted 50cm below the groundwater level; displacement sensors are installed at different depths on the foundation pit slope, settlement sensors are arranged on the surrounding buildings, roads and the top of the slope, and pore water pressure sensors are embedded in the soil of the foundation pit slope; flow sensors, pressure sensors and current sensors are respectively installed at the pump outlet, dewatering pipe and pump control circuit; temperature and humidity sensors and rainfall sensors are arranged in an open area of ​​the construction site.

[0016] As a preferred technical solution of the present invention, in step 1, the data transmission module adopts a combination of 5G / IoT and fiber optic transmission, adds a data backup unit, supports data export, and uses GPRS wireless communication as a backup communication method; the risk prediction model of the intelligent analysis and processing module adopts a BP neural network algorithm, combined with genetic algorithm optimization, the input parameters include monitoring parameters and geological parameters, and the output parameter is the risk level; the early warning module includes a graded early warning unit and a multi-channel push unit, and the early warning levels are divided into safety level, early warning level, and danger level; the emergency control module presets multiple emergency control schemes, corresponding to different risk factors, and sets up a control effect feedback unit.

[0017] As a preferred technical solution of the present invention, in step 2, the geological parameters include soil layer type, soil weight, internal friction angle, and permeability coefficient. The core hydrogeological parameters such as permeability coefficient, hydraulic conductivity, specific yield, and radius of influence are determined through on-site pumping tests. The sensors include submersible level gauges, fiber optic displacement sensors, vibrating wire pore water pressure sensors, and electromagnetic flow sensors. The measurement accuracy meets the following requirements: water level measurement error ≤ ±1cm, displacement measurement error ≤ ±0.1mm, pore water pressure measurement error ≤ ±2kPa, and flow measurement error ≤ ±1%.

[0018] As a preferred technical solution of the present invention, in step 3, the monitoring frequency is dynamically adjusted according to the construction stage. It is once every 10-30 minutes in the initial stage of precipitation and the key stage of earthwork excavation, once every 1-2 hours in the stable operation stage of the foundation pit, and automatically increased to once every 5-10 minutes when the monitoring data is close to the warning threshold.

[0019] As a preferred technical solution of the present invention, in step 4, the coupling analysis includes analyzing the correlation between the rate of rise and fall of groundwater level and soil pore water pressure and slope displacement, analyzing the matching of precipitation system flow rate, pressure and groundwater level changes, correcting the analysis results in combination with environmental parameters, and optimizing the analysis logic with reference to Darcy's law and the Dubuis assumption.

[0020] As a preferred technical solution of the present invention, in step 5, the risk prediction model is combined with digital twin technology to construct a digital twin model of the deep foundation pit, simulate the entire process of foundation pit excavation and dewatering, and realize the early prediction of risks; the model self-optimization unit adopts parallel computing technology to ensure that the model convergence time is controlled within 10 generations.

[0021] As a preferred technical solution of the present invention, in step 6, when the warning level is reached, a yellow warning is issued, warning information is pushed and a reminder is given to strengthen patrols, and the control instructions are to adjust the operating frequency of the water pumps, start and stop some water pumps and adjust the prestress of the support system; when the danger level is reached, a red warning is issued, warning information is pushed and an emergency audible and visual alarm is triggered, and the control instructions are to stop construction, start the backup dewatering equipment and emergency support measures, start depressurization and dewatering measures for the risk of confined water, and ensure that the safety factor against sudden surge at the bottom of the pit is not less than 1.05.

[0022] As a preferred technical solution of the present invention, in step 7, the archived data forms a complete monitoring archive for reference in subsequent engineering review; regular inspection includes checking the operating status of the monitoring system weekly and calibrating the sensors monthly; review and optimization includes optimizing monitoring parameter thresholds, risk prediction models and emergency control plans.

[0023] As a preferred technical solution of the present invention, for deep foundation pits with sensitive buildings and underground pipelines in the vicinity, underground pipeline displacement monitoring sensors and building settlement monitoring sensors are added to expand the monitoring range. A water control strategy of "isolation as the main method and drainage as the auxiliary method" is adopted to avoid secondary disasters. It is compatible with various dewatering methods such as well dewatering, light well point dewatering, and jet well point dewatering. The monitoring scheme can be adjusted according to the scale of the foundation pit and geological conditions.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. Achieve multi-parameter collaborative monitoring with comprehensive monitoring: This invention uses a multi-parameter monitoring module to simultaneously collect various parameters such as groundwater level, soil deformation, dewatering system operation, and environment. Combined with geological survey parameters, it breaks through the limitations of traditional single-parameter monitoring and establishes a multi-parameter coupling analysis mechanism. This mechanism can comprehensively reflect the stability of the foundation pit and the operation of the dewatering system during the deep foundation pit dewatering construction process. At the same time, it also takes into account the monitoring of the surrounding environment, avoiding risk misjudgment caused by single-parameter monitoring. The monitoring is comprehensive and meets the actual needs of the site.

[0026] 2. High degree of intelligence, achieving closed-loop management: This invention completes automatic preprocessing, coupled analysis, and risk prediction of monitoring data through an intelligent analysis and processing module. Combined with model self-optimization unit and digital twin technology, it continuously improves the accuracy of risk prediction. Through the linkage between the early warning module and the emergency control module, it realizes closed-loop management of the entire process of "monitoring-analysis-prediction-early warning-control", without much manual intervention. It effectively solves the problems of lagging monitoring data and untimely control in traditional monitoring, and greatly improves monitoring efficiency and emergency response capabilities.

[0027] 3. Accurate risk prediction and highly targeted early warning: This invention adopts an optimized BP neural network risk prediction model, combined with genetic algorithms and inverse analysis optimization algorithms, and introduces geological and environmental parameters as inputs to adapt to the deep foundation pit dewatering monitoring needs under different geological conditions, resulting in high accuracy in risk prediction. At the same time, a hierarchical early warning mechanism is set up to issue different types of early warning information according to the risk level, and pushes them through multiple channels to ensure that construction management personnel can obtain early warning information in a timely manner and take targeted measures to prevent the risk of collapse in advance.

[0028] 4. Strong emergency control linkage and high safety: The emergency control module is linked with the dewatering system and support system, and has multiple preset emergency control schemes. It can automatically output control commands based on the risk level and risk causes, adjust dewatering parameters, and activate emergency support measures. It also supports manual correction to ensure the rationality and effectiveness of control measures. For common risks such as confined water and dewatering system failures, it has set up special control measures to quickly curb the expansion of risks, reduce the incidence of foundation pit collapse accidents, and protect the lives and property of construction personnel.

[0029] 5. Wide applicability and strong scalability: This invention can flexibly adjust monitoring parameters, threshold settings, and monitoring frequencies according to different geological conditions and deep foundation pits of different scales. It is compatible with various dewatering methods such as well dewatering, lightweight wellpoint dewatering, and jet wellpoint dewatering, and is suitable for monitoring deep foundation pit dewatering and preventing collapse in various building and municipal engineering projects. At the same time, the monitoring system can add monitoring parameters and monitoring equipment according to actual needs, which is highly scalable. The monitoring data archiving function facilitates subsequent project review and technical optimization, and has high engineering practical value and promotion significance.

[0030] 6. Reduce labor costs and improve construction efficiency: This invention automates and intelligentizes the monitoring process, significantly reducing the workload of manual monitoring and lowering labor costs. At the same time, it avoids errors caused by manual monitoring and improves monitoring accuracy. By predicting risks in advance and making timely adjustments, it avoids construction stoppages caused by potential collapse hazards, ensures the smooth progress of deep foundation pit dewatering construction, and improves construction efficiency. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] This invention provides a technical solution:

[0033] A smart monitoring system for preventing collapse during dewatering in deep foundation pit construction includes the following steps:

[0034] Step 1: Setting up the monitoring system

[0035] An intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction was established. The monitoring system includes a multi-parameter monitoring module, a data transmission module, an intelligent analysis and processing module, an early warning module, and an emergency control module.

[0036] The multi-parameter monitoring module is used to collect various monitoring parameters during the deep foundation pit dewatering construction process, including groundwater level parameters, soil deformation parameters, dewatering system operation parameters, and environmental parameters. Groundwater level parameters are collected using water level sensors arranged in a ring inside and outside the foundation pit. The sensor spacing inside the pit is 5-8m, and the sensor spacing outside the pit is 8-12m. The sensors outside the pit are placed within the dewatering influence radius and inserted 50cm below the groundwater level to collect real-time groundwater level elevation and water level change rate at different monitoring points. Soil deformation parameters are collected using displacement sensors, settlement sensors, and pore water pressure sensors. The displacement sensors are installed on the foundation pit... Settlement sensors are placed at different depths on the slope of the pit, around surrounding buildings, roads, and on the top of the pit slope. Pore water pressure sensors are embedded inside the soil of the pit slope to collect data on horizontal displacement, vertical settlement, pore water pressure, and displacement rate. The operating parameters of the dewatering system are collected by flow sensors, pressure sensors, and current sensors, which are installed at the outlet of the dewatering well pump, the dewatering pipeline, and the pump control circuit, respectively, to collect data on pump flow, dewatering pipeline pressure, pump operating current, and voltage. Environmental parameters are collected by temperature and humidity sensors and rainfall sensors to collect temperature, humidity, and rainfall data at the construction site, avoiding interference from environmental factors on the monitoring results.

[0037] The data transmission module adopts a combination of wireless and wired communication. The wireless communication uses 5G / IoT technology, while the wired communication uses fiber optic transmission. This is used to transmit the monitoring data collected by the multi-parameter monitoring module to the intelligent analysis and processing module in real time. At the same time, it enables data interaction between the intelligent analysis and processing module and the early warning module and the emergency control module, ensuring the stability and real-time nature of data transmission.

[0038] The intelligent analysis and processing module has a built-in data preprocessing unit, parameter coupling analysis unit, risk prediction model and model self-optimization unit, which are used to process and analyze monitoring data and predict the risk of foundation pit collapse.

[0039] The early warning module includes a tiered early warning unit and a multi-channel push unit, which are used to issue early warning information of corresponding levels based on the risk prediction results;

[0040] The emergency control module is linked with the deep foundation pit dewatering system and support system to automatically output control commands and adjust dewatering parameters and support measures based on the early warning level and risk analysis results.

[0041] Step 2: Monitoring parameter calibration and threshold setting

[0042] Based on the geological survey report, dewatering construction plan, and design specifications for deep foundation pits, and in accordance with the requirements of standards such as the "Technical Specification for Foundation Pit Support" (JGJ120) and the "Technical Specification for Groundwater Control in Building and Municipal Engineering" (JGJ111), the calibration range and initial thresholds of various monitoring parameters are determined. Among them, the geological survey report needs to clearly define key parameters such as aquifer distribution, burial conditions, permeability coefficient, water-bearing capacity, and groundwater type. Core parameters such as aquifer permeability coefficient (K), hydraulic conductivity (T), specific yield (μ), and radius of influence (R) are determined through on-site pumping tests to provide a basis for setting monitoring parameter thresholds.

[0043] Through field tests, the sensors in the multi-parameter monitoring module were calibrated to correct measurement errors and ensure the accuracy of monitoring data. For different geological conditions (such as sand, silt, and clay), and based on empirical values ​​of the permeability coefficients of common soil layers, safety thresholds, warning thresholds, and danger thresholds were set for parameters such as groundwater level, soil displacement, and pore water pressure. The safety threshold represents the normal parameter range for stable operation of the foundation pit, the warning threshold represents the critical parameter range for the occurrence of potential risks, and the danger threshold represents the extreme parameter range for an impending collapse. Simultaneously, based on the rated parameters of the pumps and the load-bearing capacity of the dewatering pipelines, safety thresholds for the dewatering system's operating parameters were set to prevent dewatering anomalies caused by system malfunctions.

[0044] Step 3: Real-time multi-parameter collaborative monitoring

[0045] The intelligent monitoring system is activated, and the multi-parameter monitoring module collects groundwater level parameters, soil deformation parameters, dewatering system operation parameters, and environmental parameters in real time according to the preset monitoring frequency. The monitoring frequency can be dynamically adjusted according to the construction stage. In the initial stage of dewatering and the critical stage of earthwork excavation, the monitoring frequency is set to once every 10-30 minutes. In the stable operation stage of the foundation pit, the monitoring frequency is adjusted to once every 1-2 hours. When the monitoring data approaches the warning threshold, the monitoring frequency is automatically increased to once every 5-10 minutes.

[0046] The data transmission module transmits the collected monitoring data to the intelligent analysis and processing module in real time. During the transmission process, the data is encrypted to prevent data loss or tampering and to ensure the integrity and security of the monitoring data.

[0047] Step 4: Monitoring Data Preprocessing and Coupling Analysis

[0048] The intelligent analysis and processing module uses a data preprocessing unit to filter, reduce noise, and complete the received monitoring data, eliminating abnormal data (such as invalid data caused by sensor failure or abnormal fluctuation data caused by environmental interference), and using interpolation to complete missing data to ensure the validity of the data.

[0049] The parameter coupling analysis unit performs coupling analysis on various preprocessed monitoring data to establish the correlation between groundwater level changes, soil deformation, and dewatering system operating parameters. Specifically, this includes: analyzing the correlation between groundwater level rise and fall rates and soil pore water pressure and slope displacement to determine the rationality of the dewatering rate; analyzing the matching between dewatering system flow rate, pressure, and groundwater level changes to determine whether the dewatering system is operating normally; combining environmental parameters (such as rainfall) to analyze their impact on groundwater level and soil stability, and correcting the monitoring and analysis results; and, referring to Darcy's law and the Dubouy assumption, combining the pit inflow prediction model to verify the rationality of the dewatering parameters and ensure that the dewatering effect meets the pit stability requirements.

[0050] Step 5: Intelligent Risk Prediction

[0051] The intelligent analysis and processing module uses a risk prediction model to classify and predict the risk of foundation pit collapse based on the monitoring data after coupled analysis and in combination with preset parameter thresholds. The risk prediction model adopts a BP neural network algorithm combined with genetic algorithm optimization. The input parameters are pre-processed groundwater level parameters, soil deformation parameters, dewatering system operation parameters and environmental parameters. The output parameter is the foundation pit collapse risk level (divided into safe level, early warning level and dangerous level).

[0052] The model self-optimization unit regularly trains and optimizes the risk prediction model based on historical monitoring data, actual construction conditions of the foundation pit, and collapse accident cases. Combined with inverse analysis optimization algorithms, it updates parameters such as the elastic modulus of the soil based on real-time observed deformation data, reducing prediction errors, improving the accuracy of risk prediction, and ensuring that the model's prediction results are consistent with the actual situation on site. At the same time, combined with digital twin technology, a digital twin model of the deep foundation pit is constructed to simulate the entire process of foundation pit excavation and dewatering, realizing early prediction and dynamic simulation of risks.

[0053] Step 6: Tiered Early Warning and Emergency Control

[0054] Based on the risk prediction results output by the intelligent analysis and processing module, the early warning module issues early warning information of corresponding levels: when the risk level is safe, no early warning is issued, only real-time feedback of monitoring data is provided; when the risk level is warning, the graded early warning unit issues a yellow early warning, and the multi-channel push unit pushes the early warning information to construction management personnel through on-site audible and visual alarms, mobile APP push, and back-end system prompts, reminding them to strengthen on-site inspections and closely monitor changes in monitoring data; when the risk level is dangerous, the graded early warning unit issues a red early warning, the multi-channel push unit immediately pushes the early warning information, and simultaneously triggers on-site emergency audible and visual alarms, reminding on-site personnel to evacuate immediately.

[0055] The emergency control module automatically outputs emergency control commands based on the early warning level and parameter coupling analysis results, achieving coordinated control with the dewatering system and the support system: When the risk level is at the early warning level, the control command adjusts the parameters of the dewatering system, including adjusting the operating frequency of the pumps, starting and stopping some pumps, controlling the rate of rise and fall of the groundwater level, and avoiding sudden rises and falls in the groundwater level. At the same time, it adjusts the prestress of the support system to enhance slope stability. When the risk level is at the dangerous level, the control command immediately stops dewatering construction and earthwork excavation, starts the backup dewatering equipment, adjusts the dewatering parameters, quickly stabilizes the groundwater level, and simultaneously initiates emergency support measures (such as pouring emergency cushion layers and adding temporary support structures) to prevent the foundation pit from collapsing. If the monitoring data shows that the dewatering system has a fault (such as abnormal flow or excessive pressure), the emergency control module immediately issues a shutdown command to prevent the fault from escalating, and reminds maintenance personnel to carry out timely repairs to ensure the normal operation of the dewatering system. For risks caused by confined water, pressure-reducing dewatering measures are immediately initiated to reduce the pressure of the confined water head and ensure the stability of the pit bottom against sudden inrush, with a safety factor of not less than 1.05.

[0056] After receiving early warning information and control instructions, construction management personnel can manually modify the automatic control instructions according to the actual situation on site to ensure the rationality and effectiveness of emergency control measures and realize closed-loop management of "monitoring-analysis-prediction-early warning-control".

[0057] Step 7: Monitoring data archiving and review optimization

[0058] The intelligent analysis and processing module archives all monitoring data, risk prediction results, early warning information and emergency control records in real time to form a complete monitoring archive, which facilitates post-construction review and analysis and reference for subsequent similar projects.

[0059] During construction, the operational status of the monitoring system is checked regularly, and the sensors are calibrated to ensure the normal operation of the monitoring system. After construction is completed, the monitoring system, parameter thresholds, and risk prediction models are reviewed and optimized based on the monitoring records and the actual construction situation to further improve the monitoring system and enhance its applicability and reliability in subsequent deep foundation pit dewatering and collapse prevention monitoring.

[0060] To further improve monitoring accuracy and risk prediction accuracy, the present invention can be optimized as follows:

[0061] 1. In the multi-parameter monitoring module, the water level sensor adopts an immersion-type liquid level gauge, the displacement sensor adopts a fiber optic grating displacement sensor, the pore water pressure sensor adopts a vibrating wire pore water pressure sensor, and the flow sensor adopts an electromagnetic flow sensor. The measurement accuracy of each type of sensor meets the following requirements: water level measurement error ≤ ±1cm, displacement measurement error ≤ ±0.1mm, pore water pressure measurement error ≤ ±2kPa, and flow measurement error ≤ ±1%, ensuring the accuracy of the monitoring data.

[0062] 2. In the risk prediction model, geological parameters of the foundation pit (such as soil layer type, soil weight, internal friction angle, and permeability coefficient) are introduced as auxiliary input parameters. Combined with empirical values ​​of permeability coefficients of common soil layers, the relevance and accuracy of risk prediction are further improved, adapting to the monitoring needs under different geological conditions. At the same time, parallel computing technology is adopted to ensure that the model converges quickly, with the convergence time controlled within 10 generations, thereby improving the efficiency of risk prediction.

[0063] 3. The emergency control module is equipped with multiple preset emergency control schemes. Based on different risk factors (such as sudden rise in groundwater level, excessive slope displacement, dewatering system failure, and sudden surge of confined water), the corresponding control scheme is automatically matched to shorten the control response time. At the same time, a control effect feedback unit is set up to collect monitoring data after control in real time, analyze the control effect, and if the monitoring data after control still does not return to the safe range, the control scheme is automatically adjusted until the foundation pit is restored to stability.

[0064] 4. A data backup unit is added to the data transmission module to back up the monitoring data in real time to prevent data loss. It also supports data export function, which makes it convenient for construction management personnel to view and analyze the monitoring data at any time. GPRS wireless communication technology is used as a backup communication method to ensure that the monitoring data can be transmitted normally when 5G / IoT communication is interrupted.

[0065] 5. For deep foundation pits with sensitive buildings or underground pipelines nearby, add underground pipeline displacement monitoring sensors and building settlement monitoring sensors to the monitoring module to expand the monitoring range, analyze the impact of dewatering construction on the surrounding environment, adjust dewatering parameters in a timely manner, and adopt a water control strategy of "isolation as the main approach and drainage as a supplement" to avoid secondary disasters such as damage to surrounding buildings and breakage of underground pipelines, and achieve dual protection for the safety of the foundation pit and the safety of the surrounding environment.

[0066] Example 1

[0067] This embodiment provides an intelligent monitoring system for dewatering and preventing collapse during deep foundation pit construction. It is applied to the dewatering construction of a deep foundation pit in a building project. The deep foundation pit has an excavation depth of 15m and a perimeter of 80m. The geological conditions are interlayered silty clay and silty sand, with a groundwater level of 3m. The well dewatering method is adopted, and the dewatering depth is required to be reduced to 1.5m below the bottom of the foundation pit. The spacing between dewatering wells is 15m, the diameter of a single well is 600mm, and a submersible pump is built in. There are existing buildings within 5m of the foundation pit, which poses a high construction risk and requires enhanced monitoring of dewatering and preventing collapse.

[0068] Step 1: Setting up the monitoring system

[0069] An intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction was established, including a multi-parameter monitoring module, a data transmission module, an intelligent analysis and processing module, an early warning module, and an emergency control module.

[0070] The multi-parameter monitoring module includes: 12 water level sensors (submersible level gauges), arranged in a ring inside and outside the foundation pit, with 6 inside the pit at an 8m spacing and 6 outside at a 10m spacing, all inserted 50cm below the groundwater level to collect groundwater elevation and rate of water level change; 8 displacement sensors (fiber optic displacement sensors), installed at 4 different depths (2m, 5m, 8m, 12m) on the foundation pit slope, with 2 sensors at each depth; and 6 settlement sensors (vibrating wire settlement sensors), with 3 arranged around the existing perimeter of the foundation pit. At the building foundation, three sensors are placed at the top of the foundation pit slope; eight pore water pressure sensors (vibrating wire pore water pressure sensors) are embedded in the soil of the foundation pit slope at depths of 3m, 6m, 9m, and 12m, with two sensors at each depth; six flow sensors (electromagnetic flow sensors) are installed at the pump outlets of the six dewatering wells; six pressure sensors are installed on the dewatering pipes; six current sensors are installed in the pump control circuit; and two temperature and humidity sensors and one rainfall sensor are placed in an open area of ​​the construction site.

[0071] The data transmission module adopts 5G IoT + fiber optic transmission and adds a data backup unit to back up the monitoring data in real time and support data export. At the same time, GPRS wireless communication is used as a backup communication method to ensure stable data transmission.

[0072] The intelligent analysis and processing module has a built-in data preprocessing unit, parameter coupling analysis unit, BP neural network risk prediction model and model self-optimization unit. It incorporates geological exploration parameters (permeability coefficient of silty clay and silt, soil weight, internal friction angle) and combines Darcy's law and Dubuis assumption to construct a foundation pit water inflow prediction model for data processing, coupling analysis and risk prediction.

[0073] The early warning module includes a tiered early warning unit (yellow warning, red warning) and a multi-channel push unit (on-site audible and visual alarms, mobile APP, and back-end system).

[0074] The emergency control module is linked with six water pumps and the prestressed anchor cable system for foundation pit support. It has three preset emergency control schemes, which correspond to three risk factors: abnormal groundwater level, excessive slope displacement, and dewatering system failure. It also has a control effect feedback unit.

[0075] Step 2: Monitoring parameter calibration and threshold setting

[0076] Based on the geological survey report, dewatering construction plan, and the requirements of the "Technical Specification for Foundation Pit Support" (JGJ120), the permeability coefficient of silty clay was determined to be 1.2 × 10⁻⁵ cm / s and the permeability coefficient of silty sand to be 3.5 × 10⁻³ cm / s through on-site pumping tests. Combined with empirical values ​​of permeability coefficients for common soil layers, threshold values ​​for various monitoring parameters were set.

[0077] Groundwater level: The safe threshold is 1.2-1.8m below the bottom of the foundation pit, the warning threshold is 0.8-1.2m or 1.8-2.2m below the bottom of the foundation pit, and the danger threshold is <0.8m or >2.2m below the bottom of the foundation pit; Water level change rate: Safe threshold ≤0.5m / d, warning threshold 0.5-1.0m / d, danger threshold >1.0m / d;

[0078] Horizontal displacement of slope: safe threshold ≤3mm / d, warning threshold 3-5mm / d, danger threshold >5mm / d; cumulative displacement: safe threshold ≤30mm, warning threshold 30-50mm, danger threshold >50mm;

[0079] Soil pore water pressure: safe threshold ≤150kPa, warning threshold 150-200kPa, danger threshold >200kPa;

[0080] Rainfall system: Pump flow rate safety threshold 15-25 m³ / h, warning threshold 10-15 m³ / h or 25-30 m³ / h, danger threshold <10 m³ / h or >30 m³ / h; Rainfall pipeline pressure safety threshold 0.3-0.5 MPa, warning threshold 0.2-0.3 MPa or 0.5-0.6 MPa, danger threshold <0.2 MPa or >0.6 MPa;

[0081] Through field tests, all sensors were calibrated and measurement errors were corrected to ensure that the water level measurement error is ≤ ±1cm, the displacement measurement error is ≤ ±0.1mm, the pore water pressure measurement error is ≤ ±2kPa, and the flow rate measurement error is ≤ ±1%.

[0082] Step 3: Real-time multi-parameter collaborative monitoring

[0083] The monitoring system is activated. During the initial stage of precipitation and the critical stage of earthwork excavation, the monitoring frequency is set to once every 20 minutes. During the stable operation stage of the foundation pit, the monitoring frequency is adjusted to once every 1.5 hours. When the monitoring data approaches the warning threshold, the monitoring frequency is automatically increased to once every 8 minutes. The multi-parameter monitoring module collects various parameters in real time, and the data transmission module encrypts the monitoring data and transmits it to the intelligent analysis and processing module in real time, while also backing up the data.

[0084] Step 4: Monitoring Data Preprocessing and Coupling Analysis

[0085] The intelligent analysis and processing module uses a data preprocessing unit to remove invalid data caused by sensor malfunctions and uses linear interpolation to complete missing data. The parameter coupling analysis unit performs coupling analysis on the preprocessed data and finds that when the pump flow rate drops to 14 m³ / h (close to the warning threshold), the groundwater level rise rate reaches 0.6 m / d (warning threshold), the slope horizontal displacement reaches 3.2 mm / d (warning threshold), and the pore water pressure rises to 155 kPa (warning threshold). Combined with the rainfall data at the construction site (the daily rainfall was 5 mm, which has a small impact on the groundwater level), it is determined that the insufficient flow of the dewatering system leads to the rise in the groundwater level, which in turn causes the soil deformation to intensify. There is a significant correlation between the abnormal operation of the dewatering system and the groundwater level and soil deformation. At the same time, combined with the pit inflow prediction model, it is verified that the current dewatering parameters cannot meet the stability requirements of the pit.

[0086] Step 5: Intelligent Risk Prediction

[0087] The risk prediction model takes into account various preprocessed monitoring data and geological parameters, and optimizes them using a BP neural network algorithm combined with a genetic algorithm, outputting a risk level as an early warning level. The model's self-optimization unit trains the model in real time based on historical monitoring data, and updates the soil elastic modulus parameters using an inverse analysis optimization algorithm to reduce prediction errors and ensure accurate prediction results. At the same time, through digital twin model simulation, it is predicted that if timely intervention is not implemented, the groundwater level will continue to rise and the slope displacement will exceed the danger threshold.

[0088] Step 6: Tiered Early Warning and Emergency Control

[0089] The early warning module issues a yellow warning, triggering on-site audible and visual alarms. The warning information is simultaneously pushed to the mobile APP and the back-end system, reminding construction management personnel to strengthen on-site inspections.

[0090] Based on the analysis results, the emergency control module automatically outputs control commands: adjust the operating frequency of the two water pumps to increase the flow rate to 18 m³ / h, and start one standby water pump to increase the total precipitation and control the rate of rise of the groundwater level; adjust the prestress value of the support prestressed anchor cable from 150 kN to 180 kN to enhance the slope stability.

[0091] After receiving the early warning information and control instructions, the construction management personnel confirmed that the control plan was reasonable based on the actual site conditions and did not make any manual corrections; the control effect feedback unit collected the monitoring data after the control in real time and analyzed the control effect.

[0092] Step 7: Monitoring data archiving and review optimization

[0093] The intelligent analysis and processing module archives monitoring data, risk prediction results, early warning information and emergency control records in real time; after 3 hours of control, the monitoring data returns to the safe range: the groundwater level drops to 1.5m below the bottom of the foundation pit, the water level change rate is 0.4m / d, the slope horizontal displacement is 2.8mm / d, the pore water pressure is 145kPa, the water pump flow rate is 18m³ / h, and the dewatering pipeline pressure is 0.4MPa.

[0094] During construction, the monitoring system was inspected once a week and the sensors were calibrated once a month. After construction was completed, the monitoring parameter thresholds and risk prediction models were reviewed and optimized based on the monitoring records, and the monitoring experience of this deep foundation pit was applied to subsequent similar projects.

[0095] In this embodiment, the intelligent monitoring system successfully predicted and contained a risk of early warning-level collapse, ensuring the smooth progress of deep foundation pit dewatering construction. No collapse hazards or secondary disasters occurred. The monitoring efficiency was improved by more than 80% compared with traditional manual monitoring, and the risk prediction accuracy reached more than 95%. It significantly reduced labor costs, improved construction safety and efficiency, and effectively protected the safety of surrounding existing buildings, verifying the practicality and reliability of the system of this invention.

[0096] Example 2

[0097] The difference between this embodiment and Embodiment 1 is that the geological conditions of this deep foundation pit are coarse sand layer, with a groundwater level depth of 2.5m. Vacuum deep well dewatering is adopted. There are underground pipelines around the foundation pit, so it is necessary to add underground pipeline displacement monitoring sensors to monitor the displacement changes of underground pipelines and avoid underground pipeline breakage caused by dewatering construction. At the same time, considering the high permeability and easy formation of quicksand in coarse sand layer, the monitoring parameter threshold is adjusted and the water level monitoring frequency is increased. The monitoring frequency is set to once every 10 minutes in the initial stage of dewatering. The risk prediction model focuses on introducing parameters such as permeability coefficient and soil particle size distribution, optimizes the coupling analysis logic, and enhances the pertinence of risk prediction. A special control scheme for underground pipeline protection is added to the emergency control module. When the underground pipeline displacement approaches the warning threshold, the dewatering parameters are automatically adjusted, and a water control strategy of "isolation as the main method and drainage as a supplement" is adopted. A water-resistant curtain is added to cut off the hydraulic connection between the inside and outside of the pit and avoid underground pipeline breakage due to uneven settlement.

[0098] During construction, the intelligent monitoring system was used to monitor the displacement of underground pipelines in real time, successfully avoiding the problem of excessive displacement of underground pipelines caused by precipitation, ensuring the safety of the foundation pit construction and underground pipelines, and further verifying the applicability and scalability of the system of the present invention.

[0099] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction, characterized in that: Includes the following steps: Step 1: Establish an intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction. This system includes a multi-parameter monitoring module, a data transmission module, an intelligent analysis and processing module, an early warning module, and an emergency control module. The multi-parameter monitoring module collects groundwater level parameters, soil deformation parameters, dewatering system operating parameters, and environmental parameters. The data transmission module transmits monitoring data and interacts with other data in real time. The intelligent analysis and processing module processes and analyzes the monitoring data and predicts the risk of foundation pit collapse. The early warning module issues tiered early warning information. The emergency control module works in conjunction with the dewatering system and the support system to output emergency control commands. Step 2: Based on the geological survey report of the deep foundation pit, the dewatering construction plan and design specifications, and combined with the hydrogeological parameters determined by the on-site pumping test, set the calibration range, safety threshold, early warning threshold and danger threshold of various monitoring parameters, calibrate the sensors through on-site tests and correct measurement errors; Step 3: Start the monitoring system. The multi-parameter monitoring module collects various monitoring parameters in real time at a preset frequency. The data transmission module encrypts the monitoring data and transmits it to the intelligent analysis and processing module in real time. Step 4: The intelligent analysis and processing module performs preprocessing on the monitoring data, including filtering, noise reduction, and completion. Then, it performs coupled analysis on the various types of preprocessed data to establish the correlation between multiple parameters and verify the rationality of the precipitation parameters in conjunction with the pit water inflow prediction model. Step 5: The intelligent analysis and processing module uses the optimized risk prediction model to predict the risk of foundation pit collapse in stages based on the coupling analysis results and preset thresholds. The model self-optimization unit regularly combines historical data, actual construction conditions and inverse analysis optimization algorithms to optimize model parameters and improve prediction accuracy. Step 6: Based on the risk prediction results, the early warning module issues early warning information of the corresponding level and pushes it through multiple channels; The emergency control module automatically outputs control commands based on the warning level and parameter coupling analysis results, and coordinates the precipitation system and support system for emergency control, while supporting manual correction of control commands; Step 7: Archive the monitoring data, risk prediction results, early warning information and emergency control records, regularly check the monitoring system and calibrate the sensors, and review and optimize the monitoring system and model parameters after construction is completed.

2. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 1, the water level sensors of the multi-parameter monitoring module are arranged in a ring inside and outside the foundation pit, with a spacing of 5-8m inside the foundation pit and 8-12m outside the foundation pit, inserted 50cm below the groundwater level; displacement sensors are installed at different depths on the foundation pit slope; settlement sensors are arranged around the foundation pit buildings, roads and the top of the slope; pore water pressure sensors are embedded in the soil of the foundation pit slope; flow sensors, pressure sensors and current sensors are respectively installed at the pump outlet, dewatering pipe and pump control circuit; temperature and humidity sensors and rainfall sensors are arranged in an open area of ​​the construction site.

3. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 1, the data transmission module adopts a combination of 5G / IoT and fiber optic transmission, adds a data backup unit, supports data export, and uses GPRS wireless communication as a backup communication method; the risk prediction model of the intelligent analysis and processing module adopts a BP neural network algorithm, combined with genetic algorithm optimization, with input parameters including monitoring parameters and geological parameters, and output parameters being the risk level; the early warning module includes a graded early warning unit and a multi-channel push unit, with early warning levels divided into safe level, early warning level, and dangerous level; the emergency control module presets multiple emergency control schemes corresponding to different risk triggers and sets up a control effect feedback unit.

4. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 2, the geological parameters include soil layer type, soil weight, internal friction angle, and permeability coefficient. Core hydrogeological parameters such as permeability coefficient, hydraulic conductivity, specific yield, and radius of influence are determined through on-site pumping tests. The sensors include submersible level gauges, fiber optic displacement sensors, vibrating wire pore water pressure sensors, and electromagnetic flow sensors. The measurement accuracy meets the following requirements: water level measurement error ≤ ±1cm, displacement measurement error ≤ ±0.1mm, pore water pressure measurement error ≤ ±2kPa, and flow measurement error ≤ ±1%.

5. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 3, the monitoring frequency is dynamically adjusted according to the construction stage. It is once every 10-30 minutes during the initial stage of precipitation and the key stage of earthwork excavation, once every 1-2 hours during the stable operation stage of the foundation pit, and automatically increased to once every 5-10 minutes when the monitoring data approaches the warning threshold.

6. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 4, the coupling analysis includes analyzing the correlation between the rate of rise and fall of groundwater level and soil pore water pressure and slope displacement, analyzing the matching between the flow rate and pressure of the precipitation system and the changes in groundwater level, correcting the analysis results in combination with environmental parameters, and optimizing the analysis logic with reference to Darcy's law and the Dubuis assumption.

7. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 5, the risk prediction model is combined with digital twin technology to construct a digital twin model of the deep foundation pit, simulating the entire process of foundation pit excavation and dewatering, and realizing early risk prediction; the model self-optimization unit adopts parallel computing technology to ensure that the model convergence time is controlled within 10 generations.

8. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 6, at the warning level, a yellow warning is issued, warning information is pushed out and a reminder is given to strengthen patrols. The control instructions are to adjust the operating frequency of the water pumps, start and stop some water pumps, and adjust the prestress of the support system. At the danger level, a red warning is issued, warning information is pushed out and an emergency audible and visual alarm is triggered. The control instructions are to stop construction, start the backup dewatering equipment and emergency support measures, and start depressurization and dewatering measures to address the risk of confined water, ensuring that the safety factor against sudden inrush at the bottom of the pit is not less than 1.

05.

9. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to claim 1, characterized in that: In step 7, the archived data forms a complete monitoring file for reference in subsequent project review; regular checks include weekly checks on the operating status of the monitoring system and monthly sensor calibration; review and optimization include optimizing monitoring parameter thresholds, risk prediction models, and emergency control plans.

10. The intelligent monitoring system for dewatering and collapse prevention during deep foundation pit construction according to any one of claims 1-9, characterized in that: For deep foundation pits with sensitive buildings or underground pipelines nearby, additional underground pipeline displacement monitoring sensors and building settlement monitoring sensors are installed to expand the monitoring range. A water control strategy of "isolation as the main method and drainage as a supplement" is adopted to avoid secondary disasters. It is compatible with various dewatering methods such as well dewatering, light well point dewatering, and jet well point dewatering. The monitoring plan can be adjusted according to the size of the foundation pit and geological conditions.