River levee flood control pile foundation stress analysis method and system

Abstract

The invention provides a river levee flood control pile foundation stress analysis method and system, and relates to the technical field of river levee flood control pile foundation stress analysis. Multi-channel sensors are arranged on the pile top, the pile side and the pile end, and the sampling frequency is automatically switched along with the flood stage; inverting the soil saturation based on displacement and pore pressure change, dynamically correcting the permeability coefficient and the effective stress, and outputting a space-time coupling parameter matrix; determining a hazard source for the monitoring point, constructing an elliptical search domain by taking the hazard source as a center, and recursively incorporating abnormal points to form a risk area; a seepage-structure full-coupling finite element model is established in the risk area, the outside of the area is simplified through an equivalent spring boundary, and a pile foundation stress-strain field and a safety coefficient are calculated through self-adaptive time step control; and finally, generating a risk report according to safety factor classification, marking a high-risk area, automatically improving the sampling frequency and triggering early warning.

Description

Technical Field

[0001] This invention relates to the field of stress analysis technology for river embankment flood control pile foundations, specifically to a method and system for stress analysis of river embankment flood control pile foundations. Background Technology

[0002] In flood control and disaster relief projects, river embankment pile foundations are key components for bearing and transmitting water and soil pressure. Their safety is directly related to the stability of the embankment and the regional flood control effectiveness. River embankment flood control pile foundations are generally made of concrete or steel pipe piles, arranged at certain intervals at the toe and slope of the embankment to resist the lateral and vertical loads caused by flood erosion and slope sliding. During the flood season or when the water level exceeds the warning level, the hydrological load, soil seepage and structural response of the pile foundation often exhibit dynamic coupling characteristics. Traditional design relies on static geotechnical parameters and limit equilibrium theory, which cannot reflect the dynamic coupling effect in the flood process, leading to frequent pile foundation instability accidents during the flood season.

[0003] Existing technologies mainly achieve risk assessment through independent monitoring systems and offline simulations: strain gauges are deployed in the piles and pore water pressure gauges are buried in the soil. However, the data from each sensor are analyzed in isolation, lacking a synchronous correlation mechanism for the pile-water-soil interaction. Data is collected at a fixed frequency, which cannot adapt to changes in flood stages, resulting in the omission of key abrupt change signals. Risk identification relies on empirical thresholds, such as single bending moment exceeding the limit, without considering the chain reaction of seepage failure and soil parameter degradation. Furthermore, simulation models often use global static coupling calculations, which are inefficient and cannot meet the needs of real-time early warning.

[0004] The existing technology has three core defects: 1. Inaccurate dynamic parameters: It has not established a real-time inversion chain from pile foundation displacement to soil saturation, permeability coefficient, and effective stress, especially ignoring the coupling effect of pore pressure changes on soil saturation and cohesion. For example, an increase in pore pressure leads to a decrease in soil saturation and a decrease in the cohesion index, causing the calculated soil shear strength to deviate from reality; 2. Blind spots in risk propagation: It only focuses on single-point thresholds for hazard source identification and has not built a recursive extension model along the hydrodynamic direction, making it impossible to quantify the lateral propagation range of seepage failure; 3. Simulation efficiency bottleneck: The full-domain coupled calculation consumes huge resources, and it does not dynamically skip stable periods or backtrack to periods of violent fluctuations based on the parameter change rate, resulting in delayed response to critical flood conditions.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for stress analysis of river embankment flood control pile foundations, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for stress analysis of river embankment flood control pile foundations, the specific steps of which include: Step 1: Install sensor units at the riverbank pile foundation in the target area to simultaneously monitor the pile body response, hydrological load and soil condition data of the riverbank pile foundation to form a joint monitoring dataset, and switch the sampling frequency according to the changes in water level and flow velocity in the area. Step 2: Based on the coupling relationship between pile foundation displacement and pore water pressure in the joint monitoring data, the soil saturation change rate is inverted. Based on the inverted soil saturation change rate and pore water pressure, the effective stress is generated. The soil shear strength is calculated using the effective stress, and the soil parameter matrix is ​​output. Step 3: For each pile foundation, when its pile foundation bending moment or corresponding seepage gradient exceeds the set limit, it is marked as the initial hazard source. An elliptical search domain is constructed with the initial hazard source as the center, and sensing units for sudden changes in pile foundation displacement and abnormal pore water pressure are recursively included in the search domain until the search domain touches the edge of the embankment, and the final risk area is output. Step 4: Establish a fully coupled finite element model of seepage-structure for the pile foundation within the risk area. Replace the area outside the risk area with an equivalent spring boundary. Input the soil parameter matrix into the model as the initial parameters for time history simulation. During the simulation, increase the step size when the soil saturation change rate is stable, and back off and shorten the step size when the seepage gradient changes drastically. After the simulation is completed, output the stress field distribution and safety factor of the pile foundation within the risk area. Step 5: Based on the safety factor of each pile foundation, divide the risk area into multiple levels and generate a report, mark the high-risk area, automatically adjust the sampling frequency of the high-risk area and issue an early warning.

[0008] Furthermore, the logic for installing sensing units at the riverbank pile foundations in the target area to simultaneously monitor the pile body response, hydrological load, and soil condition data of the riverbank pile foundations in the target area to form a joint monitoring dataset is as follows: The pile response data includes pile bending moment and pile displacement; the hydrological load data includes water level and flow velocity; and the soil state data includes seepage gradient and pore water pressure. The method for obtaining the pile response, hydrological load, and soil state data is as follows: Strain gauges are equidistantly arranged along the axial direction of the pile foundation to measure the axial strain. Based on the axial strain, the bending moment of the pile foundation is calculated according to the beam theory of mechanics of materials. Distributed fiber optic displacement sensors are installed along the pile foundation axis to acquire the pile foundation displacement. Pressure gauges and electromagnetic current meters are arranged underwater on the outer side of the dike near the pile foundation to record the changes in water level and flow velocity, respectively. In the soil around the pile foundation, pore water pressure gauges are arranged in layers along the main flow direction and vertically to record the pore water pressure at different depths. The seepage gradient is calculated based on the difference in pore water pressure measurements at adjacent depths and the corresponding vertical distance. The raw data collected by the above-mentioned sensors are aligned with the timestamps according to a unified clock source, and the inherent differences in the acquisition cycle of each sensor and the data transmission delay are compensated and corrected. The compensated and corrected data are then combined into multi-element time series data including pile foundation bending moment, pile foundation displacement, water level, flow velocity, and pore water pressure to form a joint monitoring dataset. The sampling frequency switching logic is as follows: when the water level in the target area does not exceed the warning water level threshold and the rate of change of flow velocity is lower than the preset stability threshold, the first sampling frequency is used. Data is collected, and when the water level rises and exceeds a preset warning water level threshold, and the flow velocity shows an increasing trend for two consecutive sampling values ​​at the current sampling frequency, the system automatically switches to a second sampling frequency, which is 1.8 times the first sampling frequency. ; When the water level drops below the preset warning water level threshold and the rate of change of flow velocity is lower than the preset stability threshold, the system automatically switches back to the first sampling frequency. .

[0009] Furthermore, the soil saturation change rate is inverted. The logic for generating effective stress based on the inverted soil saturation change rate and pore water pressure is as follows: Extract pile foundation displacement and pore water pressure data from the generated joint monitoring dataset, and construct a sliding time window using the N most recent consecutive sampling points. ,in The sampling interval is... The current moment; Calculate the pile foundation displacement rate at the nth sampling point within the sliding time window. and pore water pressure change rate Based on the hydraulic-mechanical coupling relationship of soil, the rate of change of soil saturation is inverted: ; in, The rate of change of soil saturation is inverted at the nth sampling point. Let n be the pile foundation displacement at the nth sampling point. Let be the pore water pressure at the nth sampling point. This is the displacement-saturation conversion coefficient. is the pore pressure-saturation coupling coefficient, and n is the index of the sampling point within the sliding time window. The sampling points are indexed sequentially according to the time order within the sliding time window. Calculate the seepage gradient at the nth sampling point. The flow velocity at the nth sampling point is obtained using an electromagnetic flow meter. Calculate the permeability coefficient at the nth sampling point using Darcy's law. : ; The formula for calculating effective stress is as follows: ; in, The effective stress at the nth sampling point, The stress is due to the soil's own weight. The soil saturation variation factor. This represents the skeleton stress response caused by the rate of change in soil saturation. It represents the absolute value of the rate of change of soil saturation at the nth sampling point.

[0010] Furthermore, the logic for calculating the shear strength of soil based on effective stress is as follows: The modified Mohr-Coulomb criterion was used to calculate the soil shear strength at the nth sampling point. The calculation formula is as follows: ; in, The effective internal friction angle for passing the triaxial consolidation and drainage test. The effective cohesion of the soil at the pile foundation was determined by a triaxial consolidated drained shear test on undisturbed soil samples from the field. For each sampling point, the output dynamic soil parameter matrix includes: .

[0011] Furthermore, the risk area identification logic includes the following steps: At the current moment, a pile foundation is marked as an initial hazard source when it simultaneously meets the following conditions: 1) the pile foundation bending moment at the current moment. Greater than the set safety threshold 2) Current infiltration gradient Greater than the set limit ; On the horizontal plane, a horizontal ellipse is constructed with the projection of the initial hazard source onto the horizontal plane as the center of the ellipse, the main direction of the river as the major axis, and the horizontal normal as the minor axis. For the radius of the horizontal ellipse: ; in, This is the minimum distance from the initial hazard source to the riverbank boundary. Set the current infiltration gradient and limit value. The difference, The current flow rate. The reference flow velocity is taken from the average maximum flow velocity over the past 10 years. This is an empirical scaling factor; In the vertical direction, with the midpoint of the pile foundation depth as the center, extend the effective penetration depth D of the pile foundation upwards and downwards to form an elliptical cylinder with a height of 2D. The geometric intersection of the horizontal ellipse and the vertical cylinder is the three-dimensional search volume for this recursion. Within this three-dimensional search volume, each pile foundation that has not yet been included is sequentially checked to determine whether it meets the following criteria: 1) The displacement at the current moment is greater than 0.5% of the previous time step, and this increasing trend continues for at least two consecutive sampling periods; 2) The pore water pressure at the current moment deviates from the historical average of the pile foundation by more than three times the historical standard deviation; If either condition is met, the pile foundation is marked as a hazard source, and the search domain is reconstructed with it as the center to construct a new three-dimensional search volume, and the recursive expansion continues. The recursive inclusion process is triggered if any of the following conditions are met: 1) The search domain touches the riverbank boundary or the bottom of the pile foundation in the horizontal or depth direction; 2) No new anomalies are included in the danger domain during the recursive process. The projection coordinates of all included pile foundations on the horizontal plane are combined to obtain the final risk area at the current moment.

[0012] Furthermore, a fully coupled finite element model of seepage and structure is established for the pile foundation within the final risk zone at the current moment. The model is configured with multi-field coupled boundary conditions to simulate the interaction between the pile foundation, soil, and pore fluid. For the pile foundation outside the risk zone, an equivalent Winkler spring boundary is used, with the equivalent spring stiffness... for: ; in, The elastic modulus of the soil. This represents the contact area between the pile and the soil. The spring's working depth is 5 times the pile diameter; The logic of adaptive time-step control is as follows: Set the simulation step timing as ,in Let m be the total number of simulation steps, and m be the duration of the m-th simulation step. , Let m be the termination time of the simulation step. Let m be the termination time of the (m-1)th simulation step. For each simulation step, the index is: 1) For the backtracking rule: When the rate of change of the permeability gradient in two consecutive simulation time steps exceeds 10% / min, it is determined that the field changes drastically, backtracking to the previous time step and shortening the length of the current time step to 1 / 3 of the original, and re-executing the fully coupled simulation of that time step. 2) For the skip step rule: When the rate of change of soil saturation in three consecutive simulation time steps is less than 0.1% / min, it is determined that the seepage-structure field change is stable. The time step length of the next simulation step is set to 1.5 times the current time step length, and the fully coupled simulation of that time step is skipped, and only the Winkler spring boundary calculation is retained. 3) Result Output: Simulation Time After completion, the stress field distribution and safety factor of each pile foundation at each time step are extracted; Based on the pile foundation stress field distribution and soil shear strength obtained from the fully coupled simulation results, the anti-sliding moment generated at any section of each pile foundation is calculated. With sliding torque The ratio of the two values ​​indicates the safety factor of the simulation step. for: , This is the pile foundation section index, where and Both are obtained by integrating the soil shear strength and actual shear stress on the pile foundation section, multiplying by the section radius, and calculating along the area; the minimum ratio corresponds to the weakest section of the pile foundation; Summarize the simulation time for each pile foundation The stress field distribution, soil shear strength, and safety factor are analyzed to generate a complete simulation result dataset.

[0013] Furthermore, the risk grading and dynamic response logic includes: Level 1 risk is met: Level 2 risk is met: Risk-free satisfaction ; The sampling frequency in the Level 1 risk area has been increased to This triggered a Level 3 warning system (sound, light, vibration), and increased the sampling frequency in the Level 2 risk area to [number missing]. This triggers a level-two audible and visual warning; in risk-free areas, the current sampling frequency is maintained and no warning is triggered. Summarize the information of each pile foundation The system identifies the corresponding risk level, marks the boundaries of high-risk areas, and generates a risk assessment report that includes the safety factor, maximum effective stress, and early warning status for decision support.

[0014] The present invention also provides a stress analysis system for river embankment flood control pile foundations, the system being used to perform the above-described stress analysis method for river embankment flood control pile foundations, comprising: The data acquisition module is used to deploy sensing units in the river embankment pile foundation in the target area to simultaneously monitor the pile body response, hydrological load and soil condition data of the river embankment pile foundation to form a joint monitoring dataset, and switch the sampling frequency according to the changes in water level and flow velocity in the area. The parameter update module is used to invert the soil saturation change rate based on the coupling relationship between pile foundation displacement and pore water pressure in the joint monitoring data, generate effective stress based on the inverted soil saturation change rate and pore water pressure, calculate the soil shear strength with the effective stress, and output the soil parameter matrix. The risk identification module is used to mark each pile foundation as an initial hazard source when its bending moment or corresponding seepage gradient exceeds the set limit. An elliptical search domain is constructed with the initial hazard source as the center, and sensing units for sudden changes in pile foundation displacement and abnormal pore water pressure are recursively included in the search domain until the search domain touches the edge of the embankment, and the final risk area is output. The simulation optimization module is used to establish a fully coupled finite element model of seepage-structure for the pile foundation within the risk area. The area outside the risk area is replaced by an equivalent spring boundary. The soil parameter matrix is ​​input into the model as the initial parameters for time history simulation. During the simulation, the step size is increased when the soil saturation change rate is stable, and the step size is shortened when the seepage gradient changes drastically. After the simulation is completed, the stress field distribution and safety factor of the pile foundation within the risk area are output. The graded response module is used to divide the risk area into multiple levels and generate reports based on the safety factor of each pile foundation, mark high-risk areas, automatically adjust the sampling frequency of high-risk areas and issue early warnings.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention deploys multi-channel sensors at the pile top, pile side, and pile end, and automatically upgrades the sampling frequency from low frequency during the stable period to high frequency during the flood peak according to the flood stage. This not only ensures the synchronization of structural response, hydrological load, and soil condition, but also significantly shortens the time delay between changes in operating conditions and the availability of monitoring data. This enables the system to capture sudden changes in water level and seepage gradient before the flood peak arrives, thus gaining critical response time for subsequent early warning.

[0016] This invention employs an elliptical recursive search algorithm driven by seepage gradient. Centered on the initial hazard source, it extends the major axis along the main river direction and recursively incorporates strain mutation points and pore pressure anomaly points until the search domain reaches the riverbank boundary. This mechanism overcomes the limitations of single-point thresholds and accurately captures the lateral propagation range of seepage failure. By introducing an adaptive time-stepping algorithm, it skips steps when soil saturation is stable and backtracks and reduces the time step to 1 / 3 of the original when hydraulic gradient changes drastically. This ensures the accuracy of the fully coupled simulation while maintaining the accuracy of the calculation during the sudden flood change period, achieving a leap from static coupling across the entire domain to key dynamic focus.

[0017] This invention improves the monitoring frequency of the first-level risk zone by using multi-level safety factor classification and automated sampling frequency adjustment. This not only enables timely early warning but also avoids excessive consumption of the overall system's computing power, realizing an integrated solution of refined monitoring, rapid simulation, intelligent decision-making, and closed-loop optimization. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a curve showing the fitting of the major axis radius and the minor axis radius of the present invention. Figure 3 This is a bar chart showing the major axis radius, current flow velocity, and minor axis radius of the present invention. Figure 4 This is a line graph showing the minimum distance, permeability gradient difference, and major axis radius of the present invention. Figure 5 This is a fitting curve of the major axis radius versus the permeability gradient difference of this invention; Figure 6 This is a flowchart of the overall system modules of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: Please see Figures 1-6 The present invention provides a technical solution: A method for stress analysis of river embankment flood control pile foundations, the specific steps of which include: Step 1: Install sensor units at the riverbank pile foundation in the target area to simultaneously monitor the pile body response, hydrological load and soil condition data of the riverbank pile foundation to form a joint monitoring dataset, and switch the sampling frequency according to the changes in water level and flow velocity in the area. The logic for installing sensing units at the riverbank pile foundations in the target area to simultaneously monitor the pile body response, hydrological load, and soil condition data of the riverbank pile foundations in the target area to form a joint monitoring dataset is as follows: The pile response data includes pile bending moment and pile displacement; the hydrological load data includes water level and flow velocity; and the soil state data includes seepage gradient and pore water pressure. The method for obtaining the pile response, hydrological load, and soil state data is as follows: Riverbank pile foundations in the target area: refers to the location of pile foundations in specific river sections, such as bends in rivers prone to flooding. These pile foundations are usually buried in the riverbed soil to withstand the impact of water flow and earth pressure. When selecting target areas, high-risk points, such as areas with historical levee breaches or areas with weak geology, should be given priority. Pile response refers to the internal stress and deformation of the pile foundation under load, which directly reflects the structural integrity. Hydrological load refers to external hydraulic factors (water level, flow velocity), which are the main environmental loads that drive the pile foundation to bear force. Soil state refers to the physical properties of the soil around the pile. Soil instability, such as seepage failure, will indirectly affect the pile foundation. The bending moment of a pile foundation is a measure of the bending of the pile foundation. It originates from asymmetrical loads, such as water pressure differences. In practice, an excessive bending moment indicates that the pile foundation may crack. Pile foundation displacement includes vertical settlement or horizontal displacement. Excessive displacement indicates that the pile foundation is unstable and requires emergency treatment. Water level refers to the height of the river surface. Warning water levels are preset based on historical flood data, such as the local 10-year flood level. Pore water pressure refers to the pressure of water molecules in the soil. Negative pressure (suction) can enhance soil strength, while excessive positive pressure weakens the soil. Seepage gradient is the difference in pore water pressure divided by the distance, representing the seepage force. A seepage gradient greater than 1 may cause soil damage, such as quicksand in sandy soil. Strain gauges are equidistantly arranged along the axial direction of the pile foundation to measure the axial strain. Based on the axial strain, the pile foundation bending moment is converted according to the beam theory of mechanics of materials. The strain is directly converted into the bending moment (because the bending moment is not easy to measure directly). Beam theory treats the pile as a cantilever beam, and the strain is proportional to the bending moment. Strain gauges, such as resistance strain gauges or fiber Bragg grating sensors, are attached to the surface of the pile or embedded inside it to measure the minute elongation or shortening (strain) of the pile under load. They are arranged at equal intervals, for example, one every 2 meters, to ensure the capture of bending moment distribution, which often varies along the pile foundation. The strain gauges output voltage signals based on changes in resistance or optical signals, which are then calibrated and converted into strain values. Distributed fiber optic displacement sensors are installed along the pile foundation axis to acquire pile foundation displacement. Distributed fiber optic sensors, such as BOTDR or OFDR types, are laid along the entire length of the pile to continuously monitor displacement (rather than point measurement) and cover the entire pile. The operation involves installing the fiber optic cable close to the pile body, injecting laser pulses into the fiber optic cable, and analyzing the location of deformation points and the amount of displacement through backscattered signals. The principle is that the displacement causes micro-bending or strain in the fiber optic cable, which changes the wavelength of the optical signal. Pressure level gauges and electromagnetic current meters were installed underwater on the outer side of the dike near the pile foundation to record changes in water level and flow velocity, respectively. The outer side of the dike (river side) is the area where the load is directly applied. The water level gauge measures the water depth, and the current meter measures the impact force of the water flow. It is installed next to the underwater pile foundation and converts the water pressure into water level through a pressure-sensitive element. It needs to be cleaned regularly to prevent siltation. The electromagnetic current meter is based on Faraday's law. The water flow cuts the magnetic field lines to generate a voltage signal, which is converted into the flow velocity. It is placed on the water-facing side of the pile foundation to avoid turbulence interference. In the soil surrounding the pile foundation, pore water pressure gauges are installed in layers along the main river flow direction and vertically to record the pore water pressure at different depths. The seepage gradient is calculated based on the difference in pore water pressure measurements at adjacent depths and the corresponding vertical distance. Soil conditions affect pile-soil interaction. Pore water pressure gauges, such as piezoelectric or vibrating wire gauges, are embedded in the soil to monitor water pressure changes. Mainstream (river direction) and vertical stratification are used, such as capturing seepage anisotropy at 1-meter depths. The operation involves drilling and embedding a design group, covering the pile foundation's influence zone at a depth of twice the pile depth. The data reveals soil saturation and seepage trends. The raw data collected by the above-mentioned sensors are aligned with the timestamps according to a unified clock source, and the inherent differences in the acquisition cycle of each sensor and the data transmission delay are compensated and corrected. The compensated and corrected data are then combined into multi-element time series data including pile foundation bending moment, pile foundation displacement, water level, flow velocity, and pore water pressure to form a joint monitoring dataset. Raw sensor data often has time bias, such as strain gauges sampling 10 times per second and water level gauges sampling once per second, or transmission delay, such as wireless signals attenuating in water. This step eliminates these errors through time alignment and correction, ensuring that all data are on the same time reference. After synthesis, the dataset becomes a multivariate time series, that is, each time point contains all parameters, which facilitates the analysis of interaction relationships. The joint dataset is the basis of the early warning model. Using GPS or an atomic clock as a unified time, each sensor data point is precisely time-stamped, such as with UTC time. For example, all sensors synchronize to the same time server during data acquisition. For sensors with different sampling rates, correction methods include: 1) interpolating low-frequency data to high-frequency data (or vice versa), for example, interpolating water level data from 1Hz to 10Hz to match strain gauges; 2) measuring transmission delays, such as 1ms for fiber optic delay and 50ms for wireless delay, and adding time offset correction in software. For underwater or field environments, signal transmission may be slower due to distance or interference; correction uses timestamps to extrapolate the actual acquisition time. The technical principle of compensation correction uses algorithms such as Kalman filtering or linear interpolation to ensure data alignment in the time domain. The data is formatted as a table or matrix, with each row representing a point in time, such as one record per second, and columns corresponding to various parameters. Missing values ​​are filled with interpolation. After synthesis, the dataset is stored in a database, such as an SQL database or a time-series database. The joint monitoring dataset is the core of the decision support system. The jointness avoids information fragmentation, improves monitoring efficiency, and reduces data storage redundancy economically. The sampling frequency switching logic is as follows: when the water level in the target area does not exceed the warning water level threshold and the rate of change of flow velocity is lower than the preset stability threshold, the first sampling frequency is used. Data is collected, and when the water level rises and exceeds a preset warning water level threshold, and the flow velocity shows an increasing trend for two consecutive sampling values ​​at the current sampling frequency, the system automatically switches to a second sampling frequency, which is 1.8 times the first sampling frequency. ; When the water level drops below the preset warning water level threshold and the rate of change of flow velocity is lower than the preset stability threshold, the system automatically switches back to the first sampling frequency. ; Sampling frequency refers to the number of times data is collected per second. A fixed frequency may result in insufficient data during high-risk periods or waste resources during calm periods. Dynamic switching is based on real-time hydrological conditions to improve the intelligence level of the system. A stable state is defined as a water level fluctuation of less than 5% and a flow velocity change rate of less than a preset threshold. Warning water levels are based on historical data or design standards, such as a 50-year flood level. For example, a preset threshold of 15m is used to trigger the warning when the water level exceeds 15m. The growth trend refers to the increasing flow velocity value, such as sampling 1 at 1.5m / s and sampling 2 at 1.6m / s. Two consecutive samples ensure the accuracy of the trend and avoid misjudgments due to noise. This is based on the current... The sampling interval is 1.8 times, which is a compromise in engineering. It is enough to improve the resolution, such as to capture sudden changes in flow rate, but not too high to avoid data explosion.

[0022] Step 2: Based on the coupling relationship between pile foundation displacement and pore water pressure in the joint monitoring data, the soil saturation change rate is inverted. Based on the inverted soil saturation change rate and pore water pressure, the effective stress is generated. The soil shear strength is calculated using the effective stress, and the soil parameter matrix is ​​output. The logic for generating effective stress based on the inverted soil saturation change rate and pore water pressure is as follows: Extract pile foundation displacement and pore water pressure data from the generated joint monitoring dataset, and construct a sliding time window using the N most recent consecutive sampling points. ,in The sampling interval is... The current moment; Calculate the pile foundation displacement rate at the nth sampling point within the sliding time window. and pore water pressure change rate Based on the hydraulic-mechanical coupling relationship of soil, the rate of change of soil saturation is inverted: ; in, The rate of change of soil saturation is inverted at the nth sampling point. Let n be the pile foundation displacement at the nth sampling point. Let be the pore water pressure at the nth sampling point. This is the displacement-saturation conversion coefficient. is the pore pressure-saturation coupling coefficient, and n is the index of the sampling point within the sliding time window. The sampling points are indexed sequentially according to the time order within the sliding time window. This indicates that the soil is saturated with water; if seepage intensifies, This indicates soil drainage. If the water level drops, the larger the absolute value, the faster the saturation state changes, and the higher the risk to soil stability. This indicates that the pile foundation is moving towards the river side, under the thrust of the soil. This indicates that the pile foundation has rebounded and the soil has consolidated. This indicates an increase in pore water pressure, such as when the water level rises and seepage occurs. This indicates the dissipation of pore water pressure, such as in drainage. The instantaneous rate of change of soil saturation is reflected, and its rate of change is directly related to soil permeability and mechanical behavior: a high positive value indicates that the floodwater infiltrates rapidly, the soil tends to be saturated, and may lead to liquefaction, such as sandy soil or a sudden drop in strength; a high negative value indicates that the soil drains away during the receding water and its strength gradually recovers. The larger, The larger the value, the greater the pile foundation displacement. For example, if the pile foundation moves towards the river side, it indicates that the soil is pushing the pile foundation. This is usually caused by the soil absorbing water and expanding or by increased seepage force, hence the increase in saturation. The larger, The larger the pore water pressure, the more water seeps into the soil pores, and the saturation will inevitably increase. All were calibrated through soil-pile hydraulic-mechanical coupling tests. A larger value indicates that the pile foundation displacement is more sensitive to changes in saturation. A larger value indicates a more significant effect of pore water pressure on saturation. Calculate the seepage gradient at the nth sampling point. The flow velocity at the nth sampling point is obtained using an electromagnetic flow meter. Calculate the permeability coefficient at the nth sampling point using Darcy's law. : It is the permeability coefficient of the nth sampling point, that is, the ability of the soil to allow water to flow through. The larger the value, the stronger the permeability of the soil, and the smaller the value, the weaker the permeability. The higher the value, the stronger the scouring force of the water flow, and the easier it is for soil particles to be carried away. The higher the value, the stronger the seepage driving force and the higher the risk of piping. The permeability coefficient is dynamic—it changes when the soil becomes saturated or particles rearrange. ; The larger, The larger the velocity, the higher the measured flow velocity under the same seepage gradient, indicating stronger soil permeability. During flood season, when the flow velocity increases sharply, if... A sudden increase, such as from Rise to This indicates that the soil structure has been damaged (particles have been washed away). The larger, The larger the gradient, the greater the seepage gradient at the same flow rate, which means an increase in seepage resistance, possibly due to blockage of soil pores. The formula for calculating effective stress is as follows: ; in, The effective stress at the nth sampling point, The stress is due to the soil's own weight. The soil saturation variation factor. This represents the skeleton stress response caused by the rate of change in soil saturation. This represents the absolute value of the rate of change of soil saturation at the nth sampling point; The stress borne by the soil particle skeleton determines the soil strength. The higher the value, the denser the soil and the higher the shear strength. The lower the value, the more likely the soil is to liquefy or become unstable. It remains basically constant, but increases with depth; for example, 0 kPa at the surface and about 100 kPa at 5m underground. The larger the value, the more self-weight stress is offset, and the lower the effective stress. The larger the value, the more significant the weakening of effective stress by changes in soil saturation; Effective stress is a core concept in soil mechanics (Terzaghi principle). High effective stress means that soil particles are in close contact and have high shear strength, while low effective stress means that soil particles are floated by water and tend to be fluidized. The larger, The smaller the pore water pressure, the more it will counteract the contact stress between soil particles. The larger, The smaller ( Dramatic changes in soil saturation, such as rapid infiltration of floodwater, can lead to a sudden increase in lubrication or buoyancy between soil particles, weakening the stress in the soil skeleton. The logic for calculating soil shear strength based on effective stress is as follows: The modified Mohr-Coulomb criterion was used to calculate the soil shear strength at the nth sampling point. The calculation formula is as follows: ; in, The effective internal friction angle for passing the triaxial consolidation and drainage test. The effective cohesion of the soil at the pile foundation was determined by a triaxial consolidated drained shear test on undisturbed soil samples from the field. It refers to the ultimate ability of soil to resist shear failure. The larger the value, the more stable the soil. The smaller the value, the higher the risk of landslide or pile foundation instability. This refers to the chemical binding force between soil particles, which is determined by the soil type and is a constant, such as in clay. ,sand ; This refers to the mechanical interlocking force between soil particles, which is determined by the soil type and is a constant, such as in sandy soil. clay ; The soil's real-time shear failure resistance directly determines the stability of the pile foundation. When the soil pressure on the pile foundation exceeds a certain value... When the soil undergoes shear failure, the pile foundation may tilt or break. The larger, the better The larger the effective stress, the greater the frictional resistance between soil particles; and It is a constant, but needs to be determined through field tests, specifically through triaxial consolidated drained shear tests, where undisturbed soil samples are loaded to failure, and the results are fitted. and ; For each sampling point, the output dynamic soil parameter matrix includes: ; Monitor soil water absorption / drainage dynamics. Assess the real-time bearing condition of the soil skeleton. Analyze seepage stability. It is a criterion for pile foundation stability; The dynamic soil parameter matrix combines monitoring data with simulation results, i.e., physical time. With virtual time Alignment and dynamic updating of simulation parameters are used to improve accuracy, and ensemble Kalman filtering is used to fuse monitoring data with simulation models.

[0023] Step 3: For each pile foundation, when its pile foundation bending moment or corresponding seepage gradient exceeds the set limit, it is marked as the initial hazard source. An elliptical search domain is constructed with the initial hazard source as the center, and sensing units for sudden changes in pile foundation displacement and abnormal pore water pressure are recursively included in the search domain until the search domain touches the edge of the embankment, and the final risk area is output. The risk area identification logic includes the following steps: At the current moment, a pile foundation is marked as an initial hazard source when it simultaneously meets the following conditions: 1) the pile foundation bending moment at the current moment. Greater than the set safety threshold 2) Current infiltration gradient Greater than the set limit ; The larger the value, the closer the pile foundation is to the critical point of fracture; for example, exceeding the design value by 120%. The bending moment safety threshold (based on the design bending capacity of the pile foundation) is a preset value, such as for concrete piles. , This is the ultimate bending moment; The higher the value, the stronger the seepage scouring force, and the higher the risk of piping. (Critical value) ; It is the seepage gradient limit (based on the soil's impermeability), determined by geotechnical tests, such as for sandy soil. ; On the horizontal plane, a horizontal ellipse is constructed with the projection of the initial hazard source onto the horizontal plane as the center of the ellipse, the main direction of the river as the major axis, and the horizontal normal as the minor axis. For the radius of the horizontal ellipse: ; in, This is the minimum distance from the initial hazard source to the riverbank boundary. Set the current infiltration gradient and limit value. The difference, The current flow rate. The reference flow velocity is taken from the average maximum flow velocity over the past 10 years. This is an empirical scaling factor; The radius of the major axis of the ellipse (along the river direction) is the larger the value, the wider the risk spreads along the water flow direction, and the more it increases with the direction of the river flow. and Increase and increase, The radius of the minor axis of the ellipse (perpendicular to the river direction) is the larger the value, the wider the lateral spread of the risk. and Increase and increase; The higher the value, the closer the hazard source is to the inside of the dike, and the larger the range of risk impact. The larger the value of the excess seepage gradient, the greater the current seepage risk exceeds the critical value, and the search range needs to be expanded. The higher the value, the stronger the water scouring force, and the faster the risk spreads laterally. hour, Magnification: the higher the flow velocity, the wider the short axis range; historical reference values, such as those in the middle reaches of the Yangtze River. ; Control the major axis reference ratio (typical value is 0.5-1.0). Control the ratio of the short axis to the major axis (typical value is 0.3-0.6); It is the range of risk diffusion along the river direction, because seepage damage, such as piping, spreads faster along the direction of water flow; The larger, The larger the size, the farther the hazard source is from the boundary of the dike, the greater the threat to the dike, and the larger the search area is required; The larger, The larger the diameter, the greater the excess of the seepage gradient, and the longer the piping channel extends, such as... hour, , hour, ; It is the risk diffusion range perpendicular to the river direction, which is significantly affected by the flow velocity—high flow velocity erodes the soil and provides lateral support; The larger, As the size increases, the range of the major axis expands, and the minor axis expands proportionally (the elliptical shape is maintained). Geometric meaning of a horizontal ellipse: Its center is the projection of the initial hazard source onto the horizontal plane; its major axis is along the main river flow (due to faster downstream development of seepage damage); its minor axis is perpendicular to the river (slower lateral spread); and its direction is determined according to the flood season. , Real-time updates enable adaptive scaling of the risk scope; In the vertical direction, with the midpoint of the pile foundation depth as the center, extend the effective penetration depth D of the pile foundation upwards and downwards to form an elliptical cylinder with a height of 2D. The geometric intersection of the horizontal ellipse and the vertical cylinder is the three-dimensional search volume for this recursion. The center point of the vertical ellipse is the midpoint of the pile foundation depth, ensuring that the pile-soil interaction zone is covered. The larger D is, the deeper the influence range of the soil around the pile. D is usually taken as 2-3 times the pile diameter. For example, for a 1m diameter pile, D is 2m to 3m. The specific data for some sample numbers and major / minor axis radii are shown in Table 1.

[0024] Table 1

[0025] The data shows a significant positive correlation between the minimum distance and the major axis radius. As the minimum distance increases, the major axis radius increases in a stepwise manner. When the minimum distance increases from 8.2 to 28.4 (samples 1 to 5), the major axis radius increases from 0.43 to 3.75. When the minimum distance drops back to 15.7 (sample 6), the major axis radius decreases to 1.42. This verifies the core logic in the formula that the minimum distance is proportional to the major axis radius—the farther the pile foundation is from the riverbank boundary, the wider the seepage risk spreads along the river direction.

[0026] There is a strong positive correlation between the permeability gradient difference and the major axis radius, and the effect on the minimum distance has a non-linear amplification characteristic: when the permeability gradient difference increases from 0.07 to 0.22 (samples 1 to 5), the increase in the permeability gradient difference far exceeds the increase in the minimum distance; in sample 11, the permeability gradient difference is 0.06, and an abnormally low major axis radius of 0.51 appears; this reveals that the excess permeability gradient is an amplifier of risk propagation - for every 0.1 increase in the permeability gradient difference, the increase in the major axis radius is equivalent to the effect of increasing the minimum distance by 10 (as in sample 4 compared to sample 3), while a low permeability gradient difference (sample 11) significantly inhibits risk diffusion.

[0027] The relationship between flow velocity and minor axis radius exhibits a three-stage characteristic: In the low-velocity region, with a flow velocity less than 3.0, the minor axis radius is approximately 0.4 times the major axis radius (e.g., Sample 1: flow velocity 1.8, minor axis radius 0.18); in the critical region, with a flow velocity approximately 3.0, the amplification effect begins to appear (Sample 6: flow velocity 3.1, minor axis radius 0.89, equal to 0.63 times the major axis radius); and in the high-velocity region, with a flow velocity greater than 3.0, nonlinear amplification occurs (Sample 7: flow velocity 5.0, minor axis radius 4.02, equal to 0.83 times the major axis radius; this precisely corresponds to the formula for minor axis radius). Proportional to The physical essence of this is that when the flow velocity exceeds the baseline value of 3.0, the lateral scouring effect causes the risk range to expand superlinearly.

[0028] Minimum distance and seepage gradient difference jointly dominate the longitudinal range of risk major axis radius, while flow velocity regulates the lateral diffusion minor axis radius through a threshold mechanism. When the seepage gradient difference is detected to be greater than 0.15 and the minimum distance is greater than 20 (e.g., samples 4 / 5 / 7) or the flow velocity is greater than 3.0 (e.g., samples 6 / 7 / 9), an ellipsoidal domain recursive search should be initiated, and geological re-examination should be carried out for abnormal points (e.g., samples 12 / 15).

[0029] Within this three-dimensional search volume, each pile foundation that has not yet been included is sequentially checked to determine whether it meets the following criteria: 1) The displacement at the current moment is greater than 0.5% of the previous time step, and this increasing trend continues for at least two consecutive sampling periods; 2) The pore water pressure at the current moment deviates from the historical average of the pile foundation by more than three times the historical standard deviation; If either condition is met, the pile foundation is marked as a hazard source, and the search domain is reconstructed with it as the center to construct a new three-dimensional search volume, and the recursive expansion continues. If the pile foundation displacement growth rate exceeds the threshold of 0.5%, it indicates a sharp increase in soil driving force; when the pore water pressure deviates from the historical average by more than 3 times the standard deviation, it indicates abnormal seepage or soil liquefaction; if any of the conditions are met, the pile foundation is marked as a new hazard source, and the elliptical cylinder search body is reconstructed with it as the center, and the radius is recalculated. The recursive inclusion process is triggered if any of the following conditions are met: 1) The search domain touches the riverbank boundary or the bottom of the pile foundation in the horizontal or depth direction; 2) No new anomalies are included in the danger domain during the recursive process. The projection coordinates of all included pile foundations on the horizontal plane are combined to obtain the final risk area at the current moment.

[0030] Step 4: Establish a fully coupled finite element model of seepage-structure for the pile foundation within the risk area. Replace the area outside the risk area with an equivalent spring boundary. Input the soil parameter matrix into the model as the initial parameters for time history simulation. During the simulation, increase the step size when the soil saturation change rate is stable, and back off and shorten the step size when the seepage gradient changes drastically. After the simulation is completed, output the stress field distribution and safety factor of the pile foundation within the risk area. Within the final risk zone at the current moment, a fully coupled finite element model of seepage and structure is established for the pile foundation. The model is configured with multi-field coupled boundary conditions to simulate the interaction between the pile foundation, soil, and pore fluid. For the pile foundation outside the risk zone, an equivalent Winkler spring boundary is used, with the equivalent spring stiffness... for: ; in, The elastic modulus of the soil. This represents the contact area between the pile and the soil. The spring's working depth is 5 times the pile diameter; The simulation of soil constraint on pile foundations shows that the larger the value, the stronger the lateral constraint of the soil on the pile foundations, and the smaller the pile foundation deformation. It reflects the soil's ability to resist deformation; the higher the value, the harder the soil. It is the contact area between the pile side and the soil. The larger the value, the larger the range of pile-soil interaction, such as large-diameter piles; The soil outside the risk zone has a relatively small impact on the pile foundation. The Winkler foundation model (discrete spring) is used to replace the complex soil-pile interaction, which greatly reduces the amount of calculation. It is the soil reaction force required per unit displacement, height The pile foundation is tightly constrained by the soil, low Pile foundations are prone to deformation; The 5-times-pile-diameter ratio is based on the fact that the soil constraint on the pile mainly originates from the near-pile area, with the soil within 5 times the diameter providing approximately 90% of the reaction force. The logic of adaptive time-step control is as follows: Set the simulation step duration to ,in Let m be the total number of simulation steps, and m be the duration of the m-th simulation step. , Let m be the termination time of the simulation step. Let m be the termination time of the (m-1)th simulation step. For each simulation step, the index is: 1) For the backtracking rule: When the rate of change of seepage gradient in two consecutive simulation time steps exceeds 10% / min, it is determined that the field changes drastically. Backtracking to the previous time step and shortening the length of the current time step to 1 / 3 of the original length ensures that the transient seepage process is accurately captured, and the fully coupled simulation of that time step is re-executed. 2) For the skip step rule: When the rate of change of soil saturation in three consecutive simulation time steps is less than 0.1% / min, it is determined that the seepage-structure field change is stable. The time step length of the next simulation step is set to 1.5 times the current time step length, and the fully coupled simulation of that time step is skipped, and only the Winkler spring boundary calculation is retained. A threshold of less than 0.1% / min corresponds to the slow drainage / absorption stage of the soil, such as the receding water period; skipping the fully coupled simulation saves computational resources, as the fully coupled model takes about 10 times longer than the boundary model; 3) Result Output: Simulation Time After completion, the stress field distribution and safety factor of each pile foundation at each time step are extracted; Based on the pile foundation stress field distribution and soil shear strength obtained from the fully coupled simulation results, the anti-sliding moment generated at any section of each pile foundation is calculated. With sliding torque The ratio of the two values ​​indicates the safety factor of the simulation step. for: , This is the pile foundation section index, where and Both are obtained by integrating the soil shear strength and actual shear stress on the pile foundation section, multiplying by the section radius, and calculating along the area; the minimum ratio corresponds to the weakest section of the pile foundation; It reflects the soil's ability to resist pile foundation sliding; the higher the value, the more stable the pile foundation. The torque that reflects the external load pushing the pile foundation to slide is the one with the higher value, and the higher the risk of pile foundation instability; the actual shear stress reflects the real stress on the pile foundation and is solved directly by the coupled model. The weakest section is identified by taking the minimum value of the ratio of all pile foundation sections. This indicates safety; the anti-slip torque is greater than the sliding torque. For the ultimate equilibrium, There is a risk of instability, such as at a certain cross section. It may break at times; take all cross-sections The minimum value follows the shortest board effect, even if most areas of the pile foundation... If a certain cross section The system determined that the entire system was on the verge of destruction. Summarize the simulation time for each pile foundation The stress field distribution, soil shear strength, and safety factor are analyzed to generate a complete simulation result dataset.

[0031] Step 5: Based on the safety factor of each pile foundation, divide the risk area into multiple levels and generate a report, mark the high-risk area, automatically adjust the sampling frequency of the high-risk area and issue an early warning; The risk grading and dynamic response logic includes: Level 1 risk is met: Level 2 risk is met: Risk-free satisfaction ; The sampling frequency in the Level 1 risk area has been increased to This triggered a Level 3 warning system (sound, light, vibration), and increased the sampling frequency in the Level 2 risk area to [number missing]. This triggers a level-two audible and visual warning; in risk-free areas, the current sampling frequency is maintained and no warning is triggered. When the three-level sound-light-vibration warning is triggered, the sound refers to a high-pitched alarm, the light refers to a red flashing light (visible at a distance greater than 500m), and the vibration refers to the vibration of the pile foundation's built-in vibrator (tactile warning); when the two-level sound-light warning is triggered, the sound refers to an intermittent alarm, and the light refers to a solid yellow light, which is a daily warning reminder. Summarize the information of each pile foundation The system identifies the corresponding risk level, marks the boundaries of high-risk areas, and generates a risk assessment report that includes the safety factor, maximum effective stress, and early warning status for decision support.

[0032] Please see Figure 6 The present invention also provides a stress analysis system for river embankment flood control pile foundations, the system being used to perform the above-mentioned stress analysis method for river embankment flood control pile foundations, comprising: The data acquisition module is used to deploy sensing units in the river embankment pile foundation in the target area to simultaneously monitor the pile body response, hydrological load and soil condition data of the river embankment pile foundation to form a joint monitoring dataset, and switch the sampling frequency according to the changes in water level and flow velocity in the area. The parameter update module is used to invert the soil saturation change rate based on the coupling relationship between pile foundation displacement and pore water pressure in the joint monitoring data, generate effective stress based on the inverted soil saturation change rate and pore water pressure, calculate the soil shear strength with the effective stress, and output the soil parameter matrix. The risk identification module is used to mark each pile foundation as an initial hazard source when its bending moment or corresponding seepage gradient exceeds the set limit. An elliptical search domain is constructed with the initial hazard source as the center, and sensing units for sudden changes in pile foundation displacement and abnormal pore water pressure are recursively included in the search domain until the search domain touches the edge of the embankment, and the final risk area is output. The simulation optimization module is used to establish a fully coupled finite element model of seepage-structure for the pile foundation within the risk area. The area outside the risk area is replaced by an equivalent spring boundary. The soil parameter matrix is ​​input into the model as the initial parameters for time history simulation. During the simulation, the step size is increased when the soil saturation change rate is stable, and the step size is shortened when the seepage gradient changes drastically. After the simulation is completed, the stress field distribution and safety factor of the pile foundation within the risk area are output. The graded response module is used to divide the risk area into multiple levels and generate reports based on the safety factor of each pile foundation, mark high-risk areas, automatically adjust the sampling frequency of high-risk areas and issue early warnings.

[0033] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0034] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0035] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0036] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for analyzing the stress of flood control pile foundations for river embankments, characterized in that, The specific steps include: Step 1: Install sensor units at the riverbank pile foundation in the target area to simultaneously monitor the pile body response, hydrological load and soil condition data of the riverbank pile foundation to form a joint monitoring dataset, and switch the sampling frequency according to the changes in water level and flow velocity in the area. Step 2: Based on the coupling relationship between pile foundation displacement and pore water pressure in the joint monitoring data, the soil saturation change rate is inverted. Based on the inverted soil saturation change rate and pore water pressure, the effective stress is generated. The soil shear strength is calculated using the effective stress, and the soil parameter matrix is ​​output. Step 3: For each pile foundation, when its pile foundation bending moment or corresponding seepage gradient exceeds the set limit, it is marked as the initial hazard source. An elliptical search domain is constructed with the initial hazard source as the center, and sensing units for sudden changes in pile foundation displacement and abnormal pore water pressure are recursively included in the search domain until the search domain touches the edge of the embankment, and the final risk area is output. Step 4: Establish a fully coupled finite element model of seepage-structure for the pile foundation within the risk area. Replace the area outside the risk area with an equivalent spring boundary. Input the soil parameter matrix into the model as the initial parameters for time history simulation. During the simulation, increase the step size when the soil saturation change rate is stable, and back off and shorten the step size when the seepage gradient changes drastically. After the simulation is completed, output the stress field distribution and safety factor of the pile foundation within the risk area. Step 5: Based on the safety factor of each pile foundation, divide the risk area into multiple levels and generate a report, mark the high-risk area, automatically adjust the sampling frequency of the high-risk area and issue an early warning.

2. The method for stress analysis of river embankment flood control pile foundation according to claim 1, characterized in that: The logic for installing sensing units at the riverbank pile foundations in the target area to simultaneously monitor the pile body response, hydrological load, and soil condition data of the riverbank pile foundations in the target area to form a joint monitoring dataset is as follows: The pile response data includes pile bending moment and pile displacement; the hydrological load data includes water level and flow velocity; and the soil state data includes seepage gradient and pore water pressure. The method for obtaining the pile response, hydrological load, and soil state data is as follows: Strain gauges are equidistantly arranged along the axial direction of the pile foundation to measure the axial strain. Based on the axial strain, the bending moment of the pile foundation is calculated according to the beam theory of mechanics of materials. Distributed fiber optic displacement sensors are installed along the pile foundation axis to acquire the pile foundation displacement. Pressure gauges and electromagnetic current meters are arranged underwater on the outer side of the dike near the pile foundation to record the changes in water level and flow velocity, respectively. In the soil around the pile foundation, pore water pressure gauges are arranged in layers along the main flow direction and vertically to record the pore water pressure at different depths. The seepage gradient is calculated based on the difference in pore water pressure measurements at adjacent depths and the corresponding vertical distance. The raw data collected by the above-mentioned sensors are aligned with the timestamps according to a unified clock source, and the inherent differences in the acquisition cycle of each sensor and the data transmission delay are compensated and corrected. The compensated and corrected data are then combined into multi-element time series data including pile foundation bending moment, pile foundation displacement, water level, flow velocity, and pore water pressure to form a joint monitoring dataset. The sampling frequency switching logic is as follows: when the water level in the target area does not exceed the warning water level threshold and the rate of change of flow velocity is lower than the preset stability threshold, the first sampling frequency is used. Data is collected, and when the water level rises and exceeds a preset warning water level threshold, and the flow velocity shows an increasing trend for two consecutive sampling values ​​at the current sampling frequency, the system automatically switches to a second sampling frequency, which is 1.8 times the first sampling frequency. ; When the water level drops below the preset warning water level threshold and the rate of change of flow velocity is lower than the preset stability threshold, the system automatically switches back to the first sampling frequency. .

3. The method for stress analysis of river embankment flood control pile foundation according to claim 2, characterized in that: The logic for generating effective stress based on the inverted soil saturation change rate and pore water pressure is as follows: Extract pile foundation displacement and pore water pressure data from the generated joint monitoring dataset, and construct a sliding time window using the N most recent consecutive sampling points. ,in The sampling interval is... The current moment; Calculate the pile foundation displacement rate at the nth sampling point within the sliding time window. and pore water pressure change rate Based on the hydraulic-mechanical coupling relationship of soil, the rate of change of soil saturation is inverted: ； in, The rate of change of soil saturation is inverted at the nth sampling point. Let n be the pile foundation displacement at the nth sampling point. Let be the pore water pressure at the nth sampling point. This is the displacement-saturation conversion coefficient. is the pore pressure-saturation coupling coefficient, and n is the index of the sampling point within the sliding time window. The sampling points are indexed sequentially according to the time order within the sliding time window. Calculate the seepage gradient at the nth sampling point. The flow velocity at the nth sampling point is obtained using an electromagnetic flow meter. Calculate the permeability coefficient at the nth sampling point using Darcy's law. : ； The formula for calculating effective stress is as follows: ； in, The effective stress at the nth sampling point, The stress is due to the soil's own weight. The soil saturation variation factor. This represents the skeleton stress response caused by the rate of change in soil saturation. It represents the absolute value of the rate of change of soil saturation at the nth sampling point.

4. The method for stress analysis of river embankment flood control pile foundation according to claim 3, characterized in that: The logic for calculating soil shear strength based on effective stress is as follows: The modified Mohr-Coulomb criterion was used to calculate the soil shear strength at the nth sampling point. The calculation formula is as follows: ； in, The effective internal friction angle for passing the triaxial consolidation and drainage test. The effective cohesion of the soil at the pile foundation was determined by a triaxial consolidated drained shear test on undisturbed soil samples from the field. For each sampling point, the output dynamic soil parameter matrix includes: .

5. The method for stress analysis of river embankment flood control pile foundation according to claim 4, characterized in that: The risk area identification logic includes the following steps: At the current moment, a pile foundation is marked as an initial hazard source when it simultaneously meets the following conditions: 1) the pile foundation bending moment at the current moment. Greater than the set safety threshold 2) Current infiltration gradient Greater than the set limit ; On the horizontal plane, a horizontal ellipse is constructed with the projection of the initial hazard source onto the horizontal plane as the center of the ellipse, the main direction of the river as the major axis, and the horizontal normal as the minor axis. For the radius of the horizontal ellipse: ； in, This is the minimum distance from the initial hazard source to the riverbank boundary. Set the current infiltration gradient and limit value. The difference, The current flow rate. The reference flow velocity is taken from the average maximum flow velocity over the past 10 years. This is an empirical scaling factor; In the vertical direction, with the midpoint of the pile foundation depth as the center, extend the effective penetration depth D of the pile foundation upwards and downwards to form an elliptical cylinder with a height of 2D. The geometric intersection of the horizontal ellipse and the vertical cylinder is the three-dimensional search volume for this recursion. Within this three-dimensional search volume, each pile foundation that has not yet been included is sequentially checked to determine whether it meets the following criteria: 1) The displacement at the current moment is greater than 0.5% of the previous time step, and this increasing trend continues for at least two consecutive sampling periods; 2) The pore water pressure at the current moment deviates from the historical average of the pile foundation by more than three times the historical standard deviation; If either condition is met, the pile foundation is marked as a hazard source, and the search domain is reconstructed with it as the center to construct a new three-dimensional search volume, and the recursive expansion continues. The recursive inclusion process is triggered if any of the following conditions are met: 1) The search domain touches the riverbank boundary or the bottom of the pile foundation in the horizontal or depth direction; 2) No new anomalies are included in the danger domain during the recursive process. The projection coordinates of all included pile foundations on the horizontal plane are combined to obtain the final risk area at the current moment.

6. The method for stress analysis of river embankment flood control pile foundation according to claim 5, characterized in that: Within the final risk zone at the current moment, a fully coupled finite element model of seepage and structure is established for the pile foundation. The model is configured with multi-field coupled boundary conditions to simulate the interaction between the pile foundation, soil, and pore fluid. For the pile foundation outside the risk zone, an equivalent Winkler spring boundary is used, with the equivalent spring stiffness... for: ； in, The elastic modulus of the soil. This represents the contact area between the pile and the soil. The spring's working depth is 5 times the pile diameter; The logic of adaptive time-step control is as follows: Set the simulation step timing as ,in Let m be the total number of simulation steps, and m be the duration of the m-th simulation step. , Let m be the termination time of the simulation step. Let m be the termination time of the (m-1)th simulation step. For each simulation step, the index is: 1) For the backtracking rule: When the rate of change of the permeability gradient in two consecutive simulation time steps exceeds 10% / min, it is determined that the field changes drastically, backtracking to the previous time step and shortening the length of the current time step to 1 / 3 of the original, and re-executing the fully coupled simulation of that time step. 2) For the skip step rule: When the rate of change of soil saturation in three consecutive simulation time steps is less than 0.1% / min, it is determined that the seepage-structure field change is stable. The time step length of the next simulation step is set to 1.5 times the current time step length, and the fully coupled simulation of that time step is skipped, and only the Winkler spring boundary calculation is retained. 3) Result Output: Simulation Time After completion, the stress field distribution and safety factor of each pile foundation at each time step are extracted; Based on the pile foundation stress field distribution and soil shear strength obtained from the fully coupled simulation results, the anti-sliding moment generated at any section of each pile foundation is calculated. With sliding torque The ratio of the two values ​​indicates the safety factor of the simulation step. for: , This is the pile foundation section index, where and Both are obtained by integrating the soil shear strength and actual shear stress on the pile foundation section, multiplying by the section radius, and calculating along the area; the minimum ratio corresponds to the weakest section of the pile foundation; Summarize the simulation time for each pile foundation The stress field distribution, soil shear strength, and safety factor are analyzed to generate a complete simulation result dataset.

7. The method for stress analysis of river embankment flood control pile foundation according to claim 6, characterized in that: The risk grading and dynamic response logic includes: Level 1 risk is met: Level 2 risk is met: Risk-free satisfaction ; The sampling frequency in the Level 1 risk area has been increased to This triggered a Level 3 warning system (sound, light, vibration), and increased the sampling frequency in the Level 2 risk area to [number missing]. This triggers a level-two audible and visual warning; in risk-free areas, the current sampling frequency is maintained and no warning is triggered. Summarize the information of each pile foundation The system identifies the corresponding risk level, marks the boundaries of high-risk areas, and generates a risk assessment report that includes the safety factor, maximum effective stress, and early warning status for decision support.

8. A stress analysis system for river embankment flood control pile foundations, characterized in that: The system is used to execute the stress analysis method for river embankment flood control pile foundations according to any one of claims 1-7, including: The data acquisition module is used to deploy sensing units in the river embankment pile foundation in the target area to simultaneously monitor the pile body response, hydrological load and soil condition data of the river embankment pile foundation to form a joint monitoring dataset, and switch the sampling frequency according to the changes in water level and flow velocity in the area. The parameter update module is used to invert the soil saturation change rate based on the coupling relationship between pile foundation displacement and pore water pressure in the joint monitoring data, generate effective stress based on the inverted soil saturation change rate and pore water pressure, calculate the soil shear strength with the effective stress, and output the soil parameter matrix. The risk identification module is used to mark each pile foundation as an initial hazard source when its bending moment or corresponding seepage gradient exceeds the set limit. An elliptical search domain is constructed with the initial hazard source as the center, and sensing units for sudden changes in pile foundation displacement and abnormal pore water pressure are recursively included in the search domain until the search domain touches the edge of the embankment, and the final risk area is output. The simulation optimization module is used to establish a fully coupled finite element model of seepage-structure for the pile foundation within the risk area. The area outside the risk area is replaced by an equivalent spring boundary. The soil parameter matrix is ​​input into the model as the initial parameters for time history simulation. During the simulation, the step size is increased when the soil saturation change rate is stable, and the step size is shortened when the seepage gradient changes drastically. After the simulation is completed, the stress field distribution and safety factor of the pile foundation within the risk area are output. The graded response module is used to divide the risk area into multiple levels and generate reports based on the safety factor of each pile foundation, mark high-risk areas, automatically adjust the sampling frequency of high-risk areas and issue early warnings.

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