A method and system for analyzing geotechnical stress monitoring data

Abstract

The present invention relates to the technical field of geotechnical stress monitoring, and particularly to a method and system for analyzing geotechnical stress monitoring data. The method includes the following steps: obtaining geological data and construction design data of a river bridge construction area, analyzing the distribution differences of pile driving and pipe sinking, and obtaining pile driving and pipe sinking distribution difference data; performing extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data, further estimating stress transfer loss, obtaining effective stress transfer data, and performing stress impact structure instability analysis between adjacent geotechnical materials; constructing a geotechnical stress risk monitoring model based on the random forest algorithm, and sending the model to a terminal for analyzing geotechnical stress monitoring data. The present invention makes the geotechnical stress monitoring technology more accurate through the optimization of the geotechnical stress monitoring technology.

Description

Technical Field

[0001] The present invention relates to the technical field of geotechnical stress monitoring, and particularly to a method and system for analyzing geotechnical stress monitoring data. Background Art

[0002] Geotechnical engineering plays an important role in modern infrastructure construction. Especially during the construction of large-scale projects such as bridges, tunnels, and dams, the stress state of geotechnical materials directly affects the safety and stability of the project. With the continuous expansion of the project scale and the increasingly complex construction environment, the deformation, stress distribution, and mechanical properties of geotechnical bodies become more difficult to predict. Especially under special geological conditions such as river bridges, construction often involves complex engineering measures such as pile driving and pipe jacking. These operations can cause stress changes, settlement, and displacement of the surrounding geotechnical materials, and even lead to structural instability or damage of adjacent rock and soil layers, seriously affecting the safety of the project. Therefore, timely and accurate monitoring of geotechnical stress changes and effective analysis have become important links to ensure project safety. However, there is a problem with a traditional method for analyzing geotechnical stress monitoring data, which has a low accuracy in identifying structural instability caused by stress shocks, resulting in large errors in geotechnical stress monitoring. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for analyzing geotechnical stress monitoring data to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for analyzing geotechnical stress monitoring data, the method includes the following steps:

[0005] Step S1: Obtain geological data of the bridge construction area on the river and river bridge construction design data; summarize the pile driving and pipe jacking distribution differences in the river bridge construction design data to obtain pile driving and pipe jacking distribution difference data;

[0006] Step S2: Simulate the deflection of the lateral stress caused by pile driving extrusion according to the pile driving and pipe jacking distribution difference data to obtain extrusion lateral stress deflection data; estimate the stress transfer loss of the extrusion lateral stress deflection data to obtain effective stress transfer data; perform stress shock structural instability analysis between adjacent geotechnical materials based on the effective stress transfer data to obtain stress shock structural instability data between adjacent geotechnical materials;

[0007] Step S3: Build a geotechnical stress risk monitoring model based on the stress shock structural instability data using the random forest algorithm to obtain a geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0008] Preferably, step S1 includes the following steps:

[0009] Step S11: Obtain the geological data of the bridge construction area on the river channel and the river channel bridge construction design data;

[0010] Step S12: Perform construction time series marking on the river channel bridge construction design data to obtain construction design time marking data;

[0011] Step S13: Conduct pile driving sequence selection analysis on the construction design time marking data to obtain construction pile driving sequence selection data;

[0012] Step S14: Based on the construction pile driving sequence selection data, summarize the pile driving and pipe sinking distribution differences in the construction design time marking data to obtain pile driving and pipe sinking distribution difference data.

[0013] Preferably, step S2 includes the following steps:

[0014] Step S21: Based on the pile driving and pipe sinking distribution difference data, conduct vertical geological rock and soil structure analysis on the geological data of the bridge construction area to obtain vertical geological rock and soil structure data for different pile driving and pipe sinking areas;

[0015] Step S22: According to the pile driving and pipe sinking distribution difference data, perform pile driving extrusion lateral stress deflection simulation on the vertical geological rock and soil structure data to obtain extrusion lateral stress deflection data;

[0016] Step S23: Identify the stress radial influence azimuth for the extrusion lateral stress deflection data to obtain the stress radial influence azimuth;

[0017] Step S24: Estimate the stress transfer loss based on the stress radial influence azimuth to obtain effective stress transfer data;

[0018] Step S25: Based on the effective stress transfer data, conduct stress impact structure instability analysis between adjacent rock and soil in the vertical geological rock and soil structure data to obtain stress impact structure instability data between adjacent rock and soil.

[0019] Preferably, step S22 includes the following steps:

[0020] Step S221: Conduct applied force / frequency analysis on the pile driving and pipe sinking distribution difference data to obtain pile driving applied force / frequency data;

[0021] Step S222: Conduct vertical layer density analysis on the vertical geological rock and soil structure data to obtain the vertical layer density of the rock and soil;

[0022] Step S223: According to the pile driving applied force / frequency data, identify the rotation angle of the lateral contact section for applying force / frequency to the vertical layer density of the rock and soil to obtain the contact section rotation angle;

[0023] Step S224: Based on the pile driving force application intensity / frequency data and the rotation angle of the contact cross-section, evaluate the fluctuation of the internal friction strength of the rock and soil at different stratification depths, and obtain the internal friction strength fluctuation data of the rock and soil;

[0024] Step S225: Integrate the strength boundary fluctuation of the internal friction strength fluctuation data of the rock and soil to obtain the numerical value of the internal friction boundary fluctuation integral;

[0025] Step S226: Solve the internal friction interval with boundary constraints for the internal friction strength fluctuation data of the rock and soil based on the variational method and the numerical value of the internal friction boundary fluctuation integral, and obtain the internal friction interval with boundary constraints;

[0026] Step S227: Simulate the deflection of the lateral stress during pile driving extrusion according to the internal friction interval with boundary constraints, and obtain the lateral stress deflection data of the extrusion.

[0027] Preferably, step S25 includes the following steps:

[0028] Step S251: Classify the types of rock and soil for the vertical geological rock and soil structure data to obtain the classified types of the vertical geological rock and soil;

[0029] Step S252: Identify the geological fold / fault profile morphology between adjacent rock and soil for the vertical geological rock and soil structure data based on the classified types of the vertical geological rock and soil, and obtain the geological fold / fault profile morphology data;

[0030] Step S253: Evaluate the progressive superposition frequency of the effective stress transfer data to obtain the progressive superposition data of the stress; calculate the tilt angle of the fold deformation for the geological fold / fault profile morphology data to obtain the tilt angle of the fold deformation;

[0031] Step S254: Calculate the average difference in the slope of the fold surface at different depths for the geological fold / fault profile morphology data according to the tilt angle of the fold deformation, and obtain the average difference in the slope of the fold surface;

[0032] Step S255: Simulate the instability of the stress impact structure for the tilt angle of the fold deformation and the average difference in the slope of the fold surface based on the progressive superposition data of the stress, and obtain the instability data of the stress impact fold structure;

[0033] Step S256: Estimate the risk of fault dislocation for the geological fold / fault profile morphology data according to the progressive superposition data of the stress, and obtain the risk data of the stress superposition fault dislocation;

[0034] Step S257: Analyze the instability of the stress impact structure between adjacent rock and soil based on the instability data of the stress impact fold structure and the risk data of the stress superposition fault dislocation, and obtain the instability data of the stress impact structure between adjacent rock and soil.

[0035] Preferably, step S3 includes the following steps:

[0036] Step S31: Normalize the instability data of the stress impact structure to obtain the normalized instability data of the stress impact structure;

[0037] Step S32: Based on the random forest algorithm, construct a geotechnical stress risk monitoring model for the normalized instability data of the stress impact structure, obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0038] Preferably, step S32 includes the following steps:

[0039] Step S321: Divide the normalized instability data of the stress impact structure into a training set and a test set to obtain the instability training set of the stress impact structure and the instability test set of the stress impact structure respectively;

[0040] Step S322: Perform eigen-decomposition processing on the instability training set of the stress impact structure to obtain the stress eigen-subspace training sequence;

[0041] Step S323: Perform sequence random sampling processing on the stress eigen-subspace training sequence to obtain the stress impact random sampling sequence;

[0042] Step S325: Based on the random forest algorithm, construct an initial geotechnical stress risk monitoring model for the stress impact random sampling sequence to obtain the initial geotechnical stress risk monitoring model;

[0043] Step S326: Test the initial geotechnical stress risk monitoring model through the instability test set of the stress impact structure to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0044] Preferably, the present invention also provides a geotechnical stress monitoring data analysis system for performing the above-mentioned geotechnical stress monitoring data analysis method. The geotechnical stress monitoring data analysis system includes:

[0045] A caisson distribution difference induction module, configured to obtain geological data of the bridge construction area on the river channel and river channel bridge construction design data; induce the pile driving caisson distribution differences in the river channel bridge construction design data to obtain pile driving caisson distribution difference data;

[0046] The stress impact structure instability analysis module is used to perform pile driving extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data to obtain the extrusion lateral stress deflection data; estimate the stress transfer loss of the extrusion lateral stress deflection data to obtain the effective stress transfer data; perform stress impact structure instability analysis between adjacent geotechnicals based on the effective stress transfer data to obtain the stress impact structure instability data between adjacent geotechnicals;

[0047] The geotechnical stress risk monitoring model construction module is used to construct a geotechnical stress risk monitoring model based on the stress impact structure instability data by means of the random forest algorithm to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0048] The beneficial effects of the present invention are as follows. By obtaining the geological data and construction design data of the river bridge construction area and summarizing the pile driving and pipe sinking distribution differences in the construction design, accurate basic data can be provided for subsequent analysis. This process helps engineers identify the impacts of different construction plans on the geotechnical body, providing a basis for formulating scientific construction plans and optimizing design schemes. Through the detailed analysis of the pile driving and pipe sinking distribution differences, the stress unevenness and geological changes caused during construction can be effectively predicted, laying a good foundation for ensuring project safety. By performing extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data, the stress deflection phenomenon caused during the pile driving process can be deeply understood. The results of this simulation help to evaluate whether the stress generated during construction will cause the instability of the surrounding geotechnicals and timely discover potential risk points. Further estimating the stress transfer loss of the extrusion lateral stress deflection data can accurately understand the stress transfer situation between geotechnical bodies, providing accurate data support for subsequent stress impact structure instability analysis. The implementation of this step provides a scientific basis for stress control during construction, avoiding safety problems caused by improper construction. Using the random forest algorithm to construct a geotechnical stress risk monitoring model provides an efficient solution for stress risk management during geotechnical engineering construction. Through machine learning analysis of the stress impact structure instability data, the model can automatically identify potential risk areas and give early warnings in a timely manner, thus realizing intelligent monitoring and accurate risk prediction. The construction of this model not only improves the accuracy and real-time performance of monitoring, but also greatly reduces the cost and error of manual monitoring, enhancing the safety during the construction process. Finally, sending the monitoring model to the terminal can realize remote real-time monitoring and data analysis, further optimizing the construction control and decision-making process. Therefore, the present invention makes an improved treatment of a traditional method for analyzing geotechnical stress monitoring data, solves the problem that the traditional method for analyzing geotechnical stress monitoring data has low accuracy in identifying the structural instability caused by stress impact, resulting in large errors in geotechnical stress monitoring, improves the accuracy of identifying the structural instability caused by stress impact, and reduces the errors of geotechnical stress monitoring. Description of the Drawings

[0049] Figure 1 It is a schematic diagram of the step process of a method for analyzing geotechnical stress monitoring data;

[0050] Figure 2 It is Figure 1 a detailed implementation step process schematic diagram of step S2 in

[0051] Figure 3 It is Figure 1 a detailed implementation step process schematic diagram of step S3 in Detailed Implementation Manner

[0052] Please refer to Figures 1 to 3 , a method for analyzing geotechnical stress monitoring data, the method includes the following steps:

[0053] Step S1: Obtain the geological data of the bridge construction area on the river channel and the river channel bridge construction design data; summarize the pile driving and pipe sinking distribution differences in the river channel bridge construction design data to obtain pile driving and pipe sinking distribution difference data;

[0054] Step S2: Perform pile driving extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data to obtain extrusion lateral stress deflection data; estimate the stress transfer loss of the extrusion lateral stress deflection data to obtain effective stress transfer data; perform stress impact structure instability analysis between adjacent geotechnicals based on the effective stress transfer data to obtain stress impact structure instability data between adjacent geotechnicals;

[0055] Step S3: Build a geotechnical stress risk monitoring model based on the stress impact structure instability data using the random forest algorithm to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0056] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step process of a method for analyzing geotechnical stress monitoring data of the present invention. In this example, the method for analyzing geotechnical stress monitoring data includes the following steps:

[0057] Step S1: Obtain the geological data of the bridge construction area on the river channel and the river channel bridge construction design data; summarize the pile driving and pipe sinking distribution differences in the river channel bridge construction design data to obtain pile driving and pipe sinking distribution difference data;

[0058] In the embodiments of the present invention, to obtain the geological data of the bridge construction area on the river channel and the river channel bridge construction design data, a variety of data collection means are required to conduct a detailed survey of the geological conditions of the bridge construction area. First, a high-resolution geological radar device is used to perform three-dimensional scanning on the rock and soil structure of the construction area to obtain data such as the distribution of underground rock formations, soil density, water content, etc., and in combination with the borehole exploration data, the data obtained by the geological radar is further verified and supplemented. Subsequently, acoustic detection technology is used to analyze the density of the underground strata of the river channel to judge the characteristics of rock formations at different depths. For the aquifer distribution area, resistivity tomography is used for permeability detection to obtain the permeability coefficients of different rock formations. The obtained geological data is stored in the geological data database and classified and stored according to different depths and different rock formation types for subsequent analysis and use. At the same time, the bridge construction design data is obtained from the construction unit, including the pile driving depth, pile foundation material, pipe sinking method, pipe sinking distribution position, and corresponding construction process parameters. These data are stored in the design data database after standardization processing. For the pile driving and pipe sinking distribution in the construction design data, a method based on spatial statistical analysis is used to summarize the distribution of the pile driving area. First, the pile foundation layout in the bridge construction area is discretized spatially, and the construction area is divided into multiple grid units, and each grid unit contains corresponding pile driving information, such as pile driving density, pile diameter, pile spacing and other parameters. Kriging interpolation method is used to interpolate the pile driving parameters between different grid units to obtain the spatial distribution characteristics of the pile driving parameters in different construction areas. On this basis, the coefficient of variation of the pile driving and pipe sinking distribution is calculated to evaluate the pile driving uniformity in different areas. For the areas with large differences in pile foundation distribution, the stress interference coefficient between piles is further calculated to judge the influence of pile driving construction in different areas on adjacent pile foundations. Finally, the overall differences in the pile driving and pipe sinking distribution are summarized to form the pile driving and pipe sinking distribution difference data, providing data support for subsequent analysis.

[0059] Step S2: Perform a pile driving extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data to obtain the extrusion lateral stress deflection data; estimate the stress transfer loss for the extrusion lateral stress deflection data to obtain the effective stress transfer data; conduct a stress impact structure instability analysis between adjacent rock and soil based on the effective stress transfer data to obtain the stress impact structure instability data between adjacent rock and soil;

[0060] In the embodiments of the present invention, according to the data of the distribution difference of pile driving and pipe sinking, the simulation of the deflection of the lateral stress caused by pile driving extrusion is carried out. The finite element analysis method is used to construct the formation stress model of the pile driving construction process. Representative geotechnical units within the construction area are selected, and the mechanical parameters of the geotechnical materials, such as elastic modulus, Poisson's ratio, internal friction angle, cohesion, etc., are determined according to the drilling data. During the simulation process, the lateral extrusion effect of the pipe on the surrounding geotechnical materials during construction is considered, and the influence of different pipe sinking methods (such as vibrating pipe sinking, hammer-driven pipe sinking) on the deflection of the lateral stress is compared. The plastic mechanics model is used to simulate the plastic deformation generated by the geotechnical materials during pile driving, calculate the propagation path and deflection angle of the lateral stress, and obtain the data of the deflection of the extrusion lateral stress. The stress transfer loss of the extrusion lateral stress deflection data is estimated. Based on the stress transfer theory of layered geotechnical materials, a stress loss calculation model between different formations is established. For the stress transfer efficiency of different rock layers, the elastic-plastic mechanics method is used to calculate the attenuation coefficient of the construction stress. During the analysis process, the influence of the discontinuity on the stress transfer at the rock layer interface is considered, and the stress attenuation ratio between layers is calculated. For areas with rich groundwater, the calculation of the additional stress loss caused by seepage is further introduced. After comprehensive calculation, the effective stress transfer data are obtained and stored in the database to provide input data for subsequent analysis. Based on the effective stress transfer data, the instability analysis of the stress impact structure between adjacent geotechnical materials is carried out. A formation contact stress analysis model is established to simulate the stress impact effect between adjacent geotechnical units. According to the bonding characteristics between different rock layers, the interface friction coefficient and shear strength are calculated, and the potential shear failure area caused by the stress impact is analyzed. The formation displacement field analysis method is used to calculate the local settlement of the formation in the construction area and evaluate whether the construction process will cause uneven settlement of the overall foundation. During the simulation process, the influence of dynamic loads on geotechnical materials is considered, the seismic effect caused by the bridge construction process is analyzed, and the risk of instability of the local soil structure caused by dynamic load action is calculated. Finally, the instability data of the stress impact structure between adjacent geotechnical materials are obtained, providing a basis for subsequent monitoring.

[0061] Step S3: Based on the random forest algorithm, a geotechnical stress risk monitoring model is constructed for the instability data of the stress impact structure, and the geotechnical stress risk monitoring model is sent to the terminal to perform the analysis of the geotechnical stress monitoring data.

[0062] In the embodiments of the present invention, the random forest algorithm is used to construct a geotechnical stress risk monitoring model for stress impact structure instability data. The random forest algorithm consists of multiple decision trees, and each decision tree is trained based on different instability characteristic parameters, and finally a comprehensive geotechnical stress risk assessment model is formed. First, feature extraction is performed on the stress impact structure instability data, including stress impact intensity, stress action time, formation deformation rate, etc. Using data preprocessing methods, different features are normalized to eliminate the influence of different dimensions on the calculation results. The cross-validation method is used to optimize the parameters of the random forest model, and the K-fold cross-validation method is introduced to ensure the stability and generalization ability of the model. During the training process, historical geological data is used for the preliminary verification of the model, and the model is iteratively updated through newly collected geotechnical stress monitoring data. Finally, the trained geotechnical stress risk monitoring model can predict the geotechnical stress in the construction area in real time, judge potential formation instability risks, and generate corresponding monitoring warning data. The geotechnical stress risk monitoring model is sent to the terminal to support real-time monitoring and construction safety management.

[0063] Step S1 includes the following steps:

[0064] Step S11: Obtain the geological data of the bridge construction area on the river and the river bridge construction design data;

[0065] Step S12: Perform construction time series marking on the river bridge construction design data to obtain construction design time marking data;

[0066] Step S13: Perform pile driving sequence selection analysis on the construction design time marking data to obtain construction pile driving sequence selection data;

[0067] Step S14: Summarize the pile driving and pipe sinking distribution differences of the construction design time marking data according to the construction pile driving sequence selection data to obtain pile driving and pipe sinking distribution difference data.

[0068] In the embodiments of the present invention, first, geological data of a construction area on the river channel and design data of the bridge construction are obtained. The process of obtaining geological data includes arranging exploration boreholes in the range of the longitudinal coordinate points (120.560, 35.760) to (121.030, 35.910) of the river channel, drilling core samples with a depth of 50 meters along the river channel at an average interval of 200 meters, and recording the occurrence of rock masses, rock layer structures, and integrity information through on-site surveys. Different rock layers are marked for hardness according to the pre-established determination criteria for geotechnical density. During the borehole exploration, the structural characteristics of each geological profile are calibrated at a resolution of 5 meters in depth, including the particle size of rock particles, the distribution of mineral components, and the preliminary determination of pore water content. When obtaining the bridge construction design data, the pile foundation positions, immersed tube process plans, pile foundation length and diameter parameters involved in the design drawings are sorted out, and the construction plan is recorded with reference to the pre-positioning coordinate information of each pile foundation in the engineering drawings. Subsequently, the construction stage information in the overall bridge design document is sorted out, including the start time of construction, the number of days for pile driving of the bridge foundation, and the mileage of segmented operations, so as to mark the construction time sequence subsequently. Then, the bridge construction design data is operated to establish a time index in the established chronological order. By comparing the construction stage numbers recorded in the design text and tables with the date order, the pile driving working periods of each week are listed in the data table, and the start and end pile driving dates of each pile foundation are marked in days. Combining with the segmented operation arrangement of the bridge foundation, the corresponding pile driving work area numbers are specified for each period. In this way, a construction time series table including specific daily operation links, pile driving targets, and proposed immersed tube types is generated, and the time sequence is accurately calibrated in the data table in a way that the date corresponds to the pile driving mark. Then, for the marked time series table, based on multiple on-site investigations and the construction pile position layout map provided by the design institute, the pile driving sequence of each period is systematically analyzed, and a strategy to maintain stable construction efficiency between the rock layer strength and the construction convenience of the river channel is determined. The analysis methods include overall evaluation of the daily pile driving quantity, the peak load of pile driving instruments, and the geological structure stability index, classifying and sorting the rock layer bearing capacity and the cohesion of the surrounding riverbed soil according to the actual geological exploration results, and then establishing a strategy to drive piles in the areas with relatively loose geology and relatively low bearing capacity first, so as to disperse construction risks and maintain the continuous operation intensity of pile driving equipment. On this basis, a sequence list including the sequential relationship of multiple pile driving segments is formed, so as to clarify the associated distribution relationship between the early pile positions and the later pile positions. After obtaining the pile driving sequence, corresponding to the sequence and the time series table one by one, the immersed tube form and pipe diameter specification of each pile foundation on a given date are extracted. By comparing the immersed tube types and specific length parameters of the pile foundations in the same area in the original design, the differential distribution of the pile driving immersed tube methods adopted in each construction surface layer, middle layer, and deep layer on the date axis is statistically analyzed, including multi-dimensional data such as the outer diameter of the immersed tube, the wall thickness of the tube, the number of immersed tube segments, and the penetration force.During this process, the correspondence between the depths of the rock and soil interfaces and the sinking positions of the immersed tubes in the exploration records and the design documents was also compared. By systematically classifying the pile driving periods and geological profile characteristics of the surrounding pile foundations, a comparison table of the geotechnical bearing characteristics in different depth segments and the differences in the distribution of the immersed tubes during the corresponding construction periods was formed. Finally, a complete analysis result of the differences in the immersed tube layout was obtained, covering the pile position offsets, differences in the diameters of the immersed tubes, and differences in the number of pipe segments that occurred in the same area during different construction periods. These difference information was summarized in the form of a data list, thus achieving the goal of summarizing the differences in the distribution of pile driving and immersed tubes. Through the above operations, the summarized data on the differences in the distribution of pile driving and immersed tubes can directly reflect the relationship between the construction nodes of the river bridge and the layout of each pile foundation and immersed tube, providing an accurate and detailed preliminary basis for the subsequent simulation of the deflection of the extrusion lateral stress.

[0069] Step S2 includes the following steps:

[0070] Step S21: Based on the data on the differences in the distribution of pile driving and immersed tubes, perform a vertical geological geotechnical structure analysis on the geological data in the bridge construction area for different pile driving and immersed tube areas to obtain the vertical geological geotechnical structure data for different pile driving and immersed tube areas;

[0071] Step S22: According to the data on the differences in the distribution of pile driving and immersed tubes, perform a simulation of the deflection of the extrusion lateral stress on the vertical geological geotechnical structure data to obtain the extrusion lateral stress deflection data;

[0072] Step S23: Identify the azimuth of the radial influence of the stress on the extrusion lateral stress deflection data to obtain the azimuth of the radial influence of the stress;

[0073] Step S24: Estimate the stress transfer loss based on the azimuth of the radial influence of the stress to obtain the effective stress transfer data;

[0074] Step S25: Based on the effective stress transfer data, perform an instability analysis of the stress impact structure between adjacent geotechnical layers on the vertical geological geotechnical structure data to obtain the instability data of the stress impact structure between adjacent geotechnical layers.

[0075] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0076] Step S21: Based on the data on the differences in the distribution of pile driving and immersed tubes, perform a vertical geological geotechnical structure analysis on the geological data in the bridge construction area for different pile driving and immersed tube areas to obtain the vertical geological geotechnical structure data for different pile driving and immersed tube areas;

[0077] In the embodiments of the present invention, based on the corresponding information between the pile driving and pipe sinking distribution difference data and the geological data of the bridge construction area, an in-depth analysis is carried out on the vertical geological and geotechnical structures of different pile driving and pipe sinking areas. During this implementation operation, first, according to the coordinate range involved in the river bridge project, the construction positions covered by the same type of pile driving and pipe sinking are uniformly marked on the geological map, and further divided according to the outer diameter of the pipe, the penetration depth, and the distribution characteristics of the surrounding rock and soil layers. Then, based on the drilling core information obtained in the early stage, the particle size distribution, water content, and permeability coefficient of the layered rock and soil in this area are statistically classified, and the rock layer interface is paired with the actual influence range of the pile driving and pipe sinking by means of hierarchical comparison. The pipe diameter specification, wall thickness, and pile driving force performance in the pile driving and pipe sinking distribution difference data form several constraint conditions to distinguish the bearing capacity and density of the typical vertical layers in the geological data. In this process, in order to ensure the accuracy of the description of the rock and soil layer profile, multiple iterative discrete operations are used to locate the interface between the rock layer and the accumulation, and the distribution of the clay layer and the sandstone layer in the depth direction is corrected by combining the measured pore water pressure data. The longitudinal profile of the geological data is divided into discrete intervals every 10 meters in depth, and the lithology indexes in each interval are intensively compared to identify the alternating sections between the high-water-content sand layer and the high-plasticity clay layer. Thus, the vertical geological and geotechnical structure data of different pile driving and pipe sinking areas are generated, which is convenient for subsequent refined analysis of the lateral stress deflection condition. For example, assuming that the pile driving and pipe sinking distribution difference data are: the pile driving and pipe sinking distribution type is uniform distribution, and the pile driving and pipe sinking distribution density is 0.5 m-1. The geological data of the bridge construction area are: the rock and soil type is sandy soil, the rock and soil density is 1.8 g / cm3, and the rock and soil elastic modulus is 100 MPa. Using the finite element analysis method, the vertical geological and geotechnical structure analysis of the geological data of the bridge construction area is carried out for different pile driving and pipe sinking areas, including parameters such as the rock and soil type, rock and soil density, and rock and soil elastic modulus, and the vertical geological and geotechnical structure data of different pile driving and pipe sinking areas are obtained. For example, the vertical geological and geotechnical structure data obtained for different pile driving and pipe sinking areas are: the rock and soil type is sandy soil, the rock and soil density is 1.8 g / cm3, and the rock and soil elastic modulus is 100 MPa.

[0078] Step S22: Simulate the deflection of the lateral stress caused by pile driving extrusion on the vertical geological and geotechnical structure data according to the pile driving and pipe sinking distribution difference data to obtain the extrusion lateral stress deflection data;

[0079] In the embodiments of the present invention, after obtaining the vertical geological and geotechnical structure data, pile driving extrusion lateral stress deflection simulation is carried out based on the pile driving and pipe sinking distribution difference data. First, according to the geotechnical parameters of different pile driving areas, construction conditions such as the pile penetration speed, pile driving impact force, and pipe sinking depth are set. During the simulation, the vertical load and lateral extrusion force generated by pile driving are decomposed and calculated, and the lateral stress offset characteristics of the geotechnical around the pile foundation under the action of the extrusion force are analyzed emphatically. A three-dimensional elastoplastic constitutive model is used to describe the stress-strain relationship of the geotechnical. On this basis, the deformation and stress change distribution of geotechnical particles after being stressed are calculated by the Lagrangian finite element method, and the lateral stress deflection angle, stress diffusion range, and stress concentration area generated by the geotechnical at different pile driving positions are obtained. Further, the stress trajectory analysis method is used to track the stress transfer path of the soil around the pile foundation to judge the attenuation of the extrusion stress in different areas and its influence degree on the surrounding geotechnical structure.

[0080] In another embodiment, pile driving extrusion lateral stress deflection simulation is carried out on the above vertical geological and geotechnical structure data according to the pile driving and pipe sinking distribution difference data to obtain extrusion lateral stress deflection data. This operation first discretely dissects the transmission form of the lateral extrusion force according to the contact parameters between the outer diameter of the pile driving and pipe sinking and the rock and soil layers, and divides the contact area between the pipe wall and the geotechnical medium into several circumferential units. Each circumferential unit is further subdivided into 3 to 5 depth slices longitudinally, corresponding to the same or similar geotechnical mechanical characteristics. Referring to the geotechnical strength index and particle density obtained by drilling, the corresponding Poisson's ratio and internal friction angle are assigned to different depth slices in turn. Then, for these circumferential units and depth slices, the pile driving penetration force parameter is introduced successively to simulate the circumferential extrusion phenomenon of the surrounding soil caused by the pile foundation pressing down. In multiple iterative mixed operations, the tectonic stress field generated by each unit at a given penetration force is numerically discretized, and the stress distribution of each unit in the maximum circumferential compression state is statistically analyzed. Combining the superposition effect of cohesion and friction resistance, the deflection amount of the lateral deformation amplitude that appears is identified, and based on this, the lateral stresses of all units are combined, and the results are recorded to form extrusion lateral stress deflection data. During the whole process, the actual cohesion and humidity conditions of each layer of geotechnical need to be re-calibrated to ensure that the lateral stress deflection results are stable and conform to the field geotechnical characteristics.

[0081] Step S23: Identify the stress radial influence orientation for the extrusion lateral stress deflection data to obtain the stress radial influence orientation;

[0082] In the embodiments of the present invention, the radial stress influence orientation of the extrusion lateral stress deflection data is identified to obtain the radial stress influence orientation. First, according to the lateral stress deflection result obtained in the previous step, the circumferential units around the pile foundation are divided into fan-shaped blocks, and each block has an included angle interval of 15 degrees to 20 degrees to clarify the stress divergence degree of the pile driving load in different directions. For each fan-shaped block, the magnitude and direction of the stress component are calculated layer by layer in the longitudinal direction according to the depth reference point, and the segment superposition method is used to combine the data of the pipe jacking penetration depth and the rock layer thickness. In this way, a multi-directional stress vector distribution map around the pile foundation can be formed, and several sections with peak stress value higher than the threshold are marked on the map. Then, through the sector scanning method of the section, several main radial influence direction angles are statistically listed according to the ratio of the peak stress value to the mean value of the entire area around the pile. To avoid discrete deviation, in this process, the maximum and minimum stress amplitudes of several depth slices in each fan-shaped block are analyzed, and the continuity of the maximum amplitude value in the same direction is retained. The obtained direction data are integrated to obtain the azimuth information of the stress concentrated in a radial form at the periphery of the pile foundation, and finally the radial stress influence orientation around the pile is determined.

[0083] Step S24: Estimate the stress transfer loss based on the radial stress influence orientation to obtain the effective stress transfer data;

[0084] In the embodiments of the present invention, combined with the directional characteristics of the aforementioned radial influence orientation, the permeability coefficient, water content and soil cohesion in each direction fan-shaped block are comprehensively evaluated. The specific method is to first apply correction coefficients in the vertical and tangential directions to the geotechnical column units from the ground surface to the deepest pile point in each direction to characterize the absorption rate and dissipation rate of the stress energy by the geotechnical pore network. Then, combined with the radial stress field amplitude extracted in the previous step, the energy reduction ratio caused by penetration and the energy attenuation ratio caused by friction between strata are deducted in turn, and these attenuation values are compared with the reference stress amplitude to obtain the remaining stress values available for transfer. Then, the remaining stress values of multiple direction fan-shaped blocks are integrated in the same coordinate system to form an overall effective stress distribution table, and the loss rate curves in different azimuths are given. By segmentally accumulating the loss rate in the depth direction, the remaining stress transfer values corresponding to each rock and soil layer are obtained, and overall, they are summarized as effective stress transfer data for subsequent correlation analysis of geotechnical structures.

[0085] In another embodiment, the stress transfer loss estimation is based on the stress radial influence azimuth data to calculate the stress attenuation of the geotechnical materials in different regions. First, key physical parameters such as the permeability coefficient, porosity, and water content of the geotechnical materials at different depths are obtained. These parameters directly affect the diffusion and attenuation process of stress in the geotechnical materials. For the geotechnical characteristics of different regions, an exponential attenuation model is used to calculate the loss rate of the extrusion stress in different azimuths, and the diffusion trend of the stress is analyzed by the Gaussian distribution fitting method. For soil layers with higher permeability, due to the relatively loose arrangement of particles and larger porosity, the stress transfer loss is larger; while for soil layers with higher density, the stress transfer loss is smaller. Combining the stress path tracking technology, the differential calculation of the stress loss between different geotechnical depths is carried out to obtain the effective stress transfer data. The finite element back-analysis method is used to verify the rationality of the calculation results to ensure that the obtained effective stress data can truly reflect the stress state of the geotechnical materials under external forces.

[0086] Step S25: Based on the effective stress transfer data, perform an instability analysis of the stress impact structure between adjacent geotechnical materials on the vertical geological geotechnical structure data to obtain the instability data of the stress impact structure between adjacent geotechnical materials.

[0087] In the embodiment of the present invention, based on the effective stress transfer data, the instability situation of the stress impact structure between adjacent geotechnical materials is analyzed. In this process, first, the vertical geological geotechnical structure data is re-layered, the interface characteristics of different geological layers are extracted, and the mechanical state of the geotechnical boundary layer is monitored. The contact mechanics analysis method is used to calculate the shear slip trend of the geotechnical boundary layer under different stress actions to judge whether there is a possibility of structural instability. Combining the stress progressive superposition principle, the frequency response analysis of the geotechnical stress fluctuation in different regions is carried out to identify the stress concentration areas and potential risk points of stress impact. Further, using the fault morphology analysis method, the stability of the geological fault or fold structure is evaluated, and the influence degree of the stress impact on the slip of the geotechnical interface is calculated. Finally, the instability data of the stress impact structure between adjacent geotechnical materials is obtained, providing basic data support for the subsequent geotechnical stress risk monitoring.

[0088] Step S22 includes the following steps:

[0089] Step S221: Perform a force / frequency analysis on the pile driving and pipe sinking distribution difference data to obtain the pile driving force / frequency data;

[0090] Step S222: Perform a vertical layering density analysis on the vertical geological geotechnical structure data to obtain the geotechnical vertical layering density;

[0091] Step S223: Identify the rotation angle of the lateral contact section with the force / frequency applied to the geotechnical vertical layering density according to the pile driving force / frequency data to obtain the rotation angle of the contact section;

[0092] Step S224: Based on the pile driving force application intensity / frequency data and the rotational angle of the contact cross-section, evaluate the fluctuation of the internal friction strength of the geotechnical material at different stratification depths, and obtain the internal friction strength fluctuation data of the geotechnical material;

[0093] Step S225: Integrate the strength boundary fluctuation of the internal friction strength fluctuation data of the geotechnical material to obtain the numerical value of the internal friction boundary fluctuation integral;

[0094] Step S226: Based on the variational method and the numerical value of the internal friction boundary fluctuation integral, solve the internal friction interval with boundary constraints for the internal friction strength fluctuation data of the geotechnical material to obtain the internal friction interval with boundary constraints;

[0095] Step S227: According to the internal friction interval with boundary constraints, simulate the deflection of the lateral stress during pile driving extrusion to obtain the lateral stress deflection data during extrusion.

[0096] In an embodiment of the present invention, in one implementation process, first, based on the pile driving and pipe sinking distribution difference data obtained in the early stage, the pile driving force and pile driving frequency of different pile foundations during each construction period are statistically analyzed to form a discrete sequence table of pile driving force and frequency. To achieve this operation, the established daily pile driving process can be split, and the vibration times of the pile driver per hour, the single penetration force, the actual action time, and the change range of the penetration depth are collected. Subsequently, the continuous vibration peaks are read using the vibration recording device attached to the pile driver, and the peak duration is corresponded to the corresponding pile driving depth one by one, so as to double-label the force and frequency. In addition, by comparing the outer diameter, wall thickness grade of different types of pipe sinks and the local rock and soil compressive strength, the maximum pile driving frequency range that best matches the penetration force on the day is screened, and finally a pile driving force / frequency data is sorted out. After completing the analysis of force and frequency, the vertical stratification density analysis is performed on the vertical geological rock and soil structure data. To achieve this goal, it is necessary to retrieve the on-site density measurement data of the previous borehole cores and comprehensively compare the porosity, the arrangement mode of rock and soil particles, and the groundwater permeability coefficient in each depth section. The borehole core samples are subdivided according to 5-meter or 10-meter depth sections, and the particle size distribution ratio and dry density value of each sample are measured in the laboratory. Thus, according to the microscopic structure differences between adjacent depth sections, the change trend of the formation density with depth is judged. During induction, to highlight the stratification differences, a stratified statistical method can be used to give the average porosity and cohesion indexes of each soil layer or rock layer, forming a vertical stratification density data table of rock and soil. Subsequently, based on the pile driving force / frequency data, the rotation angle of the lateral contact section under the combined action of force and frequency of the vertical stratification density of rock and soil is identified. This operation requires first dividing the circumferential stress generated during pile driving into several discrete sectors, and the central angle of each sector can usually be set to 5 degrees to 10 degrees to obtain higher-precision circumferential data. Then, within each sector, by comparing the pre-statistical pile driving vibration force peak with the density data layer by layer, and using the internal friction angle and cohesion index of the soil body, the circumferential force inclination trend within the sector is judged. When the inclination trends of all sectors are superimposed, the rotation angle distribution of a specific depth section can be extracted, and finally the angle range where the lateral contact section deflects is marked on the same vertical section, forming a rotation angle data table of the contact section. Next, in combination with the pile driving force / frequency data and the rotation angle of the contact section, the fluctuation of the internal friction strength between different stratification depths of the vertical stratification density of rock and soil is evaluated. In this link, the basic friction strength value of the rock and soil corresponding to each depth stratification is used as the initial reference. By introducing the tangential offset generated by the instantaneous impact force and rotation angle during pile driving, the instantaneous detachment and re-overlap behavior of the contact surface between particles in the rock and soil during force application are observed. Through a continuous discrete calculation process, the mechanical differences in the particle contact in different directions are statistically analyzed, and iterative comparison is performed between adjacent depth stratifications to form the fluctuation data of the internal friction strength of the rock and soil.After obtaining the data of the internal friction strength fluctuation, it is necessary to perform an integral of the strength boundary fluctuation on this data to obtain the numerical value of the integral of the internal friction boundary fluctuation. This integration process relies on the principle of hierarchical scanning: in each vertical layer, the internal friction strength fluctuation curve is recorded in the form of discrete points, and then the fluctuation values between adjacent discrete points are accumulated to obtain the total interval fluctuation of each layer. Then, these total layer fluctuations are longitudinally summarized to form the cumulative integral value of the entire depth profile. This value reflects the overall fluctuation amplitude of the internal friction of the rock and soil between the upper and lower layers under the piling condition and can be used for the subsequent solution of the boundary constraint range. In the further processing step, combining the aforementioned variational method theory and the numerical value of the internal friction boundary fluctuation integral, the solution of the internal friction interval with boundary constraints for the rock and soil internal friction strength fluctuation data is carried out. Specifically, by inputting the fluctuation variance of the internal friction and the cumulative integral data information of the previous step into the variational method framework, the critical interval with large fluctuations is located using a discrete method of layer-by-layer iteration. In each depth segment, the peak and valley values of the fluctuation are selected as the segmentation points, and combined with the rotation angle information of the contact section obtained from the previous processing, the upper and lower limits of the internal friction of the rock and soil medium within this layer range are determined. The upper and lower limit values within each layer are fused to form the final internal friction interval with boundary constraints. This interval can intuitively show the bearing limit of the rock and soil to the lateral stress of piling at different depths. Finally, based on the internal friction interval with boundary constraints, a comprehensive numerical calculation of the deflection of the lateral stress of piling extrusion on the rock and soil medium in the vertical geological profile is performed, and thus the data of the deflection of the lateral stress of extrusion is obtained. At this stage, the extrusion force can be first applied to the discrete area around the pile foundation, and the upper and lower limits of the internal friction obtained previously are incorporated into the calculation to correct the rebound or plastic deformation phenomenon of each layer of soil body to the lateral stress. When each discrete cell is analyzed, it is necessary to consider the difference in internal friction of the surrounding cells and the rotation angle of the contact section at the same time, and introduce a method of layer-by-layer accumulation to record the deflection increment. After the data of all cells are converged, the overall distribution result of the deflection of the lateral stress of piling extrusion can be found in the three-dimensional space of the entire pile circumference area. This result provides comprehensive data support for the subsequent identification of the radial influence direction of the stress field and the estimation of stress loss.

[0097] Step S24 includes the following steps:

[0098] Step S241: Obtain the water content at different rock and soil depths; based on the radial influence direction of the stress, map the water content at different rock and soil depths to the water content in the stress diffusion direction to obtain the water content in the stress diffusion direction;

[0099] Step S242: Evaluate the trend of the water pressure increment between the mixed pores of the rock and soil particles based on the water content in the stress diffusion direction and the water content at different rock and soil depths to obtain the water pressure increment data of the mixed rock and soil particles;

[0100] Step S243: Evaluate the contact kinetic energy damping ratio of the increment data of the mixed water pressure of the rock and soil particles to obtain the kinetic energy damping ratio of the mixed water pressure of the particles;

[0101] Step S244: Estimate the stress transfer loss of the extrusion lateral stress deflection data based on the kinetic energy damping ratio of the mixed water pressure of the particles to obtain the effective stress transfer data.

[0102] In the embodiments of the present invention, during the acquisition of water content at different geotechnical depths, it is necessary to use a combination of borehole sampling and water sensing technology for joint measurement. First, a plurality of depth sensing measurement points are arranged in the construction area, and small-diameter drilling equipment is used to drill geotechnical samples at different depths. The drilling depth is controlled within 20 meters underground, and sampling points are arranged at 1-meter intervals. During the sampling process, the natural water content of undisturbed soil samples is measured by the rapid weighing method, and the soil samples are subjected to a constant temperature drying treatment at 105 °C using the drying method, and the mass loss is calculated to obtain water content data. For fine-grained soils, a high-frequency electromagnetic water content sensor is used to measure the conductivity, and the water content is calculated by combining with the soil type correction formula. During the data recording process, the water content data at different depths are stored according to the spatial coordinates, and a depth water distribution database is established. To ensure data accuracy, each measurement point is repeated at least three times, and the average measurement error is calculated, and the abnormal data with errors exceeding the set threshold is eliminated. During the mapping of water content in the stress diffusion direction, it is necessary to calculate the influence area of water distribution based on the radial diffusion characteristics of geotechnical stress. First, based on the geological survey data of the construction area, the stress diffusion radius of the rock and soil layer is determined, and the stress diffusion gradient in different directions is calculated according to the soil density and elastic modulus. The capillary water action between geotechnical particles at different depths is calculated using the water migration equation, and the dynamic distribution of water under stress is estimated by combining with empirical formulas. During the data processing process, spatial interpolation methods are used to calculate the water content at unmeasured points, and the weighted average algorithm is used to smooth the measurement data to eliminate local outliers. Finally, a water content database in the stress diffusion direction is established based on the calculation results, and the data is associated with the geotechnical depth coordinates to form a complete spatial mapping relationship. During the evaluation of the incremental trend of pore water pressure in the geotechnical particle mixture, it is necessary to calculate the change trend of pore water pressure by combining the water content in the stress diffusion direction and the water content at different depths. First, the water content data at each depth in the measurement area are extracted, and the seepage velocity of pore water is calculated according to the soil permeability coefficient. The volume change of pore water under stress is calculated using Biot's consolidation theory, and the numerical difference method is used to solve the water pressure increment. For saturated soil layers, the water permeability is calculated by combining with Darcy's law, and the water pressure growth trend is solved by the time step recurrence method. During the data storage process, a three-dimensional water pressure increment distribution map is constructed according to the spatial coordinates of the construction area, and the curve fitting method is used to analyze the change trend of water pressure with time. Finally, all calculation results are archived in the geotechnical particle mixture pore water pressure increment database and used for subsequent stress analysis. During the evaluation of the kinetic energy damping ratio of the particle mixture pore water pressure, it is necessary to calculate the kinetic energy loss of pore water based on the geotechnical particle mixture pore water pressure increment data. First, the water pressure increment data at different depths in the construction area are extracted, and the initial kinetic energy of water movement is calculated by combining with the fluid dynamics equation. The Navier-Stokes equation is used to calculate the flow resistance of pore water between geotechnical particles, and the numerical integration method is used to solve the attenuation rate of water pressure kinetic energy.For soil layers with different particle gradations, the energy transfer efficiency between particles is calculated using the particle contact model, and the changing trend of the damping ratio is analyzed using the non-linear fitting method. During data processing, the water dynamic loss rate is calculated based on the particle diameter distribution of geotechnical particles, and cross-validation is performed in combination with the foundation stress data. Finally, all the calculation results are stored in the particle mixed water pressure kinetic energy damping ratio database and used for stress transfer loss estimation.

[0103] During the stress transfer loss estimation process, it is necessary to calculate the effective stress transfer of the extrusion lateral stress deflection data based on the particle mixed water pressure kinetic energy damping ratio. First, extract the damping ratio data within the construction area and calculate the attenuation trend of the lateral stress in combination with the foundation stress. The stress transfer efficiency between geotechnical particles is calculated using the contact force chain analysis method, and the stress loss rate at different depths is solved in combination with the numerical integration method. For soil layers with high water content, the influence of pore water pressure is calculated using the modified Terzaghi effective stress formula, and the deflection path of the extrusion lateral stress is evaluated in combination with the dynamic stress analysis method. During data storage, a spatial database is constructed according to the stress loss distribution at different depths, and a stress loss contour map is generated using data visualization technology. Finally, all the calculation data are stored in the effective stress transfer database and used for subsequent geotechnical stress monitoring and analysis.

[0104] Step S25 includes the following steps:

[0105] Step S251: Classify the geotechnical types of the vertical geological geotechnical structure data to obtain the vertical geological geotechnical classification types;

[0106] Step S252: Identify the geological fold / fault profile morphology between adjacent geotechnicals based on the vertical geological geotechnical classification types to obtain the geological fold / fault profile morphology data;

[0107] Step S253: Evaluate the stress progressive superposition frequency of the effective stress transfer data to obtain the stress progressive superposition data; calculate the fold deformation inclination angle of the geological fold / fault profile morphology data to obtain the fold deformation inclination angle;

[0108] Step S254: Calculate the average difference in fold surface slopes at different depths for the geological fold / fault profile morphology data based on the fold deformation inclination angle to obtain the average difference in fold surface slopes;

[0109] Step S255: Simulate the instability of the stress impact structure for the fold deformation inclination angle and the average difference in fold surface slopes based on the stress progressive superposition data to obtain the stress impact fold structure instability data;

[0110] Step S256: Estimate the fault dislocation risk for the geological fold / fault profile data based on the stress progressive superposition data to obtain the stress superposition fault dislocation risk data;

[0111] Step S257: Conduct stress impact structure instability analysis between adjacent geotechnicals based on the stress impact fold structure instability data and the stress superposition fault dislocation risk data to obtain the stress impact structure instability data between adjacent geotechnicals.

[0112] In the embodiments of the present invention, the description of the core is obtained from the vertical geological and geotechnical structure data, including features such as color, grain size, mineral composition, degree of cementation, and porosity. Also, in the process of classifying the geotechnical types of the vertical geological and geotechnical structure data, it is necessary to comprehensively process the borehole exploration data, core analysis results, and geophysical logging data. First, multiple drilling points are selected within the construction area, and the core is described layer by layer, including features such as color, grain size, mineral composition, degree of cementation, and porosity. The mineral composition of the rock sample is quantitatively determined by X-ray diffraction analysis, and mechanical parameters such as compressive strength, elastic modulus, and Poisson's ratio are determined in combination with rock mechanics tests. The resistivity of rock and soil layers at different depths is measured by the resistivity tomography imaging method, and the density and structural characteristics of rock and soil are calculated by combining seismic wave velocity inversion. Based on the geotechnical mechanics classification standard, the geotechnical structure is divided into different types such as sandy soil, silty clay, and bedrock, and the classification results are cross-validated to finally form a vertical geological and geotechnical classification type database. In the process of identifying the morphological patterns of geological folds / fault profiles in the vertical geological and geotechnical structure data, it is necessary to analyze the geometric shapes and deformation characteristics of adjacent rock layers based on the geotechnical type data. First, the continuity of underground rock layers is extracted from the high-precision seismic profile data, and the characteristics of geological interfaces are identified by a multi-scale edge detection algorithm. For the bending shape of the rock layer, the curvature analysis method is used to calculate the position of the fold axis, and the position of the fault plane is determined in combination with the fault dip analysis. For the fault-developed area, the fault dislocation amplitude is calculated based on the dislocation gradient, and the fault type is judged by displacement vector analysis. The fault trace is identified by the terrain linear feature extraction method, and the fault activity is evaluated in combination with historical earthquake data. Finally, all the calculation results are stored in the geological fold / fault profile morphology database for subsequent stress analysis. In the process of evaluating the stress progressive superposition frequency, it is necessary to calculate the stress accumulation trend of rock and soil masses at different depths using the effective stress transfer data. First, the in-situ stress data of the research area are extracted, and the stress gradient change is calculated by the difference method. Based on the dynamic change data of the groundwater level, the influence of pore water pressure on stress accumulation is calculated, and periodic stress analysis is carried out in combination with historical monitoring data. The Fourier transform method is used to decompose the stress change signal in the frequency domain, and the stress change frequency over time is calculated by the short-time Fourier transform. For different geological units, the wavelet analysis method is used to extract local stress peaks and calculate the progressive superposition trend of the stress change period. All the calculation results are stored in the stress progressive superposition database and used for stress impact structure instability simulation. In the process of calculating the tilt angle of fold deformation, it is necessary to use the geological fold / fault profile morphology data to calculate the tilt angle of the fold axis. First, the fold axis trace is extracted from the geological profile data, and the fold curvature change rate is calculated by the least square fitting method. The tilt rate of the fold axis at different depths is calculated by the numerical differentiation method, and the fold tilt angle is determined in combination with the deformation characteristics of the rock layer.For non-linear deformed folds, the Bézier curve fitting method is used to calculate the local tilt change, and the depth distribution of the tilt angle is analyzed by combining the analysis of the rock layer thickness data. All calculation results are stored in the fold deformation tilt angle database and used for the subsequent calculation of the average slope difference of the fold surface. During the calculation of the average slope difference of the fold surface, it is necessary to calculate the change trend of the fold surface slope between different depths based on the fold deformation tilt angle data. First, the fold profile data is extracted, and the finite difference method is used to calculate the slope change rate between adjacent depths. For complex fold structures, the bicubic interpolation method is used to calculate the local slope difference in the non-uniform deformation area. Combining the fold axis trace data, the mean square error of the fold surface slope is calculated, and the spatial statistical analysis method is used to evaluate the stability of the slope change. All calculation results are stored in the average slope difference database of the fold surface and used for the instability simulation of the stress impact structure. During the instability simulation of the stress impact structure, it is necessary to analyze the structural stability of the rock and soil mass under external forces based on the stress progressive superposition data, the fold deformation tilt angle, and the average slope difference of the fold surface. First, the stress data at different depths are extracted, and the stress wave equation is used to calculate the stress impact effect. Combining the fold deformation tilt angle data, the deformation mode of the fold structure under the impact load is analyzed. The discrete element method is used to simulate the stress transfer between rock and soil particles, and the friction slip model is combined to calculate the instability critical point. For high-stress areas, the local stress release method is used to simulate the instability failure mode of the rock layer, and the fracture mechanics method is combined to calculate the crack propagation rate. All calculation results are stored in the stress impact fold structure instability database and used for the fault dislocation risk estimation. During the fault dislocation risk estimation process, it is necessary to evaluate the fault slip risk for the geological fold / fault profile data based on the stress progressive superposition data. First, the fault profile data is extracted, and the Coulomb friction criterion is used to calculate the critical stress of fault slip. Combining the seismic wave propagation data, the dislocation trend of the fault under different loads is analyzed. The three-dimensional finite element method is used to simulate the fault dislocation displacement field, and the stress concentration coefficient is combined to calculate the fault slip risk level. For active faults, the dynamic friction model is used to analyze the energy dissipation during the sliding process, and the historical earthquake data is combined for the fault stability assessment. All calculation results are stored in the stress superposition fault dislocation risk database and used for the instability analysis of the stress impact structure between adjacent rock and soil. During the instability analysis of the stress impact structure between adjacent rock and soil, it is necessary to calculate the overall stability of the adjacent rock and soil mass by combining the stress impact fold structure instability data and the stress superposition fault dislocation risk data. First, the stress impact data of different geological units are extracted, and the numerical integration method is used to calculate the stress transfer effect. Combining the fault dislocation risk data, the influence of the fault on the adjacent rock and soil structure is analyzed, and the contact mechanics method is used to simulate the disturbance effect of the fault slip on the stress distribution. The energy balance analysis method is used to calculate the possibility of instability of the rock and soil mass under the stress impact, and the dynamics analysis method is combined to evaluate the rock layer slip risk.Finally, all calculation results are stored in the instability database of the stress impact structure between adjacent geotechnical materials and used for geotechnical engineering safety monitoring and predictive analysis.

[0113] Step S256 includes the following steps:

[0114] Perform vector decomposition of the stress acting direction on the stress progressive superposition data to obtain stress data of each component;

[0115] Calculate the normal and tangential stress components of the fault profile based on the stress data of each component for the geological fold / fault profile data to obtain the stress component data of the fault plane;

[0116] Analyze the distribution density of fault joints for the geological fold / fault profile data to obtain the distribution density data of fault joints;

[0117] Simulate the critical bearing stress of the joint surface based on the stress component data of the fault plane for the distribution density data of fault joints to obtain the critical bearing stress of the joint surface;

[0118] Estimate the fault dislocation risk based on the critical bearing stress of the joint surface to obtain the stress superposition fault dislocation risk data.

[0119] In the embodiments of the present invention, the stress progressive superposition data is decomposed into stress acting direction vectors to obtain component stress data in each direction. During the analysis of geotechnical stress monitoring data, first, the stored stress progressive superposition data is extracted, and using the tensor decomposition method, the three-dimensional stress tensor is disassembled into principal stress components. Based on the Mohr stress circle theory, a principal stress axis rotation transformation is performed on the stress tensor to calculate the maximum, minimum, and intermediate principal stress components, and further, the principal stresses are decomposed into the local coordinate system using the coordinate transformation matrix to obtain the component stress data in a specific direction. The finite difference method is used to calculate the spatial gradient of the stress field to determine the directionality of stress changes at different positions, and the continuity of the stress field is analyzed in combination with the Gaussian curvature. Grid cells are divided within the stress field area, and for each cell, the generalized stress formula is used to calculate the normal stress and shear stress acting in different directions, forming a component stress data set in each direction. Combining the distribution characteristics of the stress field, an interpolation algorithm is used to smooth the data to reduce the influence brought by measurement errors and ensure the stability and accuracy of the component stress data in each direction. According to the component stress data in each direction, the normal and tangential stress components of the fault profile are calculated for the geological fold / fault profile morphology data to obtain the fault plane stress component data. Based on the fault profile morphology data, the normal vector of the fault plane is extracted, and combined with the component stress data in each direction, the normal stress and tangential stress on the fault plane are calculated. First, the spatial geometric projection method is used to calculate the normal direction of the fault plane in the three-dimensional coordinate system, and a local coordinate system is established to facilitate the projection calculation of stress components. Based on the Cauchy stress formula, the stress components acting on the fault plane are calculated, where the normal stress is obtained by the dot product of the stress tensor and the fault normal vector, and the tangential stress is calculated by the dot product of the stress tensor and the fault tangential vector. The local differential method is used to analyze the spatial variation of the fault plane stress, judge the stress concentration area, and analyze the distribution characteristics of the stress components at different scales in combination with the Fourier transform. For different fault types, according to the shear failure criterion, the ratio of shear stress to normal stress is calculated to determine the failure mode of the fault plane. All calculation results are stored in the fault plane stress component data set and used for subsequent fault stability analysis. The fault joint distribution density analysis is performed on the geological fold / fault profile morphology data to obtain the fault joint distribution density data. Based on the fault profile morphology data, the spatial distribution information of the joints near the fault is extracted, the edge detection of the core scan image is performed using the image processing algorithm, the joint structure is identified, and the joint spacing and density are calculated. Based on the Hough transform method, the spatial orientation of the joints is identified, and the distribution characteristics of the joints are determined in combination with the fault profile data. The Kriging interpolation method is used to perform spatial statistical analysis on the joint distribution, calculate the trend of joint density changes in different regions, and analyze the tendency of joints in combination with the fault plane dip angle data. For the high-density joint area, the Voronoi diagram segmentation method is used to divide the joint cells, and the joint density distribution of each cell is calculated.Combined with the fault slip characteristics, the fractal dimension analysis method is used to evaluate the complexity of joints and calculate their influence on fault stability. All calculation results are stored in the fault joint distribution density dataset and used for subsequent fault slip risk assessment. According to the fault plane stress component data, the critical bearing stress of joints is simulated for the fault joint distribution density data to obtain the critical bearing stress of joints. Based on the joint distribution density data and the fault plane stress component data, a stress balance equation on the joint surface is established and the bearing stress in different joint directions is calculated. The numerical iteration method is used to calculate the shear strength of the joint surface according to the Mohr-Coulomb criterion and the bearing capacity of the joint surface is calculated in combination with the normal stress of the joint surface. For joints in different directions, the rigid-plastic finite element method is used to simulate the stress concentration phenomenon on the joint surface and the critical shear stress is calculated. For the area with a small joint spacing, the particle flow method is used to simulate the stress transfer process on the joint surface under the action of external forces and the failure critical value in the stress concentration area is calculated. Combined with the spatial distribution characteristics of fault joints, the critical bearing stress of joints at different positions is calculated and the calculation results are fitted by the statistical regression method to obtain the overall distribution trend of the bearing capacity of the joint surface. All calculation results are stored in the critical bearing stress dataset of joints and used for fault dislocation risk assessment. The fault dislocation risk is estimated according to the critical bearing stress of joints to obtain the stress superposition fault dislocation risk data. Based on the critical bearing stress data of joints, the comprehensive failure probability on the fault plane is calculated and the fault slip possibility is analyzed by using the fault dislocation statistical model. The seismic dynamics analysis method is used to calculate the displacement change of the fault under different loads and the slip trend is analyzed in combination with the fault sliding friction model. The finite element method is used to simulate the dislocation characteristics of the fault under different stresses and the dislocation rate and the fault activity intensity are calculated. For the high dislocation risk area, the regression analysis is carried out in combination with the historical earthquake data to predict the future activity trend of the fault. For the complex fault system, the Markov chain Monte Carlo method is used to simulate the fault slip process and the probability distribution of the fault slip risk is calculated. All calculation results are stored in the stress superposition fault dislocation risk dataset and used for the stability assessment and early warning analysis of geotechnical engineering.

[0120] Step S3 includes the following steps:

[0121] Step S31: Normalize the stress impact structure instability data to obtain the normalized stress impact structure instability data;

[0122] Step S32: Based on the random forest algorithm, construct a geotechnical stress risk monitoring model for the normalized stress impact structure instability data to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0123] As an example of the present invention, refer to Figure 3As shown, in this example, step S3 includes:

[0124] Step S31: Normalize the instability data of the stress impact structure to obtain the normalized instability data of the stress impact structure;

[0125] In the embodiment of the present invention, the instability data of the stress impact structure is normalized to obtain the normalized instability data of the stress impact structure. First, extract the existing instability dataset of the stress impact structure, including multi-dimensional stress monitoring data and relevant information of instability events. These data usually contain stress values at multiple different time nodes and instability marks of the structure. For these data, first determine the range of the maximum and minimum values to avoid the excessive influence of extreme values in the data on subsequent processing. The data is standardized using the linear normalization method. The specific method is to perform the conversion through the following formula: Normalized data = (Original data - Minimum value) / (Maximum value - Minimum value). This method ensures that all data points are mapped to the interval from 0 to 1, making the data in different dimensions comparable and eliminating the influence of dimensions. For missing data or outliers, the interpolation method is used for filling to ensure the integrity of the data. After normalization, further data smoothing processing is performed. The sliding window method is used to smooth the data, making the data have better continuity and trend in the time series. The processed normalized dataset can effectively eliminate the deviation caused by the range difference of the original data and provide more consistent input data for subsequent model training and analysis.

[0126] Step S32: Based on the random forest algorithm, construct a geotechnical stress risk monitoring model for the normalized instability data of the stress impact structure to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0127] In the embodiments of the present invention, a geotechnical stress risk monitoring model is constructed for the normalized data of stress impact structure instability based on the random forest algorithm, and the geotechnical stress risk monitoring model is obtained and sent to the terminal to perform geotechnical stress monitoring data analysis. First, relevant features are extracted from the normalized stress impact structure instability data, including multi-dimensional data such as stress change rate, stress values before and after the instability time point, stress oscillation amplitude, etc., and a feature matrix is constructed. The feature matrix is combined with the corresponding instability labels (such as instability occurred or not occurred) to construct a training dataset for supervised learning. The random forest algorithm is used to train this training dataset. The random forest algorithm improves the accuracy and robustness of the model by constructing multiple decision trees and performing integrated decision-making. During the training process, the random forest algorithm will perform feature selection according to the different importance of features through indicators such as the Gini index or information gain, and automatically screen out the most predictive features. The hyperparameter adjustment during the training process, such as the number of trees, maximum depth, minimum sample split number, etc., is optimized through the cross-validation method to ensure the generalization ability and prediction accuracy of the model. After the training is completed, the constructed random forest model is evaluated, and the performance of the model is tested using the test set to ensure that the model performs stably and reliably on unknown data. Finally, the optimized geotechnical stress risk monitoring model is sent to the terminal device through wireless network or other communication means, enabling it to receive real-time stress monitoring data from the site and perform risk assessment on the real-time data through the model, and then provide prediction and early warning information on stress changes. The deployment and operation of this model can be automatically executed on the terminal device without manual intervention, greatly improving the efficiency and accuracy of geotechnical stress monitoring.

[0128] Step S32 includes the following steps:

[0129] Step S321: Divide the normalized data of stress impact structure instability into a training set and a test set to obtain the stress impact structure instability training set and the stress impact structure instability test set respectively;

[0130] Step S322: Perform feature decomposition processing on the stress impact structure instability training set to obtain the stress feature subspace training sequence;

[0131] Step S323: Perform sequence random sampling processing on the stress feature subspace training sequence to obtain the stress impact random sampling sequence;

[0132] Step S325: Based on the random forest algorithm, construct an initial geotechnical stress risk monitoring model for the stress impact random sampling sequence to obtain the initial geotechnical stress risk monitoring model;

[0133] Step S326: Use the stress impact structure instability test set to conduct model testing on the initial geotechnical stress risk monitoring model to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0134] In the embodiment of the present invention, the stress impact structural instability normalized data is divided into a training set and a test set to obtain a stress impact structural instability training set and a stress impact structural instability test set. First, a certain proportion of data is randomly extracted from the stress impact structural instability normalized data set as a training set and a test set. In order to ensure the representativeness of the training set and the test set, the stress data will be randomly divided according to the time sequence to avoid the deviation caused by the time correlation of the data set. In the division process, a ratio of 7:3 is adopted, that is, 70% of the data is used as the training set and 30% of the data is used as the test set. This ratio can ensure that the training set has enough data for model learning, and the test set can effectively evaluate the performance of the model. When dividing, the stress impact data is first deduplicated and cleaned, and outliers and duplicate data are eliminated to ensure the quality of the data. After division, the statistical characteristics of the training set and the test set are checked to ensure that the two have similar distribution characteristics in terms of stress fluctuations, instability states, etc., to ensure the reliability of subsequent analysis. The stress impact structural instability training set is subjected to feature decomposition processing to obtain a stress feature subspace training sequence. After obtaining the training set data, the data is first subjected to principal component analysis (PCA) for feature decomposition. PCA maps the original high-dimensional data to a low-dimensional space by finding the direction with the largest variance in the data to reduce redundant information and retain the most representative features in the data. First, the covariance matrix of the training set is calculated, and then the variance contribution of each feature is obtained by eigenvalue decomposition. Then, the features with larger variance contribution are selected as new feature subspaces to form a new feature matrix. Through this process, the dimension of the training set can be significantly reduced while maintaining the core information of the data, ensuring the computational efficiency and accuracy of subsequent model training. The obtained stress feature subspace training sequence contains important stress features in the training set, such as stress change rate, stress peak value, and stress value at the moment of instability. The stress feature subspace training sequence is subjected to sequence random sampling to obtain a stress shock random sampling sequence. The decomposed feature subspace training sequence is randomly sampled to simulate different stress shock scenarios. In the sampling process, the bootstrap method is used to extract each training sample with replacement. Through the bootstrap method, multiple different training data sets can be generated. These data sets have high diversity in sample distribution, which helps to improve the robustness of the model. Specifically, a certain number of data points are randomly selected from the training sequence each time to generate a new sub-sample set until the number of samples is the same as the original training set. After each sampling, the calculated stress shock random sampling sequence will be used as a new training input for further learning of the model. In this way, the diversity of training data is enhanced, thereby reducing the risk of model overfitting and improving generalization ability. Based on the random forest algorithm, the initial geotechnical stress risk monitoring model is constructed for the stress shock random sampling sequence to obtain the initial geotechnical stress risk monitoring model.After obtaining the stress impact random sampling sequence, the random forest algorithm is used to model this data. Random forest is an ensemble learning method that predicts by training multiple decision trees and voting by the majority. During the modeling process, multiple decision trees are first generated based on the sampling data set. Each tree uses a different subset of the data set during the training process, and each tree adopts the strategy of randomly selecting features when dividing nodes, further enhancing the diversity of the model. Each decision tree continuously splits according to the node selection criteria (such as information gain, Gini index) until the maximum tree depth or other termination conditions are reached. During the training process, the optimal number of trees is selected through cross-validation, and the training effect of each tree is evaluated. Finally, the output results of all decision trees are voted, and the classification results of each tree obtained are integrated to output the final stress risk prediction result. This initially constructed model is used for subsequent geotechnical stress risk monitoring, providing a basis for the final risk warning. The initial geotechnical stress risk monitoring model is tested through the stress impact structure instability test set to obtain the geotechnical stress risk monitoring model, and the geotechnical stress risk monitoring model is sent to the terminal to perform geotechnical stress monitoring data analysis. After the initial model construction is completed, the model is verified using the test set. The data in the test set is independent of the data in the training set to ensure the generalization ability of the model. By inputting the data in the test set into the constructed random forest model, the prediction results of the model are calculated and compared with the actual instability events to evaluate the accuracy of the model. The evaluation indicators include classification accuracy, precision, recall, F1-score, etc. Through the model testing process, the deficiencies of the model can be identified and corresponding optimization adjustments can be made. Finally, the optimized geotechnical stress risk monitoring model will be sent to the on-site monitoring terminal through communication protocols (such as wireless transmission, Bluetooth, etc.). The terminal receives this model and analyzes it based on the real-time collected stress data, providing real-time stress change monitoring and risk warning information for engineering personnel.

[0135] The present invention also provides a geotechnical stress monitoring data analysis system for performing the geotechnical stress monitoring data analysis method as described above. The geotechnical stress monitoring data analysis system includes:

[0136] The immersed tube distribution difference induction module is used to obtain the geological data of the bridge construction area on the river and the river bridge construction design data; induce the difference in the distribution of pile driving and immersed tubes for the river bridge construction design data to obtain the difference data in the distribution of pile driving and immersed tubes;

[0137] The stress impact structure instability analysis module is used to perform pile driving extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data to obtain the extrusion lateral stress deflection data; estimate the stress transfer loss of the extrusion lateral stress deflection data to obtain the effective stress transfer data; perform stress impact structure instability analysis between adjacent geotechnicals based on the effective stress transfer data to obtain the stress impact structure instability data between adjacent geotechnicals.

[0138] The geotechnical stress risk monitoring model construction module is used to construct a geotechnical stress risk monitoring model based on the stress impact structure instability data by using the random forest algorithm to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

[0139] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for analyzing geotechnical stress monitoring data, characterized in that, It includes the following steps: Step S1: Obtain the geological data of the bridge construction area on the river channel and the construction design data of the river channel bridge; summarize the pile driving and pipe sinking distribution differences in the construction design data of the river channel bridge to obtain pile driving and pipe sinking distribution difference data; Step S2: Conduct pile driving extrusion lateral stress deflection simulation based on the pile driving and pipe sinking distribution difference data to obtain extrusion lateral stress deflection data; Estimate the stress transfer loss of the extrusion lateral stress deflection data to obtain effective stress transfer data; Conduct stress impact structure instability analysis between adjacent geotechnicals based on the effective stress transfer data to obtain stress impact structure instability data between adjacent geotechnicals. Among them, Step S2 includes: Step S21: Conduct vertical geological geotechnical structure analysis of different pile driving and pipe sinking areas on the geological data of the bridge construction area based on the pile driving and pipe sinking distribution difference data to obtain vertical geological geotechnical structure data of different pile driving and pipe sinking areas; Step S22: Conduct pile driving extrusion lateral stress deflection simulation on the vertical geological geotechnical structure data according to the pile driving and pipe sinking distribution difference data to obtain extrusion lateral stress deflection data. Among them, Step S22 includes: Step S221: Conduct applied force / frequency analysis on the pile driving and pipe sinking distribution difference data to obtain pile driving applied force / frequency data; Step S222: Conduct vertical layer density analysis on the vertical geological geotechnical structure data to obtain the vertical layer density of the geotechnical; Step S223: Identify the rotation angle of the lateral contact section with applied force / frequency according to the pile driving applied force / frequency data on the vertical layer density of the geotechnical to obtain the rotation angle of the contact section; Step S224: Conduct evaluation of the strength fluctuation of the internal friction force between different layers of the geotechnical based on the pile driving applied force / frequency data and the rotation angle of the contact section on the vertical layer density of the geotechnical to obtain the strength fluctuation data of the internal friction force of the geotechnical; Step S225: Conduct integral of the strength boundary fluctuation on the strength fluctuation data of the internal friction force of the geotechnical to obtain the integral value of the internal friction force boundary fluctuation; Step S226: Solve the internal friction force interval with boundary constraints for the strength fluctuation data of the internal friction force of the geotechnical based on the variational method and the integral value of the internal friction force boundary fluctuation to obtain the internal friction force interval with boundary constraints; Step S227: Conduct pile driving extrusion lateral stress deflection simulation according to the internal friction force interval with boundary constraints to obtain extrusion lateral stress deflection data; Step S23: Identify the radial stress influence orientation of the extrusion lateral stress deflection data to obtain the radial stress influence orientation; Step S24: Estimate the stress transfer loss based on the radial stress influence orientation to obtain effective stress transfer data; Step S25: Conduct stress impact structure instability analysis between adjacent geotechnicals on the vertical geological geotechnical structure data based on the effective stress transfer data to obtain stress impact structure instability data between adjacent geotechnicals; Step S3: Construct a geotechnical stress risk monitoring model based on the stress impact structure instability data by using the random forest algorithm to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

2. The method for analyzing geotechnical stress monitoring data according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the geological data of the bridge construction area on the river channel and the construction design data of the river channel bridge; Step S12: Mark the construction time series of the construction design data of the river channel bridge to obtain the construction design time mark data; Step S13: Analyze the pile driving sequence selection for the construction design time mark data to obtain the construction pile driving sequence selection data; Step S14: Summarize the pile driving and pipe sinking distribution differences for the construction design time mark data according to the construction pile driving sequence selection data to obtain the pile driving and pipe sinking distribution difference data.

3. The method for analyzing geotechnical stress monitoring data according to claim 1, characterized in that, Step S25 includes the following steps: Step S251: Classify the geotechnical types of the vertical geological geotechnical structure data to obtain the vertical geological geotechnical classification types; Step S252: Identify the geological fold / fault profile morphology between adjacent geotechnicals based on the vertical geological geotechnical classification types to obtain the geological fold / fault profile morphology data; Step S253: Evaluate the stress progressive superposition frequency of the effective stress transfer data to obtain the stress progressive superposition data; calculate the fold deformation inclination angle for the geological fold / fault profile morphology data to obtain the fold deformation inclination angle; Step S254: Calculate the average difference in fold surface slopes between different depths for the geological fold / fault profile morphology data according to the fold deformation inclination angle to obtain the average difference in fold surface slopes; Step S255: Simulate the instability of the stress impact structure for the fold deformation inclination angle and the average difference in fold surface slopes based on the stress progressive superposition data to obtain the stress impact fold structure instability data; Step S256: Estimate the fault dislocation risk for the geological fold / fault profile morphology data according to the stress progressive superposition data to obtain the stress superposition fault dislocation risk data; Step S257: Analyze the instability of the stress impact structure between adjacent geotechnicals based on the stress impact fold structure instability data and the stress superposition fault dislocation risk data to obtain the stress impact structure instability data between adjacent geotechnicals.

4. The method for analyzing geotechnical stress monitoring data according to claim 1, wherein Step S3 includes the following steps: Step S31: Normalize the stress impact structure instability data to obtain the normalized stress impact structure instability data; Step S32: Construct a geotechnical stress risk monitoring model based on the random forest algorithm for the normalized stress impact structure instability data to obtain the geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

5. The method for analyzing geotechnical stress monitoring data according to claim 4, wherein Step S32 includes the following steps: Step S321: Divide the normalized stress impact structure instability data into a training set and a test set to obtain the stress impact structure instability training set and the stress impact structure instability test set respectively; Step S322: Perform feature decomposition processing on the stress impact structure instability training set to obtain the stress feature subspace training sequence; Step S323: Perform sequence random sampling processing on the stress feature subspace training sequence to obtain the stress impact random sampling sequence; Step S325: Construct an initial geotechnical stress risk monitoring model based on the random forest algorithm for the stress impact random sampling sequence to obtain the initial geotechnical stress risk monitoring model; Step S326: Test the initial geotechnical stress risk monitoring model with a stress impact structure instability test set to obtain a geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

6. A geotechnical stress monitoring data analysis system, characterized in that, For performing the geotechnical stress monitoring data analysis method described in claim 1, the geotechnical stress monitoring data analysis system includes: A caisson distribution difference induction module, configured to obtain geological data of the bridge construction area on the river channel and river channel bridge construction design data; induce the pile driving caisson distribution differences in the river channel bridge construction design data to obtain pile driving caisson distribution difference data; A stress impact structure instability analysis module, configured to simulate the lateral stress deflection of pile driving extrusion according to the pile driving caisson distribution difference data to obtain lateral stress deflection data of extrusion; estimate the stress transfer loss of the lateral stress deflection data of extrusion to obtain effective stress transfer data; perform stress impact structure instability analysis between adjacent geotechnicals based on the effective stress transfer data to obtain stress impact structure instability data between adjacent geotechnicals; A geotechnical stress risk monitoring model construction module, configured to construct a geotechnical stress risk monitoring model based on the stress impact structure instability data by using a random forest algorithm to obtain a geotechnical stress risk monitoring model, and send the geotechnical stress risk monitoring model to the terminal to perform geotechnical stress monitoring data analysis.

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