Application method for stress model analysis of steel pile under influence of soil and sea conditions

Abstract

The application relates to the technical field of steel pile stress model analysis, and discloses a steel pile stress model analysis application method under the influence of soil quality and sea conditions. The method constructs a relevant feature set and an index set by collecting multi-dimensional environmental parameters, generates an initial steel pile coupling model, forms a composite stress evaluation model through loading constraint conditions and multi-physical field coupling analysis, constructs a space-time evolution simulation data set based on the stress wave propagation simulation, trains a material failure judgment model to obtain a load optimization analysis model and outputs structure correction parameters, generates environmental disturbance simulation data by using the parameters, constructs a multi-level optimization model to iteratively correct the pile body structure, generates optimal load distribution parameters, and combines real-time monitoring to update the structure optimization strategy. The method can accurately analyze the steel pile stress, realizes structure optimization, and is suitable for steel pile foundation analysis in the fields of marine engineering and the like.

Description

Technical Field

[0001] This invention relates to the field of steel pile stress model analysis technology, specifically to the application method of steel pile stress model analysis under the influence of soil quality and sea conditions. Background Technology

[0002] In marine engineering, port construction, and other fields, steel piles serve as crucial foundation structures, widely used in projects such as offshore platforms, bridges, and wind power facilities. During their service life, steel piles are subjected to complex soil conditions and sea conditions, and their stress state directly affects the safety and stability of the entire structure. Accurate analysis of the stress model of steel piles under these complex environments is of paramount importance for the rational design of steel pile structures and ensuring project safety.

[0003] Currently, various methods exist for analyzing the stress model of steel piles. Some traditional methods assess the stress of steel piles by combining on-site monitoring with empirical formulas. However, this approach relies on limited monitoring data and specific engineering experience, making it difficult to comprehensively consider complex and variable soil and marine conditions. Dynamic loads such as waves and currents in the marine environment are random and complex, and soil parameters vary significantly across different regions, including soil strength, compressibility, and permeability. Traditional methods cannot accurately capture the comprehensive influence of these factors on the stress of steel piles, leading to significant errors in the analysis results.

[0004] Some numerical simulation-based methods, such as finite element analysis, can simulate the stress process of steel piles, but their handling of environmental parameters during model construction is relatively simplistic. They typically input only single soil parameters and fixed marine loads directly into the model, ignoring the temporal and spatial variations of environmental parameters and the coupling relationships between them. For example, the interaction between the soil and the steel pile is a dynamic process, influenced by various factors such as wave loads and soil deformation. Existing models cannot accurately reflect this dynamic coupling, leading to discrepancies between simulation results and actual conditions, making it difficult to meet practical engineering requirements.

[0005] Furthermore, existing methods have shortcomings in optimizing steel pile structures. Most methods adjust the steel pile structure from only a single perspective, such as simply increasing the strength of the pile material or changing the pile shape, without comprehensively considering the stress characteristics of the steel pile under different working conditions, the risk of material failure, and environmental disturbance factors. This results in the optimized steel pile structure potentially failing to effectively cope with complex soil and marine conditions in practical applications, posing structural safety hazards, while also wasting materials and increasing project costs.

[0006] As marine engineering construction expands into deep-sea and complex geological areas, higher demands are placed on the accuracy, reliability, and adaptability of steel pile stress model analysis. There is an urgent need for an effective method that can fully consider the influence of soil and sea conditions, comprehensively analyze the stress characteristics of steel piles, and achieve structural optimization to ensure the safe and efficient construction and operation of marine engineering projects. Summary of the Invention

[0007] The purpose of this invention is to provide a method for analyzing the stress model of steel piles under the influence of soil and sea conditions, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing and applying a stress model of steel piles under the influence of soil properties and sea conditions, the method comprising: Collect multi-dimensional environmental parameters and construct a set of geotechnical mechanical features and a set of marine dynamic indicators to generate an initial steel pile coupling model; Geological stratification constraints are applied to the initial steel pile coupling model to establish a dynamic boundary condition framework. Multiphysics field coupling analysis is performed in combination with soil interaction parameters to form a composite stress assessment model. Stress wave propagation simulation is performed based on the composite stress assessment model to generate pile deformation path, and a spatiotemporal evolution simulation dataset is constructed by combining environmental disturbance frequency parameters. A material failure discrimination model is established and trained using a spatiotemporal evolution simulation dataset to form a load optimization analysis model, which then outputs structural correction parameters. These parameters include at least the soil stiffness adjustment coefficient, wave impact threshold, and corrosion damage priority sequence. Based on structural correction parameters, composite stress assessment model and soil interaction parameters, environmental disturbance simulation data are generated. By combining environmental disturbance simulation data with material strength constraints, a multi-level optimization model is constructed and the pile structure is iteratively corrected to generate the optimal load distribution parameters. The current load distribution parameters are verified based on the real-time stress distribution network, the pile displacement consistency index is calculated, and the structural optimization strategy is updated by combining the optimal load distribution parameters with the historical deformation trajectory to achieve the dynamic coupling matching goal.

[0009] Preferably, the spatiotemporal evolution simulation dataset includes at least a set of stress concentration regions, material creep parameters, a set of coupled failure boundaries, and a dynamically adjusted priority sequence; The environmental disturbance simulation data includes at least soil shear displacement, pile foundation bearing capacity integrity rate, and displacement convergence evaluation index.

[0010] Preferably, the step of collecting multi-dimensional environmental parameters and constructing a set of geotechnical mechanical features and a set of marine dynamic indicators to generate an initial steel pile coupled model includes the following steps: Spatiotemporal alignment and noise filtering of multi-dimensional environmental parameters are performed to generate a standardized geomechanical feature set; Soil stratification feature extraction is performed on the geomechanical feature set, wherein the extraction includes one or more of shear wave velocity clustering, pore pressure mapping, rock stiffness classification and soil heterogeneity resolution; Based on the rock stratum stiffness classification results, an initial steel pile coupling model is constructed, wherein the model includes a set of pile nodes, a set of stress transfer edges, and material strength constraint rules; Soil contact strength analysis was performed on the initial steel pile coupling model to generate a dynamic coupling factor matrix, and marine working condition constraint labels were assigned to each pile node.

[0011] Preferably, the process of applying geological stratification constraints to the initial steel pile coupling model, establishing a dynamic boundary condition framework, and performing multiphysics coupling analysis in conjunction with soil interaction parameters to form a composite stress assessment model includes the following steps: Geological stratification constraints are applied to the initial steel pile coupled model to limit the soil deformation range, generating the first coupled analysis framework; Wave load spectrum parameters are applied to the first coupled analysis framework to establish a dynamic boundary condition framework; Multiphysics coupling analysis based on a dynamic boundary condition framework includes the following specific steps: A multi-scale coupled equation is constructed, which includes at least the soil contact stiffness equation, the wave impact force equation, and the pile displacement compatibility equation. The multi-scale coupled equation is solved iteratively by the finite element method to obtain the set of stress concentration regions, material creep parameters, and the set of coupled failure boundaries. The generation of the coupling failure boundary set includes the following steps: Based on the set of stress concentration regions, the stress gradient of each pile node is calculated; Identify regions in the dynamic boundary condition framework where the stress gradient is not greater than a preset threshold, and generate a set of coupled failure boundaries.

[0012] Preferably, the step of establishing a material failure discrimination model and training it with a spatiotemporal evolution simulation dataset to form a load optimization analysis model, and then outputting structural correction parameters, includes the following steps: A material failure discrimination model was established based on the initial steel pile coupling model; The material failure discrimination model is trained and validated using a spatiotemporal evolution simulation dataset to form a load optimization analysis model; By inputting real-time soil interaction parameters into the load optimization analysis model, the set of coupled failure boundaries can be predicted. Based on the predicted set of coupled failure boundaries, structural correction parameters are output, wherein the structural correction parameters include at least the soil stiffness adjustment coefficient, wave impact threshold, and corrosion damage priority sequence.

[0013] Preferably, the method further includes feature reconstruction of the spatiotemporal evolution simulation dataset, specifically: Based on the set of stress concentration regions, material creep parameters, and coupled failure boundary set, an initial multiphysics input tensor is constructed. The initial multiphysics input tensor is standardized and its features are reduced to generate the final multiphysics input tensor. A load-optimized label tensor is constructed based on a dynamically adjusted priority sequence. The training sample set is formed by combining the final multiphysics input tensor with the load-optimized label tensor.

[0014] Preferably, the step of outputting structural correction parameters based on the predicted coupling failure boundary set includes the following steps: Extract the pile node with the lowest concentrated stress gradient at the predicted coupled failure boundary and generate the soil stiffness adjustment coefficient. Based on the topological distribution characteristics of the predicted coupled failure boundary set, the probability density of the wave impact threshold is fitted to generate a corrosion damage priority sequence. The displacement convergence gradient direction of the predicted coupled failure boundary set is calculated and normalized to the load distribution reference vector, which is the wave impact threshold adjustment direction.

[0015] Preferably, the step of generating environmental disturbance simulation data based on structural correction parameters, composite stress assessment model, and soil interaction parameters includes the following steps: The soil stiffness adjustment coefficient is mapped to the composite stress assessment model, the wave impact threshold and corrosion damage priority sequence are matched, the pile node density in the failure area is adjusted, the composite stress assessment model is updated, the environmental disturbance triggering conditions, structural reconstruction rules and parameter correction increments are defined, and the environmental disturbance simulation model is generated. Based on the environmental disturbance simulation model, the multi-scale coupled equations are iteratively solved using the finite element method to generate environmental disturbance simulation data, specifically including: When the environmental disturbance triggering condition is met, update the soil shear displacement, pile foundation bearing capacity and displacement convergence evaluation index, and resolve the multi-scale coupled equation until the simulation termination condition is reached. The environmental disturbance triggering conditions include triggering parameter correction when the current pile foundation bearing capacity integrity rate is not greater than the preset bearing capacity threshold; the structural reconstruction rules include adjusting the wave impact threshold based on the displacement convergence gradient direction; the parameter correction increment has a piecewise exponential relationship with the current displacement convergence evaluation index.

[0016] Preferably, the step of constructing a multi-level optimization model and iteratively correcting the pile structure to generate optimal load distribution parameters includes the following steps: A multi-level optimization model is constructed, in which the optimization variables include pile node density, wave impact threshold and environmental disturbance frequency, the optimization objectives include maximizing bearing stability and minimizing stress concentration density, and the constraints include coupled failure boundary constraints and material creep threshold. The particle swarm optimization algorithm is used to initially solve the multi-level optimization model and generate an initial set of optimization schemes. Based on the initial set of optimization schemes, the ant colony algorithm is used for global optimization to generate the optimal load allocation parameters.

[0017] Preferably, the step of using the particle swarm optimization algorithm to initially solve the multi-level optimization model and generate an initial set of optimization schemes includes the following steps: A particle position coding structure is constructed based on pile node density and wave impact threshold to generate an initial population; The fitness function is calculated based on the load-bearing stability and stress concentration density. The initial population is then updated with velocity and iterated with position to generate a set of intermediate optimization schemes. Neighborhood search and elite retention filtering are performed on the intermediate optimization scheme set to generate an initial optimization scheme set.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a set of geotechnical mechanical characteristics and a set of marine dynamic indicators by collecting multi-dimensional environmental parameters, generating an initial coupled steel pile model, thus achieving comprehensive integration of complex environmental information. The multi-dimensional environmental parameters encompass data from multiple sources, including geological surveys and marine hydrological monitoring. Through spatiotemporal alignment and noise filtering, the accuracy and consistency of the data are ensured, enabling the initial coupled steel pile model to accurately reflect the actual environment in which the steel pile is located. Geological stratification constraints are applied to the model, and multiphysics coupling analysis is performed to construct a composite stress assessment model, accurately considering the interaction of various factors such as soil deformation and wave loads. By constructing multi-scale coupling equations and using the finite element method for iterative solution, key information such as the set of stress concentration regions, material creep parameters, and coupled failure boundary sets can be accurately calculated, providing a reliable basis for the stress analysis of the steel pile.

[0019] Stress wave propagation simulations were performed using a composite stress assessment model, and a spatiotemporal evolution simulation dataset was constructed by combining environmental disturbance frequency parameters. This dataset comprehensively describes the stress and deformation process of steel piles from both temporal and spatial dimensions. The dataset contains rich information such as stress concentration region sets and material creep parameters, providing ample data support for subsequent material failure identification and load optimization analysis. A material failure identification model was established and trained using the spatiotemporal evolution simulation dataset to form a load optimization analysis model. This model can accurately predict the coupled failure boundary set, and then output structural correction parameters such as soil stiffness adjustment coefficients, wave impact thresholds, and corrosion damage priority sequences. These parameters, based on a precise analysis of the actual stress and failure risk of steel piles, provide a quantitative basis for steel pile structural optimization.

[0020] Environmental disturbance simulation data was generated using structural correction parameters. A multi-level optimization model was constructed based on material strength constraints to iteratively correct the pile structure, ultimately generating the optimal load distribution parameters. This process comprehensively considered multiple factors and employed particle swarm optimization and ant colony optimization algorithms to optimize parameters such as pile node density and wave impact threshold, effectively improving the load-bearing stability of the steel pile and reducing stress concentration density. The load distribution parameters were validated using a real-time stress distribution network, and the structural optimization strategy was updated by incorporating historical deformation trajectories. This enabled dynamic coupling and matching between the steel pile structure and the complex environment, ensuring that the steel pile maintains good performance under different soil and sea conditions. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the working principle of the steel pile stress model analysis and application method under the influence of soil and sea conditions described in this invention. Figure 2 A flowchart for the formation of a composite stress assessment model; Figure 3 A flowchart for establishing a load optimization analysis model and outputting structural correction parameters; Figure 4 A flowchart for reconstructing features of a spatiotemporal evolution simulation dataset. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figures 1-4 The method for analyzing the stress model of steel piles under the influence of soil and sea conditions, as described in this invention, is specifically implemented as follows: Multi-dimensional environmental parameters were collected, and a set of geotechnical features and a set of marine dynamic indicators were constructed to generate an initial steel pile coupled model. First, the multi-dimensional environmental parameters were spatiotemporally aligned and noise filtered to remove interference information from the data, resulting in a standardized geomechanical feature set. Then, soil stratification features were extracted from the geomechanical feature set, including shear wave velocity clustering, pore pressure mapping, rock stratum stiffness classification, and soil heterogeneity resolution. Based on the rock stratum stiffness classification results, combined with the steel pile design parameters and rock stratification boundaries, nodes were arranged according to the structural characteristics of the steel pile to form a pile node set. Adjacent nodes were then connected, mechanical properties were assigned, and stress constraints were labeled to construct a stress transfer edge set. Based on the steel pile material properties and relevant specifications, upper limits and warning rules for node stress were set to form material strength constraint rules, completing the initial steel pile coupled model construction. Subsequently, the finite element method of contact mechanics was used, inputting rock stratum stiffness parameters to simulate the contact force between the pile and the soil, calculating the node contact strength, and then performing soil contact strength analysis on the initial model to generate a dynamic coupling factor matrix. Marine working condition constraint labels were assigned to each node. Marine working condition constraint labels are assigned to each pile node.

[0024] A geological stratification constraint condition is applied to the initial steel pile coupled model to establish a dynamic boundary condition framework. Multiphysics coupling analysis is then performed using soil interaction parameters to form a composite stress assessment model. In forming the composite stress assessment model, geological stratification constraints are first applied to generate the first coupled analysis framework, followed by the addition of wave load spectrum parameters to establish the dynamic boundary condition framework. Subsequently, multi-scale coupled equations are constructed using the soil interaction parameters and solved iteratively using the finite element method to obtain key data and the set of coupled failure boundaries, ultimately forming the model. This model requires no additional training; its rationality is verified by the solution results, and it can be directly used for subsequent stress wave propagation simulation, laying the foundation for generating simulation datasets of pile deformation paths and spatiotemporal evolution. Geological stratification constraints are applied to the initial steel pile coupled model to limit the soil deformation range, generating the first coupled analysis framework. Wave load spectrum parameters are then applied to the first coupled analysis framework to establish the dynamic boundary condition framework. Based on this framework, multiphysics coupling analysis is performed, constructing multi-scale coupling equations including soil contact stiffness equations, wave impact force equations, and pile displacement compatibility equations. The multi-scale coupling equations are iteratively solved using the finite element method to obtain a set of stress concentration regions, material creep parameters, and a set of coupled failure boundaries. When generating the set of coupled failure boundaries, the stress gradient of each pile node is calculated based on the set of stress concentration regions, identifying regions in the dynamic boundary condition framework where the stress gradient does not exceed a preset threshold, thus generating the set of coupled failure boundaries.

[0025] Stress wave propagation simulation is performed based on a composite stress assessment model to generate pile deformation paths. A spatiotemporal evolution simulation dataset is constructed by combining environmental disturbance frequency parameters. Based on the composite stress assessment model, corresponding simulation algorithms are used to perform stress wave propagation simulations. By recording and analyzing the stress changes and displacements at each node of the pile during the simulation, the pile deformation path is generated. Using the composite stress assessment model as a foundation, a set of stress concentration regions, material creep parameters, and a set of coupled failure boundaries are input. The simulation is performed according to the stress wave propagation law, capturing the stress changes and multi-directional displacements at each node of the pile at different times in real time. Data tracking and analysis present the complete deformation history of the pile, generating a pile deformation path that reflects its deformation characteristics, providing core support for constructing the spatiotemporal evolution simulation dataset. Simultaneously, environmental disturbance frequency parameters are taken into consideration, and information such as the set of stress concentration regions, material creep parameters, the set of coupled failure boundaries, and dynamically adjusted priority sequences are integrated to construct the spatiotemporal evolution simulation dataset.

[0026] A material failure discrimination model was established and trained using a spatiotemporal evolution simulation dataset to form a load optimization analysis model, which then outputs structural correction parameters. These parameters include at least soil stiffness adjustment coefficients, wave impact thresholds, and corrosion damage priority sequences. Based on an initial steel pile coupling model, and considering the mechanical properties of the steel pile material and the interaction characteristics with the soil, input variables and output targets were determined, and the basic structure of the material failure discrimination model was constructed. During training, a spatiotemporal evolution simulation dataset was used, and a training sample set was formed through feature reconstruction to adjust the model parameters. The accuracy of the model was verified through a validation subset, and once the target was met, a load optimization analysis model was formed. In application, real-time soil parameters were input, the coupling failure boundary set was predicted, and the structural correction parameters were output. Based on the initial steel pile coupling model, a material failure discrimination model was established. This model was trained and validated using a spatiotemporal evolution simulation dataset. By continuously adjusting the model parameters, the model was able to accurately identify material failure conditions, thus forming a load optimization analysis model. The real-time soil-mass interaction parameters are input into the load optimization analysis model to predict the coupled failure boundary set. Based on this, the pile node with the lowest stress gradient in the predicted coupled failure boundary set is extracted to generate the soil stiffness adjustment coefficient. According to the topological distribution characteristics of the predicted coupled failure boundary set, the probability density of the wave impact threshold is fitted to generate the corrosion damage priority sequence. The displacement convergence gradient direction of the predicted coupled failure boundary set is calculated, normalized to the load distribution reference vector, and the wave impact threshold adjustment direction is obtained. Finally, the structural correction parameters are output.

[0027] Based on structural correction parameters, a composite stress assessment model, and soil interaction parameters, environmental disturbance simulation data is generated. The soil stiffness adjustment coefficient is mapped to the composite stress assessment model, wave impact thresholds and corrosion damage priority sequences are matched, and pile node density in the failure area is adjusted to update the composite stress assessment model. Environmental disturbance triggering conditions, structural reconstruction rules, and parameter correction increments are defined to generate an environmental disturbance simulation model. Generating the environmental disturbance simulation model involves first mapping the soil stiffness adjustment coefficient from the structural correction parameters to the composite stress assessment model, matching the wave impact threshold and corrosion damage priority sequence, and adjusting the pile node density in the failure area to update the model. Then, environmental disturbance triggering conditions, structural reconstruction rules, and parameter correction increments are defined to comprehensively form the environmental disturbance simulation model. Based on the environmental disturbance simulation model, the multi-scale coupled equations are iteratively solved using the finite element method. When the environmental disturbance triggering condition is met (e.g., triggering parameter correction when the current pile foundation bearing capacity is not greater than a preset bearing capacity threshold), the soil shear displacement, pile foundation bearing capacity, and displacement convergence evaluation index are updated. Based on the environmental disturbance simulation model, relevant data are initially solved using the finite element method to determine if the triggering condition has been met; if so, parameters are adjusted and the solution is repeated. This "solve-determine-adjust" process is repeated to update the data until the simulation termination condition is met, generating environmental disturbance simulation data containing the specified indicators.

[0028] Combining environmental disturbance simulation data with material strength constraints, a multi-level optimization model was constructed and iteratively corrected for the pile structure to generate optimal load distribution parameters. In constructing the multi-level optimization model, pile node density, wave impact threshold, and environmental disturbance frequency were used as optimization variables, with the objectives of maximizing load-bearing stability and minimizing stress concentration density, and constraints including coupling failure boundary and material creep threshold. First, a particle swarm optimization algorithm was used to generate an initial population based on variable-based particle encoding, and the fitness function was calculated for iterative selection to obtain the initial optimization scheme. Then, an ant colony optimization algorithm was used for global optimization, iteratively correcting the pile structure to generate optimal load distribution parameters. The multi-level optimization model was constructed, where the optimization variables included pile node density, wave impact threshold, and environmental disturbance frequency; the optimization objectives were to maximize load-bearing stability and minimize stress concentration density; and the constraints included coupling failure boundary constraints and material creep threshold. The multi-level optimization model is initially solved using the particle swarm optimization (PSO) algorithm. A particle position encoding structure is constructed based on pile node density and wave impact threshold to generate an initial population. The fitness function is calculated based on bearing stability and stress concentration density, and the initial population is updated with velocity and iterated in position to generate an intermediate optimization scheme set. Neighborhood search and elite retention are then performed on the intermediate optimization scheme set to generate an initial optimization scheme set. Based on this initial optimization scheme set, an ant colony optimization (ACO) algorithm is used for global optimization to generate the optimal load allocation parameters.

[0029] The current load distribution parameters are verified using a real-time stress distribution network. The pile displacement consistency index is calculated, and the structural optimization strategy is updated by combining the optimal load distribution parameters with historical deformation trajectories to achieve dynamic coupling matching. Real-time stress data from various parts of the steel pile is collected using the real-time stress distribution network, and the current load distribution parameters are substituted into the model for verification. The rationality of the current load distribution is evaluated by calculating the pile displacement consistency index. When substituted into the verification, real-time stress data of the steel pile nodes is first collected using the real-time stress distribution network and substituted into the model to calculate the theoretical displacement. Simultaneously, actual node displacements are collected and compared to calculate the pile displacement consistency index (measuring the degree of agreement between theoretical and actual displacements). Evaluation is based on the index: a high index indicates reasonable allocation, while a low index requires adjustment based on the optimal parameters and historical trajectories to complete the rationality assessment. Combining the generated optimal load distribution parameters with historical deformation trajectories, the stress and deformation patterns of the steel pile under different working conditions are analyzed, and the structural optimization strategy is updated to ensure that the steel pile can always achieve dynamic coupling matching under different soil types and sea conditions, reaching the optimal stress state.

[0030] Example 1: The primary task in generating the initial steel pile coupled model is to handle multi-dimensional environmental parameters. These parameters encompass information from various monitoring devices and data sources, including but not limited to geological survey data and marine hydrological monitoring data. These data differ in time and space and may contain noise interference, thus requiring spatiotemporal alignment and noise filtering operations.

[0031] For the time dimension, since different monitoring devices may have different sampling frequencies and time bases, the Dynamic Time Warping (DTW) algorithm processes the data by identifying time characteristics and patterns in the data, unifying data from different sources onto the same time axis. The algorithm formula is as follows:

[0032] in: For different time series, The path matrix is ​​defined, with constraints of monotonicity and continuity to ensure data consistency over time. Spatial interpolation is performed using Gaussian process regression (GPR) to estimate data at other locations based on known monitoring point locations and data. The covariance function uses a squared exponential kernel.

[0033] in: , : Represent the spatial coordinates of two known monitoring points, Indicates the signal variance. This represents the decay rate of spatial correlation. This transforms discrete monitoring data into continuous spatially distributed data. Simultaneously, a soft thresholding filter algorithm based on a 5-level decomposition of the db4 wavelet basis is used to remove noise from the data. The threshold formula is: , in: The standard deviation of noise. Given the signal length, this algorithm can identify and remove outliers and interference signals in the data, ultimately obtaining a standardized geomechanical feature set.

[0034] After obtaining the standardized geomechanical feature set, soil stratification features need to be extracted. In the shear wave velocity clustering operation, a large amount of shear wave velocity data is collected. This data reflects the soil's ability to propagate shear waves and is closely related to the soil's properties. A clustering algorithm is used to analyze this data. Based on the principle of data similarity, this algorithm groups soils with similar wave velocity characteristics into the same layer. For example, soil samples with similar wave velocities are considered to have similar physical and mechanical properties and are grouped into one category, thus achieving soil stratification.

[0035] Pore ​​pressure mapping is the process of visually representing pore pressure data onto a soil structure model. Pore pressure is the pressure generated by pore water in soil and has a significant impact on the mechanical behavior of the soil. Through specific mapping techniques, pore pressure data is correlated with the spatial location of the soil, and the distribution of pore pressure is displayed visually in the soil structure model, facilitating subsequent stress analysis of the soil.

[0036] Rock strata stiffness classification is based on a pre-defined standard. This standard comprehensively considers various mechanical parameters of the rock strata, such as the elastic modulus and compressive strength, and classifies the rock strata into different levels according to their stiffness. For example, based on stiffness from highest to lowest, rock strata are divided into several levels, such as hard rock strata, relatively hard rock strata, and medium-stiff rock strata, clearly defining the mechanical properties of each rock strata.

[0037] Soil heterogeneity reduction aims to eliminate abnormal fluctuations in the spatial distribution of soil parameters. Due to the heterogeneity of soil in the natural environment, soil parameters may vary significantly in space, affecting the accuracy of the analysis results. Soil heterogeneity reduction algorithms smooth soil parameters, making their spatial distribution more reasonable and reducing errors caused by heterogeneity.

[0038] Based on the rock stratum stiffness classification results obtained from the soil stratification feature extraction, an initial coupled steel pile model was constructed. The pile node set in the model contains key node information of the steel piles at different locations; the positions and attributes of these nodes are determined based on the design structure and actual installation conditions of the steel piles. The stress transfer edge set defines the stress transfer relationships and paths between pile nodes, clarifying how stress is transmitted within the pile. Material strength constraint rules are formulated based on the material properties used in the steel piles, specifying the maximum stress and strain that the steel piles can withstand under different stress conditions, ensuring that the model meets actual engineering requirements.

[0039] After constructing the initial steel pile coupling model, soil contact strength analysis was performed. Finite element method (FEM) simulation was used to calculate the mechanical behavior of different soil-pile contact points. During the simulation, the mechanical parameters of the soil, the structural parameters of the pile, and their interactions were considered to calculate the strength of each contact point. These strength data were then compiled into a dynamic coupling factor matrix, which details the coupling strength relationships at each contact point, reflecting the strength of the interaction between the soil and the pile.

[0040] Simultaneously, based on different marine conditions, such as wave size, current speed, and tidal variations, corresponding marine condition constraint labels are assigned to each pile node. These labels clearly define the constraint conditions and loads experienced by each pile node under different marine conditions, enabling accurate simulation and calculation for different marine conditions during subsequent stress analysis of the steel piles. This provides fundamental data and conditions for further in-depth analysis and optimization, ensuring that the model accurately reflects the stress characteristics of the steel piles under actual marine environment and soil conditions.

[0041] Example 2: In the process of developing a composite stress assessment model, the first step is to apply geological stratification constraints to the initial steel pile coupled model. Geological survey reports are a crucial source of detailed information on soil layer distribution, containing key data such as the depth, thickness, and soil / rock type of each layer. Based on this information, boundary conditions for the deformation range of each soil layer are set. Specifically, based on the physical and mechanical properties of different soil layers, the maximum allowable deformation and direction under stress are determined. For example, softer soil layers have a relatively larger deformation range, while harder rock layers have a smaller deformation range. These boundary conditions are then applied to the initial steel pile coupled model to generate the first coupled analysis framework. This framework defines the deformation behavior of the soil within the model, making the model more consistent with actual geological conditions.

[0042] Next, wave load spectrum parameters are loaded onto the first coupled analysis framework. These parameters are obtained through statistical analysis and prediction of historical ocean wave data. During the collection of historical ocean wave data, equipment such as ocean monitoring stations and buoys are used to acquire information on wave height, period, and direction at different time periods and in different sea areas. By statistically analyzing this massive amount of data and employing professional data analysis methods, the distribution patterns and trends of wave parameters are identified. Based on this, combined with weather forecasts and ocean environmental models, future wave conditions are predicted, thus obtaining wave load spectrum parameters covering different frequencies, amplitudes, and periods. These parameters are then loaded into the first coupled analysis framework to establish a dynamic boundary condition framework, which simulates the external loads borne by the steel pile under ocean wave action.

[0043] Multiphysics coupling analysis based on a dynamic boundary condition framework requires the construction of multi-scale coupling equations. Among them, the soil contact stiffness equation is as follows: Considering the elastic modulus of soil Poisson's ratio and contact area The influence of contact stiffness is given by the soil contact stiffness equation:

[0044] in: The thickness of the contact layer between the pile and the soil is determined by the geological layer thickness and is used to describe the stiffness characteristics of the interface between the pile and the soil. By comprehensively considering these parameters, the stiffness characteristics of the contact area between the soil and the steel pile can be accurately calculated. The wave impact force equation calculates the impact force of waves on the steel pile based on wave theory and fluid mechanics principles. Based on linear wave theory and the fluid mechanics drag force formula, combined with JONSWAP wave spectrum parameters, the wave impact force formula is:

[0045] in: The density of seawater, The wave drag coefficient is... The diameter of the steel pile is... The instantaneous wave velocity, calculated from the wave spectrum, is used to calculate the impact force on the steel pile under different wave conditions. Wave theory provides a mathematical description of wave motion, while fluid mechanics principles are used to analyze the interaction between waves and the steel pile. Using these theories and principles, combined with wave load spectrum parameters, the magnitude and direction of the impact force on the steel pile under different wave conditions are calculated. The pile displacement compatibility equation ensures that the displacements of each part of the pile under stress conform to the mechanical equilibrium conditions; that is, when the pile is under stress, the displacements between its parts are coordinated, and unreasonable deformations do not occur. The pile displacement compatibility equation is as follows: in: This represents the total axial displacement of the steel pile. The number of segments for the steel pile. For the first Axial force of the pile segment For the first Segment length, The elastic modulus of the steel pile. For the first Cross-sectional area of ​​the pile segment.

[0046] The multi-scale coupled equations are solved iteratively using the finite element method (FEM). The FEM divides the steel pile and soil mass into numerous small elements, performs mechanical analysis on each element, and then integrates the element results to obtain the mechanical response of the entire system. In the iterative solution process, a set of initial parameters is first given, and the mechanical properties of each element, such as stress and strain, are calculated. The calculation results are then compared with actual conditions or preset conditions. If the results do not meet the requirements, the parameters are adjusted, and the calculation is repeated. This process is repeated until the calculation results converge, meaning that the calculation results no longer change significantly with parameter adjustments.

[0047] When generating the coupled failure boundary set, the operation is based on the stress concentration region set. This set of stress concentration regions is obtained during the solution of the multi-scale coupled equations and contains information on areas of relatively concentrated stress in the steel piles and soil. The stress gradient at each pile node is calculated using a numerical differential method. This method calculates the rate of stress change at the node by differentiating the stress values ​​of adjacent nodes. By setting an appropriate preset threshold, regions within the dynamic boundary condition framework whose stress gradient does not exceed this threshold are identified. These regions, due to their relatively gentle stress changes, are more prone to coupled failure, and are thus defined as the coupled failure boundary set. This boundary set provides crucial information for subsequent analysis of the steel pile's bearing capacity and prediction of structural failure risks, facilitating targeted measures in engineering design and maintenance to ensure the safety and stability of the steel pile structure.

[0048] Example 3: In establishing the load optimization analysis model and outputting structural correction parameters, a material failure discrimination model was first built based on the initial steel pile coupling model. The initial steel pile coupling model contained a large amount of data, including structural information, material properties, and interaction relationships between the steel pile and the soil. The material failure discrimination model was constructed using a machine learning algorithm, taking the steel pile's elastic modulus, yield strength, Poisson's ratio, and other material property parameters, as well as the axial force, bending moment, and shear force parameters borne by the steel pile under different working conditions, as inputs. By collecting a large number of existing steel pile material failure cases, key features and failure modes were extracted from the cases. This data was used as training samples, allowing the machine learning algorithm to learn the patterns and characteristics of material failure, thereby constructing a model capable of discriminating whether the steel pile material has failed.

[0049] After constructing the material failure discrimination model, it needs to be trained and validated using a spatiotemporal evolution simulation dataset. This dataset contains information on various aspects, including a set of stress concentration regions, material creep parameters, a set of coupled failure boundaries, and a dynamically adjusted priority sequence. During training, data from the spatiotemporal evolution simulation dataset are input into the material failure discrimination model batch by batch, with the mean squared error used as the loss function, as shown in the formula:

[0050] in: This represents the number of training samples; For the first Each sample is a true failure label, with 0 (not failed), 1 (critical failure), and 2 (failed). Labels are predicted for the model. The model calculates and predicts based on the input data, then compares the prediction results with known material failure scenarios in the dataset to calculate the prediction error. Based on the magnitude of the error, optimization methods such as backpropagation are used to adjust the parameters in the model, continuously improving the model's discriminative ability. After multiple rounds of training, the trained model is tested using a validation dataset to verify its accuracy and reliability on new data, ensuring that the model can stably and accurately identify material failure scenarios, thus forming a load optimization analysis model.

[0051] After forming the load optimization analysis model, real-time soil-soil interaction parameters are input into it. These parameters are acquired through field sensors and include information such as soil pressure, friction, and displacement. These parameters reflect the current interaction state between the soil and the steel pile, and are crucial for accurately predicting the stress state of the steel pile. Upon receiving the real-time soil-soil interaction parameters, the load optimization analysis model uses its internal algorithms and pre-trained model structure to analyze and predict the stress state of the steel pile, thereby obtaining the predicted results of the coupling failure boundary set.

[0052] Based on the predicted set of coupled failure boundaries, structural correction parameters are output. First, the pile node with the lowest stress gradient in the predicted coupled failure boundary set is extracted. The stress gradient reflects the degree of stress change at the pile node; by calculating the stress gradient of each pile node, regions with relatively gentle stress changes can be identified. In the predicted coupled failure boundary set, the pile node with the lowest stress gradient is often a relatively stable location but may also have potential risks. Based on the stress condition of this node, the physical and mechanical properties of the surrounding soil, and the structural parameters of the steel pile, a soil stiffness adjustment coefficient is calculated. This soil stiffness adjustment coefficient is used to correct the soil stiffness to more accurately reflect the actual supporting effect of the soil on the steel pile.

[0053] Next, based on the topological distribution characteristics of the predicted coupled failure boundary set, the probability density of the wave impact threshold is fitted. The topological distribution characteristics describe the spatial distribution shape, range, and connectivity between the components of the coupled failure boundary set. By analyzing these characteristics and using probabilistic statistical methods, such as maximum likelihood estimation, the probability density of the wave impact threshold is fitted. Assuming the wave impact threshold is... Its probability density function is By analyzing the predicted set of coupling failure boundaries, the probability density function is determined. The parameters in the data are used to obtain the probability distribution of wave impact thresholds, and then a corrosion damage priority sequence is generated. The corrosion damage priority sequence is used to determine the priority treatment order for different parts of the steel pile when facing corrosion risks, which helps to rationally arrange maintenance and repair work.

[0054] Finally, the displacement convergence gradient direction of the predicted coupled failure boundary set is calculated. The displacement convergence gradient direction represents the trend direction of the gradual stabilization of the steel pile displacement during the loading process. The displacement convergence gradient direction is determined by calculating the displacement change rate of each pile node in different directions. This direction is then normalized to obtain the load distribution reference vector. The formula for normalization is:

[0055] in, This represents the displacement convergence gradient direction vector, which is obtained by calculating the rate of change of displacement of each pile node in different directions. Representing vectors The modulus, i.e., the length of the vector, is determined by the vector. The normalized load distribution reference vector is obtained by taking the square root of the sum of the squares of each component. This refers to the direction of wave impact threshold adjustment, which guides subsequent adjustments to the wave impact threshold to optimize load distribution on the steel piles and improve the stability and safety of the steel pile structure. Through the above steps, the final output structural correction parameters include soil stiffness adjustment coefficients, wave impact thresholds, and corrosion damage priority sequences, providing an important basis for the optimization and maintenance of the steel pile structure.

[0056] Example 4: When generating environmental disturbance simulation data, we will take the steel pile foundation of an offshore wind power project as a specific example. It is assumed that the steel piles of this project are located in a complex marine geological environment, with soil layers including silty soil, silty clay and sandstone, and are subjected to waves of different intensities over a long period of time.

[0057] The soil stiffness adjustment coefficient is mapped to the composite stress assessment model. For example, the soil stiffness adjustment coefficient obtained from the predicted coupled failure boundary set in Example 3 is 0.8, which means that the stiffness parameters of the corresponding soil part in the composite stress assessment model need to be multiplied by 0.8. In operation, based on the division of the soil region in the model, the soil elements related to this adjustment coefficient are located, and the parameters reflecting soil stiffness, such as the elastic modulus and shear modulus of these elements, are adjusted accordingly. Simultaneously, the wave impact threshold and corrosion damage priority sequence are matched, assuming the wave impact threshold is 50 kN / m. 2 In the corrosion damage priority sequence, the area of ​​the steel pile closest to the water surface is listed as the highest priority. Based on this information, the pile node density in the failure area is adjusted. If a section of steel pile in silty soil is found to be a failure area in the composite stress assessment model, considering the complex stress distribution and tendency for stress concentration in this area, the pile node density in this area is increased by 20%. By refining the mesh, more nodes are added to the model to more accurately calculate the stress and displacement in this area, thereby updating the composite stress assessment model.

[0058] Define environmental disturbance triggering conditions, structural reconfiguration rules, and parameter correction increments. Environmental disturbance triggering conditions are set according to actual engineering needs. For example, a preset bearing capacity threshold of 80% is used; parameter correction is triggered when the current pile foundation bearing capacity integrity rate is no greater than 80%. In practice, if the pile foundation bearing capacity integrity rate drops to 78% at a certain moment, the environmental disturbance triggering condition is met. Structural reconfiguration rules adjust the wave impact threshold based on the displacement convergence gradient direction. Assuming the calculated displacement convergence gradient direction is downward along the pile axis, the wave impact threshold is adjusted accordingly, for example, from 50 kN / m. 2 Adjusted to 52kN / m 2This is to adapt to the current stress state of the steel pile. The parameter correction increment has a piecewise exponential relationship with the current displacement convergence evaluation index. If the displacement convergence evaluation index is at a low level, it indicates that the displacement of the steel pile has not yet stabilized, and the parameter correction increment is large at this time; as the displacement convergence evaluation index gradually increases, the parameter correction increment gradually decreases. ( The degree of displacement convergence, with a value ranging from 0 to 1. (The closer to 1, the more stable the displacement) exhibits a piecewise exponential relationship. Based on the displacement monitoring data of the pile nodes in the environmental disturbance simulation model, the displacement convergence evaluation index is calculated using the following formula: in: , The first , Maximum displacement of pile nodes at any given time. The maximum allowable displacement of the steel pile is determined by the material strength. For example, when the displacement convergence evaluation index is below 30%, the parameter correction increment is 1.5; when the index is between 30% and 60%, the correction increment is 1.2; and when the index is above 60%, the correction increment is 1.

[0059] After generating the environmental disturbance simulation model, the multi-scale coupled equations are iteratively solved using the finite element method (FEM). During the solution process, the environmental disturbance triggering conditions are monitored in real time. Taking the stress condition of a steel pile over a certain period as an example, initially, the steel pile is subjected to wave force and soil friction. The pile foundation bearing capacity integrity rate and soil shear displacement are calculated using the FEM. As time progresses, if the pile foundation bearing capacity integrity rate decreases to 78% due to increased wave intensity, the preset environmental disturbance triggering conditions are met. At this point, the soil shear displacement, pile foundation bearing capacity integrity rate, and displacement convergence evaluation index are immediately updated. Assuming the updated soil shear displacement increases from 0.05m to 0.07m, and the displacement convergence evaluation index increases from 25% to 30%, the relevant parameters are corrected according to the pre-set parameter correction increment (1.2 in this case), such as adjusting the soil friction coefficient and the elastic modulus of the steel pile. Then, the multi-scale coupled equations are resolved, and the stress and deformation of the steel pile are recalculated. This process is repeated continuously: monitoring environmental disturbance triggering conditions, updating various indicators and correcting parameters to resolve equations when conditions are met, until simulation termination conditions are reached, such as the simulation time reaching a preset 100 hours, or the change in various indicators falling below a certain minimum value. This process ultimately generates environmental disturbance simulation data. This data meticulously records the stress, deformation, and performance changes of the steel piles under different environmental disturbances, providing rich and accurate data for subsequent optimization design of the steel pile structure and evaluation of its safety and reliability. It helps engineers fully understand the actual working state of steel piles in complex environments.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing a steel pile stress model affected by soil and sea conditions, characterized in that, Includes the following steps: Collect multi-dimensional environmental parameters and construct a set of geotechnical mechanical features and a set of marine dynamic indicators to generate an initial steel pile coupling model; Geological stratification constraints are applied to the initial steel pile coupling model to establish a dynamic boundary condition framework. Multiphysics field coupling analysis is performed in combination with soil interaction parameters to form a composite stress assessment model. Stress wave propagation simulation is performed based on the composite stress assessment model to generate pile deformation path, and a spatiotemporal evolution simulation dataset is constructed by combining environmental disturbance frequency parameters. A material failure discrimination model is established and trained using a spatiotemporal evolution simulation dataset to form a load optimization analysis model, which then outputs structural correction parameters. These parameters include at least the soil stiffness adjustment coefficient, wave impact threshold, and corrosion damage priority sequence. Based on structural correction parameters, composite stress assessment model and soil interaction parameters, environmental disturbance simulation data are generated. By combining environmental disturbance simulation data with material strength constraints, a multi-level optimization model is constructed and the pile structure is iteratively corrected to generate the optimal load distribution parameters. The current load distribution parameters are verified based on the real-time stress distribution network, the pile displacement consistency index is calculated, and the structural optimization strategy is updated by combining the optimal load distribution parameters with the historical deformation trajectory to achieve the dynamic coupling matching goal.

2. The method for analyzing the steel pile stress model influenced by soil and sea conditions according to claim 1, characterized in that, The spatiotemporal evolution simulation dataset includes at least a set of stress concentration regions, material creep parameters, a set of coupled failure boundaries, and a dynamic adjustment priority sequence. The environmental disturbance simulation data includes at least soil shear displacement, pile foundation bearing capacity integrity rate, and displacement convergence evaluation index.

3. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 1, characterized in that, The process of collecting multi-dimensional environmental parameters and constructing a set of geotechnical mechanical features and a set of marine dynamic indicators to generate an initial steel pile coupled model includes the following steps: Spatiotemporal alignment and noise filtering of multi-dimensional environmental parameters are performed to generate a standardized geomechanical feature set; Soil stratification feature extraction is performed on the geomechanical feature set, wherein the extraction includes one or more of shear wave velocity clustering, pore pressure mapping, rock stiffness classification and soil heterogeneity resolution; Based on the rock stratum stiffness classification results, an initial steel pile coupling model is constructed, wherein the model includes a set of pile nodes, a set of stress transfer edges, and material strength constraint rules; Soil contact strength analysis was performed on the initial steel pile coupling model to generate a dynamic coupling factor matrix, and marine working condition constraint labels were assigned to each pile node.

4. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 1, characterized in that, The process of applying geological stratification constraints to the initial steel pile coupled model, establishing a dynamic boundary condition framework, and combining soil interaction parameters to perform multiphysics coupling analysis to form a composite stress assessment model includes the following steps: Geological stratification constraints are applied to the initial steel pile coupled model to limit the soil deformation range, generating the first coupled analysis framework; Wave load spectrum parameters are applied to the first coupled analysis framework to establish a dynamic boundary condition framework; Multiphysics coupling analysis based on a dynamic boundary condition framework includes the following specific steps: A multi-scale coupled equation is constructed, which includes at least the soil contact stiffness equation, the wave impact force equation, and the pile displacement compatibility equation. The multi-scale coupled equation is solved iteratively by the finite element method to obtain the set of stress concentration regions, material creep parameters, and the set of coupled failure boundaries. The generation of the coupling failure boundary set includes the following steps: Based on the set of stress concentration regions, the stress gradient of each pile node is calculated; Identify regions in the dynamic boundary condition framework where the stress gradient is not greater than a preset threshold, and generate a set of coupled failure boundaries.

5. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 1, characterized in that, The process of establishing a material failure discrimination model and training it with a spatiotemporal evolution simulation dataset to form a load optimization analysis model, and then outputting structural correction parameters, includes the following steps: A material failure discrimination model was established based on the initial steel pile coupling model; The material failure discrimination model is trained and validated using a spatiotemporal evolution simulation dataset to form a load optimization analysis model; By inputting real-time soil interaction parameters into the load optimization analysis model, the set of coupled failure boundaries can be predicted. Based on the predicted set of coupled failure boundaries, structural correction parameters are output, wherein the structural correction parameters include at least the soil stiffness adjustment coefficient, wave impact threshold, and corrosion damage priority sequence.

6. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 5, characterized in that, This also includes feature reconstruction of the spatiotemporal evolution simulation dataset, specifically: Based on the set of stress concentration regions, material creep parameters, and coupled failure boundary set, an initial multiphysics input tensor is constructed. The initial multiphysics input tensor is standardized and its features are reduced to generate the final multiphysics input tensor. A load-optimized label tensor is constructed based on a dynamically adjusted priority sequence. The training sample set is formed by combining the final multiphysics input tensor with the load-optimized label tensor.

7. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 6, characterized in that, The output structure correction parameters based on the predicted coupling failure boundary set include the following steps: Extract the pile node with the lowest concentrated stress gradient at the predicted coupled failure boundary and generate the soil stiffness adjustment coefficient. Based on the topological distribution characteristics of the predicted coupled failure boundary set, the probability density of the wave impact threshold is fitted to generate a corrosion damage priority sequence. The displacement convergence gradient direction of the predicted coupled failure boundary set is calculated and normalized to the load distribution reference vector, which is the wave impact threshold adjustment direction.

8. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 1, characterized in that, The process of generating environmental disturbance simulation data based on structural correction parameters, composite stress assessment model, and soil interaction parameters includes the following steps: The soil stiffness adjustment coefficient is mapped to the composite stress assessment model, the wave impact threshold and corrosion damage priority sequence are matched, the pile node density in the failure area is adjusted, the composite stress assessment model is updated, the environmental disturbance triggering conditions, structural reconstruction rules and parameter correction increments are defined, and the environmental disturbance simulation model is generated. Based on the environmental disturbance simulation model, the multi-scale coupled equations are iteratively solved using the finite element method to generate environmental disturbance simulation data, specifically including: When the environmental disturbance triggering condition is met, update the soil shear displacement, pile foundation bearing capacity and displacement convergence evaluation index, and resolve the multi-scale coupled equation until the simulation termination condition is reached. The environmental disturbance triggering conditions include triggering parameter correction when the current pile foundation bearing capacity integrity rate is not greater than the preset bearing capacity threshold; the structural reconstruction rules include adjusting the wave impact threshold based on the displacement convergence gradient direction; the parameter correction increment has a piecewise exponential relationship with the current displacement convergence evaluation index.

9. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 1, characterized in that, The process of constructing a multi-level optimization model and iteratively correcting the pile structure to generate optimal load distribution parameters includes the following steps: A multi-level optimization model is constructed, in which the optimization variables include pile node density, wave impact threshold and environmental disturbance frequency, the optimization objectives include maximizing bearing stability and minimizing stress concentration density, and the constraints include coupled failure boundary constraints and material creep threshold. The particle swarm optimization algorithm is used to initially solve the multi-level optimization model and generate an initial set of optimization schemes. Based on the initial set of optimization schemes, the ant colony algorithm is used for global optimization to generate the optimal load allocation parameters.

10. The method for analyzing and applying the stress model of steel piles under the influence of soil and sea conditions according to claim 9, characterized in that, The process of using particle swarm optimization to initially solve the multi-level optimization model and generate an initial set of optimization schemes includes the following steps: A particle position coding structure is constructed based on pile node density and wave impact threshold to generate an initial population; The fitness function is calculated based on the load-bearing stability and stress concentration density. The initial population is then updated with velocity and iterated with position to generate a set of intermediate optimization schemes. Neighborhood search and elite retention filtering are performed on the intermediate optimization scheme set to generate an initial optimization scheme set.

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