Quantitative analysis method for minimum tide level of bucket foundation barge operation

Abstract

The application relates to the field of marine engineering and structural safety, and discloses a quantitative analysis method for the minimum tide level of barge loading and unloading operation on a bucket foundation, which comprises the following steps: constructing a digital twin model considering multi-physical field coupling; collecting dynamic data of the operation site in real time through a sensor network; performing real-time self-correction on key physical parameters of the model by using an extended Kalman filtering algorithm; performing prospective simulation on the operation process under different tide conditions based on the corrected model, obtaining the probability distribution of load effect; performing reliability analysis in combination with a structural resistance model, calculating the structural failure probability under each tide level, and comparing the structural failure probability with a preset acceptable risk threshold, so that the minimum operable tide level is determined. Through digital twin and real-time data fusion, the application realizes dynamic and prospective quantitative evaluation of the barge loading and unloading operation risk, gets rid of the dependence on subjective experience, and significantly improves the scientificity, safety and economic benefit of operation decision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ocean engineering and structural safety, in particular to a method for quantitatively analyzing the minimum tide level of a bucket foundation loading operation. BACKGROUND

[0002] As a key clean and renewable energy, offshore wind power is increasingly important in the global energy structure. The bucket foundation has become a widely used foundation structure form for offshore wind turbine units due to its unique installation advantages, convenient construction, strong adaptability to geological conditions, and less environmental impact. In the construction and deployment process of the bucket foundation, the "loading operation" of loading the bucket foundation from the onshore prefabrication site to the semi-submersible barge is one of the most complex and risky key links in the entire engineering chain. This process not only involves precise control of a huge structure weighing several thousand tons, but also must cope with the complex coupling effects of various dynamic marine environmental factors such as tides, waves, and wind.

[0003] Currently, the determination of the loading operation window, especially the minimum operable tide level, still largely relies on simplified static mechanical calculations, previous engineering experience, and conservative assumptions based on design specifications. This traditional method usually deals with various uncertainties by presetting a fixed and larger safety margin, but its inherent technical defects have become increasingly prominent. Due to the lack of real-time and accurate perception of the operating site environment and in-depth analysis of the dynamic coupling effects of multiple physical fields such as structural mechanics and fluid mechanics, this decision-making mode cannot truly and dynamically reflect the instantaneous risk state of the operating system.

[0004] This limitation leads to a dilemma: on the one hand, in order to ensure safety, decision-makers often adopt overly conservative strategies, resulting in the inability to fully utilize the actual existing safety operation window, causing unnecessary delays and significant cost increases; on the other hand, when encountering extreme environmental combinations that are not fully anticipated, the systematic underestimation of risks can lead to catastrophic accidents such as structural damage and even capsizing, posing a serious safety hazard. Therefore, the existing technology has obvious deficiencies in achieving the best balance between safety and economy in loading operations, and there is an urgent need for an advanced technical means that can integrate real-time data, dynamically quantify risks, and provide accurate decision support. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a method for quantitatively analyzing the minimum tide level of a bucket foundation loading operation, which aims to solve the problem that the existing method for analyzing the minimum tide level of a bucket foundation loading operation is usually based on static theoretical data and deterministic formulas for calculation, which cannot adapt to the dynamically changing environmental factors and load conditions at the operating site, resulting in deviations between the analysis results and the actual situation. In order to ensure safety, the operation decision is often conservative, affecting the operation efficiency.

[0006] To solve the above technical problems, the present application provides a bucket foundation on barge operation minimum tidal level quantitative analysis method, which realizes dynamic and accurate determination of the minimum operable tidal level by constructing a digital twin model that can be self-corrected according to real-time data, combining forward simulation and probabilistic risk assessment.

[0007] The first aspect of the present application provides a bucket foundation on barge operation minimum tidal level quantitative analysis method, comprising the following steps:

[0008] S1: A multi-physics field coupled digital twin model containing preset key physical parameters is constructed, which is used to simulate the dynamic behavior of the semi-submersible barge, the wharf apron and the module car during the bucket foundation on barge operation process;

[0009] S2: During the on-barge operation process, real-time collection of on-site operation data including at least environmental data, motion data of the semi-submersible barge and structural response data of the wharf apron is performed;

[0010] S3: Based on the on-site operation data, the digital twin model is self-corrected; this step specifically comprises: using the digital twin model and the current key physical parameters to predict the structural response of the wharf apron at the next time, comparing the predicted structural response with the real-time collected structural response data at the next time, and updating the key physical parameters in the reverse direction according to the deviation generated by the comparison;

[0011] S4: Using the corrected digital twin model, forward simulation is performed on the subsequent process from the current time to the end of the on-barge operation to obtain the load effect distribution of the wharf apron in the subsequent process;

[0012] S5: Based on the load effect distribution and the preset structural resistance model of the wharf apron, the structural failure probability is calculated, and the minimum operable tidal level that meets the threshold is determined according to the preset acceptable risk threshold.

[0013] In one specific embodiment, in the step S1, the multi-physics field coupled digital twin model comprises: a hydrodynamic model for simulating the motion response of the semi-submersible barge under the action of waves and currents; a structural dynamics model for calculating the stress and strain of the wharf apron under the action of load; and a multi-body system dynamics model for simulating the contact and constraint relationship between components.

[0014] Preferably, in the step S1, the key physical parameters at least include one or a combination of the following: the hydrodynamic equivalent damping coefficient of the semi-submersible barge, the equivalent friction coefficient between the wharf apron and the semi-submersible barge, and the equivalent vertical stiffness of the module car tire.

[0015] In one specific embodiment, in the step S2, the structural response data is the support force data measured in real time by load sensors arranged at one or more key support points of the wharf deck.

[0016] Preferably, in the step S3, the inverse updating of the key physical parameters is realized by an extended Kalman filtering algorithm, which takes the key physical parameters as state variables to be estimated and the structural response of the wharf deck as observation, and estimates and corrects the key physical parameters through a recursive loop of prediction and updating.

[0017] In one specific embodiment, in the step S3, the inverse updating of the key physical parameters is triggered when the absolute value of the deviation between the predicted structural response and the real-time collected structural response data exceeds a preset deviation threshold.

[0018] Preferably, in the step S4, the forward simulation is realized by a Monte Carlo simulation method, which takes into account the randomness of future tide changes and environmental loads in the simulation process to obtain a plurality of simulation samples.

[0019] In one specific embodiment, in the step S5, the structural resistance model of the wharf deck defines its ultimate bearing capacity as a probability distribution model, such as a lognormal distribution or a normal distribution model.

[0020] Preferably, in the step S5, the structural failure probability is calculated in the following manner:

[0021] ;

[0022] wherein, is the probability density function of the load effect distribution obtained by the forward simulation, is the cumulative distribution function of the structural resistance model.

[0023] In one specific embodiment, the step S5 further comprises: virtually a series of decreasing tide points within a numerical fluctuation range based on the current tide; for each virtual tide point, repeating the step S4 to calculate the corresponding structural failure probability, thereby generating a risk curve of the structural failure probability varying with the tide; and determining the tide corresponding to the acceptable risk threshold on the risk curve as the minimum operational tide.

[0024] The second aspect of the present application provides a barrel foundation barge operation minimum tide quantitative analysis device, comprising:

[0025] A model construction module is configured to construct a multi-physics field coupling digital twin model containing preset key physical parameters, which is used to simulate the dynamic behavior of the semi-submersible barge, the wharf apron and the module vehicle during the bucket foundation loading operation.

[0026] A data acquisition module is configured to acquire real-time field operation data including at least environmental data, motion data of the semi-submersible barge and structural response data of the wharf apron during the loading operation.

[0027] A model correction module is connected with the model construction module and the data acquisition module, and is configured to perform self-correction on the digital twin model based on the field operation data; specifically, the digital twin model and the key physical parameters at the current time are used to predict the structural response of the wharf apron at the next time, and the predicted structural response is compared with the real-time acquired structural response data at the next time, and the key physical parameters are updated reversely according to the deviation generated by the comparison.

[0028] A risk assessment module is connected with the model correction module, and is configured to use the corrected digital twin model to perform forward-looking simulation on the subsequent process from the current time to the end of the loading operation, so as to obtain the load effect distribution of the wharf apron in the subsequent process.

[0029] A tide level determination module is connected with the risk assessment module, and is configured to calculate the structural failure probability based on the load effect distribution and a preset structural resistance model of the wharf apron, and determine the lowest operable tide level that meets the preset acceptable risk threshold.

[0030] The present application provides a bucket foundation loading operation minimum tide level quantitative analysis method. It has the following advantages:

[0031] 1. The present application constructs a high-fidelity multi-physics field coupling digital twin model, and uses real-time acquired field operation data to reversely update and self-correct the key physical parameters in the model. This model correction step enables the digital twin model to dynamically adapt to the actual physical characteristics of the operation site, overcoming the analysis deviation caused by the dependence of the prior art on fixed and idealized theoretical parameters, thereby significantly improving the accuracy of the prediction of the system dynamic behavior.

[0032] 2. The present application uses forward-looking simulation to obtain the probability distribution of the load effect, and combines the probability model of the structural resistance to calculate the structural failure probability. This probability-based risk assessment method replaces the traditional judgment method relying on a single safety factor or a deterministic threshold, and can more scientifically and comprehensively quantify the influence of uncertain factors on the structural safety, providing a continuous and quantitative risk index for operation decision-making, thereby realizing fine control of the operation safety.

[0033] 3、The present application can dynamically determine the minimum workable tide level that meets the preset safety threshold by accurately quantifying the risk. This enables the decision maker to abandon the overly conservative work standard adopted to cope with uncertainty, and can make more full use of the tidal window under the premise of ensuring safety, reduce unnecessary waiting time, and effectively improve the overall efficiency of the bucket foundation barge operation. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The system structure block diagram of an embodiment of the present application;

[0035] Figure 2 The method flowchart of an embodiment of the present application;

[0036] Figure 3 The operation scene and sensor layout schematic diagram of an embodiment of the present application;

[0037] Figure 4 The model self-correction closed-loop process schematic diagram of an embodiment of the present application;

[0038] Figure 5 The structure failure probability calculation principle schematic diagram of an embodiment of the present application;

[0039] Figure 6 The risk curve and minimum workable tide level determination schematic diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] Referring to the drawings in the specification of the present application, Figure 1 The present application provides a bucket foundation barge operation minimum tide level quantitative analysis device 100, which can be deployed in a computing device, such as a server or an industrial computer. The device 100 communicates data with a sensor network 200 deployed at the operation site and a user terminal 300 for data display.

[0042] The hardware environment of the embodiment includes: a sensor network 200 composed of multiple sensors deployed at locations such as the wharf, the semi-submersible barge, and the wharf deck; a computing device internally integrated with computing resources such as a central processor and a memory, for running various functional modules of the device 100; and a user terminal 300, such as a tablet computer or a display screen, for displaying analysis results to on-site engineers.

[0043] Specifically, the device 100 can include a model construction module 10, a data acquisition module 20, a model correction module 30, a risk assessment module 40, and a tide level determination module 50.

[0044] The model construction module 10 is configured to construct a multi-physics field coupling digital twin model containing preset key physical parameters according to input static data such as ship design parameters, structural drawings, and material properties before the start of the operation. The output of this module is an initialized and parameterized digital twin model, which is provided to the model correction module 30 and the risk assessment module 40.

[0045] The data acquisition module 20 is configured to receive and process real-time operation data through a communication interface with the sensor network 200. The sensor network 200 includes but is not limited to tide gauges for measuring tidal levels, RTK-GPS and inertial measurement units for obtaining six-degree-of-freedom motion data of the semi-submersible barge, and load sensors for measuring structural response data of the wharf deck. After time stamp alignment and preprocessing of the received data, the module outputs the synchronized operation data to the model correction module 30.

[0046] The model correction module 30 is connected to the model construction module 10 and the data acquisition module 20. The module receives the initial model from the model construction module 10 and the synchronized operation data from the data acquisition module 20. Its function is to perform model self-correction calculations, specifically: using the current key physical parameters to drive the digital twin model for prediction, comparing the prediction results with the real-time collected structural response data, and updating the key physical parameters in reverse according to the deviation between the two. The module outputs a set of updated key physical parameters to the risk assessment module 40.

[0047] The risk assessment module 40 is connected to the model correction module 30. The module receives the updated key physical parameters output by the model correction module 30 and calls the digital twin model. Its function is to perform forward-looking simulation of the subsequent process from the current time to the end of the operation based on the corrected model, thereby obtaining the load effect distribution of the wharf deck. The module outputs the calculated load effect distribution data to the tide level determination module 50.

[0048] The tide level determination module 50 is connected with the risk assessment module 40. The module receives the load effect distribution data from the risk assessment module 40. Its function is to calculate the structure failure probability based on the distribution and the preset structure resistance model, and compare the probability with the preset acceptable risk threshold, and finally determine the minimum operational tide level that meets the safety requirements. The module outputs the determined minimum operational tide level and related risk information to the user terminal 300 for display.

[0049] Referring to the accompanying drawings Figure 2 The specific embodiments of the present application will be described in detail below in combination with the flowchart.

[0050] Step S1 of the method of the present application aims to construct a high-fidelity, parameterized multi-physical field coupling digital twin model. The model is the basis for subsequent self-correction and risk assessment calculations. Before model self-correction in step S3, the key physical parameters need to have an initial value. The initial value can be determined according to one or more of the following ways:

[0051] By performing a limited number of field tests under no load or preset load conditions before the start of the operation, the initial value is obtained by inverse calculation. For example, before the module barge is loaded, the barge is excited slightly actively, and its motion response is collected to obtain the initial value of its hydrodynamic equivalent damping coefficient by inversion.

[0052] First, a hydrodynamic model of the key object semi-submersible barge in the operation is established. Specifically, based on the three-dimensional geometric model of the barge, the three-dimensional potential flow theory is applied to calculate its hydrodynamic coefficients in the frequency domain, including added mass and radiation damping. Subsequently, to meet the needs of subsequent time domain simulation, the motion equation in the frequency domain is converted to the time domain motion equation through convolution integral, which describes the six-degree-of-freedom motion response of the barge under the action of external environmental load. The time domain motion equation can be expressed as:

[0053] ;

[0054] wherein, is the mass / inertia matrix of the barge; is the added mass matrix at infinite frequency; is the delay function matrix representing the memory effect of the fluid; is the static water restoring force / torque coefficient matrix; is the total environmental excitation force / torque vector acting on the barge, including wave force, wind force and fluid force, etc.; 、 and are the displacement / angle, velocity and acceleration vectors of the barge in six degrees of freedom, respectively.

[0055] After the completion of the hydrodynamic model, the key bearing structures involved in the operation are modeled. Specifically, the finite element method is used to model the wharf apron and module car frame in detail, generating a structural dynamics model that can reflect its mechanical properties. This model is used to calculate the stress distribution, strain size and deformation of the structure under given load conditions. Further, the semi-submersible barge, module car, bucket foundation and wharf apron are integrated into a unified multi-body system dynamics model. In this model, the interaction between the components is defined, including the nonlinear contact model between the module car tire and the apron, and the hinge or support constraint model between the apron and the wharf, and the apron and the semi-submersible barge.

[0056] To enable the digital twin model to reflect the actual physical properties that are difficult to accurately estimate, and to provide a basis for subsequent model self-correction, some physical parameters in the model are parameterized to form an initial parameter vector . The selection of these parameters is based on their significant impact on the model output and their own large uncertainty. In a specific embodiment, the parameter vector at least includes:

[0057] The equivalent sway damping coefficient of the barge under a specific draft and sea conditions , which is significantly affected by viscous effects and has theoretical calculation errors; the equivalent friction coefficient between the support points of the apron and the semi-submersible barge , which is affected by the material properties of the contact surface, corrosion and wet state; and the equivalent vertical stiffness of the module car tire , which changes with tire pressure, temperature and load.

[0058] Finally, for subsequent probabilistic risk assessment, the ultimate bearing capacity of the wharf apron, i.e. the structural resistance , is defined probabilistically.

[0059] Due to the inherent randomness of factors such as material properties, manufacturing processes and geometric dimensions, the structural resistance is not a fixed value. In this embodiment, the structural resistance is modeled as a random variable subject to a lognormal distribution, with a probability density function expressed as:

[0060] ;

[0061] where is the specific value of the structural resistance; is the mean of the logarithm of the structural resistance; is the standard deviation of the logarithm of the structural resistance. The values of these two statistical parameters can be determined based on relevant structural design specifications, test statistical data of material properties or results of detailed finite element analysis.

[0062] Referring to the drawings Figure 3 , step S2 aims to provide real-time and accurate physical world input data for the subsequent model self-correction step S3. This step continuously collects multi-source heterogeneous data related to the working state through a sensor network deployed at the working site.

[0063] To ensure the consistency of data from different sources in the time dimension, the data collection system in this embodiment adopts a unified time synchronization mechanism.

[0064] Specifically, the data collection units of all sensors are aligned with a central time source through the Network Time Protocol (NTP) or GPS clock signal, ensuring that all collected data points are accompanied by high-precision, synchronized timestamps. Thus, data from different physical locations and different types of sensors can be integrated into a unified time series data set , which provides a basis for accurate comparison between model predictions and actual measurements. The time series data set specifically includes the following types of data:

[0065] Environmental data, mainly real-time tide level data at the wharf front . This data is measured by an ultrasonic or radar tide gauge installed on the fixed structure of the wharf, and its output is the real-time water surface elevation relative to a certain fixed ground reference.

[0066] Semi-submersible barge motion data, i.e., six-degree-of-freedom motion vectors describing its spatial position and attitude . This data is obtained by fusing the solutions of multiple high-precision real-time kinematic global positioning system (RTK-GPS) receivers and an inertial measurement unit (IMU) placed on the barge. This vector specifically includes three translational components (surge, sway, and heave) and three rotational components (roll angle , pitch angle , and yaw angle ).

[0067] Wharf apron structural response data. This data consists of two parts: first, by installing multiple pressure load sensors at the contact support points between the wharf apron and the semi-submersible barge deck, real-time measurement of the vertical support force borne by the apron; second, by pasting multiple strain gauges at stress concentration areas of the apron structure (such as the midspan or near the support points), real-time measurement of strain at key positions .

[0068] These structural response data directly reflect the true situation of load transfer during the loading process.

[0069] During the whole unloading operation, step S2 is continuously executed, forming an uninterrupted data stream with synchronized time stamps. The data stream is transmitted in real time to the data acquisition module 20 in the computing device, and after being processed by the data acquisition module 20, it is provided to the model correction module 30 for performing step S3.

[0070] Referring to the drawings Figure 4 Based on the continuous acquisition of field operation data in step S2, the method of the present application performs step S3, i.e., model self-correction. This step is a key link connecting the digital twin model and the physical world, and its purpose is to continuously correct and optimize the key physical parameters in the digital twin model through the feedback of real-time data, so that the digital twin model can accurately reflect the real dynamic characteristics of the operation site.

[0071] The execution of the model correction is not continuous, but is started by a preset trigger condition. Specifically, at any time step, the model correction module 30 first drives the digital twin model with the parameter vector at the current time to forward predict the wharf deck structure response at the next time, such as the support force When the actual measured value After being transmitted from the data acquisition module 20, the deviation between the two is calculated. When the absolute value of the deviation continuously exceeds a preset deviation threshold value for a plurality of time steps, the parameter reverse update program is triggered. The deviation threshold value is set according to the measurement noise level of the sensor itself and the inherent error range of the model under ideal conditions, so as to avoid unnecessary parameter adjustment caused by normal random fluctuations.

[0072] Once triggered, the parameter reverse update is started. In this embodiment, the update process is realized by applying the extended Kalman filter (EKF) algorithm. The key physical parameter vector to be updated is regarded as the state quantity of the system, and the real-time measured wharf deck structure response (such as the support force ) is regarded as the observation quantity of the system. At time , the iterative process thereof includes two stages of prediction and update:

[0073] Prediction stage:

[0074] First, the state quantity (parameter vector) is predicted a priori. Since the physical parameters change slowly in a short time, the state transition thereof can be simplified as the posteriori estimation value at the last time:

[0075] ;

[0076] At the same time, the predicted state covariance matrix is:

[0077] ;

[0078] Subsequently, based on the prior parameters , by running the digital twin model, which can be considered as a nonlinear function , the observed output of the system is predicted:

[0079] ;

[0080] The update stage:

[0081] When the actual measurement value at the moment is obtained , first, the Kalman gain is calculated:

[0082] ;

[0083] Then, the measurement residual is used to correct the prior estimation of the state quantity, and the updated parameter vector is obtained:

[0084] ;

[0085] Finally, the state covariance matrix is updated:

[0086] ;

[0087] In step S3, the Extended Kalman Filter (EKF) algorithm needs to calculate the Jacobian matrix of the observation function at the current state quantity. Since the multi-physical field coupling digital twin model is a complex, nonlinear simulation program, it is difficult to obtain the analytical form of the Jacobian matrix. Therefore, in the present embodiment, the Jacobian matrix is preferably approximated by a numerical method, such as the finite difference method.

[0088] Specifically, the th column of each column of the Jacobian matrix can be expressed as:

[0089] ;

[0090] where is a small perturbation vector applied only to the th parameter.

[0091] In the above formula, the symbols are defined as follows: and are the prior and posterior estimates of the parameter vector at ; and are the corresponding covariance matrices; The process noise covariance matrix; These are the predicted observations; For observation function exist The Jacobian matrix obtained by linearizing at the point; To measure the noise covariance matrix; It is an identity matrix.

[0092] Through the above recursive iteration, step S3 forms a closed-loop workflow of "prediction-comparison-correction". At each time correction is triggered, the model correction module 30 uses the latest measurement data to perform an optimal estimate of the parameter vector and updates the parameter vector. Output. The updated parameters are then used in the next round of prediction and risk assessment, thus enabling the digital twin model to dynamically track and adapt to the real physical process.

[0093] See attached document Figure 4 - Appendix Figure 5 This step utilizes a calibrated digital twin model with the highest fidelity currently available to perform a forward-looking simulation of the subsequent processes from the current moment until the end of the overhaul operation. The purpose is to quantify the uncertainties in the future operation process and assess their impact on structural safety.

[0094] In this embodiment, the prospective simulation employs the Monte Carlo simulation method. Implementing this method first requires simulation setup. Using the current system state (including the barge's position, attitude, velocity, and the modular vehicle's position, etc.) as initial conditions, a sufficiently large number of simulation iterations is set. For example, 1000 times, to ensure the convergence of subsequent statistical results. Uncertainties during future operation periods are defined as random variables. These random variables include at least: future environmental loads, such as the meaningful wave height of waves. Spectral peak period Its value can be sampled from a joint probability distribution model established based on long-term field observation data; future tide level changes can be modeled as a superposition of deterministic astronomical tide forecast values ​​and a random error term that follows a normal distribution.

[0095] Before each simulation begins, the system extracts a set of specific values ​​for each random variable and generates the environmental conditions for that simulation based on these values. Then, the calibrated digital twin model is run to perform a complete time-domain dynamic simulation of the entire subsequent process from the current moment until the modular vehicle is fully loaded onto the dock. This process is repeated. Each time, a new set of random sampling parameters is used.

[0096] In Monte Carlo simulation, to accurately simulate the future environmental loads, especially the random variables such as wind, wave, current, etc., the correlation between them needs to be considered. In this embodiment, the joint probability distribution of random variables (such as the significant wave height and the spectral peak period ) can be modeled by Copula function. This method can model the marginal distribution (for example and Weibull distribution respectively) and the dependence structure (for example Kendall or Spearman coefficient) between them separately, so as to retain the true correlation between variables when sampling, so that the simulation result is closer to the actual situation.

[0097] After completing times of simulation, the simulation results are processed to generate the distribution of load effect. Specifically, from the data of the time history of the bracket support force generated by each simulation (the th time, where ), the maximum value in the whole process is extracted, recorded as the maximum load effect sample . Through this step, a set of samples is obtained.

[0098] To obtain the continuous probability distribution of the maximum load effect , a non-parametric method called kernel density estimation (KDE) is used to fit the probability density function according to the above sample set. Its calculation formula is:

[0099] ;

[0100] wherein, is the variable of load effect; is the total number of Monte Carlo simulation; is the maximum load effect sample obtained in the th simulation; is the bandwidth parameter, which affects the smoothness of the estimated function; is the kernel function, which can be selected as Gaussian kernel function in this embodiment. The final output of this step is the probability density function , which is passed to step S5 for calculating the structural failure probability.

[0101] Referring to the accompanying drawings, Figure 6 this step aims to perform probabilistic assessment on the safety of the structure based on the aforementioned simulation results, and finally determine the minimum operational tide level that meets the preset safety level.

[0102] First, the structural failure probability is calculated. In the present embodiment, the structural failure of a wharf deck is defined as a limit state event, i.e. the load effect exceeds its own structural resistance . Since both the load effect and the structural resistance are modeled as random variables, the failure probability is obtained by calculating . Wherein, i.e. the probability that the value of the structural resistance of the deck itself is less than or equal to the load effect on the deck , which is solved by integrating all the value intervals of the load effect, and the specific calculation formula is:

[0103] ;

[0104] The physical meaning of this formula is: for any small load effect interval , the probability of the occurrence of the load effect is . Under this load, the condition for the structure to fail is that its resistance is less than or equal to , and the probability of this event is given by the cumulative distribution function of the structural resistance model . By multiplying these two probabilities and integrating over all possible load effect values, the total failure probability of the structure is obtained. Wherein, is the load effect probability density function obtained by Monte Carlo simulation and kernel density estimation in step S4, is the cumulative distribution function of the structural resistance probability model defined in step S1.

[0105] After obtaining the structural failure probability under a single tide level, in order to determine the minimum operable tide level, a risk curve needs to be generated. This process is achieved through an iterative calculation. First, taking the current tide level as the benchmark, a range of tide level lowering and a step size (e.g. 0.1 meters) are set to generate a series of discrete, decreasing virtual tide level points . Subsequently, for each virtual tide level point in the sequence, it is taken as a certain input boundary condition, and the complete process of step S4 is repeated. That is, for the tide level , the Monte Carlo simulation is performed again to obtain the load effect probability density function corresponding to this tide level. Then, the integral formula is used to calculate the structural failure probability under this tide level.

[0106] After the calculation of all virtual tide level points, a set of data pairs is obtained. Plotting these data pairs in a coordinate system with tide level as the horizontal coordinate and structure failure probability as the vertical coordinate, a visual risk curve of structure failure probability changing with tide level is formed.

[0107] Finally, compare the risk curve with a preset acceptable risk threshold (e.g., set according to relevant industry specifications or engineering requirements ). Find the point on the risk curve where the failure probability is equal to , and the horizontal coordinate value corresponding to this point is determined as the minimum workable tide level that meets the safety requirements under the current working condition. This result will be output to the user terminal to provide a basis for decision-making for on-site operations.

Claims

1. A method for quantitatively analyzing the minimum tide level of a bucket foundation barge operation, characterized in that, The method comprises the following steps: S1, constructing a multi-physics field coupling digital twin model: constructing a multi-physics field coupling digital twin model containing preset key physical parameters, the model is used to simulate the dynamic behavior of the semi-submersible barge, the wharf apron and the module vehicle during the on-barge operation of the bucket foundation; S2, real-time collection of on-site operation data: during the on-barge operation, real-time collection of on-site operation data including at least environmental data, motion data of the semi-submersible barge and structural response data of the wharf apron; S3, model self-correction: based on the on-site operation data, the multi-physics field coupling digital twin model is self-corrected; the step specifically comprises: using the multi-physics field coupling digital twin model and the key physical parameters at the current time, predicting the structural response of the wharf apron at the next time, and comparing the predicted structural response of the wharf apron at the next time with the real-time collected structural response data at the next time, and inversely updating the key physical parameters according to the deviation generated by the comparison; S4, implementation of forward simulation: using the corrected multi-physics field coupling digital twin model, performing forward simulation on the subsequent process from the current time to the end of the on-barge operation to obtain the load effect distribution of the wharf apron in the subsequent process; S5, risk assessment and determination of tide level: based on the load effect distribution and the preset structural resistance model of the wharf apron, risk assessment is performed and the structural failure probability is calculated, and based on the comparison between the structural failure probability and the preset acceptable risk threshold, the lowest operable tide level meeting the threshold is determined.

2. The method of quantifying the minimum tide level for a bar-based onloading operation according to claim 1, characterized in that, In the S1, the multi-physics field coupling digital twin model comprises: a hydrodynamic model for simulating the motion response of the semi-submersible barge; a structural dynamics model for calculating the stress and strain of the wharf apron; a multi-body system dynamics model for simulating the interaction between components.

3. The method of quantifying the minimum tide level for a bar-based onloading operation according to claim 1, characterized in that, In the S1, the key physical parameters at least include the hydrodynamic equivalent damping coefficient of the semi-submersible barge, the equivalent friction coefficient between the wharf apron and the semi-submersible barge, or the equivalent vertical stiffness of the module vehicle tire.

4. The method of quantifying the minimum tide level for a bar-based onloading operation according to claim 1, characterized in that, In the S2, the structural response data is the support force data measured by the load sensors arranged on the key support points of the wharf apron in real time.

5. The method of quantifying the minimum tide level for a bar-based onloading operation according to claim 1, wherein, In the model correction step of the S3, the inverse update of the key physical parameters is performed by extending the Kalman filter algorithm, taking the key physical parameters as the state variables to be estimated and taking the structural response of the wharf apron as the observation quantity, and recursively estimating and correcting.

6. The method of quantifying the minimum tide level for a bar-based onloading operation according to claim 1, wherein, In the S4, the forward simulation is performed by using the Monte Carlo simulation method, which considers the randomness of future tide level changes and environmental loads.

7. The method of quantifying the minimum tide level for a bar-based on-shore operation according to claim 1, wherein, In the S5, the structural resistance model of the wharf apron defines its ultimate bearing capacity as a probability distribution model.

8. The method of quantifying the minimum tide level for a bar-based on-shore operation according to claim 7, wherein, In the S5, the structural failure probability is calculated as follows: ； wherein, is a probability density function of the load effect distribution obtained by the prospective simulation, is a cumulative distribution function of the structural resistance model.

9. The method of quantifying the minimum tide level for a bar-based on-shore operation according to claim 1, wherein, The tide level determination of the S5 further comprises: virtually a series of decreasing tide level points based on the current tide level; for each virtual tide level point, the risk assessment is repeatedly performed to calculate the corresponding structural failure probability, thereby generating a risk curve of the structural failure probability changing with the tide level; On the risk curve, a tide level corresponding to the acceptable risk threshold is determined as the minimum workable tide level.

10. The method of quantifying the minimum tide level for a bar-based onloading operation according to claim 1, wherein, In S3, when an absolute value of deviation between the predicted structural response and the real-time collected structural response data exceeds a preset deviation threshold, reverse updating of the key physical parameter is triggered.

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