Quantitative analysis method for lowest tide level of bucket type foundation barge operation

Abstract

The invention relates to the field of ocean engineering and structural safety, and discloses a quantitative analysis method for the lowest sea level of bucket type foundation barge operation, which comprises the following steps of: constructing a digital twin model considering multi-physics field coupling; dynamic data of a working site are collected in real time through a sensor network; carrying out real-time self-correction on key physical parameters of the model by utilizing an extended Kalman filtering algorithm; based on the corrected model, performing prospective simulation on the operation process under different tide level conditions to obtain probability distribution of the load effect; and carrying out reliability analysis by combining a structural resistance model, calculating a structural failure probability under each tide level, and comparing the structural failure probability with a preset acceptable risk threshold, so as to determine the lowest operable tide level. According to the invention, through digital twinning and real-time data fusion, dynamic and foresight quantitative evaluation of the on-barge operation risk is realized, dependence on subjective experience is avoided, and scientificity, safety and economic benefits of operation decision making are remarkably improved.

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: 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; 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; S3: Based on the on-site operation data, the digital twin model is self-corrected; this step specifically includes: 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; 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; 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.

[0008] In one specific embodiment, in the step S1, the multi-physics field coupled digital twin model includes: 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.

[0009] 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.

[0010] 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.

[0011] Preferably, in the step S3, the inverse updating of the key physical parameters is realized by an extended Kalman filter 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.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] Preferably, in the step S5, the structural failure probability is calculated in the following manner: ; 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.

[0016] 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.

[0017] The second aspect of the present application provides a barrel foundation barge operation minimum tide quantitative analysis device, comprising: 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. 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. A model correction module is connected with the model construction module and the data acquisition module, and is configured to correct the digital twin model based on the field operation data; specifically, the structural response of the wharf apron at the next moment is predicted by using the digital twin model and the key physical parameters at the current moment, and the predicted structural response is compared with the real-time structural response data at the next moment, and the key physical parameters are updated reversely according to the deviation generated by the comparison. A risk assessment module is connected with the model correction module, and is configured to simulate the subsequent process from the current moment to the end of the loading operation by using the corrected digital twin model, so as to obtain the load effect distribution of the wharf apron in the subsequent process. 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.

[0018] The present application provides a bucket foundation loading operation minimum tide level quantitative analysis method. 1、The present application constructs a high-fidelity multi-physics field coupling digital twin model, and reversely updates and self-corrects the key physical parameters in the model by using the real-time field operation data. The model correction step enables the digital twin model to dynamically adapt to the actual physical characteristics of the operation site, overcomes the analysis deviation caused by the dependence of the prior art on fixed and idealized theoretical parameters, and significantly improves the accuracy of the prediction of the system dynamic behavior.

[0019] 2、The present application obtains the probability distribution of the load effect by using the forward simulation, and calculates the structural failure probability by combining the probability model of the structural resistance. This probability-based risk assessment method replaces the traditional judgment method relying on a single safety factor or a deterministic threshold, can more scientifically and comprehensively quantify the influence of uncertain factors on the structural safety, provides a continuous and quantitative risk index for the operation decision, and thus realizes the fine management and control of the operation safety.

[0020] 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 decision-makers to abandon the overly conservative work standards adopted to cope with uncertainty, and can make more full use of the tidal window while ensuring safety, reducing unnecessary waiting time, and thus effectively improving the overall efficiency of the bucket foundation barge operation. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 System structure block diagram of an embodiment of the present application; Figure 2 Method flow chart of an embodiment of the present application; Figure 3 Work scene and sensor layout diagram of an embodiment of the present application; Figure 4 Model self-correction closed-loop process diagram of an embodiment of the present application; Figure 5 Structural failure probability calculation principle diagram of an embodiment of the present application; Figure 6 Risk curve and minimum workable tide level determination diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the present application specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all. 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.

[0023] Referring to the drawings in the present application specification, 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 work site and a user terminal 300 for data display.

[0024] The hardware environment of the embodiment includes: a sensor network 200 composed of multiple sensors deployed at locations such as the wharf, semi-submersible barge and wharf apron; a computing device internally integrated with computing resources such as central processor and 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 showing the analysis results to the on-site engineers.

[0025] In particular, 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.

[0026] The model construction module 10 is configured to construct a multi-physics field coupled digital twin model containing preset key physical parameters according to input static data such as ship design parameters, structural drawings, material properties, etc. 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.

[0027] The data acquisition module 20 is configured to receive and process real-time field 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 semi-submersible barge six-degree-of-freedom motion data, and load sensors for measuring wharf deck structure response data. After time stamp alignment and preprocessing of the received data, the synchronized field operation data is output to the model correction module 30.

[0028] The model correction module 30 is connected to the model construction module 10 and the data acquisition module 20, respectively. This module receives the initial model from the model construction module 10 and the synchronized field 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 structure response data, and updating the key physical parameters in the opposite direction according to the deviation between the two. The module outputs a set of updated key physical parameters to the risk assessment module 40.

[0029] The risk assessment module 40 is connected to the model correction module 30. This 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 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.

[0030] The tide level determination module 50 is connected to the risk assessment module 40. This 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 to finally determine the minimum operable tide level that meets the safety requirements. The module outputs the determined minimum operable tide level and related risk information to the user terminal 300 for display.

[0031] Refer to the attachedFigure 2 The specific embodiments of the present application will be described in detail below in conjunction with the flow chart.

[0032] Step S1 of the method of the present application aims to construct a high-fidelity, parameterized multi-physical field coupling digital twin model. This 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: Through a finite number of field tests under no load or pre-set load conditions before the start of the operation, the initial value is obtained by inverse calculation. For example, before the module barge is transferred, the barge is excited slightly actively, the motion response is collected, and the initial value of the equivalent damping coefficient of the hydrodynamic force is obtained by inversion.

[0033] 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 the hydrodynamic coefficients in the frequency domain, including the added mass and the 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 integration, 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: ; 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.

[0034] After completing the construction of the hydrodynamic model, the key load-bearing structures involved in the operation are modeled. Specifically, the finite element method is used to model the wharf deck and the module car frame in detail, generating a structural dynamics model that can reflect the 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 deck are integrated into a unified multi-body system dynamics model. In this model, the interaction relationship between each component is defined, including the nonlinear contact model between the module car tire and the deck, and the hinged or supported constraint model between the deck and the wharf, and the deck and the semi-submersible barge.

[0035] In order to make the digital twin model reflect the actual physical characteristics of the site that are difficult to accurately predict and provide a basis for subsequent model self-correction, some physical parameters in the model are parameterized to form an initial parameter vector These parameters are chosen based on their significant impact on the model output and their own large uncertainty. In one embodiment, the parameter vector At least: Hydrodynamic equivalent sway damping coefficient of barge at specific draft and sea conditions This parameter is significantly affected by the viscosity effect, and there is a deviation in the theoretical calculation; the equivalent friction coefficient between the slab and the semi-submersible barge support point is , which is affected by the contact surface material, rust and wetness; and the equivalent vertical stiffness of the modular vehicle tire , which changes with tire pressure, temperature and load.

[0036] Finally, in order to conduct subsequent probabilistic risk assessment, the ultimate bearing capacity of the wharf slab, i.e., the structural resistance , and define it probabilistically.

[0037] Due to the inherent randomness of factors such as material properties, manufacturing process and geometric dimensions, the structural resistance is not a fixed value. Modeled as a random variable that obeys a lognormal distribution, its probability density function is Expressed as: ; in, is the specific value of the structural resistance; is the mean value 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, experimental statistical data of material properties, or the results of refined finite element analysis.

[0038] Refer to the attached Figure 3 ,Step S2 aims to provide real-time and accurate physical world input data for the subsequent ,model self-calibration step S3.,This step continuously collects multi-source heterogeneous data related to the ,operation status through a sensor network deployed at the operation site.

[0039] To ensure the consistency of data from different sources in the time dimension, the data acquisition system in this embodiment adopts a unified time synchronization mechanism.

[0040] In particular, the data acquisition units of all sensors are aligned to one central time source via network time protocol (NTP) or GPS clock signals, ensuring that all collected data points are accompanied by high-precision, synchronized time stamps. Thereby, data from different physical locations and different types of sensors can be integrated into one unified time series dataset , which provides the basis for accurate comparison of subsequent model predictions and actual measurements. The time series dataset specifically includes the following types of data: environmental data, mainly real-time tidal level data at the terminal front . This data is measured by an ultrasonic or radar tidal level gauge installed on the fixed structure of the terminal, and its output is the real-time water surface elevation relative to a certain fixed ground reference.

[0041] 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 outputs of multiple high-precision real-time kinematic global positioning system (RTK-GPS) receivers and an inertial measurement unit (IMU) installed on the barge. The vector specifically includes three translational components (surge, sway, heave) and three rotational components (roll angle , pitch angle , yaw angle ).

[0042] terminal apron structural response data. This data consists of two parts: first, vertical support forces borne by the apron are measured in real time by multiple pressure load sensors installed at the contact support points between the terminal apron and the semi-submersible barge deck ; second, strains at key locations are measured in real time by multiple strain gauges attached to stress concentration areas of the apron structure (e.g., near the midspan or support points) .

[0043] These structural response data directly reflect the true situation of load transfer during the loading process.

[0044] During the entire loading operation, step S2 is continuously executed, forming an uninterrupted, synchronized time-stamped data stream. This data stream is transmitted in real time to the data acquisition module 20 in the computing device, and after processing by the data acquisition module 20, it is provided to the model correction module 30 for execution of step S3.

[0045] Referring to the accompanying Figure 4On the basis of continuously collecting 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 it can accurately reflect the real dynamic characteristics of the operation site.

[0046] The execution of this 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 support force When the actual measured value at the next time is obtained, the deviation between the two is calculated. When the absolute value of this deviation continuously exceeds a preset deviation threshold value for a plurality of time steps, the parameter reverse update program is triggered. The setting of the deviation threshold value is based on the measurement noise level of the sensor itself and the inherent error range of the model under ideal conditions, in order to avoid unnecessary parameter adjustment caused by normal random fluctuations.

[0047] Once triggered, the parameter reverse update is started. In this embodiment, the update process is realized by applying the extended Kalman filter (EKF) algorithm. This algorithm takes the key physical parameter vector to be updated as the state quantity of the system, and takes the real-time measured wharf deck structure response (such as support force ) as the observation quantity of the system. At time , its iterative process includes two stages of prediction and update: Prediction stage: First, the state quantity (parameter vector) is predicted a priori. Since the physical parameters change slowly in a short time, the state transition can be simplified as the posteriori estimate value at the last time: ; At the same time, the prediction state covariance matrix is: ; Subsequently, based on the a priori parameter , by running the digital twin model, which can be regarded as a nonlinear function , the observation output of the system is predicted: ; Update stage: When the actual measured value at time is obtained, the Kalman gain is first calculated: ; Then, the measurement residual is calculated as The prior estimate of the state is then updated to obtain the updated parameter vector: ; Finally, the state covariance matrix is updated: ; In step S3, the Extended Kalman Filter (EKF) algorithm needs to calculate the observation function The Jacobian matrix at the current state is Since the multi-physical field coupled digital twin model is a complex, nonlinear simulation program, its analytical form of the Jacobian matrix is difficult to obtain. Therefore, in the present embodiment, the Jacobian matrix is preferably approximated by numerical methods, such as finite difference method.

[0048] Specifically, the i-th column of the Jacobian matrix can be expressed as: ; where is a small perturbation vector applied only on the i-th parameter.

[0049] In the above formula, the symbols are defined as follows: and are the prior and posterior estimates of the parameter vector at time ; and are the corresponding covariance matrices; is the process noise covariance matrix; is the predicted observation value; is the Jacobian matrix obtained by linearization at ; is the measurement noise covariance matrix; is the identity matrix.

[0050] Through the above recursive iteration, step S3 forms a closed-loop workflow of "prediction - comparison - correction". At each time of triggering correction, the model correction module 30 performs an optimal estimation of the parameter vector using the latest measurement data, and outputs the updated parameter vector . This updated parameter is then used in the next round of prediction and risk assessment, thereby realizing the dynamic tracking and adaptation of the digital twin model to the real physical process.

[0051] Referring to the attached Figure 4 ​​​- Appendix Figure 5 The step uses the corrected digital twin model with the current highest fidelity to perform a forward-looking simulation of the subsequent process from the current time to the end of the unloading operation, with the aim of quantifying the uncertainties in the future operation process and evaluating their impact on the structural safety.

[0052] In this embodiment, the forward-looking simulation adopts the Monte Carlo simulation method. The implementation of this method first requires simulation settings. With the system state at the current time (including the position, attitude, and speed of the barge, as well as the position of the module car, etc.) as the initial condition, a large enough simulation times , for example 1000 times, is set to ensure the convergence of the subsequent statistical results. The uncertainties in the future operation period are defined as random variables. These random variables at least include: the future environmental load, such as the significant wave height and spectral peak period of the wave, the values of which can be sampled from the joint probability distribution model established based on long-term observation data on site; the future tidal variation, which can be modeled as the superposition of a deterministic astronomical tide prediction value and a random error term subject to a normal distribution.

[0053] Before each simulation starts, the system extracts a set of specific values for each random variable and generates the environmental conditions for this simulation based on these values. Then, the corrected digital twin model is run to perform a complete time-domain dynamic simulation of the entire subsequent process from the current time to the complete unloading of the module car. This process is repeated times, each time using a new set of randomly sampled parameters.

[0054] In Monte Carlo simulation, to accurately simulate future environmental loads, especially random variables such as wind, wave, and current, their correlations need to be considered. In this embodiment, the joint probability distribution of random variables (such as the significant wave height and spectral peak period of the wave) can be modeled using Copula functions. This method can model the marginal distribution (e.g. and Weibull distribution) and their dependency structure (e.g. Kendall or Spearman coefficient) separately, thus preserving the true correlation between variables when sampling, making the simulation results closer to the actual situation.

[0055] After completing simulations, the simulation results are processed to generate the distribution of load effects. Specifically, from each simulation (the th, where ) generated by the time history data of the support force of the slab, the maximum value in the whole process is extracted and recorded as the maximum load effect sample Through this step, we get a A collection of samples .

[0056] To obtain the maximum load effect The continuous probability distribution of , using the non-parametric method of kernel density estimation (KDE), is fitted to its probability density function according to the above sample set The calculation formula is: ; in, is the variable of load effect; is the total number of Monte Carlo simulations; For the The maximum load effect sample obtained in this simulation; is the bandwidth parameter, and its value affects the smoothness of the estimation function; The final output of this step is the probability density function , the function is passed to step S5 for calculating the probability of structural failure.

[0057] Refer to the attached Figure 6 ,This step aims to conduct a probabilistic assessment of the safety of the structure based on the ,previous simulation results, and ultimately determine the lowest operable tide level that meets the ,preset safety level.

[0058] First, the probability of structural failure is calculated. In this embodiment, the structural failure of the dock deck is defined as a limit state event, that is, the load effect acting on the deck Exceeds its own structural resistance Due to the load effect and structural resistance are modeled as random variables, so the failure probability It is calculated by To obtain. Among them, That is, the structural resistance of the slab itself The value is less than or equal to the load effect on the slab The probability is obtained by integrating all the value intervals of the load effect. The specific calculation formula is: ; The physical meaning of this formula is: for any small load effect interval , the probability of occurrence of this load effect is Under this load, the condition for the structure to fail is that its resistance less than or equal to The probability of this event is given by the cumulative distribution function of the structural resistance model . Multiplying these two probabilities and integrating over all possible load effect values gives the total failure probability of the structure . Where, is the load effect probability density function obtained by step S4 through Monte Carlo simulation and kernel density estimation, is the cumulative distribution function of the structural resistance probability model defined in step S1.

[0059] After obtaining the structure failure probability at a single tide level, to determine the minimum operational 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 reference, a range of tide level downward exploration 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 this sequence, it is taken as a certain input boundary condition, and the complete process of step S4 is repeated. That is, for 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 structure failure probability at this tide level.

[0060] After completing 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 can form a visual risk curve of the structure failure probability changing with the tide level.

[0061] Finally, on this risk curve, the pre-set acceptable risk threshold (e.g. set according to relevant industry standards or engineering requirements ) is compared. Find the point on the risk curve where the failure probability is equal to . The horizontal coordinate value corresponding to this point is determined as the minimum operational 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 quantitative analysis method for the lowest tide level of bucket-type foundation barge operations, characterized in that: The following steps are involved: 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 dock deck, and the module vehicle during the barrel foundation barge operation; S2. Real-time collection of on-site operation data: During the barge loading 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 dock planking; S3. Model self-calibration: Based on the field operation data, the multi-physics field coupled digital twin model is self-calibrated. This step specifically comprises: using the multi-physics field coupled digital twin model and the key physical parameters at the current moment, predicting the structural response of the wharf slab at the next moment, and comparing the predicted structural response of the wharf slab at the next moment with the structural response data collected in real time at the next moment. Based on the deviation generated by the comparison, the key physical parameters are reversely updated. S4. Performing forward-looking simulation: Using the calibrated multi-physics field coupled digital twin model, performing forward-looking simulation on the subsequent process from the current moment to the completion of the barge loading operation to obtain the load effect distribution of the dock planking in the subsequent process; S5. Risk assessment and tide level determination: Based on the load effect distribution and the preset structural resistance model of the wharf slab, a risk assessment is performed and the probability of structural failure is calculated. Based on a comparison of the structural failure probability with a preset acceptable risk threshold, the lowest operable tide level that meets the threshold is determined.

2. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1 is characterized in that: In S1, the multi-physics field coupled digital twin model includes: 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 slab; A multi-body system dynamics model used to simulate the interactions between components.

3. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1 is characterized in that: In S1, the key physical parameters include at least: the hydrodynamic equivalent damping coefficient of the semi-submersible barge, the equivalent friction coefficient between the dock decking and the semi-submersible barge, or the equivalent vertical stiffness of the modular vehicle tires.

4. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1 is characterized in that: In S2, the structural response data is support force data measured in real time by load sensors arranged at key support points of the wharf planking.

5. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1 is characterized in that: In the model correction step of S3, the reverse update of the key physical parameters is performed by using an extended Kalman filter algorithm, taking the key physical parameters as state quantities to be estimated and the structural response of the wharf decking as an observed quantity, and performing recursive estimation and correction.

6. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1 is characterized in that: In S4, the forward-looking simulation adopts the Monte Carlo simulation method, which takes into account the randomness of future tide level changes and environmental loads.

7. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1 is characterized in that: In S5, the structural resistance model of the wharf slab is to define its ultimate bearing capacity as a probability distribution model.

8. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 7 is characterized in that: In S5, the structural failure probability The calculation method is: ； in, is the probability density function of the load effect distribution obtained by the forward-looking simulation, is the cumulative distribution function of the structural resistance model.

9. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1, characterized in that: The tide level determination in S5 further includes: Taking the current tide level as the benchmark, a series of decreasing tide points are simulated; For each virtual tide position, the risk assessment is repeatedly performed to calculate the corresponding structural failure probability, thereby generating a risk curve of structural failure probability varying with tide level; On the risk curve, a tide level corresponding to the acceptable risk threshold is determined as the minimum operable tide level.

10. The method for quantitatively analyzing the lowest tide level of a bucket-type foundation barge operation according to claim 1, characterized in that: In S3, when the absolute value of the deviation between the predicted structural response and the real-time acquired structural response data exceeds a preset deviation threshold, the reverse update of the key physical parameters is triggered.

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