Offshore pile bearing structure reliability analysis method considering multi-source uncertainty

Abstract

The invention discloses an offshore pile bearing structure reliability analysis method considering multi-source uncertainty. The method comprises the following steps: correcting wind wave load edge distribution data to obtain an estimated value; establishing a wind and wave load joint probability distribution model according to the estimated value; according to the wind and wave load joint probability distribution model, solving and obtaining wind speed-wave height boundary combination data corresponding to a set return period; obtaining a pile bearing structure response based on a preset single pile type offshore wind turbine finite element model; constructing a probability density evolution equation according to the response of the pile bearing structure; and on the basis of combination of a set initial condition and a probability conservation theory, obtaining an absorption boundary condition of a probability density evolution equation to obtain structural power reliability so as to realize reliability analysis of the offshore pile bearing structure. According to the method, the problem that the failure probability and the risk contribution of the single-pile fan are difficult to quantify reliably in an extreme load scene and a data scarcity condition due to the fact that only single-source uncertainty or insufficient description on load coupling is considered in a traditional method is solved.

Description

Technical Field

[0001] This invention relates to the field of structural reliability analysis technology, and in particular to a reliability analysis method for nearshore pile-bearing structures that considers multi-source uncertainties. Background Technology

[0002] During service, the structural and soil parameters of nearshore pile-supported structures often exhibit significant uncertainties. Reliability analysis methods can effectively account for the impact of such randomness or uncertainty, assess the failure probability of the structure under different loads, and thus provide a scientific basis for the design and engineering practice of nearshore pile-supported structures.

[0003] Furthermore, nearshore pile-supported structures often face the coupled effects of extreme wind and wave loads during operation, and these environmental loads themselves possess significant uncertainty. Therefore, structural reliability analysis must not only consider the uncertainties of structural and soil parameters but also encompass the influence of load randomness, i.e., conduct reliability analysis under multi-source uncertainty. This method can comprehensively consider the coupling effects between multiple random variables, providing more comprehensive theoretical support for structural reliability prediction and optimization design. Currently, reliability analysis considering multi-source uncertainties typically relies on numerical solutions to the generalized probability density evolution equation, which generally depends on the joint probability distribution of random parameters. However, the accuracy of joint probability distribution fitting models based on Copula theory is largely limited by the accuracy of the marginal distributions of random variables, thus only applicable when the sample size is sufficient. Furthermore, in situations where extreme wind and wave load data are scarce, traditional reliability analysis models often struggle to guarantee the accuracy of the calculation results, exhibiting certain limitations. Summary of the Invention

[0004] This invention provides a reliability analysis method for nearshore pile-bearing structures that considers multi-source uncertainties, in order to overcome the above-mentioned technical problems.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A reliability analysis method for nearshore pile-bearing structures considering multi-source uncertainties, specifically including the following steps: S1: Obtain edge distribution data of wind and wave loads on nearshore pile-supported structures, and correct the edge distribution data of wind and wave loads based on the Bayesian Bootstrap method to obtain Bayesian Bootstrap estimates. S2: Based on the Bayesian Bootstrap estimate, establish a joint probability distribution model of wind and wave loads based on the Copula function, and solve and obtain the wind speed-wave height boundary combination data corresponding to the set return period based on the joint probability distribution model of wind and wave loads. S3: Based on the number theory point selection method, select the dataset of pre-set nearshore pile bearing structure parameters and the boundary combination data of wind speed-wave height to obtain the set of basic random variables; Furthermore, the parameters of the near-shore pile-bearing structure include at least the elastic modulus of the pile foundation, the pile wall thickness, the internal friction angle of the soil, and the weight of the soil. S4: Based on the pre-set finite element model of a monopile offshore wind turbine, numerical simulation of the stochastic response capability of the offshore pile-bearing structure under multi-source uncertainty is performed according to the set of basic random variables to obtain the response of the pile-bearing structure. S5: Construct the probability density evolution equation based on the response of the pile-bearing structure; S6: Based on the set initial conditions and combined with the probability conservation theory, obtain the absorbing boundary conditions of the probability density evolution equation; solve the probability density evolution equation according to the absorbing boundary conditions and the initial conditions to obtain the residual probability density; perform integral processing on the residual probability density and obtain the structural dynamic reliability, thereby realizing the reliability analysis of the near-shore pile-bearing structure.

[0006] Furthermore, S1 specifically includes the following steps: S11: Obtain the edge distribution data of wind and wave loads on the nearshore pile-supported structure, and the edge distribution data of wind and wave loads is the measured sample of average wind speed or wave height. and , Indicates the measured sample The One element; S12: Define parameter quantities As a measured sample The statistical parameters include either the mean parameter or the variance parameter. S13: From uniform distribution Randomly selected from The process involves generating several random numbers, sorting them in ascending order to obtain a sequence list, and then using this sequence list to obtain sample observations. posterior probability ; And the posterior probability The expression is

[0007]

[0008]

[0009] In the formula: Represents the first in the sequence list A random number; Represents the first in the sequence list A random number; S14: Based on the Bayesian Bootstrap method, according to posterior probability For statistical parameters Make corrections to obtain the Bayesian Bootstrap estimate, i.e., the estimated value. ; And the estimated value The formula for obtaining it is

[0010]

[0011] In the formula: N Indicates the number of groups in the preset sample weight set; Represents sample observations The sample weights.

[0012] Furthermore, S2 specifically includes the following steps: S21: Based on the Bayesian Bootstrap estimate, establish a joint probability distribution model of wind and wave loads based on the Copula function; The expression for the joint probability distribution model of wind and wave loads is:

[0013] In the formula: This indicates the relationship between wind speed and wave height boundary combinations. The joint distribution function of the two variables; Represents the Copula function; and These represent the estimated values ​​after Bayesian Bootstrap correction. The obtained edge distribution functions for wind speed and wave height; This represents the correlation parameter of the Copula function estimated by maximizing the log-likelihood function; S22: Based on the joint probability distribution model of wind and wave loads, solve and obtain the boundary combination data of wind speed and wave height corresponding to the set return period; And the formula for obtaining the wind speed-wave height boundary combination data is as follows:

[0014] In the formula: Indicates the wind speed-wave height boundary combination The corresponding return period; and They represent average wind speeds, respectively. With significant wave height .

[0015] Furthermore, the probability density evolution equation constructed in S5 is expressed as follows:

[0016] In the formula: Denotes the joint probability density function; This represents the basic variables that reflect the randomness of the system's physical parameters; t Indicates time parameters; Represents a specific sample given a random parameter vector; This indicates the rate of response evolution of the pile-bearing structure. This represents the realized value of the state vector, i.e., the time history displacement at the mud surface of the pile foundation.

[0017] Furthermore, S6 specifically includes the following steps: S61: Set initial conditions, the expression of which is:

[0018] In the formula: Indicates a given initial value; This represents the Dirac function; express The probability density function; S62: Based on the given initial conditions and combined with probability conservation theory, the absorbing boundary conditions of the probability density evolution equation are obtained, and their expression is:

[0019] In the formula: This indicates the safe zone for displacement at the mud surface of the pile foundation. S63: Solve the probability density evolution equation based on the absorbing boundary conditions and initial conditions to obtain the residual probability density and denot it as... ; for the residual probability density Integrate and obtain structural dynamic reliability This enables reliability analysis of near-shore pile-bearing structures. Furthermore, the expression for integrating the residual probability density and obtaining the structural dynamic reliability is as follows: .

[0020] Beneficial Effects: This invention provides a reliability analysis method for nearshore pile-bearing structures considering multi-source uncertainties. Starting from the perspective of multi-source uncertainty modeling and solution, it establishes the joint probability distribution relationship of wind and waves based on Copula theory. Furthermore, it introduces Bayesian Bootstrap theory and proposes a correction strategy oriented towards edge distributions. The core advantage of this strategy is that it can increase the sample estimation capacity without adding additional samples, thereby significantly improving the estimation accuracy of load edge distributions under data-scarce conditions. In addition, this invention integrates multi-source uncertainty information from soil, structure, and loads, and utilizes probability density evolution theory for reliability analysis of nearshore pile-bearing structures under multi-source uncertainties. This significantly improves the computational efficiency and accuracy of reliability analysis for nearshore pile-bearing structures. Its reliability analysis results regarding the joint correlation of wind, wave, and loads and multi-source uncertainties can provide a strong reference for the design and engineering practice of nearshore pile-bearing structures. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a reliability analysis method for nearshore pile-bearing structures that considers multi-source uncertainties according to the present invention; Figure 2 This is a schematic diagram of the finite element numerical model in this embodiment; Figure 3 This is a graph showing the measured wind and wave data in this embodiment; Figure 4 This is a schematic diagram illustrating the Bayesian Bootstrap parameter correction in this embodiment; Figure 5 This is a simulation diagram of the combined wind and wave probability distribution in this embodiment; Figure 6 This is a schematic diagram of the initial point set selection in this embodiment; Figure 7 This is a schematic diagram of the probability density surface in this embodiment; Figure 8 This diagram shows the results of the dynamic reliability and failure probability of the monopile offshore wind turbine structure in this embodiment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0024] This invention belongs to the field of structural reliability analysis, focusing on the multi-source uncertainty reliability analysis problem of nearshore pile-supported structures in wind farms. In the nearshore environment, the load, material and geometric parameters, foundation soil characteristics, and time-varying nature of the sea state field of pile-supported structures all exhibit significant uncertainties. Among them, load uncertainty includes not only the uncertainties of conventional wind and wave loads, but also the correlation between wind-wave coupled loads and the sea state field. Traditional methods often only consider single-source uncertainties or provide insufficient descriptions of load coupling, making it difficult to reliably quantify the failure probability and risk contribution of monopile wind turbines under extreme load scenarios and data scarcity conditions. Therefore, this embodiment provides a reliability analysis method for nearshore pile-supported structures that considers multi-source uncertainties, such as... Figure 1 As shown, the specific steps include: S1: Obtain edge distribution data of wind and wave loads on nearshore pile-supported structures, and correct the edge distribution data of wind and wave loads based on the Bayesian Bootstrap method to obtain Bayesian Bootstrap estimates. This includes the following steps: S11: Obtain the edge distribution data of wind and wave loads on the nearshore pile-supported structure, and the edge distribution data of wind and wave loads is the measured sample of average wind speed or wave height. and , Indicates the measured sample The One element; S12: Define parameter quantities As a measured sample The statistical parameters include either the mean parameter or the variance parameter. S13: From uniform distribution Randomly selected from The process involves generating several random numbers, sorting them in ascending order to obtain a sequence list, and then using this sequence list to obtain sample observations. posterior probability ; And the posterior probability The expression is (1) (2) (3) In the formula: Represents the first in the sequence list A random number; Represents the first in the sequence list A random number; in this embodiment, by repeatedly executing step S13, a random number can be obtained. N Group( N Take a sample weight set of 1000 or more; S14: Based on the Bayesian Bootstrap method, according to posterior probability For statistical parameters Make corrections to obtain the Bayesian Bootstrap estimate, i.e., the estimated value. ; In this embodiment, the mean is used as an example, and the estimated value is corrected by Bayesian Bootstrap. The formula for obtaining it is (4) (5) In the formula: N Indicates the number of groups in the preset sample weight set; Represents sample observations Sample weights; In this embodiment, the normal distribution is used as an example, and its distribution formula is: (6) In the formula: and These are the mean and standard deviation, respectively, and both can be corrected using the Bayesian Bootstrap method to obtain corrected estimates. ; This represents the sample value, i.e., the average wind speed or wave height. S2: Based on the Bayesian Bootstrap estimate, establish a joint probability distribution model of wind and wave loads based on the Copula function, and solve and obtain the wind speed-wave height boundary combination data corresponding to the set return period based on the joint probability distribution model of wind and wave loads. Specifically, the following steps are included: S21: Based on the Bayesian Bootstrap estimate, establish a joint probability distribution model of wind and wave loads based on the Copula function; The expression for the joint probability distribution model of wind and wave loads is: (7) In the formula: This represents the boundary combination of wind speed and wave height determined by formula (6). Marginal distribution function and The joint distribution function of the two variables; Represents the Copula function; and These represent the estimated values ​​after Bayesian Bootstrap correction. The obtained edge distribution functions for wind speed and wave height; This represents the correlation parameter of the Copula function estimated by maximizing the log-likelihood function; S22: Based on the joint probability distribution model of wind and wave loads, solve and obtain the boundary combination data of wind speed and wave height corresponding to the set return period; And the formula for obtaining the wind speed-wave height boundary combination data is as follows: (8) In the formula: Indicates the wind speed-wave height boundary combination The corresponding return period; and They represent average wind speeds, respectively. With significant wave height In this embodiment, based on the multiple return periods of 50 years and 100 years widely used in marine structural design, the wind speed-wave height corresponding to a specific return period is solved using equation (8). Boundary combination, that is, given the return period, the corresponding combination can be obtained according to equation (7). and ; S3: Based on the number theory point selection method, a dataset of pre-set nearshore pile bearing structure parameters and wind speed-wave height boundary combination data are selected to obtain a series of basic random variables to form a basic random variable set; and the nearshore pile bearing structure parameters include at least the pile foundation elastic modulus, pile wall thickness, soil internal friction angle and soil weight; the number theory point selection method described in this embodiment is a known existing technical means, and will not be elaborated on further here. S4: Based on the pre-set finite element model of a monopile offshore wind turbine, numerical simulation of the stochastic response capability of the offshore pile-bearing structure under multi-source uncertainty is performed according to the set of basic random variables to obtain the response of the pile-bearing structure. Specifically, this embodiment takes a monopile offshore wind turbine as the research object and establishes a finite element calculation model using finite element software (such as Plaxis3D finite element calculation platform); further, in order to solve the response probability information of the monopile offshore wind turbine structure under multi-source uncertainty conditions such as load, soil and materials, the elastic modulus of the pile foundation is selected according to the number theory point selection method.E ), pile wall thickness ( ), soil internal friction angle ( φ ), soil weight ( γ ) and the previously obtained wind speed-wave height A basic set of random variables is obtained, and these sets are substituted into the finite element model for numerical calculation to obtain the random response corresponding to each set of variables. In this embodiment, the displacement time history at the mud surface of the offshore wind turbine pile foundation is obtained by combining the wind load borne by the blades, the wind load borne by the tower, the wave force borne by the pile foundation, and the basic set of random variables with the offshore wind turbine finite element numerical model. Generally, the wind load borne by the wind turbine structure can be divided into two parts: the wind load borne by the blades and the wind load borne by the tower. F B and the wind load borne by the tower F T : (9) (10) In the formula: To represent air density, take 1.225 kg / m³. 3 ; R T Indicates the rotor radius; and These represent average wind speed and fluctuating wind speed, respectively. This represents the drag coefficient, taken as 1.2; d Indicates the diameter of the tower body; Indicates the dimensionless thrust coefficient; Furthermore, the dimensionless thrust coefficient is defined as... (11) in: This represents the axial induction coefficient, which is set to 0.5 in this embodiment for subsequent related calculations; For average wind speed It can be expressed in the form of an exponential function: (12) In the formula: Indicates standard height The average wind speed at a location (usually 10m) is taken here from the average wind speed obtained in Table 4. Indicates the calculated height; α This represents the surface roughness coefficient, taken as 0.12; As for pulsed wind speed In engineering practice, the Davenport spectrum is typically solved using the harmonic superposition method. The specific calculation expression for the Davenport spectrum is as follows: (13) In the formula: This represents the fluctuating wind speed spectrum, also known as the Davenport spectrum. Indicates frequency; K This represents the correlation coefficient of ground roughness; x Indicates intermediate calculation parameters and ; For wave loads, at any location The wave force at the point can be solved by the following formula. (14) (15) In the formula: Represents the wave force spectrum; and Represents a set of orthogonal random variables; t Indicates time; Indicates angular frequency; Indicates frequency interval; Indicates the number of truncation frequency terms; S5: Construct the probability density evolution equation based on the response of the pile-bearing structure; its expression is as follows: (16) In the formula: Denotes the joint probability density function; This represents the basic variables that reflect the randomness of the system's physical parameters; t Indicates time parameters; Represents a specific sample given a random parameter vector; This represents the rate of response evolution of the pile-bearing structure, i.e., the rate of change of a series of random responses with respect to time. t Differentiate; This represents the realized value of the state vector, i.e., the time history displacement at the mud surface of the pile foundation; S6: Based on the set initial conditions and combined with the probability conservation theory, obtain the absorbing boundary conditions of the probability density evolution equation; solve the probability density evolution equation according to the absorbing boundary conditions and the initial conditions to obtain the residual probability density; perform integral processing on the residual probability density and obtain the structural dynamic reliability, thereby realizing the reliability analysis of the near-shore pile-bearing structure, specifically including the following steps: S61: Set initial conditions, the expression of which is: (17) In the formula: Indicates a given initial value; This represents the Dirac function; express The probability density function; S62: Based on the given initial conditions and probability conservation theory, the absorbing boundary conditions of the probability density evolution equation are obtained, and their expression is: (18) In the formula: This indicates the safe zone for displacement at the mud surface of the pile foundation. S63: Solve the probability density evolution equation based on the absorbing boundary conditions and initial conditions to obtain the residual probability density and denot it as... ; for the residual probability density Integrate and obtain structural dynamic reliability This enables reliability analysis of near-shore pile-bearing structures. In this embodiment, the method of solving the probability density evolution equation based on the absorbing boundary conditions and initial conditions is a well-known existing technique, which will not be elaborated further here. Furthermore, the expression for integrating the residual probability density and obtaining the structural dynamic reliability is as follows: (19).

[0025] The specific example in this embodiment is as follows: A numerical model is constructed using the NREL 5MW wind turbine as an example, such as... Figure 2 As shown. Furthermore, referring to the API specification (2011), a sand-soil py-spring model is used to simulate the horizontal strain-stress relationship of the soil. For the vertical force transfer of the pile-soil interaction, tz-spring and Qz-spring models are used for simulation, respectively. Specific parameters are detailed in Tables 1 and 2.

[0026] Table 1. Main parameters of soil layers around and at the pile tip

[0027] Table 2. Main structural parameters

[0028] Wind and wave load data are based on the measured annual extreme average wind speed from the Weizhou Island marine station. V s ) and corresponding significant wave height ( Records, such as Figure 3As shown in the figure. Existing research indicates that the optimal fitting distribution for annual extreme wind speed is the lognormal distribution, while the optimal probability model for significant wave height is the Gamma distribution. It is worth noting that in engineering statistics, a sample size n ≤ 30 is typically defined as a small sample. Due to the limited nature of measured data, the original sample's ability to represent the overall distribution is insufficient, and the classic MLE (Maximum Likelihood Estimation) method may produce significant estimation biases due to individual extreme data points. Therefore, this embodiment proposes an optimization and correction strategy for the edge distribution functions of wind and wave loads by introducing the Bayesian Bootstrap method (i.e., correcting the edge distribution data of wind and wave loads based on the Bayesian Bootstrap method), aiming to improve the robustness and statistical reliability of parameter estimation. The specific correction results are shown in the figure. Figure 4 As shown.

[0029] B. Construction of the joint probability distribution of wind and waves: To address the problem of marine environmental load modeling under unidirectional wind and wave conditions, a joint probability distribution model of annual extreme mean wind speed and significant wave height is constructed based on Copula theory. Specifically, a joint distribution model of four typical Copula functions—Gaussian, Clayton, Frank, and Gumbel—is constructed by combining Table 3.

[0030] Table 3. Copula Model Parameters and AIC Comparison

[0031] Note: The correlation coefficient is The standard normal cumulative distribution function; This represents the inverse cumulative distribution function of the standard normal distribution. Further, model selection was performed using the AIC information criterion, and the results are shown in Table 3. The analysis results show that the Clayton Copula model has a smaller AIC value, significantly better than other candidate models. Therefore, the Clayton Copula model was selected for subsequent analysis, and the resulting joint wind and wave distribution is shown in Table 3. Figure 5 As shown.

[0032] (20) In the formula, K Indicates the number of model parameters; L The maximum likelihood estimate is given; a smaller AIC value indicates a better fit. Detailed comparison results are shown in Table 3.

[0033] C. Solving for loads with different return periods: Based on the joint probability model constructed by Clayton Copula, and considering the multiple current period requirements such as 10 years, 20 years, and 50 years widely used in marine structure design, the wind speed-wave height boundary combination corresponding to a specific return period is solved by combining Equation (8), as shown in Table 4. Table 4. Combinations of wind and wave loads with different return periods

[0034] D. Select the initial point set and perform probability density evolution analysis. First, using parameters such as the elastic modulus of the pile foundation, the internal friction angle of the soil, the weight of the soil, the average wind speed, and the wave height as basic random variables, the corresponding initial target point set is obtained by selecting from these parameters using number theory point selection methods. Figure 6 As shown. Further, using the initial point set as basic parameters and based on the established finite element numerical model of the offshore wind turbine, the displacement time history at the mud surface of the pile foundation is calculated. Based on this, the structural response evolution rate is obtained by differentiating the displacement time history with respect to time. Furthermore, the structural response probability density surface of the joint probability density function is obtained as follows: Figure 7 As shown. Finally, the structural response dynamic reliability is obtained by solving according to the first-to-failure criterion, as shown in the figure. Figure 8 As shown.

[0035] The beneficial effects of the method described in this embodiment are: The method described in this embodiment starts from the perspective of multi-source uncertainty modeling and solution. It establishes the joint probability distribution relationship of wind and waves based on Copula theory, and then introduces Bayesian Bootstrap theory to propose a correction strategy for edge distributions. The core advantage of this strategy is that it can increase the sample estimation capacity without adding additional samples, thereby significantly improving the estimation accuracy of load edge distributions under data-scarce conditions. Furthermore, this invention integrates multi-source uncertainty information such as soil, structure, and load, and uses probability density evolution theory to conduct reliability analysis of nearshore pile-bearing structures under multi-source uncertainty. This significantly improves the computational efficiency and accuracy of reliability analysis of nearshore pile-bearing structures. Its reliability analysis results regarding the joint correlation of wind, wave, and loads and multi-source uncertainty can provide a strong reference for the design and engineering practice of nearshore pile-bearing structures.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for offshore pile-supported structure reliability analysis considering multi-source uncertainty, characterized in that, Specifically comprising the following steps: S1: Obtain the wind and wave load edge distribution data of the offshore pile supporting structure, and correct the wind and wave load edge distribution data based on the Bayesian Bootstrap method to obtain the Bayesian Bootstrap estimation value; S2: According to the Bayesian Bootstrap estimation value, establish a wind and wave load joint probability distribution model based on the Copula function, and solve and obtain the wind speed-wave height boundary combination data corresponding to the set return period according to the wind and wave load joint probability distribution model; S3: Based on the number theory point selection method, select the preset offshore pile supporting structure parameter data set and the wind speed-wave height boundary combination data to obtain the basic random variable set; And the offshore pile supporting structure parameters at least include the pile foundation elastic modulus, the pile wall thickness, the soil internal friction angle and the soil bulk density; S4: Based on the preset single-pile offshore wind turbine finite element model, the offshore pile supporting structure random response ability is numerically simulated under multi-source uncertainty according to the basic random variable set to obtain the pile supporting structure response; S5: Construct the probability density evolution equation according to the pile supporting structure response; S6: Based on the set initial condition and combined with the probability conservation theory, the absorbing boundary condition of the probability density evolution equation is obtained; the probability density evolution equation is solved according to the absorbing boundary condition combined with the initial condition to obtain the residual probability density; the residual probability density is integrated to obtain the structural dynamic reliability, and then the offshore pile supporting structure reliability analysis is realized.

2. The offshore pile-supported structure reliability analysis method considering multi-source uncertainty according to claim 1, characterized in that, The S1 specifically comprises the following steps: S11: Obtain the wind wave load edge distribution data of the offshore pile-supported structure, and the wind wave load edge distribution data is a measured sample of average wind speed or wave height and , represents the first element of the measured sample . ​ S12: defining a parameter quantity as a statistical parameter of the measured sample and the statistical parameter includes any one of a mean parameter or a variance parameter; S13: Randomly extract a number from a uniform distribution Sort the random numbers in ascending order to obtain a sequence list, and obtain sample observation values according to the sequence list Posterior probability ;​ and the posterior probability is expressed as In the formulae: represents the i-th random number in the sequence list; represents the i-th random number in the sequence list; represents the i-th random number in the sequence list; represents the i-th random number in the sequence list; S14: Based on Bayesian Bootstrap method, according to posterior probability correcting statistical parameters to obtain Bayesian Bootstrap estimation value, i.e. estimation value ; and the estimated value The acquisition formula is where: N represents the number of sets of pre-set sample weights; represents the sample weight of the sample observation value .

3. The offshore pile-supported structure reliability analysis method considering multi-source uncertainty according to claim 2, characterized in that, The S2 specifically comprises the following steps: S21: According to the Bayesian Bootstrap estimation value, a wind and wave load joint probability distribution model based on the Copula function is established; And the expression of the wind and wave load joint probability distribution model is wherein: represents a bivariate joint distribution function for the wind speed-wave height boundary combination represents a Copula function; and respectively represent the Bayesian Bootstrap corrected estimates of the wind speed, wave height marginal distribution functions; represents the correlation parameter of the Copula function estimated by maximizing the log-likelihood function;​​ S22: According to the wind and wave load joint probability distribution model, the wind speed-wave height boundary combination data corresponding to the set return period is solved and obtained; And the formula for obtaining the wind speed-wave height boundary combination data is where: denotes the wind speed-wave height boundary combination the corresponding return period; denotes the average wind speed denotes the significant wave height and the effective wave height .

4. The offshore pile-supported structure reliability analysis method considering multi-source uncertainty according to claim 3, characterized in that, The probability density evolution equation constructed in S5 has the expression wherein: denotes the joint probability density function; denotes the basic variables reflecting the randomness of the system physical parameters; t denotes the time parameter; denotes a specific sample of the given random parameter vector; denotes the response evolution speed of the pile-supported structure response; denotes the state vector realization, i.e. the time history displacement at the mud surface of the pile foundation.

5. The offshore pile-supported structure reliability analysis method considering multi-source uncertainty according to claim 4, characterized in that, The S6 specifically comprises the following steps: S61: Set the initial condition, which has the expression wherein: denotes a given initial value; denotes the Dirac function; denotes the probability density function of S62: Based on the set initial condition combined with the probability conservation theory, the absorbing boundary condition of the probability density evolution equation is obtained, which has the expression In the formula: represents the safety zone of displacement at the pile foundation mud surface; S63: Solve the probability density evolution equation according to the absorption boundary condition combined with the initial condition, obtain the residual probability density and mark it as ; integrate the residual probability density and obtain the structural dynamic reliability , and thus realize the offshore pile-supported structure reliability analysis; And the expression of the integral of the residual probability density and the structural dynamic reliability is 。

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