Method, device and medium for determining stress probability distribution of girth weld of buried pipeline

Abstract

The present invention relates to the technical field of pipeline detection, and discloses a method, device, equipment and medium for determining the probability distribution of girth weld stress of a buried pipeline. The method comprises: obtaining a plurality of moisture content data of soil in a target area within a preset time period and a plurality of buried depth data of the buried pipeline; randomly sampling a plurality of moisture content data and a plurality of buried depth data according to the probability distribution of the moisture content of the soil and the buried depth of the buried pipeline in the target area respectively corresponding to the plurality of moisture content data and the plurality of buried depth data; simulating a plurality of service environments of the buried pipeline through the sampled moisture content samples, the buried depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, so as to obtain the girth weld stress of the buried pipeline under each service environment; and realizing high reliability evaluation of the pipeline girth weld by characterizing the probability density function of the girth weld stress probability distribution of the buried pipeline under several service environments.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline detection, and in particular to a method, device, equipment and medium for determining stress probability distribution of girth welds of buried pipelines. Background Art

[0002] As global energy demand continues to grow, buried pipelines are the main mode of traditional energy transportation, and their construction and safe operation are becoming increasingly important. In the case of wide pipeline construction areas, complex and diverse terrain, and changing hydrological and soil conditions, pipeline failures may lead to severe threats to pipeline construction and operation due to geological disasters such as landslides, earthquakes, and subsidence.

[0003] Through statistical analysis of gas pipeline accidents at home and abroad, it is found that failure of pipeline girth welds due to welding and corrosion is the main reason for pipeline failure. Due to the limitations of welding technology and construction environment, long-distance pipelines inevitably produce defects, which are easy to cause further failure under external loads, resulting in major accidents, causing huge losses to the natural environment, casualties and the economy. In order to avoid major accidents in pipelines, it is very important to analyze the reliability of pipeline girth welds during service.

[0004] At present, when analyzing the reliability of buried pipeline girth welds, certain influencing factors, such as soil load, defects, material properties, etc., are used to evaluate the reliability of buried pipeline girth welds using numerical simulation. However, soil load is uncertain and random, and it is not accurate to rely on certain soil loads to determine its impact on pipeline girth welds for reliability evaluation of pipeline girth welds. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, equipment and medium for determining the probability distribution of girth weld stress of a buried pipeline, which detects the girth weld stress of the buried pipeline on the basis of considering the randomness of the soil load, and provides valuable support for the reliability assessment of the buried pipeline.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for determining the probability distribution of stress of a girth weld of a buried pipeline, comprising the following steps:

[0007] Obtaining a number of moisture content data of the soil in the target area within a preset time period, and a number of buried depth data of the buried pipelines in the target area;

[0008] A probability density function is constructed based on a plurality of moisture content data to indicate the probability distribution of soil moisture content in a target area, and a probability density function is constructed based on a plurality of depth data to indicate the probability distribution of buried pipeline depths in a target area;

[0009] According to the probability distribution of soil moisture content in the target area and the probability distribution of buried pipeline depth in the target area, a number of moisture content data and a number of buried depth data are randomly sampled respectively;

[0010] Through the functional relationship between soil moisture content and soil shear strength, soil shear strength samples corresponding to the sampled moisture content samples are obtained, and through the soil shear strength samples, the sampled burial depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, several service environments of the buried pipeline are simulated to obtain the girth weld stress of the buried pipeline under each service environment;

[0011] According to the girth weld stress of buried pipelines under several service environments, a probability density function is constructed to indicate the probability distribution of girth weld stress in the target area.

[0012] In some optional embodiments, the probability density function for indicating the probability distribution of soil moisture content in the target area, the probability density function for indicating the probability distribution of buried pipeline depth in the target area, and the probability density function for indicating the probability distribution of girth weld stress in the target area are all constructed by the following steps:

[0013] The moisture content data, the burial depth data and the girth weld stress are respectively taken as a target parameter. For each target parameter, the target parameters are divided into multiple sample intervals according to the initial distribution of the target parameters displayed by the histogram.

[0014] The maximum likelihood estimation method is used to estimate the unknown parameters in the probability distribution model according to the probability distribution function of the preset probability distribution model and the target parameters in any one or more sample intervals;

[0015] Substitute the estimated unknown parameters into the probability distribution function of the probability distribution model to obtain the probability density function corresponding to each target parameter.

[0016] In some optional embodiments, the probability distribution model includes the following types: normal distribution, lognormal distribution, Weibull distribution and Gumbel distribution;

[0017] Substituting the estimated unknown parameters into the probability distribution function of the probability distribution model to obtain the probability density function corresponding to each target parameter includes:

[0018] For each target parameter, according to each probability distribution model, obtain the corresponding probability density function;

[0019] The Akaike information content (AIC) criterion and the Bayesian information content (BIC) criterion were used to evaluate the fitting results of the probability density functions corresponding to various probability distribution models.

[0020] The probability density function with the lowest AIC index of the AIC criterion and the lowest BIC index of the BIC criterion after evaluation is taken as the probability density function corresponding to the target parameter.

[0021] In some optional embodiments, the force exerted by the soil in the target area indicated by the pipe-soil model on the buried pipeline is simulated by soil springs, and the force exerted by the soil on the buried pipeline is decomposed into soil spring forces in the following multiple directions: axial soil spring force of the pipeline, horizontal transverse soil spring force of the pipeline, vertical upward soil spring force of the pipeline, and vertical downward soil spring force of the pipeline.

[0022] In some optional embodiments, the pipeline axial soil spring force is expressed by the following formula:

[0023]

[0024] In the formula, D represents the diameter of the buried pipeline, α represents the adhesion coefficient, c represents the cohesion of the soil, and H represents the depth of the buried pipeline. represents the bulk density of soil, K0 represents the static earth pressure coefficient, and δ represents the friction angle of the pipe-soil interface;

[0025] The horizontal lateral soil spring force of the pipeline is expressed by the following formula:

[0026]

[0027] Where N ch Represents the horizontal compressive capacity coefficient of clay, N qh It represents the coefficient of horizontal compressive strength of sand;

[0028] Under the horizontal lateral soil spring force of the pipeline, the lateral yield displacement of the soil spring is expressed by the following formula:

[0029]

[0030] The vertical upward soil spring force of the pipeline is expressed by the following formula:

[0031]

[0032] Where N cν represents the vertical lift coefficient of clay, N qν It represents the vertical lift coefficient of sand;

[0033] The vertical downward soil spring force of the pipeline is expressed by the following formula:

[0034]

[0035] Where N c 、N q 、Nγ Both represent the soil's compressive capacity coefficient.

[0036] In some optional embodiments, the soil shear strength sample, the sampled burial depth sample and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area are used to simulate several service environments of the buried pipeline to obtain the girth weld stress of the buried pipeline in each service environment, including:

[0037] For each soil shear strength sample and the corresponding burial depth sample, the soil shear strength sample and the corresponding burial depth sample are respectively substituted into the formulas of the pipeline axial soil spring force, the pipeline horizontal lateral soil spring force, the pipeline vertical upward soil spring force, the pipeline vertical downward soil spring force and the lateral yield displacement of the soil spring, and the soil spring forces in multiple directions and the lateral yield displacement of the soil spring are obtained;

[0038] The soil spring forces in multiple directions and the lateral yield displacement of the soil spring are substituted into the pipe-soil model to obtain a new pipe-soil model. The new pipe-soil model is used to represent the service environment of the buried pipeline. The girth weld stress of the buried pipeline under different service environments is obtained from the new pipe-soil model.

[0039] In some optional embodiments, the soil shear strength is determined by the cohesion and friction angle of the soil, and the girth weld stress of the buried pipeline includes the bending moment stress and axial force stress of the girth weld.

[0040] An embodiment of the present invention further provides a device for determining probability distribution of girth weld stress of a buried pipeline, comprising:

[0041] A data acquisition module is used to acquire a plurality of moisture content data of the soil in the target area within a preset time period, and a plurality of buried depth data of the buried pipelines in the target area;

[0042] A first function construction module is used to construct a probability density function for indicating the probability distribution of soil moisture content in a target area according to a plurality of moisture content data, and to construct a probability density function for indicating the probability distribution of buried depth of buried pipelines in the target area according to a plurality of buried depth data;

[0043] The parameter simulation module is used to randomly sample a number of moisture content data and a number of burial depth data according to the probability distribution of soil moisture content in the target area and the probability distribution of buried pipeline depth in the target area; obtain soil shear strength samples corresponding to the sampled moisture content samples through the functional relationship between soil moisture content and soil shear strength, and simulate several service environments of the buried pipeline through the soil shear strength samples, the sampled burial depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, so as to obtain the girth weld stress of the buried pipeline under each service environment;

[0044] The second function building module is used to build a probability density function for indicating the probability distribution of girth weld stress in a target area according to girth weld stress of buried pipelines under several service environments.

[0045] An embodiment of the present invention also provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned method for determining the probability distribution of stress of the girth weld of a buried pipeline.

[0046] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for determining the probability distribution of stress of the girth weld of a buried pipeline when executed by a processor.

[0047] The method for determining the probability distribution of stress of the girth weld of a buried pipeline provided by the present invention has at least the following beneficial effects:

[0048] Through several moisture content data of soil in a certain area and several burial depth data of buried pipelines, a probability density function indicating the probability distribution of soil moisture content and the probability distribution of buried pipeline depth in the target area is constructed. Then, several moisture content data and several burial depth data are randomly sampled according to the probability distribution of the above two data to describe the randomness of the two data. Since soil moisture content will affect the shear strength of soil, the soil shear strength corresponding to the sampled moisture content data can be obtained. Then, the various service environments of buried pipelines can be simulated through the obtained soil shear strength, the sampled burial depth and the pipe-soil model that characterizes the interaction force between soil and buried pipelines in the target area. Finally, the girth weld stress of the buried pipeline under each service environment is obtained.

[0049] Not only the randomness of soil load (i.e., soil moisture content) but also the randomness of pipeline burial depth is taken into account. By constructing the girth weld stress of buried pipelines under various service environments and constructing a probability density function to indicate the probability distribution of girth weld stress in the target area, the probability distribution of girth weld stress of buried pipelines can be obtained when the soil load and pipeline burial depth are uncertain. Based on this, the reliability evaluation of pipeline girth welds can be carried out, and the evaluation results will be more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0051] Figure 1is a flow chart of a method for determining stress probability distribution of a girth weld of a buried pipeline provided according to an embodiment of the present invention;

[0052] Figure 2 is a curve diagram of moisture content of different soil layers provided according to an embodiment of the present invention;

[0053] Figure 3 A soil moisture content data histogram and different distribution fitting curves are provided according to an embodiment of the present invention;

[0054] Figure 4 A pipeline burial depth distribution fitting curve diagram provided according to an embodiment of the present invention;

[0055] Figure 5 A compaction sample diagram provided according to an embodiment of the present invention;

[0056] Figure 6 is a sheared specimen diagram provided according to an embodiment of the present invention;

[0057] Figure 7 is a curve diagram of the relationship between volume moisture content and cohesion provided according to an embodiment of the present invention;

[0058] Figure 8 is a curve relationship diagram of volumetric water content and internal friction angle provided according to an embodiment of the present invention;

[0059] Fig. 9 is a pipeline dimension diagram provided according to an embodiment of the present invention;

[0060] Fig.10 is a stress probability solution flow chart provided according to an embodiment of the present invention;

[0061] Fig.11 is a fitting curve diagram of axial force stress distribution provided according to an embodiment of the present invention;

[0062] Fig.12 It is a bending moment stress distribution fitting curve diagram provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present invention can be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0064] An embodiment of the present invention relates to a method for determining the probability distribution of stress of a girth weld of a buried pipeline. The implementation details of the method for determining the probability distribution of stress of a girth weld of a buried pipeline of this embodiment are specifically described below. The following content is only the implementation details provided for the convenience of understanding and is not necessary for implementing this solution.

[0065] The specific process of the method for determining the stress probability distribution of the girth weld of the buried pipeline in this embodiment can be as follows: Figure 1 As shown, including:

[0066] Step 101, obtaining a plurality of moisture content data of soil in a target area within a preset time period, and a plurality of buried depth data of underground pipelines in the target area.

[0067] Specifically, the Global Land Data Assimilation System (GLDAS) uses land surface modeling and data assimilation technology to provide high-precision data support for global land surface dynamics by ingesting satellite and ground observation data products, including atmospheric analysis fields, precipitation fields, etc. Therefore, the soil moisture data of this embodiment uses GLDAS data to obtain several moisture content data of the target area in the GLDAS data within a preset time period. At the same time, this embodiment obtains several buried depth data of buried pipelines in the target area, among which the deepest buried depth of the pipeline can reach 8m and the shallowest is about 1m.

[0068] In the specific implementation, Figure 2 The soil moisture content change curve of different depths of soil layers in the target area is shown, among which the 0-10cm soil layer is the topsoil, and the soil moisture content is most sensitive to precipitation and evaporation. The 100-200cm soil layer has a gentle change in moisture content, and responds slowly to precipitation and evaporation factors, and can best reflect the external environmental factors of the actual service of the pipeline. Therefore, this embodiment finally takes the 100-200cm soil layer in the target area as the specific moisture content data collection object.

[0069] Step 102: construct a probability density function indicating the probability distribution of soil moisture content in the target area according to a plurality of moisture content data, and construct a probability density function indicating the probability distribution of buried depth of buried pipelines in the target area according to a plurality of buried depth data.

[0070] Specifically, in this embodiment, the probability density function for indicating the probability distribution of soil moisture content in the target area and the probability density function for indicating the probability distribution of the buried depth of the buried pipeline in the target area are constructed in the same way, both using the following steps:

[0071] The moisture content data and the burial depth data are respectively taken as a target parameter. For each target parameter, several target parameters are divided into multiple sample intervals according to the initial distribution of several target parameters displayed by the histogram; the maximum likelihood estimation method is adopted to estimate the unknown parameters in the probability distribution model according to the probability distribution function of the preset probability distribution model and the target parameters in any one or more sample intervals; the estimated unknown parameters are substituted into the probability distribution function of the probability distribution model to obtain the probability density function corresponding to each target parameter.

[0072] In one example, the probability distribution models include the following types: normal distribution, lognormal distribution, Weibull distribution and Gumbel distribution. When the estimated unknown parameters are substituted into the probability distribution function of the probability distribution model to obtain the probability density function corresponding to each target parameter, for each target parameter, the corresponding probability density function is obtained specifically according to each probability distribution model; the Akaike information criterion AIC and the Bayesian information criterion BIC are used to evaluate the fitting results of the probability density functions corresponding to various probability distribution models respectively; the probability density function with the lowest AIC index of the AIC criterion and the lowest BIC index of the BIC criterion after evaluation is taken as the probability density function corresponding to the target parameter.

[0073] In the specific implementation, each target parameter, that is, a number of water content data and a number of burial depth data, usually obeys a certain statistical distribution law. Commonly used probability distribution models include normal distribution, lognormal distribution, Weibull distribution, and Gumbel distribution. The probability density functions and distribution functions are shown in Table 1:

[0074] Table 1

[0075]

[0076] In order to obtain the probability distribution model corresponding to the corresponding target parameters (i.e., several water content data and several burial depth data), the first step is to use the histogram method to show the approximate distribution of statistical data, and to plot the statistical data x1, x2, …, x nPerform grouping, determine the number of groups k and the group interval, and calculate the frequency of data in each divided sample interval. The number of groups m is calculated by the following formula:

[0077] k=1+3.3log 10 (n)

[0078] In the formula, k is the number of groups and n is the number of statistical samples.

[0079] The second step is to use the observed sample subset to solve the probability distribution of the unknown population. This process is called parameter estimation, which can be divided into point estimation and interval estimation. Parameter point estimation is to estimate the value of the unknown parameter with the help of some samples in the population X when one or more parameters are unknown under the condition of the distribution function type of the known population X. Common methods include moment estimation method and maximum likelihood estimation method. This paper uses maximum likelihood estimation method to solve the probability density function and distribution function.

[0080] Soil moisture is a discrete random variable. Suppose its distribution is P{X=x}=p(x;θ), θ∈Θ is known, and Θ is the range of possible values ​​of θ. n is a sample from X, then X1,X2,…,X n The joint distribution law of It is easy to know that samples X1, X2, …, X n Get its observed values ​​x1, x2,…, x n The probability is:

[0081]

[0082] Where L(θ) is a function of θ, called the likelihood function of the sample.

[0083] Sample values ​​x1,x2,…,x n The appearance of samples X1, X2, …, X n The probability L(θ) of taking this sample value is relatively large. Therefore, those sample values ​​that cannot make the sample values ​​x1, x2, …, x n The θ∈Θ that appears is used as an estimate of θ. The maximum likelihood estimation method is to use the burial depth data and soil moisture content data x1, x2,…, x n , select the likelihood function L(x1,x2,…,x n ; θ) reaches the maximum parameter value As the estimated value of parameter θ, we take make:

[0084]

[0085] So obtained with sample values ​​x1,x2,…,x n Related to, denoted as is called the maximum likelihood estimate of the parameter θ, and the statistic It is called the maximum likelihood estimator of the parameter. After taking the logarithm of the likelihood function, the partial derivative of the parameter is taken and equal to zero to obtain the desired parameter.

[0086] The third step is to obtain the parameters of each distribution type (i.e., the unknown parameters in the probability distribution model) through maximum likelihood estimation, and then compare the four distribution types to select the distribution model that best fits the actual observed sample set. This embodiment adopts the AIC criterion and the BIC criterion. The AIC criterion and the BIC criterion are used to measure the excellent fitting results of the comparative statistical model. The calculation formula is as follows:

[0087] AIC=2k-2ln(L)

[0088] Where k is the number of model parameters and L is the likelihood function used for maximum likelihood estimation.

[0089] The BIC calculation formula is as follows:

[0090] BIC = kln(n)-2ln(L)

[0091] Where n is the number of samples, L is the likelihood function, and k is the model parameter.

[0092] The smaller the AIC index and BIC index are, the better the model fit is.

[0093] After determining the optimal probability distribution model of each target parameter through the AIC and BIC indexes, assuming that the histograms of several moisture content data obtained in this embodiment and the different distribution fitting curves are as follows: Figure 3 As shown, the estimated parameter values, AIC index, and BIC index specific values ​​are shown in Table 2:

[0094] Table 2

[0095]

[0096] It can be seen that according to the AIC index and BIC index results, the soil moisture probability distribution model of this embodiment is a normal distribution model, and according to the maximum likelihood method parameter estimation results, the probability density function of several soil moisture data is obtained as follows:

[0097]

[0098] Similarly, based on the above scheme, assume that the distribution fitting curves of several burial depth data are obtained as follows: Figure 4 As shown, the estimated parameters, AIC index, and BIC index are shown in Table 3:

[0099] Table 3

[0100]

[0101]

[0102] By comparing the AIC and BIC indexes, Gumbel distribution is the optimal distribution type. The probability density function solution results of several burial depth data are as follows:

[0103]

[0104] Step 103 , randomly sampling a plurality of moisture content data and a plurality of burial depth data according to the probability distribution of soil moisture content in the target area and the probability distribution of buried pipeline depth in the target area.

[0105] Step 104, through the functional relationship between soil moisture content and soil shear strength, obtain the soil shear strength sample corresponding to the sampled moisture content sample, and simulate several service environments of the buried pipeline through the soil shear strength sample, the sampled burial depth sample and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, so as to obtain the girth weld stress of the buried pipeline under each service environment.

[0106] In the specific implementation, since the buried pipeline is surrounded by soil, the parameter change of the soil body will directly affect the load of the pipeline. Therefore, the uncertainty of soil parameters (i.e., soil moisture content) is taken into account in this embodiment, and the influence of the uncertainty of soil parameters on the stress of the girth weld of the buried pipeline is analyzed. The soil moisture content is one of the main reasons that directly affect the shear performance parameters of the soil. Therefore, before step 103 and step 104, in order to determine the law between the soil moisture content and the shear strength performance of the soil, this embodiment performs a remolded soil direct shear test on the soil in the target area.

[0107] Specifically, the test soil is typical red clay, and the soil sampling depth is 1-2m. First, let the soil sample air dry naturally, then crush the soil sample through a 2mm sieve, put it into a plate, and put it into a 110-degree constant temperature drying box until the moisture in the soil sample is completely evaporated. The samples are configured according to the mass moisture content of 18%, 21%, 24%, 27%, 30%, 33%, 36%, 39%, and 42%. After being fully saturated for 12 hours, they are compacted 25 times each time through a CNC electric compactor, divided into three layers. For each group of moisture content, 10 ring knife samples are made, and direct shear tests are carried out with vertical pressures of 100, 200, and 300Kpa respectively. Each group of moisture content is sheared three times, and a total of 9 samples are required. Since there are steps such as soil saturation and compaction in the sample configuration process, soil moisture loss may occur. In order to ensure the true moisture content of the final ring knife sample, the remaining 1 ring knife sample is used for the ring knife dry density test and to measure the true moisture content. The soil sample compacted before the test is as follows Figure 5 As shown, the sample after cutting is Figure 6 shown.

[0108] The test data were processed according to the Mohr-Coulomb formula (the following formula) of soil mechanics, where the internal friction angle and cohesion are two key variables of soil shear strength, that is, the soil shear strength is determined by the cohesion and friction angle of the soil, and the relationship is:

[0109]

[0110] In the formula, τ represents the soil shear strength, c represents the cohesion, represents the internal friction angle, and σ represents the normal stress.

[0111] Since the moisture content in GLDAS data is volumetric moisture content, first convert the mass moisture content to volumetric moisture content using the following formula:

[0112] w v =w*ρ

[0113] In the formula, w v represents volumetric moisture content, w represents mass moisture content, and ρ represents soil dry density.

[0114] Assume that the test data is as shown in Table 4:

[0115] Table 4

[0116]

[0117]

[0118] like Figure 7As shown in the figure, the cohesion gradually increases with the increase of volumetric moisture content, reaches a peak at 30.95%, and then gradually decreases with the increase of volumetric moisture content. The piecewise function is used for fitting. The mass moisture content of 22.86% to 30.95% is linearly fitted, and the mass moisture content of 30.95% to 50.4% is cubic polynomially fitted. The fitting R 2 The fitting result is 0.96, which is a good one. The segmented fitting results of volume moisture content and cohesion are shown in formula 10. The fitting R2 of the interval from 30.95% to 50.4% is 0.96, which is a good one. The fitting curve is shown in the figure below. Figure 7 shown.

[0119] Among them, the fitting results of volume moisture content and cohesion are shown in the following formula:

[0120]

[0121] The fitting results of volumetric water content and internal friction angle are shown in the following formula: 2 is 0.96, and the fitting effect is good. The fitting curve is shown in Figure 8 shown.

[0122]

[0123] Based on this test, the functional relationship between volumetric moisture content and cohesion and internal friction angle can be obtained. Therefore, based on the above probability density function used to indicate the probability distribution of soil moisture content in the target area, the law of change in soil shear strength performance in the rainy season can be obtained.

[0124] In addition, in order to determine the influence of soil parameters on the buried pipeline, this embodiment also establishes a pipe-soil model for characterizing the interaction force between the soil and the buried pipeline in the target area. Among them, the models used to describe the interaction force between the pipe and the soil are currently mainly divided into three categories: elastic foundation beam model, solid contact model, and soil spring model. The pipe-soil model of this embodiment is based on the soil spring model, and the force of the soil in the target area indicated by the pipe-soil model on the buried pipeline is simulated by the soil spring.

[0125] In this pipe-soil model, the soil force on the buried pipeline will be decomposed into the soil spring forces in the following directions: the axial soil spring force of the pipeline, the horizontal lateral soil spring force of the pipeline, the vertical upward soil spring force of the pipeline, and the vertical downward soil spring force of the pipeline.

[0126] The axial soil spring force of the pipeline is expressed by the following formula:

[0127]

[0128] In the formula, D represents the diameter of the buried pipeline, α represents the adhesion coefficient, c represents the cohesion of the soil, and H represents the depth of the buried pipeline. represents the bulk density of soil, K0 represents the static earth pressure coefficient, and δ represents the friction angle of the pipe-soil interface.

[0129] and,

[0130]

[0131] Where φ represents the internal friction angle of the soil, and f' represents the correlation coefficient of the anti-corrosion layer on the pipeline surface.

[0132] The horizontal lateral soil spring force of the pipeline is expressed by the following formula:

[0133]

[0134] Where N ch Represents the horizontal compressive capacity coefficient of clay, N qh It represents the horizontal compressive capacity coefficient of sand.

[0135] Under the horizontal lateral soil spring force of the pipeline, the lateral yield displacement of the soil spring is expressed by the following formula:

[0136]

[0137] The vertical upward soil spring force of the pipeline is expressed by the following formula:

[0138]

[0139] Where N cν represents the vertical lift coefficient of clay, N qν Represents the vertical uplift coefficient of sand.

[0140] The vertical downward soil spring force of the pipeline is expressed by the following formula:

[0141]

[0142] Where N c 、N q 、N γ Both represent the soil's compressive capacity coefficient.

[0143] At this time, in step 103 and step 104, several service environments of the buried pipeline are simulated based on the above content. The simulation method can adopt the Monte Carlo method, which is also called the statistical simulation method, which simulates the real working conditions by generating a large number of random samples, and finally obtains the solution to the problem (i.e., the girth weld stress of this embodiment). The girth weld stress of the buried pipeline described in this embodiment specifically includes the bending moment stress and axial force stress of the girth weld.

[0144] In this embodiment, a pipe model with an elbow (i.e., a pipe model) is established based on the actual pipeline laying conditions in the target area. The pipe diameter is 1.016 m, the pipe has a 50-degree hot bend elbow, and the curvature radius is 5D, i.e., 5.08 m. The straight pipe section of the elbow is connected by welding to produce four circumferential welds A, B, C, and D. The specific dimensions of the pipeline are as follows: Fig. 9 Then, a finite element analysis model (i.e., pipe-soil model) was established using ABAQUS, in which the pipe used PIPE31 units, the pipe axial spring used SPRING-A units, and the vertical upward, vertical downward, and horizontal transverse springs used SPRING-1 units.

[0145] The pipeline constitutive model is ideal elastic. The pipeline is subjected to internal pressure load, temperature difference, and self-gravity. The pipeline material parameters and boundary conditions are shown in Table 5:

[0146] Table 5

[0147]

[0148] Fig. 9 The bending moment and axial force at the nodes A, B, C, and D are the bending moment and axial force at the circumferential weld.

[0149] Since the geometric model of the buried pipeline (such as the above-mentioned curved pipe model) is determined, but the sampled moisture content data is different each time random sampling is performed, the corresponding soil shear strength is different, and the buried depth of the pipeline is also different. These are regarded as changes in soil parameters, and the changes in the soil parameters are only related to the relevant parameters of the soil spring when the pipe-soil model is established. Therefore, in this embodiment, a new pipe-soil model is actually generated by modifying the soil parameters in the original pipe-soil model.

[0150] Based on this, when simulating several service environments of buried pipelines, for each soil shear strength sample and the corresponding burial depth sample, the soil shear strength sample and the corresponding burial depth sample are respectively substituted into the formulas of the pipeline axial soil spring force, the pipeline horizontal lateral soil spring force, the pipeline vertical upward soil spring force, the pipeline vertical downward soil spring force and the lateral yield displacement of the soil spring, and the soil spring forces in multiple directions and the lateral yield displacement of the soil spring are solved; the soil spring forces in multiple directions and the lateral yield displacement of the soil spring are substituted into the pipe-soil model to obtain a new pipe-soil model, and the new pipe-soil model is used to represent the service environment of the buried pipeline, so as to use the new pipe-soil model to obtain the girth weld stress of the buried pipeline under different service environments.

[0151] In the specific implementation, firstly, a soil spring force and yield displacement calculation template is established, and a pipe-soil finite element parameterized calculation template file is established. Through the probability density function obtained above for indicating the probability distribution of soil moisture content in the target area and the probability density function for indicating the probability distribution of the buried depth of the buried pipeline in the target area, the functional relationship between the internal friction angle and cohesion and the soil moisture content, a Monte Carlo simulation scheme is designed, and the soil moisture content and the pipeline burial depth are randomly sampled. The sampled moisture content samples are assigned to the moisture content and the internal friction angle and cohesion function to obtain the internal friction angle and cohesion samples. Then, the internal friction angle, cohesion, and pipeline burial depth sample parameters (i.e., soil parameters) are assigned to the soil spring force and yield displacement calculation template, and the yield displacement and ultimate force of various soil springs are obtained as soil spring sample parameters (i.e., the maximum axial force in all directions of the above soil springs). By assigning the soil spring sample parameters to the pipe-soil finite element calculation template (i.e., the original pipe-soil model), a new pipe-soil model can be obtained, and then the bending moment and axial force at the girth weld of the new pipe-soil model can be obtained.

[0152] In one example, the above process is implemented by ISIGHT software, such as Fig.10 As shown. Among them, the Monte Carlo module sets the sampling method and the number of sampling times, and defines the probability density function of the moisture content data and the burial depth data. The script module 1 defines the functional relationship between the internal friction angle, cohesion (i.e., soil shear strength) and soil moisture content. The table module imports the soil spring force and yield displacement calculation template, and defines the relevant soil spring parameters. The stiffness and displacement data of the soil spring are allocated in the script module 2. The interactive component module imports the parameterized pipe-soil finite element calculation template (i.e., pipe-soil model), and defines the relevant pipeline parameters to realize the real-time modification and output functions of the relevant variables. The Abaqus module is called to run the modified INP file. The modified INP file is the above-mentioned new pipe-soil model, so as to obtain the axial force and bending moment of the girth weld. The script module 3 processes and obtains the stress data.

[0153] Step 105 , constructing a probability density function for indicating probability distribution of girth weld stress in a target area according to girth weld stress of buried pipelines under several service environments.

[0154] Specifically, the construction of a probability density function for indicating the probability distribution of the girth weld stress in the target area can refer to the above-mentioned scheme for constructing a probability density function for indicating the probability distribution of the soil moisture content in the target area and a probability density function for indicating the probability distribution of the buried depth of the buried pipeline in the target area, that is, the girth weld stress can also be regarded as the target parameter in this scheme, and the probability density function corresponding to the target parameter is solved by this scheme.

[0155] Assuming that the Monte Carlo module in the ISIGHT software simulation scheme adopts a general sampling method and the number of sampling is 1000 times, the final axial force stress distribution fitting curve is as follows: Fig.11 The axial force stress is shown in Table 6:

[0156] Table 6

[0157]

[0158] By comparing the AIC and BIC indexes, the normal distribution is the optimal distribution type, and the probability density function of the axial force stress distribution after 1000 simulations is as follows:

[0159]

[0160] The bending moment stress distribution fitting curve is as follows Fig.12 The bending moment stress is shown in Table 7:

[0161] Table 7

[0162]

[0163] By comparing the AIC and BIC indexes, the normal distribution is the optimal distribution type, and the probability density function of the moment stress distribution after 1000 simulations is as follows:

[0164]

[0165] The calculation shows that the minimum axial stress at the circumferential weld at point A is 134 MPa, the maximum is 144 MPa, and the difference is 10 MPa. The minimum bending stress is 150 MPa, the maximum is 350 MPa, and the difference is as high as 200 MPa, indicating that in the rainy season or when the precipitation is large, the soil shear performance parameters vary greatly, resulting in a large variation in the load stress of the pipeline. Therefore, the random distribution load of the soil (i.e., the randomness of the soil moisture content) is an important factor threatening the safe operation of the pipeline, especially in an environment with heavy rainfall and drastic changes in soil moisture content. This result highlights the important engineering significance of in-depth research on the impact of soil parameter randomness on pipeline safety.

[0166] In this embodiment, a probability density function indicating the probability distribution of soil moisture content and the probability distribution of buried pipeline depth in a target area is constructed through a plurality of moisture content data of soil in a certain area and a plurality of buried depth data of buried pipelines. Then, a plurality of moisture content data and a plurality of buried depth data are randomly sampled according to the probability distribution of the above two types of data to describe the randomness of the two types of data. Since the soil moisture content will affect the soil shear strength, the soil shear strength corresponding to the sampled moisture content data can be obtained. Then, various service environments of buried pipelines can be simulated through the obtained soil shear strength, the sampled buried depth and the pipe-soil model that characterizes the interaction force between soil and buried pipelines in the target area. Finally, the girth weld stress of the buried pipeline under each service environment is obtained. Not only the randomness of soil load (i.e., soil moisture content) but also the randomness of pipeline burial depth is taken into account. By constructing the girth weld stress of buried pipelines under various service environments and constructing a probability density function to indicate the probability distribution of girth weld stress in the target area, the probability distribution of girth weld stress of buried pipelines can be obtained when the soil load and pipeline burial depth are uncertain. Based on this, the reliability evaluation of pipeline girth welds can be carried out, and the evaluation results will be more accurate.

[0167] The step division of the various methods above is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of the present invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the protection scope of the invention.

[0168] Another embodiment of the present invention relates to a device for determining the probability distribution of stress of a girth weld of a buried pipeline. The implementation details of the device for determining the probability distribution of stress of a girth weld of a buried pipeline of this embodiment are specifically described below. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for implementing this solution. The device for determining the probability distribution of stress of a girth weld of a buried pipeline of this embodiment includes:

[0169] A data acquisition module is used to acquire a plurality of moisture content data of the soil in the target area within a preset time period, and a plurality of buried depth data of the buried pipelines in the target area;

[0170] A first function construction module is used to construct a probability density function for indicating the probability distribution of soil moisture content in a target area according to a plurality of moisture content data, and to construct a probability density function for indicating the probability distribution of buried depth of buried pipelines in the target area according to a plurality of buried depth data;

[0171] The parameter simulation module is used to randomly sample a number of moisture content data and a number of burial depth data according to the probability distribution of soil moisture content in the target area and the probability distribution of buried pipeline depth in the target area; obtain soil shear strength samples corresponding to the sampled moisture content samples through the functional relationship between soil moisture content and soil shear strength, and simulate several service environments of the buried pipeline through the soil shear strength samples, the sampled burial depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, so as to obtain the girth weld stress of the buried pipeline under each service environment;

[0172] The second function building module is used to build a probability density function for indicating the probability distribution of girth weld stress in a target area according to girth weld stress of buried pipelines under several service environments.

[0173] It is not difficult to find that this embodiment is a device embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details and technical effects mentioned in the above embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0174] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0175] Another embodiment of the present invention relates to a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the stress probability distribution of the buried pipeline girth weld in the above-mentioned embodiments.

[0176] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0177] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0178] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0179] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions for a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0180] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for determining the probability distribution of stress of girth weld of buried pipeline, characterized in that: include: Obtaining a number of moisture content data of the soil in the target area within a preset time period, and a number of buried depth data of the buried pipelines in the target area; A probability density function is constructed based on a plurality of moisture content data to indicate the probability distribution of soil moisture content in a target area, and a probability density function is constructed based on a plurality of depth data to indicate the probability distribution of buried pipeline depths in a target area; According to the probability distribution of soil moisture content in the target area and the probability distribution of buried pipeline depth in the target area, a number of moisture content data and a number of buried depth data are randomly sampled respectively; Through the functional relationship between soil moisture content and soil shear strength, soil shear strength samples corresponding to the sampled moisture content samples are obtained, and through the soil shear strength samples, the sampled burial depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, several service environments of the buried pipeline are simulated to obtain the girth weld stress of the buried pipeline under each service environment; According to the girth weld stress of buried pipelines under several service environments, a probability density function is constructed to indicate the probability distribution of girth weld stress in the target area; The force exerted by the soil in the target area indicated by the pipe-soil model on the buried pipeline is simulated by soil springs, and the force exerted by the soil on the buried pipeline is decomposed into soil spring forces in the following multiple directions: axial soil spring force of the pipeline, horizontal transverse soil spring force of the pipeline, vertical upward soil spring force of the pipeline, and vertical downward soil spring force of the pipeline; The pipeline axial soil spring force is expressed by the following formula: In the formula, D represents the diameter of the buried pipeline, α represents the adhesion coefficient, c represents the cohesion of the soil, and H represents the depth of the buried pipeline. represents the bulk density of soil, K0 represents the static earth pressure coefficient, and δ represents the friction angle of the pipe-soil interface; The horizontal lateral soil spring force of the pipeline is expressed by the following formula: Where N ch Represents the horizontal compressive capacity coefficient of clay, N qh It represents the coefficient of horizontal compressive strength of sand; Under the horizontal lateral soil spring force of the pipeline, the lateral yield displacement of the soil spring is expressed by the following formula: The vertical upward soil spring force of the pipeline is expressed by the following formula: Where N cν represents the vertical lift coefficient of clay, N qν It represents the vertical lift coefficient of sand; The vertical downward soil spring force of the pipeline is expressed by the following formula: Where N c 、N q 、N γ Both represent the soil's compressive capacity coefficient.

2. The method for determining the probability distribution of stress of girth weld of buried pipeline according to claim 1, characterized in that: The probability density function for indicating the probability distribution of soil moisture content in the target area, the probability density function for indicating the probability distribution of buried pipeline depth in the target area, and the probability density function for indicating the probability distribution of girth weld stress in the target area are all constructed by the following steps: The moisture content data, the burial depth data and the girth weld stress are respectively taken as a target parameter. For each target parameter, the target parameters are divided into multiple sample intervals according to the initial distribution of the target parameters displayed by the histogram. The maximum likelihood estimation method is used to estimate the unknown parameters in the probability distribution model according to the probability distribution function of the preset probability distribution model and the target parameters in any one or more sample intervals; Substitute the estimated unknown parameters into the probability distribution function of the probability distribution model to obtain the probability density function corresponding to each target parameter.

3. The method for determining the probability distribution of stress of girth weld of buried pipeline according to claim 2, characterized in that: The probability distribution model includes the following types: normal distribution, lognormal distribution, Weibull distribution and Gumbel distribution; Substituting the estimated unknown parameters into the probability distribution function of the probability distribution model to obtain the probability density function corresponding to each target parameter includes: For each target parameter, according to each probability distribution model, obtain the corresponding probability density function; The Akaike information content (AIC) criterion and the Bayesian information content (BIC) criterion were used to evaluate the fitting results of the probability density functions corresponding to various probability distribution models. The probability density function with the lowest AIC index of the AIC criterion and the lowest BIC index of the BIC criterion after evaluation is taken as the probability density function corresponding to the target parameter.

4. The method for determining the probability distribution of stress of girth weld of buried pipeline according to claim 1, characterized in that: The soil shear strength samples, the sampled burial depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area are used to simulate several service environments of the buried pipeline to obtain the girth weld stress of the buried pipeline under each service environment, including: For each soil shear strength sample and the corresponding burial depth sample, the soil shear strength sample and the corresponding burial depth sample are respectively substituted into the formulas of the pipeline axial soil spring force, the pipeline horizontal lateral soil spring force, the pipeline vertical upward soil spring force, the pipeline vertical downward soil spring force and the lateral yield displacement of the soil spring, and the soil spring forces in multiple directions and the lateral yield displacement of the soil spring are obtained; The soil spring forces in multiple directions and the lateral yield displacement of the soil spring are substituted into the pipe-soil model to obtain a new pipe-soil model. The new pipe-soil model is used to represent the service environment of the buried pipeline. The girth weld stress of the buried pipeline under different service environments is obtained from the new pipe-soil model.

5. The method for determining the probability distribution of stress of girth weld of buried pipeline according to claim 1, characterized in that: The soil shear strength is determined by the cohesion and friction angle of the soil, and the girth weld stress of the buried pipeline includes the bending moment stress and axial force stress of the girth weld.

6. A device for determining probability distribution of stress of girth weld of buried pipeline, characterized in that: include: A data acquisition module is used to acquire a plurality of moisture content data of the soil in the target area within a preset time period, and a plurality of buried depth data of the buried pipelines in the target area; A first function construction module is used to construct a probability density function for indicating the probability distribution of soil moisture content in a target area according to a plurality of moisture content data, and to construct a probability density function for indicating the probability distribution of buried depth of buried pipelines in the target area according to a plurality of buried depth data; The parameter simulation module is used to randomly sample a number of moisture content data and a number of burial depth data according to the probability distribution of soil moisture content in the target area and the probability distribution of buried pipeline depth in the target area; obtain soil shear strength samples corresponding to the sampled moisture content samples through the functional relationship between soil moisture content and soil shear strength, and simulate several service environments of the buried pipeline through the soil shear strength samples, the sampled burial depth samples and the pipe-soil model used to characterize the interaction force between the soil and the buried pipeline in the target area, so as to obtain the girth weld stress of the buried pipeline under each service environment; The second function construction module is used to construct a probability density function for indicating the probability distribution of girth weld stress in a target area according to girth weld stress of buried pipelines under several service environments; The force exerted by the soil in the target area indicated by the pipe-soil model on the buried pipeline is simulated by soil springs, and the force exerted by the soil on the buried pipeline is decomposed into soil spring forces in the following multiple directions: axial soil spring force of the pipeline, horizontal transverse soil spring force of the pipeline, vertical upward soil spring force of the pipeline, and vertical downward soil spring force of the pipeline; The pipeline axial soil spring force is expressed by the following formula: In the formula, D represents the diameter of the buried pipeline, α represents the adhesion coefficient, c represents the cohesion of the soil, and H represents the depth of the buried pipeline. represents the bulk density of soil, K0 represents the static earth pressure coefficient, and δ represents the friction angle of the pipe-soil interface; The horizontal lateral soil spring force of the pipeline is expressed by the following formula: Where N ch Represents the horizontal compressive capacity coefficient of clay, N qh It represents the coefficient of horizontal compressive strength of sand; Under the horizontal lateral soil spring force of the pipeline, the lateral yield displacement of the soil spring is expressed by the following formula: The vertical upward soil spring force of the pipeline is expressed by the following formula: Where N cν represents the vertical lift coefficient of clay, N qν It represents the vertical lift coefficient of sand; The vertical downward soil spring force of the pipeline is expressed by the following formula: Where N c 、N q 、N γ Both represent the soil's compressive capacity coefficient.

7. A computer device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the stress probability distribution of the buried pipeline girth weld as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for determining the stress probability distribution of the girth weld of a buried pipeline as claimed in any one of claims 1 to 5 is implemented.

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