A method for evaluating the residual bearing capacity of a corroded pipeline based on sampling statistical parameters

Abstract

A kind of corrosion pipeline residual ultimate bearing capacity evaluation method based on sampling statistical parameters, comprising: obtaining the sampling data of corrosion pipeline basic parameters and the corrosion region to be evaluated;Sample data is mapped to pipeline coordinate system and preprocessed, and sample data set of corrosion region is established;Based on sample data set, a group of statistical parameters representing the overall characteristics of the corrosion region are extracted, and corrosion length coefficient, corrosion ring coefficient and corrosion position coefficient are constructed according to axial expansion characteristics, ring expansion characteristics and position sensitive characteristics;A residual ultimate bearing capacity reduction factor model is constructed with the group of statistical parameters, corrosion length coefficient, corrosion ring coefficient and corrosion position coefficient as input, and the output reduction factor;The residual ultimate bearing capacity of the corrosion pipeline is calculated according to the complete pipeline ultimate bearing capacity and the reduction factor, and the evaluation result is output.The present application can realize accurate and stable evaluation of the bearing capacity of complex non-uniform corrosion pipeline under the condition of limited sampling data.

Description

Technical Field

[0001] This invention relates to the technology for evaluating the integrity and structural strength of subsea oil and gas pipelines, and in particular to a method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters. Background Technology

[0002] Subsea pipelines are critical infrastructure in offshore oil and gas development and transportation systems. Operating long-term in the marine environment characterized by high salinity, high humidity, complex flow fields, and various environmental loads, both their inner and outer surfaces are susceptible to corrosion damage in different forms. External surface corrosion is typically related to factors such as seawater environment, seabed contact, cathodic protection status, localized erosion, and external mechanical wear. Internal surface corrosion is usually related to factors such as carbon dioxide, hydrogen sulfide, water content, flow velocity fluctuations, particle erosion, and localized stagnant environments in the transported medium. Compared to land-based pipelines, subsea pipelines operate in a more complex environment with more diverse corrosion mechanisms. Actual corrosion morphologies often exhibit significant non-uniformity, randomness, localized concentration, and the coexistence of multiple defects. Common forms include pitting corrosion, uniform corrosion, localized sheet corrosion, abrasive corrosion, and erosion corrosion. Currently, the assessment of the remaining ultimate bearing capacity of corroded pipelines in engineering primarily employs standard methods, empirical methods, semi-empirical methods, and finite element numerical analysis. Existing standard and empirical methods typically use maximum corrosion depth, average corrosion depth, defect length, or several interaction parameters as inputs to perform geometric equivalence analysis of the corroded area before assessing residual strength or bearing capacity. While finite element numerical analysis can consider local corrosion geometry, material nonlinearity, and stress-strain response in greater detail, it usually requires high precision in corroded surface reconstruction, modeling processes, computational resources, and analytical experience. On the other hand, corrosion detection data for subsea pipelines in actual engineering projects are mostly obtained in the form of discrete thickness measurements, defect point data, internal inspection results, image recognition results, or point cloud data. This type of data itself contains information on the depth distribution, spatial expansion, and location distribution of the corroded area. However, existing methods often neglect to systematically transform these sampled data into effective input parameters that can be directly used for residual ultimate bearing capacity assessment.

[0003] Currently, the assessment of the remaining ultimate bearing capacity of corroded subsea pipelines mainly relies on the equivalent geometric parameters of defects. This involves simplifying the description of the corroded area using maximum corrosion depth, average corrosion depth, defect length, or a few equivalent parameters, and then conducting strength or bearing capacity analysis accordingly. This method has some applicability in scenarios involving single, regular defects, isolated defects, or rapid engineering estimations. Meanwhile, while high-fidelity finite element methods can be used for detailed analysis of complex corrosion geometries, their engineering applications are typically limited by the complexity of corrosion surface reconstruction, modeling, and computational costs. Overall, for complex, non-uniform corrosion scenarios on the inner and outer surfaces of subsea pipelines, existing technologies still face the following prominent problems and technological gaps:

[0004] 1. Existing methods mostly rely on single defects or a small number of equivalent geometric parameters to describe the corrosion region, which is difficult to fully reflect the overall statistical characteristics of complex corrosion regions. For non-uniform corrosion regions composed of multiple corrosion pits, local thinning areas, wear zones, erosion zones, and irregular corrosion patches, using only parameters such as maximum depth, average depth, or equivalent length for characterization can easily lead to a significant loss of corrosion morphology information, resulting in an insufficient description of the true corrosion state.

[0005] 2. Existing methods do not adequately consider the spatial distribution characteristics of corrosion zones. Actual corrosion not only exhibits differences in depth amplitude but also axial expansion, circumferential dispersion, local aggregation, related length characteristics, and defect connectivity. These spatial statistical properties directly affect local stress redistribution, plastic development, and ultimate bearing capacity. Existing methods typically struggle to systematically incorporate this spatial information into mechanical evaluation models.

[0006] 3. Existing methods give little consideration to the location effect of corrosion. Under axial loads, internal pressure, external pressure, bending moment, or combined loads, the local stress state, plastic development path, and failure sensitivity of the corrosion zone vary depending on its location on the circumference, axis, and wall surface. Especially when internal and external surface corrosion coexist or are dominant, the impact on the remaining ultimate bearing capacity may differ significantly due to the different wall surface locations. Existing technologies generally lack assessment methods that establish a unified quantitative relationship between these location characteristics and bearing capacity degradation.

[0007] 4. Although the high-fidelity finite element method can handle complex corrosion geometry in detail, it has high requirements for the accuracy of corrosion surface reconstruction, modeling cost, computational resources and analysis experience. It is not conducive to conducting rapid engineering evaluation directly on discrete sampling data in the field, and it is also difficult to form a simple, unified and scalable method in integrity management.

[0008] 5. Existing technologies have not yet developed a systematic evaluation method capable of extracting the depth distribution characteristics, spatial expansion characteristics, and location-sensitive characteristics of corrosion zones from limited sampling data, and further establishing their relationship with the reduction of the remaining ultimate bearing capacity. This makes it difficult to effectively transform the large amount of available sampling information in engineering into the key input parameters required for bearing capacity analysis.

[0009] Therefore, in the face of complex and non-uniform corrosion on the inner or outer surfaces of subsea pipelines, there is an urgent need to propose a residual ultimate bearing capacity assessment method that can make full use of sampling data, take into account both statistical characterization and mechanical rationality, and is suitable for rapid engineering evaluation, so as to improve the technical support capabilities for subsea pipeline integrity management, maintenance decision-making, and operational safety assessment.

[0010] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0011] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] A method for assessing the residual ultimate bearing capacity of corroded pipelines based on sampling statistical parameters includes the following steps:

[0014] S1. Obtain material parameters, structural dimension parameters, and corrosion depth sampling data of the corroded pipeline and the corrosion area to be evaluated.

[0015] S2. Map the sampled data to the pipeline coordinate system and preprocess it to establish a sample dataset of the corrosion area;

[0016] S3. Based on the sample dataset, extract a set of statistical parameters that characterize the overall features of the corrosion region, and construct corrosion length coefficient and corrosion circumferential coefficient according to the axial expansion features and circumferential expansion features of the corrosion region, and construct corrosion position coefficient according to the position sensitivity features.

[0017] S4. Construct a model for the remaining ultimate bearing capacity reduction factor. The model takes the statistical parameter set, corrosion length coefficient, corrosion circumferential coefficient and corrosion location coefficient as inputs, and outputs a reduction factor to characterize the degree of reduction of the pipeline's ultimate bearing capacity due to corrosion.

[0018] S5. Based on the ultimate bearing capacity of the intact pipeline under non-corrosion conditions and the reduction factor, calculate the remaining ultimate bearing capacity of the corroded pipeline and output the evaluation results.

[0019] The present invention has the following beneficial effects:

[0020] This invention provides a method for assessing the residual ultimate bearing capacity of corroded pipelines based on sampling statistical parameters. This method uses surface corrosion sampling data of subsea pipelines as a foundation, moving away from relying on single maximum depth, average depth, or a few equivalent geometric parameters. Instead, it constructs depth distribution statistical parameters, spatial distribution statistical parameters, and location-sensitive statistical parameters to comprehensively characterize complex and non-uniform corrosion morphologies, thereby improving the completeness and accuracy of the corrosion state description. Furthermore, this invention constructs corrosion length coefficient, corrosion circumferential coefficient, and corrosion location coefficient, and distinguishes the different roles of corrosion shape-related parameters and corrosion location-related parameters in the reduction factor model. This allows corrosion volume damage and location-sensitive damage to be mapped to the residual ultimate bearing capacity in a hierarchical manner, providing clear physical interpretation.

[0021] This invention employs a hierarchical modeling approach to construct a residual ultimate bearing capacity reduction factor model, which includes at least a corrosion depth damage index, a propagation damage index, and a corrosion location-sensitive index. The corrosion depth damage index comprehensively reflects the impact of local extreme weakening and inhomogeneity on the bearing section; the propagation damage index comprehensively reflects the impact of axial expansion, circumferential coverage, and the proportion of high-damage areas on the failure path; and the corrosion location-sensitive index, based on a two-dimensional location-sensitive weight function and a wall location weight function, quantifies the relative hazard of the corrosion area under different stress locations and wall conditions. The model parameters are calibrated through finite element numerical simulations, publicly available experimental data, or engineering case data. The calibration target is to ensure that the square of the correlation coefficient between the predicted reduction factor and the target reduction factor approaches 1, guaranteeing the accuracy and reliability of the model.

[0022] This invention is applicable not only to axial compression conditions, but also to internal pressure, external pressure, bending moment, and combined load conditions, and can be extended to cases of internal surface corrosion, external surface corrosion, and simultaneous corrosion of both internal and external surfaces. When both internal and external surfaces are corroded simultaneously, the corrosion depth damage index and the expansion damage index of the internal and external surfaces can be calculated separately. A joint reduction factor model is then established by coupling the corrosion location coefficient, which comprehensively considers the wall weights of both internal and external surfaces, resulting in good adaptability to both operating conditions and wall surfaces.

[0023] This invention, even in the absence of a complete and realistic reconstruction of the corroded surface, can still establish a residual ultimate bearing capacity assessment process based on limited sampling data. This avoids the adverse effects of high-precision corrosion geometry reconstruction, complex finite element modeling, and high computational costs on engineering application, thus improving the feasibility of subsea pipeline integrity management and engineering applications. This invention provides a systematic evaluation approach that lies between traditional empirical equivalent methods and highly complex detailed analysis methods. It can fully utilize field sampling data, detection data, or identification data, providing new technical means for the safety evaluation, maintenance decision-making, and life management of subsea corroded pipelines, and has significant potential for widespread application.

[0024] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0025] Figure 1 This is the overall flowchart of the present invention, which is a method for evaluating the remaining ultimate bearing capacity of corroded subsea pipelines based on sampling statistical parameters.

[0026] Figure 2 This is a schematic diagram showing the distribution of corrosion zones in the axial-circumferential coordinate system of a subsea pipeline.

[0027] Figure 3 This is a schematic diagram showing the establishment of sampling points and datasets for corrosion on the outer surface of a subsea pipeline.

[0028] Figure 4 This is a schematic diagram showing the establishment of sampling points and datasets for corrosion on the inner surface of a subsea pipeline.

[0029] Figure 5 This is a schematic diagram of a numerical simulation model of an irregularly corroded pipeline on the seabed.

[0030] Figure 6 This is a schematic diagram for verifying the effectiveness of the method of the present invention. Detailed Implementation

[0031] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0032] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0033] This invention aims to provide a method for assessing the residual ultimate bearing capacity of corroded subsea pipelines based on sampling statistical parameters. It overcomes the shortcomings of existing methods, such as difficulty in fully utilizing pipeline surface corrosion sampling data, inaccurate characterization of complex non-uniform corrosion, and inability to simultaneously consider the comprehensive influence of corrosion depth distribution, spatial expansion characteristics, and location sensitivity on the residual ultimate bearing capacity. This improves the accuracy, stability, and engineering applicability of residual ultimate bearing capacity prediction. Specifically, it includes the following aspects:

[0034] 1. In existing technologies, when only limited discrete sampling data is available and the true corrosion surface cannot be fully reconstructed, a simplified description of the corrosion region is usually only possible using a single maximum depth, average depth, or equivalent geometric parameter. This results in a large amount of effective information in the corrosion morphology being difficult to incorporate into the evaluation process. This invention improves the ability to describe the true state of complex corrosion regions by statistically characterizing the corrosion sampling data and extracting a set of statistical parameters that reflect the overall characteristics of the corrosion region.

[0035] 2. In existing technologies, the corrosion depth distribution information, spatial discreteness information, local aggregation information, and related length information contained in the sampling data have not been systematically converted into characteristic parameters that can be directly used for mechanical analysis. This invention extracts depth distribution statistical parameters, spatial distribution statistical parameters, and location-sensitive statistical parameters from corrosion sample data, enabling the sampling information to be effectively converted into input parameters for the bearing capacity assessment model.

[0036] 3. In existing technologies, there is a lack of a unified parameter system that can simultaneously reflect the corrosion zone length effect, circumferential position effect, and wall position effect for corrosion assessment of subsea pipelines under internal pressure, axial load, bending moment, external pressure, or combined loads. This invention constructs a corrosion length coefficient and a corrosion position coefficient to characterize the expansion characteristics and position-sensitive effects of the corrosion zone.

[0037] 4. In existing technologies, there is a lack of a unified, clear, and easily applied mapping relationship between the impact of complex, non-uniform corrosion regions on the residual ultimate bearing capacity of pipelines. This invention establishes a residual ultimate bearing capacity reduction factor model based on statistical parameters, corrosion length coefficient, and corrosion location coefficient, enabling the overall adverse effects of complex corrosion regions to be mapped to the residual ultimate bearing capacity of pipelines through a unified reduction relationship.

[0038] 5. In existing technologies, traditional empirical equivalent methods have limited ability to represent complex corrosion scenarios, while high-complexity fine analysis methods suffer from problems such as high requirements for geometric reconstruction, complex modeling, and difficulty in engineering application. This invention proposes a technical route based on sampling data acquisition, data preprocessing and sample construction, statistical parameter and coefficient construction, reduction factor establishment, and residual ultimate bearing capacity calculation output, forming a system evaluation method for complex non-uniform corrosion on the surface of subsea pipelines.

[0039] See Figure 1 This invention provides a method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters, comprising the following steps:

[0040] Step S1: Obtain the material parameters, structural dimension parameters, and corrosion depth sampling data of the corroded pipeline and the area to be evaluated.

[0041] In some embodiments, the corrosion sampling data in step S1 is derived from the corrosion area on the inner and / or outer surfaces of the pipe and is represented in the form of a corrosion sample set. Each sample point includes at least axial coordinates, circumferential coordinates, corrosion depth, and wall identification parameters for distinguishing whether the corrosion is located on the inner or outer surface.

[0042] Step S2: Map the sampled data to the pipeline coordinate system and perform preprocessing to establish a sample dataset of the corrosion area.

[0043] In some embodiments, the step S2 of mapping the sampled data to the pipeline coordinate system and performing preprocessing to establish a corrosion area sample dataset specifically includes: first, mapping the corrosion sampled data to a two-dimensional coordinate system with the axial coordinate as the horizontal axis and the circumferential coordinate as the vertical axis; then, preprocessing the mapped data, the preprocessing including removing outliers, repairing missing values, unifying the units and coordinate references, resampling the sampling points according to the detection resolution, interpolating or smoothing the discrete data, fusing data from different sources, and classifying and labeling or unifying the internal and external surface corrosion data; after preprocessing, a sample dataset is formed that at least contains the sampling point location coordinates, the corresponding corrosion depth value, and the identification information of whether the corrosion is located on the inner or outer wall.

[0044] Step S3: Based on the sample dataset, extract a set of statistical parameters that characterize the overall features of the corrosion region, and construct corrosion length coefficient and corrosion circumferential coefficient according to the axial expansion characteristics and circumferential expansion characteristics of the corrosion region, and construct corrosion position coefficient according to the position sensitivity characteristics.

[0045] In some embodiments, the extraction of the statistical parameter set characterizing the overall features of the corrosion region in step S3 specifically includes:

[0046] Extract depth distribution statistical parameters, which are obtained by statistical analysis of the corrosion depth values ​​of all sample points, including at least the average corrosion depth, maximum corrosion depth, unbiased sample standard deviation, coefficient of variation, and characteristic quantile depth obtained by fitting the cumulative distribution function of corrosion depth.

[0047] Spatial distribution statistical parameters are extracted, including: constructing a two-dimensional autocorrelation function based on discrete sampling points, calculating the axial autocorrelation function and circumferential autocorrelation function of the corrosion depth field, setting correlation thresholds respectively, and solving for the axial correlation length and circumferential correlation angle; and, by setting a corrosion depth threshold, calculating the proportion of the area of ​​the region with a corrosion depth exceeding the threshold to the total area of ​​the corrosion region, to obtain the proportion of the area of ​​the highly corroded region.

[0048] Extract location-sensitive statistical parameters, including at least a corrosion location coefficient; the corrosion location coefficient is constructed based on a two-dimensional location-sensitive weighting function and a wall location weighting function, wherein the two-dimensional location-sensitive weighting function is determined based on the nominal stress distribution of the complete pipeline under a preset load condition and a reference load level.

[0049] In some embodiments, the construction of corrosion length coefficient and corrosion circumferential coefficient in step S3 specifically includes: constructing a dimensionless corrosion length coefficient based on the projected length of the corrosion region in the axial direction of the pipe, combined with the effective length, outer diameter and wall thickness of the pipe, to characterize the influence of the axial expansion of the corrosion region on the bearing capacity; and constructing a dimensionless corrosion circumferential coefficient based on the projected angle of the corrosion region in the circumferential direction, combined with the circumferential angle, to characterize the influence of the circumferential coverage of the corrosion region on the bearing capacity.

[0050] In some embodiments, the construction of the corrosion location coefficient in step S3 specifically includes: first, constructing a two-dimensional location-sensitive weighting function based on the nominal stress distribution of the complete pipeline under preset load conditions and reference load levels, and defining a wall location weighting function to distinguish the relative influence of corrosion on the inner and outer surfaces; then, calculating the product of the two-dimensional location-sensitive weight and the wall location weight for each sample point to obtain the comprehensive location weight; finally, obtaining the corrosion location coefficient based on the weighted ratio of the comprehensive location weight to the corrosion depth.

[0051] When only a single wall surface is corroded, the wall surface position weight of the corresponding wall surface is set to 1; when corrosion exists on both the inner and outer surfaces, the inner wall position weight and the outer wall position weight are preset according to the dominant load conditions, or determined by normalizing the reference hazard levels of the inner and outer walls of the complete pipeline under the corresponding conditions.

[0052] Step S4: Construct a model for the remaining ultimate bearing capacity reduction factor. The model takes the statistical parameter set, corrosion length coefficient, corrosion circumferential coefficient, and corrosion location coefficient as inputs and outputs a reduction factor that characterizes the degree of reduction of the pipeline's ultimate bearing capacity due to corrosion.

[0053] In some embodiments, the construction of the residual ultimate bearing capacity reduction factor model in step S4 includes:

[0054] A hierarchical modeling approach is used to construct a parametric model, which includes at least a corrosion depth damage index, a propagation damage index, and a corrosion location sensitivity index. The corrosion depth damage index is constructed from the ratio of characteristic quantile depth to wall thickness, the corrosion depth variation coefficient, and the ratio of the difference between the maximum corrosion depth and the average corrosion depth to the wall thickness. The propagation damage index is constructed from the corrosion length coefficient, the corrosion circumferential coefficient, the ratio of axial correlation length to pipe geometric features, the ratio of circumferential correlation angle to circumferential angle, and the proportion of highly corroded area. The corrosion location sensitivity index is the corrosion location coefficient.

[0055] The reduction factor model is expressed in polynomial form as follows: the reduction factor equals 1 minus the product of the first model parameter and the corrosion depth damage index and the extended damage index, and then minus the product of the second model parameter and the corrosion location sensitivity index.

[0056] The model parameters are obtained by constructing a calibration sample set containing multiple corroded pipe samples and fitting and calibrating the model with the goal that the square of the correlation coefficient between the target reduction factor of each sample and the model predicted reduction factor approaches 1.

[0057] In some embodiments, the calibration process of the model parameters includes: First, constructing a calibration sample set, each sample containing a set of statistical parameters, corrosion length coefficient, corrosion circumferential coefficient, corrosion location coefficient, and the corresponding ultimate bearing capacity of the intact pipeline and the ultimate bearing capacity of the corroded pipeline obtained from finite element numerical simulation, public experiments, or engineering case data; then, for each calibration sample, calculating the ratio of the ultimate bearing capacity of the intact pipeline to the ultimate bearing capacity of the corroded pipeline as the target reduction factor for that sample; finally, substituting the input parameters of each sample into the preset reduction factor model, and iterating the model parameters step by step using particle swarm optimization or other optimization algorithms, so that the square of the correlation coefficient between the model prediction reduction factor and the corresponding target reduction factor of all samples is greater than a preset threshold (particularly preferred to be 0.9), thereby determining the model parameters.

[0058] In some embodiments, the method is applicable to single wall corrosion and simultaneous corrosion of inner and outer surfaces; in step S4, when the pipeline has both inner and outer surface corrosion, the corrosion depth damage index and the expansion damage index of the corrosion area on the inner and outer surfaces are calculated respectively, and the corrosion location coefficient considering the weight of the inner and outer surface walls is calculated. Then, a joint reduction factor model is established based on the coupling of the inner surface corrosion depth damage index, the outer surface corrosion depth damage index, the inner surface expansion damage index, the outer surface expansion damage index and the corrosion location coefficient.

[0059] Step S5: Calculate the remaining ultimate bearing capacity of the corroded pipeline based on the ultimate bearing capacity of the intact pipeline under non-corrosion conditions and the reduction factor, and output the evaluation results.

[0060] In some embodiments, the calculation of the remaining ultimate bearing capacity of the corroded pipeline in step S5 specifically includes: determining the baseline ultimate bearing capacity of the intact pipeline under the corresponding working condition in the non-corrosion state according to the load condition of the pipeline to be evaluated, wherein the working condition includes axial compression, axial tension, internal pressure, external pressure, bending moment or combined load; the baseline ultimate bearing capacity of the intact pipeline is determined by standard formula, theoretical model, analytical model, engineering calculation model or finite element numerical calculation; multiplying the baseline ultimate bearing capacity by the reduction factor to obtain the remaining ultimate bearing capacity of the corroded pipeline under the current load condition, and outputting an evaluation conclusion including the remaining ultimate bearing capacity, the reduction factor value and the corrosion risk level.

[0061] The proposed method for assessing the residual ultimate bearing capacity of corroded pipelines based on sampling statistical parameters can quantitatively assess the bearing capacity of complex, non-uniformly corroded subsea pipelines under limited sampling data conditions. This improves the accuracy of corrosion state characterization and the relevance of residual ultimate bearing capacity assessment, helps reduce assessment errors caused by the difficulty in reasonably equivalencing complex corrosion morphologies, and enhances the engineering efficiency of subsea pipeline integrity management, inspection and maintenance decisions, and operational safety assessment.

[0062] The following further describes specific embodiments of the present invention, algorithm examples, and experimental verification.

[0063] A method for assessing the remaining ultimate bearing capacity of corroded subsea pipelines based on sampling statistical parameters includes the following steps:

[0064] Step S1: Obtain material parameters, structural dimensional parameters, and corrosion depth sampling data of the corroded subsea pipeline.

[0065] Obtain basic parameters of the subsea pipeline to be evaluated, including but not limited to the pipeline's outer diameter. Wall thickness Effective length The material's elastic modulus, yield strength, tensile strength, elastic modulus, Poisson's ratio, and corresponding load condition parameters. These load condition parameters include one or more of the following: internal pressure, axial compressive load, axial tensile load, bending moment, external pressure, or a combination thereof.

[0066] Obtain corrosion sampling data for the area to be evaluated. The corrosion sampling data originates from the corrosion area on the pipe surface (including the outer and inner surfaces) and includes one or more of the following: discrete thickness measurement data, corrosion depth detection point data, defect data output from internal or external inspection, corrosion boundary data obtained from image recognition, three-dimensional point cloud data, acoustic inspection data, and other data that can characterize the geometric features of the corrosion area.

[0067] In a preferred embodiment, such as Figure 3 and Figure 4 As shown, quadrilateral mesh sampling is used for the entire computational pipeline, assuming a total of [number] samples are obtained. The corrosion sample point, then the first Each sample point can be represented as:

[0068]

[0069] in, Indicates the first The axial coordinates of each sample point Indicates the first The circumferential coordinates of each sample point Indicates the first Corrosion depth of each sample point These parameters indicate the wall surface markings used to distinguish whether corrosion is located on the inner or outer surface of the pipe. Preferably, the following parameters can be used:

[0070]

[0071] The corrosion sample data can then be represented as:

[0072]

[0073] In a preferred embodiment, the minimum quadrilateral grid size selected for sampling, i.e., the sampling resolution, should be sufficient to characterize irregular corrosion features. However, to avoid storage problems caused by large amounts of data, variable resolution sampling can be used according to the corrosion type, i.e., the sampling resolution is reduced in areas where the corrosion depth does not change significantly, and the sampling resolution is increased in areas where the corrosion depth changes significantly.

[0074] Step S2: Establish a sample dataset of corroded areas and perform preprocessing.

[0075] When the original coordinate form of the corrosion sample set is inconsistent with the pipeline coordinate system, it needs to be converted to a unified pipeline coordinate system first; when the corrosion sample set is consistent with the pipeline coordinate system, preprocessing can be performed directly.

[0076] The pipeline coordinate system is preferably an axial-circumferential two-dimensional coordinate system, specifically represented as follows: Figure 2 As shown, if necessary, it can be further extended to a multi-dimensional coordinate representation method that includes radial corrosion depth and wall markings.

[0077] The preprocessing includes one or more of the following: removing outliers, repairing missing values, unifying units and coordinate references, resampling sampling points according to detection resolution, interpolation or smoothing, fusing data from different sources, and classifying and labeling or uniformly mapping internal and external surface corrosion data.

[0078] After preprocessing, each sampling point is retained. Corresponding discrete corrosion depth value This forms a discrete corrosion depth field dataset with a number of data points. Preferably, the subsequent calculation of spatial statistical parameters in this invention is directly based on discrete sampling points, thus maintaining consistency with the actual detection data format. If necessary, interpolation or gridding processing can also be performed on the discrete sampling data.

[0079] Step S3: Extract corrosion statistical characteristic parameters and construct corrosion characteristic coefficients

[0080] Based on the preprocessed discrete corrosion region sample dataset, a set of statistical parameters characterizing the overall features of the corrosion region is extracted. This set of statistical parameters includes one or more of depth distribution statistical parameters, spatial distribution statistical parameters, and location-sensitive statistical parameters. All statistical parameters are calculated under the corrosion parameters of the pipe wall on the same side. For cases where both the inner and outer surfaces are corroded, the depth distribution statistical parameters and spatial distribution statistical parameters are calculated separately, while the location-sensitive statistical parameters can take into account both inner and outer surface corrosion conditions.

[0081] The depth distribution statistical parameters are used to characterize the degree to which corrosion weakens the effective load-bearing cross section of the pipeline and the extent of development of local weak areas. These include, but are not limited to, average corrosion depth, maximum corrosion depth, standard deviation, coefficient of variation, skewness, kurtosis, several characteristic quantile depths, and extreme value statistics.

[0082] Preferably, the markings are placed on a fixed wall surface. under conditions ( Average corrosion depth Maximum corrosion depth and unbiased sample standard deviation and corrosion depth variation coefficient They are respectively:

[0083]

[0084] The cumulative distribution function of the random variable corrosion depth is: Then the feature quantile depth satisfy:

[0085]

[0086] in, A reference value of 0.90, 0.95, or 0.99 can be used.

[0087] in, Reflects the overall average thinning degree of the corroded area. and Reflects the level of extreme local weakening. and This reflects the dispersion and non-uniformity of corrosion depth within the region. For residual ultimate bearing capacity problems, local extreme weakening and non-uniformity can lead to local stress redistribution, premature plastic development, and increased failure susceptibility.

[0088] Spatial distribution statistical parameters are used to characterize the expansion, aggregation, and correlation of corrosion in the axial and circumferential directions. These include, but are not limited to, axial correlation length, circumferential correlation length, area ratio of highly corroded regions, local aggregation degree, pitting density, corrosion length coefficient, flux parameters, and spatial directionality parameters.

[0089] Preferably, the markings are on a fixed wall surface. under conditions ( Since the autocorrelation function is calculated directly based on discrete sampling points, it adopts the estimation form of discrete sample points.

[0090] Two-dimensional discrete autocorrelation function It can be defined as:

[0091]

[0092] in, For the coordinate difference to satisfy , The set of sample points , These are the axial spacing tolerance and the circumferential spacing tolerance, respectively. This represents the number of elements in the set of corresponding point pairs.

[0093] Preferably, based on the two-dimensional autocorrelation function, characteristic correlation parameters in the axial and circumferential directions are further extracted. The axial autocorrelation function and the circumferential autocorrelation function are as follows:

[0094]

[0095] Preferably, when or Attenuate to a preset threshold At that time, axial correlation length Circumferential related angles It can be defined as:

[0096]

[0097] in, Preferred selection Or 0.5.

[0098] When corrosion exists on both the inner and outer surfaces, the axial correlation length and the circumferential correlation length are calculated separately, and the maximum value is selected as the correlation length for subsequent calculations.

[0099] Furthermore, let the corrosion depth threshold be... The proportion of highly corroded areas This can be satisfied through statistics The proportion of sampling points or the sampling grid area weights are used for estimation.

[0100] Among them, the axial correlation length Reflecting the degree of continuous axial corrosion spread, the circumferential correlation length Reflects the extent of corrosion coverage in the circumferential direction, and the percentage of area with high corrosion. This reflects the proportion of highly damaged areas within the overall corrosion zone. When the axial extension is longer, the circumferential coverage is wider, and the area of ​​locally highly corroded regions is larger, the weakening of local cross-sections will be more concentrated or develop over a wider area, thus affecting the ultimate failure path and load-bearing capacity.

[0101] The axial and circumferential expansion characteristics of the corrosion region constitute a dimensionless corrosion length coefficient. Corrosion Circulation Coefficient Preferably, it is determined by the axial projection length of the corrosion region. Circular projection angle of the corrosion area Given the pipe geometry parameters, it can be constructed in the following form:

[0102]

[0103] The location-sensitive statistical parameters characterize the relative influence of the corrosion location on the remaining ultimate bearing capacity under given load and boundary conditions. These include, but are not limited to, one or more of the following: circumferential location weight of the corrosion zone, axial location weight, wall location weight, weighted average corrosion depth of the critical load area, and corrosion location coefficient. Since the hazard of a sampling point is typically determined by both its axial and circumferential locations, this invention preferably employs a point coordinate-based method. The two-dimensional position sensitivity function is further considered to take into account the influence of the wall position.

[0104] Preferably, the first The comprehensive location weight of each sample point It can be defined as:

[0105]

[0106] in, For two-dimensional position-sensitive weighting functions, The wall position weighting function is used. These are the load condition parameters, including load type, location, and magnitude. The load condition parameters can be defined as follows, based on their values: 1-Axial load; 2-Radial pressure; 3-Bending moment load; 4-Combined load.

[0107] In a preferred embodiment, the two-dimensional position-sensitive weighting function can be constructed based on the nominal stress distribution of the complete pipe under a given load, and the wall position weighting function... Using a segmented approach, it can be represented as follows:

[0108] (15)

[0109] (16)

[0110] in Indicates the complete pipeline under the preset load type and at the preset reference load level, position The nominal stress at the point; the denominator is the maximum absolute value of the nominal stress under the same reference state. The preset reference load level is preferably a certain proportion of the unit load level, the standardized load level, or the load level corresponding to the complete pipeline reference bearing capacity under finite element analysis. and These represent the wall weights of the inner and outer surfaces, respectively. Preferably, when only a single wall surface is corroded, the wall position weight corresponding to that surface is set to 1; that is, when only the inner surface is corroded, the weight is set to 1. When only the outer surface is corroded, take When both the inner and outer surfaces are corroded, preferably, the... and Based on the pre-set dominant load condition: when the internal pressure condition is dominant, the preferred option is... When external pressure or local instability of the outer wall dominates the operating conditions, the preferred method is to take... When axial or bending conditions dominate and the difference between the inner and outer walls is not significant, the preferred option is to choose... .

[0111] Furthermore, a corrosion location coefficient can preferably be defined. :

[0112]

[0113] in, , .

[0114] Furthermore, corrosion parameters can be divided into corrosion shape-related parameters and corrosion location-related parameters, and then dimensionless processing can be performed:

[0115]

[0116] in, This represents a set of parameters related to the corrosion shape. This represents a set of parameters related to the location of corrosion.

[0117] Step S4: Construct the residual ultimate bearing capacity reduction factor model

[0118] Considering the different influence mechanisms of corrosion shape parameters, corrosion region location parameters, and corrosion type and load condition parameters on the remaining ultimate bearing capacity, this invention preferably adopts a layered modeling approach to construct the remaining ultimate bearing capacity reduction factor model. Preferably, a corrosion depth damage index is defined. Extended damage indicators and corrosion location sensitive indicators as follows:

[0119]

[0120] Furthermore, the preferred reduction factor model can be expressed in polynomial form as follows:

[0121]

[0122] In the above model, the model parameters ~ The calibration is determined by using finite element numerical simulation results, publicly available test results, existing engineering case data, or a combination thereof as reference samples.

[0123] Furthermore, the calibration process of the model parameters includes: constructing a calibration sample set containing multiple corroded pipe samples; calculating the target reduction factor based on the ultimate bearing capacity of the complete pipe and the ultimate bearing capacity of the corresponding corroded pipe; inputting the statistical parameter set in the calibration sample into the preset reduction factor model; constructing the error function and solving the model parameters through least squares fitting, nonlinear regression or error minimization methods.

[0124] Preferably, let the first The ultimate bearing capacity of the complete pipeline of each calibration sample is: The corresponding ultimate bearing capacity of the corroded pipeline is Then the target reduction factor for:

[0125]

[0126] Let the reduction factor predicted by the model be... Therefore, it is preferable to use the square of the correlation coefficient as the objective function:

[0127]

[0128] in, To determine the sample size, The average of the reduction factors for the objective function. The closer the value is to 1, the more accurate the model parameters are. ~ .

[0129] Step S5: Calculate the corrosion force on the pipeline and output the evaluation results.

[0130] Based on the geometric parameters, material parameters, and load conditions of the pipeline to be evaluated, determine the ultimate bearing capacity of the intact pipeline under non-corrosion conditions. The ultimate bearing capacity of the intact pipeline can be determined through standard formulas, theoretical models, analytical models, or engineering calculation models. The commonly used formula for calculating the ultimate bearing capacity of thick-walled pipelines in the standard is as follows:

[0131] When the load condition is axial compression, the ultimate axial bearing capacity of the complete long pipe and the short pipe. :

[0132] (26)

[0133] The ultimate radial bearing capacity of the complete pipeline under external / internal pressure load conditions. :

[0134] (27)

[0135] The ultimate bending bearing capacity of the complete pipe when the load condition is bending moment. :

[0136] (28)

[0137] in, The elastic modulus of the material. For the tensile strength of the material, For the material's yield strength, .

[0138] Furthermore, the ultimate bearing capacity of the complete pipeline, including the above-mentioned working conditions and composite load conditions. It can also be obtained through finite element numerical calculation.

[0139] In summary, based on the ultimate bearing capacity of the complete pipeline and the reduction factor... Calculate the remaining ultimate bearing capacity of the corroded pipeline. Its expression can be:

[0140] (29)

[0141] in, This indicates the remaining ultimate bearing capacity of the corroded pipeline under the corresponding load conditions.

[0142] Example 1

[0143] To further clarify the features and effects of the present invention, a section of a corroded subsea pipeline was used as an example to evaluate its remaining ultimate bearing capacity under axial compressive load.

[0144] Step S1: Determine the outer diameter of the seabed corrosion pipeline Wall thickness Effective length Material parameters and load conditions are obtained, and discrete corrosion characteristic sampling data of its surface are acquired.

[0145] The sampling data includes multiple corrosion sample points, each of which contains at least location coordinates and corresponding corrosion depth information. A corrosion sample set is established, represented as follows: In the formula Here are the coordinates of the sample points. The corrosion depth at this coordinate. These are the parameters of the corroded wall surface at this coordinate system.

[0146] Step S2: Map the corrosion sample points to a unified pipeline coordinate system, and perform outlier handling, missing data repair, and necessary smoothing or interpolation on the original data.

[0147] Step S3: Calculate the corrosion characteristic parameters of the pipeline surface, including the average corrosion depth. Maximum corrosion depth Feature quantile depth Unbiased sample standard deviation Axial related length Circumferential related angles Corrosion location coefficient Percentage of highly corrosive areas Corrosion length coefficient and circumferential coefficient .

[0148] The method for calculating the corrosion characteristic parameters can be expressed as follows: , , , , .

[0149] Furthermore, for submarine pipelines with single external surface corrosion, the corrosion location coefficient is... It can be represented as In the formula For the complete pipeline under load conditions, the parameters are as follows: The two-dimensional position-sensitive weight function is calculated below.

[0150] Further, a corrosion length coefficient is constructed based on the distribution characteristics of the corrosion area. With circumferential coefficient Mathematically, this can be represented as: , In the formula The axial projection length of the corroded region. The angle of the annular projection of the corroded area.

[0151] Step S4: Based on the parameter set obtained above, establish the reduction factor model and calibrate the model parameters.

[0152] The reduction factor model can be established from two perspectives: corrosion shape-related parameters and corrosion location-related parameters. A corrosion depth damage index is defined. Extended damage indicators and corrosion location sensitive indicators Mathematically, they are respectively represented as , , Furthermore, a polynomial reduction factor model is established, expressed as: In the absence of publicly available data on corroded pipe surfaces and experimental data, model parameters can be calibrated by establishing a finite element dataset. ~ .

[0153] The process of calibrating model parameters includes establishing multiple finite element models similar to the corrosion type of the submarine pipeline to be evaluated, calculating the target reduction factor based on the ultimate bearing capacity of the complete pipeline and the remaining ultimate bearing capacity of the corresponding corroded pipeline, inputting the statistical parameter set in the calibration sample into the preset reduction factor model, and finally constructing an error function and calculating the square of the correlation coefficient to determine the model parameters.

[0154] The method for calculating the target reduction factor can be expressed as follows: In the formula For the first The ultimate bearing capacity of the complete pipeline for each calibration sample, and the corresponding ultimate bearing capacity of the corroded pipeline are: .

[0155] The calculation of the square of the correlation coefficient can be expressed as follows: In the formula, To determine the sample size, The average of the target reduction factor. The parameters and model are considered valid.

[0156] Step S5: Calculate the ultimate bearing capacity of the intact pipeline and the remaining ultimate bearing capacity of the submarine corroded pipeline under axial compression load conditions.

[0157] The ultimate bearing capacity of the intact pipeline under axial compressive load conditions is calculated according to the method in DNV-ST-F101 specification, comparing the axial ultimate bearing capacity of long and short pipelines. It can be represented as

[0158]

[0159] In the formula The elastic modulus of the material. For the tensile strength of the material, This represents the yield strength of the material.

[0160] Furthermore, the axial residual ultimate bearing capacity of a subsea corrosion-prone pipeline can be expressed as: .

[0161] Specifically, the implementation examples are described in detail below:

[0162] In specific step S1, we consider a pipe with irregular external surface corrosion, and the relevant parameters are shown in Table 1. In this embodiment, a short pipe is selected for calculation.

[0163] Table 1. Piping Parameters

[0164]

[0165] In specific step S3, the corrosion location coefficient To determine the model, the actual stress-strain relationship obtained from the tensile test of X65 material was first input into Abaqus to establish a complete, corrosion-free pipeline model. Then, the axial compression process under displacement-controlled loading was simulated using the Dynamic / Implicit method. Considering both computational efficiency and accuracy, the mesh configuration was as follows: 2 meshes in the radial thickness direction, 50mm mesh size in the axial direction, 200 meshes in the circumferential direction, and C3D8R element type. Next, the stress values ​​at the nodes on the outer surface of the pipeline were extracted when the ultimate bearing capacity was reached. The weight values ​​for each point are calculated. Then, these weight values ​​are mapped to the erosion sampling dataset using linear interpolation to obtain the positional weight for each sampling point. Finally, based on... Corrosion location coefficient was calculated .

[0166] Furthermore, the error between the calculation results of the numerical simulation model established in step S3 and the calculation results of the short pipe in DNV-ST-F101 can be expressed as follows: The calculated result is 0.14% (as shown in Table 2). The calculation demonstrates the effectiveness of the numerical simulation model and its potential use in establishing a parameter calibration dataset.

[0167] Table 2. Complete Pipeline Calculation Results

[0168]

[0169] The specific steps are: S4 reduction factor model parameters. ~ The calibration process includes the following steps.

[0170] First, based on the Python language and Abaqus script interface, 33 sets of irregular seabed corrosion pipe models were quickly established. The mesh configuration of the non-corrosion area was consistent with that in step S3. The axial compression process was simulated using the Dynamic / Implicit method. The displacement-result curves during displacement-controlled loading were extracted, and the axial residual ultimate bearing capacity of 32 irregular seabed corrosion pipes was obtained. Figure 5 This is a schematic diagram of an irregularly corroded pipeline model on the seabed. Tables 3a and 3b show the statistical characteristic parameters and numerical simulation results for 33 groups of irregularly corroded pipelines on the seabed. In this embodiment, the outer surface grid size is the sampling resolution.

[0171] Furthermore, the 32 irregularly corroded pipe models established in this embodiment have characteristic quantile depths. Selecting feature probabilities during calculation Therefore, for non-fully corroded pipelines This can be ignored during model parameter calibration. , that is to say .

[0172] Furthermore, statistical parameters are substituted into the reduction factor model, and the model parameters are iterated step by step using the particle swarm optimization method. ~ The constructed objective function satisfy The model's computational results are made as close as possible to the numerical simulation results to meet the evaluation computational requirements. Table 4 lists the parameter values ​​obtained after calibration, where the objective function... .

[0173] In specific step S5, the formula for evaluating the axial residual ultimate bearing capacity of irregularly corroded pipelines on the seabed is expressed as follows:

[0174]

[0175] in , , , , .

[0176] Specifically, 40 additional numerical simulation models of irregularly corroded subsea pipelines were established to verify the effectiveness of the calibration formula in step S5. The corrosion depth distribution characteristics of #V1~#V40 in Table 5 differ from those of #C1~#C33, with most having a maximum corrosion depth of 15.12 mm. This is because during the rapid modeling process using Python scripts, when the randomly generated maximum corrosion depth exceeds the pipeline wall thickness, it is scaled down by a certain ratio, with the maximum value not exceeding 15.12 mm. The error data in Tables 5a and 5b are compared with... Figure 6 The results show that the method for evaluating the axial residual ultimate bearing capacity of irregularly corroded subsea pipelines established based on this invention has good effectiveness and feasibility. The method for calculating the error is as follows: .

[0177] Example 2

[0178] To further illustrate the applicability of the present invention under internal pressure, external pressure, bending moment, and combined loads, the differences in the steps of the present invention under the case of single wall corrosion are further explained based on Example 1.

[0179] Specifically, steps S1 and S2 are basically the same as in Example 1, but the load condition parameters need to be adjusted according to the actual evaluation content.

[0180] Specifically, in step S3, a complete pipeline analysis model is established under the corresponding load condition, and the position weight function is extracted.

[0181] Specifically, in step S4, based on the corrosion shape characteristic parameter set and corrosion location characteristic parameters extracted and calculated in the previous steps, the influence of irregular corrosion defects on the pipeline's remaining ultimate bearing capacity is considered from the perspective of pipeline failure modes. A reduction factor model is pre-designed, preferably in polynomial or exponential form. Further optimization and adjustment are performed during the parameter calibration process.

[0182] Specifically, the ultimate bearing capacity of the complete pipeline reference in step S5 is denoted as... The remaining ultimate bearing capacity of an irregularly corroded subsea pipeline under the corresponding load condition can be expressed by a mathematical expression. To conduct an evaluation.

[0183] Furthermore, when irregular corrosion exists on both the inner and outer surfaces of the pipe, the corrosion shape-related parameters are calculated separately for the inner and outer surfaces in step S3 to establish a parameter set. and Corrosion location coefficient During calculation, and Based on the pre-set dominant load condition: when the internal pressure condition is dominant, the preferred option is... When external pressure or local instability of the outer wall dominates the operating conditions, the preferred method is to take... When axial or bending conditions dominate and the difference between the inner and outer walls is not significant, the preferred option is to choose... .

[0184] Furthermore, in step S4, the reduction factor model can be based on , , , and Coupling is established. Further optimization and adjustments are made during the parameter calibration process.

[0185] In summary, this invention provides a method for assessing the residual ultimate bearing capacity of corroded pipelines based on sampling statistical parameters. This method addresses the widespread and complex non-uniform corrosion scenarios on the inner and outer surfaces of subsea pipelines, overcoming the shortcomings of traditional methods that rely on a single maximum depth or a small number of equivalent geometric parameters, leading to information loss and difficulty in characterizing spatial distribution features and location-sensitive effects. Starting from finite discrete sampling data, this invention maps the sampling data to the pipeline coordinate system and establishes a sample dataset. It extracts three types of statistical parameters: depth distribution, spatial distribution, and location sensitivity. Simultaneously, it constructs corrosion length coefficient, corrosion circumferential coefficient, and corrosion location coefficient, and then establishes a reduction factor model based on hierarchical indices (including corrosion depth damage index, extended damage index, and location-sensitive index), ultimately achieving a systematic mapping from sampling information to residual ultimate bearing capacity. This technical approach balances the comprehensiveness of statistical characterization with the rationality of mechanical assessment, effectively improving the accuracy and engineering applicability of the assessment without requiring a complete reconstruction of the actual corroded surface.

[0186] Table 3a

[0187]

[0188] Table 3b

[0189]

[0190] Table 4

[0191]

[0192] Table 5a

[0193]

[0194] Table 5b

[0195]

[0196] Table 5c

[0197]

[0198] Table 5d

[0199]

[0200] Table 5e

[0201]

[0202] Table 5f

[0203]

[0204] Compared with the prior art, the present invention has at least the following significant technical advantages:

[0205] 1. Based on the surface corrosion sampling data of subsea pipelines, this invention no longer relies solely on a single maximum depth, average depth, or a small number of equivalent geometric parameters. Instead, it constructs depth distribution statistical parameters, spatial distribution statistical parameters, and location-sensitive statistical parameters to comprehensively characterize complex non-uniform corrosion morphology, thereby improving the completeness and authenticity of the corrosion state description.

[0206] 2. This invention integrates corrosion depth characteristics, spatial expansion characteristics, and stress location effects into the remaining ultimate bearing capacity assessment framework, which can simultaneously reflect the depth weakening effect, axial and circumferential expansion effect, and dangerous stress location effect of the corrosion zone, thereby improving the assessment capability of the remaining ultimate bearing capacity of complex corroded subsea pipelines.

[0207] 3. By constructing corrosion length coefficient, corrosion circumferential coefficient and corrosion location coefficient, and distinguishing the different roles of corrosion shape-related parameters and corrosion location-related parameters in the reduction factor model, this invention enables corrosion volume damage and location-sensitive damage to be mapped to the remaining ultimate bearing capacity in a layered manner, thus providing a clearer physical interpretation.

[0208] 4. The reduction factor model established in this invention is not only applicable to axial compression conditions, but also to internal pressure, external pressure, bending moment and combined load conditions, and can be extended to internal surface corrosion, external surface corrosion and simultaneous internal and external surface corrosion, and has good adaptability to working conditions and wall surfaces.

[0209] 5. Even in the absence of a complete and realistic reconstruction of the corroded surface, this invention can still establish a residual ultimate bearing capacity assessment process based on limited sampling data. This avoids the adverse effects of high-precision corrosion geometry reconstruction, complex finite element modeling, and high computational costs on engineering promotion, and is conducive to improving the feasibility of submarine pipeline integrity management and engineering applications.

[0210] 6. This invention provides a systematic evaluation approach that lies between traditional empirical equivalent methods and highly complex detailed analysis methods. It can make full use of on-site sampling data, detection data, or identification data, providing new technical means for safety evaluation, maintenance decision-making, and life management of submarine corrosion pipelines, and has good application value.

[0211] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0212] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0213] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0214] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc or CD-ROM; magnetic surface memory can be disk storage or magnetic tape storage. The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0215] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0216] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0217] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0218] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0219] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0220] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0221] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0222] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0223] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters, characterized in that, Includes the following steps: S1. Obtain material parameters, structural dimension parameters, and corrosion depth sampling data of the corroded pipeline and the corrosion area to be evaluated. S2. Map the sampled data to the pipeline coordinate system and preprocess it to establish a sample dataset of the corrosion area; S3. Based on the sample dataset, extract a set of statistical parameters that characterize the overall features of the corrosion region, and construct corrosion length coefficient and corrosion circumferential coefficient according to the axial expansion features and circumferential expansion features of the corrosion region, and construct corrosion position coefficient according to the position sensitivity features. S4. Construct a model for the remaining ultimate bearing capacity reduction factor. The model takes the statistical parameter set, corrosion length coefficient, corrosion circumferential coefficient and corrosion location coefficient as inputs, and outputs a reduction factor to characterize the degree of reduction of the pipeline's ultimate bearing capacity due to corrosion. The construction of the residual ultimate bearing capacity reduction factor model in step S4 includes: constructing a parameterized model using a hierarchical modeling approach. The model includes at least a corrosion depth damage index, an extended damage index, and a corrosion location sensitivity index. The corrosion depth damage index is constructed from the ratio of characteristic quantile depth to wall thickness, the corrosion depth variation coefficient, and the ratio of the difference between the maximum corrosion depth and the average corrosion depth to the wall thickness. The extended damage index is constructed from the corrosion length coefficient, the corrosion circumferential coefficient, the ratio of axially related length to pipe geometric features, the ratio of circumferential related angle to circumferential angle, and the proportion of highly corroded area. The corrosion location sensitivity index is the corrosion location coefficient. S5. Based on the ultimate bearing capacity of the intact pipeline under non-corrosion conditions and the reduction factor, calculate the remaining ultimate bearing capacity of the corroded pipeline and output the evaluation results.

2. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, In step S1, the corrosion sampling data comes from the corrosion area on the inner and / or outer surface of the pipe and is represented in the form of a corrosion sample set. Each sample point includes at least axial coordinates, circumferential coordinates, corrosion depth, and wall identification parameters used to distinguish whether the corrosion is located on the inner or outer surface.

3. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, Step S2, which maps the sampled data to the pipeline coordinate system and performs preprocessing to establish a corrosion area sample dataset, specifically includes: First, mapping the corrosion sampled data to a two-dimensional coordinate system with the axial coordinate as the horizontal axis and the circumferential coordinate as the vertical axis; then, preprocessing the mapped data, which includes removing outliers, repairing missing values, unifying the units and coordinate references, resampling the sampling points according to the detection resolution, interpolating or smoothing discrete data, fusing data from different sources, and classifying and labeling or unifying the internal and external surface corrosion data; after preprocessing, a sample dataset is formed that at least contains the sampling point location coordinates, the corresponding corrosion depth values, and information indicating whether the corrosion is located on the inner or outer wall.

4. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, Step S3, which involves extracting a set of statistical parameters characterizing the overall features of the corroded area, specifically includes: Extract depth distribution statistical parameters, which are obtained by statistical analysis of the corrosion depth values ​​of all sample points, including at least the average corrosion depth, maximum corrosion depth, unbiased sample standard deviation, coefficient of variation, and characteristic quantile depth obtained by fitting the cumulative distribution function of corrosion depth. Spatial distribution statistical parameters are extracted, including: constructing a two-dimensional autocorrelation function based on discrete sampling points, calculating the axial autocorrelation function and circumferential autocorrelation function of the corrosion depth field, setting correlation thresholds respectively, and solving for the axial correlation length and circumferential correlation angle; and, by setting a corrosion depth threshold, calculating the proportion of the area of ​​the region with a corrosion depth exceeding the threshold to the total area of ​​the corrosion region, to obtain the proportion of the area of ​​the highly corroded region. Extract location-sensitive statistical parameters, including at least a corrosion location coefficient; the corrosion location coefficient is constructed based on a two-dimensional location-sensitive weighting function and a wall location weighting function, wherein the two-dimensional location-sensitive weighting function is determined based on the nominal stress distribution of the complete pipeline under a preset load condition and a reference load level.

5. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, The construction of corrosion length coefficient and corrosion circumferential coefficient in step S3 specifically includes: constructing a dimensionless corrosion length coefficient based on the projected length of the corrosion region in the axial direction of the pipe, combined with the effective length, outer diameter and wall thickness of the pipe, to characterize the influence of the axial expansion of the corrosion region on the bearing capacity; and constructing a dimensionless corrosion circumferential coefficient based on the projected angle of the corrosion region in the circumferential direction, combined with the circumferential angle, to characterize the influence of the circumferential coverage of the corrosion region on the bearing capacity.

6. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, The construction of the corrosion location coefficient in step S3 specifically includes: constructing a two-dimensional location-sensitive weighting function based on the nominal stress distribution of the complete pipeline under preset load conditions and reference load levels, and defining a wall location weighting function to distinguish the relative influence of corrosion on the inner and outer surfaces; calculating the product of the two-dimensional location-sensitive weight and the wall location weight for each sample point to obtain the comprehensive location weight; and obtaining the corrosion location coefficient based on the weighted ratio of the comprehensive location weight to the corrosion depth. When only a single wall surface is corroded, the wall surface position weight is set to 1. When both the inner and outer surfaces are corroded, the inner wall position weight and the outer wall position weight are preset according to the dominant load conditions, or determined by normalizing the reference hazard levels of the inner and outer walls of the complete pipeline under the corresponding conditions.

7. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, The reduction factor model described in step S4 is expressed in polynomial form as follows: the reduction factor equals 1 minus the product of the first model parameter and the corrosion depth damage index and the extended damage index, and then minus the product of the second model parameter and the corrosion location sensitivity index. The model parameters are obtained by constructing a calibration sample set containing multiple corroded pipe samples and fitting and calibrating the model with the goal that the square of the correlation coefficient between the target reduction factor of each sample and the model predicted reduction factor approaches 1.

8. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 7, characterized in that, The calibration process for the model parameters includes: constructing a calibration sample set, where each sample contains a set of statistical parameters, corrosion length coefficient, corrosion circumferential coefficient, corrosion location coefficient, and the corresponding ultimate bearing capacity of the intact pipeline and the ultimate bearing capacity of the corroded pipeline, obtained from finite element numerical simulation, public experiments, or engineering case data; for each calibration sample, calculating the ratio of the ultimate bearing capacity of the intact pipeline to the ultimate bearing capacity of the corroded pipeline as the target reduction factor for that sample; substituting the input parameters of each sample into the preset reduction factor model, and iterating the model parameters step by step using particle swarm optimization or other optimization algorithms until the square of the correlation coefficient between the model prediction reduction factor and the corresponding target reduction factor for all samples is greater than a preset threshold, thereby determining the model parameters.

9. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, The method is applicable to single wall corrosion and simultaneous corrosion of inner and outer surfaces. In step S4, when the pipeline has both inner and outer surface corrosion, the corrosion depth damage index and the expansion damage index of the corrosion area on the inner and outer surfaces are calculated respectively. The corrosion location coefficient that takes into account the weight of the inner and outer surface walls is also calculated. Then, a joint reduction factor model is established based on the coupling of the inner surface corrosion depth damage index, the outer surface corrosion depth damage index, the inner surface expansion damage index, the outer surface expansion damage index and the corrosion location coefficient.

10. The method for evaluating the remaining ultimate bearing capacity of corroded pipelines based on sampling statistical parameters according to claim 1, characterized in that, The calculation of the remaining ultimate bearing capacity of the corroded pipeline in step S5 specifically includes: determining the baseline ultimate bearing capacity of the intact pipeline under the corresponding working conditions in the non-corrosion state, based on the load conditions of the pipeline to be evaluated; the working conditions include axial compression, axial tension, internal pressure, external pressure, bending moment, or combined load; the baseline ultimate bearing capacity of the intact pipeline is determined by standard formulas, theoretical models, analytical models, engineering calculation models, or finite element numerical calculations; multiplying the baseline ultimate bearing capacity by the reduction factor to obtain the remaining ultimate bearing capacity of the corroded pipeline under the current load conditions, and outputting an evaluation conclusion including the remaining ultimate bearing capacity, the reduction factor value, and the corrosion risk level.

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