CN112001104B - Buried pipeline service performance evaluation method, computer readable medium and equipment - Google Patents
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Abstract
The invention relates to a buried pipeline service performance evaluation method, a computer readable medium and equipment. A service performance evaluation method for buried pipelines comprises the following steps: s1, constructing a buried pipeline model by combining an influence variable, and acquiring a regression equation of pipeline stress and the influence variable; s2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation; s3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline. The method can directly solve the stress of various buried pipelines under multiple diseases and complex service conditions by adopting the established pipeline service performance evaluation model, calculate the contribution percentage of variables contained in an equation to the pipeline stress, and predict the residual life of the pipeline.
Description
Technical Field
The invention relates to the field of pipeline performance evaluation, in particular to a buried pipeline service performance evaluation method, a computer readable medium and equipment.
Background
The buried pipeline built in early stage has a long service life, and under the coupling action of various internal and external factors, various diseases such as pipe bottom void, inner wall corrosion, pipe body cracking, leakage and the like coexist, so that the possibility of accidents such as pipe explosion, pavement collapse and the like is greatly increased. Therefore, how to evaluate the service state, the remaining life, the repair priority, etc. of these old pipelines has become a serious problem for municipal authorities.
At present, an analytic method and a semi-empirical method are mainly used for solving the pipeline stress. Although these methods have been widely used in practice, they have severely limited application because of a variety of assumptions. For example, three-dimensional effects are ignored, pipe-soil interactions cannot be analyzed using Winkler springs to reflect nonlinear characteristics, etc. The most commonly used buried pipeline performance evaluation method in the existing pipeline service performance evaluation method is to determine defect scores according to defect types and severity, calculate average values and maximum values of defect coefficients according to the defect points, wherein the defect coefficients with the larger scores are structural and functional defect coefficients of a pipeline section, and finally conduct pipeline defect grade division according to intervals to which the structural and functional defect coefficients of the pipeline section belong. This evaluation method has the following problems: (1) The adoption of a single length average index can not reflect the real situation of the pipeline defect; (2) Only evaluating according to the apparent detection result, and not distinguishing the relationship between the defect and the pipeline material; (3) lack of scientific quantification and theoretical basis in the evaluation process; (4) the remaining life of the pipe cannot be predicted. Meanwhile, most of the existing evaluation methods for the service condition of other pipelines are based on statistical methods and reliability theory, the evaluation results are highly dependent on the number of samples, and the evaluation results cannot truly reflect the service condition of the pipelines under the condition that the samples with enough capacity are difficult to obtain, so that the evaluation results cannot be popularized and applied in a large scale.
Patent document with application number of CN 201910159966.2 discloses a method for detecting service life of a buried PE gas pipeline with defects, and the gas pipeline is tested to obtain structural size and operation parameters of the gas pipeline; intercepting a gas pipeline to prepare a curved compact sample; stretching the sample at a preset frequency f, wherein the stretching stress in the stretching process directly and reciprocally changes at a maximum preset stress Fmax and a minimum preset stress Fmin; determining the limit defect size according to the first failure principle based on reliability and finite element analysis and calculation; and (5) establishing a fatigue crack propagation model, calculating the fatigue cycle times, and determining the residual life of the buried PE gas pipeline containing the defects. The invention can accurately estimate the residual life of the buried PE gas pipeline containing the defects. But still does not efficiently solve the above problems.
Therefore, the existing buried pipeline performance evaluation field has defects and needs to be improved and improved.
Disclosure of Invention
In view of the above-described shortcomings of the prior art, an object of the present invention is to provide a buried pipeline service performance evaluation method, computer-readable medium, and apparatus, capable of scientifically quantifying the performance results of a buried pipeline while being able to predict the remaining life of the pipeline.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a service performance evaluation method for buried pipelines comprises the following steps:
s1, constructing a buried pipeline model by combining an influence variable, and acquiring a regression equation of pipeline stress and the influence variable;
s2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation;
s3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline.
Preferably, the step S1 specifically includes:
s11, constructing a buried pipeline model, simultaneously acquiring influence variables of service performance of the pipeline, setting different influence variable conditions, and respectively acquiring pipeline stress;
s12, carrying out dimensionless treatment on the influence variable in the buried pipeline model, determining the relation with the pipeline stress, and constructing a regression equation;
s13, solving regression equation coefficients to enable the prediction accuracy of the regression equation to meet the prediction requirement.
Preferably, the step S12 specifically includes:
s121, carrying out dimensionless treatment on the influence variables to obtain dimensionless variables, wherein the number of the dimensionless variables is determined through pi theory;
s122, expressing the pipeline stress as a function form of a dimensionless variable, and carrying out fitting analysis based on a multi-element nonlinear regression method to obtain a regression equation.
Preferably, in the method for evaluating service performance of a buried pipeline, in step S13, a least square method is used to solve the regression equation coefficient; the initial value of the regression equation is obtained by using a random selection method particle swarm algorithm.
Preferably, in the method for evaluating service performance of a buried pipeline, in step S11, the step of constructing the buried pipeline model includes:
s111, respectively establishing a disease-free pipeline primary model and a disease-free pipeline primary model of a pipeline soil structure according to a design working condition, respectively carrying out sensitivity analysis on model sizes and network sizes of the disease-free pipeline primary model and the disease-free pipeline primary model, and adjusting corresponding model parameters;
s112, performing full-scale test on the primary model of the disease-free pipeline and the primary model of the disease-free pipeline respectively, and obtaining the optimal primary model of the disease-free pipeline as a buried pipeline model through verification.
Preferably, the step S3 specifically includes:
s31, determining the maximum stress of the pipeline;
s32, determining an influence variable extremum through the relation curve;
s33, performing sensitivity analysis on the influence variable according to the regression equation to obtain a contribution value of the influence variable to the pipeline stress;
s34, acquiring variable measurement data and the annual average increment rate of defects corresponding to the influence variables;
s35, obtaining the residual service life of the pipeline.
Preferably, the step S33 specifically includes:
s331, carrying out disturbance to a certain extent on one-dimensional variables in the influence variables to obtain new input influence variables;
s332, inputting the input variable into the regression equation, and calculating a Speermann level correlation coefficient between an output value before disturbance and an output value after disturbance;
s333, normalizing the spearman level correlation coefficient to obtain the contribution value of each dimension influence variable to the pipeline stress.
Preferably, in the method for evaluating service performance of a buried pipeline, a calculation formula of the residual life of the pipeline is as follows:
wherein N is the remaining lifetime; c is the annual average increasing rate of the defect corresponding to the influence variable; c (C) h-cri The maximum stress corresponds to the influence variable extremum; c (C) h Data is measured for the current variable.
A computer readable medium storing computer software which, when executed by a computer, implements the buried pipeline service performance assessment method.
An electronic device includes a processor and a memory; the memory stores computer software; the processor is used for executing the computer software to realize the service performance evaluation method of the buried pipeline.
Compared with the prior art, the buried pipeline service performance evaluation method, the computer readable medium and the equipment provided by the invention have the following beneficial effects:
1. the invention can establish various buried pipeline service performance evaluation models under multiple diseases and complex service conditions, and compared with the existing pipeline evaluation method, the invention overcomes the defects of excessive assumption conditions, insufficient theoretical support and the like;
2. the method can directly solve the stress of various buried pipelines under multiple diseases and complex service conditions by adopting the established pipeline service performance evaluation model, calculate the contribution percentage of variables contained in an equation to the pipeline stress, and predict the residual life of the pipeline;
3. the method can be applied to the establishment of various pipeline service performance evaluation models such as concrete pipelines, ductile cast iron pipelines, PCCP pipelines, HDPE pipelines, PE pipelines and the like, can provide a new thought for solving the problems, and can provide theoretical support for the departments such as municipal administration, water service, water conservancy and the like to evaluate the service performance of various pipelines by using the established pipeline stress evaluation model.
Drawings
FIG. 1 is a flow chart of a buried pipeline service performance assessment method provided by the invention;
FIG. 2 is a specific flowchart of step S1 in the method for evaluating service performance of a buried pipeline provided by the invention;
FIG. 3 is a flow chart of the construction of the buried pipeline model provided by the present invention;
FIG. 4 is a flow chart of obtaining regression equations provided by the present invention;
FIG. 5 is a specific flowchart of step S3 in the method for evaluating service performance of a buried pipeline according to the present invention;
FIG. 6 is a flow chart of a sensitivity analysis provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and more specific, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, the invention provides a service performance evaluation method for a buried pipeline, comprising the following steps:
s1, constructing a buried pipeline model by combining an influence variable, and acquiring a regression equation of pipeline stress and the influence variable;
in this embodiment, the buried pipeline model constructed in this step is constructed from influencing variables (such as corrosion, void, crack, groundwater, fault zone, soft foundation, etc.) that significantly affect the service performance of the pipeline in view of the defect problem that occurs in the research pipeline. Preferably, the buried pipeline model is constructed as a finite element model. In general, the method for evaluating the service performance of the pipeline provided by the invention can be used for concrete pipelines, ductile cast iron pipelines, PCCP (Prestressed Concrete Cylinder Pipe, prestressed steel cylinder concrete) pipelines, HDPE (High Density Polyethylene ) pipelines, PE (polyethylene) pipelines and the like, and the influence variables of the different pipelines are different, but the method is the same, and the concrete pipelines are taken as examples for detailed description in the following embodiment.
Referring to fig. 2, in this embodiment, the step S1 specifically includes:
s11, constructing a buried pipeline model, simultaneously acquiring influence variables of service performance of the pipeline, setting different influence variable conditions, and respectively acquiring pipeline stress;
specifically, a full-scale test scheme for verifying the model is designed by referring to the designed finite element model; performing full-scale test to obtain test data; establishing a three-dimensional refined finite element model of the buried pipeline based on finite element software, wherein the three-dimensional refined finite element model can accurately reflect structural characteristics (such as bell and spigot, rubber rings and the like), defects (such as corrosion, void, cracks and the like) and complex service conditions (such as groundwater, fault zone, soft foundation and the like) of the pipeline, and simultaneously obtain the corresponding maximum pipeline stress of the buried pipeline according to different service conditions; in this embodiment, a model is built according to the corresponding design conditions (for example, there is a corrosion defect condition) on a concrete pipeline, and then the pipeline stress of the concrete pipeline at this time is calculated according to the characteristics of the responsive concrete material through a finite element model, and different design conditions are set respectively and input into the finite element model, so as to obtain a plurality of groups of different design conditions, namely, corresponding pipeline stresses, and whether the concrete pipeline model is reasonable or not can be known. The influencing variables include: corrosion length (C) l ) Depth of corrosion (C) h ) Corrosion width (C) w ) Crack length (L) l ) Crack depth (L) h ) Length of run-out (V) l ) Depth of void (V) h ) Width of void (V) w ) A burial depth (h), a lateral earth pressure coefficient (k), a pipe base strength (E) b ) Strength of backfill (E) c ) Ground water level (h) w ) Traffic load (P). Of course, when constructing the finite element model, corresponding basic parameters are also required as the framework, wherein the basic parameters comprise pipe diameter (D), wall thickness (t), total length of pipe (L), elastic modulus of pipe (Ep) and soil body weight (gamma). In general, during an evaluation processThe basic parameters are not changed after being set.
Referring to fig. 3, in a preferred embodiment, in step S11, the step of constructing the buried pipeline model includes:
s111, respectively establishing a disease-free pipeline primary model and a disease-free pipeline primary model of a pipeline soil structure according to a design working condition, respectively carrying out sensitivity analysis on model sizes and network sizes of the disease-free pipeline primary model and the disease-free pipeline primary model, and adjusting corresponding model parameters;
specifically, the construction process of the buried pipeline model is as follows:
(1) And (3) establishing a model: a pipe soil body structure Model is built in an ABAQUS Model module, and pipe base materials, thickness, supporting angles, pipe inner diameters, wall thicknesses, effective lengths and the like can be determined according to relevant specifications according to the type of the researched pipe. Pipeline diseases, complex service conditions and the like can be set item by item according to the design of the design working condition.
(2) Imparting material properties: giving Material properties of each part of the model in a Material module of ABAQUS and inputting related parameters, wherein a nonlinear elastoplastic constitutive model such as a mole-coulomb model, a Duncan-Zhang Moxing model and the like is adopted for soil mass; tube-based materials typically employ a wire elastic model; the piping material configuration and related parameters are selected according to the type of piping under study.
(3) Mesh dissection: to ensure model mesh quality, mesh size may be formulatedWherein C is s For shear wave velocity (m/s), Δl is the maximum grid cell size (m), f is the excitation frequency (Hz), and ω is the circulation frequency of the excitation (rad/s). In addition, the soil grids near the pipeline part should be encrypted, the pipeline and the dry soil body can adopt a three-dimensional entity eight-node reduction integral unit (C3D 8R), the saturated soil can adopt a three-dimensional entity eight-node stress-hole pressure coupling reduction integral unit (C3D 8 RP), the rubber sealing ring can adopt a three-dimensional entity eight-node coordination reduction integral unit (C3D 8 RH), and in addition, all grids need to be subjected to hourglass control.
(4) Contact attribute setting: the model contact attribute is set in an Interaction module of ABAQUS, specifically, the surface-surface contact or the point-surface contact is set at a pipe-soil, socket-rubber ring and socket-rubber ring contact interface, tangential friction contact models can be selected from 'Penalty, frictionless, rough, lagrange Multiplie', normal friction attribute can be selected from 'Hard, exponential, linear', and the like, and a proper friction model can be selected according to the type of a researched pipeline.
(5) Boundary condition setting: if power calculation is adopted, a viscoelastic-plastic artificial power boundary or an infinite element power boundary condition is adopted for the model boundary so as to reduce the superposition effect of stress waves at the boundary; if static force calculation is adopted, limiting the degree of freedom of normal displacement of the four side surfaces, the bottom surface and the two ends of the pipeline of the calculation model; if the influence of groundwater is considered, the interfaces of saturated soil, dry soil and saturated soil and pipelines are taken as infiltration surfaces, the pore pressure on the infiltration surfaces is 0, and the boundary conditions of the pore pressures are according to p=ρ w ×g×(z 0 -h i ) Setting, wherein ρ w Density of water, g is gravity acceleration, z 0 Is the vertical coordinate value of 0 hole pressing surface, h i Is the vertical coordinate value of any point below the 0-hole pressing surface.
(6) Ground stress balance: creating a ground stress balance analysis Step (Geostatic) in a Step module, wherein the type is "Fixed", adding the gravity in the z direction for the whole model in a Load module, and submitting calculation to obtain a result file geo; and (3) copying the Model (Model 2), leading in geo.odb in a predefined field in the Load module, re-submitting calculation, realizing ground stress balance, and when the balanced pipeline displacement is smaller than 1e-05, indicating that the balance effect is better, otherwise, repeatedly leading in the odb file newly obtained each time until the requirement is met.
(7) The analysis step is as follows: model 2 was replicated, leaving behind the ground stress balance analysis steps, after which the other analysis steps required for the calculation were created.
(8) Load setting: in the Load module, the ground stress analysis step still applies gravity, and the rest analysis steps set corresponding Load variables and Load amplitudes according to the design working conditions in the step S11.
(9) Creating a calculation name in the Job module, and clicking to Submit (Submit) to calculate to acquire the pipeline stress of the current model.
S112, performing full-scale test on the primary model of the disease-free pipeline and the primary model of the disease-free pipeline respectively, and obtaining the optimal primary model of the disease-free pipeline as a buried pipeline model through verification.
Specifically, sensitivity analysis is performed on model size, grid size, unit type, friction attribute and the like, reliability of the model is verified according to test data, and parameters of the buried pipeline model are adjusted according to analysis results. All buried pipeline models obtained according to different design working conditions are submitted to calculation to obtain corresponding pipeline stress, the output pipeline stress is determined by pipeline materials, the maximum main stress is generally output for brittle materials such as concrete, and Mises stress is output for elastoplastic materials such as ductile cast iron pipes. It should be noted that the buried pipeline model is obtained by optimizing a primary model of a pipeline with diseases, and when corresponding disease data are all empty, the buried pipeline model has similar external characteristics to the primary model of the pipeline without diseases.
S12, carrying out dimensionless treatment on the influence variable in the buried pipeline model, determining the relation with the pipeline stress, and constructing a regression equation;
referring to fig. 4, in this embodiment, the step S12 specifically includes:
s121, carrying out dimensionless treatment on the influence variables to obtain dimensionless variables, wherein the number of the dimensionless variables is determined through pi theory;
s122, expressing the pipeline stress as a function form of a dimensionless variable, and carrying out fitting analysis based on a multi-element nonlinear regression method to obtain a regression equation.
Specifically, all influencing variables in the buried pipeline model are subjected to dimensionless treatment, the number of the dimensionless variables is determined by pi theory, the form of the dimensionless variables can be specifically determined according to the physical meaning of the variables, for example, the corresponding forms of the dimensionless variables in the concrete pipeline can be as follows:the pipeline stress is expressed as a function of a dimensionless variable, which may be:
fitting analysis is carried out based on a multi-element nonlinear regression method, and the function form is continuously adjusted according to the fitting condition and the error analysis until an optimal evaluation model is found; in this embodiment, the regression equation in the concrete pipeline model (of course, the corresponding method for other types of pipelines is similar) is preferably:
wherein sigma max Is the maximum stress; alpha 1 、……α 14 Evaluating coefficients of the expression equation for the pipeline; beta 1 、……β 14 Evaluating the index parameters of the expression equation for the pipeline; c (C) l For the length of corrosion, C h For depth of corrosion, C w Is of corrosion width, L l Is the crack length L h Is the depth of the crack, V l Is of a run-out length, V h Is the depth of the void, V w Is the void width, h is the burial depth, k is the lateral soil pressure coefficient, E b For the strength of the tube base, E c Is the backfill soil strength, h w The underground water level is adopted, and P is traffic load; d is pipe diameter, t is wall thickness, L is total length of pipe, E p The elastic modulus of the pipeline is that of the soil body, and gamma is that of the soil body.
S13, solving regression equation coefficients to enable the prediction accuracy of the regression equation to meet the prediction requirement. And verifying the prediction precision of the provided model by using test data or a newly established finite element model, so that the prediction precision of the regression model constructed by the regression equation meets the prediction requirement.
In the present embodiment, in the step S13, the regression equation coefficient is solved by using a least square method; the initial value of the regression equation is obtained by using a random selection method particle swarm algorithm.
Specifically, the solving step of the regression coefficient of the regression model formed by solving the regression equation is as follows:
reading all influence variables of the regression model;
carrying out dimensionless treatment on all influence variables, and defining the input and output of the proposed regression model;
and solving a model regression coefficient by utilizing a least square method principle, wherein the initial value selection can be obtained by adopting a random selection method or a particle swarm algorithm.
S2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation;
s3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline.
Specifically, the step of predicting the residual life of the pipeline takes the corroded concrete pipeline as an example, the residual variables in the regression model are kept unchanged, the relation between the corrosion depth and the maximum main stress of the concrete pipeline is calculated, the corrosion depth corresponding to the ultimate tensile strength of the concrete pipeline is found out, and the residual life of the corroded concrete pipeline can be predicted by using the annual growth rate of the corrosion depth. Of course, the relationship between the remaining variables in the proposed model and the mechanical response of the pipeline can also be calculated to predict the remaining life of the pipeline. The method is similar for other types of pipes.
Referring to fig. 5, in this embodiment, the step S3 specifically includes:
s31, determining the maximum stress of the pipeline; the obtaining mode of the maximum stress of the pipeline is obtained by a finite element analysis method of a buried pipeline model according to different pipeline materials,
s32, determining an influence variable extremum through the relation curve;
s33, performing sensitivity analysis on the influence variable according to the regression equation to obtain a contribution value of the influence variable to the pipeline stress;
referring to fig. 6, in this embodiment, the step S33 specifically includes:
s331, carrying out disturbance to a certain extent on one-dimensional variables in the influence variables to obtain new input influence variables; carrying out disturbance to a certain extent on the one-dimensional vector (namely, the influence variable needing to obtain the contribution value) to obtain a new input variable; in the implementation, if the contribution values of all the influence variables need to be obtained, the disturbance is needed to be carried out on all the influence variables respectively; it should be noted here that the preferred disturbance amplitude is 5% -20%, further preferably 10% of the corresponding influencing variable;
s332, inputting the input variable into the regression equation, and calculating a Speermann level correlation coefficient between an output value before disturbance and an output value after disturbance; calculating output values (namely pipeline stress) corresponding to the input variables (namely all influencing variables in corresponding design working conditions) according to the proposed regression model;
s333, normalizing the spearman level correlation coefficient to obtain the contribution value of each dimension influence variable to the pipeline stress. Calculating a spearman grade correlation coefficient between a pipeline stress output value under the condition of a design working condition before disturbance and a pipeline stress output value under the condition of the design working condition after disturbance; the spearman scale correlation coefficient is normalized to obtain the relative contribution percentage of each control variable to the maximum stress of the pipeline. The corresponding spearman coefficient evaluation and standardization are realized by adopting a spearman coefficient correlation technique method, and the invention is not limited.
S34, acquiring variable measurement data and the annual average increment rate of defects corresponding to the influence variables;
s35, obtaining the residual service life of the pipeline.
In this embodiment, the calculation formula of the remaining life of the pipe is as follows:
wherein N is the remaining lifetime; c is the annual average increasing rate of the defect corresponding to the influence variable; c (C) h-cri The maximum stress corresponds to the influence variable extremum; c (C) h Data is measured for the current variable.
In conclusion, the invention can establish various buried pipeline service performance evaluation models under multiple diseases and complex service conditions, and compared with the existing pipeline evaluation method, the invention overcomes the defects of excessive assumption conditions, insufficient theoretical support and the like. The pipeline service performance evaluation model established by the method provided by the invention can directly solve the stress of various buried pipelines under multiple diseases and complex service conditions, calculate the contribution percentage of variables contained in an equation to the pipeline stress, and predict the residual life of the pipeline. The method can be applied to the establishment of various pipeline service performance evaluation models such as concrete pipelines, ductile cast iron pipelines, PCCP pipelines, HDPE pipelines, PE pipelines and the like, can provide a new thought for solving the problems, and can provide theoretical support for the departments such as municipal administration, water and water conservancy to evaluate the service performance of various pipelines by using the established pipeline stress evaluation model.
Correspondingly, the invention also provides a computer readable medium, which stores computer software, and the computer software realizes the method for evaluating the service performance of the buried pipeline when being executed by a computer. In particular, the readable medium may be a medium that exists independently, or may be an internal component of an electronic device, which is not limited by the present invention. When the readable medium is a stand-alone medium, it can be used in connection with a host computer having a processor capable of running the computer software.
The invention also provides an electronic device, which comprises a processor and a memory; the memory stores computer software; the processor is used for executing the computer software to realize the method for evaluating the service performance of the buried pipeline. Specifically, the number of the processors is not limited, and one or more processors may execute the computer software in the memory alone or in combination; the memory may be a readable medium provided by the present invention, or may be other devices in the art, where the number is not limited, and one or more memories may be provided, and each memory stores the computer software.
It will be understood that equivalents and modifications will occur to those skilled in the art in light of the present invention and their spirit, and all such modifications and substitutions are intended to be included within the scope of the present invention as defined in the following claims.
Claims (6)
1. The service performance evaluation method for the buried pipeline is characterized by comprising the following steps of:
s1, constructing a buried pipeline model by combining an influence variable, and acquiring a regression equation of pipeline stress and the influence variable; the step S1 specifically includes:
s11, constructing a buried pipeline model, simultaneously acquiring influence variables of service performance of the pipeline, setting different influence variable conditions, and respectively acquiring pipeline stress; the step of constructing the buried pipeline model comprises the following steps:
s111, respectively establishing a disease-free pipeline primary model and a disease-free pipeline primary model of a pipeline soil structure according to a design working condition, respectively carrying out sensitivity analysis on model sizes and network sizes of the disease-free pipeline primary model and the disease-free pipeline primary model, and adjusting corresponding model parameters;
s112, performing full-scale test on the primary model of the disease-free pipeline and the primary model of the disease-free pipeline respectively, and taking the primary model of the disease-free pipeline which is obtained through verification as a buried pipeline model
S12, carrying out dimensionless treatment on the influence variable in the buried pipeline model, determining the relation with the pipeline stress, and constructing a regression equation;
s13, solving regression equation coefficients to enable the prediction accuracy of the regression equation to meet the prediction requirement;
s2, constructing a relation curve between the influence variable and the pipeline stress based on the regression equation;
s3, measuring variable measurement data in the actual working condition of the pipeline, and predicting the service life of the pipeline; the step S3 specifically includes:
s31, determining the maximum stress of the pipeline;
s32, determining an influence variable extremum through the relation curve;
s33, performing sensitivity analysis on the influence variable according to the regression equation to obtain a contribution value of the influence variable to the pipeline stress; the method specifically comprises the following steps:
s331, carrying out disturbance to a certain extent on one-dimensional variables in the influence variables to obtain new input influence variables;
s332, inputting the input variable into the regression equation, and calculating a Speermann level correlation coefficient between an output value before disturbance and an output value after disturbance;
s333, normalizing the Szelman class correlation coefficient to obtain a contribution value of each dimension of influence variable to the pipeline stress;
s34, acquiring variable measurement data and the annual average increment rate of defects corresponding to the influence variables;
s35, obtaining the residual service life of the pipeline.
2. The method for evaluating service performance of a buried pipeline according to claim 1, wherein said step S12 specifically comprises:
s121, carrying out dimensionless treatment on the influence variables to obtain dimensionless variables, wherein the number of the dimensionless variables is determined through pi theory;
s122, expressing the pipeline stress as a function form of a dimensionless variable, and carrying out fitting analysis based on a multi-element nonlinear regression method to obtain a regression equation.
3. The method for evaluating service performance of a buried pipeline according to claim 1, wherein in the step S13, the regression equation coefficient is solved using a least square method; the initial value of the regression equation is obtained by using a random selection method particle swarm algorithm.
4. The method for evaluating service performance of a buried pipeline according to claim 1, wherein the calculation formula of the residual life of the pipeline is:
wherein N is the remaining lifetime; c is the year of the defect corresponding to the affected variableThe rate is increased; c (C) h-cri The maximum stress corresponds to the influence variable extremum; c (C) h Data is measured for the current variable.
5. A computer readable medium storing computer software which, when executed by a computer, implements the method for evaluating service performance of a buried pipeline according to any one of claims 1 to 4.
6. An electronic device comprising a processor and a memory; the memory stores computer software; the processor is used for executing the computer software to realize the method for evaluating the service performance of the buried pipeline according to any one of claims 1 to 4.
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