CN116305947B - Buried pipeline stress prediction method, safety evaluation method, equipment and storage medium - Google Patents
Buried pipeline stress prediction method, safety evaluation method, equipment and storage medium Download PDFInfo
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Abstract
The application provides a buried pipeline stress prediction method, a safety evaluation method, equipment and a storage medium, and relates to the technical field of pipelines. The method comprises the steps of obtaining influence factor data of a buried pipeline to be predicted, wherein the influence factor data are used for representing data of stress influence of the buried pipeline to be predicted, and the influence factors comprise pipeline diameter, pipeline wall thickness, pipeline burial depth, pipeline internal pressure, vehicle weight and soil type; and determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and the pre-constructed stress prediction formula, wherein the stress prediction formula is a buried pipeline stress prediction formula under the action of the rolling load of the vehicle constructed based on pi theorem, is quick and accurate, does not need professional numerical simulation personnel to participate in modeling and analysis, saves time and labor, and is suitable for buried pipeline stress prediction under various working conditions.
Description
Technical Field
The application relates to the technical field of pipelines, in particular to a buried pipeline stress prediction method, a safety evaluation method, safety evaluation equipment and a storage medium.
Background
With the rapid development of the urban process and the continuous improvement of engineering technology level, pipelines are widely used for conveying petroleum, natural gas and other media, and have become one of the most important infrastructures for maintaining modern life. However, due to some irresistible factors, long-distance oil and gas pipelines crossing things, longitudinally crossing the north and the south and communicating overseas inevitably intersect roads, and the load born by the oil and gas pipelines at the intersecting section mainly comprises constant loads such as the dead weight of a pipeline structure, soil pressure and the like and dynamic loads such as rolling loads of vehicles, ground stacking loads, earthing loads, temperature loads, pipeline internal pressure (or conveying medium internal pressure) and the like. Among these, vehicle crush loads are one of the most dominant dynamic loads acting on buried oil and gas pipelines (or simply "buried pipelines"). Under the combined action of the loads, the pipeline can deform, even crack and the like, so that accidents such as oil gas leakage, fire and the like occur, and huge losses are caused. Therefore, accurately obtaining the maximum Mi Saisi (Mises) stress to which the buried pipeline is subjected under the rolling load of the vehicle is a key ring for judging the safety state of the buried pipeline.
In the related art, a finite element modeling method is generally adopted to calculate the maximum Mi Saisi stress of the buried pipeline under the action of rolling load of a vehicle, but the method needs professional numerical simulation personnel to build a model and analyze the model, and is time-consuming and labor-consuming and lacks versatility.
Disclosure of Invention
The application provides a buried pipeline stress prediction method, a safety evaluation method, equipment and a storage medium, which are used for solving the problems that the existing method is time-consuming and labor-consuming and lacks versatility.
In a first aspect, the present application provides a method for predicting stress of a buried pipeline, comprising: obtaining influence factor data of the buried pipeline to be predicted, wherein the influence factor data are used for representing data of stress influence of the buried pipeline to be predicted, and the influence factors comprise pipeline diameter, pipeline wall thickness, pipeline burial depth, pipeline internal pressure, vehicle weight and soil type; and determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and a pre-constructed stress prediction formula, wherein the stress prediction formula is constructed based on pi theorem and used for the stress prediction formula of the buried pipeline under the action of the rolling load of the vehicle.
In one possible implementation, the stress prediction formula satisfies the following formula:
wherein sigma Mises Maximum rice for buried pipelineStress of Sies, f Mises P as a stress prediction function of heavy vehicle Missis e The pressure is designed for the pipeline, t is the wall thickness of the pipeline, D is the diameter of the pipeline, h is the burial depth of the pipeline and P w Is the internal pressure of the pipeline, m is the weight of the vehicle, E s Is soil elastic modulus, pi 1 、π 2 、π 3 、π 4 、π 5 To influence the dimensionless parameters of the stress of the heavy truck Mi Saisi, pi 1 Dividing the wall thickness of the pipeline by the diameter of the pipeline, pi 2 Dividing the pipe depth by the pipe diameter, pi 3 Dividing the internal pressure of the pipeline by the design pressure of the pipeline, pi 4 Is thatπ 5 Dividing the elastic modulus of soil by the design pressure of the pipeline, f 1 As the stress function of the heavy truck Mi Saisi corresponding to the wall thickness of the pipeline, f 2 For the stress function of the heavy truck Mi Saisi corresponding to the pipeline burial depth, f 3 As the stress function of the corresponding heavy truck Mi Saisi in the pipeline internal pressure, f 4 For the stress function of the heavy truck Mi Saisi corresponding to the weight of the truck, f 5 A is a stress function of a heavy truck Mi Saisi corresponding to the elastic modulus of soil 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 、a 11 And a 12 Is constant.
In one possible implementation, the stress prediction formula is constructed by:
analyzing to obtain influence factors of the buried pipeline;
based on pi theorem, it is determined that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions as follows:
f 3 ＝(a 7 π 3 +a 8 )
f 4 ＝(a 9 π 4 +a 10 )
f 5 ＝[a 11 π 5 +a 12 )
and multiplying different stress functions of the heavy truck Mi Saisi by adopting a nonlinear fitting method to obtain a stress prediction formula.
In a possible implementation manner, based on pi theorem, determining different influencing factors respectively corresponds to different heavy truck Mi Saisi stress functions as follows, including: classifying buried pipelines with different pipeline diameters; based on pi theorem, different influencing factors are determined to correspond to different heavy truck Mi Saisi stress functions as shown below under different pipeline diameters.
In a possible implementation manner, before determining that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions according to pi theorem, the method further comprises: according to the influence factor data of the buried pipeline, establishing a numerical simulation model corresponding to the buried pipeline by adopting numerical simulation software; parameterizing the numerical simulation model to obtain a parameterized numerical simulation model; inputting the influence factor data into a parameterized numerical simulation model, and performing batch calculation to obtain a calculation result; and analyzing influence factors of the buried pipeline according to the calculation result, and establishing a database.
In a second aspect, the present application provides a method for evaluating safety of a buried pipeline, comprising: obtaining the maximum Mi Saisi stress of the target buried pipeline, wherein the maximum Mi Saisi stress of the target buried pipeline is obtained by the buried pipeline stress prediction method of the first aspect; evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and the safety grade judgment criterion of the target buried pipeline; determining protective measures of the target buried pipeline according to the risk level; wherein, the security level decision criterion is: when the maximum Mi Saisi stress is smaller than the preset multiple of the allowable stress of the pipeline, the risk is low; when the maximum Mi Saisi stress is greater than or equal to a preset multiple of the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipeline allowable stress, the risk is medium; when the maximum Mi Saisi stress is greater than or equal to the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipe yield stress, a higher risk is attributed; high risk exists when the maximum Mi Saisi stress is greater than or equal to the tubing yield stress.
In one possible embodiment, evaluating the risk rating of the target buried pipeline based on the maximum Mi Saisi stress and the safety rating criteria comprises: if the target buried pipeline is defect-free, evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and a safety grade judging criterion; if the target buried pipeline is defective, the risk level of the target buried pipeline is estimated according to the maximum Mi Saisi stress, the safety level judgment criterion and the pipeline interior detection data or the excavation detection data of the target buried pipeline.
In a third aspect, the present application provides a buried pipeline stress prediction apparatus comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring influence factor data of a buried pipeline to be predicted, the influence factor data are used for representing data of stress influence of the buried pipeline to be predicted, and the influence factors comprise pipeline diameter, pipeline wall thickness, pipeline buried depth, pipeline internal pressure, vehicle weight and soil type;
the first determining module is used for determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and a pre-constructed stress prediction formula, wherein the stress prediction formula is a buried pipeline stress prediction formula under the action of a vehicle rolling load constructed on the basis of pi theorem.
In one possible implementation, the stress prediction formula satisfies the following formula:
wherein sigma Mises To the maximum Mi Saisi stress of the buried pipeline, f Mises P as a stress prediction function of heavy vehicle Missis e The pressure is designed for the pipeline, t is the wall thickness of the pipeline, D is the diameter of the pipeline, h is the burial depth of the pipeline and P w Is the internal pressure of the pipeline, m is the weight of the vehicle, E s Is elastic to soilModulus, pi 1 、π 2 、π 3 、π 4 、π 5 To influence the dimensionless parameters of the stress of the heavy truck Mi Saisi, pi 1 Dividing the wall thickness of the pipeline by the diameter of the pipeline, pi 2 Dividing the pipe depth by the pipe diameter, pi 3 Dividing the internal pressure of the pipeline by the design pressure of the pipeline, pi 4 Is thatπ 5 Dividing the elastic modulus of soil by the design pressure of the pipeline, f 1 As the stress function of the heavy truck Mi Saisi corresponding to the wall thickness of the pipeline, f 2 For the stress function of the heavy truck Mi Saisi corresponding to the pipeline burial depth, f 3 As the stress function of the corresponding heavy truck Mi Saisi in the pipeline internal pressure, f 4 For the stress function of the heavy truck Mi Saisi corresponding to the weight of the truck, f 5 A is a stress function of a heavy truck Mi Saisi corresponding to the elastic modulus of soil 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 、a 11 And a 12 Is constant.
In one possible embodiment, the buried pipeline stress prediction apparatus may further include an analysis module, a second determination module, and a fitting module, where the analysis module, the second determination module, and the fitting module may be configured to construct a stress prediction formula, where:
The analysis module may be specifically configured to: analyzing to obtain influence factors of the buried pipeline;
the second determining module may be specifically configured to: based on pi theorem, it is determined that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions as follows:
f 3 ＝(a 7 π 3 +a 8 )
f 4 ＝(a 9 π 4 +a 10 )
f 5 ＝[a 11 π 5 +a 12 )
the fitting module may be specifically configured to: and multiplying different stress functions of the heavy truck Mi Saisi by adopting a nonlinear fitting method to obtain a stress prediction formula.
In a possible implementation manner, the second determining module may be further configured to: classifying buried pipelines with different pipeline diameters; based on pi theorem, different influencing factors are determined to correspond to different heavy truck Mi Saisi stress functions as shown below under different pipeline diameters.
In a possible implementation, the analysis module may also be used to: according to the influence factor data of the buried pipeline, establishing a numerical simulation model corresponding to the buried pipeline by adopting numerical simulation software; parameterizing the numerical simulation model to obtain a parameterized numerical simulation model; inputting the influence factor data into a parameterized numerical simulation model, and performing batch calculation to obtain a calculation result; and analyzing influence factors of the buried pipeline according to the calculation result, and establishing a database.
In a fourth aspect, the present application provides a buried pipeline safety assessment device comprising:
an acquisition module for acquiring a maximum Mi Saisi stress of the target buried pipeline, the maximum Mi Saisi stress of the target buried pipeline being obtained by the buried pipeline stress prediction method of the first aspect;
the evaluation module is used for evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and the safety grade judgment criterion of the target buried pipeline;
the protection module is used for determining the protection measures of the target buried pipeline according to the risk level;
wherein, the security level decision criterion is: when the maximum Mi Saisi stress is smaller than the preset multiple of the allowable stress of the pipeline, the risk is low; when the maximum Mi Saisi stress is greater than or equal to a preset multiple of the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipeline allowable stress, the risk is medium; when the maximum Mi Saisi stress is greater than or equal to the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipe yield stress, a higher risk is attributed; high risk exists when the maximum Mi Saisi stress is greater than or equal to the tubing yield stress.
In a possible embodiment, the evaluation module may be specifically configured to: if the target buried pipeline is defect-free, evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and a safety grade judging criterion; if the target buried pipeline is defective, the risk level of the target buried pipeline is estimated according to the maximum Mi Saisi stress, the safety level judgment criterion and the pipeline interior detection data or the excavation detection data of the target buried pipeline.
In a fifth aspect, the present application provides an electronic device, comprising: memory and a processor. The memory is used for storing program instructions; the processor is configured to invoke the program instructions in the memory to perform the buried pipeline stress prediction method of the first aspect and/or the processor is configured to invoke the program instructions in the memory to perform the buried pipeline security assessment method of the second aspect.
In a sixth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions that, when executed, implement the buried pipeline stress prediction method of the first aspect, and/or implement the buried pipeline security assessment method of the second aspect.
In a seventh aspect, the present application provides a computer program product comprising a computer program for implementing the buried pipeline stress prediction method of the first aspect when the computer program is executed by a processor and/or for implementing the buried pipeline security assessment method of the second aspect when the computer program is executed by a processor.
According to the method, the safety evaluation method, the equipment and the storage medium for predicting the stress of the buried pipeline, the influence factor data of the buried pipeline to be predicted are obtained, and are used for representing the data of the influence of the stress of the buried pipeline to be predicted, wherein the influence factors comprise the diameter of the pipeline, the wall thickness of the pipeline, the burial depth of the pipeline, the internal pressure of the pipeline, the vehicle weight and the soil type; and determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and the pre-constructed stress prediction formula, wherein the stress prediction formula is a buried pipeline stress prediction formula under the action of the rolling load of the vehicle constructed based on pi theorem, is quick and accurate, does not need professional numerical simulation personnel to participate in modeling and analysis, saves time and labor, and is suitable for buried pipeline stress prediction under various working conditions.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for predicting stress of a buried pipeline according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a finite element model according to an embodiment of the present application;
FIG. 4 is a top view of a spatial distribution of rolling load of a vehicle according to an embodiment of the present application;
FIG. 5 is a schematic diagram showing a comparison of a predicted value and a calculated value of stress of a buried pipeline according to an embodiment of the present application;
FIG. 6 is a flow chart of a method for evaluating safety of a buried pipeline according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a stress prediction apparatus for buried pipelines according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a safety evaluation device for buried pipelines according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, article, or apparatus.
In the related art, researchers perform field test analysis aiming at the fact that the rolling load of vehicles acts on buried pipelines, mainly research the distribution rule of soil-to-earth pressure and establish a finite element model, but the finite element simulation result is not compared with the field test result for verification, the accuracy of the finite element simulation result is to be questioned, and the method is only suitable for single working conditions and lacks versatility.
Aiming at the problems, the application provides a buried pipeline stress prediction method, which is characterized in that the data of influencing factors of a rolling load of a vehicle on a buried pipeline are collected, a numerical simulation model is constructed, and the constructed numerical simulation model is parameterized so as to calculate the collected data in batches; and according to a calculation result, analyzing a functional relation between the influence factors and the maximum Mi Saisi stress of the buried pipeline, and pre-constructing a stress prediction formula (or a stress semi-empirical formula) by adopting a nonlinear fitting method. Based on a pre-constructed stress prediction formula, the obtained influence factor data of the rolling load of the vehicle on the buried pipeline can be directly substituted to obtain the maximum Mi Saisi stress of the buried pipeline, the stress prediction of the buried pipeline can be rapidly and accurately realized, and the modeling and analysis are not needed by professional numerical simulation personnel, so that the method is time-saving and labor-saving, and is suitable for the stress prediction of the buried pipeline under various working conditions.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application. As shown in fig. 1, the application scenario includes a first client 11, a server 12, and a second client 13, where the number of the first client 11 and the second client 13 may be at least one. In practical applications, researchers can collect the influence factor data of the practical buried pipeline, such as the related data of the pipeline diameter, the pipeline wall thickness, the pipeline buried depth, the pipeline internal pressure and the vehicle weight, the soil type and the like, and send the influence factor data to the server 12 for storage through the first client 11 or the second client 13. Correspondingly, after obtaining the impact factor data, the server 12 may substitute the impact factor data into a pre-constructed stress prediction formula, thereby predicting the maximum Mi Saisi stress of the buried pipeline.
Further, the risk level of the buried pipeline can be evaluated according to the predicted maximum Mi Saisi stress in combination with a safety level judgment criterion, and corresponding protective measures are given.
It should be noted that the server 12 may be replaced by a server cluster or other computing device with a certain computing power. The first client 11 and the second client 13 may be mobile phones, computers, notebooks, or personal digital assistants (Personal Digital Assistant, PDA for short), etc. In addition, the method for predicting the stress of the buried pipeline can be used for predicting the stress of the buried pipeline of petroleum, natural gas and the like.
A buried pipeline stress prediction method according to an exemplary embodiment of the present application will be described below with reference to fig. 2 in conjunction with the application scenario of fig. 1. It should be noted that the above application scenario is only shown for the convenience of understanding the spirit and principle of the present application, and the embodiment of the present application is not limited by the application scenario shown in fig. 1.
Fig. 2 is a flow chart illustrating a method for predicting stress of a buried pipeline according to an embodiment of the present application.
As shown in fig. 2, the method for predicting the stress of the buried pipeline according to the embodiment of the application comprises the following steps:
s201: and acquiring influence factor data of the buried pipeline to be predicted, wherein the influence factor data are used for representing data of influence of stress on the buried pipeline to be predicted, and the influence factors comprise pipeline diameter, pipeline wall thickness, pipeline burial depth, pipeline internal pressure, vehicle weight and soil type.
The buried pipeline is a pipeline which is buried under the road after the oil gas pipeline and the like intersect the road, and at the moment, the buried pipeline can bear constant loads such as dead weight of the pipeline structure and soil pressure and dynamic loads such as rolling load of vehicles and internal pressure of the pipeline, and the rolling load of the vehicles is one of the most main dynamic loads acting on the buried pipeline. Wherein the vehicle rolling load includes a heavy vehicle rolling load and the like.
When a rolling load of a vehicle acts on the buried pipeline, the influence factors of the maximum Mi Saisi stress to which the buried pipeline is subjected include: the method comprises the steps of measuring the diameter of a pipeline, the wall thickness of the pipeline, the burial depth of the pipeline, the internal pressure of the pipeline, the vehicle weight and the soil type, wherein the related data of the diameter of the pipeline, the wall thickness of the pipeline, the burial depth of the pipeline and the internal pressure of the pipeline can be obtained through on-site measurement, the soil type can be obtained through on-site data acquisition, and the vehicle weight can be set according to the standard vehicle weight allowing the road to be on the road.
S202: and determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and a pre-constructed stress prediction formula, wherein the stress prediction formula is constructed based on pi theorem and used for the stress prediction formula of the buried pipeline under the action of the rolling load of the vehicle.
In the step, the obtained influence factor data are directly substituted into a pre-constructed stress prediction formula, so that the maximum Mi Saisi stress of the buried pipeline to be predicted can be obtained, wherein the unit is megapascal (MPa). Wherein the pre-constructed stress prediction formula satisfies the following formula:
wherein sigma Mises To the maximum Mi Saisi stress of the buried pipeline, f Mises P as a stress prediction function of heavy vehicle Missis e The pressure is designed for the pipeline, t is the wall thickness of the pipeline, D is the diameter of the pipeline, h is the burial depth of the pipeline and P w Is the internal pressure of the pipeline, m is the weight of the vehicle, E s Is soil elastic modulus, pi 1 、π 2 、π 3 、π 4 、π 5 To influence the dimensionless parameters of the stress of the heavy truck Mi Saisi, pi 1 Dividing the wall thickness of the pipeline by the diameter of the pipeline, pi 2 Dividing the pipe depth by the pipe diameter, pi 3 Dividing the internal pressure of the pipeline by the design pressure of the pipeline, pi 4 Is thatπ 5 Dividing the elastic modulus of soil by the design pressure of the pipeline, f 1 As the stress function of the heavy truck Mi Saisi corresponding to the wall thickness of the pipeline, f 2 For the stress function of the heavy truck Mi Saisi corresponding to the pipeline burial depth, f 3 As the stress function of the corresponding heavy truck Mi Saisi in the pipeline internal pressure, f 4 For the stress function of the heavy truck Mi Saisi corresponding to the weight of the truck, f 5 A is a stress function of a heavy truck Mi Saisi corresponding to the elastic modulus of soil 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 、a 11 And a 12 Is constant.
For example, a stress prediction formula of the buried pipeline under the action of rolling load of the vehicle can be constructed based on pi theorem, and each influencing factor (or parameter), namely the pipeline diameter (D), the pipeline wall thickness (t), the pipeline burial depth (h) and the pipeline internal pressure (P) w ) Vehicle weight (m) and soil elastic modulus (E) s ) Functional relation with the maximum Mi Saisi stress of the buried pipeline, and correspondingly distributing each parameter to a mathematical expression with representativenessOr a variable function), i.e., the heavy truck Mi Saisi stress function f in the above embodiment 1 、f 2 、f 3 、f 4 And f 5 In (a), in (b). Wherein the influence factor analysis also gives an important correlation between the influence factors, i.e. pi in the above embodiment 1 、π 2 、π 3 、π 4 And pi 5 These parameters may form part of the various variable functions in the stress prediction formula.
Alternatively, the stress prediction formula may multiply the respective variable functions based on the concept of direct combination multiplication, as shown in formula (1):
σ Mises ＝f Mises (π 1 ，π 2 ，π 3 ，π 4 ，π 5 ）P e ＝f 1 ·f 2 ·f 3 ·f 4 ·f 5 ·P e (1)
based on the above embodiments, the stress prediction formula may be constructed by:
analyzing to obtain influence factors of the buried pipeline;
based on pi theorem, it is determined that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions as follows:
f 3 ＝(a 7 π 3 +a 8 )
f 4 ＝(a 9 π 4 +a 10 )
f 5 ＝(a 11 π 5 +a 12 ) (2)
and multiplying different stress functions of the heavy truck Mi Saisi by adopting a nonlinear fitting method to obtain a stress prediction formula. The nonlinear fitting method can be realized by software such as MATLAB, python.
Specifically, after the formula (2) is obtained according to the pi theorem and according to the influence factor analysis, the formula (2) is substituted into the formula (1), and the important interrelationship among the influence factors is combined to obtain a final stress prediction formula of the buried pipeline under the action of the rolling load of the vehicle, as shown in the formula (3):
According to the buried pipeline stress prediction method provided by the embodiment of the application, the maximum Mi Saisi stress of the buried pipeline can be directly predicted by substituting the influence factor data of the buried pipeline under the action of the rolling load of the vehicle into the pre-constructed stress prediction formula, so that the buried pipeline stress prediction method is rapid and accurate, does not need professional numerical simulation personnel to participate in modeling and analysis, saves time and labor, and is suitable for the buried pipeline stress prediction under various working conditions.
In some embodiments, determining that different influencing factors correspond to different heavy truck Mi Saisi stress functions, respectively, based on pi theorem, includes: classifying buried pipelines with different pipeline diameters; based on pi theorem, different influencing factors are determined to correspond to different heavy truck Mi Saisi stress functions as shown below under different pipeline diameters.
For example, the collected data is classified according to the pipeline diameters, so that the stress function of the heavy truck Mi Saisi can be obtained according to different pipeline diameters, and then the stress prediction formulas corresponding to different pipeline diameters are fitted, so that the accuracy of the stress prediction formulas can be improved.
Corresponding to the above embodiment, before determining that different influencing factors correspond to different heavy truck Mi Saisi stress functions respectively as follows based on pi theorem, the method further includes: according to the influence factor data of the buried pipeline, establishing a numerical simulation model corresponding to the buried pipeline by adopting numerical simulation software; parameterizing the numerical simulation model to obtain a parameterized numerical simulation model; inputting the influence factor data into a parameterized numerical simulation model, and performing batch calculation to obtain a calculation result; and analyzing influence factors of the buried pipeline according to the calculation result, and establishing a database.
The numerical simulation software can be nonlinear numerical simulation analysis software (such as ABAQUS, ANSYS, simdroid and the like), and a numerical simulation model (or a finite element model) of the pipeline mechanical response under the action of the rolling load of the vehicle can be established through the ABAQUS. As shown in fig. 3, the upper left corner of fig. 3 is a numerical simulation model, the upper right corner is a cross-sectional view of the numerical simulation model, and the lower left corner is a graph showing the modeling direction, and it can be seen that the numerical simulation model includes a roadbed, a field soil, a buried pipeline, and a cover plate.
In addition, the established numerical simulation model also needs to consider pipelines, pipeline internal pressure, soil gravity and the like, fixed constraint can be applied to the soil at the bottom of the numerical simulation model, displacement of the four-side constraint of the soil perpendicular to the side surface of the numerical simulation model is set, surface-surface contact is set between the buried pipeline and the soil, hard contact is set at the normal direction of the contact surface, constraint is carried out by tangential selection of a penalty function, and the friction coefficient is 0.3.
Further, still taking fig. 3 as an example, the lower right corner diagram in fig. 3 represents a buried pipeline, and in the specific modeling, the modeled pipeline may be subjected to finite element mesh division according to practical situations, for example, a rolled portion of a vehicle may be finely divided, and a portion which cannot be rolled by the vehicle may be coarsely divided, so as to balance the calculation amount and the accuracy of the model. It can be understood that the more the grid division is, the larger the calculation amount is, and the more accurate the calculation result is.
In some embodiments, it may be assumed in the numerical simulation model that the pressure on the wheel-to-road surface is evenly distributed, and that the actual contact area of the tire with the ground is calculated as a rectangular area. Fig. 4 is a top view of a spatial distribution of rolling load of a vehicle according to an embodiment of the present application. As shown in FIG. 4, the vehicle has 10 wheels, and the contact area between the single wheel and the ground is 0.3 m long and 0.25m wide, so that the total contact area between the vehicle and the ground is 0.75 square meters (m 2 ). Wherein the front wheels are a group of single wheels, the rear wheels are a group of two wheels, the distance between the front wheels and the middle wheels is 3.78m, and the rear wheels and the middle wheelsThe distance was 1.4m, and the distance between the left and right wheel center lines was 2.55m.
For example, an open source language (e.g., python) may be used to parameterize the numerical simulation model, and perform batch calculation for specific buried pipeline parameters, such as buried pipelines in the shanxi-beijing road section, to obtain a calculation result; and a database (or a working condition summary table) is established according to the calculation result.
The database established according to the buried pipeline parameters of the Shaanxi-Beijing road section comprises pipeline diameter, pipeline wall thickness, pipeline buried depth, pipeline internal pressure, vehicle weight, soil type, pipeline materials and working condition number. Specifically as shown in table 1:
TABLE 1
Among them, soil types include sand and clay, pipe materials include X80, X70 and X60, and pipe diameters include 1219mm, 1016mm, 914mm, 711mm and 660mm. The number of working conditions can be obtained by multiplying constants, for example, when the diameter of a pipeline is 1219mm, the wall thickness of the pipeline is 4, the burial depth of the pipeline is 3, the internal pressure of the pipeline is 4, the weight of the vehicle is 4, the soil type is 2, 4×3×4×4×2=384, the diameters of the other pipelines are similar, and the details are not repeated here. Finally 2040 groups of working conditions can be obtained, various conditions are covered, and the wide application of the stress prediction method provided by the application is facilitated.
When the diameter of the pipeline is 1219mm, the value range of the wall thickness of the pipeline is 18.4-33 mm, the value range of the internal pressure of the pipeline is 2.5-12 MPa, and the design pressure of the pipeline is 12MPa.
When the diameter of the pipeline is 1016mm, the value range of the wall thickness of the pipeline is 14.6-27.5 mm, the value range of the internal pressure of the pipeline is 3-10 MPa, and the design pressure of the pipeline is 10MPa.
When the diameter of the pipeline is 914mm, the value range of the wall thickness of the pipeline is 12.3-25 mm, the value range of the internal pressure of the pipeline is 2-7.8 MPa, and the design pressure of the pipeline is 7.8MPa.
When the diameter of the pipeline is 711mm, the value range of the wall thickness of the pipeline is 7.9-17.5 mm, the value range of the internal pressure of the pipeline is 2-10 MPa, and the design pressure of the pipeline is 10MPa.
When the diameter of the pipeline is 660mm, the value range of the wall thickness of the pipeline is 7.14-12.7 mm, the value range of the internal pressure of the pipeline is 2.5-6.4 MPa, and the design pressure of the pipeline is 6.4MPa.
The value range of the pipeline burial depth is 1-2 m, the value range of the vehicle mass (or the vehicle weight) is 40-150 t, and the value range of the soil elastic modulus is 10-20 MPa.
In addition, a in the above formula (2) 1 To a 12 The different pipe diameters take different values for constants, respectively, which can be determined during the construction of the stress prediction formula. The constant term values of the stress prediction formula of the buried pipeline under the action of the rolling load of the vehicle are specifically shown in table 2:
TABLE 2
In Table 2, the fitting degree R 2 The fitting degree of the regression line to the observed value is the fitting degree of the maximum Mi Saisi stress of the buried pipeline and the maximum Mi Saisi stress actually observed under different working conditions predicted by the stress prediction formula constructed by the application. R is R 2 Maximum value is 1, R 2 The closer the value of (2) is to 1, the better the fitting degree of the regression line to the observed value is; conversely, R is 2 The smaller the value of (c) is, the worse the fitting degree of the regression line to the observed value is.
FIG. 5 is a schematic diagram showing a comparison of a predicted value and a calculated value of stress of a buried pipeline according to an embodiment of the present application. As shown in fig. 5, under 2040 groups of different working conditions, the maximum Mi Saisi stress of the buried pipeline calculated by the numerical simulation model is compared with the maximum Mi Saisi stress of the buried pipeline under the action of the rolling load of the vehicle predicted by the stress prediction formula provided by the application, wherein the abscissa represents the reference value calculated by the numerical simulation model, and the ordinate represents the predicted value obtained by the stress prediction formula. It can be seen that the Mi Saisi stress predicted value obtained by the stress prediction formula provided by the application is uniformly distributed near the 1:1 reference line, so that the overall prediction effect is better, and the prediction accuracy is higher.
Fig. 6 is a flow chart of a method for evaluating safety of a buried pipeline according to an embodiment of the application. As shown in fig. 6, the method for evaluating the safety of the buried pipeline in the embodiment of the application comprises the following steps:
s601: the maximum Mi Saisi stress of the target buried pipeline is obtained, and the maximum Mi Saisi stress of the target buried pipeline is obtained by the buried pipeline stress prediction method of the first aspect.
The maximum Mi Saisi stress is obtained by referring to the above embodiment, and will not be described here.
S602: and evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and the safety grade judgment criterion of the target buried pipeline.
Wherein, the security level decision criterion is: when the maximum Mi Saisi stress is smaller than the preset multiple of the allowable stress of the pipeline, the risk is low; when the maximum Mi Saisi stress is greater than or equal to a preset multiple of the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipeline allowable stress, the risk is medium; when the maximum Mi Saisi stress is greater than or equal to the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipe yield stress, a higher risk is attributed; high risk exists when the maximum Mi Saisi stress is greater than or equal to the tubing yield stress.
In practical situations, the early warning (or warning) color of the risk level (or the safety state) of the pipeline can be set to be red, orange, yellow and green, wherein the red represents high risk, and the weakness degree (or the danger degree) of the pipeline is 4; "orange" stands for "higher risk", the pipe is vulnerable to grade 3; "yellow" stands for "medium risk", the pipe is vulnerable to grade 2; "green" means "low risk", the pipeline is vulnerable to grade 1. For example, the correspondence between the safety state evaluation grade and the weakness degree of the non-defective buried pipeline under the action of the rolling load of the vehicle and the early warning color is shown in table 3:
TABLE 3 Table 3
| Evaluation grade | Low risk | Risk in | Higher risk | High risk |
| Degree of weakness | Level 1 | Level 2 | 3 grade | Grade 4 |
| Early warning color | Green colour | Yellow colour | Orange color | Red color |
In addition, the preset multiple of the allowable stress of the pipeline may be 0.9. The safety level (or risk level) criteria for a defect-free buried pipeline under vehicle crush load are shown in table 4:
TABLE 0
| Risk level | Criteria for |
| Low risk | σ Mises ＜0.9[σ] |
| Risk in | 0.9[σ]≤σ Mises ＜[σ] |
| Higher risk | [σ]≤σ Mises ＜σ s |
| High risk | σ Mises ≥σ s |
Wherein σ in Table 4 Mises Maximum Mi Saisi stress [ sigma ] of buried pipeline under rolling load of vehicle ]Allowable stress for pipeline, sigma s Is the yield stress of the pipe.
In practice, the allowable stress of a pipe is a value obtained by multiplying the basic yield stress of the pipe material by the mass coefficient, wherein the mass coefficient refers to the mass coefficient of a longitudinal welded joint of the material (welded pipe, welded pipe fitting) or the mass coefficient of a casting. Illustratively, when the pipe material is X80, the pipe yield stress is 80X 7-5=555 MPa, and the pipe allowable stress is 555X 0.9= 499.5MPa; when the pipeline material is X70, the yield stress of the pipeline is 70 multiplied by 7-5=485 MPa, and the allowable stress of the pipeline is 485 multiplied by 0.9=427.5 MPa. The pipe allowable stress and the pipe yield stress of other pipe materials can be obtained by similar methods, and are not described herein.
S603: and determining the protective measures of the target buried pipeline according to the risk level.
The protective measures comprise limiting the traffic flow and the weight of the vehicle, installing monitoring facilities, setting up warning marks, covering a cover plate or a steel plate and the like.
In some embodiments, evaluating the risk level of the target buried pipeline according to the maximum Mi Saisi stress and safety level decision criteria further comprises: if the target buried pipeline is defect-free, evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and a safety grade judging criterion; if the target buried pipeline is defective, the risk level of the target buried pipeline is estimated according to the maximum Mi Saisi stress, the safety level judgment criterion and the pipeline interior detection data or the excavation detection data of the target buried pipeline.
For example, when a defect-free buried pipeline is in different safety states, different protection and treatment measures need to be taken on the buried pipeline, and the specific measures are as follows:
(1) When the risk level of the evaluation is low risk (green), it is recommended to strengthen supervision without taking special precautions.
(2) When the risk level of the evaluation is medium risk (yellow), the related departments should be recommended to limit the weight and the flow of the road section, install monitoring facilities and set up warning marks. For road sections where the traffic flow is difficult to control on site, a reinforced concrete cover plate with the thickness not less than 20 centimeters (cm) and the width at least covering the whole road surface width is paved above the buried pipeline of the road section.
(3) When the risk grade is high risk (orange), a reinforced concrete cover plate with the thickness not less than 20cm and the width at least covering the whole pavement width is paved above the buried pipeline, and the cover plate width is recommended to be wider than the pavement by two meters; for the road section that can't in time be under construction, can lay the steel sheet that thickness is not less than 30mm temporarily, and guarantee that the width is not less than the road surface width. Meanwhile, the related departments should be recommended to limit the weight and the flow of the road section, install monitoring facilities and set up warning marks.
(4) When the risk level is high (red), the buried pipeline should be protected by paving a cover culvert. And simultaneously, the related departments are recommended to limit the weight and the flow of the road section, the monitoring facilities are installed, and the warning mark is built.
In addition, for buried pipelines in Shaanxi-Beijing road sections and other buried pipelines with defects in the buried pipelines in service, such as pipeline pits, pipeline cracks and the like, more influencing factors need to be considered when evaluating the safety state of the buried pipelines under the action of rolling load of vehicles. Such as the need to incorporate buried pipeline internal test data or excavation test data, etc. If the buried pipeline with the defects is in a heavy truck rolling section, carrying out applicability evaluation according to the defect type and the defect size, and adopting operations such as paving a cover culvert, monitoring, reinforcing and repairing, replacing a pipe and the like according to an evaluation result.
In summary, the present application has at least the following advantages:
1. by substituting the influence factor data of the buried pipeline under the action of the rolling load of the vehicle into a pre-constructed stress prediction formula, the maximum Mi Saisi stress of the buried pipeline can be directly predicted, the method is quick and accurate, and does not need professional numerical simulation personnel to participate in modeling and analysis, so that time and labor are saved.
2. The stress prediction formula fitted according to various working conditions comprises various actual conditions, and is beneficial to the wide application of the stress prediction formula provided by the application.
The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.
Fig. 7 is a schematic structural diagram of a stress prediction apparatus for buried pipelines according to an embodiment of the present application. For convenience of explanation, only portions relevant to the embodiments of the present application are shown. As shown in fig. 7, the buried pipeline stress prediction apparatus 70 includes: an acquisition module 71 and a first determination module 72. Wherein:
the obtaining module 71 is configured to obtain influence factor data of the buried pipeline to be predicted, where the influence factor data is used to characterize data of influence of stress on the buried pipeline to be predicted, and the influence factors include a pipeline diameter, a pipeline wall thickness, a pipeline buried depth, a pipeline internal pressure, a vehicle weight and a soil type;
the first determining module 72 is configured to determine a maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and a pre-constructed stress prediction formula, where the stress prediction formula is a buried pipeline stress prediction formula under the action of a rolling load of the vehicle constructed based on pi theorem.
In one possible implementation, the stress prediction formula satisfies the following formula:
wherein sigma Mises To the maximum Mi Saisi stress of the buried pipeline, f Mises P as a stress prediction function of heavy vehicle Missis e The pressure is designed for the pipeline, t is the wall thickness of the pipeline, D is the diameter of the pipeline, h is the burial depth of the pipeline and P w Is the internal pressure of the pipeline, m is the weight of the vehicle, E s Is soil elastic modulus, pi 1 、π 2 、π 3 、π 4 、π 5 To influence the dimensionless parameters of the stress of the heavy truck Mi Saisi, pi 1 Dividing the wall thickness of the pipeline by the diameter of the pipeline, pi 2 Dividing the pipe depth by the pipe diameter, pi 3 Dividing the internal pressure of the pipeline by the design pressure of the pipeline, pi 4 Is thatπ 5 Dividing the elastic modulus of soil by the design pressure of the pipeline, f 1 As the stress function of the heavy truck Mi Saisi corresponding to the wall thickness of the pipeline, f 2 For the stress function of the heavy truck Mi Saisi corresponding to the pipeline burial depth, f 3 As the stress function of the corresponding heavy truck Mi Saisi in the pipeline internal pressure, f 4 For the stress function of the heavy truck Mi Saisi corresponding to the weight of the truck, f 5 A is a stress function of a heavy truck Mi Saisi corresponding to the elastic modulus of soil 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 、a 11 And a 12 Is constant.
In one possible embodiment, the buried pipeline stress prediction apparatus may further include an analysis module, a second determination module, and a fitting module (not shown). Wherein the analysis module, the second determination module, and the fitting module may be configured to construct a stress prediction formula, wherein:
The analysis module may be specifically configured to: analyzing to obtain influence factors of the buried pipeline;
the second determining module may be specifically configured to: based on pi theorem, it is determined that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions as follows:
f 3 ＝(a 7 π 3 +a 8 )
f 4 ＝(a 9 π 4 +a 10 )
f 5 ＝(a 11 π 5 +a 12 )
the fitting module may be specifically configured to: and multiplying different stress functions of the heavy truck Mi Saisi by adopting a nonlinear fitting method to obtain a stress prediction formula.
In a possible implementation manner, the second determining module may be further configured to: classifying buried pipelines with different pipeline diameters; based on pi theorem, different influencing factors are determined to correspond to different heavy truck Mi Saisi stress functions as shown below under different pipeline diameters.
In a possible implementation, the analysis module may also be used to: according to the influence factor data of the buried pipeline, establishing a numerical simulation model corresponding to the buried pipeline by adopting numerical simulation software; parameterizing the numerical simulation model to obtain a parameterized numerical simulation model; inputting the influence factor data into a parameterized numerical simulation model, and performing batch calculation to obtain a calculation result; and analyzing influence factors of the buried pipeline according to the calculation result, and establishing a database.
Fig. 8 is a schematic structural diagram of a safety evaluation device for buried pipelines according to an embodiment of the present application. For convenience of explanation, only portions relevant to the embodiments of the present application are shown. As shown in fig. 8, the buried pipeline safety evaluating apparatus 80 includes: an acquisition module 81, an evaluation module 82 and a protection module 83. Wherein:
an obtaining module 81, configured to obtain a maximum Mi Saisi stress of the target buried pipeline, where the maximum Mi Saisi stress of the target buried pipeline is obtained by the buried pipeline stress prediction method of the first aspect;
an evaluation module 82 for evaluating the risk level of the target buried pipeline according to the maximum Mi Saisi stress and the safety level criterion of the target buried pipeline;
a protection module 83, configured to determine a protection measure of the target buried pipeline according to the risk level;
wherein, the security level decision criterion is: when the maximum Mi Saisi stress is smaller than the preset multiple of the allowable stress of the pipeline, the risk is low; when the maximum Mi Saisi stress is greater than or equal to a preset multiple of the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipeline allowable stress, the risk is medium; when the maximum Mi Saisi stress is greater than or equal to the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipe yield stress, a higher risk is attributed; high risk exists when the maximum Mi Saisi stress is greater than or equal to the tubing yield stress.
In one possible implementation, the evaluation module 82 may be specifically configured to: if the target buried pipeline is defect-free, evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and a safety grade judging criterion; if the target buried pipeline is defective, the risk level of the target buried pipeline is estimated according to the maximum Mi Saisi stress, the safety level judgment criterion and the pipeline interior detection data or the excavation detection data of the target buried pipeline.
The stress prediction device and the safety evaluation device for the buried pipeline provided by the embodiment of the application have similar implementation principle and technical effects to those of the above embodiment, and specific reference can be made to the above embodiment, and details are not repeated here.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 9, the electronic device 90 includes: at least one processor 910, memory 920, communication interface 930, and system bus 940. The memory 920 and the communication interface 930 are connected to the processor 910 through the system bus 940 and complete communication with each other, the memory 920 is used for storing instructions, the communication interface 930 is used for communicating with other devices, and the processor 910 is used for calling the instructions in the memory to execute the above-mentioned buried pipeline stress prediction method and/or the scheme of the embodiment of the buried pipeline security assessment method, and the specific implementation manner and technical effect are similar, and are not repeated herein.
The processor 910 mentioned in fig. 9 may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP for short), etc.; digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field programmable gate arrays (Field Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
The Memory 920 may include a random access Memory (Random Access Memory, RAM) and may further include a static random access Memory (Static Random Access Memory, SRAM), an electrically erasable programmable Read-Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), an erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), a programmable Read-Only Memory (Programmable Read-Only Memory, PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk, such as at least one magnetic disk Memory.
The communication interface 930 is used to enable communication between the buried pipeline stress prediction device and/or the buried pipeline security assessment device and other devices (e.g., clients).
The system bus 940 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The system bus 940 may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
It will be appreciated by those skilled in the art that the electronic device shown in fig. 9 is not limiting and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
Embodiments of the present application also provide a computer-readable storage medium having stored therein computer-executable instructions that, when executed, implement the above buried pipeline stress prediction method and/or that, when executed, implement the above buried pipeline security assessment method of the second aspect.
Embodiments of the present application also provide a computer program product comprising a computer program which, when executed, implements a buried pipeline stress prediction method as above, and/or which, when executed, implements a buried pipeline security assessment method as above in the second aspect.
The embodiment of the application also provides a chip for running the instruction, wherein the chip is used for executing the buried pipeline stress prediction method of any method embodiment, and/or the chip is used for executing the buried pipeline safety assessment method of any method embodiment.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and provide corresponding operation entries for the user to select authorization or rejection.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (8)
1. A method of stress prediction for a buried pipeline, comprising:
obtaining influence factor data of a buried pipeline to be predicted, wherein the influence factor data are used for representing data of stress influence on the buried pipeline to be predicted, and the influence factors comprise pipeline diameter, pipeline wall thickness, pipeline buried depth, pipeline internal pressure, vehicle weight and soil type;
determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and a pre-constructed stress prediction formula, wherein the stress prediction formula is a buried pipeline stress prediction formula under the action of vehicle rolling load constructed based on pi theorem;
the stress prediction formula satisfies the following formula:
wherein sigma Mises To the maximum Mi Saisi stress of the buried pipeline, f Mises P as a stress prediction function of heavy vehicle Missis e The pressure is designed for the pipeline, t is the wall thickness of the pipeline, D is the diameter of the pipeline, h is the burial depth of the pipeline and P w Is the internal pressure of the pipeline, m is the weight of the vehicle, E s Is soil elastic modulus, pi 1 、π 2 、π 3 、π 4 、π 5 To influence the dimensionless parameters of the stress of the heavy truck Mi Saisi, pi 1 Dividing the wall thickness of the pipeline by the diameter of the pipeline, pi 2 Dividing the pipe depth by the pipe diameter, pi 3 Dividing the internal pressure of the pipeline by the design pressure of the pipeline, pi 4 Is thatπ 5 Dividing the elastic modulus of soil by the design pressure of the pipeline, f 1 As the stress function of the heavy truck Mi Saisi corresponding to the wall thickness of the pipeline, f 2 For the stress function of the heavy truck Mi Saisi corresponding to the pipeline burial depth, f 3 As the stress function of the corresponding heavy truck Mi Saisi in the pipeline internal pressure, f 4 For the stress function of the heavy truck Mi Saisi corresponding to the weight of the truck, f 5 A is a stress function of a heavy truck Mi Saisi corresponding to the elastic modulus of soil 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 、a 11 And a 12 Is a constant;
the stress prediction formula is constructed by:
analyzing to obtain influence factors of the buried pipeline;
based on pi theorem, it is determined that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions as follows:
f 3 ＝(a 7 π 3 +a 8 )
f 4 ＝(a 9 π 4 +a 10 )
f 5 ＝(a 11 π 5 +a 12 )
and multiplying different stress functions of the heavy truck Mi Saisi by adopting a nonlinear fitting method to obtain the stress prediction formula.
2. The method for predicting the stress of the buried pipeline according to claim 1, wherein determining different influencing factors respectively corresponds to different stress functions of the heavy truck Mi Saisi based on pi theorem, comprises:
Classifying buried pipelines with different pipeline diameters;
based on pi theorem, different influencing factors are determined to correspond to different heavy truck Mi Saisi stress functions as shown below under different pipeline diameters.
3. The method for predicting stress of a buried pipeline according to claim 2, wherein before determining that different influencing factors correspond to different stress functions of the heavy truck Mi Saisi based on pi theorem, respectively, further comprises:
according to the influence factor data of the buried pipeline, establishing a numerical simulation model corresponding to the buried pipeline by adopting numerical simulation software;
parameterizing the numerical simulation model to obtain a parameterized numerical simulation model;
inputting the influence factor data into the parameterized numerical simulation model, and performing batch calculation to obtain a calculation result;
and analyzing influence factors of the buried pipeline according to the calculation result, and establishing a database.
4. A method for evaluating the safety of a buried pipeline, comprising:
obtaining a maximum Mi Saisi stress of a target buried pipeline, the maximum Mi Saisi stress of the target buried pipeline being obtained by the method of any one of claims 1 to 3;
Evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress and the safety grade judgment criterion of the target buried pipeline;
determining protective measures of the target buried pipeline according to the risk level;
wherein the security level decision criteria are: when the maximum Mi Saisi stress is smaller than a preset multiple of the allowable stress of the pipeline, the risk is low; when the maximum Mi Saisi stress is greater than or equal to the preset multiple of the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipeline allowable stress, the risk is medium; when the maximum Mi Saisi stress is greater than or equal to the pipeline allowable stress and the maximum Mi Saisi stress is less than the pipe yield stress, a higher risk is attributed; when the maximum Mi Saisi stress is greater than or equal to the tubing yield stress, it is a high risk.
5. The method of claim 4, wherein said evaluating the risk level of the target buried pipeline according to the maximum Mi Saisi stress and safety level decision criteria comprises:
if the target buried pipeline is defect-free, evaluating the risk level of the target buried pipeline according to the maximum Mi Saisi stress and a safety level judgment criterion;
And if the target buried pipeline is defective, evaluating the risk grade of the target buried pipeline according to the maximum Mi Saisi stress, the safety grade judging criterion and the pipeline internal detection data or the excavation detection data of the target buried pipeline.
6. A buried pipeline stress prediction apparatus, comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring influence factor data of a buried pipeline to be predicted, and the influence factor data are used for representing data of stress influence on the buried pipeline to be predicted;
the first determining module is used for determining the maximum Mi Saisi stress of the buried pipeline to be predicted according to the influence factor data and a pre-constructed stress prediction formula, wherein the stress prediction formula is a buried pipeline stress prediction formula under the action of vehicle rolling load constructed based on pi theorem;
the stress prediction formula satisfies the following formula:
wherein sigma Mises To the maximum Mi Saisi stress of the buried pipeline, f Mises P as a stress prediction function of heavy vehicle Missis e The pressure is designed for the pipeline, t is the wall thickness of the pipeline, D is the diameter of the pipeline, h is the burial depth of the pipeline and P w Is the internal pressure of the pipeline, m is the weight of the vehicle, E s Is soil elastic modulus, pi 1 、π 2 、π 3 、π 4 、π 5 To influence the dimensionless parameters of the stress of the heavy truck Mi Saisi, pi 1 Dividing the wall thickness of the pipeline by the diameter of the pipeline, pi 2 Dividing the pipe depth by the pipe diameter, pi 3 Dividing the internal pressure of the pipeline by the design pressure of the pipeline, pi 4 Is thatπ 5 Dividing the elastic modulus of soil by the design pressure of the pipeline, f 1 As the stress function of the heavy truck Mi Saisi corresponding to the wall thickness of the pipeline, f 2 For the stress function of the heavy truck Mi Saisi corresponding to the pipeline burial depth, f 3 As the stress function of the corresponding heavy truck Mi Saisi in the pipeline internal pressure, f 4 For the stress function of the heavy truck Mi Saisi corresponding to the weight of the truck, f 5 A is a stress function of a heavy truck Mi Saisi corresponding to the elastic modulus of soil 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 、a 11 And a 12 Is a constant;
the buried pipeline stress prediction device further comprises: an analysis module, a second determination module and a fitting module,
the analysis module, the second determination module, and the fitting module are configured to construct a stress prediction formula, where:
the analysis module is specifically used for: analyzing to obtain influence factors of the buried pipeline;
the second determining module is specifically configured to: based on pi theorem, it is determined that different influencing factors respectively correspond to different heavy truck Mi Saisi stress functions as follows:
f 3 ＝(a 7 π 3 +a 8 )
f 4 ＝(a 9 π 4 +a 10 )
f 5 ＝(a 11 π 5 +a 12 )
the fitting module is specifically configured to: and multiplying different stress functions of the heavy truck Mi Saisi by adopting a nonlinear fitting method to obtain the stress prediction formula.
7. An electronic device, comprising: a memory and a processor;
the memory is used for storing program instructions;
the processor is configured to invoke the program instructions in the memory to perform the buried pipeline stress prediction method according to any of claims 1 to 3, and/or the processor is configured to invoke the program instructions in the memory to perform the buried pipeline security assessment method according to claim 4 or 5.
8. A computer readable storage medium having stored therein computer executable instructions that when executed implement the buried pipeline stress prediction method of any one of claims 1 to 3 and/or implement the buried pipeline security assessment method of claim 4 or 5.
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