CN116305949A - Method for realizing water supply pipeline damage and service life prediction based on form procedure - Google Patents
Method for realizing water supply pipeline damage and service life prediction based on form procedure Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000006378 damage Effects 0.000 title claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims abstract description 24
- 238000004880 explosion Methods 0.000 claims abstract description 10
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 230000007797 corrosion Effects 0.000 claims description 19
- 238000005260 corrosion Methods 0.000 claims description 19
- 239000002689 soil Substances 0.000 claims description 8
- 230000001186 cumulative effect Effects 0.000 claims description 7
- 238000009933 burial Methods 0.000 claims description 4
- 230000009172 bursting Effects 0.000 claims description 4
- 230000005484 gravity Effects 0.000 claims description 4
- 238000012502 risk assessment Methods 0.000 abstract description 8
- 230000007547 defect Effects 0.000 abstract description 6
- 238000011161 development Methods 0.000 abstract description 2
- 239000011159 matrix material Substances 0.000 description 4
- 238000000611 regression analysis Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 230000001052 transient effect Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The invention discloses a method for realizing water supply pipeline damage and service life prediction based on a form procedure, which comprises the following steps: inserting the macro code into Excel development options, and inputting parameter values of the buried water supply pipeline to be tested; calling a macro command Critical_ack_Length, and calculating based on the parameter value of the buried water supply pipeline to be detected to obtain a calculation result; and judging the leakage or pipe explosion condition of the pipeline based on the calculation result, and completing the state evaluation analysis of the buried water supply pipeline. The method provided by the invention overcomes the defect that modeling analysis is time-consuming and labor-consuming in reliability theory correlation calculation. Relevant parameters are input, corresponding macro commands are called, an iHLRF-x algorithm is used for iterative computation of reliability index beta under a first-order reliability method FORM framework, instantaneous failure probability, average residual life of a predicted pipeline and other results can be automatically computed, automatic drawing is achieved, and safety risk assessment of the buried water supply pipeline is completed.
Description
Technical Field
The invention belongs to the field of underground engineering safety, and particularly relates to a method for realizing water supply pipeline damage and service life prediction based on a form procedure.
Background
Pipeline engineering is known as "lifeline" engineering. The buried water supply pipeline can generate a pipe wall corrosion phenomenon in the service process, so that the normal service function of the buried water supply pipeline is degraded, the pipeline is damaged, and even secondary disasters such as collapse of surrounding ground, unstable and damaged underground adjacent structures and the like are caused, so that the safety and stability of an underground space structure and the life and property safety of people are threatened greatly. The method has extremely important significance for pipeline safety maintenance work by evaluating the failure probability of the pipeline in the current state and predicting the average residual service life of the pipeline.
The existing safety risk assessment about pipelines mainly focuses on calculating failure probability by adopting reliability theory, is less common in life prediction analysis, and is less common in coupling all influence factors to realize pipeline safety risk assessment. For example, a method for evaluating reliability of a pipeline containing corrosion defects (application publication number: CN 105404776A) calculates failure probability of the corrosion defects by adopting a Monte Carlo method according to a pressure insertion point value, only analyzes influence of pipeline operation pressure factors, and cannot predict service life of the pipeline without considering condition of a next time node.
At present, modeling analysis is mostly carried out by using MATLAB and other software on pipeline life prediction, and certain probability analysis basic knowledge and experience of numerical programming are needed, so that a convenient and visual method for calculating pipeline failure probability and predicting residual service life of a pipeline is very necessary to be developed.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for realizing water supply pipeline damage and service life prediction based on a FORM procedure, which uses an iHLRF-x algorithm to iteratively calculate a reliability index beta under a FORM framework of a first-order reliability method, overcomes the defect that modeling analysis is time-consuming and labor-consuming in reliability theory related calculation, and can rapidly perform buried water supply pipeline safety risk assessment.
In order to achieve the above object, the present invention provides a method for realizing water supply pipeline damage and service life prediction based on a form procedure, comprising:
recording parameter information of the buried water supply pipeline to be detected into an Excle;
batch calculation is carried out on the parameters of the buried water supply pipeline to be detected, and the state characteristics of the water supply pipeline are obtained;
and judging the leakage or pipe bursting condition of the pipeline based on the state characteristics of the water supply pipeline, and completing the state evaluation analysis of the buried water supply pipeline.
Optionally, the parameters of the buried water supply pipeline to be tested comprise pipeline parameters, soil parameters, loads and corrosion parameters;
the pipeline parameters comprise pipeline wall thickness, pipeline outer diameter, burial depth, service duration, expected service duration, ultimate tensile strength and fracture toughness;
the soil body parameters comprise the gravity, the elastic modulus and the lateral soil pressure coefficient;
the load includes road load and pipeline internal pressure;
the corrosion parameters include corrosion pit long half shaft, corrosion pit short half shaft, corrosion pit depth, axial corrosion rate, and circumferential corrosion rate.
Optionally, a macro command critical_ack_length is used for batch calculation of the parameters of the buried water supply pipeline to be measured.
Optionally, the water supply line condition characteristics include nominal tensile stress, stress concentration coefficient, and tube burst critical crack length.
Optionally, the calculation formula of the nominal tensile stress is:
wherein sigma (x) is the nominal tensile stress, x is the vector of the relevant physical parameter composition, D is the outer diameter of the pipeline, W is the road load, gamma is the gravity, h is the burial depth, E p Is the modulus of elasticity of the pipeline, E s Is the modulus of elasticity, P is the internal pressure of the pipeline, T is the wall thickness, k is the lateral soil pressure coefficient, a 1 -a 7 And b 1 -b 7 Is a model constant;
the calculation formula of the stress concentration coefficient is as follows:
wherein SCF is stress concentration factor, v is Poisson's ratio, a is etch pit half-axis, R is pipeline radius, b is etch pit half-axis, c is etch pit depth, α 1 -α 7 And beta 1 -β 7 Is a model constant;
the calculation formula of the critical crack length of the pipe explosion is as follows:
wherein K is c Is fracture toughness, sigma t Is ultimate tensile strength, L c Is the critical crack length of the squib.
Optionally, based on the status feature of the water supply pipeline, determining leakage or pipe bursting of the pipeline, and completing the evaluation analysis of the status of the buried water supply pipeline includes:
setting an ultimate tensile strength, comparing the product of the stress concentration coefficient and the nominal tensile stress with the ultimate tensile strength, and simultaneously comparing the two times of the length of the corrosion pit long half shaft and the critical crack length of the pipe explosion, and judging the leakage or the pipe explosion of the pipeline according to the comparison result;
completing the buried water supply line condition assessment analysis if the line leaks or bursts;
and acquiring a pipeline service life prediction curve based on a macro command FORM_Recursive and a macro command pred_Curves, and completing the state evaluation analysis of the buried water supply pipeline.
Optionally, based on the macro command form_reactive and the macro command pred_curves, obtaining the pipeline service life prediction curve includes:
based on an iHLRF-x algorithm, calling the macro command FORM_Recursive, determining an iterative algorithm of the optimal step length, and obtaining a reliability index;
obtaining failure probability based on the reliability index;
and predicting based on the failure probability, calling the macro command pred_Curves, and obtaining the pipeline service life prediction curve.
Optionally, the pipeline service life prediction curve includes an instantaneous failure probability, an accumulated failure probability, and an average remaining life.
The invention has the following beneficial effects:
the method for realizing the damage and service life prediction of the water supply pipeline based on the form procedure can simply and efficiently finish the safety risk assessment of the buried water supply pipeline, is quick in calculation and wide in applicability, does not need complex numerical analysis experience and software programming knowledge, is simple to operate and easy to realize, and can provide important basis for actual engineering; the method overcomes the defect that modeling analysis is time-consuming and labor-consuming in reliability theory correlation calculation. Relevant parameters are input, corresponding macro commands are called, an iHLRF-x algorithm is used for iterative computation of reliability index beta under a first-order reliability method FORM framework, instantaneous failure probability, average residual life of a predicted pipeline and other results can be automatically computed, automatic drawing is achieved, and safety risk assessment of the buried water supply pipeline is completed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a schematic flow chart of a method for implementing water supply line damage and service life prediction based on a form procedure according to an embodiment of the present invention;
FIG. 2 is a diagram of a parameter input interface and a calculation result according to an embodiment of the present invention;
FIG. 3 is a graph showing the prediction of service life of a buried water supply line according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
As shown in fig. 1, the method for realizing water supply pipeline damage and service life prediction based on the table-based process in the embodiment comprises the following steps:
step one: inserting macro codes into Excel development options, and inputting relevant parameters of a buried water supply pipeline and corresponding uncertainty information thereof according to the existing engineering data and a geological survey report, wherein the uncertainty information is shown as physical parameter values of the buried water supply pipeline in table 1;
TABLE 1
The irregular etch pits are idealized as equivalent ellipsoidal etch pits. Inputting the parameters into corresponding cells of Excel, wherein the variation coefficients corresponding to the low uncertainty, the medium uncertainty and the high uncertainty are respectively 0.1, 0.25 and 0.5. The emphasis of this embodiment is to calculate the instantaneous failure probability and predict the remaining life of the buried water supply line, and thus the situation before the current length of service is not considered.
Step two, calling a macro command Critical_Crack_Length in Excel, wherein the macro command is embedded with a nominal tensile stress sigma (x), a stress concentration coefficient SCF and a bursting tube Critical crack length L c The calculation result will be used to determine whether a leak or pipe burst has occurred in the pipeline.
The calculation formula of the nominal tensile stress sigma (x) is as follows:
wherein: σ (x) is the maximum stress in the pipeline, i.e. the nominal tensile stress; x is a vector of related physical parameters; e (E) p Is the modulus of elasticity of the pipeline; a, a 1 -a 7 And b 1 -b 7 Is a model constant, and the best fit coefficient of the nominal tensile stress sigma (x) is calculated by the regression analysis of the multiple sets of finite element results, as shown by the model constant of the nominal tensile stress determined by the regression analysis of table 2.
TABLE 2
| Regression coefficient | a 1 | a 2 | a 3 | a 4 | a 5 | a 6 | a 7 |
| Value taking | 0.12 | 4.08 | -1.76×10 4 | 7.65×10 4 | 4.17×10 6 | -3.23×10 7 | -3.55×10 7 |
| Regression coefficient | b 1 | b 2 | b 3 | b 4 | b 5 | b 6 | b 7 |
| Value taking | 0.086 | 0.94 | 0.89 | 0.88 | 0.94 | -0.51 | -0.71 |
The VBA programming code to calculate the nominal tensile stress σ (x) is:
Function stress_max(Ep，vp，t，D，h，Es，k，gama，W，p)
Dim Alpha1，Alpha2，Alpha3，Alpha4，Alpha5，Alpha6，Alpha7
Dim Beta1，Beta2，Beta3，Beta4，Beta5，Beta6，Beta7
Alpha1＝0.12：Beta1＝0.086
Alpha2＝4.08：Beta2＝0.94
Alpha3＝-1760000：Beta3＝0.89
Alpha4＝76500：Beta4＝0.88
Alpha5＝4170000：Beta5＝0.94
Alpha6＝-32300000：Beta6＝-0.51
Alpha7＝-35500000：Beta7＝-0.71
Ep＝Ep*10 6 ：vp＝vp：t＝t*10 -3 ：
D＝D*10 -3 ：h＝h*10 -3
Es＝Es*10 3 ：k＝k：gama＝gama
W＝W：P＝P
Item2＝Alpha2*(P/Es) Beta3 /((t/D) Beta4 *(W/(gama*D 2 *h)+1) Beta5 )
Item3＝Alpha3*(t/D) Beta6 *(W/(gama*D 2 *h)+1) Beta7 /(Alpha4*Ep/Es+Alpha5*P/Es+Alpha6*h/D+Alpha7*k)
Item4＝(W+gama*D 2 *h)/D 2
Sigma＝Item1*(Item2+Item3)*Item4
Sigma＝Sigma/1000
stress_max＝Int(Sigma)
End Function
the calculation formula of the stress concentration coefficient SCF is as follows:
wherein: v is poisson's ratio; r is the pipeline radius; alpha 1 -α 7 And beta 1 -β 7 Is a model constant, and the best fit coefficient of the calculated stress concentration coefficient SCF obtained by regression analysis of a plurality of groups of finite element results is shown as the stress concentration coefficient model constant determined by regression analysis of table 3.
TABLE 3 Table 3
| Regression coefficient | α 1 | α 2 | α 3 | α 4 | α 5 | α 6 | α 7 |
| Value taking | 2.8×10 -5 | 3×10 -5 | 7.096 | 3×10 -6 | 3×10 -5 | 0.011 | 0.797 |
| Regression coefficient | β 1 | β 2 | β 3 | β 4 | β 5 | β 6 | β 7 |
| Value taking | 1.071 | 2.09 | 11.677 | 0.733 | 1.348 | 5.755 | 0.84 |
The VBA programming code for calculating the stress concentration factor SCF is:
Function SCF_ellip(a，b，c，D，t，vp)
Dim Alpha1，Alpha2，Alpha3，Alpha4，Alpha5，Alpha6，Alpha7As Double
Dim Beta1，Beta2，Beta3，Beta4，Beta5，Beta6，Beta7As Double
Dim Item1，Item2，Item3，Item4As Double
Alpha1＝0.000028：Beta1＝1.071
Alpha2＝0.00003：Beta2＝2.09
Alpha3＝7.096：Beta3＝11.677
Alpha4＝0.000003：Beta4＝0.733
Alpha5＝0.00003：Beta5＝1.348
Alpha6＝0.011：Beta6＝5.755
Alpha7＝0.797：Beta7＝0.84
m＝(3*(1-vp 2 )) 1/4 /2
Item1＝a/Sqr((D/2)*t)
Item2＝b/Sqr((D/2)*t)
Item3＝c/Sqr((D/2)*t)
Item4＝c/Application.WorksheetFunction.max(0.01，(t-c))
SCF_ellip＝1+m*((Alpha1*Item1 Beta1 +Alpha2*Item2 Beta2 +Alpha3*Item3 Beta3 )/(Alpha4*Item1 Beta4 +Alpha5*Item2 Beta5 +Alpha6*Item3 Beta6 ))*Alpha7*Item4 Beta7
End Function
critical crack length L of pipe explosion c The calculation formula of (2) is as follows:
the formulas (1) to (3) are all realized in an Excel platform through VBA programming and are embedded in a macro command Critical_ack_Length.
Step three: substituting the calculation result of the step two into the step (4) to judge whether leakage or pipe explosion occurs in the pipeline,
in this embodiment, fig. 2 shows a parameter input interface and calculation and judgment results according to the method. The calculation result of this example does not satisfy the above equation, no leakage or pipe explosion occurs, and the pipeline state is estimated by probability analysis.
Step four, calling a macro command FORM_Recursive in Excel, wherein the macro command is embedded with an iHLRF-x algorithm, and calculating a reliability index beta through an iterative algorithm for determining the optimal step length, wherein the reliability index beta represents the minimum distance from a mean value vector to a most probable failure point vector, namely:
wherein n is * Representing the most probable failure point vector in normal space parameters, n *T A transpose vector representing the most likely failure point vector; r is R -1 Is the inverse of the correlation matrix R;
a most likely point of failure vector component value representing an ith variable evaluated in x-space; />
And->
Respectively representing the equivalent normal distribution mean and standard deviation of the ith variable in normal space.
The probability P of failure is obtained according to the reliability index beta f The method comprises the following steps:
P f ＝Φ(-β) (6)
where Φ (·) represents a standard normal distribution cumulative distribution function.
The iterative formula for calculating the reliability index beta under the framework of the iHLRF-x algorithm is as follows:
monitoring function m (x):
iterative determination of optimal step size lambda k ：
Convergence criteria:
wherein x is k Is the vector of the kth iteration comprising a random variable in x space; the x space refers to an original data space, namely an original distribution space obeyed by parameters in the probability theory; x is x k+1 Is a vector containing a random variable in the x-space in the k+1-th iteration, and the x satisfying the formula (11) in the last iteration k+1 Defined as x new ，x k+1 ＝x new At this time x new Is the most probable failure point vector; lambda (lambda) k Is the step size;
is the search direction of the x space; />
Is the mean vector of the random variables contained in the kth iteration in normal space N, N represents normal space,/I>
Is a transformation matrix, K is a correlation matrix of all random variables,>
represents a limit state function g (x k ) Gradient vector of>
Representing gradient vector +.>
Is the transposed vector of>
Is the equivalent normal average value of the ith variable in normal space, g (x) is the limit state function, m (x) k ) Is x k The corresponding value of the monitoring function,
is x k The corresponding monitoring function gradient, n is penalty factor, p and q are parameters with values ranging from 0 to 1, j is optimal integer solution, beta k+1 Is the reliability index of iteration k+1 times, beta k Is a reliability index of iterated k times, g (x new ) Is the function value corresponding to the most likely failure point vector.
The formulas (5) to (11) are all realized by VBA programming on an Excel platform and embedded in a macro command FORM_Recursive, so that the instantaneous failure probability of a pipeline is analyzed and calculated, and the specific codes are as follows:
Sub FORM_Recursive()
′At first，define the following vector names in the FORM main spreadsheet
′1.Perfunc＝Performance function，or limit state surface
′2.beta＝reliability index
′3.dp＝column vector for design point values
′4.xi＝column vector for the current trial point values：x_k
′5.xii＝column vector for the new trial point values：x_k+1
′6.Pgradient
＝column vector for the LSS gradient at the current trial point values：x_k
′7.corrmat＝correlation matrix
′8.lamda
＝the optimum step length constant used for iHLRF_x algorithm
′9.mk＝merit function usedfor iHLRF_x ALGORITHM
beta_0＝10′FORM reliability index
NLSS＝0′Number of LSS evaluatiohs
Range(″xi″).Value＝Range(″meanv0″).Value
For k＝1To100′number of iteratiohs for converged reliability index
Range(″dp″).Value＝Range(″xi″).Value
beta_0＝Range(″beta″).Value
NLSS＝NLSS+1
Perfunc_0＝Range(″Perfunc″).Value
dp_N＝Range(″MEANV″).Rows.Count
For i＝1To dp_N
xdum Range(″dp″).Cells(i，1).Value
delta_xi＝xdum*0.0001′perturbation length
＝xxx％of the mean value
Range(″dp″).Cells(i，1).Value＝xdum+delta_xi
NLSS＝NLSS+1
Perfunc_i＝Range(″Perfunc″).Value
Pgradient_i＝(Perfunc_i-Perfunc_0)/delta_xi
Range(″Pgradient″).Cells(i，1).Value＝Pgradient_i
Range(″dp″).Cells(i，1).Value＝xdum
Next i
mk_dk
＝Range(″Armijo″).Value′only used for original Armijo rule in below
lamda1＝1
Range(″lamda″).Value＝lamda1
Range(″dp″).Value＝Range(″xi″).Value
Range(″dp″).Value＝Range(″xii″).Value
NLSS＝NLSS+1
Perfunc_1＝Range(″Perfunc″).Value
beta_1＝Range(″beta″).Value
mk1＝Range(″mk″).Value
Nmax＝5′maximum number of trials for the step length lamda
For j＝1To Nmax
lamda2＝0.5 j′ in Armijo rule，b＝0.5
Range(″lamda″).Value＝lamda2
Range(″dp″).Value＝Range(″xi″).Value
Range(″dp″).Value＝Range(″xii″).Value
NLSS＝NLSS+1
Perfunc_2＝Range(″Perfunc″).Value
mk2＝Range(″mk″).Value
beta_2＝Range(″beta″).Value
On Error Resume Next
If mk2＜mk1-0.1*lamda2*mk_dk Then
′If mk2＜mk1*0.95Then′simplified shrinking method
lamda1＝lamda2
mk1＝mk2
beta_1＝beta_2
Perfunc_1＝Perfunc_2
Else：Exit For
End If
Next j
Range(″dp″).Value＝Range(″xi″).Value
Range(，″lamda″).Value＝lamda1
tol_beta＝Abs(beta_0-beta_1)
tol_lss＝Abs(Perfunc_1)
If tol_beta＜0.01And tol_lss＜0.01Then
Exit For
Else：
Range(″xi″).Value
＝Range(″xii″).Value′replace xi+1with xi，check ranges
beta_0＝beta_1
End If
Next k
Range(″lamda″).Cells(2，1).Value＝NLSS′Number of LSS evaluations
Range(″lamda″).Cells(3，1).Value＝k
End Sub
in this embodiment, an initial iteration point x is first selected in the x space k Initial x k Can take the average value point x 0 Define the function g (x k )。
Will x k，1 (i.e. x k The first component of (a) becomes x k，1 +Δx 1 Wherein Deltax 1 Is a prescribed minor variation. Other terms in the equation remain unchanged, and the function g (x k，1 ) Is a new value of (c).
Calculating x 1 The change in the function value caused by a small change in the value, i.e. Δg (x k，1 )＝g(x k，1 )-g（x k ). At this time g (x) k ) For x 1 The derivative is approximately equal to the difference quotient deltag (x k，1 )/Δx 1 。
For vector x k Each component of (i.e. Δg (x) k，j ) Repeating the above steps to obtain gradient vector Shang g (x) k，j )。
Calculating the search direction according to (8), (10)
Optimum step length lambda k Then, a new vector x is calculated by using an iterative formula (7) k+1 。
From the following components
Calculating a reliability index beta, wherein +_>
Seen as a new vector x k+1 . Using a new vector x k+1 Repeating the above steps untilTo the convergence of the vector x and beta values, and satisfies the following two equations: beta (beta) k+1 -β k |≤ε 1 ，|g(x new )|≤ε 2 Wherein ε is 1 And epsilon 2 Is a predetermined small amount.
The failure probability of the pipeline when the service time of the pipeline is 50 years is calculated to be 4.44%.
And fifthly, calling a macro command pred_Curves in Excel, outputting a failure probability and average residual life prediction curve in the subsequent service process of the pipeline, and completing the state evaluation analysis of the buried water supply pipeline.
The prediction curve of the failure probability and the average residual life in the subsequent service process of the pipeline comprises the instantaneous failure probability P f (t) cumulative failure probability F r (T) and average remaining lifetime μ (T) r ) A curve.
At a certain moment t, the instantaneous failure probability of the pipeline being broken is the risk rate h (t), namely the limit state function g (x i Probability of t) < 0:
P f (t)＝h(t)＝P f [g(x i ，t)＜0]＝Φ(-β) (12)
wherein x is i Is a vector of state parameters.
By calculating P for multiple points of service of the pipeline f (t) drawing a pipeline transient failure probability curve. Pipeline cumulative failure probability function F r (t) and transient failure probability function P f The relationship of (t) is as follows:
after the cumulative failure probability of the pipeline is obtained, the average residual service life of the pipeline can be calculated by the following formula:
formulas (12) to (14) are all realized in an Excel platform through VBA programming and embedded in a macro command pred_Curves, and the instantaneous failure probability is calculated and outputRate P f (t) cumulative failure probability F r (T) and average remaining lifetime μ (T) r ) The curves, specific codes are as follows:
Sub Pred_Curves()
Application.StatusBar
＝″Lifetime Probability Analysis in Progress，Please Be Patient…″
Application.ScreenUpdating＝False
Sheets(″B-CoupledCorrossionModel″).Select
On Error Resume Next
Range(″A35：B60″).ClearContents
Range(″LF_start″).Value＝Range(″LF_0″).Value
For i＝2To50
If Range(″LF_start″).Cells(i-1，1).Value+5＜
＝Range(″LF_0″).Value+Range(″LF_1″).Value Then
Range(″LF_start″).Cells(i，1).Value
＝Range(″LF_start″).Cells(i-1，1).Value+5
Else
Exit For
End If
Next i
TTF_iHLRFx
Worksheets(″ACAPFP_main″).ChartObjects.Delete
Worksheets(″B-CoupledCorrossionModel″).Select
ActiveSheet.ChartObjects(″Prediction Curves″).Activate
ActiveChart.PlotArea.Select
ActiveChart.ChartArea.Copy
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in the embodiment, when the service length of the pipeline is calculated to be 50 years and the expected service length is calculated to be 50 years, the pipeline failure probability P is calculated every 5 years f Obtaining the instantaneous failure probability P f (t) cumulative failure probability F r (T) and average remaining lifetime μ (T) r ) Curves, as shown in figure 3.
The invention aims to overcome the defects of a safety risk assessment method of a buried water supply pipeline, and provides a method for realizing water supply pipeline damage and service life prediction based on a form procedure, which has good practicability. By means of an Excel platform, an iHLRF-x algorithm is embedded in a VBA macro program, instantaneous failure probability of a buried water supply pipeline is calculated by comprehensively considering various complex influence factors, and a pipeline service life prediction curve is given. The invention does not need time and labor to carry out modeling analysis, can quickly realize pipeline security risk assessment, and provides a simple and efficient discrimination means for actual engineering.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method for implementing water supply line damage and service life prediction based on a form procedure, comprising:
recording parameter information of the buried water supply pipeline to be detected into an Excle;
batch calculation is carried out on the parameters of the buried water supply pipeline to be detected, and the state characteristics of the water supply pipeline are obtained;
and judging the leakage or pipe bursting condition of the pipeline based on the state characteristics of the water supply pipeline, and completing the state evaluation analysis of the buried water supply pipeline.
2. The method for realizing water supply pipeline damage and service life prediction based on the form process according to claim 1, wherein the to-be-detected buried water supply pipeline parameters comprise pipeline parameters, soil parameters, loads and corrosion parameters;
the pipeline parameters comprise pipeline wall thickness, pipeline outer diameter, burial depth, service duration, expected service duration, ultimate tensile strength and fracture toughness;
the soil body parameters comprise the gravity, the elastic modulus and the lateral soil pressure coefficient;
the load includes road load and pipeline internal pressure;
the corrosion parameters include corrosion pit long half shaft, corrosion pit short half shaft, corrosion pit depth, axial corrosion rate, and circumferential corrosion rate.
3. The method for implementing water supply line damage and service life prediction based on table procedures as recited in claim 1, wherein macro command critical_ack_length is used in batch calculation of the buried water supply line parameters to be tested.
4. The method for implementing water supply line damage and service life prediction based on a table routine of claim 1, wherein the water supply line status characteristics include nominal tensile stress, stress concentration coefficient, and pipe burst critical crack length.
5. The method for implementing water supply line damage and service life prediction based on a table routine of claim 4,
the calculation formula of the nominal tensile stress is as follows:
wherein sigma (x) is the nominal tensile stress, x is the vector of the relevant physical parameter composition, D is the outer diameter of the pipeline, W is the road load, gamma is the gravity, h is the burial depth, E p Is the modulus of elasticity of the pipeline, E s Is the modulus of elasticity, P is the internal pressure of the pipeline, T is the wall thickness, k is the lateral soil pressure coefficient, a 1 -a 7 And b 1 -b 7 Is a model constant;
the calculation formula of the stress concentration coefficient is as follows:
wherein SCF is stress concentration factor, v is Poisson's ratio, a is etch pit half-axis, R is pipeline radius, b is etch pit half-axis, c is etch pit depth, α 1 -α 7 And beta 1 -β 7 Is a model constant;
the calculation formula of the critical crack length of the pipe explosion is as follows:
wherein K is c Is fracture toughness, sigma t Is ultimate tensile strength, L c Is the critical crack length of the squib.
6. The method for implementing water supply line damage and service life prediction based on a table routine of claim 5, wherein determining a leakage or pipe burst condition of a line based on the water supply line status characteristics, completing the buried water supply line status assessment analysis comprises:
setting an ultimate tensile strength, comparing the product of the stress concentration coefficient and the nominal tensile stress with the ultimate tensile strength, and simultaneously comparing the two times of the length of the corrosion pit long half shaft and the critical crack length of the pipe explosion, and judging the leakage or the pipe explosion of the pipeline according to the comparison result;
completing the buried water supply line condition assessment analysis if the line leaks or bursts;
and acquiring a pipeline service life prediction curve based on a macro command FORM_Recursive and a macro command pred_Curves, and completing the state evaluation analysis of the buried water supply pipeline.
7. The method for implementing water supply line destruction and service life prediction based on a table-based process of claim 6, wherein obtaining the line service life prediction curve based on a macro command form_reactive and a macro command pred_curves comprises:
based on an iHLRF-x algorithm, calling the macro command FORM_Recursive, determining an iterative algorithm of the optimal step length, and obtaining a reliability index;
obtaining failure probability based on the reliability index;
and predicting based on the failure probability, calling the macro command pred_Curves, and obtaining the pipeline service life prediction curve.
8. The method for implementing water supply line damage and service life prediction based on a table routine of claim 7, wherein the line service life prediction curve includes instantaneous failure probability, cumulative failure probability, and average remaining life.
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