Probabilistic Analysis of the Collapse Pressure of Corroded Pipelines

2016 ◽  
Author(s):  
Nara Oliveira ◽  
Helio Bisaggio ◽  
Theodoro Netto
Keyword(s):  
Monte Carlo ◽  
Structural Reliability ◽  
Limit State ◽  
Monte Carlo Integration ◽  
Experimental Program ◽  
Small Scale ◽  
Collapse Pressure ◽  
First Order ◽  
Operational Life ◽  
Corrosion Defects

Oil and gas offshore pipelines are one of the main components of a subsea system. A major accident can have a great economic impact due to loss of revenue and the expenses involving actions to mitigate damages to the environment. Therefore, investment in accident prevention through a carefully designed inspection and maintenance plan is necessary. In this scenario, many companies have changed their procedures to ensure the structural integrity of their pipelines — from a model that incorporates empirical safety factors and periodic inspections to another, based on methods that consider concepts of structural reliability to establish risk based inspections. The collapse pressure of pipelines containing corrosion defects is usually predicted by deterministic methods, either numerically or through empirical formulations. The severity of each individual corrosion defect can be determined by comparing the differential pressure during operation with the estimated collapse pressure. However, loads and resistance parameters have uncertainties which define the basic reliability problem. These uncertainties are related to the geometric and material parameters of the pipe and the operational conditions. In recent years, many studies have been developed using reliability concepts in order to predict the probability of failure of a corroded pipeline at any given time. The main problem in assuring the integrity and safe operation of pipelines lies in obtaining the necessary accurate prediction of their future condition. A simple deterministic procedure for estimating the collapse pressure of pipes with narrow and long defects has been recently proposed by Netto (2010). This formulation was based on a combined small-scale experimental program and nonlinear numerical analyses accounting for different materials and defect geometries. Probabilistic failure analyses of pipelines considering different failure mechanisms have been performed by different authors over the last decade. Limit state functions similar to the mentioned above, coupled with reliability algorithms such as the first-order second-moment (FOSM) iterative method, the Monte Carlo integration method, and the first-order and second-order reliability methods (FORM/SORM) are generally used. The analyses take into account the natural spread of material properties, geometric and operational parameters, and the uncertainties associated with the sizing of eventual corrosion defects. In this paper, Netto’s deterministic formulation and the crude Monte Carlo method were used to obtain the reliability of corroded pipelines under external hydrostatic pressure. This approach provides a method to predict the probability of collapse of a corroded pipeline along its operational life. It applies concepts of structural reliability to evaluate the detrimental effect of corrosion damages, giving the basis to develop a risk based maintenance strategy.

Collapse Experiments and Reliability Analyses of Corroded Pipes for Offshore Applications

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
N. Oliveira ◽  
T. A. Netto
Keyword(s):  
Structural Reliability ◽  
Simple Procedure ◽  
Experimental Tests ◽  
External Pressure ◽  
Experimental Program ◽  
Small Scale ◽  
Collapse Pressure ◽  
Operational Conditions ◽  
Operational Life ◽  
Corrosion Defects

Abstract The collapse pressure of subsea pipelines containing corrosion defects is usually predicted by deterministic methods, either numerically or through empirical formulations. A simple deterministic procedure for estimating the collapse pressure of pipes with narrow and long defects has been recently proposed by Netto, T. A. (2009, “On the Effect of Narrow and Long Corrosion Defects on the Collapse Pressure of Pipelines,” Appl. Ocean Res., 31(2), pp. 75–81) and Netto, T. A. (2010, “A Simple Procedure for the Prediction of the Collapse Pressure of Pipelines With Narrow and Long Corrosion Defects—Correlation With New Experimental Data,” Appl. Ocean Res., 32(1), pp. 132–134). The formulation was based on a combined small-scale experimental program and nonlinear numerical analyses accounting for different materials and defect geometries. This paper presents additional experimental tests on corroded pipes under external pressure. The collapse pressure calculated using the equation proposed by Netto is compared with this new set of experiments and also with test results available in open literature. These results are used to estimate the equation uncertainty. A sensitivity analysis is also performed to identify how geometric parameters of the defects influence the reduction of collapse pressure. However, loads and resistance parameters have uncertainties. These uncertainties are related to the geometric and material parameters of the pipe and the operational conditions. To account for these uncertainties, a method to predict the probability of collapse of a corroded pipeline along its operational life is proposed. The methodology is illustrated through a case study in which concepts of structural reliability are used to evaluate the detrimental effect of corrosion damages in a pipeline, providing the basis to develop a risk-based maintenance strategy.

A Simple Procedure for the Prediction of the Collapse Pressure of Pipelines With Narrow and Long Corrosion Defects: Uncertainty and Sensibility Analysis

2018 ◽  
Author(s):  
Nara Oliveira ◽  
Theodoro Netto
Keyword(s):  
Simple Procedure ◽  
Experimental Tests ◽  
External Pressure ◽  
Experimental Program ◽  
Small Scale ◽  
Test Results ◽  
Collapse Pressure ◽  
Operational Conditions ◽  
Sensibility Analysis ◽  
Corrosion Defects

The collapse pressure of pipelines containing corrosion defects is usually predicted by deterministic methods, either numerically or through empirical formulations. The severity of each individual corrosion defect can be determined by comparing the differential pressure during operation with the estimated collapse pressure. A simple deterministic procedure for estimating the collapse pressure of pipes with narrow and long defects has been recently proposed by Netto (2010). This formulation was based on a combined small-scale experimental program and nonlinear numerical analyses accounting for different materials and defect geometries. However, loads and resistance parameters have uncertainties which define the basic reliability problem. These uncertainties are mailyrelated to the geometric and material parameters of the pipe and the operational conditions. This paper presents additional experimental tests on corroded pipes under external pressure. The collapse pressure calculated using the equation proposed by Netto (2010) is compared with this new set of experiments and also with test results available in open literature. These results are used to estimate the equation uncertainty. Finally, a sensitivity analysis is performed to identify how geometric parameters of the defects influence the reduction of collapse pressure.

Pipeline Remaining Life Estimation Based on Concepts of Structural Reliability and Bayesian Inference

2014 ◽  
Author(s):  
Helio da Cunha Bisaggio ◽  
Theodoro Antoun Netto
Keyword(s):  
Structural Reliability ◽  
Limit State ◽  
Probability Of Failure ◽  
Operational Parameters ◽  
Simple Method ◽  
Life Estimation ◽  
Predicted Probability ◽  
Operational Life ◽  
Corrosion Defects ◽  
State Functions

In this paper, structural reliability concepts are used in conjunction with DNV Recommended Practice RP-F101 [1] formulation to establish the limit state functions of corroded pipes. The model takes into account the natural spread of material properties, geometric and operational parameters, and the uncertainties associated with the sizing of eventual corrosion defects to determine the probability of failure at a given time. Bayesian and reliability concepts are used to estimate the evolution of a pre-defined distribution of defects obtained, for instance, from an inspection campaign. By comparing the predicted probability of failure with the reliability acceptance criteria the operator can schedule defect repairs and establish inspection intervals with more confidence. Thus, a simple method to predict the probability of failure of a corroded pipeline along its operational life is proposed to provide the basis to develop a risk based maintenance strategy.

IMPERFECTION SENSITIVITY AND RELIABILITY USING SIMPLE BAR-SPRING MODELS FOR STABILITY

2013 ◽  
Vol 13 (03) ◽  
pp. 1250075 ◽  
Author(s):  
VAHID ZEINODDINI MEIMAND ◽  
LORI GRAHAM-BRADY ◽  
BENJAMIN WILLIAM SCHAFER
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Ultimate Strength ◽  
Structural Reliability ◽  
Plastic Hinge ◽  
Continuous Systems ◽  
Plastic Collapse ◽  
Imperfection Sensitivity ◽  
Element Analysis ◽  
First Order

The objective of this paper is to demonstrate how simple bar-spring models can illustrate elementary and advanced structural behavior, including stability, imperfection sensitivity, and plastic collapse. In addition, the same bar-spring models also provide a ready means for assessing structural reliability. Bar-spring models for a column (both post-buckling stable and unstable), a frame, and a plate are all developed. For each model the influence of geometric imperfections are explicitly introduced and the ultimate strength considering plastic collapse of the supporting springs derived. The developed expressions are compared to material and geometric nonlinear finite element analysis models of analogous continuous systems, using yield surface based plastic hinge beam elements (in MASTAN) for the column and frame and shell elements (in ABAQUS) for the plate. The results show excellent qualitative agreement, and surprisingly good quantitative agreement. The developed bar-spring models are used in Monte Carlo simulations and in the development of first order Taylor Series approximations to provide the statistics of the ultimate strength as used in structural reliability calculations. Good agreement between conventional first order second moment assumptions and the Monte Carlo simulations of the bar-spring models is demonstrated. It is intended that the developed models provide a useful illustration of basic concepts central to structural stability and structural reliability.

First-Order Reliability Method for Structural Reliability Analysis in the Presence of Random and Interval Variables

2015 ◽  
Vol 1 (4) ◽  
Author(s):  
Umberto Alibrandi ◽  
C. G. Koh
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Computational Cost ◽  
Computational Effort ◽  
Stochastic Dynamic ◽  
First Order Reliability Method ◽  
First Order ◽  
Structural Reliability Analysis ◽  
Reliability Method

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis in the presence of random parameters and interval uncertain parameters. In the proposed formulation, the hybrid problem is reduced to standard reliability problems, where the limit state functions are defined only in terms of the random variables. Monte Carlo simulation (MCS) for hybrid reliability analysis (HRA) is presented, and it is shown that it requires a tremendous computational effort; FORM for HRA is more efficient but still demanding. The computational cost is significantly reduced through a simplified procedure, which gives good approximations of the design points, by requiring only three classical FORMs and one interval analysis (IA), developed herein through an optimization procedure. FORM for HRA and its simplified formulation achieve a much improved efficiency than MCS by several orders of magnitude, and it can thus be applied to real-world engineering problems. Representative examples of stochastic dynamic analysis and performance-based engineering are presented.

Performance of In-Line Inspection Tools and Their Influence on the Structural Reliability of Corroded Pipeline

2006 ◽  
Author(s):  
Jose´ de Jesu´s Leal Carvajalino ◽  
Fa´bio de Castro Marangone ◽  
Jose´ Luiz de Franc¸a Freire
Keyword(s):  
Sequential Analysis ◽  
Structural Reliability ◽  
Field Measurements ◽  
Visual Basic ◽  
Probability Of Failure ◽  
First Order ◽  
Reliability Method ◽  
Minimum Number ◽  
Corrosion Defects ◽  
Reference Tool

This paper presents: i) the assessment of in-line inspection (ILI) tools’ performance in the measurement of defects caused by corrosion; ii) different methods for calculating the probability of failure (POF) of corroded pipeline based on the ILI report. The ILI report is compared to the geometry of defects measured by a reference tool (field measurements) and the errors associated with each measurement system are analyzed and assessed through different statistical methods. The minimum number of field measurements necessary to verify the performance of the ILI in sizing the corrosion defects is determined by implementing a test based on sequential analysis. The POF of a pipeline is calculated using two methods: i) first order reliability method (FORM) and ii) propagation of uncertainties. The comparison between calculated and acceptable POF enables the determining of the next reinspection period. When the calculated POF exceeds the acceptable POF before completing the amount of time desired for the next inspection, the developed procedure enables determining the number of repairs that must be made to reach the desired time when the next ILI will be performed. Finally, a software in Visual Basic® language was developed to implement this work.

Neural network approach to model the limit state surface for reliability analysis

2003 ◽  
Vol 40 (6) ◽  
pp. 1235-1244 ◽  
Author(s):  
Anthony TC Goh ◽  
Fred H Kulhawy
Keyword(s):  
Neural Network ◽  
Reliability Index ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
First Order Reliability Method ◽  
First Order ◽  
The Neural Network ◽  
Reliability Method ◽  
Geotechnical Structures

Structural reliability methods are often used to evaluate the failure performance of geotechnical structures. A common approach is to use the first-order reliability method. Its popularity results from the mathematical simplicity of the method, since only second moment information (mean and coefficient of variation) on the random variables is required. The probability of failure is then assessed by an index known commonly as the reliability index. One critical aspect in determining the reliability index is the explicit definition of the limit state surface of the system. In a problem involving multi-dimensional random variables, the limit state surface is the boundary separating the safe domain from the "failure" (or lack of serviceability) domain. In many complicated and nonlinear problems where the analyses involve the use of numerical procedures such as the finite element method, this surface may be difficult to determine explicitly in terms of the random variables, and therefore the limit state can only be expressed implicitly rather than in a closed-form solution. It is proposed in this paper to use an artificial intelligence technique known as the back-propagation neural network algorithm to model the limit state surface. First, the failure domain is found through repeated point-by-point numerical analyses with different input values. The neural network is then trained on this set of data. Using the optimal weights of the neural network connections, it is possible to develop a mathematical expression relating the input and output variables that approximates the limit state surface. Some examples are given to illustrate the application and accuracy of the proposed approach.Key words: first-order reliability method, geotechnical structures, limit state surface, neural networks, reliability.

scholarly journals Non-deterministic Approach for Reliability Evaluation of Steel Portal Frame

2019 ◽  
Vol 5 (8) ◽  
pp. 1684-1697
Author(s):  
Hawraa Qasim Jebur ◽  
Salah Rohaima Al-Zaidee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Limit State ◽  
Computational Cost ◽  
Probability Of Failure ◽  
Ultimate Limit State ◽  
Axial Forces ◽  
Portal Frame ◽  
Ultimate Limit

In recent years, more researches on structural reliability theory and methods have been carried out. In this study, a portal steel frame is considered. The reliability analysis for the frame is represented by the probability of failure, P_f, and the reliability index, β, that can be predicted based on the failure of the girders and columns. The probability of failure can be estimated dependent on the probability density function of two random variables, namely Capacity R, and Demand Q. The Monte Carlo simulation approach has been employed to consider the uncertainty the parameters of R, and Q. Matlab functions have been adopted to generate pseudo-random number for considered parameters. Although the Monte Carlo method is active and is widely used in reliability research, it has a disadvantage which represented by the requirement of large sample sizes to estimate the small probabilities of failure. This is leading to computational cost and time. Therefore, an Approximated Monte Carlo simulation method has been adopted for this issue. In this study, four performances have been considered include the serviceability deflection limit state, ultimate limit state for girder, ultimate limit state for the columns, and elastic stability. As the portal frame is a statically indeterminate structure, therefore bending moments, and axial forces cannot be determined based on static alone. A finite element parametric model has been prepared using Abaqus to deal with this aspect. The statistical analysis for the results samples show that all response data have lognormal distribution except of elastic critical buckling load which has a normal distribution.

Improving the first-order structural reliability estimation by Monte Carlo simulation

2017 ◽  
Vol 170 (7) ◽  
pp. 532-540 ◽  
Author(s):  
Mohsen Ali Shayanfar ◽  
Mohammad Ali Barkhordari ◽  
Moien Barkhori ◽  
Mohammad Barkhori
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Reliability Estimation ◽  
First Order

Influence Research of Correlation of Random Variables on Structural Reliability

2011 ◽  
Vol 243-249 ◽  
pp. 245-250
Author(s):  
Yan Feng Fang ◽  
Li Yan Chen ◽  
Hua Xi Gao
Keyword(s):  
Monte Carlo ◽  
Correlation Coefficient ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
State Equation ◽  
Maximum Intensity ◽  
State Function ◽  
Sampling Techniques ◽  
Limit State Function

In this paper, the influence of correlation of variables on structural reliability is discussed. Using importance, condition and duality sampling techniques of Monte Carlo method, accepted accuracy can be obtained. For the limit state function, the correlation of random variables will influence structural reliability, and the influence can be described. For the case of positive correlation, reliability will increase as the the correlation coefficient raise. For the case of negative correlation, reliability will drop as the correlation coefficient raise. The level of influence depends on the slope of limit state equation in standardized coordinate. When k=1, the influence attains maximum intensity for both cases.

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