Reliability-based lifecycle management for corroding pipelines

Abstract Corrosion-induced damage is a major source of deterioration in infrastructure and industrial systems such as bridges, offshore and onshore structures, and underground oil and gas pipelines. The uncertainty is pervasive in the parameters affecting the evolution of corrosion process. Risk assessment and management of these systems require a suitable dynamic description of the corrosion process that sufficiently accounts for the underlying uncertainty and subsequently propagates it into the lifecycle reliability assessment of these systems. In this paper, we present a novel approach for reliability-based life cycle management of buried pipelines subjected to corrosion damage. We view our main contributions as twofold. First, a probabilistic model for time evolution of corrosion growth is constructed from available data using polynomial chaos formalism. The model is used to systematically propagate the underlying uncertainty into the limit state functions and the lifecycle reliability analysis. Second, we propose a computationally efficient and accurate optimization strategy using polynomial surrogate in order to solve the stochastic optimization associated with the lifecycle management of buried pipelines. The proposed method facilitates the optimization of maintenance scheduling to achieve minimum expected lifecycle cost by allowing implementation of a gradient-based optimization scheme. This is generally a challenging task due to the discontinuous nature of the objective function with respect to design variables. We examine the sensitivity of the optimum maintenance scheduling with respect to the different measures related to failure probability representing different risk strategies. The proposed development provides an uncertainty-aware decision support tool for making more informed decision regarding the lifecycle management of corroding pipelines. This formalism can also be adapted to other deterioration mechanisms that result in damage-induced structural failure over the lifetime.

[1]  Amir M. Alani,et al.  Reliability based life cycle cost optimization for underground pipeline networks , 2014 .

[2]  D. Tortorelli,et al.  Gradient based design optimization under uncertainty via stochastic expansion methods , 2016 .

[3]  Dongbin Xiu,et al.  The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..

[4]  Francisco Caleyo,et al.  Reliability assessment of buried pipelines based on different corrosion rate models , 2013 .

[5]  Robert Y. Liang,et al.  A clustering approach for assessing external corrosion in a buried pipeline based on hidden Markov random field model , 2015 .

[6]  W. T. Martin,et al.  The Orthogonal Development of Non-Linear Functionals in Series of Fourier-Hermite Functionals , 1947 .

[7]  Christian Cremona Reliability updating of welded joints damaged by fatigue , 1996 .

[8]  André T. Beck,et al.  Optimal inspection and design of onshore pipelines under external corrosion process , 2014 .

[9]  Florian Heiss,et al.  Likelihood approximation by numerical integration on sparse grids , 2008 .

[10]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[11]  Arthur J. Helmicki,et al.  Condition Assessment for Bridge Management , 1996 .

[12]  Roger G. Ghanem,et al.  On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data , 2006, J. Comput. Phys..

[13]  Wenxing Zhou,et al.  System reliability of corroding pipelines considering stochastic process-based models for defect growth and internal pressure , 2013 .

[14]  Dongbin Xiu,et al.  High-Order Collocation Methods for Differential Equations with Random Inputs , 2005, SIAM J. Sci. Comput..

[15]  Hui Wang,et al.  Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines , 2016 .

[16]  Wenxing Zhou,et al.  System reliability of corroding pipelines , 2010 .

[17]  M. Urquidi-Macdonald,et al.  PERFORMANCE COMPARISON BETWEEN A STATISTICAL MODEL, A DETERMINISTIC MODEL,AND AN ARTIFICIAL NEURAL NETWORK MODEL FOR PREDICTING DAMAGE FROM PITTING C ORROSION , 1994 .

[18]  M. Rosenblatt Remarks on a Multivariate Transformation , 1952 .

[19]  Dan M. Frangopol,et al.  Optimization of bridge maintenance strategies based on multiple limit states and monitoring , 2010 .

[20]  Francisco Caleyo,et al.  Technical Note: Field Study—Pitting Corrosion of Underground Pipelines Related to Local Soil and Pipe Characteristics , 2010 .

[21]  Gerhart I. Schuëller,et al.  Design of maintenance schedules for fatigue-prone metallic components using reliability-based optimization , 2010 .

[22]  Wenxing Zhou,et al.  Optimal Design of Onshore Natural Gas Pipelines , 2011 .

[23]  Terje Haukaas,et al.  Optimal inspection planning for onshore pipelines subject to external corrosion , 2013, Reliab. Eng. Syst. Saf..

[24]  Dan M. Frangopol,et al.  Lifetime reliability-based optimization of reinforced concrete cross-sections under corrosion , 2009 .

[25]  Wenxing Zhou,et al.  Probabilistic characterisation of metal-loss corrosion growth on underground pipelines based on geometric Brownian motion process , 2015 .

[26]  Mauricio Sánchez-Silva,et al.  Life-cycle performance of structures subject to multiple deterioration mechanisms , 2011 .

[27]  F. Caleyo,et al.  Probability distribution of pitting corrosion depth and rate in underground pipelines: A Monte Carlo study , 2009 .

[28]  Dan M. Frangopol,et al.  Lifetime-oriented multi-objective optimization of structural maintenance considering system reliability, redundancy and life-cycle cost using GA , 2009 .

[29]  Dan M. Frangopol,et al.  Minimum expected cost-oriented optimal maintenance planning for deteriorating structures: application to concrete bridge decks , 2001, Reliab. Eng. Syst. Saf..

[30]  Han Ping Hong,et al.  Inspection and maintenance planning of pipeline under external corrosion considering generation of new defects , 1999 .

[31]  Francisco Caleyo,et al.  Markov Chain Models for the Stochastic Modeling of Pitting Corrosion , 2013 .

[32]  R. Melchers Modeling of Marine Immersion Corrosion for Mild and Low-Alloy Steels—Part 1: Phenomenological Model , 2003 .

[33]  Claude Gabrielli,et al.  A review of the probabilistic aspects of localized corrosion , 1990 .

[34]  Philip A. Scarf,et al.  Extrapolation of Extreme Pit Depths in Space and Time , 1990 .

[35]  Roger G. Ghanem,et al.  Polynomial chaos representation of spatio-temporal random fields from experimental measurements , 2009, J. Comput. Phys..

[36]  P. Laycock,et al.  ORDER STATISTICS FOR PITS, PITTING AND OTHER LOCALIZED CORROSION PHENOMENA , 1993 .

[37]  Michael Havbro Faber,et al.  Sensitivities in Structural Maintenance Planning , 1996 .

[38]  D. D. Theodorakopoulos,et al.  A knowledge-based system for maintenance planning of highway concrete bridges , 2005, Adv. Eng. Softw..

[39]  Christian Soize,et al.  Maximum likelihood estimation of stochastic chaos representations from experimental data , 2006 .

[40]  Jian-Hua Li,et al.  Predicting corrosion remaining life of underground pipelines with a mechanically-based probabilistic model , 2009 .

[41]  Daniel Straub,et al.  Risk based inspection planning for structural systems , 2005 .

[42]  F. Caleyo,et al.  Predictive Model for Pitting Corrosion in Buried Oil and Gas Pipelines , 2009 .

[43]  R. Ghanem,et al.  Polynomial chaos decomposition for the simulation of non-gaussian nonstationary stochastic processes , 2002 .

[44]  Ross B. Corotis,et al.  Reliability-based bridge design and life cycle management with Markov decision processes☆ , 1994 .

[45]  Dan M. Frangopol,et al.  Optimization of bridge maintenance strategies based on structural health monitoring information , 2011 .

[46]  M. Ahammed,et al.  Prediction of remaining strength of corroded pressurised pipelines , 1997 .

[47]  Alaa Chateauneuf,et al.  Reliability analysis and inspection updating by stochastic response surface of fatigue cracks in mixed mode , 2011 .

[48]  Jan Drewes Achenbach,et al.  Optimization of inspection schedule for a surface-breaking crack subject to fatigue loading , 2007 .

[49]  Omar M. Knio,et al.  Spectral Methods for Uncertainty Quantification , 2010 .

[50]  Franck Schoefs,et al.  Stochastic improvement of inspection and maintenance of corroding reinforced concrete structures placed in unsaturated environments , 2012 .

[51]  R. Rackwitz,et al.  Cost-benefit optimization and risk acceptability for existing, aging but maintained structures , 2008 .

[52]  Robert E. Melchers,et al.  Pitting Corrosion of Mild Steel in Marine Immersion Environment—Part 1: Maximum Pit Depth , 2004 .

[53]  John L. Hudson,et al.  Cooperative Stochastic Behavior in Localized Corrosion I. Model , 1997 .

[54]  D. Xiu Numerical Methods for Stochastic Computations: A Spectral Method Approach , 2010 .

[55]  Mark Stephens,et al.  A Comprehensive Approach to Corrosion Management Based on Structural Reliability Methods , 2006 .