Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines. The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter. The measurement errors associated with the ILI tools are considered in the Bayesian inference. The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta, Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.     

First-order reliability method-based system reliability analyses of corroding pipelines considering multiple defects and failure modes

A methodology that employs the first-order reliability method is proposed to evaluate the time-dependent system reliability of a segment of a pressurised steel pipeline containing multiple active corrosion defects. The methodology considers the leak and burst failure modes of the pipe segment and takes into account the correlations among limit state functions at different corrosion defects. The methodology involves first constructing two linearised equivalent limit state functions for the pipe segment in the standard normal space and then evaluating the probabilities of leak and burst of the segment incrementally over time based on the equivalent limit state functions. The applicability and accuracy of the proposed methodology is illustrated through system reliability analyses of three pipeline examples, each containing ten active corrosion defects whose growth over time is characterised by the linear, nonlinear and homogeneous gamma process-based models.     

Probabilistic characterisation of metal-loss corrosion growth on underground pipelines based on geometric Brownian motion process

This article describes a geometric Brownian motion process-based model to characterise the growth rate of the depth of corrosion defects on underground steel pipelines based on inspection data subjected to measurement uncertainties. To account for the uncertainties from different sources, the hierarchical Bayesian method is used to formulate the growth model, and the Markov Chain Monte Carlo simulation techniques are used to numerically evaluate the probabilistic characteristics of the model parameters. The growth model considers the bias and random scattering error associated with the in-line inspection (ILI) tool as well as the correlations between the random scattering errors associated with different ILI tools. The application of the growth model is illustrated through an example involving real ILI data collected from an in-service pipeline in Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.     

Bayesian Modeling of External Corrosion in Underground Pipelines Based on the Integration of Markov Chain Monte Carlo Techniques and Clustered Inspection Data

In this study, a model is developed to assess external corrosion in buried pipelines based on the unification of Bayesian inferential structure derived from Markov chain Monte Carlo techniques using clustered inspection data. This proposed stochastic model combines clustering algorithms that can ascertain the similarity of corrosion defects and Monte Carlo simulation that can give an accurate probability density function estimation of the corrosion rate. The metal loss rate is chosen as the indicator of corrosion damage propagation, obeying a generalized extreme value (GEV) distribution. Bayesian theory was employed to update the probability distribution of metal loss rate as well as the GEV parameters in order to account for the model uncertainty. The proposed model was validated with direct and indirect inspection data extracted from a 110‐km buried pipeline system.     

Multi-objective maintenance strategy for in-service corroding pipelines using genetic algorithms

Abstract

The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints.     

A condition assessment model for oil and gas pipelines using integrated simulation and analytic network process

Even though they are safe and economical transportation means of gas and oil products around the world, pipelines can be subject to failure and degradation generating hazardous consequences and irreparable environmental damages. Therefore, gas and oil pipelines need to be effectively monitored and assessed for optimal and safe operation. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models used corrosion features as the sole factor to assess the condition of pipelines. Therefore, the objective of this paper was to develop a condition assessment model of oil and gas pipelines that considers several factors besides corrosion. The proposed model, which uses both analytic network process and Monte Carlo simulation, considers the uncertainty of the factors affecting pipeline condition and the interdependency relationships between them. The performance of the model was tested on an existing offshore gas pipeline in Qatar and was found to be satisfactory. The model will help pipeline operators to assess the condition of oil and gas pipelines and hence prioritise their inspections and rehabilitation requirements.     

Life-cycle reliability assessment of reinforced concrete bridges under multiple hazards

This paper proposes a novel probabilistic methodology for estimating the life-cycle reliability of existing reinforced concrete (RC) bridges under multiple hazards. The life-cycle reliability of an RC bridge pier under seismic and airborne chloride hazards is compared to that of a bridge girder under traffic and airborne chloride hazards. When conducting a life-cycle reliability assessment of existing RC bridges, observational data from inspections can provide the corrosion level in reinforcement steel. Random variables related with the prediction of time-variant steel weight loss can be updated based on the inspection results using Sequential Monte Carlo Simulation (SMCS). This paper presents a novel procedure for identifying the hazards that most threaten the structural safety of existing RC bridges, as well as the structural components with the lowest reliability when these bridges are exposed to multiple hazards. The proposed approach, using inspection results associated with steel weight loss, provides a rational reliability assessment framework that allows comparison between the life-cycle reliabilities of bridge components under multiple hazards, helping the prioritisation of maintenance actions. The effect of the number of inspection locations on the updated reliability is considered by incorporating the spatial steel corrosion distribution. An illustrative example is provided of applying the proposed life-cyle reliability assessment to a hypothetical RC bridge under multiple hazards.     

考虑相关性的地下管网系统抗震可靠度分析方法

城市供水、供气、供热等地下管网是重要的生命线工程系统,由于管网中不同管线的结构属性、场地环境、地震动荷载等参数的相关性,管线结构抗震失效事件存在相关性。文章建立相关失效管网系统抗震可靠度问题的解析模型,给出随机变量相关系数在管线可靠度至管网系统可靠度的传递关系。利用基于Nataf变换和正交变换的一次二阶矩法,建立含相关随机变量的管线结构抗震可靠度解析模型;基于结构体系可靠度理论,求解管线结构失效相关系数;采用网络可靠度算法,计算相关失效管网系统节点连通可靠度。同时,给出基于相关随机变量抽样的Monte Carlo随机模拟解法。算例分析结果表明,在管段结构功能函数起控制作用的随机变量的强相关性,对管网系统可靠度影响较大;与传统的管线失效独立假定相比,管线失效事件的强相关性可使并联管网系统可靠度减小15%以上;管网系统拓扑结构的并联冗余性越高,管线失效相关性对系统可靠度的影响越大。     

Prediction of Onset of Corrosion in Concrete Bridge Decks Using Neural Networks and Case-Based Reasoning

Abstract:   This article proposes a methodology for predicting the time to onset of corrosion of reinforcing steel in concrete bridge decks while incorporating parameter uncertainty. It is based on the integration of artificial neural network (ANN), case-based reasoning (CBR), mechanistic model, and Monte Carlo simulation (MCS). A probabilistic mechanistic model is used to generate the distribution of the time to corrosion initiation based on statistical models of the governing parameters obtained from field data. The proposed ANN and CBR models act as universal functional mapping tools to approximate the relationship between the input and output of the mechanistic model. These tools are integrated with the MCS technique to generate the distribution of the corrosion initiation time using the distributions of the governing parameters. The proposed methodology is applied to predict the time to corrosion initiation of the top reinforcing steel in the concrete deck of the Dickson Bridge in Montreal. This study demonstrates the feasibility, adequate reliability, and computational efficiency of the proposed integrated ANN-MCS and CBR-MCS approaches for preliminary project-level and also network-level analyses.     

Locating monitoring wells in groundwater systems using embedded optimization and simulation models

In this paper, a new methodology is proposed for optimally locating monitoring wells in groundwater systems in order to identify an unknown pollution source using monitoring data. The methodology is comprised of two different single and multi-objective optimization models, a Monte Carlo analysis, MODFLOW, MT3D groundwater quantity and quality simulation models and a Probabilistic Support Vector Machine (PSVM). The single-objective optimization model, which uses the results of the Monte Carlo analysis and maximizes the reliability of contamination detection, provides the initial location of monitoring wells. The objective functions of the multi-objective optimization model are minimizing the monitoring cost, i.e. the number of monitoring wells, maximizing the reliability of contamination detection and maximizing the probability of detecting an unknown pollution source. The PSVMs are calibrated and verified using the results of the single-objective optimization model and the Monte Carlo analysis. Then, the PSVMs are linked with the multi-objective optimization model, which maximizes both the reliability of contamination detection and probability of detecting an unknown pollution source. To evaluate the efficiency and applicability of the proposed methodology, it is applied to Tehran Refinery in Iran.     

Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines

In this work, a novel stochastic model framework for predicting the external corrosion growth in buried pipeline structures has been developed, and a reliability-based temporal and spatial maintenance strategy is presented. The spatial correlation of soil properties is modelled via hidden Markov random field. The temporal correlation of the corrosion rate is characterised by the geometric Brownian bridge process. A Bayesian inferential framework is employed to estimate the model parameters of the corrosion growth model using in-line inspection data. The proposed corrosion growth model was validated with actual inspection data. In the reliability analysis, the impact of device detectability is considered and hence the estimated failure probability is more realistic. The proposed maintenance strategy is directly based on the time-specific and location-specific failure probability. The application of the proposed model and maintenance strategy is illustrated through a real-life pipeline system. The results indicate that the proposed maintenance strategy is an adaptive and dynamic scheme that is able to improve the efficiency of inspections.     

Fuzzy-based optimised subset simulation for reliability analysis of engineering structures

This paper presents a numerical strategy for the efficient reliability assessment of engineering structures with random variables and fuzzy variables using fuzzy based optimised subset simulation (SS) approach. The proposed method relies on the performance function of the structure, which involves probability distribution functions and fuzzy variables for the modelling of the structural system. The values of the fuzzy variables for every alpha level are first obtained using the membership function. Therefore, the set values of the fuzzy variable bound the reliability of the structure, and this is evaluated using optimisation and efficient SS approach. The rationale behind the proposed strategy is to locate a failure domain or region where the objective function is minimised or maximised and compute the reliability using SS. The proposed algorithm in this study inherits the benefits of direct Monte Carlo approach in propagating the uncertainties associated with structural parameters but also demonstrate more robustness against the latter. The methodology can be applied to any engineering structures, and the applicability is demonstrated here, using a buried pipeline.     

基于成本的长输管道腐蚀缺陷检测及维修规划

提出了基于成本的长输管道腐蚀缺陷的检测及维修模型,将总成本分为检测和维修2部分,根据总成本最小原则确定最优检测及维修规划。最优的检测及维修,是在保证管道在设计工作寿命内的最大腐蚀深度小于满足强度要求的最大腐蚀裕量的基础上,使管道生命周期内总的期望费用最小。针对腐蚀缺陷的发展过程,以最早产生缺陷的尺寸为界限进行划分,对不同腐蚀程度缺陷进行分类修复。通过比较在不同的状态点下进行检测修复所产生的检测和维修总费用,确定最优的检测及维修规划。     

探究市政燃气管道泄露检测技术的现状及发展

管道输送属于燃气输送的重要形式。市政燃气管道运行的可靠性与安全性,直接影响着城市日常生活。在燃气管道运行过程中,可能会出现受损泄露问题。为此,本文对市政燃气管道泄露检测技术的现状和发展进行了深入探究。     

Experimental evaluation of a vibration-based leak detection technique for water pipelines

Conventional water pipeline leak detection surveys employ labour-intensive acoustic techniques, which are usually expensive and not amenable for continuous monitoring of distribution systems. Many previous studies attempted to address these limitations by proposing and evaluating a myriad of continuous, long-term monitoring techniques. However, these techniques have difficulty to identify leaks in the presence of pipeline system complexities (e.g. T-joints), offered limited compatibility with popular pipe materials (e.g. PVC), and were in some cases intrusive in nature. Recently, a non-intrusive pipeline surface vibration-based leak detection technique has been proposed to address some of the limitations of the previous studies. This new technique involves continuous monitoring of the change in the cross-spectral density of surface vibration measured at discrete locations along the pipeline. Previously, the capabilities of this technique have been demonstrated through an experimental campaign carried out on a simple pipeline set-up. This paper presents a follow-up evaluation of the new technique in a real-size experimental looped pipeline system located in a laboratory with complexities, such as junctions, bends and varying pipeline sizes. The results revealed the potential feasibility of the proposed technique to detect and assess the onset of single or multiple leaks in a complex system.     

Inspection and maintenance planning of pipeline under external corrosion considering generation of new defects

Safe operation of aging pipeline systems under external corrosion can be achieved through inspection and maintenance programs. Tools used for the pipeline inspection are uncertain in detecting a corrosion defect and in sizing a detected defect. The process of generation of new corrosion pits is an uncertain process. These uncertainties must be taken into account in the reliability analysis and in the pipeline inspection and maintenance planning. In this paper the effect of corrosion defect size on the remaining pipeline strength is modeled by a Markov process. Analytical solution of the probability transition matrix is obtained by solving the Kolmogorov forward differential equation. The matrix of probability transition function, the probability of defect detection and the probability distribution of sizing a detected defect is incorporated in estimating the probability of failure. The generation of new corrosion defects is modeled by a Poisson process. The optimal inspection and maintenance schedules are selected based on the reliability constraint. The sensitivity of optimal inspection schedule to the quality of inspection tools and to maintenance criteria is illustrated through examples.     

Application of subset simulation methods to reliability benchmark problems

This paper presents the reliability analysis of three benchmark problems using three variants of Subset Simulation. The original version of Subset Simulation, SubSim/MCMC, employs a Markov chain Monte Carlo (MCMC) method to simulate samples conditional on intermediate failure events; it is a general method that is applicable to all the benchmark problems. SubSim/Splitting is a variant of Subset Simulation applicable to first-passage problems involving deterministic dynamical systems. It makes use of trajectory splitting for generating conditional samples. Another variant, SubSim/Hybrid, uses a combined MCMC/Splitting strategy and so it has the advantages of MCMC and splitting; it is applicable to uncertain and deterministic dynamical systems. Results show that all three Subset Simulation variants are effective in high-dimensional problems and that some computational efficiency can be gained by exploiting and incorporating system characteristics into the simulation procedure.     

Slope reliability and back analysis of failure with geotechnical parameters estimated using Bayesian inference

A Bayesian approach is proposed for the inference of the geotechnical parameters used in slope design. The methodology involves the construction of posterior probability distributions that combine prior information on the parameter values with typical data from laboratory tests and site investigations used in design. The posterior distributions are often complex, multidimensional functions whose analysis requires the use of Markov chain Monte Carlo (MCMC) methods. These procedures are used to draw representative samples of the parameters investigated, providing information on their best estimate values, variability and correlations. The paper describes the methodology to define the posterior distributions of the input parameters for slope design and the use of these results for evaluation of the reliability of a slope with the first order reliability method (FORM). The reliability analysis corresponds to a forward stability analysis of the slope where the factor of safety (FS) is calculated with a surrogate model from the more likely values of the input parameters. The Bayesian model is also used to update the estimation of the input parameters based on the back analysis of slope failure. In this case, the condition FS = 1 is treated as a data point that is compared with the model prediction of FS. The analysis requires a sufficient number of observations of failure to outbalance the effect of the initial input parameters. The parameters are updated according to their uncertainty, which is determined by the amount of data supporting them. The methodology is illustrated with an example of a rock slope characterised with a Hoek-Brown rock mass strength. The example is used to highlight the advantages of using Bayesian methods for the slope reliability analysis and to show the effects of data support on the results of the updating process from back analysis of failure.     

A sensor-driven structural health prognosis procedure considering sensor performance degradation

This article presents a general framework for sensor-driven structural health prognosis and its application to probabilistic maintenance scheduling. Continuously collected sensor data is used to update the parameters of the stochastic structural degradation model. Uncertainty in sensor data (i.e. measurement error) is explicitly modelled as an evolving stochastic process. The proposed framework utilises Bayesian theorem and Markov Chain Monte Carlo (MCMC) sampling to calculate the posterior distributions of stochastic parameters of the structural degradation model. Bayesian updating allows the use of dynamic diagnostic information with prior knowledge for improved prognosis including risk analysis and remaining useful life (RUL) estimation. Although the proposed sensor-driven structural health prognosis procedure is illustrated with a fatigue-related example, it is applicable to more general applications such as corrosion and pavement cracking. A case study of the fatigue details found in a prototype steelgirder bridge has been conducted to demonstrate the proposed prognosis and maintenance scheduling procedure.     

新技术在济钢煤气管网系统中的探索与应用

济钢部分管网系统老化严重,由于腐蚀等原因,煤气管道、膨胀器等设施漏点频繁出现。文章介绍了济钢充分发挥广大职工的聪明才智,将职工自行开发的器具以及外部的新技术充分利用到管网系统中去,保障管网系统的安全稳定运行。