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.     

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.     

Evaluating the system reliability of corroding pipelines based on inspection data

This paper presents a methodology for evaluating the time-dependent system reliability of a pressurised gas pipeline segment containing multiple active metal-loss corrosion defects. The methodology incorporates three distinctive failure modes of the pipe segment due to corrosion, namely small leak, large leak and rupture. The growth of the depth of individual corrosion defect is assumed to follow a power-law function of time. The Bayesian updating and Markov Chain Monte Carlo (MCMC) simulation techniques are used to quantify the parameters of the power-law growth model based on data obtained from multiple inspections carried out at different times. The simple Monte Carlo and MCMC techniques are combined to evaluate the system reliability. A numerical example involving an in-service gas pipeline located in Alberta, Canada, is used to illustrate the proposed methodology. Results of the sensitivity analysis suggest that the use of a defect-specific or segment-specific growth model for the defect depth has a marked impact on the evaluated system reliability. The proposed methodology can be incorporated in reliability-based pipeline corrosion management programmes to assist integrity engineers in making informed decisions about defect repair and mitigation.     

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。     

Life-Cycle Performance of RC Bridges: Probabilistic Approach

This article addresses the problem of reliability assessment of reinforced concrete (RC) bridges during their service life. First, a probabilistic model for assessment of time-dependent reliability of RC bridges is presented, with particular emphasis placed on deterioration of bridges due to corrosion of reinforcing steel. The model takes into account uncertainties associated with materials properties, bridge dimensions, loads, and corrosion initiation and propagation. Time-dependent reliabilities are considered for ultimate and serviceability limit states. Examples illustrate the application of the model. Second, updating of predictive probabilistic models using site-specific data is considered. Bayesian statistical theory that provides a mathematical basis for such updating is outlined briefly, and its implementation for the updating of information about bridge properties using inspection data is described in more detail. An example illustrates the effect of this updating on bridge reliability.     

Inspection and maintenance planning of underground pipelines under the combined effect of active corrosion and residual stress

In this paper, a maintenance policy is proposed for pipelines subjected to active corrosion and residual stress, by taking into account imperfect inspection results. The degradation of the pipeline is induced by uniform corrosion, leading to losses of the pipe wall thickness. Localized corrosion is not considered herein, as neither pitting nor crevice corrosion are strongly influenced by external loading conditions and, hence, are not critical in structural strength considerations. When the corroded layers are removed, strain relaxation occurs, causing a redistribution of residual stresses. In parallel, the inspection is applied to detect the corrosion defects, namely the thickness of the corroded layer, and it has a detection threshold under which no corrosion rate can be measured. Due to uncertainties, each inspection is affected by the probability of detecting small defects and the probability of wrong assessment in terms of defect existence and size. The present work aims at integrating imperfect inspection results in the cost model for corroded pipelines, where the failure probabilities are computed by reliability methods. A numerical application on a gas pipe shows the influence of corrosion rates and residual stresses on the optimal maintenance planning.     

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.     

RELIABILITY BASED OPTIMAL INSPECTION AND MAINTENANCE FOR PIPELINE UNDER CORROSION

Nondestructive inspection tools used for pipeline inspection are uncertain in detecting corrosion pits and in sizing detected defects. Probability-based optimal inspection schedule analysis must taken these uncertainties into account. In this paper, the probability of time to failure is formulated as integral equations with domain of integration expressed as unions and intersections of domains of failure, defect detection, defect nondetection and maintenance criterion. The rate of defect detection as a function of defect size and the maintenance criterion are used as filters to eliminate the defects that are not fit for service in an expected remaining service life after inspection. Simulation procedure is given to estimate the probability distribution of time to failure by using the integral equations. To facilitate the probabilistic analysis, a standard uniformly distributed variate is introduce and used in defining the domain of detected defect and the domain of nondetected defect. The advantages of using the proposed simulation procedure are discussed. Optimal inspection schedules are selected based on the minimum value of the maximum probability of time to failure before inspections and before the time at the end of service life. Effect of inspection quality and maintenance criterion on probability of time to failure and on selecting optimal inspection schedule is presented through an illustrative application study.     

埋地管道腐蚀检测与评价技术

介绍了土壤腐蚀环境的检测、管道防腐层检测技术、管体腐蚀损伤检测技术及杂散电流检测技术,探讨了管道剩余强度评价方法及管道剩余寿命预测方法。     

Defect distribution and reliability assessment of wind turbine blades

In this paper, two stochastic models for the distribution of defects in wind turbine blades are proposed. The first model assumes that the individual defects are completely randomly distributed in the blade. The second model assumes that the defects occur in clusters of different size, based on the assumption that one error in the production process tends to trigger several defects. For both models, additional information, such as number, type, and size of the defects, is included as stochastic variables.In a numerical example, the reliability is estimated for a generic wind turbine blade model both with and without defects in terms of delaminations. The reliability of the blade decreases when defects are included. However, the distribution of the defects influences how much the reliability is decreased. It is also shown how non-destructive inspection (NDI) after production can be used to update the reliability for the wind turbine blade using Bayesian statistics.     

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.     

埋地燃气钢管腐蚀检测及防腐对策

阐述了埋地燃气钢管防腐的重要意义,通过对某管道燃气公司埋地燃气钢管腐蚀检测,分析了产生腐蚀的原因,提出了减缓腐蚀的方法与对策,对该公司的外加电流深井阳极阴极保护法的防腐实践进行总结与探讨.     

热力管道状态检测技术研究

分析了国内外管道检测技术的原理、特点与应用领域,以及在热力管道检测中的适用性。在可通行管沟中可采用红外热像仪检测法对热力管道进行局部检测。采用分布式光纤测温系统可对管沟敷设热力管道和直埋敷设热力管道实现全程连续检测。管道内检测技术包括测径器检测法、闭路电视（CCTV）管道检测法、超声波检测法、漏磁检测法,这些方法尽管在石油管道检测中非常有效,但由于热力管道的高温和检测设备进出困难,仍应用较少。管道内检测技术对于管道的快速检测、完整性评价意义重大,值得进一步研究。     

里地燃气钢管腐蚀检测及防腐对策

阐述了埋地燃气钢管防腐的重要意义,通过对某管道燃气公司埋地燃气钢管腐蚀检测,分析了产生腐蚀的原因,提出了减缓腐蚀的方法与对策,对该公司的外加电流深井阳极阴极保护法的防腐实践进行总结与探讨。     

佛山市第二水源输水干管过河沉管施工

通过对佛山市第二水源输水干管过河钢管采用沉管技术进行施工的实例,阐述了过河管道沉管工程的主要施工工艺、管段加工、内外防腐及其质量检验方法等应注意的问题,以推广该技术的应用。     

基于深度学习的排水管道缺陷内窥检测智能识别系统研究

目前国内各城市已普遍采用管道机器人深入管道内部摄取视频影像,有效获取到可供管道缺陷检测的一手资料,但缺陷识别大部分依靠人工目视识别,耗时耗力,生产周期长。利用福州市勘测院多年累积的管道检测数据,基于Pytorch深度学习框架、建立了排水管道缺陷内窥检测智能识别系统,包括:数据预处理,残差神经网络设计与训练、系统集成等。重点实现了三级组合识别模型建构(二分类,类型识别,等级识别),解决了系统准确度等技术难题。经生产实践表明:模型准确率高,可有效提高管道健康状况检查质量和效率。     

城市燃气管道应力腐蚀分析

随着城市燃气管道运营年代的推移,管道的寿命也面临着挑战,其中应力腐蚀是造成管道事故的主要原因。文章给出了城市燃气管道应力腐蚀模型,提出可以根据应力腐蚀过程采取有效措施,对燃气管道预先检测和维护,避免发生管道事故。     

Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections and facilitate bridge access. Any such inspection must consider the design of the UAV, the choice of cameras, data acquisition, geometrical resolution, safety regulations and pilot protocols. The Placer River Trail Bridge in Alaska recently served as a test bed for a UAV inspection methodology that integrates these considerations. The end goal was to produce a three-dimensional (3D) model of the bridge using UAV-captured images and a hierarchical Dense Structure-from-Motion algorithm. To maximise the quality of the model and its benefits to inspectors, this goal guided UAV design and mission planning. The resulting inspection methodology integrates UAV design, data capture and data analysis together to provide an optimised 3D model. This model provides inspection documentation while enabling the monitoring of defects. The developed methodology is presented herein, as well as analyses of the 3D models. The results are compared against models generated through laser scanning. The findings demonstrate that the UAV inspection methodology provided superior 3D models with the accuracy to resolve defects and support the needs of infrastructure managers.     

埋地钢管外防腐层直接检测技术与方法

根据多年检测地下管道外防腐层的实践经验,系统地论述了地下管道外防腐层检测前沿的几种理论方法。通过对这些理论方法和检测技术的分析,以期能对我国油气等埋地管网腐蚀评价的技术规范制定、实际管道腐蚀检测的实施、埋地管网腐蚀评价起到指导和借鉴作用。