Database development and uncertainty treatment for estimating pipe failure rates and rupture frequencies

Estimates of failure rates for nuclear power plant piping systems are important inputs to Probabilistic Risk Assessments (PRA) and risk informed applications of PRA. Such estimates are needed for initiating event frequencies for Loss of Coolant Accidents and internal flooding events and for risk informed evaluations of piping system in-service inspection programs. A critical issue in the estimation of these parameters is the treatment of uncertainties, which can exceed an order of magnitude deviation from failure rate point estimates. Sources of uncertainty include failure data reporting issues, scarcity of data, poorly characterized component populations, and uncertainties about the physical characteristics of the failure mechanisms and root causes. A methodology for quantifying these uncertainties using a Bayes' uncertainty analysis method was developed for the EPRI risk informed in-service inspection program and significantly enhanced in subsequent applications. In parallel with these efforts, progress has been made in the development of pipe failure databases that contain the quantity and quality of information needed to support piping system reliability evaluations. Examples are used in this paper to identify technical issues with previous published estimates of pipe failure rates and the numerical impacts of these issues on the pipe failure rates and rupture frequencies are quantified.     

General network reliability problem and its efficient solution by Subset Simulation

Complex technological networks designed for distribution of some resource or commodity are a pervasive feature of modern society. Moreover, the dependence of our society on modern technological networks constantly grows. As a result, there is an increasing demand for these networks to be highly reliable in delivering their service. As a consequence, there is a pressing need for efficient computational methods that can quantitatively assess the reliability of technological networks to enhance their design and operation in the presence of uncertainty in their future demand, supply and capacity. In this paper, we propose a stochastic framework for quantitative assessment of the reliability of network service, formulate a general network reliability problem within this framework, and then show how to calculate the service reliability using Subset Simulation, an efficient Markov chain Monte Carlo method that was originally developed for estimating small failure probabilities of complex dynamic systems. The efficiency of the method is demonstrated with an illustrative example where two small-world network generation models are compared in terms of the maximum-flow reliability of the networks that they produce.     

供水管网地震功能的失效分析

在地震发生后,供水管网一般处于渗漏工作状态。为了评价城市供水管网的抗震能力,需要进行震后带渗漏供水管网的水力分析,对震后供水管网的流分析方法进行了改进,并建立了震后带渗漏供水管网的水力分析模型,通过连通性分析和连续性方程求解,计算得到震后节点的供水量和节点压力,进而评价供水管网的地震功能。另外,以某供水管网为例,演示了该方法的可行性。为供水管网的抗震设计、城市供水系统的抗震能力预测、提高震后救灾恢复的效率具有重要的指导作用。     

Pipe break prediction based on evolutionary data-driven methods with brief recorded data

Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.     

基于物理机制的随机地震动场中供水管网抗震功能反应分析

城市供水管网是生命线工程的重要组成部分,由于受到工程场地地震动随机性的影响,其系统结构响应和服务功能也应呈现显著的随机性。传统的埋地管线地震反应分析既不能考虑管网拓扑结构的影响,也难以反映随机地震动的作用,同时,管线的结构响应未能落实到系统的功能性评价之上。为了解决上述三个难题,该文在工程场地地震动随机场物理模型的基础之上,通过引入埋地管网地震反应分析模型,结合概率密度演化方法,实现了具有非线性特性的埋地管网的随机地震反应分析;进而,结合管网流体的瞬变流分析理论,实现了城市供水管网的动态抗震功能反应分析,为供水管网的抗震功能可靠度研究奠定了基础。     

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterise four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company.     

Water pipeline failure due to water hammer effects

A numerical model has been established in order to simulate the propagation of pressure waves in water networks. The present model formulation is based on a system of partial hyperbolic differential equations. This system has been solved via the characteristics method. The current model provides the necessary data and the necessary damping of water hammer waves, taking into account the structure of the pipe network and the pressure loss. The numerical algorithm estimates the maximum pressure values resulting from the water hammer when closing valves in the network and consequently, the maximum stresses in the pipes have been calculated. In the case of simultaneous closing of several valves, the over pressure can exceed the admissible pressure. In this case, the severity of a defect such as a corrosion crater (pit) has been estimated by computing a safety factor for the stress distribution at the defect tip. This allows the applied notch stress intensity factor to be obtained. To investigate the defect geometry effects, semi‐spherical and semi‐elliptical defects are deemed to exist in up to one‐half of the thickness of the pipe wall. The outcomes have been introduced into the structural integrity assessment procedure (SINTAP) failure diagram assessment (FAD) in order to obtain the safety factor value. Conventionally, it is considered that a failure hazard exists if this safety factor is less than two.     

Weibull-based methodology for condition assessment of cast iron water mains and its application

A qualitative and quantitative understanding of how cast iron water distribution pipes fail in service would facilitate a targeted approach to the management of rehabilitation in the water industry. This paper proposes a technique for assessing the condition of pipes, based on strength characteristics obtained from small samples; this offers an alternative way of estimating the likelihood of failure to current methodologies based on pit‐depth measurements. Examination of recovered pipe samples indicates that the strength of the cast iron pipe reduces over time as a result of corrosion, although other time‐dependent processes, such as fatigue, may also contribute to this degradation. Taken with previous work, this paper suggests that the variation in strength of small samples removed from cast iron water distribution pipes can be characterized using Weibull methods. It is argued that the Weibull modulus provides a useful indicator of the condition of the pipe. Using scaling arguments, inherent in the Weibull methodology, it is then possible to use data from small samples to predict the likely strength characteristics of water distribution pipes in the ground, which is reasoned to be a good measure of the potential performance of the pipe in service. The Weibull approach is applied to a number of different data sets obtained from testing samples extracted from a range of pipes, which have seen service at various locations in the Thames Water region. One of these data sets was from locations where failure had occurred in service. It is shown that the use of Weibull analysis can identify pipes in the network that have degraded the most significantly. A methodology is suggested whereby this information taken with other performance indicators can be used to identify the local regions where rehabilitation is required most urgently. Alternatively, it can be used to identify those regions of the network, which are in good condition and unlikely to need repair or replacement work.     

Risk classification and uncertainty propagation for virtual water distribution systems

While the secrecy of real water distribution system data is crucial, it poses difficulty for research as results cannot be publicized. This data includes topological layouts of pipe networks, pump operation schedules, and water demands. Therefore, a library of virtual water distribution systems can be an important research tool for comparative development of analytical methods. A virtual city, “Micropolis”, has been developed, including a comprehensive water distribution system, as a first entry into such a library. This virtual city of 5000 residents is fully described in both geographic information systems (GIS) and EPANet hydraulic model frameworks. A risk classification scheme and Monte Carlo analysis are employed for an attempted water supply contamination attack. Model inputs to be considered include uncertainties in: daily water demand, seasonal demand, initial storage tank levels, the time of day a contamination event is initiated, duration of contamination event, and contaminant quantity. Findings show that reasonable uncertainties in model inputs produce high variability in exposure levels. It is also shown that exposure level distributions experience noticeable sensitivities to population clusters within the contaminant spread area. High uncertainties in exposure patterns lead to greater resources needed for more effective mitigation strategies.     

Fuzzy Decision Model: Evaluating and Selecting Open Banking Business Partners

The finance supply chain has always been a different supply chain compared to product supply chain being a service supply chain. Open Banking (OB) is one of the most important milestones since the beginning of financial technology innovation and service supply chain. As these are activities provided by traditional banks, non-bank financial institutions also provide financial service with access to consumer banking, transactional and other financial data to develop financial applications and services tailored to their customers. The development of financial technology, “Open banking”, promotes financial services to begin this transformation. However, evaluating and selecting open banking business partners from multiple perspectives for banks are underexplored. Thus, the authors in this research proposed a hybrid multi-criteria decision-making model which includes a Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) model and a Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) model with supports from seasoned domain experts. The contribution of this research is a proposition of a fuzzy decision model for evaluating and selecting open banking business partners. The model successfully determined a suitable open bank provider in order to assist decision makers decide objectively.     

Statistical models for the analysis of water distribution system pipe break data

The deterioration of pipes leading to pipe breaks and leaks in urban water distribution systems is of concern to water utilities throughout the world. Pipe breaks and leaks may result in reduction in the water-carrying capacity of the pipes and contamination of water in the distribution systems. Water utilities incur large expenses in the replacement and rehabilitation of water mains, making it critical to evaluate the current and future condition of the system for maintenance decision-making. This paper compares different statistical regression models proposed in the literature for estimating the reliability of pipes in a water distribution system on the basis of short time histories. The goals of these models are to estimate the likelihood of pipe breaks in the future and determine the parameters that most affect the likelihood of pipe breaks. The data set used for the analysis comes from a major US city, and these data include approximately 85,000 pipe segments with nearly 2500 breaks from 2000 through 2005. The results show that the set of statistical models previously proposed for this problem do not provide good estimates with the test data set. However, logistic generalized linear models do provide good estimates of pipe reliability and can be useful for water utilities in planning pipe inspection and maintenance.     

供热管道地震易损性分析

为了研究供热管道的地震易损性,采用有限元分析方法,建立了典型供热管道分析模型,选取典型地震波作为输入,通过数值模拟手段,对供热管道在地震作用下的应力反应进行了计算分析。基于三态破坏准则对单体管道地震破坏概率进行了分析,给出了供热管道不同地震烈度下的破坏概率。根据管道破坏服从泊松分布的基本假定,对不同烈度下一定长度供热管道地震破坏处数进行了估计,同时与历史地震中管道震害经验统计结果进行了对比分析,初步评述了供热管道的抗震性能。     

A physical probabilistic model to predict failure rates in buried PVC pipelines

For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown to be appropriate to model uncertainty in predicted pipe lifetime. The Weibull hazard function for pipe lifetime is then used to estimate the expected failure rate (per pipe length/per year) as a function of pipe age. To validate the model, predicted failure rates are compared to aggregated failure data from 17 UK water utilities obtained from the United Kingdom Water Industry Research (UKWIR) National Mains Failure Database. In the absence of actual operating pressure data in the UKWIR database, typical values from Australian water utilities were assumed to apply. While the physical probabilistic failure model shows good agreement with data recorded by UK water utilities, actual operating pressures from the UK is required to complete the model validation.     

Risk evaluation by modeling of passing behavior on two-lane rural highways

Passing maneuver on rural two-lane highways is a complex task, which has a significant effect on capacity, level of service and safety. The maneuver is conditioned on the gap between two successive vehicles on the opposing lane. The minimum time to collision, defined as the remaining gap between the passing vehicle and the oncoming vehicle at the end of the passing process, expresses a measure of the risk involved in the passing maneuver.This paper develops a model that explains the minimum time to collision. The model formulation is based on the analysis of drivers’ passing decisions on two-lane rural highways using an interactive driving simulator. The simulator enables the collection of vehicle speeds and positions for different road and traffic scenarios. In addition to the driver simulator, participants responded to a questionnaire which collected information about their socio-demographic characteristics.The composed dataset was analyzed and processed to develop a model that predicts the risk associated with the passing behavior. Tobit regression models were found to be more suitable, in comparison to ordinary least square models and Hazard-based Duration models. The explanatory variables tested represent road geometry, traffic conditions and drivers’ characteristics. It was found that while the traffic related variables had the most important effect on the measure of risk chosen, factors related to the geometric design and the driver characteristics also had a significant contribution.     

The bullwhip effect in hybrid supply chain

As the service is becoming the strategic preference in the manufacturing industry, more and more manufacturing companies provide customers with total solutions by integrating services into their core product offerings, which is usually implemented in so-called hybrid supply chain (HSC). The HSC is formed as a chain by a group of manufacturers and service providers who work together to offer total solutions to customers. In HSC, customers’ responses to variable service levels and the interaction between inventory strategy and service capacity strategy have a great impact on supply chain instability, which represents an important concern in supply chain research. To tackle this problem, we establish a system dynamics model, by taking the HSC for an elevator company in China as an example. Firstly, we analyzed the oscillation characteristics of service flow and product flow. Then, we proposed the performance metrics of bullwhip effect in HSC. Finally, based on the model, we find that the bullwhip effect of HSC could be smoothened by incorporating service capacity adjustment strategy into the inventory replenishment policy.     

3G客户关系稳定度建模与应用

为了定量评价使用3G业务的客户与运营商之间的关系,对3G客户的稳定度进行了研究,提出了基于数据挖掘(DM)和层次分析法(AHP)的3G客户关系稳定度模型。将模型应用于某电信公司,得出3G客户稳定度评分分布。该模型为运营商更有效地开展3G客户关系维系及服务等工作提供了有力支撑。     

随机需求下鲜活农产品RFID技术投资决策研究

考虑一个零售商和一个生产商组成的单周期两级鲜活农产品供应链,采用RFID技术可以明显改善鲜活农产品的新鲜度和实体损耗;讨论了消费者需求随机的情况下RFID标签成本对供应链上下游企业利润的影响;分析了RFID技术使用前后集中型和分散型供应链模型中各方的最优期望利润、订货量等问题;计算出两类模型中RFID技术标签成本的临界值。结果表明,集中型和分散型供应链模型,零售商和生产商可承担的RFID技术标签成本临界值相同;RFID技术对新鲜度和实体损耗改善越明显,企业投资的意愿越强。     

大口径埋地钢管在地震断层作用下破坏模式的研究

利用壳有限元模型模拟了在土耳其地震和集集地震中埋地供水钢管在地震断层错动作用下的实际震害。从理论上得到一等效边界,引入到壳有限元模型的两端,因此只需对在断层附近发生大变形的管段进行壳分析。与梁有限元模型分析结果的比较,壳有限元模型能够更好地揭示断层作用下埋地钢管的各种屈曲破坏模式。通过区分断层两侧场地条件相同和相异这两种情况,很好地模拟了1999年土耳其地震中一根2.2m口径供水钢管在断层附近的三处破坏和集集地震中一根2.0m口径供水钢管在断层附近的二处破坏,探讨了断层两侧场地条件的分布形式对埋地管道破坏模式的影响。还详细地解释了土耳其地震供水钢管第三处破坏产生的原因。     

Discrimination between accelerated life models via Approximate Bayesian Computation

Accelerated life testing (ALT) is widely used in high-reliability product estimation to get relevant information about an item's performance and its failure mechanisms. To analyse the observed ALT data, reliability practitioners need to select a suitable accelerated life model based on the nature of the stress and the physics involved. A statistical model consists of (i) a lifetime distribution that represents the scatter in product life and (ii) a relationship between life and stress. In practice, several accelerated life models could be used for the same failure mode and the choice of the best model is far from trivial. For this reason, an efficient selection procedure to discriminate between a set of competing accelerated life models is of great importance for practitioners. In this paper, accelerated life model selection is approached by using the Approximate Bayesian Computation (ABC) method and a likelihood-based approach for comparison purposes. To demonstrate the efficiency of the ABC method in calibrating and selecting accelerated life model, an extensive Monte Carlo simulation study is carried out using different distances to measure the discrepancy between the empirical and simulated times of failure data. Then, the ABC algorithm is applied to real accelerated fatigue life data in order to select the most likely model among five plausible models. It has been demonstrated that the ABC method outperforms the likelihood-based approach in terms of reliability predictions mainly at lower percentiles particularly useful in reliability engineering and risk assessment applications. Moreover, it has shown that ABC could mitigate the effects of model misspecification through an appropriate choice of the distance function.     

Global sensitivity analysis and aggregation of risk in multi-product supply chain networks

Manufacturing firms manage complex supply chain networks which are exposed to a plethora of hazard events. An essential part of the risk management process is the calculation of the stand-alone risk exposures of the product-specific supply chains, but also of the entire multi-product system. In this paper, first, a global sensitivity analysis of the statistical supply chain risk model is conducted. This method helps the decision-makers to understand the risk of the model they are using. Second, a methodology for risk aggregation in multi-product supply chain networks is proposed. The real-world data is used to analyse and validate the model. Supply chain managers equipped with the proposed method will better cope with the risk in supply chains for different product configurations.