Reliability estimation of reinforced slopes to prioritize maintenance actions

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8 ≥ 105 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods

Guidelines for Analysis of Data Related to Ageing of Nuclear Power Plant Components and Systems

This guideline is intended to provide practical methods for practitioners to use in analyzing component and system reliability data, with a focus on detection and modeling of ageing. The emphasis is on frequentist and Bayesian approaches, implemented with MS EXCEL and the open-source software package WinBUGS. The methods described in this document can be implemented with other software packages.JRC.F.5-Safety of present nuclear reactor

Probabilistic Modeling and Bayesian Inference of Metal-Loss Corrosion with Application in Reliability Analysis for Energy Pipelines

The stochastic process-based models are developed to characterize the generation and growth of metal-loss corrosion defects on oil and gas steel pipelines. The generation of corrosion defects over time is characterized by the non-homogenous Poisson process, and the growth of depths of individual defects is modeled by the non-homogenous gamma process (NHGP). The defect generation and growth models are formulated in a hierarchical Bayesian framework, whereby the parameters of the models are evaluated from the in-line inspection (ILI) data through the Bayesian updating by accounting for the probability of detection (POD) and measurement errors associated with the ILI data. The Markov Chain Monte Carlo (MCMC) simulation in conjunction with the data augmentation (DA) technique is employed to carry out the Bayesian updating. Numerical examples that involve both the simulated and actual ILI data are used to validate the proposed Bayesian formulation and illustrate the application of the methodology. A simple Monte Carlo simulation-based methodology is further developed to evaluate the time-dependent system reliability of corroding pipelines in terms of three distinctive failure modes, namely small leak, large leak and rupture, by incorporating the corrosion models evaluated from the Bayesian updating methodology. An example that involves three sets of ILI data for a pipe joint in a natural gas pipeline located in Alberta is used to illustrate the proposed methodology. The results of the reliability analysis indicate that ignoring generation of new defects in the reliability analysis leads to underestimations of the probabilities of small leak, large leak and rupture. The generation of new defects has the largest impact on the probability of small leak

A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System

Reliability represents one of the most important attributes of software quality. Assessing the reliability of software embedded in the safety of highlycritical systems is essential. Unfortunately, there are many factors influencing software reliability that cannot be measured directly. Furthermore, the existing models and approaches for assessing software reliability have assumptions and limitations which are not directly acceptable for all systems, such as reactor protection systems. This paper presents the result of a study which aims to conduct quantitative assessment of the software reliability at the reactor protection system (RPS) of RSG-GAS based on software development life cycle. A Bayesian network (BN) is applied in this research and used to predict the software defect in the operation which represents the software reliability. The availability of operation failure data, characteristics of the RPS components and their operation features, prior knowledge on the software development and system reliability, as well as relevant finding from references were considered in the assessment and the construction of nodes on causal network model. The structure of causal model consists of eight nodes including design quality, problem complexity, and defect inserted in the software. The calculation result using Agenarisk software revealed that software defect in the operation of RPS follows binomial statistic distribution with the mean of 1.393. This number indicated the high software maturity level and high capability of the organization. The improvement of software defect concentration range on the posterior distribution compared with the prior’s is also identified. The result achieved is valuable for furtherreliability estimation by introducingnew evidence and experience data, and by setting up an appropriate plan in order to enhance software reliability in the RPS

Review of Quantitative Software Reliability Methods

The current U.S. Nuclear Regulatory Commission (NRC) licensing process for digital systems rests on deterministic engineering criteria. In its 1995 probabilistic risk assessment (PRA) policy statement, the Commission encouraged the use of PRA technology in all regulatory matters to the extent supported by the state-of-the-art in PRA methods and data. Although many activities have been completed in the area of risk-informed regulation, the risk-informed analysis process for digital systems has not yet been satisfactorily developed. Since digital instrumentation and control (I&C) systems are expected to play an increasingly important role in nuclear power plant (NPP) safety, the NRC established a digital system research plan that defines a coherent set of research programs to support its regulatory needs. One of the research programs included in the NRC's digital system research plan addresses risk assessment methods and data for digital systems. Digital I&C systems have some unique characteristics, such as using software, and may have different failure causes and/or modes than analog I&C systems; hence, their incorporation into NPP PRAs entails special challenges. The objective of the NRC's digital system risk research is to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems into NPP PRAs, and (2) using information on the risks of digital systems to support the NRC's risk-informed licensing and oversight activities. For several years, Brookhaven National Laboratory (BNL) has worked on NRC projects to investigate methods and tools for the probabilistic modeling of digital systems, as documented mainly in NUREG/CR-6962 and NUREG/CR-6997. However, the scope of this research principally focused on hardware failures, with limited reviews of software failure experience and software reliability methods. NRC also sponsored research at the Ohio State University investigating the modeling of digital systems using dynamic PRA methods. These efforts, documented in NUREG/CR-6901, NUREG/CR-6942, and NUREG/CR-6985, included a functional representation of the system's software but did not explicitly address failure modes caused by software defects or by inadequate design requirements. An important identified research need is to establish a commonly accepted basis for incorporating the behavior of software into digital I&C system reliability models for use in PRAs. To address this need, BNL is exploring the inclusion of software failures into the reliability models of digital I&C systems, such that their contribution to the risk of the associated NPP can be assessed

Systematic Review for Water Network Failure Models and Cases

As estimated in the American Society of Civil Engineers 2017 report, in the United States, there are approximately 240,000 water main pipe breaks each year. To help estimate pipe breaks and maintenance frequency, a number of physically-based and statistically-based water main failure prediction models have been developed in the last 30 years. Precious review papers focused more on the evolution of failure models rather than modeling results. However, the modeling results of different models applied in case studies are worth reviewing as well. In this review, we focus on research papers after Year 2008 and collect latest cases without repetition. A total of 64 papers are qualified following the selection criteria. Detailed information on models and cases are summarized and compared. Chapter 2 provides a summary and review of failure models and discusses the limitation of current models. Chapter 3 provides a comprehensive review of collected cases, which include network characteristics and factors. Chapter 4 focuses on the main findings from collected papers. We conclude with insights and suggestions for future model selection for pipe failure analysis

Software Reliability Modeling

International audienceSoftware Reliability Modelin