A risk perspective suitable for resilience engineering

In recent years, resilience engineering has been given considerable attention among safety researchers and analysts. The area represents a new way of thinking about safety. Whereas conventional risk management approaches are based on hindsight knowledge, failure reporting and risk assessments calculating historical data-based probabilities, resilience engineering looks for ways to enhance the ability of organisations to be resilient in the sense that they recognise, adapt to and absorb variations, changes, disturbances, disruptions and surprises. The implications of this shift in thinking are many. We focus in this paper on the understanding of the risk concept and how risk can be assessed and treated. The traditional ways of looking at risk are not suitable for use in resilience engineering, but other risk perspectives exist. A main purpose of this paper is to draw attention to such perspectives, in particular one category of perspectives where probability is replaced by uncertainty in the definition of risk. We argue that the basic ideas of resilience engineering can be supported by such risk perspectives.     

Decision analysis on fire safety design based on evaluating building fire risk to life

This study presents a framework of decision analysis on fire safety design alternatives based on evaluating building fire risk to life. A probabilistic risk assessment method for occupant life is presented with consideration of some uncertainties of evacuation process and fire development at first. For occupant evacuation time assessment, occupant pre-movement time is characterized by normal distribution. For onset time to untenable conditions assessment, its uncertainty is considered as probability distribution according to the range of design fires. Based on event tree technique, probable fire scenarios are analyzed with consideration of the effect of fire protection systems, employees extinguishing, etc. It is difficult to make a precise assessment on probability and consequence of every fire scenario, but their lower bound and upper bound can be achieved based on statistical data. Therefore, Supersoft Decision Theory [Malmnäs, P.E., 1995. Methods of Evaluation in Supersoft Decision Theory. Department of Philosophy, Stockholm University, 365 Stockholm; Johansson, H., Malmnäs, P.E., 2000. Application of supersoft decision theory in fire risk assessment. Journal of Fire Protection Engineering 14, 55–84] that allows decision maker to utilize vague statement is utilized to integrate with risk assessment to make decision on different fire safety design alternatives. To express how to make decision on different fire safety design alternatives based on evaluating building fire risk to life, two hypothetical fire safety design alternatives for a commercial building are presented.     

A new approach to quantitative assessment of reliability of passive systems

The objective of this paper is to show how probabilistic reliability can be assessed for complex systems in the absence of statistical data on their operating experience, based on performance evaluation of the dominant underlying physical processes. The approach is to distinguish between functional and performance probabilities when dealing with the quantification of the overall probability of a system to perform a given function in a given period of time (reliability). In the case of systems where sufficient statistical operating experience data are available, one can focus the quantitative evaluation entirely on the assessment of the functional probability for a given active item (e.g. a pump) by assuming that the specification, layout, construction and installation is such that the item is providing the assigned performance, e.g. in the form of generating the required flow rate. This is how traditional probabilistic safety assessments (PSAs) focus the reliability analysis for the various safety features on the calculation of values for the availability per demand. In contrast, for various systems relevant in advanced technical applications, such as passive safety features in innovative reactor designs, it is essential to evaluate both functional and performance probabilities explicitly and combine the two probabilities later on. This is of course due to the strong reliance of passive safety systems on inherent physical principles. In practice, this means that, for example, in case of a passive cooling system based on natural circulation of a given medium, one has to evaluate and to assess the probability to have a medium condition and a flow rate such that a cladding temperature, represented by a probability distribution, can be hold at a required level. A practical example of this method is given for the case of the reliability assessment of a residual passive heat removal system. General conclusions are drawn regarding reliability estimation of complex, interconnected systems in the absence of statistical performance data, such as for infrastructures.     

Risk-based reliability assessment under epistemic uncertainty

Existing risk in production systems has a direct relationship with unreliability of these systems. Under such circumstances, the approach to maximize the reliability should be replaced with a risk-based reliability assessment approach. Calculating the absolute reliability for systems and complex processes, when we are not provided with any data on failure, is extremely complex and difficult. Until now, studies of reliability assessment have been based on the probability theory, in which the failure time is anticipated after determining the type of size distributions. However, in this paper, the researchers have developed an approach to apply the possibility theory instead of the probability theory. Instead of using absolutely qualitative methods, this new approach applies the Dempster–Shafer Theory. It is obvious when there are insufficient data; an index is needed to make a decision. Then, a novel method is proposed and used in a real case study in order to determine the reliability of production systems based on risk when the available data are not sufficient, helping us to make decisions. After calculating the failure probability and analyzing the assessment matrix and risk criteria, we may conclude that the failure risk of equipment is reduced while the system reliability is increased.     

Rare event risk analysis – application to iceberg collision

To design an engineering system, testing in extreme conditions is at least recommended if not required. There are ambiguities about how to define an extreme state and how to consider it in the design of a system or its operation. The probability estimation of such an event is challenging due to data scarcity, especially in many engineering domains, e.g. offshore development. In this study, available techniques for analyzing the probability of extreme events are examined for their suitability in engineering applications, and a framework is proposed for rare event risk analysis. The framework is comprised of three phases. In the first phase, the outlier based extreme value theory is implemented to estimate the rare event probability. The maximum likelihood criterion is used to estimate the extreme distribution parameters. In the second phase, the rare event is considered as a heavy tail event, and the tail index is estimated through the Hill and the SmooHill estimator. In the third phase, The uncertainty analysis is conducted, and the risk is computed. The proposed methodology is tested for extreme iceberg risk assessment on large offshore structures in the Flemish Pass basin. For this specific case, the estimated design extreme iceberg speed was 4.31 km/h, with an occurrence probability of 3.61E-06.     

Sudden-onset disaster resilience considering functionality improvement planning: An upstream oil and gas company

The present paper presents a quantitative approach to evaluate organizational resilience in sudden-onset disasters considering preparedness actions (PAs). The resilience triangle concept is extended and gradual improvement of the level of functionality (LF) strategy is examined as PAs. Robustness and rapidity measures are considered as loss of LF and recovery time to indicate the overall disaster resilience. Besides, the resourcefulness and redundancy measures are considered pre-determined response plans and PAs aimed to improve disaster resilience. Two mathematical models are developed by applying these measures. Thereafter, graphical analytics are conducted to demonstrate the effectiveness of PAs and provide a better outlook for decision-makers. Then, mathematical analyses are conducted to show how the PAs affect resilience measures. It is mathematically proved that PAs exert considerable impacts on recovery time, loss of LF, and robustness. To illustrate the application of the proposed approach, it is applied to an upstream oil and gas company in the field of exploration and production. The results suggest that the approach is significantly effective in disaster response, planning, and mitigation.     

Methodology for computer aided fuzzy fault tree analysis

Probabilistic risk assessment (PRA) is a comprehensive, structured and logical analysis method aimed at identifying and assessing risks of complex process systems. PRA uses fault tree analysis (FTA) as a tool to identify basic causes leading to an undesired event, to represent logical dependency of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event.To conduct a quantitative fault tree analysis, one needs a fault tree along with failure data of the basic events (components). Sometimes it is difficult to have an exact estimation of the failure rate of individual components or the probability of occurrence of undesired events due to a lack of sufficient data. Further, due to imprecision in basic failure data, the overall result may be questionable. To avoid such conditions, a fuzzy approach may be used with the FTA technique. This reduces the ambiguity and imprecision arising out of subjectivity of the data.This paper presents a methodology for a fuzzy based computer-aided fault tree analysis tool. The methodology is developed using a systematic approach of fault tree development, minimal cut sets determination and probability analysis. Further, it uses static and dynamic structuring and modeling, fuzzy based probability analysis and sensitivity analysis.This paper also illustrates with a case study the use of a fuzzy weighted index and cutsets importance measure in sensitivity analysis (for system probabilistic risk analysis) and design modification.     

PSA中人因失误模型化研究

主要研究PSA模型如何考虑人因失误的影响,系统地提出如何在电厂系统模型中建立相对应的人因失误分析模型。利用事件树把系统故障和人因失误相结合的方法,探讨如何最大可能地真实描述事故后的操纵员行为,确定重要人因事件发展序列以及根据系统响应确定合理可分析的人因题头,建立完整的人因失误模型化的体系,并以实例说明具体分析过程。此项研究能够较好地描述硬件可靠性和人因可靠性之间的关联关系,降低HRA出力并满足PSA对于事故后人员行为的概率分析需求。     

A risk concept applicable for both probabilistic and non-probabilistic perspectives

In engineering risk assessments, probability is the common tool used to describe the epistemic uncertainties about unknown quantities. Probability is considered a main component of risk. However, a number of alternative approaches exist for representing and describing uncertainties in risk assessments, including possibility theory and evidence theory. For these approaches and theories a probability-based risk definition cannot in general serve as a conceptual framework for risk assessments. A broader risk perspective is required. The purpose of the present paper is to present such a perspective and show how both the probabilistic and the alternative approaches and theories can be supported by this perspective. The key feature of this perspective is that uncertainty replaces probability in the definition of risk.     

Risk assessment of rare events

Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.     

An imprecise Fault Tree Analysis for the estimation of the Rate of OCcurrence Of Failure (ROCOF)

The paper proposes an imprecise Fault Tree Analysis in order to characterize systems affected by the lack of reliability data. Differently from other research works, the paper introduces a classification of basic events into two categories, namely Initiators and Enablers. Actually, in real industrial systems some events refer to component failures or process parameter deviations from normal operating conditions (Initiators), whereas others refer to the functioning of safety barriers to be activated on demand (Enablers). As a consequence, the output parameter of interest is not the classical probability of occurrence of the top event, but its Rate of OCcurrence (ROCOF) over a stated period of time. In order to characterize the basic events, interval-valued information supplied by experts are properly aggregated and propagated to the top. To this purpose, the Dempster–Shafer Theory of evidence is proposed as a more appropriate mathematical framework than the classical probabilistic one. The proposed methodology, applied to a real industrial scenario, can be considered a helpful tool to support risk managers working in industrial plants.     

Designing for safety in passenger ships utilizing advanced evacuation analyses—A risk based approach

This paper describes a novel set of well-defined evacuation scenarios for use in advanced evacuation analyses of passenger ships according to present maritime safety regulations. The scenarios are based on a recently performed risk assessment of passenger ship evacuation and can be related to actual accident scenarios, covering the major hazards passenger ships are exposed to. Furthermore, a risk-based methodology for using the set of scenarios in evacuation performance evaluation is proposed and it is demonstrated how the scenarios can be used to relate actual design options to the overall level of risk associated with the ship.The paper includes a brief introduction and describes the background for developing the evacuation scenarios. The results from a recently performed risk assessment is reviewed and it is explained how this can be used as basis for deriving a complete set of realistic evacuation scenarios. Furthermore, it is outlined how to use the evacuation scenarios to estimate the overall risk associated with a specific passenger ship. Finally, possible future developments of the maritime safety regulations have been discussed and it has been demonstrated how the proposed set of scenarios will facilitate the emergence of truly risk based probabilistic safety regulations.     

地震灾害下城市供水网络韧性评估及优化研究

为提升城市供水网络应对地震灾害的能力,基于性能响应函数(PRF)方法,引入基础设施网络均衡理论,设计综合考虑城市供水网络物理状态和输送能力的性能时程响应函数,构建贯穿结构和功能双维度的地震灾害下城市供水网络韧性评估方法;从灾害概率、灾害后果、恢复速度、恢复程度等4个关键因素出发,提出城市供水网络网状扩张、管道延性改造、增加恢复资源和提升恢复储备等韧性优化策略;以我国华东某市供水网络为例,定量评估不同优化策略下的韧性提升效果,提出不同决策场景下恢复资源r和恢复储备b的优化配置方法。结果表明:管网延性改造是提升供水网络韧性的有效措施,网状扩张能提升供水网络性能,但对韧性提升无明显作用,网状扩张后要注意增加网络恢复资源和恢复储备。     

Modeling perceived collision risk in port water navigation

An increase in the likelihood of navigational collisions in port waters has put focus on the collision avoidance process in port traffic safety. The most widely used on-board collision-avoidance system is the automatic radar plotting aid which is a passive warning system that triggers an alert based on the pilot’s pre-defined indicators of distance and time proximities at the closest point of approaches in encounters with nearby vessels. To better help pilot in decision making in close quarter situations, collision risk should be considered as a continuous monotonic function of the proximities and risk perception should be considered probabilistically. This paper derives an ordered probit regression model to study perceived collision risks. To illustrate the procedure, the risks perceived by Singapore port pilots were obtained to calibrate the regression model. The results demonstrate that a framework based on the probabilistic risk assessment model can be used to give a better understanding of collision risk and to define a more appropriate level of evasive actions.     

Evaluation of risk and cost using an age-dependent unavailability modelling of test and maintenance for standby components

The improvement of safety in the process industries is related to assessment and reduction of risk in a cost-effective manner. This paper addresses the trade-off between risk and cost related to standby safety systems. An age-dependent unavailability model that integrates the effects of the test and maintenance (T&M) activities as well as component ageing is developed and represents the basis for calculating risk. The repair “same-as-new” process is considered regarding the T&M activities. Costs are expressed as a function of the selected risk measure. The time-averaged function of the selected risk measure is obtained from probabilistic safety assessment, i.e. the fault tree analysis. This function is further extended with inclusion of additional parameters related to T&M activities as well as ageing parameters related to component ageing. In that sense, a new model of system unavailability, incorporating component ageing and T&M costs, is presented. The testing strategy is also addressed. Sequential and staggered testings are compared. The developed approach is applied on a standard safety system in nuclear power plant although the method is applicable to standby safety systems that are tested and maintained in other industries as well. The results show that the risk-informed surveillance requirements differ from existing ones in technical specifications, which are deterministically based. Moreover, the presented approach achieves a significant reduction in system unavailability over a relatively small increase of total T&M costs.     

Epistemic uncertainty in fault tree analysis approached by the evidence theory

Process plants may be subjected to dangerous events. Different methodologies are nowadays employed to identify failure events, that can lead to severe accidents, and to assess the relative probability of occurrence. As for rare events reliability data are generally poor, leading to a partial or incomplete knowledge of the process, the classical probabilistic approach can not be successfully used. Such an uncertainty, called epistemic uncertainty, can be treated by means of different methodologies, alternative to the probabilistic one. In this work, the Evidence Theory or Dempster–Shafer theory (DST) is proposed to deal with this kind of uncertainty. In particular, the classical Fault Tree Analysis (FTA) is considered when input data are supplied by experts in an interval form. The practical problem of information acquisition from experts is discussed and two realistic scenarios are proposed. A methodology to propagate such an uncertainty through the fault tree up to the Top Event (TE) and to determine the belief measures is supplied. The analysis is illustrated by means of two simple series/parallel systems. An application to a real industrial safety system is finally performed and discussed.     

A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment

In highly complex industries, capturing and employing expert systems is significantly important to an organization's success considering the advantages of knowledge-based systems. The two most important issues within the expert system applications in risk and reliability analysis are the acquisition of domain experts' professional knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition processes. The members of an expert panel regularly represent different experiences and knowledge. Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in current expert system applications in probabilistic risk assessment could not accurately represent the increasingly complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Z-numbers to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate the application and feasibility of the proposed methodology.     

极端海况下海洋石油结构的风险评估

根据中国海上石油工业缺乏独立的风险评价体系的现状 ,笔者对极端海况进行了环境荷载的联合概率分析 ,利用随机模拟技术 ,求解结构的失效概率 ,提出结构失效分析的新方法 ,并利用DNV提供的历史数据 ,对结构失效的后果做了适当分析。以埕北 12 C井组平台作为实例 ,对平台甲板高程 (AirGap)失效分析进行了实例计算。结果表明 ,使用多维联合极值分布理论及相应的求解方法 ,是离岸工程结构物风险评估的重要手段。     

Risk assessment of chemical process considering dynamic probability of near misses based on Bayesian theory and event tree analysis

With the development of modern automatic control systems, chemical accidents are of low frequency in most chemical plants, but once an accident happens, it often causes serious consequences. Near-misses are the precursor of accidents. As the process progresses, near misses caused by abnormal fluctuation of process variables may eventually lead to accidents. However, variables that may lead to serious consequences in the production process cannot update the risk in the life cycle of the process by traditional risk assessment methods, which do not pay enough attention to the near misses. Therefore, this paper proposed a new method based on Bayesian theory to dynamically update the probability of key variables associated with process failure risk and obtain the risk change of the near-misses. This article outlines the proposed approach and uses a chemical process of styrene production to demonstrate the application. In this chemical process, the key variables include flow rate, liquid level, pressure and temperature. In order to study the dynamic risk of the chemical process with consideration of near misses, according to the accumulated data of process variables, firstly the abnormal probability of the variables and the failure rate of safety systems associated with the variables were updated with time based on Bayesian theory. On the basis of the dynamic probability of key process variables, an event tree of possible consequences caused by variable anomalies was established. From the logical relationship of the event tree, the probability of different consequences can be obtained. The results show that the proposed risk assessment method based on Bayesian theory can overcome the shortcomings of traditional analysis methods. It shows the dynamic characteristics of the probability of different near misses, and achieves the dynamic risk analysis of chemical process accidents.     

Risk assessment of major hazards: the effect of ground slope

Liquefied gases, such as chlorine and ammonia, are stored in large quantities at industrial sites. If released accidentally, they form a heavy gas cloud that has the potential to kill or injure large numbers of people. The dispersion of such a cloud is thus of interest to the risk assessment community [Nussey, Pantony, & Smallwood, 1992. HSE’s risk assessment tool, RISKAT. In: Major Hazards: Onshore and Offshore. pp. 607–638].Little is understood about the effect of slope on risk. Here, the risk (probability) of being exposed to the gas cloud, given a release, is considered; probability language is needed because wind direction is assumed to be a random variable.This paper shows how the risk of being exposed to toxic gas released over a slope may be estimated using simple physical modelling.The physical model used is that of Tickle [J. Hazard. Mater. 49 (1996) 29], who showed that a finite-volume instantaneous release on an inclined plane can form a stable wedge-shaped cloud that moves down the line of greatest slope. Nonzero windspeeds are accounted for by following Tickle’s suggestion of vectorially adding windspeed to the advection induced by the slope.A range of windspeeds and slopes are considered. The slopes substantially affect the risk in the sense that the predicted risk contours are far from circularly symmetric.