Identification and classification of dynamic event tree scenarios via possibilistic clustering: Application to a steam generator tube rupture event

This paper illustrates a method to identify and classify scenarios generated in a dynamic event tree (DET) analysis. Identification and classification are carried out by means of an evolutionary possibilistic fuzzy C-means clustering algorithm which takes into account not only the final system states but also the timing of the events and the process evolution. An application is considered with regards to the scenarios generated following a steam generator tube rupture in a nuclear power plant. The scenarios are generated by the accident dynamic simulator (ADS), coupled to a RELAP code that simulates the thermo-hydraulic behavior of the plant and to an operators’ crew model, which simulates their cognitive and procedures-guided responses.A set of 60 scenarios has been generated by the ADS DET tool. The classification approach has grouped the 60 scenarios into 4 classes of dominant scenarios, one of which was not anticipated a priori but was “discovered” by the classifier. The proposed approach may be considered as a first effort towards the application of identification and classification approaches to scenarios post-processing for real-scale dynamic safety assessments.     

Representing context, cognition, and crew performance in a shutdown risk assessment

This paper presents a potentially practical treatment of dynamic operator-system interactions. The approach employs a dynamic event tree framework to explicitly address plant dynamics, system indications, crew-plant interactions, time available, crew cognition, errors of commission (mistakes), and multiple planning and diagnosis possibilities. The approach is applied in an analysis of a hypothesize medium break loss of coolant accident for a test reactor that occurs during plant shutdown. This transient was selected on the basis of: a significant cognitive component being present, high consequences being associated with operator actions, and the importance of event timing to scenario progression. The results presented show how quantitative risk predictions are affected by the treatment of dynamics, and demonstrate how non-proceduralized recovery actions and a number of performance shaping factors (e.g., crew experience, stress, and confidence) can be explicitly treated. Insights and lessons learned regarding the performance of a dynamic assessment are also presented.     

飞行机组人为差错风险分析方法研究

提出了一个新的飞行机组人为差错风险评估方法F HECA,识别和分析航空公司的机组人为差错风险。该方法在对机组人为差错类型进行总结和分类的基础上,选取人为差错严重度作为评价指标,将该指标中的3个变量人为差错概率、人为差错后果严重度、人为差错影响概率与灰色综合评价法结合,定量评估机组人为差错的严重度,实现机组人为差错风险评估。通过实例分析验证此方法可用于分析机组人为差错风险,为飞行训练和飞行操作手册内容的改进提供技术支持,是民航人为因素研究中可借鉴的一种方法。     

A stochastic programming approach to manufacturing flow control

This paper proposes and tests an approximation of the solution of a class of piecewise deterministic control problems, typically used in the modeling of manufacturing flow processes. This approximation uses a stochastic programming approach on a suitably discretized and sampled system. The method proceeds through two stages: (i) the Hamilton-Jacobi-Bellman (HJB) dynamic programming equations for the finite horizon continuous time stochastic control problem are discretized over a set of sampled times; this defines an associated discrete time stochastic control problem which, due to the finiteness of the sample path set for the Markov disturbance process, can be written as a stochastic programming problem; and (ii) the very large event tree representing the sample path set is replaced with a reduced tree obtained by randomly sampling over the set of all possible paths. It is shown that the solution of the stochastic program defined on the randomly sampled tree converges toward the solution of the discrete time control problem when the sample size increases to infinity. The discrete time control problem solution converges to the solution of the flow control problem when the discretization mesh tends to zero. A comparison with a direct numerical solution of the dynamic programming equations is made for a single part manufacturing flow control model in order to illustrate the convergence properties. Applications to larger models affected by the curse of dimensionality in a standard dynamic programming techniques show the possible advantages of the method.     

A comparison of stochastic programming and bi-objective optimisation approaches to robust airline crew scheduling

A prominent problem in airline crew scheduling is the pairings or Tour-of-Duty planning problem. The objective is to determine a set of pairings (or Tours-of-Duty) for a crew group to minimise the planned cost of operating a schedule of flights. However, due to unforeseen events the performance in operation can differ considerably from planning, sometimes causing significant additional recovery costs. In recent years there has been a growing interest in robust crew scheduling. Here, the aim is to find solutions that are “cheap” in terms of planned cost as well as being robust, meaning that they are less likely to be disrupted in case of delays. Taking the stochastic nature of delays into account, Yen and Birge (Transp Sci 40:3–14, 2006) formulate the problem as a two-stage stochastic integer programme and develop an algorithm to solve this problem. Based on the contradictory nature of the goals, Ehrgott and Ryan (J Multi-Criteria Decis Anal 11:139–150, 2002) formulate a bi-objective set partitioning model and employ elastic constraint scalarisation to enable the solution by set partitioning algorithms commercially used in crew scheduling software. In this study, we compare the two solution approaches. We improve the algorithm of Yen and Birge (Transp Sci 40:3–14, 2006) and implement both methods with a commercial crew scheduling software. The results of both methods are compared with respect to characteristics of robust solutions, such as the number of aircraft changes for crew. We also conduct experiments to simulate the performance of the obtained solutions. All experiments are performed using actual schedule data from Air New Zealand.     

Non-linear stochastic response of marine vehicles

The dynamic behaviour of marine vehicles in extreme sea states is a matter of great concern following some recent and dramatic mishaps. The complex problem of its prediction can be approached from the study, yet of broader scope, of non-linear dynamic systems subjected to stochastic excitations. However, a general non-linear stochastic dynamic theory is not yet available. A new technique, the so-called linearize-and-match method, for predicting the response statistics of non-linear systems, is presented. Essentially, the technique involves the construction of an infinite series of linear systems aimed at the prediction of the response statistical moments of a given order. The linear systems are successively defined by linearizing the original, non-linear system and matching the Volterra functional model response statistics to the desired order. The linear system for predicting second order statistics is shown to coincide with the one obtained using the method of equivalent linearization. Response probability distributions can be constructed from the knowledge of such statistics. Particular attention is devoted to the distribution of maximum entropy and its justification in such underdetermined moment problems. Finally, applications to the roll motion of ships serve to exemplify as well as to assess the accuracy and the versatility of the overall method. Response distributions of maxima so predicted compare very well with digital simulation estimates.     

A method for analysing the reliability of a transmission grid

The paper describes a probabilistic method for transmission grid security evaluation. Power system security is the ability of the power system to withstand sudden disturbances such as short circuits. The method presented here uses event and fault trees and combines them with power system dynamic simulations. Event trees model the substation protection and trip operations after line faults. Different event tree end states (fault duration, circuit breaker trips) are simulated with power system dynamic analysis program. The dynamic analysis results (power system post-fault states) are then classified into secure, alert, emergency and system breakdown. The probabilities, minimal cut sets and grid level importance measures (Fussell-Vesely, risk increase and decrease factors) are calculated for the total and partial system breakdown. In this way, the relative importance of the substation devices regarding to the system breakdown can be reached. Also the more and less likely contributing factors to system breakdown are received. With this method, an existing 400 kV transmission grid with its line fault and device failure statistics is analysed.     

The British Airways human factors reporting programme

This paper describes three safety programmes that provide BA flight operations with feedback on operational quality. Two of these are flight data recording and air safety reporting. These earlier programmes are described primarily to set in context the development of the most recent of the three, the human factors reporting programme (HFR). This is a confidential programme that records and analyses data using a distinctly different process from most reporting programmes. It requests not only information about ‘What’ happened in an event but ‘Why’ the event occurred and ‘How’ the crew dealt with the problem. Analysis uses ‘factors’ describing crew actions and the influences on crew actions. Actions and influences can describe safety positive as well as negative events and the analysis produces causal event sequence diagrams of each reported event. The paper describes these processes and offers some illustrative examples taken from the database.     

Robust design with dynamic characteristics using stochastic sequential quadratic programming

Robust design with dynamic characteristics is an important off-line quality engineering technique for improving product quality over a range of input conditions by reducing variations caused by uncontrolled factors. Since several studies have indicated that there are important limitations to Taguchi's S/N ratio analysis, the solution procedure for dynamic systems deserves further investigation. This paper proposes a stochastic optimization modeling procedure to overcome the difficulty in Taguchi's method to accommodate dynamic characteristics. The main idea underlying the proposed method is to minimize the total variations on quality characteristics while attaining the target performance over a range of input conditions. Due to the nonlinear nature of the stochastic optimization model, two stochastic versions of sequential quadratic programming respectively embedded with a Monte Carlo simulation and numerical approximations are devised to solve the problem. In the robust design of a temperature control circuit often discussed in dynamic problems, the proposed method performs efficiently and effectively. Compared with the Taguchi method, the design solved in this paper has smaller variations, indicating that the proposed method is a promising technique for dynamic-characteristic robust design.     

A systematic approach to analysing errors of commission from diagnosis failure in accident progression

The accident scenarios of a nuclear power plant are composed of an initiating event (IE), additional events/failures and human inappropriate actions, the combinations of which lead to irreversible consequences. In such a dynamic situation, operators should diagnose the occurring events/failures (including an initiating event and additional events) and assess the related situations utilising the available resources such as operating procedures or human–machine systems to control and maintain the plant in a stable condition. The misdiagnosis or diagnosis failure of the occurring events could cause critical human inappropriate actions that aggravate the plant condition, which is termed as errors of commission (EOCs). This paper presents a methodology for analysing the potential for diagnosis failure of the initiating and additional events and the consequent EOC events, based on the operating procedures, in the accident scenarios of nuclear power plants. The method to be presented categorizes the diagnostic situations in the accident scenarios into three cases according to the structure of the emergency operating procedures (EOPs) and the time of the occurring events: (1) the diagnosis of an initiating event, (2) the diagnosis of both an initiating event and an additional event when an additional event occurs prior to the performance of the diagnosis procedure, and (3) the diagnosis of an additional event when an additional events occurs after the performance of the diagnosis procedure. The application of the method is illustrated through three case example scenarios: (1) the power-operated relief valve (PORV) or the pressurizer safety valve (PSV) LOCA, (2) the loss of all feedwater (LOAF) event (loss of main feedwater*loss of auxiliary feedwater), (3) the sequence of<the station blackout (SBO)*loss of turbine-driven (or diesel-driven) auxiliary feedwater*PSV stuck-open*recovery of AC power>.     

Some implications of an event-based definition of exposure to the risk of road accident

This paper proposes a new definition of exposure to the risk of road accident as any event, limited in space and time, representing a potential for an accident to occur by bringing road users close to each other in time or space of by requiring a road user to take action to avoid leaving the roadway. A typology of events representing a potential for an accident is proposed. Each event can be interpreted as a trial as defined in probability theory. Risk is the proportion of events that result in an accident. Defining exposure as events demanding the attention of road users implies that road users will learn from repeated exposure to these events, which in turn implies that there will normally be a negative relationship between exposure and risk. Four hypotheses regarding the relationship between exposure and risk are proposed. Preliminary tests support these hypotheses. Advantages and disadvantages of defining exposure as specific events are discussed. It is argued that developments in vehicle technology are likely to make events both observable and countable, thus ensuring that exposure is an operational concept.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the IDAC Model

This is the first in a series of five papers that discuss the information, decision, and action in crew context (IDAC) model for human reliability analysis (HRA). An example application of this modeling technique is also discussed in this series. The model is developed to probabilistically predict the responses of the nuclear power plant control room-operating crew during an accident for use in probabilistic risk assessments. The operator response spectrum includes cognitive, emotional, and physical activities during the course of the accident. This paper provides an overview of the IDAC architecture and principles of implementation as a HRA model. IDAC includes a crew model of three types of operators: decision maker, action taker, and consultant. Within the crew context, each individual operator's behaviors are simulated through a cognitive model under the influence of a number of explicitly modeled performance-influencing factors.     

钢筋砼结构非线性随机地震响应分析

考虑地震作用和结构参数的随机性,建立了钢筋砼结构药非线性随机动力学模型。文中导出了随机结构动力分析的非线性随机有限元法的增量列式,并据此对多层钢筋砼结构进行了弹塑性随机地震响应分析。计算结果与该建筑物的实际震害作了对比,效果良好。还讨论了动力模型中随机变量对响应量的影响。     

Self‐regularized pseudo time‐marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non‐linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least‐square solution is through a regularized Gauss–Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo‐dynamical GNM (PD‐GNM) update equation addresses the major numerical difficulty associated with the near‐zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo‐dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo‐dynamic ensemble Kalman filter (PD‐EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of the PD‐EnKF by proposing an inner iteration within every time step. Results using the pseudo‐dynamic strategy obtained through PD‐EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD‐EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright © 2009 John Wiley & Sons, Ltd.     

Measuring the effect of police surveillance on the prevention of traffic accidents

An approach for estimating the effect of preventive measures taken against traffic accidents is presented. A simple stochastic model as to the process of accident occurrence is provided and the relationship among vulnerable behavior, collisions, and police surveillance is formalized. Here vulnerable behavior is defined as the state prior to the collision in the process of accident occurrence, and police surveillance is selected as one of the preventive measures against the accident.

The procedures by which the model can be applied in practice are shown for the case of intersection accidents, and the effects of some intersection surveillance forms on accident prevention are calculated. The results suggest that police surveillance which affects vulnerable behavior reduces accidents and that each collision category differently has the most effective surveillance form.

It is concluded that vulnerable behavior, which increases the likelihood of accident occurrence, provides a more sensitive safety measure than accidents.     

Footprint of knowledge acquisition improvement in failure diagnosis analysis

Fault tree analysis (FTA) as an effective and efficient risk assessment tool are widely used to analyze the reliability of a complex system. In this context, FTA can properly improve the safety performance of the system by preventing an event which may lead to occurrence of a catastrophic accident. However, traditional FTA is still suffering from dynamic structure demonstration and importantly epistemic uncertainty processing. In this study, a novel methodology is introduced using Bayesian updating mechanism to deal with dynamic structure and 2‐tuple fuzzy set named as intuitionistic fuzzy numbers are employed to cope with subjectivity of uncertainty processing. Accordingly, the most critical system components which affect the system reliability are recognized by using an appropriate sensitivity analysis method. The proposed methodology is then applied on a real case study application (a brake fluid filling system) in order to examine the effectiveness and feasibility of the approach. The results illustrated that the new methodology can have enough benefits for diagnosing the systems' faults compared with listing approaches of safety and reliability analysis. In terms of empirical case study, “electromotor failure” was evaluated as the second most critical basic event in conventional‐based approaches, whereas in the novel methodology “high pressure liquefied material” was recognized as the second one.     

Stochastic analysis of interphase effects on elastic modulus and yield strength of nylon 6/clay nanocomposites

The multifunctional properties of polymer clay nanocomposites (PCNs) can be related to the interaction of clays, polymer and interphase region. Several experimental, analytical and numerical studies have been conducted to characterize the mechanical behavior of PCNs. The elastic behavior of PCNs is well documented in the literature but their other material properties like yield strength are rather vague. On the other hand, the variation of material parameters and the stochastic nature of interphase region hinder the use of deterministic methods. In this study, a stochastic analysis along with a hierarchical multiscale method is used to analyze the effect of interphase properties on the macroscopic properties of PCNs. Since the interphase layer is expected to be weaker than the polymer matrix, a weakening coefficient is defined to describe the interphase properties based on the matrix properties. This weakening coefficient and the interphase thickness are considered as the stochastic inputs. The elastic modulus and yield strength of nylon 6/clay nanocomposites are calculated using the stochastic multiscale framework. The uncertainty propagation and sobol sensitivity analysis are performed to study the effect of random inputs on the elastic modulus and yield strength of PCNs. Despite the wide range of input variations, the accuracy of the proposed stochastic multiscale framework for the prediction of the PCNs properties is estimated by validating our results against the available experimental data in the literature.

     

Stochastic finite element analysis of a cable-stayed bridge system with varying material properties

Stochastic seismic finite element analysis of a cable-stayed bridge whose material properties are described by random fields is presented in this paper. The stochastic perturbation technique and Monte Carlo simulation (MCS) method are used in the analyses. A summary of MCS and perturbation based stochastic finite element dynamic analysis formulation of structural system is given. The Jindo Bridge, constructed in South Korea, is chosen as a numerical example. The Kocaeli earthquake in 1999 is considered as a ground motion. During the stochastic analysis, displacements and internal forces of the considered bridge are obtained from perturbation based stochastic finite element method (SFEM) and MCS method by changing elastic modulus and mass density as random variable. The efficiency and accuracy of the proposed SFEM algorithm are evaluated by comparison with results of MCS method. The results imply that perturbation based SFEM method gives close results to MCS method and it can be used instead of MCS method, especially, if computational cost is taken into consideration.     

A stochastic approach to consequence tree analysis

In this paper a stochastic approach to consequence tree analysis is considered. A consequence tree is a set of events logically combined by OR and AND connections that occur in sequence, some being prerequisites for others. Consequence trees are applicable to failure propagation in plants. Facilitating paths and inhibiting cuts are defined and considered. The distribution of the time the system needs to reach a certain top event is obtained. Probability weights are defined that can be used to obtain the weakest link in the consequence tree.     

Modeling secondary accidents identified by traffic shock waves

The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident.