9 research outputs found

    Systematic Representation of Relationship Quality in Conflict and Dispute: for Construction Projects

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    The construction industry needs to move towards more relational procurement procedures to reduce extensive losses of value and avoid conflicts and disputes. Despite this, the actual conceptualization and assessment of relationships during conflict and dispute incidents seem to be neglected. Via a review of literature, relationship quality is suggested as a systematic framework for construction projects. General system theory is applied and a framework consistent of four layers respectively labelled as triggering, antecedent, moderation and outcome is suggested. Two different case studies are undertaken to represent the systematic framework; which verifies that changes in contracting circumstances and built environment culture can affect the identified layers.Through system reliability theories a fault tree is derived to represent a systematic framework of relationship quality. The combinations of components, causes, and events for two case studies are mapped out through fault tree. By analysing the fault tree the combination of events that lead to relationship deterioration may be identified. Consequently the progression of simple events into failure is formulized and probabilities allocated. Accordingly the importance and the contribution of these events to failure become accessible. The ability to have such indications about relationship quality may help increase performance as well as sustainable procurement. Paper Type: Research articl

    Methods of fault tree analysis and their limits

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    Modeling and analysis of process failures using probabilistic functional model

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    Failure analysis is an important tool for effective safety management in the chemical process industry. This thesis applies a probabilistic approach to study two failure analysis techniques. The first technique focuses on fault detection and diagnosis (FDD), while the second is on vulnerability analysis of plant components. In formulating the FDD strategy, a class of functional model called multilevel flow modeling (MFM) was used. Since this model is not commonly used for chemical processes, it was tested on a crude distillation unit and validated using a simulation flowsheet implemented in Aspen HYSYS (Version 8.4) to demonstrate its suitability. Within the proposed FDD framework, probabilistic information was added by transforming the MFM model into its equivalent fault tree model to provide the ability to predict the likelihood of component’s failure. This model was then converted into its equivalent Bayesian network model using HUGIN 8.1 software to facilitate computations. Evaluations of the system on a heat exchanger pilot plant highlight the capability of the model in detecting process faults and identifying the associated root causes. The proposed technique also incorporated options for multi – state functional outcomes, in addition to the typical binary states offered by typical MFM model. The second tool proposed was a new methodology called basic event ranking approach (BERA), which measures the relative vulnerabilities of plant components and can be used to assist plant maintenance and upgrade planning. The framework was applied to a case study involving toxic prevention barriers in a typical process plant. The method was compared to some common importance index methodologies, and the results obtained ascertained the suitability of BERA to be used as a tool to facilitate risk based decisions in planning maintenance schedules in a process plant

    The monotonicity of component importance measures in linear consecutive-k-out-of-n systems

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    The monotonicity behavior of component importance measures in linear consecutive-k-out-of-n systems is studied through the formulation of a number of importance measures for these systems when all components are assumed to be independently and equally reliable. For the system with unequally reliable components, specific formulations are also developed. The structure and reliability functions for linear consecutive-k-out-of-n systems are derived through the notion of minimal-path vector representation of system structure;Three types of component importance measures are considered in this study: first, importance measures based on critical vectors which include the Birnbaum and Barlow-Proschan measures; second, importance measures based on minimal-path vectors, which include the Deegan-Packel measure; and third, importance measures based on minimal-cut vectors, which include the Vesely-Fussel measure. It is shown that, for k ≤ n ≤ 2k, these measures are always monotone. However, for n \u3e 2k, only the Deegan-Packel and Vesely-Fussel measures are monotone, whereas the Birnbaum and Barlow-Proschan measures are shown to be nonmonotone for the entire domain of component reliability p, where n is in the range (2k + 1,3k + 1). For n \u3e 3k + 1, the Birnbaum measure is nonmonotone for certain p and k, and the Barlow-Proschan measure is nonmonotone when n is sufficiently large

    Determining the set of the most important components for system reliability

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    Mere značajnosti (Importance measures) predstavlјaju načine merenja, tj. brojčanog iskazivanja značajnosti pojedinih komponenata u sistemu sa aspekta ukupne pouzdanosti sistema. Merama značajnosti je moguće odrediti (izdvojiti) komponente najznačajnije za pouzdanost sistema. Od šezdesetih godina, kada je koncept mera značajnosti prvi put uveden, do danas postoji neprekidno interesovanje za ovu oblast, tako da se, pored primene tradicionalnih mera značajnosti, neprestano uvode i definišu nove mere radi njihove primene na specifične sisteme. Opšti nedostatak mera značajnosti, nezavisno od kategorije kojoj pripadaju, je taj što se one utvrđuju za svaku pojedinačnu komponentu, a tek nakon toga se može izdvojiti skup najznačajnijih komponenata zadate kardinalnosti...Importance measures are numerical representations of the importance of each system’s component considering total system reliability. Using importance measures, the most important components for system reliability can be determined. Since the sixties, when the concept of importance measures was first introduced, there is a constant interest in this area, so that, in addition to the traditional importance measures, new measures for specific systems observed are continually introduced and defined. The general weak point of importance measures, irrespective of the category they belong to, is that they are determined for each individual component, and only afterwards a certain number of most important components can be set aside..

    Protection des Infrastructures Essentielles par Advanced Modélisation, simulation et optimisation pour l’atténuation et résilience de défaillance en cascade

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    Continuously increasing complexity and interconnectedness of modern critical infrastructures, together with increasingly complex risk environments, pose unique challenges for their secure, reliable, and efficient operation. The focus of the present dissertation is on the modelling, simulation and optimization of critical infrastructures (CIs) (e.g., power transmission networks) with respect to their vulnerability and resilience to cascading failures. This study approaches the problem by firstly modelling CIs at a fundamental level, by focusing on network topology and physical flow patterns within the CIs. A hierarchical network modelling technique is introduced for the management of system complexity. Within these modelling frameworks, advanced optimization techniques (e.g., non-dominated sorting binary differential evolution (NSBDE) algorithm) are utilized to maximize both the robustness and resilience (recovery capacity) of CIs against cascading failures. Specifically, the first problem is taken from a holistic system design perspective, i.e. some system properties, such as its topology and link capacities, are redesigned in an optimal way in order to enhance system’s capacity of resisting to systemic failures. Both topological and physical cascading failure models are applied and their corresponding results are compared. With respect to the second problem, a novel framework is proposed for optimally selecting proper recovery actions in order to maximize the capacity of the CI network of recovery from a disruptive event. A heuristic, computationally cheap optimization algorithm is proposed for the solution of the problem, by integrating foundemental concepts from network flows and project scheduling. Examples of analysis are carried out by referring to several realistic CI systems.Sans cesse croissante complexité et l'interdépendance des infrastructures critiques modernes, avec des environs de risque plus en plus complexes, posent des défis uniques pour leur exploitation sûre, fiable et efficace. L'objectif de la présente thèse est sur la modélisation, la simulation et l'optimisation des infrastructures critiques (par exemple, les réseaux de transmission de puissance) à l'égard de leur vulnérabilité et la résilience aux défaillances en cascade. Cette étude aborde le problème en modélisant infrastructures critiques à un niveau fondamental, en se concentrant sur la topologie du réseau et des modèles de flux physiques dans les infrastructures critiques. Un cadre de modélisation hiérarchique est introduit pour la gestion de la complexité du système. Au sein de ces cadres de modélisation, les techniques d'optimisation avancées (par exemple, non-dominée de tri binaire évolution différentielle (NSBDE) algorithme) sont utilisés pour maximiser à la fois la robustesse et la résilience (capacité de récupération) des infrastructures critiques contre les défaillances en cascade. Plus précisément, le premier problème est pris à partir d'un point de vue de la conception du système holistique, c'est-à-dire certaines propriétés du système, tels que ses capacités de topologie et de liaison, sont redessiné de manière optimale afin d'améliorer la capacité de résister à des défaillances systémiques de système. Les deux modèles de défaillance en cascade topologiques et physiques sont appliquées et leurs résultats correspondants sont comparés. En ce qui concerne le deuxième problème, un nouveau cadre est proposé pour la sélection optimale des mesures appropriées de récupération afin de maximiser la capacité du réseau d’infrastructure critique de récupération à partir d'un événement perturbateur. Un algorithme d'optimisation de calcul pas cher heuristique est proposé pour la solution du problème, en intégrant des concepts fondamentaux de flux de réseau et le calendrier du projet. Exemples d'analyse sont effectués en se référant à plusieurs systèmes de CI réalistes

    Risk Assessment and Management of Petroleum Transportation Systems Operations

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    Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs

    Modelling safety critical systems with ageing components, with application to underground railway risk and hazards

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    In this thesis methodologies for modelling risk on ageing systems are developed. In the first stages of the thesis, two systems on an underground railway are used to demonstrate the modelling approach. In the latter stages of this thesis the modelling approach is expanded further, presenting a method for optimisation of a phased maintenance strategy, an inclusion of uncertainty in model outputs and an approach to model size reduction. Initially, a Petri net modelling approach is proposed to predict the derailment caused by component failures on a Switch and Crossing (S&C). A holistic methodology is adopted such that components of the system are divided into subsets of interconnected modules at a system level. Degradation within each module is idealized through a sequence of discrete states of wear until final failure occurs. Monte Carlo analysis is used to numerically evaluate the resulting Petri net. Through this methodology, different maintenance strategies, such as partial replacement, complete replacement, and opportunistic maintenance, are tested, to evaluate their influence on the final risk of derailment and predicted system state over time. This work includes a more in-depth modelling approach for S&C than that available in literature. This improves on the state of the art by removing assumptions of perfect maintenance and inspection. In addition, the approach includes modelling of dependencies between components, that are introduced through shared maintenance actions. Secondly, a Petri net modelling approach is applied to an automatic fire protection system to assess the probability of system failure, throughout the system life. Components are modelled with individual Petri nets, which are connected by a phased asset management strategy. The model is solved numerically via Monte Carlo simulation and component failure probabilities are combined using logic developed through Fault Tree analysis. For each time period, this application gives the probability of detection, deluge and alarm system failure, along with the number of maintenance actions, system tests and false system activations. The key contributions from this work include a detailed model for the interlocking fire protection systems and the application of a phased asset management strategy. This phased strategy allows the modelling of different maintenance approaches that are applied at different times depending on the system age. This approach demonstrates an increased functionality in comparison to modelling approaches currently available for fire protection systems, In addition, the modelling approach is extended further towards an optimal risk-based asset management decision making tool. The model for the fire protection systems is used as an application and is extended to give a measure of risk and whole-life cost. This extended model forms the basis of a two-stage optimisation approach within the framework of a phased asset management strategy. A Simulated Annealing algorithm is combined with a Genetic Algorithm to reduce system level risk and whole-life cost. A method for the incorporation of uncertainty in predicted model outputs is also presented. Novel aspects within this work include: the development of the optimisation approach for a phased asset management strategy and the developed algorithm for quantifying model output uncertainty given uncertain input parameters. The optimization of a phased system shows improvements on current model optimisation examples as it allows different strategies to be applied at different phases of the system lifecycle. It allows these phases to be determined in an automatic manner. The inclusion of uncertainty estimates on model outputs improves current Petri net modelling approaches, where uncertainty in input parameters is not included, as it allows decisions based on modelling outcomes to be more fully informed. Finally, a method is presented that can be applied to large system level Petri net models to produce equivalent model at a reduced computational cost. The method consists of generating a reduced Petri net which approximates the behaviour of its larger counterpart with a shorter simulation time. Parameters in this reduced structure are updated following a combined Approximate Bayesian Computation and Subset Simulation framework. Novel contributions from this work include: the proposed reduction approach, a method for using this reduction approach to improve model optimisation efficiency and the exploration of the reduction approach to justify model structure selection. These improve on approaches for model reduction available in literature, which are commonly rule based and so less flexible. In addition, model choice is typically user defined without quantifiable evidence for the suitability of the selected model structure

    Modelling safety critical systems with ageing components, with application to underground railway risk and hazards

    Get PDF
    In this thesis methodologies for modelling risk on ageing systems are developed. In the first stages of the thesis, two systems on an underground railway are used to demonstrate the modelling approach. In the latter stages of this thesis the modelling approach is expanded further, presenting a method for optimisation of a phased maintenance strategy, an inclusion of uncertainty in model outputs and an approach to model size reduction. Initially, a Petri net modelling approach is proposed to predict the derailment caused by component failures on a Switch and Crossing (S&C). A holistic methodology is adopted such that components of the system are divided into subsets of interconnected modules at a system level. Degradation within each module is idealized through a sequence of discrete states of wear until final failure occurs. Monte Carlo analysis is used to numerically evaluate the resulting Petri net. Through this methodology, different maintenance strategies, such as partial replacement, complete replacement, and opportunistic maintenance, are tested, to evaluate their influence on the final risk of derailment and predicted system state over time. This work includes a more in-depth modelling approach for S&C than that available in literature. This improves on the state of the art by removing assumptions of perfect maintenance and inspection. In addition, the approach includes modelling of dependencies between components, that are introduced through shared maintenance actions. Secondly, a Petri net modelling approach is applied to an automatic fire protection system to assess the probability of system failure, throughout the system life. Components are modelled with individual Petri nets, which are connected by a phased asset management strategy. The model is solved numerically via Monte Carlo simulation and component failure probabilities are combined using logic developed through Fault Tree analysis. For each time period, this application gives the probability of detection, deluge and alarm system failure, along with the number of maintenance actions, system tests and false system activations. The key contributions from this work include a detailed model for the interlocking fire protection systems and the application of a phased asset management strategy. This phased strategy allows the modelling of different maintenance approaches that are applied at different times depending on the system age. This approach demonstrates an increased functionality in comparison to modelling approaches currently available for fire protection systems, In addition, the modelling approach is extended further towards an optimal risk-based asset management decision making tool. The model for the fire protection systems is used as an application and is extended to give a measure of risk and whole-life cost. This extended model forms the basis of a two-stage optimisation approach within the framework of a phased asset management strategy. A Simulated Annealing algorithm is combined with a Genetic Algorithm to reduce system level risk and whole-life cost. A method for the incorporation of uncertainty in predicted model outputs is also presented. Novel aspects within this work include: the development of the optimisation approach for a phased asset management strategy and the developed algorithm for quantifying model output uncertainty given uncertain input parameters. The optimization of a phased system shows improvements on current model optimisation examples as it allows different strategies to be applied at different phases of the system lifecycle. It allows these phases to be determined in an automatic manner. The inclusion of uncertainty estimates on model outputs improves current Petri net modelling approaches, where uncertainty in input parameters is not included, as it allows decisions based on modelling outcomes to be more fully informed. Finally, a method is presented that can be applied to large system level Petri net models to produce equivalent model at a reduced computational cost. The method consists of generating a reduced Petri net which approximates the behaviour of its larger counterpart with a shorter simulation time. Parameters in this reduced structure are updated following a combined Approximate Bayesian Computation and Subset Simulation framework. Novel contributions from this work include: the proposed reduction approach, a method for using this reduction approach to improve model optimisation efficiency and the exploration of the reduction approach to justify model structure selection. These improve on approaches for model reduction available in literature, which are commonly rule based and so less flexible. In addition, model choice is typically user defined without quantifiable evidence for the suitability of the selected model structure
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