A risk-informed approach to setting economically-justifiable maintenance strategies for railway tracks

The global railway infrastructure is carrying ever increasing amounts of railway and freight traffic which in turn is causing accelerated rates of infrastructure deterioration. Given the pressure to increase track utilization, the ageing infrastructure on which much of the railway transport systems are founded, and the constrained budgets under which the infrastructure is managed, appropriate maintenance needs to be predicted, prioritized, planned and carried out efficiently and economically. This doctoral research aims to develop a means of appraising railway track maintenance strategies economically while taking into account the associated risks and uncertainties. To this end, this research proposes, a Whole Life Cycle Cost Analysis (WLCCA) under uncertainty, while considering the direct and indirect costs of track maintenance, and the benefits to train operation, users, safety and the environment. The developed risk-informed approach is demonstrated via case studies on three different route types within the UK mainline railway network

Robust design of bridges : robustness analysis of Sjölundaviadukt bridge

Robustness of structural systems is as yet not explicitly defined nor is there a clearly defined method for incorporating robustness in design/construction. Robustness can be simply defined as the ability of a structural system to survive unforeseen/extraordinary exposures or circumstances that would otherwise cause it to fail. The structure must have enough residual capacity during and after the event to maintain at least some of its intended function intact. The level of robustness of a structure has to be analyzed in terms of the causes and consequences of failure; i.e. the consequences of structural damages should not be disproportional to the original cause (see 2.1 (3) of EN 1990:2002). This master thesis deals with the robustness of bridge structures. It examines common circumstances of failure and investigates methods and strategies towards incorporating structural robustness into the design of bridges. A robustness analysis is conducted for the Sjölundaviadukten Bridge; a 5-span post-tensioned frame bridge in Malmö

A risk-informed decision support tool for the strategic asset management of railway track infrastructure

The provision of safe, efficient, reliable and affordable railway transport requires the railway track infrastructure to be maintained to an appropriate condition. Given the constrained budgets under which the infrastructure is managed, maintenance needs to be predicted in advance of track failure, prioritized and identified risks and uncertainties need to be considered within the decision-making process. This paper describes a risk-informed approach that can be used to economically justify railway track infrastructure conditions by comparing on a life-cycle basis infrastructure maintenance costs, train operating costs, travel time costs, safety, social and environmental impacts. The approach represents a step-change for the railway industry as it will enable economic maintenance standards to be derived which considers the needs of the infrastructure operator, but also those of users, train operating companies and the environment. Further, the risk-informed capability of the tool enables asset managers to deal with uncertainties associated with forecasting costs and the effects of track maintenance, and unavailability of data. The Monte Carlo simulation technique and a Fuzzy reasoning approach are used to address safety data uncertainties through probabilistic risk assessment allied to expert opinion. The approach is illustrated using data from three routes on the UK mainline railway network. The results demonstrate that the approach can be used to support strategic and tactical levels of railway asset management to inform plausible design and maintenance strategies that realise the maximum benefit for the available budget. </jats:p

Uncertainty analysis of large risk assessment models with applications to the railway safety and standards board safety risk model

Probabilistic risk analysis aims to assess the safety risk of a system so that actions can then be taken to improve safety. Uncertainty however always exists in modelling. For more informed decision making, uncertainty in the outputs of the model must be assessed through uncertainty analysis. This research focuses on parameter uncertainty of a risk model composed of fault trees and event trees. Research questions include: (1) how to model the subjective uncertainty in the basic events and the consequences; (2) how to propagate the uncertainty in the input parameters through fault trees and event trees to obtain uncertainty in the output. Structured approaches are developed to elicit the covariance matrix of the basic events and to model dependence among the consequences. To calculate the uncertainty propagation, a model is developed to mimic fault trees and event trees; an analytical solution and a simulation-based method are developed for assessing the uncertainty propagation, which are implemented independently and therefore crosscheck each other. The developments can be used for subjective uncertainty assessment of Fault-tree and Event-tree models. With the developed methods, a reasonable elicitation workload is required to model the subjective uncertainty in the input parameters; the assessments can be monitored during the elicitation process. The methods for assessing the uncertainty in the output can work efficiently for large fault trees and event trees. Two case studies have been conducted with the Safety Risk Model (SRM) developed by Rail Safety and Standard Board (RSSB), UK. In the two case studies, the developed methods are deployed and experts were confident in making the required assessments. The feasibility of the developments is validated by the case studies

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

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

Reliability assessment of freight wagon passing through railway turnouts using adaptive Kriging surrogate model

Railway turnout (RT) is a crucial component of railway infrastructure that consists of several components. Assessing the derailment probability of freight wagons passing through the turnout is crucial for quantifying failure risks and optimizing the performance of the freight wagon-turnout system (FWTS). However, existing assessment methods often require extensive model evaluations and impose substantial computational costs. To address this issue, an efficient reliability analysis method is established for assessing the derailment risk at RTs. Firstly, a dynamic model is developed to capture the wheel-rail dynamic interaction and the numerical model is validated by field tests. Secondly, to reduce the computational cost in the reliability analysis, an efficient adaptive Kriging method based on an error stopping criteria and a learning function is adopted to estimate the failure probabilities under multiple failure modes of wheel derailments. Based on the efficient learning function and convergence criterion, accurate failure probability results can be obtained with a small number of multibody and finite element coupled dynamic simulations. Furthermore, the prediction accuracy of the proposed method in capturing random characteristics for FWTS is evaluated. Finally, the influence of the evolution of rail wear on the failure probability is further discussed

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

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