In countries with severe winters such like Canada, winter road maintenance (WRM) operations, such as plowing, salting and sanding, play an indispensible role in maintaining good road surface conditions and keeping roads safe. WRM is, however, also costly, both monetarily and environmentally. The substantial direct and indirect costs associated with WRM have stimulated significant interest in quantifying the safety and mobility benefits of winter road maintenance, such that systematic cost-benefit assessment can be performed. A number of studies have been initiated in the past decade to identify the links between winter road safety and factors related to weather, road, and maintenance operations. However, most of these studies have focused on the effects of adverse weather on road safety. Limited efforts have been devoted to the problem of quantifying the safety benefits of winter road maintenance under specific road weather conditions. Moreover, the joint effects of and complex interactions between road driving conditions, traffic and maintenance and their impact on traffic safety have rarely been studied. This research aims to determine the effect of WRM on road safety during snow storm events and develop models that can be used to quantify the safety benefit of alternative winter road maintenance policies, strategies and practices. Two integral aspects of collision risk were investigated, namely, collision frequency and severity. Collision frequency models were developed using winter storm collision data compiled for six winter seasons (2000 to 2006) for a total of 31 highway routes across Ontario. A comprehensive measure, namely, road surface condition index (RSI), was proposed to represent the road surface conditions during a variety of snow events. RSI was used as a surrogate measure to capture the effects of WRM. Other factors related to weather, traffic and road features were also accounted for in the analysis. Problems associated with data aggregation were also investigated. For this purpose, two different datasets were formed, namely, event-based data (EBD) which aggregates data by snow storm events and hourly based data (HBD) which includes hourly records of collision counts and other related factors. These two data sets of different aggregation levels were then used to investigate the effects of data aggregation and correlation (within – event) as well as to develop models for different purposes of benefit analyses. For EBD, Negative Binomial models and Generalized Negative Binomial models were calibrated whereas for HBD, Generalized Negative Binomial models and multilevel Poisson Lognormal models were calibrated. Generalized Negative Binomial models were found to best fit the data for both datasets. It was found that addition of site specific variables improves model fit. RSI and exposure were found significant for all the models and datasets. Weather factors such as visibility, wind speed, precipitation, and air temperature were also found to have statistically significant effects on collision frequency. All the models were consistent in terms of effects of different variables. The EBD models are useful to quantify the effect of different maintenance service standards and policies with limited information on the details of the weather events and traffic. On the other hand, HBD models have a higher level of reliability capable of providing more accurate estimates on road accidents. As a result, they are useful for determining the effects of different treatment operations. Several examples were employed to demonstrate the application of the developed models, such as quantifying the benefits of alternative maintenance operations and evaluating the effects of different service standards using safety as a performance measure. To enable a comprehensive risk analysis, collisions under both all-weather conditions and snow storm conditions over the six winter seasons were analyzed to identify the relationship between collision severity and various factors related to road weather and surface conditions, road characteristics, traffic, and vehicles etc., on collision severity. A multilevel modeling framework was introduced to capture the inherent hierarchy between collisions, vehicles and persons involved within the collision data. For each collision data set, three alternative severity models, namely, multinomial models, ordered logit models and binary logit models, were calibrated and compared. It was found that multilevel multinomial logit models were best fit to the data. Moreover issues related to different levels of aggregation were also discussed and results from occupant based data were found to be more reasonable and in line with general literature. Different individual, vehicle, environment and accident location factors were found to have a statistically significant effect on the injury severity levels. Contributing factors at the individual and vehicle levels include driver condition, driver sex, driver age, position in vehicle, use of safety device such as seat belt, vehicle type, vehicle age and vehicle condition. Roadway and environment factors include number of lanes, speed limit, road alignment, RSI/road surface condition, wind speed, and visibility. Other factors include light, and traffic volume. Two case studies were conducted to demonstrate the application of the developed models in conjunction with the accident frequency models for cost benefit analysis. This research was the first to investigate the direct link between road surface conditions and collisions at an operational level. It has been shown that the developed models are capable of evaluating alternative winter road maintenance policies and operations and assessing the safety benefit of a particular winter road maintenance strategy or decision. This research is also the first to conduct an in-depth analysis on the problem of winter road safety at a disaggregate level that captures detailed temporal variation (e.g., hourly and by storm event)) within small spatial aggregation units (road sections corresponding to actual patrol routes). The safety models developed from this research could be easily incorporated into a decision support tool for conducting what-if analysis of alternative winter road maintenance policies and methods. Moreover these models could provide a mechanism to estimate road safety level based on road surface as well as weather and traffic conditions and therefore could potentially be used for generating safety related information for travelers as part of a winter traffic management scheme. Directions for future work are also provided at the end of this document.

The increasing complexity of today’s vehicles gives drivers help with everything from adaptive cruisecontrol to warning lights for low fuel level. But the increasing functionality also increases the risk offailures in the system. To prevent system failures, different safety analytic methods can be used, e.g.,fault trees and/or FMEA-tables. These methods are generally performed manually, and due to thegrowing system size the time spent on safety analysis is growing with increased risk of human errors. If the safety analysis can be automated, lots of time can be saved. This thesis investigates the possibility to generate fault trees from safety requirements as wellas which additional information, if any, that is needed for the generation. Safety requirements are requirements on the systems functionality that has to be fulfilled for the safety of the system to be guaranteed. This means that the safety of the truck, the driver, and the surroundings, depend on thefulfillment of those requirements. The requirements describing the system are structured in a graphusing contract theory. Contract theory defines the dependencies between requirements and connectsthem in a contract structure. To be able to automatically generate the fault tree for a system, information about the systems failure propagation is needed. For this a Bayesian network is used. The network is built from the contract structure and stores the propagation information in all the nodes of the network. This will result in a failure propagation network, which the fault tree generation will be generated from. The failure propagation network is used to see which combinations of faults in the system can violate thesafety goal, i.e., causing one or several hazards. The result of this will be the base of the fault tree. The automatic generation was tested on two different Scania systems, the fuel level displayand the dual circuit steering. Validation was done by comparing the automatically generated trees withmanually generated trees for the two systems showing that the proposed method works as intended. The case studies show that the automated fault tree generation works if the failure propagationinformation exists and can save a lot of time and also minimize the errors made by manuallygenerating the fault trees. The generated fault trees can also be used to validate written requirementsto by analyzing the fault trees created from them.

In countries with severe winters such like Canada, winter road maintenance (WRM) operations, such as plowing, salting and sanding, play an indispensible role in maintaining good road surface conditions and keeping roads safe. WRM is, however, also costly, both monetarily and environmentally. The substantial direct and indirect costs associated with WRM have stimulated significant interest in quantifying the safety and mobility benefits of winter road maintenance, such that systematic cost-benefit assessment can be performed. A number of studies have been initiated in the past decade to identify the links between winter road safety and factors related to weather, road, and maintenance operations. However, most of these studies have focused on the effects of adverse weather on road safety. Limited efforts have been devoted to the problem of quantifying the safety benefits of winter road maintenance under specific road weather conditions. Moreover, the joint effects of and complex interactions between road driving conditions, traffic and maintenance and their impact on traffic safety have rarely been studied. This research aims to determine the effect of WRM on road safety during snow storm events and develop models that can be used to quantify the safety benefit of alternative winter road maintenance policies, strategies and practices. Two integral aspects of collision risk were investigated, namely, collision frequency and severity. Collision frequency models were developed using winter storm collision data compiled for six winter seasons (2000 to 2006) for a total of 31 highway routes across Ontario. A comprehensive measure, namely, road surface condition index (RSI), was proposed to represent the road surface conditions during a variety of snow events. RSI was used as a surrogate measure to capture the effects of WRM. Other factors related to weather, traffic and road features were also accounted for in the analysis. Problems associated with data aggregation were also investigated. For this purpose, two different datasets were formed, namely, event-based data (EBD) which aggregates data by snow storm events and hourly based data (HBD) which includes hourly records of collision counts and other related factors. These two data sets of different aggregation levels were then used to investigate the effects of data aggregation and correlation (within – event) as well as to develop models for different purposes of benefit analyses. For EBD, Negative Binomial models and Generalized Negative Binomial models were calibrated whereas for HBD, Generalized Negative Binomial models and multilevel Poisson Lognormal models were calibrated. Generalized Negative Binomial models were found to best fit the data for both datasets. It was found that addition of site specific variables improves model fit. RSI and exposure were found significant for all the models and datasets. Weather factors such as visibility, wind speed, precipitation, and air temperature were also found to have statistically significant effects on collision frequency. All the models were consistent in terms of effects of different variables. The EBD models are useful to quantify the effect of different maintenance service standards and policies with limited information on the details of the weather events and traffic. On the other hand, HBD models have a higher level of reliability capable of providing more accurate estimates on road accidents. As a result, they are useful for determining the effects of different treatment operations. Several examples were employed to demonstrate the application of the developed models, such as quantifying the benefits of alternative maintenance operations and evaluating the effects of different service standards using safety as a performance measure. To enable a comprehensive risk analysis, collisions under both all-weather conditions and snow storm conditions over the six winter seasons were analyzed to identify the relationship between collision severity and various factors related to road weather and surface conditions, road characteristics, traffic, and vehicles etc., on collision severity. A multilevel modeling framework was introduced to capture the inherent hierarchy between collisions, vehicles and persons involved within the collision data. For each collision data set, three alternative severity models, namely, multinomial models, ordered logit models and binary logit models, were calibrated and compared. It was found that multilevel multinomial logit models were best fit to the data. Moreover issues related to different levels of aggregation were also discussed and results from occupant based data were found to be more reasonable and in line with general literature. Different individual, vehicle, environment and accident location factors were found to have a statistically significant effect on the injury severity levels. Contributing factors at the individual and vehicle levels include driver condition, driver sex, driver age, position in vehicle, use of safety device such as seat belt, vehicle type, vehicle age and vehicle condition. Roadway and environment factors include number of lanes, speed limit, road alignment, RSI/road surface condition, wind speed, and visibility. Other factors include light, and traffic volume. Two case studies were conducted to demonstrate the application of the developed models in conjunction with the accident frequency models for cost benefit analysis. This research was the first to investigate the direct link between road surface conditions and collisions at an operational level. It has been shown that the developed models are capable of evaluating alternative winter road maintenance policies and operations and assessing the safety benefit of a particular winter road maintenance strategy or decision. This research is also the first to conduct an in-depth analysis on the problem of winter road safety at a disaggregate level that captures detailed temporal variation (e.g., hourly and by storm event)) within small spatial aggregation units (road sections corresponding to actual patrol routes). The safety models developed from this research could be easily incorporated into a decision support tool for conducting what-if analysis of alternative winter road maintenance policies and methods. Moreover these models could provide a mechanism to estimate road safety level based on road surface as well as weather and traffic conditions and therefore could potentially be used for generating safety related information for travelers as part of a winter traffic management scheme. Directions for future work are also provided at the end of this document.

Made available in DSpace on 2014-10-09T12:37:05Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:56:35Z (GMT). No. of bitstreams: 1 01916.pdf: 2131779 bytes, checksum: a61fe66919fac88782f178ab80945a27 (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

Made available in DSpace on 2014-10-09T12:32:57Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:06:10Z (GMT). No. of bitstreams: 1 17894.pdf: 2451441 bytes, checksum: a74787cbf4366c714393f0684a412955 (MD5) / Dissertacao (Mestrado Profissionalizante em Lasers em Odontologia) / IPEN/D-MPLO / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP; Faculdade de Odontologia, Universidade de São Paulo, São Paulo

Made available in DSpace on 2014-10-09T12:37:05Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:56:35Z (GMT). No. of bitstreams: 1 01916.pdf: 2131779 bytes, checksum: a61fe66919fac88782f178ab80945a27 (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

Made available in DSpace on 2014-10-09T12:32:57Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:06:10Z (GMT). No. of bitstreams: 1 17894.pdf: 2451441 bytes, checksum: a74787cbf4366c714393f0684a412955 (MD5) / Dissertacao (Mestrado Profissionalizante em Lasers em Odontologia) / IPEN/D-MPLO / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP; Faculdade de Odontologia, Universidade de São Paulo, São Paulo

Yes / System safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios. / This work was funded by the DEIS H2020 project (Grant Agreement 732242).

<p>In the next few decades, the nuclear industry worldwide is expected to launch a set of reactors with advanced reactor designs. Generation-IV (GEN-IV) reactors are to display superior safety by incorporating additional passive safety concepts as well as improving accident management and minimization of consequences. Canada is in the early stages of conceiving its GEN-IV reactor design – the Supercritical Water Reactor (SCWR). The proposed design is based on the existing CANDU configurations and is expected to offer significant advances in thermal efficiency, fuel cycle sustainability, and relative cost of energy. Of particular interest is the reactor's ability to use inherent or passive safety concepts which will translate to the reactor being walk-away safe in an accident.</p> <p>Steam generators in CANDU remove decay heat by thermosyphoning in a loss of Class-IV power accident. This natural circulation process was a passive feature in GEN-II and GEN-III CANDUs. The SCWR's direct thermodynamic cycle implies steam generators are no longer incorporated into the design. This thesis examines how the SCWR compensates for the removal of a passive safety system element and the difference to the overall safety of the reactor following accidents. These results will be compared to the traditional CANDU's response in accidents to demonstrate the added value of this new reactor in maintaining the goal of no widespread core damage. Comparisons were also made between the SCWR and similar GEN-IV reactors in terms of design and response to various initiating events.</p> <p>Probabilistic Risk Analysis is used in this thesis to assess the SCWR design options. Although the SCWR is in the pre-conceptual design phase, the results of such risk assessment studies could affect the design, operation, and licensing of this new reactor. Future studies can build on this work to conduct more detailed analyses to characterise the SCWR's safety and reliability.</p> / Master of Applied Science (MASc)

Les systèmes modernes sont caractérisés par l’intégration de plusieurs composants de technologies diverses interagissant dans le but d’offrir de plus en plus de fonctionnalités aux utilisateurs. La complexité croissante dans ces systèmes pluridisciplinaires dits mécatroniques nécessite la mise en place de nouveaux processus, outils et méthodes pour la conception, l’analyse et la validation de ces derniers en respectant les contraintes de coût et de délais imposés par la concurrence. Ces systèmes doivent également satisfaire des contraintes de fiabilité et surtout de sûreté de fonctionnement. Seule une intégration du processus d’analyse de sûreté de fonctionnement tout au long du processus de développement peut assurer la satisfaction de ces contraintes de manière optimale.Les travaux de cette thèse ont pour objectif de contribuer à l’intégration des analyses de sûreté de fonctionnement dans le processus d’ingénierie système basée sur SysML afin de rendre ces analyses plus rapides et plus efficaces. Pour ce faire, nous avons traité les axes suivants : la formalisation d’une méthodologie de conception basée sur SysML et qui sera le support des analyses de sûreté de fonctionnement ; l’extension du langage SysML afin de pouvoir intégrer des spécificités des systèmes mécatroniques ainsi que des aspects de sûreté de fonctionnement dans le modèle système; l’exploration automatique des modèles SysML afin d’en extraire les données nécessaires pour l’élaboration des artefacts de la SdF et la génération (semi)/automatique de ces derniers (FMEA et FTA). Nous avons également intégré la vérification formelle d’exigences de sûreté de fonctionnement.Cette méthodologie nommée SafeSysE a été appliquée sur des cas d’étude du domaine de l’aéronautique : EMA (Electro-Mechenical Actuator) et WBS (Wheel Brake System). / Modern systems are getting more complex due to the integration of several interacting components with different technologies in order to offer more functionality to the final user. The increasing complexity in these multi-disciplinary systems, called mechatronic systems, requires new appropriate processes, tools and methodologies for their design, analysis and validation whilst remaining competitive with regards to cost and time-to-market constraints.The main objective of this thesis is to contribute to the integration of safety analysis in a SysML-based systems engineering approach in order to make it more efficient and faster. To achieve this purpose, we tackled the following axes: formalizing a SysML-based design methodology that will be the support for safety analyses; providing an extension of SysML in order to enable the integration of specific needs for mechatronic systems modeling as well as safety concepts in the system model; allowing the automated exploration of the SysML models in order to extract necessary information to elaborate safety artefacts (such as FMEA and FTA) and the semi-automated generation of the latters. We have also integrated formal verification to verify if the system behaviors satisfy some safety requirements.The proposed methodology named SafeSysE was applied to case studies from the aeronautics domain: EMA (Electro Mechanical Actuator) and WBS (Wheel Brake System).