Due to the scarcity of Human Reliability Analysis (HRA) data, one of the key elements of any HRA analysis is use of engineering judgment. The Electric Power Research Institute (EPRI) HRA Calculator guides the user through the steps of any HRA analysis and allows the user to choose among analytical HRA methods. It applies Accident Sequence Evaluation Program (ASEP), Technique for Human Error Rate Prediction (THERP), the HCR/ORE Correlation, and the Caused Based Decision Tree Method (CBDTM). This program is intended to produce consistent results among different analysts provided that the initial information is similar. Even with this analytical approach, an HRA analyst must still render several judgments. The objective of this study was to evaluate the use of engineering judgment applied to the quantification of post-initiator actions using the HRA Calculator. The Comanche Peak Steam Electric Station (CPSES) Level 1 Probabilistic Risk Assessment (PRA) HRA was used as a database for examples and numerical comparison. Engineering judgments were evaluated in the following ways: 1) Survey of HRA experts. Two surveys were completed, and the participants provided a range of different perspectives on how they individually apply engineering judgment. 2) Numerical comparison among the three methods. 3) Review of CPSES HRA and identification of judgments and the effects on the overall results of the database. The results of this study identified thirteen areas in which an HRA analyst must interpret and render judgments on how to quantify a Human Error Probability (HEP) and recommendations are provided on how current industry practitioners render these same judgments. The areas are: identification and definition of actions to be modeled, identification and definition of actions to be modeled, definition of critical actions, definition of cognitive portion of the action, choice of methodology, stress level, rule-, skill- or knowledge-based designation, timing information, training, procedures, human interactions with hardware, recoveries and dependencies within an action, and review of final HEP.

Standard techniques used to analyze a system's response with uncertain system parameters or inputs, are generally Importance sampling methods. Sampling methods require a large number of simulation runs before the system output statistics can be analyzed. As model fidelity increases, sampling techniques become computationally infeasible, and Reliability methods have gained popularity as an analysis method that requires significantly fewer simulation runs. Reliability analysis is an analytic technique which finds a particular point in the design space that can accurately be related to the probability of system failure. However, application to dynamic systems have remained limited. In the following thesis a First Order Reliability Method (FORM) is used to determine the failure probability of a dynamic system due to system/input uncertainties. A pendulum cart system is used as a case study to demonstrate the FORM on a dynamic system. Three failure modes are discussed which correspond to the maximum pendulum angle, the maximum system velocity, and a combined requirement that neither the maximum pendulum angle or system velocity are exceeded. An explicit formulation is generated from the implicit formulation using a Response Surface Methodology, and the FORM is performed using the explicit estimate. Although the analysis converges with minimal simulation computations, attempts to verify FORM results illuminate current limitations of the methodology. The results of this initial study conclude that, currently, sampling techniques are necessary to verify the FORM results, which restricts the potential applications of the FORM methodology. Suggested future work focuses on result verification without the use of Importance sampling which would allow Reliability methods to have widespread applicability. / text

Excavation activities like open cutting and tunneling work may cause ground movements. Many of these activities are performed in urban areas where many structures and facilities already exist. These activities are close enough to affect adjacent structures. It is therefore important to understand how the ground movements due to excavations influence nearby structures. The goal of the proposed research is to investigate and develop analytical methods for addressing uncertainty during observation-based, adaptive design of deep excavation and tunneling projects. Computational procedures based on a Bayesian probabilistic framework are developed for comparative analysis between observed and predicted soil and structure response during construction phases. This analysis couples the adaptive design capabilities of the observational method with updated reliability indices, to be used in risk-based design decisions. A probabilistic framework is developed to predict three-dimensional deformation profiles due to supported excavations using a semi-empirical approach. The key advantage of this approach for practicing engineers is that an already common semi-empirical chart can be used together with a few additional simple calculations to better evaluate three-dimensional displacement profiles. A reliability analysis framework is also developed to assess the fragility of excavation-induced infrastructure system damage for multiple serviceability limit states. Finally, a reliability analysis of a shallow circular tunnel driven by a pressurized shield in a frictional and cohesive soil is developed to consider the inherent uncertainty in the input parameters and the proposed model. The ultimate limit state for the face stability is considered in the analysis. The probability of failure that exceeding a specified applied pressure at the tunnel face is estimated. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.

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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).

A simulation-based reliability analysis method is presented and evaluated. This method is intended for problems for which most probable point of failure (MPP) search-based methods fail or provide inaccurate results, and for which Monte Carlo simulation and its variants are too costly to apply. This may occur in the evaluation of complex engineering problems of low failure probability. The method used to address this problem is a variant of conditional expectation and works by sampling on the failure boundary without relying on the MPP. The effectiveness of the method is compared to a selection of other commonly available reliability methods considering a variety of analytical as well as more complex engineering problems. The results indicate that the method has the potential to deliver solutions of high efficiency and accuracy for a wide range of difficult reliability problems.

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Although Autonomous Vehicles (AVs) are quickly becoming a reality, there is much that needs to be understood before mainstream commercialization can occur. One critical issue is the interplay between multiple fields of engineering. Whereas the first part of this work is a granular treatment of a specific issue, the second part simultaneously examines numerous fields within the transportation industry. In the surge to understand and develop AVs, researchers tend to study specific subdivisions within the "vehicle engineering umbrella". In particular, mechanical and civil engineers study vehicle dynamics in two different levels of specificity. Mechanical engineers typically investigate small-scale dynamic behavior which applies to a single vehicle, such as vehicle-terrain interactions or the behavior of mechanical components. On the other hand, civil engineers tend to study kinematic behavior: the behavior of platoons as it pertains to large-scale traffic flow. Regardless of the scale of study, each subdivision has a set of performance metrics. Due to the differences among subdivisions, some performance metrics may (unintentionally) compete. Compromises must be made in the design stage to produce a vehicle which caters to an appropriate audience. The first part of this work features two major contributions to bridge the gap between the dynamic and kinematic perspectives. One is the application of Design Envelopes that establishes a framework to balance constraints and assess design tradeoffs arising from each viewpoints. Three Design Envelopes are introduced to reach compromises on a vehicle's velocity, acceleration, and jerk. Another contribution is a methodology to tune the parameters of a car-following model analytically. Current tuning practices require empirically collected traffic count data, which is cumbersome to obtain. Analytically parameterizing car-following models facilitates more robust planning and encompasses both the dynamic and kinematic perspectives. The second contribution utilizes these Design Envelopes to improve a currently-existing speed profile generator. Integrating the Design Envelopes reformulates the existing algorithm as a constrained LQR problem, which enhances ride comfort and maintains dynamic stability for not just one vehicle, but a platoon. Simulations demonstrate that the refined algorithm can reduce the travel time on a specific route by 3-4.4%. More importantly, the simulations demonstrate it is possible to synthesize multiple engineering fields to enhance AV design. The second part of this work features two contributions aimed at revisions to modern-day highway design policies based on the concept of combining microscopic and macroscopic principles. One common belief is that AVs should drive better than the best human drivers, which suggests operating at or close to the vehicle's theoretical handling limits. Operating in this manner requires a thorough understanding of the associated risks, particularly the risks stemming from uncertainty. This is especially pertinent as there are many inherently probabilistic quantities that are conveniently treated as deterministic in vehicle performance simulations, such as the coefficient of friction. This is a questionable practice when operating on the precipice of compromised safety. Thus, the second part of this work probabilistically examines the chance of handling loss given the amount of tire-road friction and driver acceleration. The result is a mathematically rigorous quantification of a safety margin for various road conditions and driver ability levels. Changes to the official US highway design handbook are recommended based on the findings. / Doctor of Philosophy / Autonomous vehicles (AVs) are quickly becoming a reality. In the surge to understand and develop AVs, researchers tend to study specific subdivisions within the vehicle engineering umbrella. In particular, mechanical and civil engineers study vehicle dynamics in two different levels of specificity. Mechanical engineers typically investigate the dynamics of a single vehicle, such as vehicle-terrain interactions or how various mechanical components operate. On the other hand, civil engineers tend to study traffic flow, which involves platoons (large groups of vehicles). Regardless of the scale of study, each subdivision has a set of performance metrics. Due to the differences among subdivisions, some performance metrics may (unintentionally) compete. Compromises must be made in the design stage to produce a vehicle which caters to an appropriate audience. This work features four main contributions. The first contribution is the application of Design Envelopes that establishes a framework to balance constraints arising from the different ways of studying vehicle dynamics. Three Design Envelopes are introduced to reach compromises on various facets of a vehicle's behavior, such as the vehicle's speed. The second contribution utilizes these Design Envelopes to improve a currently-existing speed profile generator. The current speed profile generator determines how to smoothly transition between two speeds (such as needing to decelerate to remain under a speed limit), but the ride may be uncomfortable to passengers. Integrating the Design Envelopes into the algorithm enhances the ride comfort for not just one vehicle, but a platoon. Simulations demonstrate that the refined algorithm can reduce the travel time on a specific route by 3-4.4%. The third contribution examines how horizontal curves on highways are designed, and a revision based on an acceleration-based safety margin is proposed. Finally, the fourth contribution considers important design variables probabilistically to establish a link between a motorist's acceleration and the chance of a tire skidding failure, which can impact the way straightaway road segments are designed to accommodate sudden braking maneuvers. As a whole, this work demonstrates it is possible to synthesize multiple engineering fields to enhance both current and future (full-scale AV implementation) roadway design.

La conception de lanceurs est un problème d’optimisation multidisciplinaire dont l’objectif est de trouverl’architecture du lanceur qui garantit une performance optimale tout en assurant un niveau de fiabilité requis.En vue de l’obtention de la solution optimale, les phases d’avant-projet sont cruciales pour le processus deconception et se caractérisent par la présence d’incertitudes dues aux phénomènes physiques impliqués etaux méconnaissances existantes sur les modèles employés. Cette thèse s’intéresse aux méthodes d’analyse et d’optimisation multidisciplinaire en présence d’incertitudes afin d’améliorer le processus de conception de lanceurs. Trois sujets complémentaires sont abordés. Tout d’abord, deux nouvelles formulations du problème de conception ont été proposées afin d’améliorer la prise en compte des interactions disciplinaires. Ensuite, deux nouvelles méthodes d’analyse de fiabilité, permettant de tenir compte d’incertitudes de natures variées, ont été proposées, impliquant des techniques d’échantillonnage préférentiel et des modèles de substitution. Enfin, une nouvelle technique de gestion des contraintes pour l’algorithme d’optimisation ”Covariance Matrix Adaptation - Evolutionary Strategy” a été développée, visant à assurer la faisabilité de la solution optimale. Les approches développées ont été comparées aux techniques proposées dans la littérature sur des cas tests d’analyse et de conception de lanceurs. Les résultats montrent que les approches proposées permettent d’améliorer l’efficacité du processus d’optimisation et la fiabilité de la solution obtenue. / Launch vehicle design is a Multidisciplinary Design Optimization problem whose objective is to find the launch vehicle architecture providing the optimal performance while ensuring the required reliability. In order to obtain an optimal solution, the early design phases are essential for the design process and are characterized by the presence of uncertainty due to the involved physical phenomena and the lack of knowledge on the used models. This thesis is focused on methodologies for multidisciplinary analysis and optimization under uncertainty for launch vehicle design. Three complementary topics are tackled. First, two new formulations have been developed in order to ensure adequate interdisciplinary coupling handling. Then, two new reliability techniques have been proposed in order to take into account the various natures of uncertainty, involving surrogate models and efficient sampling methods. Eventually, a new approach of constraint handling for optimization algorithm ”Covariance Matrix Adaptation - Evolutionary Strategy” has been developed to ensure the feasibility of the optimal solution. All the proposed methods have been compared to existing techniques in literature on analysis and design test cases of launch vehicles. The results illustrate that the proposed approaches allow the improvement of the efficiency of the design process and of the reliability of the found solution.

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