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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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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