Este trabalho apresenta desenvolvimentos na modelagem mecânica de estruturas de barras em concreto armado, bem como no acoplamento entre modelos de confiabilidade e otimização do tipo RBDO para obtenção de dimensões ótimas, respeitando os requisitos de segurança especificados em projeto. Quanto à modelagem mecânica via Método dos Elementos Finitos (MEF), além do comportamento não-linear geométrico e dos materiais, foi considerada a contribuição dos mecanismos complementares de resistência ao cisalhamento, dados pelo engrenamento de agregados e efeito de pino das armaduras longitudinais. Além disso, um modelo simplificado que avalia a contribuição da armadura transversal também foi proposto. Foi desenvolvida uma formulação de otimização que deixa a posição da linha neutra livre, ao contrário de formulações existentes. Esta formulação resultou em projetos mais economicos dos que aqueles encontrados na literatura. Na questão do acoplamento de confiabilidade e otimização, foram exploradas melhorias no Método de Superfície de Resposta e no acoplamento direto via Método de Confiabilidade de Primeira Ordem e Técnica dos Gradientes Numéricos. Estas resultaram em maior precisão dos resultados e aumento na velocidade de convergência. Os modelos mecânicos, incluindo análise não-linear e mecanismos complementares, a formulação de otimização e as técnicas de confiabilidade foram implementados em um programa computacional para dimensionamento ótimo de elementos em concreto armado. O programa foi utilizado na resolução de vários problemas-exemplo. Verificou-se que a consideração dos mecanismos complementares de resistência ao cisalhamento produziram acréscimo na carga última, quando comparadas com as respostas sem tais efeitos. Verificou-se também que os mesmos mecanismos produziram um aumento, até mais significativo, nos índices de confiabilidade obtidos. As dimensões ótimas de elementos estruturais também foram comparadas, considerando-se modelos lineares e não-lineares dos materiais. O estudo mostrou que os custos da estrutura otimizada são menores, quando se considera os efeitos de comportamento não-linear dos materiais. / This work presents some developments in the mechanical modeling of reinforced concrete bar structures, as well in the coupling of reliability and RBDO optimization models, with the purpose of obtaining optimal dimensions considering the safety requirements specified in design. As for the mechanical modeling via Finite Element Method (FEM), in addition to geometrical and material nonlinear behaviors, the contribution of shear resistance complementary mechanisms (aggregate interlock and dowel action of longitudinal reinforcement) was taken into account. Moreover, a simplified model that evaluates the contribution of shear reinforcement was also proposed. In an improvement of existing formulations, an optimization scheme was developed which leaves the position of the neutral axis free. This improvement resulted in more economical cross-sections, than those found in the literature. With respect to the coupling of reliability and optimization methods, improvements were sought in the Response Surface Method and in the direct coupling via First Order Reliability and Numerical Gradients methods. These improvements resulted in greater precision and in increased convergence speed. The mechanical models, including non linear effects and complementary mechanisms , the optimization and reliability formulations were implemented in a computational code for the optimum design of reinforced concrete structures. The program was used to solve a number of example problems. It was found that the complementary mechanisms resulted in an increase of ultimate loads, when compared to the response obtained without these effects. These mechanisms also resulted in an even greater increase of the elements reliability. Optimal dimensions of the structural elements were also compared, considering linear and non-linear material models. The cost of the optimum structure was found to be smaller when non linear effects are taken into account.

Au stade de projet amont, les ingénieurs utilisent souvent des modèles de basse fidélité possédant de larges erreurs. Les approches déterministes prennent implicitement en compte les erreurs par un choix conservatif des paramètres aléatoires et par l'ajout de facteurs de sécurité dans les contraintes de conception. Une fois qu'une solution est proposée, elle est analysée par un modèle haute fidélité (test futur): une re-conception peut s'avérer nécessaire pour restaurer la fiabilité ou améliorer la performance, et le modèle basse fidélité est calibré pour prendre en compte les résultats de l'analyse haute-fidélité. Mais une re-conception possède un coût financier et temporel. Dans ce travail, les effets possibles des tests futurs et des re-conceptions sont intégrés à une procédure de conception avec un modèle basse fidélité. Après les Chapitres 1 et 2 qui donnent le contexte de ce travail et l'état de l'art, le Chapitre 3 analyse le dilemme d'une conception initiale conservatrice en terme de fiabilité ou ambitieuse en termes de performances (avec les re-conceptions associées pour améliorer la performance ou la fiabilité). Le Chapitre 4 propose une méthode de simulation des tests futurs et de re-conception avec des erreurs épistémiques corrélées spatialement. Le Chapitre 5 décrit une application à une fusée sonde avec des erreurs à la fois aléatoires et de modèles. Le Chapitre 6 conclut le travail. / At the initial design stage, engineers often rely on low-fidelity models that have high uncertainty. In a deterministic safety-margin-based design approach, uncertainty is implicitly compensated for by using fixed conservative values in place of aleatory variables and ensuring the design satisfies a safety-margin with respect to design constraints. After an initial design is selected, high-fidelity modeling is performed to reduce epistemic uncertainty and ensure the design achieves the targeted levels of safety. High-fidelity modeling is used to calibrate low-fidelity models and prescribe redesign when tests are not passed. After calibration, reduced epistemic model uncertainty can be leveraged through redesign to restore safety or improve design performance; however, redesign may be associated with substantial costs or delays. In this work, the possible effects of a future test and redesign are considered while the initial design is optimized using only a low-fidelity model. The context of the work and a literature review make Chapters 1 and 2 of this manuscript. Chapter 3 analyzes the dilemma of whether to start with a more conservative initial design and possibly redesign for performance or to start with a less conservative initial design and risk redesigning to restore safety. Chapter 4 develops a generalized method for simulating a future test and possible redesign that accounts for spatial correlations in the epistemic model error. Chapter 5 discusses the application of the method to the design of a sounding rocket under mixed epistemic model uncertainty and aleatory parameter uncertainty. Chapter 6 concludes the work.

abstract: This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that are performed to obtain probability of failure and reliability of structures. Next, decoupled RBDO procedure is proposed with a new reliability analysis formulation with sensitivity analysis, which is performed to remove the highly reliable constraints in the RBDO, thereby reducing the computational time and function evaluations. Followed by implementation of the reliability analysis concepts and RBDO in finite element 2D truss problems and a planar beam problem are presented and discussed. / Dissertation/Thesis / M.S. Civil and Environmental Engineering 2012

Research in the field of reliability based design is mainly focused on two sub-areas: The computation of the probability of failure and its integration in the reliability based design optimization (RBDO) loop. Four papers are presented in this work, representing a contribution to both sub-areas. In the first paper, a new Second Order Reliability Method (SORM) is presented. As opposed to the most commonly used SORMs, the presented approach is not limited to hyper-parabolic approximation of the performance function at the Most Probable Point (MPP) of failure. Instead, a full quadratic fit is used leading to a better approximation of the real performance function and therefore more accurate values of the probability of failure. The second paper focuses on the integration of the expression for the probability of failure for general quadratic function, presented in the first paper, in RBDO. One important feature of the proposed approach is that it does not involve locating the MPP. In the third paper, the expressions for the probability of failure based on general quadratic limit-state functions presented in the first paper are applied for the special case of a hyper-parabola. The expression is reformulated and simplified so that the probability of failure is only a function of three statistical measures: the Cornell reliability index, the skewness and the kurtosis of the hyper-parabola. These statistical measures are functions of the First-Order Reliability Index and the curvatures at the MPP. In the last paper, an approximate and efficient reliability method is proposed. Focus is on computational efficiency as well as intuitiveness for practicing engineers, especially regarding probabilistic fatigue problems where volume methods are used. The number of function evaluations to compute the probability of failure of the design under different types of uncertainties is a priori known to be 3n+2 in the proposed method, where n is the number of stochastic design variables. / <p>QC 20160317</p>

In this thesis, several Reliability-based Design Optimization (RBDO) methods and algorithms for airplane floor grid layout optimization are proposed. A general RBDO process is proposed and validated by an example. Copula as a mathematical method to model random variable correlations is introduced to discover the correlations between random variables and to be applied in producing correlated data samples for Monte Carlo simulations. Based on Hasofer-Lind (HL) method, a correlated HL method is proposed to evaluate a reliability index under correlation. As an alternative method for computing a reliability index, the reliability index is interpreted as an optimization problem and two nonlinear programming algorithms are introduced to evaluate reliability index. To evaluate the reliability index by Monte Carlo simulation in a time efficient way, a kriging-based surrogate model is proposed and compared to the original model in terms of computing time. Since in RBDO optimization models the reliability constraint obtained by MCS does not have an analytical form, a kriging-based response surface is built. Kriging-based response surface models are usually segment functions that do not have a uniform expression over the design space; however, most optimization algorithms require a uniform expression for constraints. To solve this problem, a heuristic gradient-based direct searching algorithm is proposed. These methods and algorithms, together with the RBDO general process, are applied to the layout optimization of aircraft floor grid structural design.