The main objective of the topology optimization is to fulfill the objective function with the minimum amount of material. This reduces the overall cost of the structure and at the same time reduces the assembly, manufacturing and maintenance costs because of the reduced number of parts in the final structure. The concept of reliability analysis can be incorporated into the deterministic topology optimization method; this incorporated scheme is referred to as Reliability-based Topology Optimization (RBTO). In RBTO, the statistical nature of constraints and design problems are defined in the objective function and probabilistic constraint. The probabilistic constraint can specify the required reliability level of the system. In practical applications, however, finding global optimum in the presence of uncertainty is a difficult and computationally intensive task, since for every possible design a full stochastic analysis has to be performed for estimating various statistical parameters. Efficient methodologies are therefore required for the solution of the stochastic part and the optimization part of the design process. This research will explore a reliability-based synthesis method which estimates all the statistical parameters and finds the optimum while being less computationally intensive. The efficiency of the proposed method is achieved with the combination of topology optimization and stochastic approximation which utilizes a sampling technique such as Latin Hypercube Sampling (LHS) and surrogate modeling techniques such as Local Regression and Classification using Artificial Neural Networks (ANN). Local regression is comparatively less computationally intensive and produces good results in case of low probability of failures whereas Classification is particularly useful in cases where the reliability of failure has to be estimated with disjoint failure domains. Because classification using ANN is comparatively more computationally demanding than Local regression, classification is only used when local regression fails to give the desired level of goodness of fit. Nevertheless, classification is an indispensible tool in estimating the probability of failure when the failure domain is discontinuous. Representative examples will be demonstrated where the method is used to design customized meso-scale truss structures and a macro-scale hydrogen storage tank. The final deliverable from this research will be a less computationally intensive and robust RBTO procedure that can be used for design of truss structures with variable design parameters and force and boundary conditions.

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.

This thesis attempts to resolve the contradictory relationship between the ecological merits of wood construction and the significant material intensity of recent free form timber surface structures. The building industry is now adept in the design and construction of freeform surface architecture, however new challenges have been introduced with the environmentally conscious desire to build these structures in wood. Lacking the formal versatility of steel and concrete, wood introduces a great deal of difficulty in the realization of complex form at an architectural scale. Powerful digital design and fabrication tools have recently made it possible to model, analyze and construct these buildings, but at the cost of heavy structural solutions that involve energy intensive fabrication processes and significant material waste. This approach contradicts the ecological benefits of wood, and raises the question of whether it is possible to achieve free and expressive form in timber surface architecture while maintaining an economy of means and material. This question is addressed through the development of a generative design tool for the creation of material conscious free form timber surface architecture. The formation of the tool is informed by the field of computational morphogenesis, which draws from the natural growth processes of biological structures in the virtual synthesis of form. The tool is conceived as a morphogenetic material system, which consists of a generative algorithm that integrates material, structure and form in a single computational process. Specific material saving techniques deployed in the algorithm draw from existing research in timber shell design and material optimization. Established methods in the use of geodesic lines for the structural patterning of wood shells and stress driven material distribution make up the core concepts deployed in the algorithm. The material system is developed, refined and tested through the design and construction of an experimental free form timber lattice.

Difficult gradient calculations, converging to a local optimum without exploring the design space adequately, too much dependency on the starting solution, lacking capabilities to treat discrete and mixed design variables are the main drawbacks of conventional optimization techniques. So evolutionary optimization methods received significant interest amongst researchers in the optimization area. Genetic algorithms (GAs) and simulated annealing (SA) are the main representatives of evolutionary optimization methods. These techniques emerged as powerful and modern strategies to efficiently deal with the difficulties encountered in conventional techniques, and therefore rightly attracted a substantial interest and popularity. The underlying concepts of these techniques and thus their algorithmic models have been devised by establishing between the optimization task and events occurring in nature. While, Darwin&amp / #8217 / s survival of the fittest theory is mimicked by GAs, annealing process of physical systems are employed to SA. On the other hand, dome structures are among the most preferred types of structures for large unobstructed areas. Domes have been of a special interest in the sense that they enclose a maximum amount of space with a minimum surface. This feature provides economy in terms of consumption of constructional materials. So merging these two concepts make it possible to obtain optimum designs of dome structures. This thesis is concerned with the use of GAs and SA in optimum structural design of dome structures, which range from some relatively simple problems to the problems of increased complexity. In this thesis, firstly both techniques are investigated in terms of their practicality and applicability to the problems of interest. Then numerous test problems taken from real life conditions are studied for comparing the success of the proposed GA and SA techniques with other discrete and continuous optimization methods. The results are discussed in detail to reach certain recommendations contributing to a more efficient use of the techniques in optimum structural design of pin-jointed 3-D dome structures.

This thesis investigates topology optimization techniques for periodic continuum structures at the macroscopic level. Periodic structures are increasingly used in the design of structural systems and sub-systems of buildings, vehicles, aircrafts, etc. The duplication of identical or similar modules significantly reduces the manufacturing cost and greatly simplifies the assembly process. Optimization of periodic structures in the micro level has been extensively researched in the context of material design, while research on topology optimization for macrostructures is very limited and has great potential both economically and intellectually. In the present thesis, numerical algorithms based on the bi-directional evolutionary structural optimization method (BESO) are developed for topology optimization for various objectives and constraints. Soft-kill (replacing void elements with soft elements) formulations of topology optimization problems for solid-void solutions are developed through appropriate material interpolation schemes. Incorporating the optimality criteria and algorithms for mesh-independence and solution-convergence, the present BESO becomes a reliable gradient based technique for topology optimization. Additionally, a new combination of genetic algorithms (GAs) with BESO is developed in order to stochastically search for the global optima. These enhanced BESO algorithms are applied to various optimization problems with the periodicity requirement as an extra constraint aiming at producing periodicity in the layout. For structures under static loading, the present thesis addresses minimization of the mean compliance and explores the applications of conventional stiffness optimization for periodic structures. Furthermore, this thesis develops a volume minimization formulation where the maximum deflection is constrained. For the design of structures subject to dynamic loading, this thesis develops two different approaches (hard-kill and soft-kill) to resolving the problem of localized or artificial modes. In the hard-kill (completely removing void elements) approach, extra control measures are taken in order to eliminate the localized modes in an explicit manner. In the soft-kill approach, a modified power low material model is presented to prevent the occurrence of artificial and localized modes. Periodic stress and strain fields cannot be assumed in structures under arbitrary loadings and boundaries at the macroscopic level. Therefore being different from material design, no natural base cell can be directly extracted from macrostructures. In this thesis, the concept of an imaginary representative unit cell (RUC) is presented. For situations when the structure cannot be discretized into equally-sized elements, the concept of sensitivity density is developed in order for mesh-independent robust solutions to be produced. The RUC and sensitivity density based approach is incorporated into various topology optimization problems to obtain absolute or scaled periodicities in structure layouts. The influence of this extra constraint on the final optima is investigated based on a large number of numerical experiments. The findings shown in this thesis have established appropriate techniques for designing and optimizing periodic structures. The work has provided a solid foundation for creating a practical design tool in the form of a user-friendly computer program suitable for the conceptual design of a wide range of structures.

Made available in DSpace on 2016-12-12T20:25:13Z (GMT). No. of bitstreams: 1 Geovane Augusto Haveroth.pdf: 5753124 bytes, checksum: 03000a94208725070d4dd679b75ac321 (MD5) Previous issue date: 2015-11-30 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In this work, a comprehensive study is developed aiming to the application of the semianalytical sensitivity method using complex variables (SAC) at truss structures considering geometric and material nonlinear behavior. Special emphasis is given to path dependent problems and the appropriate treatment for internal variables updating, which is an aspect not found in the literature by the author and applicable to problems with plasticity and damage behavior. Previous studies show that in path independent problems the SAC method has great efficiency and storage economy, since the operations are performed at the element level. In this work it is verified that when applied to path dependent problems, the method has the same efficiently detected for the independent counterpart, but at the expense of a little higher storage cost, however, the operations remain at the element level. In order to perform the mentioned study, the finite element formulation and some sensitivity evaluation methodologies of structural responses are presented in detail, including the proposed by the author. Finally, a comparative study between the different sensitivity methods is made for problems dominated by rigid body rotation, problems involving discontinuities sensitivity coefficients and for cellular structures. / Neste trabalho realiza-se um abrangente estudo que visa a aplicação do método semi-analítico de análise de sensibilidade, utilizando variáveis complexas (SAC) em estruturas treliçadas, considerando o comportamento não linear geométrico e material. Tal pesquisa foca principalmente nos problemas dependentes da trajetória e no tratamento adequado para a atualização das variáveis internas, sendo este um aspecto não encontrado na revisão bibliográfica pelo autor e aplicável em problemas que envolvem plasticidade e dano. Estudos anteriores mostram que em problemas independentes da trajetória o método SAC apresenta grande eficiência e economia de armazenamento, uma vez que as operações são realizadas no nível do elemento. Verifica-se que este método quando aplicado em problemas dependentes da trajetória, apresenta amesma eficiência detectada na contraparte independente comumcusto de armazenamento um pouco mais elevado. Contudo, as operações ainda se mantém no nível do elemento. Com a finalidade de realizar tal estudo, a formulação de elementos finitos e as diferentes metodologias para avaliar a sensibilidade de respostas estruturais, tanto para problemas dependentes quanto independentes da trajetória, são apresentadas em detalhes. Por fim, realizasse um estudo comparativo entre os diferentes métodos de sensibilidade em problemas que sejam dominados por rotação de corpo rígido, que possuam descontinuidades nos coeficientes da sensibilidade e em estruturas celulares.

A otimização estrutural, utilizando ferramentas computacionais é um grande campo de pesquisa na atualidade. Os métodos utilizados, dependendo da complexidade do problema, demandam um grande custo computacional, e por isso vem sendo avaliandas várias técnicas para diminuí-lo. Uma delas é o emprego de técnicas de aproximação de análises, dentre as quais destacam-se as redes neurais, que combinadas aos métodos de otimização e de análises clássicos conseguem bons resultados e reduzem significativamente o tempo de processamento. O emprego dos compósitos laminados como material estrutural vem crescendo nos últimos tempos, incentivado pela suas excelentes propriedades mecânicas e baixo peso. Em consenso com todo o esforço científico dedicado a essa área, o presente trabalho visa a implementação de uma ferramenta computacional capaz de otimizar estruturas complexas fabricadas com tais materiais, a um baixo custo computacional. Com isto em mente, é desenvolvido um sistema de otimização, aproveitando módulos implementados previamente para a análise estática linear e não linear através do método dos elementos finitos (MEF), e o módulo de otimização por algoritmos genéticos. Serão desenvolvidos os módulos de análise modal, para otimizar também estruturas com critérios baseados em freqüências e modos, e o modulo de redes neurais de tipo perceptron para aproximações das análises feitas através do MEF. Alguns exemplos são apresentados para demonstrar que bons resultados são obtidos com a utilização de redes neurais artificiais, cujo treinamento permite poupar tempo computacional proveniente do grande número de análises usualmente necessárias no processo de otimização. / Structural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, artificial neural networks may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and Artificial Neural Networks (ANN) to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to optimize problems based in frequencies and modal criteria, and a module with perceptron ANN is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.

Com base no crescimento exponencial das populações urbanas, a demanda por espaço para habitação tem crescido vertiginosamente. Para atender a estas necessidades, edificações cada vez mais altas e mais esbeltas são projetadas e vãos cada vez maiores são utilizados. Novos materiais são criados e aprimorados para que seja extraído o máximo de desempenho com o menor custo. Deste modo, esta dissertação tem como objetivo o estudo do comportamento e otimização do projeto estrutural de edifícios. Para tal, considera-se ao longo do estudo o projeto de uma edificação de concreto armado com 47 metros de altura e 15 pavimentos, submetida às ações das cargas usuais de projeto atuantes sobre edifícios residenciais, além das cargas de vento. No que tange ao desenvolvimento do modelo computacional são empregadas técnicas usuais de discretização, via método dos elementos finitos, por meio do programa ANSYS. Inicialmente, a resposta estática e dinâmica do modelo estrutural é obtida e comparada com base nos valores limites propostos por normas de projeto. A partir de análises qualitativas e quantitativas desenvolvidas sobre a resposta estrutural do modelo em estudo são utilizadas técnicas de otimização com o objetivo de modificar e aprimorar o desempenho estrutural do edifício analisado. / Based on the exponential growth of urban populations, the demand for space for housing has grown dramatically. To meet these needs, building ever higher and more slender are designed and increasing spans has been used. New materials are created and improved to be extracted maximum performance at the lowest cost. Thus, this research work aims to study the behaviour and optimization of buildings structural design. To do this, it is considered throughout the study the design of a reinforced concrete building with 47 meters high and 15 floors, subjected to the actions of usual design loadings on residential buildings in addition to wind loads. Regarding the development of the computational model, usual mesh refinement techniques are used, based on the finite element method simulations, and implemented in the ANSYS program. Initially, the structural model static and dynamic response is obtained and compared, based on the limiting values proposed by design standards. Based on the developed qualitative and quantitative analyses on the investigated structural model response, optimization techniques are used in order to modify and improve the structural performance of the analysed building.

A otimização estrutural, utilizando ferramentas computacionais é um grande campo de pesquisa na atualidade. Os métodos utilizados, dependendo da complexidade do problema, demandam um grande custo computacional, e por isso vem sendo avaliandas várias técnicas para diminuí-lo. Uma delas é o emprego de técnicas de aproximação de análises, dentre as quais destacam-se as redes neurais, que combinadas aos métodos de otimização e de análises clássicos conseguem bons resultados e reduzem significativamente o tempo de processamento. O emprego dos compósitos laminados como material estrutural vem crescendo nos últimos tempos, incentivado pela suas excelentes propriedades mecânicas e baixo peso. Em consenso com todo o esforço científico dedicado a essa área, o presente trabalho visa a implementação de uma ferramenta computacional capaz de otimizar estruturas complexas fabricadas com tais materiais, a um baixo custo computacional. Com isto em mente, é desenvolvido um sistema de otimização, aproveitando módulos implementados previamente para a análise estática linear e não linear através do método dos elementos finitos (MEF), e o módulo de otimização por algoritmos genéticos. Serão desenvolvidos os módulos de análise modal, para otimizar também estruturas com critérios baseados em freqüências e modos, e o modulo de redes neurais de tipo perceptron para aproximações das análises feitas através do MEF. Alguns exemplos são apresentados para demonstrar que bons resultados são obtidos com a utilização de redes neurais artificiais, cujo treinamento permite poupar tempo computacional proveniente do grande número de análises usualmente necessárias no processo de otimização. / Structural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, artificial neural networks may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and Artificial Neural Networks (ANN) to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to optimize problems based in frequencies and modal criteria, and a module with perceptron ANN is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.

CAPES / Materiais compósitos laminados são utilizados em diversos setores da indústria, principalmente nas áreas automobilística de competição e aeroespacial, pois apresentam relações resistência-peso e rigidez-peso muito superiores aos materiais metálicos em geral. Estruturas fabricadas a partir desses materiais são normalmente finas e, consequentemente, estão sujeitas à flambagem. Requisitos tradicionais de projeto normalmente levam em conta a flambagem mas, para alguns casos, o projeto é conservador, visto que a estrutura pode ainda ser funcional no regime de pósflambagem. Entretanto, o comportamento nesse regime é não-linear, além da dificuldade de se estimar quando ocorre a falha da estrutura, o que torna a análise mais complexa e onerosa em relação à uma análise de flambagem linear. Nesse contexto está inserido o presente trabalho, que visa encontrar as orientações das fibras que maximizam as cargas de flambagem e de pós-flambagem de estruturas compósitas, usando no processo de otimização metamodelos para aliviar o custo computacional. Duas técnicas de metamodelagem são utilizadas e testadas: redes neurais artificiais e regressão de vetores de suporte, com ênfase para a última. Em combinação com os metamodelos são empregadas duas metaheurísticas de otimização desenvolvidas recentemente: o algoritmo harmony search e o algoritmo de vaga-lumes. Vários problemas com diferentes níveis de dificuldade são apresentados e discutidos. Os melhores resultados de otimização foram obtidos com o algoritmo de vaga-lumes associado ao metamodelo de regressão de vetores de suporte, mostrando que tais técnicas são promissoras na solução dessa classe de problemas. Como uma das principais contribuições desta tese tem-se a adaptação/implementação da técnica de regressão de vetores de suporte para problemas de empilhamento de lâminas em estruturas compósitas, particularmente na otimização em flambagem e pósflambagem. Além disso, foram realizados avanços na modelagem do comportamento e da otimização em pós-flambagem com a utilização de critérios de falha e de dano para compósitos. / Laminated composite materials are applied in many industrial sectors, particularly in competition automotive and aerospace fields, since they have strength-to-weight and stiffness-to-weight ratios much higher than the metals in general. Structures made by these materials are usually thin and hence they are subject to buckling. Traditional design requirements usually take into account the buckling, but in some cases the design is conservative since the structure can still be functional in the postbuckling regime. However, the behavior in this regime is nonlinear, in addition of being difficult to evaluate when the failure of the structure takes place, which makes the analysis more complex and computational expensive if compared to a linear buckling analysis. Within this context this work is inserted, which aims to find the orientations of the fibers that maximize the buckling and postbuckling load of composite structures using metamodels in the optimization process to alleviate the computational cost. Two metamodeling techniques are used and tested: artificial neural networks and support vector regression, with emphasis on the latter. In combination with the metamodels, two recently developed metaheuristics, the harmony search algorithm and the firefly algorithm, are employed. Several problems, with different levels of difficulty, are presented and discussed. The best optimization results were obtained with the firefly algorithm associated with the support vector regression metamodel, showing that these techniques are promising to solve this class of problems. One of the main contributions of this thesis is the adaptation/implementation of support vector regression for layup orientation sequence problems of composite structures, in particular for buckling and postbuckling optimizations. Moreover, advances were made in the modeling of the behavior and optimization in postbuckling regime using failure and damage criteria for composites.