There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data.

La libéralisation du marché de l'électricité a introduit plusieurs nouveaux sujets de recherche intéressants dans la zone du système électrique. Cette thèse aborde l'un des problèmes fascinants parmi eux: l'étude de la génération distribuée à la fois renouvelable et classique d'intégration dans les réseaux de distribution. De Gestionnaires de Réseau de Distribution (GRD) point de vue, il est intéressant de développer une méthodologie globale qui considère les différentes technologies de production décentralisée (GD) comme une option pour la fourniture à la demande. Dans cette thèse, le problème de planification a été modélisé avec la méthodologie de multi-objectif. Cela aidera le planificateur de la prise de décision tout en sachant les arbitrages entre les fonctions objectives. Afin de trouver le front de Pareto optimale du problème, un hybride génétique-immunes algorithme est proposé. La méthode floue satisfaisant est utilisé pour trouver la solution finale. Divers objectifs comme le coût, les pertes actifs, d'émissions et de la satisfaction de contraintes techniques ont été prises en compte. Les variables de décision sont les stratégies de renforcement des réseaux de distribution et aussi les décisions d'investissement concernant les modules GD, dans le cas où GRD peut investir dans des modules de DG aussi. Un autre aspect qui rend les modèles proposés plus flexible, est compte tenu des incertitudes sur les paramètres d'entrée. Les incertitudes des données d'entrée ont été traitées de trois manières différentes à savoir : probabiliste, possibiliste et finalement mélangés possibiliste-probabilistes. Dans cette thèse, deux types de modèles ont été développés: centralisé et dégroupé modèle de planification GD. Dans les deux modèles, le GRD est responsable de fournir un réseau fiable et performant pour ses clients sur son territoire. Dans le contexte de planification centralisée, le GRD est autorisé à faire des investissements dans les modules de la GD. Dans ce modèle, la taille optimale, nombre d'unités de la GD, l'emplacement, la technologie et de la GD, calendrier des investissements dans les modules de GD à la fois et les composants du réseau sont déterminés. Le modèle développé ne sera pas seulement utile dans le contexte de la planification centralisée, mais est également applicable aux marchés de l'énergie d'autres qui ont besoin pour évaluer, surveiller et guider les décisions des développeurs GD. Dans le modèle de planification de la GD dégroupé, le GRD n'est pas autorisé à prendre des décisions d'investissement dans les options de la GD. Les variables de décision du GRD sont limités à renfort de réseau, le placement de condensateurs, la reconfiguration du réseau et des technologies de réseau intelligent. / The process of deregulation that has involved electricity markets has introduced several new interesting research topics in power system area. This thesis addresses one of the fascinating issues among them: the study of distributed generation both renewable and conventional integration in distribution networks. From the distribution network operator (DNO)'s point of view, it is interesting to develop a comprehensive methodology which considers various distributed generation technologies as an option for supplying the demand. In this thesis, the planning problem has been multi-objectively modeled. This will help the planner in decision making while knowing the trade-offs between the objective functions. for finding the Pareto optimal front of the problem a hybrid Genetic-Immune algorithm is proposed. The fuzzy satisfying method is used to find the final solution. Various objectives like cost, active losses, emission and the technical constraint satisfaction have been taken into account. The decision variables are the distribution network reinforcement strategies and also the investment decisions regarding DG units, in case where DNO can invest in DG units too. Another aspect which makes the proposed models more flexible, is considering the uncertainties of the input parameters. The uncertainties of input data have been treated in three different ways namely, probabilistic, possibilistic and finally mixed possibilistic-probabilistic methods. In this thesis, two types of models have been developed: centralized and unbundled DG planning model. In both models, the DNO is responsible to provide a reliable and efficient network for his costumers in its territory. In centrally controlled planning context, the DNO is authorized to make investment in DG units. In this model, the optimal size, number of DG units, location, DG technology and timing of investment in both DG units and network components are determined. The developed model will not only be useful in the centrally controlled planning context but also is applicable to other power markets that need to assess, monitor and guide the decisions of DG developers. In unbundled DG planning model, the DNO is not authorized to make investment decisions in DG options. The decision variables of DNO are limited to feeder/substation expansion/reinforcement, capacitor placement, network reconfiguration and smart grid technologies.

Com o aperfeiçoamento de materiais compósitos de alto desempenho, surge a possibilidade do desenvolvimento de estruturas inteligentes, onde atuadores e sensores piezelétricos estão integrados na estrutura com sistemas de controle adequados para a atenuação de vibrações. Projetos multidisciplinares se tornam cada vez mais complexos e sofisticados, envolvendo diversas fontes de incertezas que devem ser analisadas e quantificadas. O escopo principal desse trabalho visa o estudo da propagação de incertezas em estruturas de materiais compósitos laminados com atuadores e sensores piezelétricos, onde entradas e parâmetros do projeto podem ser fontes aleatórias e/ou nebulosas. Para atingir esse objetivo é adotada a metodologia fuzzy, com a aplicação de otimização de cortes-α. Essa técnica é utilizada na presença de informações vagas ou imprecisas acerca da aleatoriedade presente. Nesse estudo projetam-se, através do método dos elementos finitos, estruturas em forma de placa e casca de material compósito laminado com atuadores e sensores piezelétricos acoplados, controlados pelos reguladores Linear Quadratic Regulator (LQR) e Linear Quadratic Gaussian (LQG). Inicialmente são realizados estudos de otimização para encontrar a melhor localização dos componentes piezelétricos pelos Gramianos de controlabilidade e observabilidade, assim como os fatores de ponderação das leis de controle. O desenvolvimento é realizado no espaço modal reduzido visando um melhor desempenho computacional. As métricas escolhidas para avaliação do controle de vibração e análise das saídas incertas do sistema são baseadas nas energias cinética, potencial e elétrica. Também apresentam-se estudos de envelopes relacionados ao deslocamentos e às frequências naturais da estrutura devido às incertezas. Os resultados mostraram que as otimizações por corte-α para tratar números fuzzy nesse tipo de problema são robustas e eficientes, encontrando-se valores extremos das saídas desejadas. Além de ser um método não intrusivo, também pode ser utilizado em problemas com um número elevado de parâmetros incertos como entrada. / The possibility of developments of smart structures arises with high performance composite materials improvements, where piezoelectric actuators and sensors are embedded into the structures, following a suitable control laws for vibration attenuation. Multidisciplinary projects are becoming highly complex and sophisticated, involving several sources of uncertainty that should be analyzed and quantified. The main objective for this work is to study the uncertainty propagation in composite laminate structures with embedded piezoelectric actuators and sensors, considering random and/or fuzzy sources for the inputs and design parameters. To accomplish this objective, it is adopted the fuzzy α-cut optimizations methodology. This technique is used when the available information related to the actual randomness is vague or imprecise. In this study, laminated composite shells and plates structures are designed and analyzed by the finite element method, where embedded piezoelectric actuators and sensors controlled by Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) are present. Initially, optimization analyses are executed to find the best arrangement for the piezoelectric material using controllability and observability Gramians metrics, as well as the best controller parameters. This study is developed in the reduced modal space looking for computational costs savings. The chosen rating metrics for the vibration control and uncertainty analysis are based on kinetic, potential and electrical energies. Structural displacements and natural frequency envelopes due uncertainty are also studied and presented. The results have shown that the fuzzy α-cut optimizations methodology is robust and efficient to find extreme values for the sought outputs. In addition to being a non-intrusive method, it is also able to deal with a large number of uncertain input parameters.

Com o aperfeiçoamento de materiais compósitos de alto desempenho, surge a possibilidade do desenvolvimento de estruturas inteligentes, onde atuadores e sensores piezelétricos estão integrados na estrutura com sistemas de controle adequados para a atenuação de vibrações. Projetos multidisciplinares se tornam cada vez mais complexos e sofisticados, envolvendo diversas fontes de incertezas que devem ser analisadas e quantificadas. O escopo principal desse trabalho visa o estudo da propagação de incertezas em estruturas de materiais compósitos laminados com atuadores e sensores piezelétricos, onde entradas e parâmetros do projeto podem ser fontes aleatórias e/ou nebulosas. Para atingir esse objetivo é adotada a metodologia fuzzy, com a aplicação de otimização de cortes-α. Essa técnica é utilizada na presença de informações vagas ou imprecisas acerca da aleatoriedade presente. Nesse estudo projetam-se, através do método dos elementos finitos, estruturas em forma de placa e casca de material compósito laminado com atuadores e sensores piezelétricos acoplados, controlados pelos reguladores Linear Quadratic Regulator (LQR) e Linear Quadratic Gaussian (LQG). Inicialmente são realizados estudos de otimização para encontrar a melhor localização dos componentes piezelétricos pelos Gramianos de controlabilidade e observabilidade, assim como os fatores de ponderação das leis de controle. O desenvolvimento é realizado no espaço modal reduzido visando um melhor desempenho computacional. As métricas escolhidas para avaliação do controle de vibração e análise das saídas incertas do sistema são baseadas nas energias cinética, potencial e elétrica. Também apresentam-se estudos de envelopes relacionados ao deslocamentos e às frequências naturais da estrutura devido às incertezas. Os resultados mostraram que as otimizações por corte-α para tratar números fuzzy nesse tipo de problema são robustas e eficientes, encontrando-se valores extremos das saídas desejadas. Além de ser um método não intrusivo, também pode ser utilizado em problemas com um número elevado de parâmetros incertos como entrada. / The possibility of developments of smart structures arises with high performance composite materials improvements, where piezoelectric actuators and sensors are embedded into the structures, following a suitable control laws for vibration attenuation. Multidisciplinary projects are becoming highly complex and sophisticated, involving several sources of uncertainty that should be analyzed and quantified. The main objective for this work is to study the uncertainty propagation in composite laminate structures with embedded piezoelectric actuators and sensors, considering random and/or fuzzy sources for the inputs and design parameters. To accomplish this objective, it is adopted the fuzzy α-cut optimizations methodology. This technique is used when the available information related to the actual randomness is vague or imprecise. In this study, laminated composite shells and plates structures are designed and analyzed by the finite element method, where embedded piezoelectric actuators and sensors controlled by Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) are present. Initially, optimization analyses are executed to find the best arrangement for the piezoelectric material using controllability and observability Gramians metrics, as well as the best controller parameters. This study is developed in the reduced modal space looking for computational costs savings. The chosen rating metrics for the vibration control and uncertainty analysis are based on kinetic, potential and electrical energies. Structural displacements and natural frequency envelopes due uncertainty are also studied and presented. The results have shown that the fuzzy α-cut optimizations methodology is robust and efficient to find extreme values for the sought outputs. In addition to being a non-intrusive method, it is also able to deal with a large number of uncertain input parameters.

Com o aperfeiçoamento de materiais compósitos de alto desempenho, surge a possibilidade do desenvolvimento de estruturas inteligentes, onde atuadores e sensores piezelétricos estão integrados na estrutura com sistemas de controle adequados para a atenuação de vibrações. Projetos multidisciplinares se tornam cada vez mais complexos e sofisticados, envolvendo diversas fontes de incertezas que devem ser analisadas e quantificadas. O escopo principal desse trabalho visa o estudo da propagação de incertezas em estruturas de materiais compósitos laminados com atuadores e sensores piezelétricos, onde entradas e parâmetros do projeto podem ser fontes aleatórias e/ou nebulosas. Para atingir esse objetivo é adotada a metodologia fuzzy, com a aplicação de otimização de cortes-α. Essa técnica é utilizada na presença de informações vagas ou imprecisas acerca da aleatoriedade presente. Nesse estudo projetam-se, através do método dos elementos finitos, estruturas em forma de placa e casca de material compósito laminado com atuadores e sensores piezelétricos acoplados, controlados pelos reguladores Linear Quadratic Regulator (LQR) e Linear Quadratic Gaussian (LQG). Inicialmente são realizados estudos de otimização para encontrar a melhor localização dos componentes piezelétricos pelos Gramianos de controlabilidade e observabilidade, assim como os fatores de ponderação das leis de controle. O desenvolvimento é realizado no espaço modal reduzido visando um melhor desempenho computacional. As métricas escolhidas para avaliação do controle de vibração e análise das saídas incertas do sistema são baseadas nas energias cinética, potencial e elétrica. Também apresentam-se estudos de envelopes relacionados ao deslocamentos e às frequências naturais da estrutura devido às incertezas. Os resultados mostraram que as otimizações por corte-α para tratar números fuzzy nesse tipo de problema são robustas e eficientes, encontrando-se valores extremos das saídas desejadas. Além de ser um método não intrusivo, também pode ser utilizado em problemas com um número elevado de parâmetros incertos como entrada. / The possibility of developments of smart structures arises with high performance composite materials improvements, where piezoelectric actuators and sensors are embedded into the structures, following a suitable control laws for vibration attenuation. Multidisciplinary projects are becoming highly complex and sophisticated, involving several sources of uncertainty that should be analyzed and quantified. The main objective for this work is to study the uncertainty propagation in composite laminate structures with embedded piezoelectric actuators and sensors, considering random and/or fuzzy sources for the inputs and design parameters. To accomplish this objective, it is adopted the fuzzy α-cut optimizations methodology. This technique is used when the available information related to the actual randomness is vague or imprecise. In this study, laminated composite shells and plates structures are designed and analyzed by the finite element method, where embedded piezoelectric actuators and sensors controlled by Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) are present. Initially, optimization analyses are executed to find the best arrangement for the piezoelectric material using controllability and observability Gramians metrics, as well as the best controller parameters. This study is developed in the reduced modal space looking for computational costs savings. The chosen rating metrics for the vibration control and uncertainty analysis are based on kinetic, potential and electrical energies. Structural displacements and natural frequency envelopes due uncertainty are also studied and presented. The results have shown that the fuzzy α-cut optimizations methodology is robust and efficient to find extreme values for the sought outputs. In addition to being a non-intrusive method, it is also able to deal with a large number of uncertain input parameters.

Uncertainties are present in the inverse analysis of damage identification with respect to the given measurements, mainly the modelling uncertainties and the measurement uncertainties. Modelling uncertainties occur due to constructing a representative model of the real structure through finite element modelling, and representing damage in the real structures through changes in material parameters of the finite element model (assuming smeared crack approach). Measurement uncertainties are always present in the measurements despite the accuracy with which the measurements are measured or the precision of the instruments used for the measurement. The modelling errors in the finite element model are assumed to be encompassed in the updated uncertain parameters of the finite element model, given the uncertainties in the measurements and in the prior uncertainties of the parameters. The uncertainties in the direct measurement data are propagated to the estimated output data. Empirical models from codal provisions and standard recommendations are normally used for prediction of long-time deformations in concrete structures. Uncertainties are also present in the creep and shrinkage models, in the parameters of these models, in the shrinkage and creep mechanisms, in the environmental conditions, and in the in-situ measurements. All these uncertainties are needed to be considered in the damage identification and prediction of long-time deformations in concrete structures. In the context of modelling uncertainty, uncertainties can be categorized into aleatory or epistemic uncertainty. Aleatory uncertainty deals with the irresolvable indeterminacy about how the uncertain variable will evolve over time, whereas epistemic uncertainty deals with lack of knowledge. In the field of damage detection and prediction of long time deformations, aleatory uncertainty is modeled through probabilistic analysis, whereas epistemic uncertainty can be modeled through (1) Interval analysis (2) Ellipsoidal modeling (3) Fuzzy analysis (4) Dempster-Shafer evidence theory or (5) Imprecise probability. Many a times it is di cult to determine whether a particular uncertainty is to be considered as an aleatory or as an epistemic uncertainty, and the model builder makes the distinction. The model builder makes the choice based on the general state of scientific knowledge, on the practical need for limiting the model sophistication to a significant engineering importance, and on the errors associated with the measurements. Measurement uncertainty can be stated as the dispersion of real data resulting from systematic error (instrumental error, environmental error, observational error, human error, drift in measurement, measurement of wrong quantity) and random error (all errors apart from systematic errors). Most of instrumental errors given by the manufacturers are in terms of plus minus ranges and can be better represented through interval bounds. The vagueness involved in the representation of human error, observational error, and drift in measurement can be represented through interval bounds. Deliberate measurement of wrong quantity through cheaper and more convenient measurement units can lead to bad quality data. Quality of data can be better handled through interval analysis, with good quality data having narrow width of interval bounds and bad quality data having wide interval bounds. The environmental error, the electronic noise coming from transmitting the data and the random errors can be represented through probability distribution functions. A major part of the measurement uncertainties is better represented through interval bounds and the other part, is better represented through probability distributions. The uncertainties in the direct measurement data are propagated to the estimated output data (in damage identification techniques, the damaged parameters, and in the long-time deformation, the uncertain parameters of the deformation models, which are then used for the prediction of long-time deformations). Uncertainty based damage identification techniques and long-time deformations in concrete structures require further studies, when the measurement uncertainties are expressed through interval bounds only, or through both interval and probability using imprecise techniques. The thesis is divided into six chapters. Chapter 1 provides a review of existing literature on uncertainty based techniques for damage identification and prediction of long-time deformations in concrete structures. A brief review of uncertainty based methods for engineering applications is made, with special highlight to the need of interval analysis and imprecise probability for modeling uncertainties in the damage identification techniques. The review identifies that the available techniques for damage identification, where the uncertainties in the measurements and in the structural and material parameters are expressed in terms of interval bounds, lack e ciency, when the size of the damaged parameter vector is large. Studies on estimating the uncertainties in the damage parameters when the uncertainties in the measurements are expressed through imprecise probability analysis, are also identified as problems that will be considered in this thesis. Also the need for estimating the short-term time period, which in turn helps in accurate prediction of long-time deformations in concrete structures, along with a cost effective and easy to use system of measuring the existing prestress forces at various time instances in the short-time period is noted. The review identifies that most of modelers and analysts have been inclined to select a single simulation model for the long-time deformations resulted from creep, shrinkage and relaxation, rather than take all the possibilities into consideration, where the model selection is made based on the hardly realistic assumption that we can certainly select a correct, and the lack of confidence associated with model selection brings about the uncertainty that resides in a given model set. The need for a single best model out of all the available deformation models is needed to be developed, when uncertainties are present in the models, in the measurements and in the parameters of each models is also identified as a problem that will be considered in this thesis. In Chapter 2, an algorithm is proposed adapting the existing modified Metropolis Hastings algorithm for estimating the posterior probability of the damage indices as well as the posterior probability of the bounds of the interval parameters, when the measurements are given in terms of interval bounds. A damage index is defined for each element of the finite element model considering the parameters of each element are intervals. Methods are developed for evaluating response bounds in the finite element software ABAQUS, when the parameters of the finite element model are intervals. Illustrative examples include reinforced concrete beams with three damage scenarios mainly (i) loss of stiffness, (ii) loss of mass, and (iii) loss of bond between concrete and reinforcement steel, that have been tested in our laboratory. Comparison of the prediction from the proposed method with those obtained from Bayesian analysis and interval optimization technique show improved accuracy and computational efficiency, in addition to better representation of measurement uncertainties through interval bounds. Imprecise probability based methods are developed in Chapter 3, for damage identifi cation using finite element model updating in concrete structures, when the uncertainties in the measurements and parameters are imprecisely defined. Bayesian analysis using Metropolis Hastings algorithm for parameter estimation is generalized to incorporate the imprecision present in the prior distribution, in the likelihood function, and in the measured responses. Three different cases are considered (i) imprecision is present in the prior distribution and in the measurements only, (ii) imprecision is present in the parameters of the finite element model and in the measurement only, and (iii) imprecision is present in the prior distribution, in the parameters of the finite element model, and in the measurements. Illustrative examples include reinforced concrete beams and prestressed concrete beams tested in our laboratory. In Chapter 4, a steel frame is designed to measure the existing prestressing force in the concrete beams and slabs when embedded inside the concrete members. The steel frame is designed to work on the principles of a vibrating wire strain gauge and is referred to as a vibrating beam strain gauge (VBSG). The existing strain in the VBSG is evaluated using both frequency data on the stretched member and static strain corresponding to a fixed static load, measured using electrical strain gauges. The crack reopening load method is used to compute the existing prestressing force in the concrete members and is then compared with the existing prestressing force obtained from the VBSG at that section. Digital image correlation based surface deformation and change in neutral axis monitored by putting electrical strain gauges across the cross section, are used to compute the crack reopening load accurately. Long-time deformations in concrete structures are estimated in Chapter 5, using short-time measurements of deformation responses when uncertainties are present in the measurements, in the deformation models and in the parameters of the deformation models. The short-time period is defined as the least time up to which if measurements are made available, the measurements will be enough for estimating the parameters of the deformation models in predicting the long time deformations. The short-time period is evaluated using stochastic simulations where all the parameters of the deformation models are defined as random variables. The existing deformation models are empirical in nature and are developed based on an arbitrary selection of experimental data sets among all the available data sets, and each model contains some information about the deformation patterns in concrete structures. Uncertainty based model averaging is performed for obtaining the single best model for predicting the long-time deformation in concrete structures. Three types of uncertainty models are considered namely, probability models, interval models and imprecise probability models. Illustrative examples consider experiments in the Northwestern University database available in the literature and prestressed concrete beams and slabs cast in our laboratory for prediction of long-time prestress losses. A summary of contributions made in this thesis, together with a few suggestions for future research, are presented in Chapter 6. Finally the references that were studies are listed.