[pt] Esta tese busca utilizar os novos conceitos físicos relacionados à física do estado sólido e à mecânica estatística - teoria do caos e geometria fractal - na análise do comportamento de sistemas dinâmicos não-lineares. Mais pormenorizadamente, trata-se de estudar o comportamento de um modelo numérico elasto-plástico com função de escoamento de Mohr-Coulomb, usualmente empregado em simulações de materiais geológicos - cimentados ou não -, quando submetido a carregamentos externos, situação esta geralmente encontrada em problemas afeitos à mecânica dos solos e das rochas (p/ex., estabilidade de taludes e escavações subterrâneas). Mostra-se que tal modelo geomecânico de muitos corpos (many-body) interagentes é conduzido espontaneamente, ao longo de sua evolução temporal, à chamada criticalidade auto-organizada (self- organized criticality - SOC), estado caracterizado por apresentar evolução na fronteira entre ordem e caos, sensibilidade extrema a qualquer pequena perturbação, e desenvolvimento de interações espaço-temporais de longo alcance. Como a evolução de qualquer sistema dinâmico pode ser vista como um fluxo ininterrupto de informações entre suas partes constituintes, avaliou-se, para tal sistema, a entropia de Tsallis, formulação original proposta pelo físico brasileiro Constantino Tsallis, do Centro Brasileiro de Pesquisas Físicas (CBPF), tendo se mostrado adequada à sua descrição. Em especial, determinou-se para tal sistema, pela primeira vez, o valor do índice entrópico, que parametriza a aludida forma entrópica alternativa. Ademais, como é característico de sistemas fora do equilíbrio regidos por uma dinâmica de limiar, mostra-se que tal sistema geomecânico, durante o seu desenvolvimento, teve a sua simetria translacional inicial quebrada, sendo substituída pela simetria por escala, auto-semelhante (i.é., fractal). Em decorrência, o modelo exibe a chamada invariância discreta de escala (discrete scale invariance - DSI), fruto do processo mesmo de ruptura progressiva do material heterogêneo. Especificamente, as simulações numéricas sugeriram que o processo de ruptura progressiva do material elasto-plástico se dá por uma transferência multiplicativa de tensões, em diferentes escalas de observação hierarquicamente dispostas, acarretando o aparecimento de sinais bastante peculiares, caracterizados por desvios oscilatórios sistemáticos do padrão em lei de potência, o que possibilita a previsão de sua ruína, quando ainda em fase preparatória. Assim, esta pesquisa mostrou a eficiência de tal método de previsão, aplicado, pela primeira vez, não somente aos resultados das simulações numéricas do referido modelo geomecânico, como aos ensaios de laboratório em rochas sedimentares, realizados no Centro de Pesquisas da Petrobrás (CENPES). Por fim, é interessante assinalar que o material elasto-plástico investigado neste trabalho teve seu comportamento compartilhado por um modelo matemático bastante simples, fundamentado na função binomial multifractal, reconhecida por descrever processos multiplicativos em diferentes escalas. / [en] This thesis aims at applying new concepts from solid state physics and statistical mechanics - chaos theory and fractal geometry - to the study of nonlinear dynamic systems. More precisely, it deals with a two-dimensional continuum elastoplastic Mohr-Coulomb model, commonly used to simulate pressure-sensitive materials (e.g., soils, rocks and concrete) subjected to stress-strain fields, normally found in general soil or rock mechanics problems (e.g., slope stability and underground excavations). It is shown that such many-body system is spontaneously driven to a state at the edge of chaos, called self- organized criticality (SOC), capable of developing long- range interactions in space and long-range memory in time. A new entropic form proposed by C. Tsallis is presented and shown that it is the suitable theoretical framework to deal with these problems. Furthermore, the index q of the Tsallis entropy, which measures the degree of non- additivity of the system, is calculated, for the first time, for an elastoplastic model. In addition, as is usual in non-equilibrium systems with threshold dynamics, the model changes its symmetry, from translational to fractal (that is, self-similar), leading to what is called discrete scale invariance. It is shown that this special type of scale invariance, characterized by systematic oscillatory deviations from the fundamental power-law behavior, can be used to predict the failure of heterogeneous materials, while the process is still being build-up, i.e., from precursory signals, typical of progressive failure processes. Specifically, this framework was applied, for the first time, not only to the elastoplastic geomechanical model, but to laboratory tests in sedimentary rocks as well. Finally, it is interesting to realize that the above- mentioned behaviors are also displayed by the binomial multifractal function, known to adequately describe multiplicative cascading processes.

It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed.

It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed.

It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate