Most of the reinforced concrete systems are monolithic. During construction, concrete from the bottom of the deepest beam to the top of slab, is placed at once. Therefore the slab serves as the top flange of the beams. Such a beam is referred to as T-beam. In a floor system made of T-beams, the compressive stress is a maximum over the web, dropping between the webs. The distribution of compressive stress on the flange depends on the relative dimensions of the cross section, span length, support and loading conditions. For simplification, the varying distribution of compressive stress can be replaced by an equivalent uniform distribution. This gives us an effective flange width, which is smaller than the real flange width. In various codes there are recommendations for effective flange width formulas. But these formulas are expressed only in terms of span length or flange and web thicknesses and ignore the other important variables. In this thesis, three-dimensional finite element analysis has been carried out on continuous T-beams under different loading conditions to assess the effective flange width based on displacement criterion. The formulation is based on a combination of the elementary bending theory and the finite element method, accommodating partial interaction in between. The beam spacing, beam span length, total depth of the beam, the web and the flange thicknesses are considered as independent variables. Depending on the type of loading, the numerical value of the moment of inertia of the transformed beam crosssection and hence the effective flange width are calculated. The input data and the finite element displacement results are then used in a nonlinear regression analysis and two explicit design formulas for effective flange width have been derived. Comparisons are made between the proposed formulas and the ACI, Eurocode, TS-500 and BS-8110 code recommendations.

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies.

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies.

A new reliability-based probabilistic model is developed for the estimation of liquefaction-induced lateral ground spreading, taking into consideration the uncertainties within the model functional form and the descriptive variables as well. The new model is also introduced as performance-based probabilistic engineering tool.

Os controladores eletrônicos de pulverização visam minimizar a variação das taxas de insumos aplicadas no campo. Eles fazem parte de um sistema de controle, e permitem a compensação da variação de velocidade de deslocamento do pulverizador durante a operação. Há vários tipos de controladores eletrônicos de pulverização disponíveis no mercado e uma das formas de selecionar qual o mais eficiente nas mesmas condições, ou seja, em um mesmo sistema de controle, é quantificar o tempo de resposta do sistema para cada controlador específico. O objetivo desse trabalho foi estimar os tempos de resposta para mudanças de velocidade de um sistema eletrônico de pulverização via modelos de regressão não lineares, estes, resultantes da soma de regressões lineares ponderadas por funções distribuição acumulada. Os dados foram obtidos no Laboratório de Tecnologia de Aplicação, localizado no Departamento de Engenharia de Biossistemas da Escola Superior de Agricultura \"Luiz de Queiroz\", Universidade de São Paulo, no município de Piracicaba, São Paulo, Brasil. Os modelos utilizados foram o logístico e de Gompertz, que resultam de uma soma ponderada de duas regressões lineares constantes com peso dado pela função distribuição acumulada logística e Gumbell, respectivamente. Reparametrizações foram propostas para inclusão do tempo de resposta do sistema de controle nos modelos, com o objetivo de melhorar a interpretação e inferência estatística dos mesmos. Foi proposto também um modelo de regressão não linear difásico que resulta da soma ponderada de regressões lineares constantes com peso dado pela função distribuição acumulada Cauchy seno hiperbólico exponencial. Um estudo de simulação foi feito, utilizando a metodologia de Monte Carlo, para avaliar as estimativas de máxima verossimilhança dos parâmetros do modelo. / The electronic controllers spray aimed at minimizing the variation of inputs rates applied in the field. They are part of a control system, and allow for compensation for variation spray travel speed during operation. There are several types of electronic spray controllers on the market and one way to select which more efficient under the same conditions, ie in the same system of control, is to quantify the system response time for each specific driver. The objective of this study was to estimate the response times for changes in speed of an electronic spraying system via nonlinear regression models, these resulting from the sum of weighted linear regressions for cumulative distribution functions. Data were obtained on the Application Technology Laboratory, located in the Department of Biosystems Engineering from College of Agriculture \"Luiz de Queiroz\", University of Sao Paulo, in Piracicaba, Sao Paulo, Brazil. The models used were the logistic and Gompertz, resulting from a weighted sum of two constant linear regressions with weight given by the cumulative distribution function logistics and Gumbell respectively. Reparametrization been proposed for inclusion in the control system response time models, in order to improve the statistical interpretation and inference of the same. It has also been proposed a non-linear regression model two-phase which is the weighted sum of constant linear regressions weight given by a cumulative distribution function exponential hyperbolic sine Cauchy in which a simulation study was conducted using the methodology of Monte Carlo to evaluating the maximum likelihood estimates of the model parameters.

This thesis deals with solutions of nonlinear regression problems using R programming language. The introductory theoretical part is devoted to familiarization with the principles of solving nonlinear regression models and of their applications in the program R. In both, theoretical and practical part, the most famous and used differentiator algorithms are presented, particularly the Gauss-Newton's and of the steepest descent method, for estimating the parameters of nonlinear regression. Further, in the practical part, there are some demo solutions of particular tasks using nonlinear regression methods. Overall, a large number of graphs processed by the author is used in this thesis for better comprehension.

A fully automated Temperature Dependent Hall (TDH) measurement setup has been assembled for the purposes of this study. This TDH setup is capable of measuring samples in the 20 K to 370 K temperature range. Sample sizes of up to 20 mm × 20 mm can be accommodated by the custom designed and manufactured sample holder. Samples with a resistance in the 1Ω to 250 MΩ range can be measured with this setup provided that the mobility of the sample is greater than 1 cm²/Vs. The computer program controlling the automated measurement processwas written in LabView™ version 6.1. Single crystal Zinc Oxide (ZnO) was the material under investigation in this study. Bulk ZnO samples grown by three different methods, namely pressurized melt growth, seeded chemical vapor transport (SCVT) growth and hydrothermal growth, were measured in the 20 K to 370 K range. The effect of annealing in argon atmosphere in the 550 ºC to 930 ºC range was investigated on all three ZnO types. In addition, hydrogen-implanted layers on semi-insulating hydrothermally grown ZnO were studied. These samples were annealed in the 200 ºC to 400 ºC range and Hall measurements in the 20 K to 330 K range were performed. Programs were written to fit, wherever possible, the obtained temperature dependent carrier concentration and mobility profiles to suitable theoretical models. The carrier concentration data was fitted to a multi-donor single acceptor charge balance equation for the purpose of extracting donor concentrations and activation energies. Before fitting, the data was corrected for the Hall scattering factor and, where necessary, for two-layer effects particularly a degenerate surface conduction channel that developed through annealing on the SCVT-grown and hydrothermally grown samples. The acceptor concentrations of the samples were obtained by fitting the mobility data to a model based on D.L. Rode’s method of solving the Boltzmann transport equation. Scattering mechanisms included in the model were piezoelectric and deformation potential acoustic modes, polar optic modes and ionized impurity scattering. It was found that the mobility data did not fit the model very well without assigning questionable values to other parameters, in this case the deformation potential. Plausible values for the acceptor concentration were however obtained. The carrier concentration data fitted the model well, but due to the large number of parameters to be extracted (up to six parameters in the case of three donors) there was often not much certainty in the extracted values This study shows that TDH analysis is a valuable tool to assess the quality of semiconductors. Bulk and degenerate surface (or interfacial) conduction are separated with relative ease, and shallow defect concentrations as well as compensation level concentrations could be extracted. The generally observed uncertainty in values obtained in the multi-parameter regression of carrier concentration data indicates that supplementary techniques such as photoluminescence are needed to support results obtained by the TDH technique. / Dissertation (MSc (Physics))--University of Pretoria, 2007. / Physics / MSc / unrestricted

The forecast of the waste production is an important information for planning in waste management. The historical data often consists of short time series, therefore traditional prognostic approaches fail. The mathematical model for forecasting of future waste production based on spatially distributed data with hierarchically structure is suggested in this thesis. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The emphasis is on decomposition of extensive model into subtasks, which lead to a simpler implementation. The output of this thesis is tool tested within case study on municipal waste production data in the Czech Republic.

No description available.

Composite indicators, also called indices, are widely used synthetic measures for ranking and benchmarking alternatives across complex concepts. The aim of constructing a composite indicator is, among other things, to simplify and condense the information of a plurality of underlying indicators. However, to avoid misleading results, it is important to ensure that the construction is performed in a transparent and representative manner. To this end, this thesis aims to aid the construction of the Electricity Supply Resilience Index (ESRI) – which is a novel energy index, developed within the Future Resilient Systems (FRS) programme at the Singapore-ETH Centre (SEC) – by looking at the complementary and fundamental component of index aggregation, namely the weighting of the indicators. Normally, weights are assigned to reflect the relative importance of each indicator, based on stakeholders’ or decision-makers’ preferences. Consequently, the weights are often perceived to be importance coefficients, independent from the dataset under analysis. However, it has recently been shown that the structure of the dataset and correlations between the indicators often have a decisive effect on each indicator’s importance in the index. In fact, their importance rarely coincides with the assigned weights. This phenomenon is sometimes referred to as implicit weights. The aim of this thesis is to assess the implicit weights in the aggregation of ESRI.  For this purpose, a six-step analytical framework, based on a novel variance-based sensitivity analysis approach, is presented and applied to ESRI. The resulting analysis shows that statistical dependencies between ESRI’s underlying indicators have direct implications on the outcome values – the equal weights assigned a-priori do not correspond to an equal influence from each indicator. Furthermore, when attempting to optimise the weights to balance the contribution of each indicator, it is found that this would require a highly unbalanced set of weights and come at the expense of representing the indicators in an effective manner. Thereby, it can be concluded that there are significant dependencies between the indicators and that their correlations need to be accounted for to achieve a balanced and representative index construction. Guided by these findings, this thesis provides three recommendations for improving the statistical representation and conceptual coherence of ESRI. These include: (1) avoid aggregating a negatively correlated indicator – keep it aside, (2) remove a conceptually problematic indicator – revise its construction or conceptual contribution, and (3) aggregate three collinear and conceptually intersecting indicators into a sub-index, prior to aggregation – limit their overrepresentation. By revising the index according to these three recommendations, it is found that ESRI showcases a greater conceptual and statistical coherence. It can thus be concluded that the analytical framework, proposed in this thesis, can aid the development of representative indices. / Kompositindikatorer (eller index) är populära verktyg som ofta används vid rankning och benchmarking av olika alternativ utifrån komplexa koncept. Syftet med att konstruera ett index är, bland annat, att förenkla och sammanfatta informationen från ett flertal underliggande indikatorer. För att undvika missvisande resultat är det därmed viktigt att konstruera index på ett transparent och representativt sätt. Med detta i åtanke, avser denna uppsats att stödja konstruktionen av Electricity Supply Resilience Index (ESRI) – vilket är ett nyutvecklat energiindex, framtaget inom Future Resilient Systems (FRS) programmet på Singapore-ETH Centre (SEC). Detta görs genom att studera ett vanligt fenomen (s.k. implicita vikter) som gör sig gällande i ett av konstruktionsstegen, då de underliggande indikatorerna ska viktas och aggregeras till ett index. I detta steg tilldelas vanligtvis vikter till de enskilda indikatorerna som ska spegla deras relativa betydelse i indexet. Det har dock nyligen visats att datastrukturen och korrelationer mellan indikatorerna har en avgörande påverkan på varje indikators betydelse i indexet, vilket ibland kan vara helt oberoende av vikten de tilldelats. Detta fenomen kallas ibland för implicita vikter, då de ej är explicit tilldelade utan uppkommer från datastrukturen. Syftet med denna uppsatts är således att undersöka de implicita vikterna i aggregationen av ESRI.  För detta ändamål sker en tillämpning och utökning av en nyutvecklad variansbaserad känslighetsanalys, baserad på olinjär regression, för bedömning av implicita vikter i kompositindikatorer. Resultaten från denna analys visar att statistiska beroenden mellan ESRIs underliggande indikatorer har direkt inverkan på varje indikators betydelse i indexet. Detta medför att vikterna ej överensstämmer med indikatorernas betydelse. Följaktligen utförs en vikt-optimering, för att balansera bidraget från varje indikator. Utifrån resultaten av denna vikt-optimering kan det konstateras att det inte är tänkbart att balansera bidraget från varje indikator genom att justera vikterna. Om så görs, skulle det ske på bekostnad av att kunna representera varje indikator på ett effektivt sätt. Därmed kan slutsatsen dras att det finns tydliga beroenden mellan indikatorer och att deras korrelationerna måste tas i hänsyn för att uppnå en balanserad och representativ indexkonstruktion. Utifrån dessa insikter presenteras tre rekommendationer för att förbättra den statistiska representationen och konceptuella samstämmigheten i ESRI. Dessa innefattar: (1) Undvik att aggregera en negativt korrelerad indikator - behåll den vid sidan av, (2) ta bort en konceptuellt problematisk indikator - revidera dess konstruktion eller konceptuella bidrag, och (3) sammanställ tre kollinära och konceptuellt överlappande indikatorer i ett sub-index, före aggregering - begränsa deras överrepresentation. När dessa rekommendationer implementerats står det klart att den reviderade ESRI påvisar en förbättrad konceptuell och statistisks samstämmighet. Därmed kan det fastställas att det analytiska verktyg som presenteras i denna uppsats kan bidra till utvecklingen av representativa index.