A suite of NLFEA programs, VecTor, has been developed at the University of Toronto. However, this software still requires the development of other functions to execute some types of analyses. One of the required functions is the consideration of transient creep strain in the heat transfer analysis. Moreover, there is a strong need to develop a general graphics-based post-processor applicable to VecTor programs. The first objective of this thesis is to develop a function considering the effect of the transient creep strain, because it can have significant influence on the behaviour of concrete under elevated temperatures. The second purpose of this thesis is to construct the new analysis visualization features compatible with entire suite of VecTor programs. As the result, the modified post-processor, JANUS, has had its abilities expanded significantly.

A suite of NLFEA programs, VecTor, has been developed at the University of Toronto. However, this software still requires the development of other functions to execute some types of analyses. One of the required functions is the consideration of transient creep strain in the heat transfer analysis. Moreover, there is a strong need to develop a general graphics-based post-processor applicable to VecTor programs. The first objective of this thesis is to develop a function considering the effect of the transient creep strain, because it can have significant influence on the behaviour of concrete under elevated temperatures. The second purpose of this thesis is to construct the new analysis visualization features compatible with entire suite of VecTor programs. As the result, the modified post-processor, JANUS, has had its abilities expanded significantly.

VecTor is a suite of computer programs developed for the nonlinear finite element analysis of reinforced concrete structures. Due to the substantial nature of output data produced by the programs, accessing pertinent analysis information is not easily accomplished. A graphics-based post-processor would greatly improve the overall utility of the VecTor programs by allowing the multitude of information to be visually displayed and manipulated for the purposes of data synthesis and rapid verification of results. The intent of this manual is to demonstrate a post-processor program which reads and displays the results of VecTor-based analyses in a robust and straightforward manner. The proposed post-processor program, named Janus, will provide the user with the capability to display both local and global response characteristics. Janus will allow the user to comprehensively recall and manipulate structural analysis results on a model-wide basis as well as display pertinent information for individually specified elements of interest.

VecTor is a suite of computer programs developed for the nonlinear finite element analysis of reinforced concrete structures. Due to the substantial nature of output data produced by the programs, accessing pertinent analysis information is not easily accomplished. A graphics-based post-processor would greatly improve the overall utility of the VecTor programs by allowing the multitude of information to be visually displayed and manipulated for the purposes of data synthesis and rapid verification of results. The intent of this manual is to demonstrate a post-processor program which reads and displays the results of VecTor-based analyses in a robust and straightforward manner. The proposed post-processor program, named Janus, will provide the user with the capability to display both local and global response characteristics. Janus will allow the user to comprehensively recall and manipulate structural analysis results on a model-wide basis as well as display pertinent information for individually specified elements of interest.

Rotor dynamic analysis, which requires extensive amount of data and rigorous analytical processing, has been eased by the advent of powerful and affordable digital computers. By incorporating the processor and a graphical interface post processor in a single set up, this program offers a consistent and efficient approach to rotor dynamic analysis. The graphic user interface presented in this program effectively addresses the inherent complexities of rotor dynamic analyses by linking the required computational algorithms together to constitute a comprehensive program by which input data and the results are exchanged, analyzed and graphically plotted with minimal effort by the user. Just by selecting an input file and appropriate options as required, the user can carry out a comprehensive rotor dynamic analysis (synchronous response, stability analysis, critical speed analysis with undamped map) of a particular design and view the results with several options to save the plots for further verification. This approach helps the user to modify the design of turbomachinery quickly, until an efficient design is reached, with minimal compromise in all aspects.

Most existing telephone networks transmit narrowband coded speech which has been bandlimited to 4 kHz. Compared with normal speech, this speech has a muffled quality and reduced intelligibility, which is particularly noticeable in sounds such as /s/, /f/ and /sh/. Speech which has been bandlimited to 8 kHz is often coded for this reason, but this requires an increase in the bit rate. Wideband enhancement is a scheme that adds a synthesized highband signal to narrowband speech to produce a higher quality wideband speech signal. The synthesized highband signal is based entirely on information contained in the narrowband speech, and is thus achieved at zero increase in the bit rate from a coding perspective. Wideband enhancement can function as a post-processor to any narrowband telephone receiver, or alternatively it can be combined with any narrowband speech coder to produce a very low bit rate wideband speech coder. Applications include higher quality mobile, teleconferencing, and internet telephony. This thesis examines in detail each component of the wideband enhancement scheme: highband excitation synthesis, highband envelope estimation, and narrowband-highband envelope continuity. Objective and subjective test measures are formulated to assess existing and new methods for all components, and the likely limitations to the performance of wideband enhancement are also investigated. A new method for highband excitation synthesis is proposed that uses a combination of sinusoidal transform coding-based excitation and random excitation. Several new techniques for highband spectral envelope estimation are also developed. The performance of these techniques is shown to be approaching the limit likely to be achieved. Subjective tests demonstrate that wideband speech synthesized using these techniques has higher quality than the input narrowband speech. Finally, a new paradigm for very low bit rate wideband speech coding is presented in which the quality of the wideband enhancement scheme is improved further by allocating a very small bitstream for highband envelope and gain coding. Thus, this thesis demonstrates that wideband speech can be communicated at or near the bit rate of a narrowband speech coder.

[ES] La cuantificación de la incertidumbre predictiva es de vital importancia para producir predicciones hidrológicas confiables que soporten y apoyen la toma de decisiones en el marco de la gestión de los recursos hídricos. Los post-procesadores hidrológicos son herramientas adecuadas para estimar la incertidumbre predictiva de las predicciones hidrológicas (salidas del modelo hidrológico). El objetivo general de esta tesis es mejorar los métodos de post-procesamiento hidrológico para estimar la incertidumbre predictiva de caudales mensuales. Esta tesis pretende resolver dos problemas del post-procesamiento hidrológico: i) la heterocedasticidad y ii) la función de verosimilitud intratable. Los objetivos específicos de esta tesis son tres. Primero y relacionado con la heterocedasticidad, se propone y evalúa un nuevo método de post-procesamiento llamado GMM post-processor que consiste en la combinación del esquema de modelado de probabilidad Bayesiana conjunta y la mezcla de Gaussianas múltiples. Además, se comparó el desempeño del post-procesador propuesto con otros métodos tradicionales y bien aceptados en caudales mensuales a través de las doce cuencas hidrográficas del proyecto MOPEX. A partir de este objetivo (capitulo 2), encontramos que GMM post-processor es el mejor para estimar la incertidumbre predictiva de caudales mensuales, especialmente en cuencas de clima seco. Segundo, se propone un método para cuantificar la incertidumbre predictiva en el contexto de post-procesamiento hidrológico cuando sea difícil calcular la función de verosimilitud (función de verosimilitud intratable). Algunas veces en modelamiento hidrológico es difícil calcular la función de verosimilitud, por ejemplo, cuando se trabaja con modelos complejos o en escenarios de escasa información como en cuencas no aforadas. Por lo tanto, se propone el ABC post-processor que intercambia la estimación de la función de verosimilitud por el uso de resúmenes estadísticos y datos simulados. De este objetivo específico (capitulo 3), se demuestra que la distribución predictiva estimada por un método exacto (MCMC post-processor) o por un método aproximado (ABC post-processor) es similar. Este resultado es importante porque trabajar con escasa información es una característica común en los estudios hidrológicos. Finalmente, se aplica el ABC post-processor para estimar la incertidumbre de los estadísticos de los caudales obtenidos desde las proyecciones de cambio climático, como un caso particular de un problema de función de verosimilitud intratable. De este objetivo específico (capitulo 4), encontramos que el ABC post-processor ofrece proyecciones de cambio climático más confiables que los 14 modelos climáticos (sin post-procesamiento). De igual forma, ABC post-processor produce bandas de incertidumbre más realista para los estadísticos de los caudales que el método clásico de múltiples conjuntos (ensamble). / [CA] La quantificació de la incertesa predictiva és de vital importància per a produir prediccions hidrològiques confiables que suporten i recolzen la presa de decisions en el marc de la gestió dels recursos hídrics. Els post-processadors hidrològics són eines adequades per a estimar la incertesa predictiva de les prediccions hidrològiques (eixides del model hidrològic). L'objectiu general d'aquesta tesi és millorar els mètodes de post-processament hidrològic per a estimar la incertesa predictiva de cabals mensuals. Els objectius específics d'aquesta tesi són tres. Primer, es proposa i avalua un nou mètode de post-processament anomenat GMM post-processor que consisteix en la combinació de l'esquema de modelatge de probabilitat Bayesiana conjunta i la barreja de Gaussianes múltiples. A més, es compara l'acompliment del post-processador proposat amb altres mètodes tradicionals i ben acceptats en cabals mensuals a través de les dotze conques hidrogràfiques del projecte MOPEX. A partir d'aquest objectiu (capítol 2), trobem que GMM post-processor és el millor per a estimar la incertesa predictiva de cabals mensuals, especialment en conques de clima sec. En segon lloc, es proposa un mètode per a quantificar la incertesa predictiva en el context de post-processament hidrològic quan siga difícil calcular la funció de versemblança (funció de versemblança intractable). Algunes vegades en modelació hidrològica és difícil calcular la funció de versemblança, per exemple, quan es treballa amb models complexos o amb escenaris d'escassa informació com a conques no aforades. Per tant, es proposa l'ABC post-processor que intercanvia l'estimació de la funció de versemblança per l'ús de resums estadístics i dades simulades. D'aquest objectiu específic (capítol 3), es demostra que la distribució predictiva estimada per un mètode exacte (MCMC post-processor) o per un mètode aproximat (ABC post-processor) és similar. Aquest resultat és important perquè treballar amb escassa informació és una característica comuna als estudis hidrològics. Finalment, s'aplica l'ABC post-processor per a estimar la incertesa dels estadístics dels cabals obtinguts des de les projeccions de canvi climàtic. D'aquest objectiu específic (capítol 4), trobem que l'ABC post-processor ofereix projeccions de canvi climàtic més confiables que els 14 models climàtics (sense post-processament). D'igual forma, ABC post-processor produeix bandes d'incertesa més realistes per als estadístics dels cabals que el mètode clàssic d'assemble. / [EN] The predictive uncertainty quantification in monthly streamflows is crucial to make reliable hydrological predictions that help and support decision-making in water resources management. Hydrological post-processing methods are suitable tools to estimate the predictive uncertainty of deterministic streamflow predictions (hydrological model outputs). In general, this thesis focuses on improving hydrological post-processing methods for assessing the conditional predictive uncertainty of monthly streamflows. This thesis deal with two issues of the hydrological post-processing scheme i) the heteroscedasticity problem and ii) the intractable likelihood problem. Mainly, this thesis includes three specific aims. First and relate to the heteroscedasticity problem, we develop and evaluate a new post-processing approach, called GMM post-processor, which is based on the Bayesian joint probability modelling approach and the Gaussian mixture models. Besides, we compare the performance of the proposed post-processor with the well-known exiting post-processors for monthly streamflows across 12 MOPEX catchments. From this aim (chapter 2), we find that the GMM post-processor is the best suited for estimating the conditional predictive uncertainty of monthly streamflows, especially for dry catchments. Secondly, we introduce a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be challenging to estimate the likelihood itself in hydrological modelling, especially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. With this aim in mind (chapter 3), we prove that the conditional predictive distribution is similarly produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor), qualitatively speaking. This finding is significant because dealing with scarce information is a common condition in hydrological studies. Finally, we apply the ABC post-processing method to estimate the uncertainty of streamflow statistics obtained from climate change projections, such as a particular case of intractable likelihood problem. From this specific objective (chapter 4), we find that the ABC post-processor approach: 1) offers more reliable projections than 14 climate models (without post-processing); 2) concerning the best climate models during the baseline period, produces more realistic uncertainty bands than the classical multi-model ensemble approach. / I would like to thank the Gobernación del Huila Scholarship Program No. 677 (Colombia) for providing the financial support for my PhD research. / Romero Cuellar, J. (2019). Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/133999