In the past, transportation related economic analysis has considered agency related costs only. However, transportation managers are moving towards more holistic economic analysis including road user and environmental costs and benefits. In particular, transportation air pollution is causing increasing harm to health and the environment. Transport managers are now considering related emissions in transport economical analyses, and have established strategies to help meet Kyoto Protocol targets, which specified a fifteen percent reduction in Canada's emissions related to 1990 levels within 2008-2012.<p>The objectives of this research are to model heavy vehicle emissions using a emissions computer model which is able to assess various transport applications, and help improve holistic economic transport modeling. Two case studies were evaluated with the model developed.<p>Firstly, the environmental benefits of deploying weigh-in-motion systems at weigh stations to pre-sort heavy vehicles and reduce delays were assessed. The second case study evaluates alternative truck sizes and road upgrades within short heavy oilfield haul in Western Canada. <p>The model developed herein employed a deterministic framework from a sensitivity analysis across independent variables, which identified the most sensitive variables to primary field state conditions. The variables found to be significant included idling time for the weigh-in-motion case study, road stiffness and road grades for the short heavy haul oilfield case study.<p>According to this research, employing WIM at weigh stations would reduce annual Canadian transportation CO₂ emissions by nearly 228 kilo tonnes, or 1.04 percent of the Canadian Kyoto Protocol targets. Regarding direct fuel savings, WIM would save from 90 to 190 million litres of fuel annually, or between $59 and $190 million of direct operating costs.<p>Regarding the short heavy oil haul case study, increasing allowable heavy vehicle sizes while upgrading roads could decrease the annual emissions, the fuel consumption, and their associated costs by an average of 68 percent. Therefore, this could reduce each rural Saskatchewan municipality's annual CO₂ emissions from 13 to 26.7-kilo tonnes, which translates to 0.06 and 0.12 percent of the Canadian Kyoto Protocol targets or between $544,000 and $ 1.1 million annually. <p>Based on these results, the model demonstrates its functionality, and was successfully applied to two typical transportation field state applications. The model generated emissions savings results that appear to be realistic, in terms of potential Kyoto targets, as well as users cost reductions and fuel savings.

No description available.

This study presents different aspects on the use of deterministic methods including Artificial Neural Networks (ANNs), and linear and nonlinear regression, as well as probabilistic methods including Bayesian inference and Monte Carlo methods to develop reliable solutions for challenging problems in geotechnics. This study addresses the theoretical and computational advantages and limitations of these methods in application to: 1) prediction of the stiffness and strength of stabilized organic soils, 2) determination of unknown foundations for bridges vulnerable to scour, and 3) uncertainty quantification for one-dimensional diffusion processes. ANNs were successfully implemented in this study to develop nonlinear models for the mechanical properties of stabilized organic soils. ANN models were able to learn from the training examples and then generalize the trend to make predictions for the stiffness and strength of stabilized organic soils. A stepwise parameter selection and a sensitivity analysis method were implemented to identify the most relevant factors for the prediction of the stiffness and strength. Also, the variations of the stiffness and strength with respect to each factor were investigated. A deterministic and a probabilistic approach were proposed to evaluate the characteristics of unknown foundations of bridges subjected to scour. The proposed methods were successfully implemented and validated by collecting data for bridges in the Bryan District. ANN models were developed and trained using the database of bridges to predict the foundation type and embedment depth. The probabilistic Bayesian approach generated probability distributions for the foundation and soil characteristics and was able to capture the uncertainty in the predictions. The parametric and numerical uncertainties in the one-dimensional diffusion process were evaluated under varying observation conditions. The inverse problem was solved using Bayesian inference formulated by both the analytical and numerical solutions of the ordinary differential equation of diffusion. The numerical uncertainty was evaluated by comparing the mean and standard deviation of the posterior realizations of the process corresponding to the analytical and numerical solutions of the forward problem. It was shown that higher correlation in the structure of the observations increased both parametric and numerical uncertainties, whereas increasing the number of data dramatically decreased the uncertainties in the diffusion process.

Ce travail de thèse concerne la modélisation du canal de propagation dans les milieux industriels. Dans ce contexte, le canal de propagation a un comportement différent de celui classiquement rencontré en indoor. Cela est dû à l'aménagement des bâtiments qui sont plus grand et ouverts ainsi qu'à la présence de machines, d'objets mobiles et d'autres matériaux métalliques rencontrés dans ces environnements. Ainsi, les modèles de canaux indoor existants ne sont plus valides. L'utilisation de modèles déterministes comme alternative est possible mais limitée en raison du temps de calcul qui en découle.Pour répondre à cette problématique, nous proposons un modèle hybride de canal s'inspirant d'une méthode à tracer de rayons 3D et du modèle WINNER. L'originalité de ce modèle repose sur son caractère hybride consistant, en prétraitement, à partitionner l'environnement en zones de visibilité ou de non-visibilité «faible» et «forte» sur des critères déterministes liés à la propagation des ondes. Un modèle statistique, type WINNER, reprenant le concept de cluster est ensuite "joué" au sein de chacune des ces zones reproduisant ainsi fidèlement l'évolution des paramètres caractéristiques des clusters identifiés. Nous avons validé notre modèle en le comparant d'abord à un modèle déterministe et ensuite à la mesure. Sa robustesse ainsi que celle de WINNER sont testées en les simulant dans trois environnements différents et en les comparant au modèle déterministe à tracer de rayons. / This thesis focuses on the modeling of the propagation channel in industrial environments. In this context, the propagation channel has a different behavior than typically encountered in indoor. This is due to the construction of buildings that are larger and open and the presence of machines, moving objects and metal materials encountered in these environments. Thus, the existing indoor channel models are not valid. Using deterministic models as an alternative is possible, but limited by the computing time.To address this problem, we propose a hybrid channel model for communications in industrial environments inspired by a ray tracing method and Winner model. The originality of this model is its hybrid nature consisting, in preprocessing, in partitionning the environment in areas of visibility or non-visibility «weak» and «strong» based on deterministic criteria related to the wave propagation. A statistical model, as WINNER , using the concept of cluster is then played in each of these areas and faithfully reproducing the evolution of the characteristic parameters of the identified clusters. We have validated our model by comparing it firstly to a deterministic model and then to measurement. Its robustness as well that of WINNER are tested by simulating them in three different environments and by comparing them with the deterministic model.

Master’s thesis deals with the problems of heat exchanger network synthesis and compare the present methods with emphasis on Pinch Design Method and deterministic method. Based on theoretical formulation of deterministic model the computer program for heat exchanger network synthesis was developed in the software Maple environment. Developed software implementation of deterministic method has been applied to several case studies.