Span-to-depth ratio is an important bridge design parameter that affects structural behaviour, construction costs and aesthetics. A study of 86 constant-depth girders indicates that conventional ratios have not changed significantly since 1958. These conventional ratios are now questionable, because recently developed high-strength concrete has enhanced mechanical properties that allow for slenderer sections. Based on material consumption, cost, and aesthetics comparisons, the thesis determines optimal ratios of an 8-span highway viaduct constructed with high-strength concrete. Three bridge types are investigated: cast-in-place on falsework box-girder and solid slabs, and precast segmental span-by-span box-girder. Results demonstrate that total construction cost is relatively insensitive to span-to-depth ratio over the following ranges of ratios: 10-35, 30-45, and 15-25 for the three bridge types respectively. This finding leads to greater freedom for aesthetic expressions because, compared to conventional values (i.e. 18-23, 22-39, and 16-19), higher ranges of ratios can now be selected without significant cost premiums.

External sulphate attack on concrete can lead to cracking, expansion and sometimes loss of cohesiveness of hardened cement paste. Therefore, aside from using sulphate resistant cementitious binders, it is important to design concrete which can resist sulphate penetration. In this research, both ASTM C1202 and NT Build 492 electrical migration tests were modified such that sulphate rather than chloride penetration resistances were measured. Modifications included exposing concrete specimens to Na2SO4 rather than NaCl solutions and measuring the depth of sulphate penetration visually using BaCl2+KMnO4 rather than AgNO3 solution. Nine concrete mixtures of varying w/cm, slag replacement and cement types were tested in both original standard tests and modified tests to evaluate the influence of these material variables on test results and compare chloride to sulphate results. It was found that while migration coefficients and total charge passing were lower for sulphate, the influence of material variables were relatively similar.

This study aims to present and evaluate a new method for measuring the distribution of snow within built-up environments by differencing elevations collected by an Airborne Laser Scanner (ALS) before, and during peak snow accumulation. Few efforts have been made to study the distribution of snow within built-up environments due to the false assumption that high-intensity rainfall is the main contributor to peak yearly runoff rates. Traditional techniques for measuring snow are often difficult to replicate in built-up environments due to incompatibility of methods and barriers such as buildings, roads and private property. Light Detection and Ranging (LiDAR) technology, specifically ALSs, have previously been used to characterize the distribution of snow under forest canopy, and in remote mountain environments. This study investigates and assesses the utility of high resolution, non-intrusive ALS data for estimating the depth and distribution of snow within the town of Uxbridge, Ontario. ALS flights for this study were completed before the onset of snow accumulation, as well as near peak snow accumulation for the winters of 2010 and 2011. Pre and post snow accumulation ALS measured elevations were differenced to estimate the depth of the snowpack across the entire study area at a resolution of 0.5 m. Ground measurements of snow depth were also completed within 24 hours of each of the winter flights. The LiDAR-estimated and ground-measured snow depths were compared using Spearman's rank correlation coefficient as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results from this thesis show that: 1) Snow depths estimated by differencing elevations from two ALS flights show a MAE of 3 cm and an RMSE of 10 cm when compared to ground-measured snow depths. (2) There is a strong, statistically significant relationship (ρ = 0:82, p < 0:001) between LiDAR-estimated and ground-measured snow depths. (3) An average bias of -3 cm was found for the entire dataset showing an underestimation in the LiDAR-estimated snow depths most likely caused by the effects of low lying vegetation on the fall ALS measurements. The results presented in this study demonstrate that ALSs are capable of providing high spatial resolution snow depth estimates within built-up environments. Furthermore, snow depth measurements made using an ALS can be used to increase the current body of knowledge on the distribution and re-distribution of snow within built-up environments. Snow distributions measured by an ALS could also be used for future development and verification of urban hydrological models.

A numerical solution algorithm based on finite volume method is developed for unsteady, two-dimensional, depth-averaged shallow water flow equations. The model is verified using test cases from the literature and free surface data obtained from measurements in a laboratory flume. Experiments are carried out in a horizontal, rectangular channel with vertical solid boxes attached on the sidewalls to obtain freesurface data set in flows where three-dimensionality is significant. Experimental data contain both subcritical and supercritical states. The shallow water equations are solved on a structured, rectangular grid system. Godunov type solution procedure evaluates the interface fluxes using an upwind method with an exact Riemann solver. The numerical solution reproduces analytical solutions for the test cases successfully. Comparison of the numerical results with the experimental two-dimensional free surface data is used to illustrate the limitations of the shallow water equations and improvements necessary for better simulation of such cases.

In very few mobile robotic applications stereo vision based navigation and mapping is used because dealing with stereo images is very hard and very time consuming. Despite all the problems, stereo vision still becomes one of the most important resources of knowing the world for a mobile robot because imaging provides much more information than most other sensors. Real robotic applications are very complicated because besides the problems of finding how the robot should behave to complete the task at hand, the problems faced while controlling the robot&rsquo / s internal parameters bring high computational load. Thus, finding the strategy to be followed in a simulated world and then applying this on real robot for real applications is preferable. In this study, we describe an algorithm for object recognition and cognitive map formation using stereo image data in a 3D virtual world where 3D objects and a robot with active stereo imaging system are simulated. Stereo imaging system is simulated so that the actual human visual system properties are parameterized. Only the stereo images obtained from this world are supplied to the virtual robot. By applying our disparity algorithm, depth map for the current stereo view is extracted. Using the depth information for the current view, a cognitive map of the environment is updated gradually while the virtual agent is exploring the environment. The agent explores its environment in an intelligent way using the current view and environmental map information obtained up to date. Also, during exploration if a new object is observed, the robot turns around it, obtains stereo images from different directions and extracts the model of the object in 3D. Using the available set of possible objects, it recognizes the object.

In recent years, artificial neural networks (ANNs) have been applied to estimate in many areas of hydrology and hydraulic engineering. In this thesis, multilayered feedforward backpropagation algorithm was used to establish for the prediction of unit discharge q (m3/s/m) in a rectangular free overfall. Researchers&rsquo / experimental data were used to train and validate the network with high reliability. First, an appropriate ANN model has been established by considering determination of hidden layer and node numbers related to training function and training epoch number. Then by applying sensitivity analysis, parameters involved in and their effectiveness relatively has been determined in the phenomenon. In the scope of the thesis, there are two case studies. In the first case study, ANN models reliability has been investigated according to the training data clustered and the results are given by comparing to regression analysis. In the second case, ANN models&rsquo / ability in establishing relations with different data clusters is investigated and effectiveness of ANN is scrutinized.

In this thesis, we introduce a concept of directional quantile envelopes, the intersection of the halfspaces determined by directional quantiles, and show that they allow for explicit probabilistic interpretation, compared to other multivariate quantile concepts. Directional quantile envelopes provide a way to perform multivariate quantile regression: to ``regress contours'' on covariates. We also develop theory and algorithms for an important application of multivariate quantile regression in biometry: bivariate growth charts. We prove that directional quantiles are continuous and derive their closed-form expression for elliptically symmetric distributions. We provide probabilistic interpretations of directional quantile envelopes and establish that directional quantile envelopes are essentially halfspace depth contours. We show that distributions with smooth directional quantile envelopes are uniquely determined by their envelopes. We describe an estimation scheme of directional quantile envelopes and prove its affine equivariance. We establish the consistency of the estimates of directional quantile envelopes and describe their accuracy. The results are applied to estimation of bivariate extreme quantiles. One of the main contributions of this thesis is the construction of bivariate growth charts, an important application of multivariate quantile regression. We discuss the computation of our multivariate quantile regression by developing a fast elimination algorithm. The algorithm constructs the set of active halfspaces to form a directional quantile envelope. Applying this algorithm to a large number of quantile halfspaces, we can construct an arbitrary exact approximation of the direction quantile envelope. In the remainder of the thesis, we exhibit the connection between depth contours and directional regression quantiles (Laine, 2001), stated without proof in Koenker (2005). Our proof uses the duality theory of primal-dual linear programming. Aiming at interpreting halfspace depth contours, we explore their properties for empirical distributions, absolutely continuous distributions and certain general distributions. Finally, we propose a generalized quantile concept, depth quantile, inspired by halfspace depth (Tukey, 1975) and regression depth (Rousseeuw and Hubert, 1999). We study its properties in various data-analytic situations: multivariate and univariate locations, regression with and without intercept. In the end, we show an example that while the quantile regression of Koenker and Bassett (1978) fails, our concept provides sensible answers. / Statistics

Multiterminal (MT) video coding is a practical application of the MT source coding theory. For MT source coding theory, two problems associated with achievable rate regions are well investigated into in this thesis: a new sufficient condition for BT sum-rate tightness, and the sum-rate loss for quadratic Gaussian MT source coding. Practical code design for ideal Gaussian sources with quadratic distortion measure is also achieved for cases more than two sources with minor rate loss compared to theoretical limits. However, when the theory is applied to practical applications, the performance of MT video coding has been unsatisfactory due to the difficulty to explore the correlation between different camera views. In this dissertation, we present an MT video coding scheme under the H.264/AVC framework. In this scheme, depth camera information can be optionally sent to the decoder separately as another source sequence. With the help of depth information at the decoder end, inter-view correlation can be largely improved and thus so is the compression performance. With the depth information, joint estimation from decoded frames and side information at the decoder also becomes available to improve the quality of reconstructed video frames. Experimental result shows that compared to separate encoding, up to 9.53% of the bit rate can be saved by the proposed MT scheme using decoder depth information, while up to 5.65% can be saved by the scheme without depth camera information. Comparisons to joint video coding schemes are also provided.

Reservoir characterization is one of the most important problems in petroleum engineering. It involves forward reservoir modeling that predicts the fluid behavior in the reservoir and inverse problem that calibrates created reservoir models with given data. In this dissertation, we focus on two problems in the field of reservoir characterization: depth of investigation in heterogeneous reservoirs, and history matching and uncertainty quantification of channelized reservoirs. The concept of depth of investigation is fundamental to well test analysis. Much of the current well test analysis relies on analytical solutions based on homogeneous or layered reservoirs. However, such analytic solutions are severely limited for heterogeneous and fractured reservoirs, particularly for unconventional reservoirs with multistage hydraulic fractures. We first generalize the concept to heterogeneous reservoirs and provide an efficient tool to calculate drainage volume using fast marching methods and estimate pressure depletion based on geometric pressure approximation. The applicability of proposed method is illustrated using two applications in unconventional reservoirs including flow regime visualization and stimulated reservoir volume estimation. Due to high permeability contrast and non-Gaussianity of channelized permeability field, it is difficult to history match and quantify uncertainty of channelized reservoirs using traditional approaches. We treat facies boundaries as level set functions and solve the moving boundary problem (history matching) with the level set equation. In addition to level set methods, we also exploit the problem using pixel based approach. The reversible jump Markov Chain Monte Carlo approach is utilized to search the parameter space with flexible dimensions. Both proposed approaches are demonstrated with two and three dimensional examples.

The physical properties of the protective passive films formed on the surface of stainless steels under electrochemical polarization in different electrolytes were studied. The structure of these films was analyzed as a function of depth using photoelectron spectroscopy (PES). Depth profiling (using PES) of the surface layer was achieved by either changing the angle of incidence to achieve different analysis depths (ARXPS), by argon ion etching, or by varying the energy of the incoming x-rays by the use of synchrotron radiation. The use of hard x-rays with high resolution (HAXPES) provided novel quantified information about the nickel content underneath the passive films. A complex environment was found in these surface layers composed of an outermost monolayer of iron on top of a layer of chromium hydroxides covering an underlayer of chromium oxides. Molybdenum was enriched in the interface between the metal and oxide. Nickel is enriched underneath the passive film and therefore nickeloxides are only present in the surface layer in low concentrations. A comparison was performed on austenitic and duplex stainless prepared by hot isostatically pressed (HIP) or cast and forged processes. HIP stainless steel was produced using the burgeoning technique of pressing gas atomized powders together. The structure of these steels is far more homogenous with a lower porosity than that of the conventionally prepared equivalents. It was shown that hot HIP austenitic steel had better pitting corrosion resistance than its conventional counterpart. Finally, the duplex steel was cycled in a Li-ion battery to explore its potential application as a current collector. It was shown that the passive film formed in the organic solvents is similar in composition and thickness to the films formed in aqueous solutions. However, it is doubtful if steel could be used as current collector in batteries due to its high reactivity with lithium.