Shear-wall dominant multi-story reinforced concrete structures, constructed by using a special tunnel form technique are commonly built in countries facing a substantial seismic risk, such as Chile, Japan, Italy and Turkey. In 1999, two severe urban earthquakes struck Kocaeli and D&uuml / zce provinces in Turkey with magnitudes (Mw) 7.4 and 7.1, respectively. These catastrophes caused substantial structural damage, casualties and loss of lives. In the aftermath of these destructive earthquakes, neither demolished nor damaged shear-wall dominant buildings constructed by tunnel form techniques were reported. In spite of their high resistance to earthquake excitations, current seismic code provisions including the Uniform Building Code and the Turkish Seismic Code present limited information for their design criteria. This study presents experimental investigation of the panel unit having H-geometry. To investigate the seismic behavior of panel buildings, two prototype test specimens which have H wall design were tested at the Structural Mechanics Laboratory at METU. The experimental work involves the testing of two four-story, 1/5-scale reinforced concrete panel form building test specimens under lateral reversed loading, simulating the seismic forces and free vibration tests. Free vibration tests before and after cracking were done to assess the differences between the dynamic properties of uncracked and cracked test specimens. A moment-curvature program named Waller2002 for shear walls is developed to include the effects of steel strain hardening, confinement of concrete and tension strength of concrete. The moment-curvature relationships of panel form test specimens showed that walls with very low longitudinal steel ratios exhibit a brittle flexural failure with very little energy absorption. Shear walls of panel form test specimens have a reinforcement ratio of 0.0015 in the longitudinal and vertical directions. Under gradually increasing reversed lateral loading, the test specimens reached ultimate strength, as soon as the concrete cracked, followed by yielding and then rupturing of the longitudinal steel. The displacement ductility of the panel form test specimens was found to be very low. Thus, the occurrence of rupture of the longitudinal steel, as also observed in analytical studies, has been experimentally verified. Strength, stiffness, energy dissipation and story drifts of the test specimens were examined by evaluating the test results.

This thesis proposes machine learning algorithms for processing geometry by example. Each algorithm takes as input a collection of shapes along with exemplar values of target properties related to shape processing tasks. The goal of the algorithms is to output a function that maps from the shape data to the target properties. The learned functions can be applied to novel input shape data in order to synthesize the target properties with style similar to the training examples. Learning such functions is particularly useful for two different types of geometry processing problems. The first type of problems involves learning functions that map to target properties required for shape interpretation and understanding. The second type of problems involves learning functions that map to geometric attributes of animated shapes required for real-time rendering of dynamic scenes. With respect to the first type of problems involving shape interpretation and understanding, I demonstrate learning for shape segmentation and line illustration. For shape segmentation, the algorithms learn functions of shape data in order to perform segmentation and recognition of parts in 3D meshes simultaneously. This is in contrast to existing mesh segmentation methods that attempt segmentation without recognition based only on low-level geometric cues. The proposed method does not require any manual parameter tuning and achieves significant improvements in results over the state-of-the-art. For line illustration, the algorithms learn functions from shape and shading data to hatching properties, given a single exemplar line illustration of a shape. Learning models of such artistic-based properties is extremely challenging, since hatching exhibits significant complexity as a network of overlapping curves of varying orientation, thickness, density, as well as considerable stylistic variation. In contrast to existing algorithms that are hand-tuned or hand-designed from insight and intuition, the proposed technique offers a largely automated and potentially natural workflow for artists. With respect to the second type of problems involving fast computations of geometric attributes in dynamic scenes, I demonstrate algorithms for learning functions of shape animation parameters that specifically aim at taking advantage of the spatial and temporal coherence in the attribute data. As a result, the learned mappings can be evaluated very efficiently during runtime. This is especially useful when traditional geometric computations are too expensive to re-estimate the shape attributes at each frame. I apply such algorithms to efficiently compute curvature and high-order derivatives of animated surfaces. As a result, curvature-dependent tasks, such as line drawing, which could be previously performed only offline for animated scenes, can now be executed in real-time on modern CPU hardware.

This thesis proposes machine learning algorithms for processing geometry by example. Each algorithm takes as input a collection of shapes along with exemplar values of target properties related to shape processing tasks. The goal of the algorithms is to output a function that maps from the shape data to the target properties. The learned functions can be applied to novel input shape data in order to synthesize the target properties with style similar to the training examples. Learning such functions is particularly useful for two different types of geometry processing problems. The first type of problems involves learning functions that map to target properties required for shape interpretation and understanding. The second type of problems involves learning functions that map to geometric attributes of animated shapes required for real-time rendering of dynamic scenes. With respect to the first type of problems involving shape interpretation and understanding, I demonstrate learning for shape segmentation and line illustration. For shape segmentation, the algorithms learn functions of shape data in order to perform segmentation and recognition of parts in 3D meshes simultaneously. This is in contrast to existing mesh segmentation methods that attempt segmentation without recognition based only on low-level geometric cues. The proposed method does not require any manual parameter tuning and achieves significant improvements in results over the state-of-the-art. For line illustration, the algorithms learn functions from shape and shading data to hatching properties, given a single exemplar line illustration of a shape. Learning models of such artistic-based properties is extremely challenging, since hatching exhibits significant complexity as a network of overlapping curves of varying orientation, thickness, density, as well as considerable stylistic variation. In contrast to existing algorithms that are hand-tuned or hand-designed from insight and intuition, the proposed technique offers a largely automated and potentially natural workflow for artists. With respect to the second type of problems involving fast computations of geometric attributes in dynamic scenes, I demonstrate algorithms for learning functions of shape animation parameters that specifically aim at taking advantage of the spatial and temporal coherence in the attribute data. As a result, the learned mappings can be evaluated very efficiently during runtime. This is especially useful when traditional geometric computations are too expensive to re-estimate the shape attributes at each frame. I apply such algorithms to efficiently compute curvature and high-order derivatives of animated surfaces. As a result, curvature-dependent tasks, such as line drawing, which could be previously performed only offline for animated scenes, can now be executed in real-time on modern CPU hardware.

Vascular diseases are among the leading causes of death in Canada and around the globe. A major underlying cause of most such medical conditions is atherosclerosis, a gradual accumulation of plaque on the walls of blood vessels. Particularly vulnerable to atherosclerosis is the carotid artery, which carries blood to the brain. Dangerous narrowing of the carotid artery can lead to embolism, a dislodgement of plaque fragments which travel to the brain and are the cause of most strokes. If this pathology can be detected early, such a deadly scenario can be potentially prevented through treatment or surgery. This not only improves the patient's prognosis, but also dramatically lowers the overall cost of their treatment. Medical imaging is an indispensable tool for early detection of atherosclerosis, in particular since the exact location and shape of the plaque need to be known for accurate diagnosis. This can be achieved by locating the plaque inside the artery and measuring its volume or texture, a process which is greatly aided by image segmentation. In particular, the use of ultrasound imaging is desirable because it is a cost-effective and safe modality. However, ultrasonic images depict sound-reflecting properties of tissue, and thus suffer from a number of unique artifacts not present in other medical images, such as acoustic shadowing, speckle noise and discontinuous tissue boundaries. A robust ultrasound image segmentation technique must take these properties into account. Prior to segmentation, an important pre-processing step is the extraction of a series of features from the image via application of various transforms and non-linear filters. A number of such features are explored and evaluated, many of them resulting in piecewise smooth images. It is also proposed to decompose the ultrasound image into several statistically distinct components. These components can be then used as features directly, or other features can be obtained from them instead of the original image. The decomposition scheme is derived using Maximum-a-Posteriori estimation framework and is efficiently computable. Furthermore, this work presents and evaluates an algorithm for segmenting the carotid artery in 3D ultrasound images from other tissues. The algorithm incorporates information from different sources using an energy minimization framework. Using the ultrasound image itself, statistical differences between the region of interest and its background are exploited, and maximal overlap with strong image edges encouraged. In order to aid the convergence to anatomically accurate shapes, as well as to deal with the above-mentioned artifacts, prior knowledge is incorporated into the algorithm by using weak geometric priors. The performance of the algorithm is tested on a number of available 3D images, and encouraging results are obtained and discussed.

Stress-strain relations for unconfined and confined concrete are proposed to overcome some shortcomings of existing commonly used models. Specifically, existing models are neither easy to invert nor integrate to obtain equivalent rectangular stress-block parameters for hand analysis and design purposes. The stress?strain relations proposed are validated for a whole range of concrete strengths and confining stresses. Then, closed form expressions are derived for the equivalent rectangular stress-block parameters. The efficacy of the results is demonstrated for hand analysis applied for deriving the moment-curvature performance of a confined concrete column. Results are compared with those obtained from a computational fiber-element using the proposed stress-strain model and another widely used model; good agreement between the two is observed. The model is then utilized in the development of a new structural system that utilizes the positive attributes of timber and concrete to form a parallel. Timber has the advantage of being a light weight construction material, easy to handle, is environmentally friendly. However, large creep deflections and significant issues with sound transmission (the footfall problem) generally limit timber use to small spans and low rise buildings. Concrete topping on timber sub-floors mitigate some of these issues, but even with well engineered wood systems, the spans are relatively short. In this study, a new structural system called structural boxed-concrete, which utilizes the positive attributes of both timber and reinforced concrete to form a parallel system (different from timber-concrete composite system) is explored. A stress-block approach is developed to calculate strength and deformation. An analytical stress-block based moment-curvature analysis is performed on the timber-boxed concrete structural elements. Results show that the structural timber-boxed concrete members may have better strength and ductility capacities when compared to an equivalent ordinary reinforced concrete member.

Increasingly stringent pollution and emission controls have caused a rise in the use of combustors operating under lean, premixed conditions. Operating lean (excess air) lowers the level of nitrous oxides (NOx) emitted to the environment. In addition, concerns over climate change due to increased carbon dioxide (CO2) emissions and the need for energy independence in the United States have spurred interest in developing combustors capable of operating with a wide range of fuel compositions. One method to decrease the carbon footprint of modern combustors is the use of high hydrogen content (HHC) fuels. The objective of this research is to develop tools to better understand the physics of turbulent flame propagation in highly stretch sensitive premixed flames in order to predict their behavior at conditions realistic to the environment of gas turbine combustors. This thesis presents the results of an experimental study into the flame propagation characteristics of highly stretch-sensitive, turbulent premixed flames generated in a low swirl burner (LSB). This study uses a scaling law, developed in an earlier thesis from leading point concepts for turbulent premixed flames, to collapse turbulent flame speed data over a wide range of conditions. The flow and flame structure are characterized using high speed particle image velocimetry (PIV) over a wide range of fuel compositions, mean flow velocities, and turbulence levels. The first part of this study looks at turbulent flame speeds for these mixtures and applies the previously developed leading points scaling model in order to test its validity in an alternate geometry. The model was found to collapse the turbulent flame speed data over a wide range of fuel compositions and turbulence levels, giving merit to the leading points model as a method that can produce meaningful results with different geometries and turbulent flame speed definitions. The second part of this thesis examines flame front topologies and stretch statistics of these highly stretch sensitive, turbulent premixed flames. Instantaneous flame front locations and local flow velocities are used to calculate flame curvatures and tangential strain rates. Statistics of these two quantities are calculated both over the entire flame surface and also conditioned at the leading points of the flames. Results presented do not support the arguments made in the development of the leading points model. Only minor effects of fuel composition are noted on curvature statistics, which are mostly dominated by the turbulence. There is a stronger sensitivity for tangential strain rate statistics, however, time-averaged values are still well below the values hypothesized from the leading points model. The results of this study emphasize the importance of local flame topology measurements towards the development of predictive models of the turbulent flame speed.

Vascular diseases are among the leading causes of death in Canada and around the globe. A major underlying cause of most such medical conditions is atherosclerosis, a gradual accumulation of plaque on the walls of blood vessels. Particularly vulnerable to atherosclerosis is the carotid artery, which carries blood to the brain. Dangerous narrowing of the carotid artery can lead to embolism, a dislodgement of plaque fragments which travel to the brain and are the cause of most strokes. If this pathology can be detected early, such a deadly scenario can be potentially prevented through treatment or surgery. This not only improves the patient's prognosis, but also dramatically lowers the overall cost of their treatment. Medical imaging is an indispensable tool for early detection of atherosclerosis, in particular since the exact location and shape of the plaque need to be known for accurate diagnosis. This can be achieved by locating the plaque inside the artery and measuring its volume or texture, a process which is greatly aided by image segmentation. In particular, the use of ultrasound imaging is desirable because it is a cost-effective and safe modality. However, ultrasonic images depict sound-reflecting properties of tissue, and thus suffer from a number of unique artifacts not present in other medical images, such as acoustic shadowing, speckle noise and discontinuous tissue boundaries. A robust ultrasound image segmentation technique must take these properties into account. Prior to segmentation, an important pre-processing step is the extraction of a series of features from the image via application of various transforms and non-linear filters. A number of such features are explored and evaluated, many of them resulting in piecewise smooth images. It is also proposed to decompose the ultrasound image into several statistically distinct components. These components can be then used as features directly, or other features can be obtained from them instead of the original image. The decomposition scheme is derived using Maximum-a-Posteriori estimation framework and is efficiently computable. Furthermore, this work presents and evaluates an algorithm for segmenting the carotid artery in 3D ultrasound images from other tissues. The algorithm incorporates information from different sources using an energy minimization framework. Using the ultrasound image itself, statistical differences between the region of interest and its background are exploited, and maximal overlap with strong image edges encouraged. In order to aid the convergence to anatomically accurate shapes, as well as to deal with the above-mentioned artifacts, prior knowledge is incorporated into the algorithm by using weak geometric priors. The performance of the algorithm is tested on a number of available 3D images, and encouraging results are obtained and discussed.

This Ph.D. thesis specifically deals with the issue of metamorphosis of 3D objects represented as 3D triangular meshes. The objective is to elaborate a complete 3D mesh morphing methodology which ensures high quality transition sequences, smooth and gradual, consistent with respect to both geometry and topology, and visually pleasant. Our first contributions concern the two different approaches of parameterization: a new barycentric mapping algorithm based on the preservation of the mesh length ratios, and a spherical parameterization technique, exploiting a Gaussian curvature criterion. The experimental evaluation, carried out on 3D models of various shapes, demonstrated a considerably improvement in terms of mesh distortion for both methods. In order to align the features of the two input models, we have considered a warping technique based on the CTPS C2a radial basis function suitable to deform the models embeddings in the parametric domain maintaining a valid mapping through the entire movement process. We show how this technique has to be adapted in order to warp meshes specified in the parametric domains. A final contribution consists of a novel algorithm for constructing a pseudo-metamesh that avoids the complex process of edge intersections encountered in the state-of-the-art. The obtained mesh structure is characterized by a small number of vertices and it is able to approximate both the source and target shapes. The entire mesh morphing framework has been integrated in an interactive application that allows the user to control and visualize all the stages of the morphing process

Application of refined ultimate state theories and use of high strength materials have resulted in longer spans and smaller depths of reinforced and prestressed concrete structures. Consequently, the condition of the limiting deflection rather than the strength requirement often is the governing design criterion. Long-term deflections might be up to 3 to 4 times larger than the short-term deflections. Such increments are caused by complex physical effects such as concrete creep, shrinkage and cracking, bond defects, etc. Long-term concrete creep and shrinkage deformations govern prestress losses. Structural analysis can be carried out either by traditional design code methods or numerical techniques. Although design code methods ensure safe design, they have significant limitations. Different techniques are used for strength, deflection, crack width and prestress loss analyses. Besides, most of the simplified approaches do not assess such factors as concrete shrinkage, cracking or tension stiffening. Based on a large number of empirical expressions and factors, they lack physical interpretation and do not reveal the actual stress-strain state of cracked structures. On the other hand, numerical techniques are universal and can take into account each physical effect. However, inadequacies made in the prediction of each effect might lead to significant inaccuracies when integral magnitudes such as deflection are to be assessed. Consequently, the predictions by the numerical... [to full text]

We have developed an analytical technique for single molecule force spectroscopy (SMFS) data that avoids filtering prior to analysis and performs pattern recognition to identify distinct SMFS events. The technique characterizes the signal similarity between all curves in a data set and generates a hierarchical clustering tree, from which clusters can be identified, aligned, and examined to identify key patterns. This procedure was applied to alpha-lactalbumin (aLA) on polystyrene substrates with flat and nanoscale curvature, and bacteriorhodopsin (bR) adsorbed on mica substrates. Cluster patterns identified for the aLA data sets were associated with different higher-order protein-protein interactions. Changes in the frequency of the patterns showed an increase in the monomeric signal from flat to curved substrates. Analysis of the bR data showed a high level of multiple protein SMFS events and allowed for the identification of a set of characteristic three-peak unfolding events.