<p>In order to better understand work hardening behavior of pure magnesium, latent hardening experiments with magnesium single crystals were carried out. Large magnesium single crystal samples were first deformed in tension, and subsequently sectioned for secondary tension tests. Three different initial crystallographic orientations of magnesium single crystals were prepared: crystals A and B were oriented for basal <a> slip, while crystal C was oriented for {1 012} twinning. The latent hardening test from type A and B crystals revealed that the LHR in coplanar system arising from basal - basal dislocations interactions was close to unity, whereas the LHR in non-coplanar system arising from basal - 2nd order pyramidal dislocation interactions was around 15. The interactions between {1012} twins and various slip dislocations were investigated using type C crystals. It was found that the {10 12} twins contributed to large strain hardening from at least a dynamic Hall - Petch mechanism. However, the role of {10 12} twins is complicated and further studies to understand the hardening mechanism due to {10 12} twinning are necessary.</p> / Master of Science (MS)

In conversations, stories, news reporting, and other forms of natural language, understanding requires participants to make assumptions (hypothesis) based on background knowledge, a process called entailment. These assumptions may then be supported, contradicted, or refined as a conversation or story progresses and additional facts become known and context changes. It is often the case that we do not know an aspect of the story with certainty but rather believe it to be the case; i.e., what we know is associated with uncertainty or ambiguity. In this research a method has been developed to identify different contexts of the input raw text along with specific features of the contexts such as time, location, and objects. The method includes a two-phase SVM classifier along with a voting mechanism in the second phase to identify the contexts. Rule-based algorithms were utilized to extract the context elements. This research also develops a new context˗aware text representation. This representation maintains semantic aspects of sentences, as well as textual contexts and context elements. The method can offer both graph representation and First-Order-Logic representation of the text. This research also extracts a First-Order Logic (FOL) and XML representation of a text or series of texts. The method includes entailment using background knowledge from sources (VerbOcean and WordNet), with resolution of conflicts between extracted clauses, and handling the role of context in resolving uncertain truth.

The problem of price volatility as it pertains to material and labor is a major source of risk and financial distress for all the participants in the construction industry. The overarching goal of this dissertation is to address this problem from both viewpoints of risk analysis and risk management. This dissertation offers three independent papers addressing this goal. In the first paper using the Engineering News Record Construction Cost Index (ENR CCI), a predictive model is developed. The model uses General Autoregressive Conditional Heteroscedastic (GARCH) approach which facilitates both forecasting of the future values of the CCI, and capturing and quantifying its volatilities as a separate measure of risk through the passage of time. GARCH (1,1) was recognized as the best model. The maximum volatility was observed in October 2008 and results showed persistent volatility of the CCI in the case of external economic shocks. In the second paper using the same cost index (ENR CCI), the methodology of the first paper is integrated with Value at Risk concept to cautiously estimate the escalation factor in both short and long-term construction projects for avoiding cost overrun due to price volatilities and inflation. Proposed methodology was also applied to two construction projects in which the estimated escalation factors revealed satisfactory performances in terms of accuracy and reliability. Finally, the third paper addresses the price volatility from the view of risk management. It entails two objectives of identifying and ranking of potential management strategies. The former is achieved via in-depth literature review and questionnaire interviews with industry experts. The latter is done using Analytic Hierarchy Process (AHP). Quantitative risk management methods, alike those offered in foregoing papers are considered as one of the candidates in dealing with the price volatility risk. Cost, risk allocation and duration were perceived as the most significant criteria (project indicators) in construction projects. Also, Integrated Project Delivery (IPD) with respect to project duration; quantitative risk management methods with respect to the cost; and Price Adjustment Clauses (PAC) with respect to the risk allocation, were recognized as the top strategies to manage the risk of price volatilities.

Recent advances in digital technology have had a significant influence on the quality and speed of sharing and communicating project information in the architecture, engineering, and construction (AEC) industry. The process of acquiring the design intent in order to develop and communicate project schedules, as critical components of project delivery, have similarly been benefitting from such progress. With the relatively recent techniques of Building Information Modeling (BIM) and its capability to integrate the facility design with its construction schedule, meaningul strides have been made in improving the information flow and eventually visualizing the final schedule in 4D. However, the need for faster and more efficient ways of generating both the schedule and its 4D visualization has been growing as it directly impacts the overhead cost, and hence the bottomline, of projects. Lack of direct integration and logical interoperability between the various computer systems used for these processes deprives the industry of the power of synergy that could have resulted from such explicit assimilation of the product and process models and their respective sub-processes. This research develops an approach that interprets 3D building information models into a source of direct input information to generate initial construction schedules for commercial building projects, which ultimately leads to automated visualization of the produced schedule in 4D BIM. By integrating an intermediate product model and generically predefined activities at domains level, it generates initial activities that capture the scope of the work in the design. The method also incorporates semi-automated sequencing algorithms that take into account the logic of support in structural construction and other factors related to work access and user preferences. The methodology has been implemented in a computer application built to substantiate its feasibility and then evaluated with the help of volunteer professionals in the industry by using test cases. The implementation and the tests conducted demonstrated that the developed methodology is feasible and can be considered as a step forward towards complete automation in the industry, while there are still various aspects open for improvement.

A series of heat transfer tests using a single pass slot shaped channel utilizing varying configurations of trapezoid shaped turbulence enhancement strip or varying configurations of high velocity jets issuing from side channels are studied. Thermochromatic Liquid Crystal techniques are used to determine local heat transfer coefficients, which are converted to normalized Nusselt values. The results show a marked improvement over traditional heat transfer enhancements used in the mid-span region of first stage turbine blades.

This work addresses the micro- and nano-behaviors in metals through constitutive modeling and experiments. The size effect encountered during nanoindentation experiments, which is known as indentation size effect (ISE), is investigated for metal materials. The ISE is believed to be related with the strain gradient at small indentation depths. Geometrically necessary dislocations (GNDs) are formed in order to accommodate the strain gradient, which results in the increase in the material hardness. The grain boundaries in polycrystalline materials play an important role on the material hardness during nanoindentation experiments as there is a soften-hardening segment in the curve of hardness as a function of the indentation depth. The grain boundaries act as barriers of the movement of dislocations that leads to an increase of the dislocation density. The increasing dislocation density gives the additional increase in material hardness, resulting in the hardening phenomenon. The classical continuum mechanics has to be enhanced with the strain gradient plasticity theory in order to address the ISE. Using the strain gradient plasticity theory, a material intrinsic length scale parameter is incorporated. The length scale bridges the gap between the behaviors in macro-scale and the micro-/nano-scale. The ISE of different materials can be characterized using this length scale parameter. The length scale parameter is determined through the strain plasticity model, the nanoindetation experiments using continuous stiffness measurement (CSM) mode in determining the fitting parameters and the finite element method (FEM) in determining the equivalent plasticity strain in the expression of the length scale. The rate dependency of ISE is investigated and it shows that the hardness increases with the increasing strain rate. The length scales at different strain rates are determined and they decrease with the increasing strain rate, leading to the increase in the hardness. The grain boundary effect on the hardness is verified as there is no hardening-softening phenomenon in single crystalline materials as there is no grain boundary in them. The grain boundary effect is further isolated by using bicrystalline materials. The single grain boundary is investigated through nanoindentation experiments from different distances to the grain boundary. The results show that the hardness increase as the distance decreases, providing a new type of size effect.

Affect (emotion) recognition has many applications, such as human assistive robotics, human computer interaction and empathic agents, virtual tutoring, marketing, surveillance, and counseling. Previous research has focused primarily on unimodal or bimodal affect recognition (facial expressions and speech). This research developed multimodal emotion recognition by using data from facial expressions, head position, hand movement, body posture and speech. A novel hybrid event driven fusion technique was used to combine data from multiple input channels at the feature level and decision level. Position and temporal data from tracked feature points was used for training a support vector machine based classifier. New rule based features in addition to existing geometric, kinetic and 3D features were created. An emotional key word look-up using speech recognition technology was incorporated in the recognition process. The research developed a real time affect estimation system that accurately predicts multiple emotions, intensity of the emotions and maintains the context history of recognized emotions.

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