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No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Determining the fatigue properties (Manson-Coffin and Ramberg-Osgood parameters) for a steel material requires time consuming and expensive testing. In the early stages of a design process, it is not feasible to perform this testing. To help solve this problem numerous researchers have developed estimation methods to estimate the Manson-Coffin parameters from monotonic properties data. Additionally, other researchers have compared the results from these various estimation methods for large material classifications. However, a comprehensive comparison of these estimation methods has not been made for steels in different heat treatment states. More accurate results for the best estimation method can be made with smaller classifications, which have more consistent properties. In this research, best estimation methods are determined for six steel heat treatments. In addition to looking at steel heat treatment classifications, the estimation of the Ramberg-Osgood parameters is also examined through the compatibility conditions. Without them, the approach of estimating the fatigue properties using the estimation methods would not be practically useful. Finally, in the comparison of the estimation methods, an appropriate statistical comparison methodology is utilized; multiple contrasts comparison. This methodology is implemented into the comparison of the different estimation methods, by comparing the estimated lives and the experimental lives as a regression so that the entire life range can be considered. The estimation methods can also be utilized to get estimates of the variability of the fatigue properties given the variability of the monotonic properties data, since there is a functional relationship developed between the two sets of material properties. This variability is necessary for a stochastic design process, in order to obtain a more optimally designed component or structure. Overall the estimation methods have a number of practical applications within a fatigue design process. Their use and implementation needs to be supplemented by the appropriate knowledge of their limitations and for what classifications they give the best results. An expert system is developed to summarize this knowledge to assist an engineer. This research aims to provide this knowledge and expands their use to account for variability in fatigue properties for stochastic analysis.

Optimal strategies under worst-case scenarios have been studied in Bernard et al. [2013a]. Bernard et al. utilize copulas to construct cost-efficient strategies with a predefined dependence structure in the tail between the payoff and the market. In their study they show that such strategies with state-dependent copula constraints dominate traditional diversification strategies in terms of the provided protection in the states of market downturns. We derive similar strategies, however using correlation constraints instead of copula constraints in the tail. We found that for an investor seeking negative dependence with the market, it is cheaper to construct a strategy with conditional correlation constraint in the tail. However, the constructed strategies with conditional correlation constraints do not provide sufficient protection in bad states of the economy. Therefore, when analyzing a strategy, negative correlation with the market in the tail is not a sufficient indicator for the protection level in the event of a market crisis.

The goal of this thesis is to study and model the Diel Vertical Migration (DVM) pattern using machine learning methods. We choose an Almost Periodic Function as the mathematical model and fit the monthly averaged migration data into a 5-term Fourier series whose coefficients and frequency are functions of time. The resulting function captures the general characteristics of the DVM pattern whose period is similar yet undergoes gradual changes over time. Further correlation analyses show that the monthly averaged distribution of zooplankton and various environmental factors are strongly correlated. Therefore, we adjust the function so that the coefficients and frequency are functions of environmental factors. Besides, we also examine the pattern on finer time scales using classification algorithms. We build classifiers which predict zooplankton existence at different depths based on a set of environmental measurements. Experiments demonstrate that both of the above methods are valid in modeling the DVM pattern.