A fragility function quantifies the probability that a structural system reaches an undesirable limit state, conditioned on the occurrence of a hazard of prescribed intensity level. Multiple sources of uncertainty are present when estimating fragility functions, e.g., record-to-record variation, uncertain material and geometric properties, model assumptions, adopted methodologies, and scarce data to characterize the hazard. Advances in the last decades have provided considerable research about parameter selection, hazard characteristics and multiple methodology for the computation of these functions. However, there is no clear path on the type of methodologies and data to ensure that accurate fragility functions can be computed in an efficient manner. Fragility functions are influenced by the selection of a methodology and the data to be analyzed. Each selection may lead to different levels of accuracy, due to either increased potential for bias or the rate of convergence of the fragility functions as more data is used. To overcome this difficulty, it is necessary to evaluate the level of agreement between different statistical models and the available data as well as to exploit the information provided by each piece of available data. By doing this, it is possible to accomplish more accurate fragility functions with less uncertainty while enabling faster and widespread analysis. In this dissertation, two methodologies are developed to address the aforementioned challenges. The first methodology provides a way to quantify uncertainty and perform statistical model selection to compute seismic fragility functions. This outcome is achieved by implementing a hierarchical Bayesian inference framework in conjunction with a sequential Monte Carlo technique. Using a finite amount of simulations, the stochastic map between the hazard level and the structural response is constructed using Bayesian inference. The Bayesian approach allows for the quantification of the epistemic uncertainty induced by the limited number of simulations. The most probable model is then selected using Bayesian model selection and validated through multiple metrics such as the Kolmogorov-Smirnov test. The subsequent methodology proposes a sequential selection strategy to choose the earthquake with characteristics that yield the largest reduction in uncertainty. Sequentially, the quantification of uncertainty is exploited to consecutively select the ground motion simulations that expedite learning and provides unbiased fragility functions with fewer simulations. Lastly, some examples of practices during the computation of fragility functions that results i n undesirable bias in the results are discussed. The methodologies are implemented on a widely studied twenty-story steel nonlinear benchmark building model and employ a set of realistic synthetic ground motions obtained from earthquake scenarios in California. Further analysis of this case study demonstrates the superior performance when using a lognormal probability distribution compared to other models considered. It is concluded by demonstrating that the methodologies developed in this dissertation can yield lower levels of uncertainty than traditional sampling techniques using the same number of simulations. The methodologies developed in this dissertation enable reliable and efficient structural assessment, by means of fragility functions, for civil infrastructure, especially for time-critical applications such as post-disaster evaluation. Additionally, this research empowers implementation by being transferable, facilitating such analysis at community level and for other critical infrastructure systems (e.g., transportation, communication, energy, water, security) and their interdependencies.

In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.

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I confronted empirical habitat data (1994-2004) and population data (1988-2005) with ecological theory on habitat dynamics, recruitment, survival, and dispersal to develop predictive relationships between landcover variation and population dynamics. I focus on Florida Scrub-Jays, although one chapter presents a model for the potential influence of habitat restoration on viability of the Florida Grasshopper Sparrow. Both species are unique to Florida landscapes that are dominated by shrubs and grasses and maintained by frequent fires. Both species are declining, even in protected areas, despite their protected status. I mapped habitat for both species using grid polygon cells to quantify population potential and habitat quality. A grid cell was the average territory size and the landcover unit in which habitat-specific recruitment and survival occurred. I measured habitat-specific recruitment and survival of Florida Scrub-Jays from 1988-2008. Data analyses included multistate analysis, which was developed for capture-recapture data but is useful for analyzing many ecological processes, such as habitat change. I relied on publications by other investigators for empirical Florida Grasshopper Sparrow data. The amount of potential habitat was greatly underestimated by landcover mapping not specific to Florida Scrub-Jays. Overlaying east central Florida with grid polygons was an efficient method to map potential habitat and monitor habitat quality directly related to recruitment, survival, and management needs. Most habitats for both species were degraded by anthropogenic reductions in fire frequency. Degradation occurred across large areas. Florida Scrub-Jay recruitment and survival were most influenced by shrub height states. Multistate modeling of shrub heights showed that state transitions were influenced by vegetation composition, edges, and habitat management. Measured population declines of 4% per year corroborated habitat-specific modeling predictions. Habitat quality improved over the study period but not enough to recover precariously small populations. The degree of landcover fragmentation influenced mean Florida Scrub-Jay dispersal distances but not the number of occupied territories between natal and breeding territories. There was little exchange between populations, which were usually further apart than mean dispersal distances. Florida Scrub-Jays bred or delayed breeding depending on age, sex, and breeding opportunities. I show an urgent need also for Florida Grasshopper Sparrow habitat restoration given that the endangered bird has declined to only two sizeable populations and there is a high likelihood for continued large decline. A major effect of habitat fragmentation identified in this dissertation that should apply to many organisms in disturbance prone systems is that fragmentation disrupts natural processes, reducing habitat quality across large areas. Humans have managed wildland fire for > 40,000 years, so it should be possible to manage habitat for many endangered species that make Florida's biodiversity unique. This dissertation provides methods to quantify landscape units into potential source and sink territories and provides a basis for applying adaptive management to reach population and conservation goals.

The time-course of interaction formation or breaking can be studied using LigandTracer, and the data obtained from an experiment can be analyzed using a model of ligand-target binding kinetics. There are different kinetic models, and the choice of model is currently motivated by knowledge about the interaction, which is problematic when the knowledge about the interaction is unsatisfactory. In this project, a Bayesian model selection procedure was implemented to motivate the model choice using the data obtained from studying a biological system. The model selection procedure was implemented for four kinetic models, the 1:1 model, the 1:2 model, the bivalent model and a new version of the bivalent model.Bayesian inference was performed on the data using each of the models to obtain the posterior distributions of the parameters. Afterwards, the Bayes factor was approximated from numerical calculations of the marginal likelihood. Four numerical methods were implemented to approximate the marginal likelihood, the Naïve Monte Carlo estimator, the method of Harmonic Means of the likelihood, Importance Sampling and Sequential Monte Carlo. When tested on simulated data, the method of Importance Sampling seemed to yield the most reliable prediction of the most likely model. The model selection procedure was then tested on experimental data which was expected to be from a 1:1 interaction and the result of the model selection procedure did not agree with the expectation on the experimental test dataset. Therefore no reliable conclusion could be made when the model selection procedure was used to analyze the interaction between the anti-CD20 antibody Rituximab and Daudi cells. / Interaktioner kan analyseras med hjälp av LigandTracer. Data från ett LigandTracer experiment kan sedan analyseras med avseende på en kinetisk modell. Det finns olika kinetiska modeller, och modellvalet motiveras vanligen utifrån tidigare kunskap om interaktionen, vilket är problematiskt när den tillgängliga informationen om en interaktion är otillräcklig. I det här projektet implementerades en Bayesiansk metod för att motivera valet av modell utifrån data från ett LigandTracer experiment. Modellvalsmetoden implementerades för fyra kinetiska modeller, 1:1 modellen, 1:2 modellen, den bivalenta modellen och en ny version av den bivalenta modellen. Bayesiansk inferens användes för att få fram aposteriorifördelningarna för de olika modellernas parametrar utifrån den givna datan. Sedan beräknades Bayes faktor utifrån numeriska approximationer av marginalsannolikeheten. Fyra numeriska metoder implementerades för att approximera marginalsannolikheten; Naïve Monte Carlo estimator, det harmoniska medelvärdet av likelihood-funktionen, Importance Sampling och Sekventiell Monte Carlo. När modellvalsmetoden testades på simulerad data gav metoden Importance Sampling den mest tillförlitliga förutsägelsen om vilken modell som generade datan. Metoden testades också på experimentell data som förväntades följa en 1:1 interaktion och resultatet avvek från det förväntade resultatet. Följaktligen kunde ingen slutsas dras av resultet från modelvalsmetoden när den sedan används för att analysera interaktionen mellan anti-CD antikroppen Rituximab och Daudi-celler.

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