Scholarship & Research
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Item Investigation of crack arrest fracture toughness of laboratory-manufactured polycrystalline ice(Montana State University - Bozeman, College of Engineering, 2021) Alcorn, Derek West; Chairperson, Graduate Committee: Edward E. AdamsApproximately 50% of ice mass loss from ice sheets is due to icebergs breaking off in a process called calving. Icebergs are created through the incremental growth of crevasses, which are large fractures in the ice. Crevasse propagation and iceberg calving predictions within ice sheet models conflict with direct observations of crevasse processes. Current ice sheet models assume that a crevasse will propagate until it reaches a depth where the stress intensity factor at the crack tip is less than that of crack initiation, however, this is likely an oversimplification as current models over estimate crevasse depth. A more robust model would also account for the crack arrest fracture toughness, a measure of how well a material can stop an already propagating crack. Here, we calculate crack arrest fracture toughness for samples of laboratory-manufactured polycrystalline ice. These samples were created using a radial freezing technique with a reproducible grain size distribution of 0.95 mm + or - 0.28 mm analyzed by cross-polarized light. Specimens were notched and brought to failure via a short-rod fracture toughness test at controlled temperatures and a constant displacement rate in a commercial mechanical testing apparatus with an environmental chamber. The presented data agrees with short-rod fracture toughness data collected from ice cores at the Filchner- Ronne Ice Shelf in Antarctica, demonstrating quasi-stable crack growth behavior. Results show the crack arrest fracture toughness of laboratory-manufactured polycrystalline ice is approximately 25 - 50% of fracture toughness. Using the crack arrest fracture toughness determined in this study would further increase modeled crevasse depth, indicating more analysis is required. Future studies can incorporate these data to more accurately determine crevasse penetration depth and improve iceberg calving predictions within ice sheet models.Item An investigation modeling risk of wildlife-vehicle collisions in Montana, USA(Montana State University - Bozeman, College of Engineering, 2019) Bell, Matthew Andrew; Chairperson, Graduate Committee: Yiyi Wang and Damon Fick (co-chair)Road ecologists and transportation engineers have been exploring new methods to adapt to the environmental and motorist safety concerns involving wildlife-vehicle collisions. There are over one-million crashes with large-bodied wildlife every year in the U.S. that result in substantial property damage and personal injuries. Recent studies modeling these collisions identify where they cluster, and the landscape, road, and driver characteristics that influence the likelihood of a collision along short road corridors and small geographic regions. This research expands on current knowledge and attempts to model the risk of wildlife-vehicle collisions on a large geographic scale. This research investigates different analysis methods and creates predictive models that will estimate the risk of a wildlife-vehicle collision as drivers travel across multiple ecosystems. Different analysis units were created to extract two similar datasets that are modeled against two different response variables -- reported collisions and roadkill locations. Regularization is used to help with feature selection. Negative binomial regression models are built to predict risk. Random forest machine learning helps better understand the percent of variance explained by the variables in each model. A range of statistical measurements were taken to compare the non-nested models. The best performing model is applied to the seasonal division of data. Yearlong and seasonal risk is mapped onto the road network and color-coded to show the differences in risk on Montana's road network. The maps capture the changes in risk throughout the year, they generally match where wildlife-vehicle collisions actually happen, and even coincides with published work on the locations of collision hotspots in Montana. This research is the basis for future complex real-time risk-mapping models that can be integrated into smart technology and developed into on-board driver alert systems. With the advancements of autonomous vehicle, it is possible to incorporate real-time driving data into models that will analyze wildlife-vehicle collision risk based on vehicle location, season, time of day and driving habits. This can increase driver safety by informing them when they are traveling in areas where wildlife-vehicle collisions are more likely to happen, and can be especially helpful while driving on unfamiliar roads.