Abstract: Artificial Neural Networks (ANNs) are used to predict vertical surface movement when soils expand and contract with changes in soil moisture caused by climatic conditions. Temperature and rainfall data, soil property data, and soil moisture measurements are used for training ANNs to simulate the movement of spread footings at a field site in Arlington, Texas. A research team from Texas A&M University surveyed the footing movement monthly over a two-year period. The performance of the ANNs is evaluated by comparing the predictions to the observed movements. Data for temperature and rainfall are available for each month of the two-year field study, but soil moisture data (on which soil shrinking/swelling is predominantly dependent) is only available for seven of those months. Therefore, two ANNs were used in series. The first ANN estimates soil moisture for the months when data are not available, while the second ANN uses both measured and estimated soil moisture as training input to estimate vertical movement.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).