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Emerging Technologies And Advanced Analyses For Non-Invasive Near-Surface Site Characterization, Aser Abbas
Emerging Technologies And Advanced Analyses For Non-Invasive Near-Surface Site Characterization, Aser Abbas
All Graduate Theses and Dissertations, Fall 2023 to Present
This dissertation introduces novel techniques for estimating the soil small-strain shear modulus (Gmax) and damping ratio (D), crucial for modeling soil behavior in various geotechnical engineering problems. For Gmax estimation, a machine learning approach is proposed, capable of generating two-dimensional (2D) images of the subsurface shear wave velocity, which is directly related to Gmax. The dissertation also presents a method for estimating frequency dependent attenuation coefficients from ambient vibrations collected using 2D arrays of seismic sensors deployed across the ground surface. These attenuation coefficients can then be used in an inversion process …
Ensemble Machine Learning At The Edge Using The Codec Classifier Structure And Weak Learners Guided By Mutual Information, Aj Beckwith
All Graduate Theses and Dissertations, Fall 2023 to Present
The Codec Classifier is a low-computation, low-memory tree ensemble method that dramatically improves feasibility of image classification on resource-constrained edge devices. It achieves advantages over other tree ensemble methods due the separation of encoder and decoder tasks in the classifier. The encoder partitions feature space, and the decoder labels the regions in the partition. This functional separation of tasks enables the encoder design (partitioning) to be guided by maximizing the mutual information (MI) between class labels and the features (i.e. the encoded representation of the data) without regard to the error performance of the classifier. Experiments show maximizing MI leads …
Techniques To Overcome Energy Storage Limitations In Electric Vehicles, Matthew J. Hansen
Techniques To Overcome Energy Storage Limitations In Electric Vehicles, Matthew J. Hansen
All Graduate Theses and Dissertations, Fall 2023 to Present
Electric vehicles are becoming increasingly popular, battery limitations (cost, size, and weight) complicate electric vehicle adoption. While important research on battery development is ongoing, this dissertation discusses two main approaches to overcome those limitations within the existing battery technology paradigm. Those thrusts are: improving battery health through an optimal charging strategy and minimizing necessary battery size through dynamic wireless power transfer. In this dissertation, relevant literature is discussed, with opportunities for further development considered. Within the two thrusts, three objectives sharpen the focus of the research presented here. First, a planning tool is defined for a battery electric bus fleet. …
Artificial Intelligence In Landscape Architecture: A Survey Of Theory, Culture, And Practice, Phillip J. Fernberg
Artificial Intelligence In Landscape Architecture: A Survey Of Theory, Culture, And Practice, Phillip J. Fernberg
All Graduate Theses and Dissertations, Fall 2023 to Present
This dissertation explores the role of artificial intelligence (AI) in shaping the landscape architecture profession. It looks at how AI has evolved in the field, its current influence, and its potential to change research, teaching, and professional practice. The research includes a detailed review of existing literature to identify trends in AI applications and gaps in knowledge. It also examines landscape architects' attitudes towards AI, revealing a mix of enthusiasm for its benefits and concerns about its impact on creativity and design processes, and proposes new ways of thinking about and working with AI. The work brings a unique perspective …