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Full-Text Articles in Engineering
Machine Learning On Acoustic Signals Applied To High-Speed Bridge Deck Defect Detection, Yao Chou
Machine Learning On Acoustic Signals Applied To High-Speed Bridge Deck Defect Detection, Yao Chou
Theses and Dissertations
Machine learning techniques are being applied to many data-intensive problems because they can accurately provide classification of complex data using appropriate training. Often, the performance of machine learning can exceed the performance of traditional techniques because machine learning can take advantage of higher dimensionality than traditional algorithms. In this work, acoustic data sets taken using a rapid scanning technique on concrete bridge decks provided an opportunity to both apply machine learning algorithms to improve detection performance and also to investigate the ways that training of neural networks can be aided by data augmentation approaches. Early detection and repair can enhance …
Groundwater Level Mapping Tool: Development Of A Web Application To Effectively Characterize Groundwater Resources, Steven William Evans
Groundwater Level Mapping Tool: Development Of A Web Application To Effectively Characterize Groundwater Resources, Steven William Evans
Theses and Dissertations
Groundwater is used worldwide as a major source for agricultural irrigation, industrial processes, mining, and drinking water. An accurate understanding of groundwater levels and trends is essential for decision makers to effectively manage groundwater resources throughout an aquifer, ensuring its sustainable development and usage. Unfortunately, groundwater is one of the most challenging and expensive water resources to characterize, quantify, and monitor on a regional basis. Data, though present, are often limited or sporadic, and are generally not used to their full potential to aid decision makers in their groundwater management.This thesis presents a solution to this under-utilization of available data …
Predicting Hardness Of Friction Stir Processed 304l Stainless Steel Using A Finite Element Model And A Random Forest Algorithm, Tyler Alan Mathis
Predicting Hardness Of Friction Stir Processed 304l Stainless Steel Using A Finite Element Model And A Random Forest Algorithm, Tyler Alan Mathis
Theses and Dissertations
Friction stir welding is an advanced welding process that is being investigated for use in many different industries. One area that has been investigated for its application is in healing critical nuclear reactor components that are developing cracks. However, friction stir welding is a complicated process and it is difficult to predict what the final properties of a set of welding parameters will be. This thesis sets forth a method using finite element analysis and a random forest model to accurately predict hardness in the welding nugget after processing. The finite element analysis code used and ALE formulation that enabled …
An Atomistic Approach For The Survey Of Dislocation-Grain Boundary Interactions In Fcc Nickel, Devin William Adams
An Atomistic Approach For The Survey Of Dislocation-Grain Boundary Interactions In Fcc Nickel, Devin William Adams
Theses and Dissertations
It is well known that grain boundaries (GBs) have a strong influence on mechanical properties of polycrystalline materials. Not as well-known is how different GBs interact with dislocations to influence dislocation movement. This work presents a molecular dynamics study of 33 different FCC Ni bicrystals subjected to mechanical loading to induce incident dislocation-GB interactions. The resulting simulations are analyzed to determine properties of the interaction that affect the likelihood of transmission of the dislocation through the GB in an effort to better inform mesoscale models of dislocation movement within polycrystals. It is found that the ability to predict the slip …
Machine Learning Methods For Nanophotonic Design, Simulation, And Operation, Alec Michael Hammond
Machine Learning Methods For Nanophotonic Design, Simulation, And Operation, Alec Michael Hammond
Theses and Dissertations
Interest in nanophotonics continues to grow as integrated optics provides an affordable platform for areas like telecommunications, quantum information processing, and biosensing. Designing and characterizing integrated photonics components and circuits, however, remains a major bottleneck. This is especially true when complex circuits or devices are required to study a particular phenomenon.To address this challenge, this work develops and experimentally validates a novel machine learning design framework for nanophotonic devices that is both practical and intuitive. As case studies, artificial neural networks are trained to model strip waveguides, integrated chirped Bragg gratings, and microring resonators using a small number of simple …