Engineering Commons^™

Elastic Sensing Skin For Monitoring Of Concrete Structures, Emmanuel Abiodun Ogunniyi

Theses and Dissertations

Soft elastomeric capacitors (SECs) are emerging as potential low-cost solutions for monitoring cracks and strains in concrete infrastructure, a crucial aspect of structural health monitoring. Effective long-term monitoring of civil infrastructure can reduce the risk of structural failures and potentially reduce the cost and frequency of inspections. However, deploying structural health monitoring (SHM) technologies for bridge monitoring is expensive, especially long-term, due to the density of sensors required to detect, localize, and quantify cracks. Previous research on soft elastomeric capacitors (SEC) has shown their viability for low-cost monitoring of cracks in transportation infrastructure. However, when deployed on concrete for strain …

Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song

Theses and Dissertations

Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …

Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez

Theses and Dissertations

Currently, heat transfer in many industries is the limiting factor for innovation, especially in the energy sector. For example, maximizing thermal conductivity of ceramic coatings in power plant devices improves the overall electrical to thermal energy ratio, whereas minimizing thermal conductivity is required for desirable heat-to-electricity conversion in thermoelectric devices. As such, rapid discovery of new materials with extreme thermal conductivity values is quintessential for the near-future deployment of current and developing energy applications.

The vibrational properties of crystalline materials are essential for their ability to conduct heat. Fundamentally, the restorative atomic forces of displaced atoms are sufficient to represent …

Leveraging Programmable Switches To Enhance The Performance Of Networks: Active And Passive Deployments, Elie Kfoury

Theses and Dissertations

The performance of networks today is drastically affected by: 1) switches equipped with large buffers, referred to as “bloated buffers”: due to the lack of programmability and traffic visibility in legacy switches, operators nowadays configure large buffers statically without considering the characteristics or dynamics of flows. Such buffers increase the delays on packets, causing the Quality of Service (QoS) of networked applications (e.g., voice over IP, web browsing) to degrade; 2) switches forwarding packets on a best-effort basis: traffic crossing a switch is heterogeneous in many ways. Mixing such traffic in a single queue without any QoS measures can drastically …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor

Senior Theses

Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …

Deep Learning Based Generative Materials Design, Yong Zhao

Theses and Dissertations

Discovery of novel functional materials is playing an increasingly important role in many key industries such as lithium batteries for electric vehicles and cell phones. However experimental tinkering of existing materials or Density Functional Theory (DFT) based screening of known crystal structures, two of the major current materials design approaches, are both severely constrained by the limited scale (around 250,000 in ICSD database) and diversity of existing materials and the lack of a sufficient number of materials with annotated properties. How to generate a large number of physically feasible, stable, and synthesizable crystal materials and build accurate property prediction models …

Algorithmic Robot Design: Label Maps, Procrustean Graphs, And The Boundary Of Non-Destructiveness, Shervin Ghasemlou

Theses and Dissertations

This dissertation is focused on the problem of algorithmic robot design. The process of designing a robot or a team of robots that can reliably accomplish a task in an environment requires several key elements. How the problem is formulated can play a big role in the design process. The ability of the model to correctly reflect the environment, the events, and different pieces of the problem is crucial. Another key element is the ability of the model to show the relationship between different designs of a single system. These two elements can enable design algorithms to navigate through the …

Assessment Of Classifiers For Potential Voice-Enabled Transportation Apps, Md Majbah Uddin

Theses and Dissertations

Transportation apps are playing a positive role for today’s technology-driven users. They provide users with a convenient and flexible tool to access transportation data and services, as well as collect and manage data. In many of these apps, such as Google Maps, their operations rely on the effectiveness of the voice recognition system. For the existing and new apps to be truly effective, the built-in voice recognition system needs to be robust (i.e., being able to recognize words spoken in different pitch and tone). The goal of this study is to assess three post-processing classifiers (i.e., bag-of-sentences, support vector machine, …