Engineering Commons^™

An In Depth Examination Of Educational, Family, Economic, And Personal Determinants On Academic Performance, Engagement, And Stress: A Comprehensive Study Among Engineering Students, Oumaima Larif

Theses and Dissertations

This study explores the effects of family, education, economic, and personal factors on students’ decisions to pursue engineering as a profession and their long-term impact on performance as engineering students. We adopted a mixed-method approach, collecting data through surveys administered to undergraduate and graduate engineering students at Mississippi State University. The study results revealed that family, education, economic, and personal factors profoundly influence students' decisions to study engineering. We found that parental expectations, background information, and socioeconomic status, in conjunction with cultural norms, values, gender expectations, and religious beliefs, affect students. Additionally, this study identified gaps in the existing literature …

Asphalt Durability Improvements By Way Of Specification Advancements, Jessica V. Lewis

Theses and Dissertations

The objective of this dissertation is to provide content through which industry and agencies can adjust and/or update standards and specifications, as appropriate, to accommodate the present-day needs of the asphalt industry towards producing more durable asphalt. A total of 78 asphalt mixtures and 9 asphalt binders were evaluated for up to 10 years of field aging to provide content that was analyzed to meet this objective. A full-scale aging site in Columbus, Mississippi was the focal point of the aging experiments. A major conclusion from this work was that Cantabro Mass Loss (CML) testing of cores was able to …

Integration Of Machine Learning Techniques With Computational Fluid Dynamics For Enhanced Backward-Facing Step Flow, Yahya Mekkaoui

Theses and Dissertations

This research presents a novel integrated framework combining computational fluid dynamics (CFD) with machine learning techniques to enhance the analysis of backward-facing step flows. A high-fidelity OpenFOAM CFD simulation was developed and validated against experimental data, accurately predicting the reattachment length (��/�� ≈ 6.18) and velocity profiles throughout the domain. Machine learning models, including Random Forest, Gradient Boosting, and Support Vector Regression, were integrated through a robust data pipeline, with the ensemble approach demonstrating superior performance (RMSE of 1.18 m/s, ��2 of 0.951). Feature importance analysis revealed pressure (32%) and turbulent kinetic energy (28%) as the dominant physical parameters governing …

Atomistic Modeling Of Mg And Al Systems Using Rapid Artificial Neural Network Derived Interatomic Potential, Edward Omozusi Obasuyi

Theses and Dissertations

Machine learning derived interatomic potentials have proven to be effective tools in simulation that mimic the accuracy of ab-initio calculations to the sub meV/atom level. They offer the advantage of computational speed operating at linear scaling with respect to the system’s size making them more efficient than classical potentials like MEAM (modified embedded atom methods). In this work, interatomic potentials are created based on the rapid artificial neural network (RANN) formalism for magnesium (Mg), Aluminum (Al) and their alloys. From previous works, the RANN formalism produces high-fidelity atomic models with accurate force fields for several metals, offering new insights into …

Removal Of Harmful Algal Bloom Toxin, Microcystin-Lr Via Graphene Coated Polymers, Justin Douglas Puhnaty

Theses and Dissertations

This study investigates a novel 3D-printed graphene-coated polymer (GCP) for the removal of Microcystin-LR (MC-LR), a harmful cyanotoxin produced during harmful algal blooms (HABs). While graphene nanoplatelets (GnPs) exhibit high adsorption capacity, their powdered form limits practical application. To address this, GnPs were coated onto 3D-printed poly(lactic acid) (PLA) substrates, enabling enhanced handling for field deployment. Surface characterization using laser confocal microscopy, Raman spectroscopy, and thermogravimetric analysis confirmed GnP distribution and coating uniformity. Batch adsorption experiments revealed pseudo-second-order kinetics, a maximum adsorption capacity (qmax) of 596 μg/g, and adsorption behavior best described by the Langmuir isotherm. Statistical analysis and comparison …

Raad-Llm: Adaptive Anomaly Detection Using Llms And Rag Integration, Alicia Russell-Gilbert

Theses and Dissertations

Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies that are adaptive, transferable, and capable of integrating domain-specific knowledge. This work presents RAAD-LLM, a novel framework for adaptive anomaly detection, leveraging large language models (LLMs) integrated with Retrieval-Augmented Generation (RAG). This approach addresses the aforementioned PdM challenges. By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time-series data without requiring fine-tuning on specific datasets. The framework's adaptability mechanism enables it to adjust its understanding of normal operating conditions …

Biogenic Synthesis Of Metallic And Bimetallic Oxide Nanoparticles For Water Treatment Applications, Gunarani G.I

Theses and Dissertations

Access to clean water is a quality-of-life indicator. The availability of clean water apart from water scarcity is marred due to contamination by heavy metals and synthetic dyes, posing a grave environmental and public health challenge. This necessitates the implementation of advanced and sustainable treatment solutions towards water remediation. This research work focusses on the fabrication and application of biogenic and chemically synthesized metallic and bimetallic nanoparticles for the efficient removal of uranium, chromium, and toxic dyes from aqueous environments. The study particularly focuses on zero-valent (C-Fe and B-Fe) and bimetallic (C-NiFe and B-NiFe) nanoparticles, evaluating their adsorption capabilities and …

Improvement Of Hanford Waste Vitrification Algorithms With Machine Learning, Uncertainty Quantification, And Gradient-Based Optimization, Lagrande L. Gunnell

Theses and Dissertations

Over 200 million liters of nuclear waste is stored in tank farms in Hanford, Washington, USA. An ongoing effort to immobilize non-solid nuclear waste is through waste vitrification, where waste is mixed and melted with glass-forming chemicals (GFCs) to form a waste glass. Algorithms to determine the ideal mixing recipes are formalized as constrained optimization problems to maximize waste loading in the glass product, with constraints on several properties of the glass melt and final product. These properties are on the glass melt, such as viscosity, electrical conductivity, and corrosion rates, as well as the final glass product, such as …

Selecting Green Wall Adoption Best Solutions Using Optimization: Addressing Barriers And Maximizing Benefits, Samar Nadeem

Theses and Dissertations

This research explores the potential of green walls as a sustainable solution to mitigate urban challenges and align with global climate change objectives. Despite their ecological and social benefits, green wall implementation faces significant barriers, hindering their adoption. Consequently, this study aims to develop a comprehensive framework to identify the obstacles, propose feasible solutions, and provide actionable strategies for promoting green walls. This framework incorporates an optimization model specifically designed for government-led solutions, evaluating their feasibility based on administrative feasibility, community acceptance, technical feasibility, industrial acceptance, and long-term adoption potential. By quantifying and prioritizing solutions based on their cost, feasibility, …

Cross-Dataset Fairness Evaluation Of Transformer-Based Sentiment Models, Sara Zuiran

Theses and Dissertations

With the growing exploration of Natural Language Processing (NLP) systems in decision-making environments, it is essential to evaluate technical and ethical aspects of the dataset and the NLP model to improve fairness. To assess fairness, the thesis examines demographic imbalances in sentiment classification models by evaluating transformer-based models fine-tuned on the Stanford Sentiment Treebank version 2 dataset (SST-2) against the demographically annotated Comprehensive Assessment of Language Model dataset (CALM). This work identifies performance disparities in sentiment prediction across demographic groups by examining sensitive attributes such as gender and race. The study evaluates both the RoBERTa and MentalBERT transformer models using …

Development Of A 3-Dof Contactless Magnetic Joint Actuator, Elle Whitney

Theses and Dissertations

The magnetic joint actuator is a relatively recent research technology that expands on the brushless DC motor to encompass rotation in three degrees-of-freedom. These actuators involve a stator with a series of electromagnetic coils oriented normal to a spherical permanent magnet rotor which can be controlled by modulating the current of the stator coils. The development of a magnetic joint actuator that operates without a physical connection between the stator and the rotor has wide applications for robotics in dust-sensitive environments, where a device with bearings would otherwise falter. This work details the design, sensing, and control of such a …

Improving Home Security Through User Centered Device Positioning, Lalith Nadipalli

Theses and Dissertations

In today’s world, where technology is advancing rapidly and security threats are becoming more complex, the need for effective home safety measures is more critical than ever. Homeowners are increasingly turning to a variety of smart devices, such as smoke detectors, carbon monoxide detectors, and security cameras, to protect their living spaces against potential dangers like burglary, fire, and environmental hazards. These devices offer essential protection, acting as both early warning systems and visual surveillance tools. However, their effectiveness largely hinges on how well they are placed within the home. Proper placement of these safety devices ensures that they provide …

C-Band Air To Ground Communication System For Uav, Mubark Alghamdi

Theses and Dissertations

C band spectrum 5030-5091 MHz is allocated for command-and-control communication services with unmanned aircraft systems. This document evaluates the possibility of using 3GPP 5G standards for provisioning of such services. Channelization of the spectrum and major system parameters are proposed. The performance of the proposed system in channel fading environment is evaluated using MATLAB based simulations. The evaluations examine SINR, and average throughput of the proposed system at different altitudes of the unmanned aircraft system. MATLAB simulations are used to evaluate system performance in free-space environments. Signal-to-Interference-plus-Noise Ratio (SINR) and Reference Signal Received Power (RSRP) are predicted at different UAS …

The Impact Of System Transparency On Perceived System Reliability, Perceived System Usability, And Information Clarity In Self-Driving Car Systems, Uditkumar Nair

Theses and Dissertations

In human-computer interaction (HCI), the development of autonomous vehicle (AV) technology has created new difficulties, especially in building user confidence as well as understanding of system functioning. The effect of system transparency on user- centered outcomes, such as perceived usability, perceived system reliability, and information clarity, is examined in this thesis. In order to evaluate their experiences in both ordinary and high-stakes driving situations, participants engaged with both system- transparent user interfaces (TUIs) and non-transparent user interfaces (NTUIs) across a number of experimental scenarios. In order to assess how well each interface conveyed system logic and actions, the study included …

Developing End-To-End Imitation Learning For Asteroid Proximity Operations, Patrick David Quinn

Theses and Dissertations

Asteroid exploration remains a popular topic in the scientific community, however hurdles still exist for controlling spacecraft within the asteroid environment. Communication delays often require the usage of limited onboard computing hardware for navigation. Additionally, long mission timelines must be accommodated with highly efficient fuel use. Considering these issues, it is apparent that any guidance, navigation, and control (GNC) system in these spacecraft should emphasize both computational and fuel efficiency in its design. Furthermore, the integration of a robust state estimation system is necessary for the successful deployment of such systems. The development of a controller aiming to address these …

Mixed-Mode Fracture Analysis Of Additively Manufactured Periodic Orthotropic Functionally Graded Microcellular Materials, Behnam Shahbazian

Theses and Dissertations

The quest for safety, efficiency, and innovation in aerospace engineering demands a relentless focus on understanding how materials and structures behave under extreme and complicated loading conditions. Among the many challenges faced by engineers in this field, predicting and preventing structural failure stands out as both a fundamental necessity and a complex problem. Fracture mechanics, the science of how materials fail under stress, is a cornerstone of this effort. Whether it is the fuselage of an aircraft enduring cyclic loading or the lightweight components of a spacecraft resisting impact, the ability to accurately predict fracture behavior directly influences the reliability …

Alternative Robust Design Formulation And Exploration Strategies For Managing Uncertainties In The Realization Of Complex Engineering Systems, Pooja Mukundan

Theses and Dissertations

The design of complex engineered systems often relies on simulation-based models, which inherently introduce uncertainties. Managing these uncertainties is critical to ensure robust system performance. From a decision-based design (DBD) perspective, robust design metrics such as the Design Capability Index (DCI) and Error Margin Index (EMI) are employed to formulate and solve problems under uncertainty. However, existing formulations of these metrics rely on first-order Taylor series approximations for variance estimation, which are often inadequate in the presence of highly nonlinear or multimodal performance functions.

This thesis proposes alternate formulations of DCI and EMI, which use the multiple-point method of variance …

Autonomous Defect Detection In Additive Manufacturing, John Samuel Poindexter Iv

Theses and Dissertations

Additive manufacturing (AM) is the process of creating a product by applying layers of material to build up to the final product. AM has revolutionized the field of manufacturing and has allowed more complex products to be manufactured at reasonable cost, quicker manufacturing time, and optimized material usage. Traditional forms of manufacturing were based on subtractive manufacturing, where by a product would be created by removing material from its source. AM is a new manufacturing process and faces significant challenges when it comes to manufacturing due to defects. Defects can vary from type and severity and can overall weaken or …

Expressive And Interpretable User Engagement Prediction Using Multivariate Survival Processes, Akshay Aravamudan

Theses and Dissertations

The ability to characterize how information diffuses online is of paramount importance to stakeholders that are interested in tasks such as proposing solutions for mitigating and countering dis/misinformation, predicting user engagement of content in social media, planning marketing campaigns to roll-out products and planning dissemination of political campaign messaging among others. One such facet of learning the dynamics of information diffusion is the ability to predict user engagement or the popularity of a single piece of information as it spreads through an online medium. Existing works in this regard mainly either obfuscate user level information or utilize frameworks that are …

Use Of Pushed-In Pencel Pressuremeter Test For Shallow Foundation Design In Florida Fine Sands, Brhane Weldeanenya Ygzaw

Theses and Dissertations

A comprehensive evaluation was conducted on the performance, consistency, and practical application of various in-situ geotechnical testing methods for predicting the settlement and bearing capacity of shallow foundations in Florida’s fine sands. The methods assessed include the Standard Penetration Test (SPT), Cone Penetration Test (CPT), Dilatometer Test (DMT), pushed-in PENCEL Pressuremeter Test (PPMT), and the pre-bored TEXAM Pressuremeter Test. A dual approach controlled indoor testing at the FDOT State Materials Office (SMO) and field investigations at three representative sites (Kingsley, Trenton, and the UCF research site), formed the experimental foundation.

Over thirty settlement prediction techniques and thirteen bearing capacity estimation …

The Prototype Of A Fully Automated Pressuremeter For Various Geotechnical Applications, Anuar Akchurin

Theses and Dissertations

The geotechnical engineering field requires high-quality in-situ testing equipment that ensures consistency in the test execution. This research focuses on developing and testing a prototype of a fully automated pressuremeter control unit to allow the industry to obtain reliable and controlled stress-strain data, thereby improving efficiency and reducing testing time.

The objectives of this work are to design and develop a system with the necessary components to perform the pressuremeter test, integrate the testing procedure into the state machine design, enable automated testing, and validate the results by comparing the stress-strain data with that obtained from the existing hand-operated pressuremeter …

Advancing Precision And Autonomy In Agriculture And Medical Imaging Through Ai And Computer Visions, Tan-Hanh Pham

Theses and Dissertations

Deep learning has revolutionized numerous fields by enhancing precision, automation, and decision-making capabilities. This dissertation explores its applications in agriculture and medical image processing, introducing novel methodologies to improve accuracy and efficiency in these domains. These fields hold critical societal importance -- agriculture underpins global food security and sustainability, while medical imaging drives advancements in diagnostics and personalized healthcare, both benefiting significantly from data-driven innovations. In agriculture, deep learning is applied to precision spray systems through droplet analysis. Specifically, a generative model is designed to create synthetic droplet images, addressing the challenge of limited training samples, which are expensive …

Cross-Frame Effects And Design Optimization For Skewed Steel I-Girder Bridges, Nisha Sthapit

Theses and Dissertations

Cross-frames are critical for load distribution and stability in steel I-girder bridges with complex geometry, especially during construction. However, they are often designed with standardized cross-sections that are uniform throughout the bridge. This thesis presents an optimization approach for cross-frame cross-sections for skewed steel I-girder bridges with integral and stub abutments with skews ranging from 15⁰-60⁰. This optimization, using Method of Moving Asymptotes (MMA), minimizes girder flange lateral bending stress while maintaining or reducing the total volume of the cross-frames from the original design. Validated 3D finite element models in CSI Bridge showed that the optimization achieved around 20% decrease …

Evaluating Structural Response Of Steel I-Girder Bridges Under Different Support Conditions: Construction Loading In Skewed Straight And Thermal Loading In Horizontally Curved Configuration, Bikesh Sedhain

Theses and Dissertations

Skewed and horizontally curved steel I-girder bridges are commonly designed and constructed to connect existing roadways and navigate obstacles in densely populated areas. However, these bridges can introduce significant challenges to bridge design and analysis, including complex lateral movements and torsional effects. Simplified support representation techniques that are commonly adopted in analysis and design may not accurately capture these effects, which lead to potential misestimations of bridge responses. This research aims to evaluate skewed and curved steel I-girder bridges for complicated loading and support conditions – skewed integral abutment bridges were studied during pre-composite deck placement and horizontally curved bridges …

Porous Geopolymer Composite For Geotechnical Applications, Karla Viviana Sierra Flores

Theses and Dissertations

This research investigates the development of a porous geopolymer cement grout for soil grouting applications, aiming to reduce carbon emissions associated with Portland cement while maintaining critical performance characteristics such as strength and permeability. Class F fly ash and metakaolin were used as aluminosilicate precursors, activated by sodium silicate and sodium hydroxide solutions. The addition of hydrogen peroxide served as a foaming agent to introduce porosity by creating a thermodynamic reaction that releases oxygen gas during mixing. Compressive strength, porosity, and hydraulic conductivity were evaluated, with results showing that metakaolin significantly increased compressive strength due to its smaller particle size …

Development Of A New Seismic Metamaterial For Passive Vibration Control Of Civil Structures, Shayan Khosravi

Theses and Dissertations

Seismic metamaterials are engineered structures that control mechanical wave propagation through frequency bandgaps, making them useful for structural control and seismic hazard mitigation. This study introduces the Passive Friction Seismic Metamaterial Base Isolation (PFSMBI) system, which combines frequency bandgaps and solid friction energy dissipation to improve the seismic performance of civil structures. The PFSMBI consists of a lattice structure with identical cells connected by springs and dampers, shifting a building’s natural frequency away from earthquake excitations. A dynamic model analyzes the system’s absolute acceleration, drift responses, and base shear force, optimizing parameters such as frequency ratios, mass ratios, and the …

Vibration-Based Algorithm For Diagnostic And Prognostic Condition Monitoring Of Railroad Bearings And Wheels, Jeffery Ray Pams

Theses and Dissertations

Bearing and wheel failure accounts for the majority of equipment-related train derailments on US railroads. Current wayside monitoring systems have been insufficient in the prevention of catastrophic derailments prompting the rail industry to explore the possible integration of onboard condition monitoring systems into their rail operations. The University Transportation Center for Railway Safety (UTCRS) in partnership with Hum Industrial Technology, Inc., has developed a wireless onboard system intended for the condition monitoring of railroad bearings and wheels. This thesis presents the development, evaluation, and implementation of an algorithm designed to be used in conjunction with these systems to provide railroad …

Analysis And Modeling Of Porous Media Networks In Petroleum Reservoirs Through Microscale Thermography And Core Flooding, Alan Petrovich-Mar

Theses and Dissertations

Reservoir rocks are porous materials that are an important area of study since they are present in reservoir sites rich in crude oil. It is important to understand the spreading phenomena on the rock surface and through the porous structure during fluid flow through these porous materials. For a complex system like reservoir rocks, factors such as spatially varying chemical composition and topography, multidimensional porosity, temperature, humidity, and existing oil/water in the pores can affect wettability and be a detriment to oil recovery.

The interaction of core samples with different porosity and permeability with water after proper cleaning procedures and …

Synthesis And Characterization Of Composite Hydrogels For Tissue Engineering: An Investigation On Mechanical Properties And Biocompatibility, Ismael Lamas Jr.

Theses and Dissertations

This study investigates the fabrication and characterization of Polyvinyl Alcohol (PVA)/agar double-network (DN) hydrogels for biomedical applications, focusing on mechanical strength, biocompatibility, and tissue engineering potential. Techniques like swelling ratio analysis and Fourier-transform infrared spectroscopy (FTIR), alongside antibacterial and cytotoxicity assays, were employed to evaluate the hydrogels' properties. Results show that increasing agar content enhances mechanical and physical properties, with swelling ratios increasing by 300% when agar makes up 6% of the hydrogel, compared to 140% without agar. A positive correlation was observed between agar concentration and compressive and tensile moduli. Load-relaxation rates, essential for viscoelastic properties, also improved with …

Development And Tribological Characterization Of Uhmwpe-Tin Composite Coatings Deposited By Electrostatic Spraying, Jarrod Keith Perez

Theses and Dissertations

The increasing demand for inert, wear-resistant, cost-efficient, environmentally friendly, and low-friction coatings has driven extensive research into polymer-based solutions. Among these, ultra-high-molecular-weight polyethylene (UHMWPE) has emerged as a strong candidate due to its low density, anticorrosion properties, self-lubricating nature, affordability, and ease of processing. However, its low load-bearing capacity limits its widespread use in high-stress tribological applications. To overcome this limitation, this project investigates the development and characterization of UHMWPE composite coatings reinforced with titanium nitride (TiN) particles at varying concentrations (2.5, 5, and 10 wt%) to enhance their mechanical properties and wear resistance.