Engineering Commons^™

Accident Data Analytics And Ai-Based Auditory Enhancement For Highway Construction Safety, Thinh Nguyen

All Dissertations

This research develops data-driven and AI-enhanced approaches to improve worker safety in highway construction environments. The study integrates advanced analytics and auditory signal enhancement to identify high-risk worker behaviors and improve real-time safety warnings. The work consists of three core studies. The first study applies Sequential Pattern Mining and Social Network Analysis to more than 1,000 construction accident reports to identify high-risk worker actions and their hidden sequential relationships with accident types and consequences. These insights reveal sector-specific risk patterns, supporting the creation of targeted, data-informed safety interventions. The second study introduces an AI-based auditory enhancement model built on a …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

Purifying Circular Rna Therapeutics, Karen Guillen-Cuevas

All Dissertations

Circular RNA (circRNA) has emerged as a promising therapeutic modality due to its enhanced stability and resistance to exonuclease degradation compared with linear mRNA. However, its stability and functionality depend strongly on achieving high purity, and current methods such as size-exclusion chromatography suffer from peak overlap, low yields, and poor scalability. This dissertation addresses these limitations by developing ultrafiltration-based strategies to purify circRNA directly from in vitro transcription (IVT) and self-splicing (circularization) reactions. First, the feasibility of ultrafiltration was demonstrated using polyethersulfone membranes, where sieving coefficients and critical flux values of circRNA, precursor RNA, and nicked RNA were measured to …

End-To-End Direct Current For Standalone Power Network, Eyad Ahmad Aldarsi

All Dissertations

The primary issue that faces the humanity is climate change and because of that greenhouse gases (GHGs) emissions are rising. The consequences of this issue have emphasized the necessity for replacing the dominant method of electricity generation, which mostly utilizes fossil fuels, to be centered on access to all, green, low-cost, and renewable sources of energy. Currently, photovoltaics (PV) and wind turbines are the two technologies so far that can convert the source of renewable energy, which is produced by sun irradiance and wind respectively, into large-scale electric power that can be distributed into the electricity grid. The work in …

Thermal Management Of Power Electronic Converters In Electric Motor Drives Using Active Thermal Control And Active Cooling, S M Imrat Rahman

All Dissertations

The application of power electronic converters (PEC) in electric vehicles (EVs) has significantly increased due to their enhanced controllability and flexibility. Additionally, Permanent Magnet Synchronous Machines (PMSMs) are favored in EVs for their higher torque, efficiency, performance, and heat dissipation capabilities compared to asynchronous machines. The reliable operation of power converters in PMSM drives is crucial for EV operation, and this is driving interest in developing innovative and sustainable technologies that ensure the safe and reliable functioning of PECs. One of the main challenges in achieving reliable PECs is designing effective thermal management systems to control junction temperature and reduce …

Surface Reaction And Transportation Of Radium-226 In Marine Systems, Jiusi Wang

All Dissertations

Historical uses of radium (Ra)-containing self-luminous paint, such as on gauges and dials in naval ships, have led to the potential for such paint, and thus Ra, to be released into the marine environment. Radium-226 (²²⁶Ra) behavior in marine systems is not fully described in the literature, presenting a knowledge gap in the process of evaluating potential ecological impact of Ra in marine systems. This study explores various mechanisms contributing to Ra environmental transport in marine systems on a laboratory scale, including leaching, sorption, and diffusion. ²²⁶Ra leaching from watch hand dials containing radioactive luminous paint is …

Machine Learning-Enabled Safety-Critical Model Predictive Control For Uncertain Dynamical Systems, Hossein Nejatbakhsh Esfahani

All Dissertations

This dissertation explores the interactions between Model Predictive Control (MPC), Safety-critical Control, and Artificial Intelligence (AI)/Machine Learning (ML) methods, with a particular focus on Reinforcement Learning (RL) and Bayesian Optimization (BO). We then leverage AI/ML to address several challenges in the control design and safe operation of uncertain dynamical systems. In many applications, ranging from autonomous vehicles and robotics to energy systems and industrial processes, ensuring safety is as essential as satisfying control objectives. The use of Control Barrier Functions (CBFs) within the MPC framework recently has emerged as a powerful tool to guarantee safety by enforcing constraints in optimal …

Reverse (Bio)Engineering: A Machine Learning Approach To Optimize Baseball Pitcher Health And Performance, Robert C. Moore

All Dissertations

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. Analysts routinely use ball flight data to identify the attributes of top performing pitchers, finding that the best pitchers throw with optimal combinations of release speed and spin to precise locations. However, for certain pitchers, the throwing motion required to produce optimal ball flight places exceedingly high biomechanical load on the elbow, and consequently injury rates continue to rise. This …

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

All Dissertations

Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

All Dissertations

This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Integrating Dft And Machine Learning To Predict Structural Properties In High Entropy Alloys, Nathan Linton

All Dissertations

In the past decade, a paradigm shift in the design of metal alloys has been observed. These new alloys are commonly referred to as high entropy alloys (HEAs), multi-principal element alloys (MPEAs), or complex, concentrated alloys (CCAs). In contrast to conventional alloys, which consist of one main element (for example 80%) with other elements in small amounts, HEAs are made of four or more main elements ranging from 5 to 35% each element. Due to the large presence of multiple elements, HEAs have shown substantial material property improvements over conventional alloys such as steel. For example, they have high ductility …

Unveiling The Interactions Between The Antibiotic Resistome And Microplastics Influenced By Trace Elements And Ppcps In Wastewater Treatment Plants, Tahira Tasneem Rahman

All Graduate Theses and Dissertations, Fall 2023 to Present

Antibiotic resistance is a growing global concern, and wastewater treatment plants play a critical role in controlling its spread. These facilities process water containing microplastics (small plastic particles), trace elements like copper and zinc, and chemicals from everyday products like pharmaceuticals and personal care products. Together, these pollutants may contribute to the survival and spread of bacteria containing antibiotic resistance genes, but their combined effects are not fully understood. This study investigated how these pollutants behave and interact in wastewater treatment plants using two approaches: direct monitoring of real treatment plants and controlled lab experiments using synthetic wastewater. Results showed …

Developing Standardized Testing Datasets For Benchmarking Automated Quality Control Algorithm Performance With Aquatic Sensor Data, Ehsan Kahrizi

All Graduate Theses and Dissertations, Fall 2023 to Present

Advances in water monitoring technologies have led to a large increase in the amount of data collected from rivers, lakes, and other water systems. However, ensuring that these data are accurate and reliable remains a major challenge. Traditional data quality checks are done manually by a technician, which can be slow, inconsistent, and not practical for real-time monitoring. This research addresses these challenges by developing standardized datasets for testing computer-based methods that automatically detect and correct errors in water data. Using information from the Logan River Observatory in northern Utah, we created a step-by-step process to identify, categorize, and label …

Cognitive Dynamics In Undergraduate Engineering Education: Effects Of Need For Cognitive Closure And Achievement Goal Orientation On Cognitive Engagement, Zain Ul Abideen

All Graduate Theses and Dissertations, Fall 2023 to Present

Cognitive and motivational factors play an important role in students’ engagement during problem-solving, which can lead to academic and professional success. Among these factors, Need for cognitive closure (NFCC) and achievement goal orientation (AGO) have been studied extensively individually, but their interaction and impact on engineering students’ problem-solving is still not explored. NFCC refers to the person’s desire for quick answers, and AGO is the student’s approach to learning and success. This research investigated the impact of NFCC on gender and engineering major, and the relation between NFCC (five facets) and AGO (Mastery, Performance) at their different levels. Additionally, this …

Enabling Scalable Camera-Based Streamflow Monitoring With Serverless Cloud Computing, Sajan Neupane

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis introduces a modern and automated method for monitoring rivers and streams using cameras and cloud-based software tools. Traditional monitoring methods often rely on sensors placed directly in the water, which can be expensive, hard to install, and difficult to maintain, especially in remote or rough terrain. In contrast, this approach uses cameras to observe water flow from a distance, offering a safer and more cost-effective alternative. However, camera systems come with their own challenges, such as collecting large amounts of image data, transferring it reliably from the field, and analyzing it quickly enough to be useful. This research …

Design, Control, And Optimization Of Unfolding-Based Ac-Dc Topologies With Three-Port Resonant Converters For Electric Vehicle Battery Charging Applications, Aditya Zade

All Graduate Theses and Dissertations, Fall 2023 to Present

The global effort to reduce greenhouse gas emissions and reliance on fossil fuels has made electric mobility a cornerstone of sustainable transportation. This transition is driving demand for advanced charging infrastructure and more efficient power conversion systems. Power converters play a central role, not only in enabling reliable EV charging but also in integrating renewable energy sources with the grid. Because of the large amount of power involved, these converters must operate efficiently, reliably, and at low cost. Conventional high-power chargers often use two stages of conversion, which are effective but limited by size and energy losses. To overcome these …

Quicklime-Based Self-Healing Concrete For Bridge Deck Applications, Mehrnoosh Nazari

All Graduate Theses and Dissertations, Fall 2023 to Present

Concrete is one of the most widely used construction materials in the world. However, over time, it can crack and deteriorate, especially on roads and bridges exposed to harsh weather and deicing salts. Inspired by the long-lasting durability of ancient Roman concrete, this study explores the self-healing capabilities of concrete incorporating quicklime (calcium oxide) aggregates to extend the service life of bridge decks. For this purpose, three concrete mixtures were developed using local materials: one without quicklime (C-Q 0%) and two with 2% (C-Q 2%) and 4% quicklime (C-Q 4%). These mixtures were tested for fresh properties, strength, durability, and …

Biochar As Additive And Modifier In Bitumen And Asphalt Mixtures, Chathurika Dassanayake, Nuha S. Mashaan

Research outputs 2022 to 2026

Incorporating biochar into pavement materials is a novel and environmentally sustainable approach that aligns with global sustainability goals and advances greener pavement technologies. Studies have shown that biochar significantly enhances the strength, durability, and stability of pavements, while also contributing to sustainability by lowering the carbon footprint associated with traditional construction materials. Additionally, the incorporation of biochar contributes to the sustainability of asphalt engineering by reducing reliance on petroleum-based products and promoting the valorization of biomass. The primary objective of this review is to critically evaluate and synthesize existing research on the use of biochar in bitumen and asphalt mixtures, …

Nanao/Organocatalyat Sio2/4-(2-Aminoethyl)-Morpholine As A New, Reusable, And Efficacious Catalyst For The Synthesis Of Polyhydroquinolines Derivatives And Antibacterially Active Evaluation, Leila Amiri-Zirtol, Zahra Karimi, Javad Farahbakhsh, Ahmad Gholami, Seyedeh Narjes Abootalebi

Research outputs 2022 to 2026

In this study, a new nanocomposite comprising 4-(2-Aminoethyl)-morpholine, an organic catalyst, was prepared on the surface of silica. The absence of metal in the catalyst structure contributes to its environmental friendliness. This novel nanocatalyst was used for multi-component reactions (MCRs). Having a nano size for the composite enhances the contact between the raw materials and the catalytic surface, leading to significant advancement in the reaction. The synthesized composite was identified and evaluated using FT-IR, EDX, EDX-Mapping, TGA, XRD, BET, TEM, and FE-SEM analysis. The characteristic analysis confirmed the synthesis of both nano-silica/4-(2-Aminoethyl)-morpholine catalyst and polyhydroquinoline. The composite’s catalytic properties for …

Photocatalytic Performance Of N-Doped Ti3o5 Nano-Catalyst For Phenolic Compounds Removal From Industrial Wastewaters, Mehdi Narimani, Maryam Gonbadi, Mahsa Navabi, Fatemeh Khezri-Shooshtari, Amin Ale Ebrahim, Mohammad Mahdi Zerafat

Research outputs 2022 to 2026

This study is focused on the synthesis and evaluation of TiO2 and N-doped Ti3O5 for the photocatalytic degradation of phenol as a noxious substance commonly present in the industrial process effluents with harmful effects on human and the environment. The research addresses the critical environmental challenge posed by phenol-contaminated effluents from petrochemical industries. Leveraging Response Surface Methodology with Box-Behnken design, 17 experiments were conducted to analyze the impact of independent variables such as photo-catalyst dosage, pH, and irradiation time on degradation efficiency. Notably, the average crystallite sizes were determined as 20 nm for TiO2 and 58.3 nm for N-doped Ti3O5. …

Optimization Of Energy Management In Malaysian Microgrids Using Fuzzy Logic-Based Ems Scheduling Controller, Mohammad Nur-E-Alam, Tarek Abedin, Nur Aini Samsudin, Jana Petrů, Abdulwasa Bakr Barnawi, Manzoore Elahi M. Soudagar, T. M.Yunus Khan, Muhammad Nasir Bashir, Mohammad Aminul Islam, Boon Kar Yap, Tiong Sieh Kiong

Research outputs 2022 to 2026

The microgrid (MG) faces significant security issues due to the two-way power and information flow. Integrating an Energy Management System (EMS) to balance energy supply and demand in Malaysian microgrids, this study designs a Fuzzy Logic Controller (FLC) that considers intermittent renewable sources and fluctuating demand patterns. FLC offers a flexible solution to energy scheduling effectively assessed by MATLAB/Simulink simulations. The microgrid consists of PV, battery, grid, and load. A Maximum Power Point Tracking (MPPT) controller helps to extract the maximum PV output and manages the power storage by providing or absorbing excess power. System analysis is performed by observing …

Optimization Of Size And Siting Of Distributed Generation In Unbalanced Distribution Systems: A Literature Review, Pema Dorji, Stefan Lachowicz, Octavian Bass

Research outputs 2022 to 2026

Renewable energy sources (RES) are essential for meeting the rising global electricity demand while reducing greenhouse gas emissions from conventional generation. As traditional systems approach capacity saturation, the integration of RES into power grids becomes increasingly vital. However, the intermittent and variable nature of RES introduces significant technical, economic, and operational challenges. This review focuses on the optimal planning and integration of distributed generation in unbalanced distribution systems, which more accurately reflect real-world power network conditions. Emphasis is placed on siting and sizing strategies aimed at enhancing voltage stability, minimizing power losses, and reducing system costs and emissions. The review …

Design Of A Novel Robust Adaptive Fractional-Order Model Predictive Controller For Boost Converter Using Grey Wolf Optimization Algorithm, Chao Peng, Seyyed Morteza [email protected] Ghamari, Hasan Mollaee, Omid Rezaei

Research outputs 2022 to 2026

Boost converters play a crucial role in power electronics but present control challenges due to their non-minimum phase behavior and nonlinear dynamics at high switching frequencies. To address these issues, this work proposes a Fractional-order adaptive Model Predictive Control (FO-MPC) framework incorporating Exponential Regressive Least Squares (ERLS) for system identification. Traditional MPC frameworks often rely on accurate mathematical models, which are difficult to obtain in real-world scenarios. This adaptive modelling approach based on ERLS identification method eliminates the need for precise system models, improving robustness and adaptability under parameter variations. Additionally, a FO derivative term enhances damping, stability, and noise …

Biopolymer-Based Membranes And Their Application In Per- And Polyfluorinated Substances Removal: Perspective Review, Roham Ghanbari, Ratish Permala, Stefan Iglauer, Masoumeh Zargar

Research outputs 2022 to 2026

Over the past 2 decades, per- and polyfluoroalkyl substances (PFAS) have attracted considerable attention due to their widespread occurrence and adverse effects on both the environment and human health. PFAS possess strong carbon‑fluorine bonds that confer exceptional chemical stability and resistance to natural degradation; their fluorinated chains are highly hydrophobic and oleophobic, while their polar functional groups contribute to amphiphilic behavior and water solubility. Certain PFAS thus pose serious risks to living organisms, especially mammals. Membrane filtration is recognized as a particularly effective technique, achieving removal efficiencies greater than 99 % for long-chain-PFAS (including anionic, cationic, and zwitterionic PFAS molecules). …

Confinement-Dependent Transient Mi For The Hoek-Brown (H-B) Strength Criterion, Hao Li, Leo Pel, Zhenjiang You, David Smeulders

Research outputs 2022 to 2026

The growing global demand for mineral resources necessitates deeper excavation, where increasing in situ stresses pose significant geomechanical challenges. Consequently, an accurate strength assessment of deep-seated rock masses is crucial for ensuring the safety and reliability of underground excavations. The Hoek-Brown (H-B) strength criterion remains a widely applied rock strength criterion. However, its conventional form struggles to characterize the full nonlinear evolution of rock strength from pre-critical to critical conditions across varying confining stresses. To address this limitation, this study proposes a Confinement-dependent Transient mi Model (CTM) as an enhancement to the H-B criterion. By incorporating confinement-sensitive variations of mi, …

The Microbial Factor In Subsurface Hydrogen Behavior: Implications For Wettability And Interfacial Dynamics, Hamid Esfandyari, Raziallah Jafari Jozani, Aliakbar Hassanpouryouzband, Farhid Hemmatzadeh, Manouchehr Haghighi, Stefan Iglauer, Alireza Keshavarz, Abbas Zeinijahromi

Research outputs 2022 to 2026

Microbial activity plays a significant role in subsurface hydrogen behavior, with implications for underground storage and natural hydrogen systems. This study examines how microbial processes influence the wettability and solid–liquid interfacial characteristics of key subsurface minerals, calcite, dolomite, quartz, and gypsum, in hydrogen-brine-rock systems under realistic subsurface conditions. Wettability directly affects hydrogen distribution, flow dynamics, and trapping, making it a critical factor for both storage and natural recovery applications. Experiments were conducted under high-pressure, high-temperature conditions, with SEM/EDS analysis used to characterize interfacial modifications and mineral surface changes across three scenarios: clean, organic acid-aged, and microbial-aged. Microbial aging consistently increased …

A Critical Review On Selecting Performance Evaluation Metrics For Supervised Machine Learning Models In Wastewater Quality Prediction, Hoda Khoshvaght, Ratish Ramyad Permala, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

While numerous machine learning (ML) models have been applied to wastewater treatment plant (WWTP) quality prediction tasks, significantly less attention has been paid to the selection and interpretation of performance evaluation metrics. Most studies rely on general-purpose regression metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). Although widely used, these metrics differ considerably in terms of interpretability, sensitivity to data anomalies, and suitability for dynamic, noisy environments like WWTPs. This review, based on a systematic literature analysis of 27 performance evaluation metrics, critically …

Enhanced Degradation Of Triclosan Using Aerated Hydrodynamic Cavitation: Turbulence-Based Modelling And Economic Evaluation, Mehdi Khiadani, Ensiyeh Taheri, Veena Bobade, Ali Fatehizadeh

Research outputs 2022 to 2026

Pharmaceutical and personal care product (PPCP)-contaminated wastewater, particularly with triclosan (TCS), poses a significant environmental concern due to TCS persistence, ecotoxicity, and endocrine-disrupting effects, necessitating the development of efficient and sustainable remediation technologies. In this study, the degradation of TCS using hydrodynamic cavitation coupled with air injection (aerated HC process) is investigated as a green and chemical-free treatment method for wastewater treatment. Initially, the optimum operating conditions of the standalone HC process were determined by varying the inlet pressure and flow rate, which were found to be 2.34 bar and 0.45 L/s, respectively, corresponding to the highest turbulence frequency level …

Optimizing Zeolite Catalysts For Efficient Ldpe Plastic Waste Pyrolysis, Hilda Porawati, Mazwan Mazwan, Sukadi Sukadi

Journal of Mechanical Engineering Science and Technology (JMEST)

The problem of plastic waste, particularly from low-density polyethylene (LDPE) plastic bags, poses a significant challenge in environmental management due to its non-biodegradable nature and widespread use in daily life. This study evaluates the influence of catalyst types on the characteristics and energy efficiency of liquid fuel products produced from LDPE waste pyrolysis. The three treatments compared were no catalysts, natural zeolite catalyst, and synthetic zeolite catalysts, each subjected to three different temperature variations: 250°C, 300°C, and 350°C. The observed parameters in this study were volume, product mass, cetane index, density, sulfur content, viscosity, flash point, calorific value, and energy …

The Impact Of Pine Resin-Epoxy Ratios On Fire Resistance And Acoustic Performance Of Banana Fiber Composites, Cokorda Istri Putri Kusuma Kencanawati, Dewa Ngakan Ketut Putra Negara, I Gede Andy Andika Parahita

Journal of Mechanical Engineering Science and Technology (JMEST)

Because of their low environmental impact and biodegradability, composite materials derived from natural fibers and resins have drawn interest as sustainable substitutes for synthetic materials in thermal and acoustic applications. Two promising natural components are banana stem fiber and pine resin, but they need to be modified due to their mechanical limitations and flammability. Epoxy resin is commonly used to enhance structural performance; however, its effects on acoustic and fire resistance properties require further investigation. This study examined how different epoxy resin contents affected the sound absorption and fire resistance of composites made from banana stem fiber and pine resin. …