Engineering Commons^™

Load Rating Evaluation Of Deteriorated Prestressed Channel Girders, Alexander Zane Henderson

Theses and Dissertations

The South Carolina Department of Transportation (SCDOT) is conducting a multi-year effort to load rate its inventory of over 9,400 bridges. This includes many bridges that are load posted due to potential structural considerations (e.g., outdated design loads, members whose capacity is difficult to assess, structural degradation). The number of load-posted bridges in South Carolina is expected to increase significantly due to recent efforts from SCDOT to assess the current state of bridge infrastructure. It is expected that the increased scrutiny may result in more load postings, which in turn may lead to restrictions on truck routes, potential bridge closures, …

Evaluation Of Preferential Vaporization Characteristics Of Isolated Single Droplet Combustion In A Converging Channel, Claire Elizabeth Dixon

Theses and Dissertations

Preferential vaporization of isolated single droplets was evaluated in this study. A previously verified lab-scale experiment using flow acceleration to counteract the effects of buoyancy in a converging channel with a decreasing cross section was utilized to investigate the single isolated droplet combustion. The previous experimentation revealed differences between results and values predicted by a one-dimensional numerical simulation. To investigate if the observed differences were the result of turbulent flow and the development of a boundary layer within the channel, a model was designed and analyzed in Ansys Fluent.

The developed Ansys simulation indicated that turbulent flow and growth of …

Advancing The Engineering Workforce By Enhancing Diversity, Equity, And Inclusion In The Engineering Classroom, Phoneia Hughes Myers

Theses and Dissertations

Society’s dependence on engineered products is steadily increasing. This dependence requires the engineering workforce to continually innovate and design new advancements. In pursuit of this, engineering employers are implementing diversity, equity, and inclusion initiatives to attract prime candidates and retain employees. For employers to achieve the goals of their initiatives, engineering colleges and universities must produce a diverse pool of graduates. The purpose of this thesis report is to investigate how the diversity, equity, and inclusion concept can be implemented into engineering education to assist with producing diverse graduating classes. A universal methodology is proposed to provide a roadmap on …

Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang

Theses and Dissertations

Gene expression is the fundamental differentiation and development process of life. Although all cells in an organism have essentially the same DNA, cell types and activities vary due to changes in gene expression. Gene expression can be influenced by many gene regulations. RNA editing contributes to the variety of RNA and proteins by allowing single nucleotide substitution. Reverse transcription can alter the expression status of genes by inducing genetic diversity and polymorphism via novel insertions, deletions, and recombination events. Gene regulation is critical to normal development because it enables cells to respond rapidly to environmental changes. However, identifying gene regulations …

Speckle Development And Application In Uav-Based Digital Image Correlation For Transportation Infrastructure Monitoring And Assessment, Taylor Wyatt

Theses and Dissertations

A critical aspect of civil infrastructure is structural health monitoring, of which digital image correlation (DIC) has been proven to be an effective and appropriate method. There is sufficient literature on the use of stationary DIC, but an unexplored aspect is DIC-equipped unmanned aircraft systems. It has been verified in University of South Carolina labs but has not been proved in a field environment. One of the challenges of field implementation is large scale speckle application. This work focusses on identifying and addressing challenges in bringing UAV-DIC technology from the lab to the field. This includes the development of optimized …

Evaluation Of A Droplet Spraying/Misting System To Enhance Leachate Evaporation And Reduce Leachate Treatment Costs: A Case Study At The Three Rivers Solid Waste Authority Landfill, Kaitlen Drafts, Suzie Boxman, Scott Ribes, Mike Terry, Bryan Staley, Nicole Berge

Faculty Publications

Three Rivers Solid Waste Authority (TRSWA) operates a MSW landfill outside Jack-son, South Carolina at which leachate is stored in a collection pond then trucked to a local wastewater treatment plant (WWTP) for treatment. This landfill operates a droplet spraying/misting system (referred to as the Lilypad system) to enhance leachate evaporation and ultimately reduce the quantity of leachate in the pond that requires subsequent treatment. Little work investigating the efficacy in using such a system to enhance leachate evaporation has been reported. The overall goal associated with this study was to quantify the amount of evaporation enhanced by the droplet …

Dynamic Multi-Dimensional Numerical Transport Study Of Lithium-Ion Battery Active Material Microstructures For Automotive Applications, Joseph S. Lopata, Taylor R. Garrick, Fengkun Wang, Han Zhang, Yangbing Zeng, Sirivatch Shimpalee

Faculty Publications

In support of GM’s traction battery efforts, we derived and implemented a method to describe the electrochemical performance of a battery cell considering the nuances of the electrode microstructure at the anode and the cathode and the corresponding impact on the electrochemical transport in the solid and liquid phases. To assess the capability of the method, we compared model results from the microstructure framework with the commonly used continuum-level porous electrode model, commonly referred to as the pseudo-2-dimensional model, or the Newman Model. The microstructure modeling framework was applied to simulate the electrochemical and transport processes within the battery cell …

Knowledge Graph Empowered Machine Learning Pipelines For Improved Efficiency, Reusability, And Explainability, Revathy Venkataramanan, Aalap Tripathy, Martin Foltin, Hong Yung Yip, Annmary Justine, Amit Sheth

Publications

Artificial intelligence (AI) pipelines are complex, heavily parameterized, and expensive to execute in terms of time and computational resources. Consequently, it is onerous to run experiments with all possible parameter combinations to achieve an optimal solution. However, these AI experiments can be optimized by recommending relevant parameters to commence the experiments, reducing search space significantly, which can be fine tuned further. The relevant parameters can be identified by observing the metadata of pipelines executed in the past, and the relevant pipeline with relevant parameters can be recommended to the user. Currently, there are various metadata frameworks that automatically record the …

Investigation Of Electrically Isolated Capacitive Sensing Skins On Concrete To Reduce Structure/Sensor Capacitive Coupling, Emmanuel Ogunniyi, Alexander Vareen, Austin Downey, Simon Laflamme, Jian Li, Caroline Bennett, William Collins, Hongki Jo, Alexander Henderson, Paul Ziehl

Faculty Publications

Damage to bridges can result in partial or complete structural failures, with fatal consequences. Cracks develop in concrete infrastructure from fatigue loading, vibrations, corrosion, or unforeseen structural displacement. 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 technologies for crack detection on bridges is expensive, especially long-term, due to the density of sensors required to detect, localize, and quantify cracks. Previous research on soft elastomeric capacitors (SECs) has shown their viability for low-cost monitoring of cracks in transportation infrastructure. However, when deployed …

Perspective On Predictive Modeling: Current Status, New High-Order Methodology And Outlook For Energy Systems, Dan Gabriel Cacuci

Faculty Publications

This work presents a perspective on deterministic predictive modeling methodologies, which aim at extracting best-estimate values for model responses and parameters along with reduced predicted uncertainties for these best-estimate values. The two oldest such methodologies are the data-adjustment method, which stems from the nuclear energy field, and the data-assimilation method, which is implemented in the geophysical sciences. Both of these methodologies attempt to minimize, in the least-square sense, a user-defined functional that represents the discrepancies between computed and measured model responses. These two methodologies were briefly reviewed and shown to be inconsistent even to first-order in the sensitivities of the …

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance For Telehealth: The Mental Health Case, Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth

Publications

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed …

Cook-Gen: Robust Generative Modeling Of Cooking Actions From Recipes, Revathy Venkataramanan, Kaushik Roy, Kanak Ray, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit Sheth

Publications

As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, food recommendation systems analyze recipe instructions to assess nutritional contents and provide recipe recommendations. The recent and remarkable successes of generative AI methods, such as auto-regressive large language models, can lead to robust methods for a more comprehensive understanding of recipes for healthy food recommendations beyond surface-level nutrition content assessments. In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and …

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Kalyan, Amit Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because of a lack of training on safety-constrained and specialized clinical process knowledge (Pro-Know). In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and ProKnow that healthcare professionals use (ProKnow-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (ProKnow-algo). We demonstrate the …

Second Victim Syndrome And Organizational Support For Healthcare Providers: A Scoping Review, K R. Petryszyn, J P. Young, E R. Neil, J E. Benedict, L E. Eberman

Clinical Practice in Athletic Training

Introduction: Healthcare providers may experience critical incident, medical error, or other adverse patient events in their clinical practice. Those that do encounter such events, may experience second victim syndrome (SVS), a condition in which providers feel psychological, cognitive, or physical reactions rendering care in these instances. Those with SVS may experience symptoms such as anxiety, depression, or burnout. Organizational support may mediate the impacts of SVS after an adverse patient event. We conducted a scoping review to explore and synthesize the literature on the support strategies implemented by healthcare organizations in the United States, for healthcare providers, after adverse patient …

Moment-Based Reinforcement Learning For Ensemble Control, Yao-Chi Yu, Vignesh Narayanan, Jr-Shin Li

Publications

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics are robustly compensated. In …

Tutorial - Shodhguru Labs: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Suitability Of Quantized Devs-Lim Methods For Simulation Of Power Systems, Navid Gholizadeh

Theses and Dissertations

This research presents an investigation of behavioral intricacies of the Quantized DEVS Latency Insertion Method (QDL) method and proposes resolutions to the previously unsolved and unexplained discrepancies between the reference solution and the QDL method. QDL method is a combination of two other methods namely the Latency Insertion Method (LIM) and Linear Implicit Quantized State (LIQSS)method. This technique is rigorously evaluated across a diverse array of systems and scenarios, with the aim of unearthing nuanced insights into its respective functionalities.

The research seeks to discern novel attributes of this method while gauging its comparative efficacy against conventional discrete-time methodologies, both …

Ierl: Interpretable Ensemble Representation Learning - Combining Crowdsourced Knowledge And Distributed Semantic Representations, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth

Publications

Large Language Models (LLMs) encode meanings of words in the form of distributed semantics. Distributed semantics capture common statistical patterns among language tokens (words, phrases, and sentences) from large amounts of data. LLMs perform exceedingly well across General Language Understanding Evaluation (GLUE) tasks designed to test a model’s understanding of the meanings of the input tokens. However, recent studies have shown that LLMs tend to generate unintended, inconsistent, or wrong texts as outputs when processing inputs that were seen rarely during training, or inputs that are associated with diverse contexts (e.g., well-known hallucination phenomenon in language generation tasks). Crowdsourced and …

Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Publications

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …

A Semantic Web Approach To Fault Tolerant Autonomous Manufacturing, Fadi El Kalach, Ruwan Wickramarachchi, Ramy Harik, Amit Sheth

Publications

The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future factories are required to be agile, allowing for more customized production, and resistance to disturbances. Such production lines would be able to reallocate resources as needed and minimize downtime while keeping up with market demands. These systems must be capable of complex decision-making based on parameters such as machine status, sensory/IoT data, and inspection results. Current manufacturing lines lack this complex capability and instead focus on low-level decision-making on the machine level without utilizing the generated data to its full extent. This …

Knowledge Graph Guided Semantic Evaluation Of Language Models For User Trust, Kaushik Roy, Tarun Garg, Vedant Palit, Yuxin Zi, Vignesh Narayanan, Amit Sheth

Publications

A fundamental question in natural language processing is - what kind of language structure and semantics is the language model capturing? Graph formats such as knowledge graphs are easy to evaluate as they explicitly express language semantics and structure. This study evaluates the semantics encoded in the self-attention transformers by leveraging explicit knowledge graph structures. We propose novel metrics to measure the reconstruction error when providing graph path sequences from a knowledge graph and trying to reproduce/reconstruct the same from the outputs of the self-attention transformer models. The opacity of language models has an immense bearing on societal issues of …

Acm Web Conference 2023, Usha Lokala, Kaushik Roy, Utkarshani Jaimini, Amit Sheth

Publications

Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic …

L3 Ensembles: Lifelong Learning Approach For Ensemble Of Foundational Language Models*, Aidin Shiri, Kaushik Roy, Amit Sheth, Manas Gaur

Publications

Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices. This necessitates the development of a Lifelong Learning (L3) framework that continuously adapts to a stream of Natural Language Processing (NLP) tasks efficiently. We propose an approach that focuses on extracting meaningful representations from unseen data, constructing a structured knowledge base, and improving task performance incrementally. We conducted experiments on various NLP tasks to validate its effectiveness, including benchmarks like GLUE and SuperGLUE. We measured good performance across the accuracy, training efficiency, and knowledge transfer metrics. Initial experimental results show that the proposed L3 …

The Troubling Emergence Of Hallucination In Large Language Models--An Extensive Definition, Quantification, And Prescriptive Remediations, Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das

Publications

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a finegrained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) …

Tutorial - Shodhguru Labs: Optimization And Hyperparameter Tuning For Neural Networks, Kaushik Roy

Publications

Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This paper presents a comprehensive exploration of optimization strategies and hyperparameter tuning techniques for neural networks. Neural networks have emerged as a powerful and versatile class …

Evaluating The Influence Of A Droplet Spraying/Misting System To Enhance Ammonia Volatilization From A Leachate Storage Pond: A Case Study At The Three Rivers Solid Waste Authority, Kaitlen Drafts, Suzie Boxman, Scott Ribes, Mike Terry, Bryan Staley, Joseph Flora, Nicole Berge

Faculty Publications

The Three Rivers Solid Waste Authority (TRSWA) operates a MSW landfill outside Jackson, South Carolina (USA) at which leachate ammonia concentrations are of concern. The landfill operates a droplet spraying/misting system (known as the Lilypad system) in their pond to enhance both leachate evaporation and, possibly, ammonia volatilization. The overall goals of this study were to determine the fate of nitrogen in the pond and to ultimately quantify the role the Lilypad system plays in enhancing ammonia removal. To accomplish the study goals, an empirical model based on collected leachate and mist samples, climatological data, and pond hy-draulic data was …

Precipitation Trends In North And South Carolina, Usa, Giacomo Moragila, Erika Brattich, Gregory J. Carbone

Faculty Publications

Study region

North and South Carolina, USA.

Study focus

Intense precipitation poses risks to life and property. Its frequency can change in response to global-scale drivers, but its spatial expression can vary seasonally and regionally, and be dependent on how it is measured and what analysis period is used. We investigate forty-four historical stations from the U.S. Historical Climatology Network (USHCN) across North and South Carolina to determine trends in the pluviometric regime defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). New hydrological insights for the region Most of the stations in this area do not …

A Focused Review On Structures And Ionic Conduction Mechanisms In Inorganic Solid-State Proton And Hydride Anion Conductors, Shichen Sun, Qiming Tang, Kangkang Zhang, Yeting Wen, Aidan Billings, Kevin Huang

Faculty Publications

Solid-state proton and hydride anion conductors are an important family of materials as electrolytes for solid state electrochemical cells such as fuel cells, batteries, sensors, and gas separation membranes. Searching for new proton and hydride-anion conductors has been an active research area for many decades. The focus of this article is on reviewing the types and mechanisms of each proton/hydride-anion conductor developed and their pros and cons. This review starts off with the most studied and most promising perovskite structured oxides as proton conductors, followed by other types of perovskite-related structures such as the Ruddlesden–Popper phase, pyrochlores and rare earth …

Higher Corrections Of The Ilkovich Equation, S. Jon Chapman, Charles W. Monroe, Shiv Krishna Madi Reddy, Alexander Van-Brunt, Ralph E. White

Faculty Publications

A short-time asymptotic analysis is performed to establish corrections of the Ilkovich equation, which describes the polarographic response of a dropping mercury electrode. The convective diffusion equation governing diffusion limited reactant flux for small drop times is solved by a regular perturbation based on powers of the sixth root of time. This produces a framework within which higher terms of the Ilkovich equation can be derived systematically. As well as reproducing Ilkovich’s original formula and verifying Newman’s correction of Koutecky’s first-order term, we calculate the second-order term for the first time. The calculation is compared to the Newman–Levich procedure and …

Bio-Oil Production From Hydrothermal Liquefaction Of Pennisetum Purpureum X Pennisetum Typhoideum, Tossapon Katongtung, Sanphawat Phtomphithak, Thossaporn Onsree, Nakorn Tippayawong, Jochen A. Lauterbach

Faculty Publications

Among renewable and sustainable energy resources, biomass plays a vital role. Agricultural residues/wastes, energy crops, and lignocellulosic biomass could potentially be major feedstocks for biorefineries. In Thailand, one of the most interesting energy crops is hybrid giant Juncao grass (GJG) or Pennisetum purpureum × Pennisetum typhoideum. GJG can be easily grown and has relatively high yields under tropical climates. Herein, conversion of GJG to biofuels via hydrothermal liquefaction (HTL) was investigated using batch reactors under varying reaction temperatures of 250–350 ◦C and biomass-to-deionized water concentrations of 15–25 wt% at a fixed residence time of 30 min. Changes in temperature and …