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Full-Text Articles in Engineering

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 Jan 2023

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 …


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 Jan 2023

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 …


Ierl: Interpretable Ensemble Representation Learning - Combining Crowdsourced Knowledge And Distributed Semantic Representations, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth Jan 2023

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 Jan 2023

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 …


Implementing Dei In Aviation Education: Coping And Addressing Mental Health Concerns, Jorge L. D. Albelo Ph.D., Michael F. O'Toole Ph.D., Samantha Bowyer Dec 2022

Implementing Dei In Aviation Education: Coping And Addressing Mental Health Concerns, Jorge L. D. Albelo Ph.D., Michael F. O'Toole Ph.D., Samantha Bowyer

Publications

In recent years, different global events have led to increased awareness of the benefits of promoting diversity, equity, and inclusion in the workplace and education. Notably, the aviation industry is seeing increased research initiatives to promote DEI among all generations. Nevertheless, given the rising concerns about mental health in higher education, this paper sought to connect coping and addressing mental health through implementing DEI teachings in aviation education. Integrating DEI in the aviation classroom can be challenging, as many faculty members might feel uncomfortable addressing the topic in their courses. Consequently, the researchers proposed and tested an aviation education approach …


A Machine Learning Approach Towards Analyzing Impact Of Surface Weather On Expect Departure Clearance Times In Aviation, Dothag Truong, Shlok Misra, Godfrey V. D'Souza Nov 2022

A Machine Learning Approach Towards Analyzing Impact Of Surface Weather On Expect Departure Clearance Times In Aviation, Dothag Truong, Shlok Misra, Godfrey V. D'Souza

Publications

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, the weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stops and Expect Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline’s operating network. The use of time-series data mining can be used to assess and quantify …


Event-Triggered Optimal Adaptive Control Of Partially Unknown Linear Continuous-Time Systems With State Delay, Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan Nov 2022

Event-Triggered Optimal Adaptive Control Of Partially Unknown Linear Continuous-Time Systems With State Delay, Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan

Publications

This paper proposes an event-triggered optimal adaptive output feedback control design approach by utilizing integral reinforcement learning (IRL) for linear time-invariant systems with state delay and uncertain internal dynamics. In the proposed approach, the general optimal control problem is formulated into the game-theoretic framework by treating the event-triggering threshold and the optimal control policy as players. A cost function is defined and a value functional, which includes the delayed system output, is considered. First, by using the value functional and applying stationarity conditions using the Hamiltonian function, the output game delay algebraic Riccati equation (OGDARE) and optimal control policy are …


Tutorial: Knowledge-Infused Learning For Autonomous Driving (Kl4ad), Ruwan Wickramarachchi, Cory Henson, Sebastian Monka, Daria Stepanova, Amit Sheth Oct 2022

Tutorial: Knowledge-Infused Learning For Autonomous Driving (Kl4ad), Ruwan Wickramarachchi, Cory Henson, Sebastian Monka, Daria Stepanova, Amit Sheth

Publications

Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and …


Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth Oct 2022

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

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, …


A Protocol For Coupling Volumetrically Dynamic In Vitro Experiments To Numerical Physiology Simulation For A Hybrid Cardiovascular Model, Abraham Umo, Ethan Kung Oct 2022

A Protocol For Coupling Volumetrically Dynamic In Vitro Experiments To Numerical Physiology Simulation For A Hybrid Cardiovascular Model, Abraham Umo, Ethan Kung

Publications

Objective: The Physiology Simulation Coupled Experiment (PSCOPE) is a hybrid modeling framework that enables a physical fluid experiment to operate in the context of a closed-loop computational simulation of cardiovascular physiology. Previous PSCOPE methods coupled rigid experiments to a lumped parameter network (LPN) of physiology but are incompatible with volumetrically dynamic experiments where fluid volume varies periodically. We address this limitation by introducing a method capable of coupling rigid, multi-branch, and volumetrically dynamic in-vitro experiments to an LPN. Methods: Our proposed method utilizes an iterative weighted-averaging algorithm to identify the unique solution waveforms for a given PSCOPE model. We confirm …


Defining Aviation “Skills” To Ensure Effective, Safe, And Efficient Evaluations: A Qualitative Study, Jorge L. D. Albelo Ph.D., Haydee M. Cuevas, Marisa Aguiar, Christopher Piccone, Karlene Petitt, Raquel Villagomez Oct 2022

Defining Aviation “Skills” To Ensure Effective, Safe, And Efficient Evaluations: A Qualitative Study, Jorge L. D. Albelo Ph.D., Haydee M. Cuevas, Marisa Aguiar, Christopher Piccone, Karlene Petitt, Raquel Villagomez

Publications

The present qualitative case study strives to define the term skill within aviation, drawing from the cognitive psychology, organizational psychology, and training literature as well as input from subject matter experts in the aviation industry. A review of the published literature revealed no consensus for defining what constitutes a skill. While some definitions follow a task-based approach, others emphasize more cognitively based representations. Moreover, a formal, commonly accepted definition of the term skill within the aviation domain is lacking. The researchers employed a qualitative case study methodology to extract true descriptions from the subject matter experts to bound and expand …


Sme Coffee Hour: Human Factors: Dirty Dozennorms And Complacency, Linda Vee Weiland Oct 2022

Sme Coffee Hour: Human Factors: Dirty Dozennorms And Complacency, Linda Vee Weiland

Publications

Presentation about the relevance between communication, language, complacency and norms related to flight safety.


Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth Oct 2022

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …


Metaversekg: Knowledge Graph For Engineering And Design Application In Industrial Metaverse, Utkarshani Jaimini, Tongtao Zhang, Georgia Olympia Brikis Oct 2022

Metaversekg: Knowledge Graph For Engineering And Design Application In Industrial Metaverse, Utkarshani Jaimini, Tongtao Zhang, Georgia Olympia Brikis

Publications

While the term Metaverse was first coined by the author Neal Stephenson in 1992 in his science fiction novel “Snow Crash”, today the vision of an integrated virtual world is becoming a reality across different sectors. Applications in gaming and consumer products are gaining traction, industrial metaverse applications are, still in their early stages of development with one of the challenges being interoperability across various metaverse development platforms and existing software tools. In this work we propose the use of a knowledge graph based semantic data exchange layer, the Metaverse Knowledge Graph, to enable seamless transfer of information across platforms. …


Towards Efficient Scoring Of Student-Generated Long-Form Analogies In Stem, Thilini Wijesiriwardene, Ruwan Wickramarachchi, Valerie L. Shalin, Amit P. Sheth Sep 2022

Towards Efficient Scoring Of Student-Generated Long-Form Analogies In Stem, Thilini Wijesiriwardene, Ruwan Wickramarachchi, Valerie L. Shalin, Amit P. Sheth

Publications

Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature …


Height Information Aided 3d Real-Time Large-Scale Underground User Positioning, Houbing Song, Chengkai Tang, Cunle Zhang, Lingling Zhang, Yi Zhang Sep 2022

Height Information Aided 3d Real-Time Large-Scale Underground User Positioning, Houbing Song, Chengkai Tang, Cunle Zhang, Lingling Zhang, Yi Zhang

Publications

Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large‐scale underground mining environment. To solve this problem, this study proposes large‐scale underground 3D real‐time positioning method with seam height assistance. This method uses the ultrawide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise3D positioning. The simulation results show that the proposed real‐time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large‐scale underground users.


Rocket Measurements Of Electron Energy Spectra From Earth’S Photoelectron Production Layer, Aroh Barjatya, Shantanab Debchoudhury, Glyn A. Collinson, Alex Glocer, Dennis Chornay, Et Al. Aug 2022

Rocket Measurements Of Electron Energy Spectra From Earth’S Photoelectron Production Layer, Aroh Barjatya, Shantanab Debchoudhury, Glyn A. Collinson, Alex Glocer, Dennis Chornay, Et Al.

Publications

Photoelectrons are crucial to atmospheric physics. They heat the atmosphere, strengthen 28 planetary ambipolar electric fields, and enhance the outflow of ions to space. However, 29 there exist only a handful of measurements of their energy spectrum near the peak of 30 photoproduction. We present calibrated energy spectra of pristine photoelectrons at their 31 source by a prototype Dual Electrostatic Analyzer (DESA) instrument flown on July 11 32 2021 aboard the Dynamo-2 sounding rocket (NASA № 36.357). Photopeaks arising from 33 30.4nm He-II spectral line were observed throughout the flight above 120km. DESA also 34 successfully resolved the rarely observed …


Comprehensive Review Of Heat Transfer Correlations Of Supercritical Co2 In Straight Tubes Near The Critical Point: A Historical Perspective, Douglas C. Lopes, Yang Chao, Vinusha Dasarla, Neil P. Sullivan, Mark Ricklick, Sandra Boetcher, Douglas Cabrera Lopes Aug 2022

Comprehensive Review Of Heat Transfer Correlations Of Supercritical Co2 In Straight Tubes Near The Critical Point: A Historical Perspective, Douglas C. Lopes, Yang Chao, Vinusha Dasarla, Neil P. Sullivan, Mark Ricklick, Sandra Boetcher, Douglas Cabrera Lopes

Publications

An exhaustive review was undertaken to assemble all available correlations for supercritical CO2 in straight, round tubes of any orientation with special attention paid to how the wildly varying fluid properties near the critical point are handled. The assemblage of correlations, and subsequent discussion, is presented from a historical perspective, starting from pioneering work on the topic in the 1950s to the modern day. Despite the growing sophistication of sCO2 heat transfer correlations, modern correlations are still only generally applicable over a relatively small range of operating conditions, and there has not been a substantial increase in predictive capabilities. Recently, …


Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth Jul 2022

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of ‘depression’, our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …


Preliminary Study Of Shape-Memory Alloy Torsional Tubes As Thermal Management Actuators Under Non-Ideal Conditions, Paula Sanjuan Espejo, Samuel Desloover, Devon Hardy, Mark Ricklick, Frederick Calkin, David Foutch Jun 2022

Preliminary Study Of Shape-Memory Alloy Torsional Tubes As Thermal Management Actuators Under Non-Ideal Conditions, Paula Sanjuan Espejo, Samuel Desloover, Devon Hardy, Mark Ricklick, Frederick Calkin, David Foutch

Publications

Shape-memory alloys (SMAs) have been used in many engineering applications because of their shape-memory effect and pseudoelasticity. SMA behavior is well understood under steady and constant temperature and loading conditions, whereas transient and non-ideal conditions effects should be further investigated. In this research, SMA torque tubes are studied for use in thermal management applications as self-regulated actuators responding to a process fluid with changes in temperature, with the goal of improved system efficiency by keeping components at an optimal temperature. When utilized in a thermal management configuration, it is likely that the SMA’s thermal environment will be different than that …


Predicting The Impact Of Covid-19 On Air Transportation Volumes, Dothang Truong Jun 2022

Predicting The Impact Of Covid-19 On Air Transportation Volumes, Dothang Truong

Publications

COVID-19 has significant impacts on air transportation. This paper aims to predict domestic and international transportation volumes during the pandemic. Daily trips by distance are novel variables in the prediction. Additionally, COVID-19 severity, vaccination, and economic index are other predictors. Artificial Neural Networks and Monte Carlo simulations were used to develop and validate the predictive models using data from various sources in 2021. The findings confirm the importance of daily trips by distance and vaccination as significant predictors. Airlines can use the models to predict air transportation volumes and formulate appropriate strategies to meet the air travel demand and improve …


Can Language Models Capture Graph Semantics? From Graphs To Language Model And Vice-Versa, Tarun Garg, Kaushik Roy, Amit Sheth Jun 2022

Can Language Models Capture Graph Semantics? From Graphs To Language Model And Vice-Versa, Tarun Garg, Kaushik Roy, Amit Sheth

Publications

Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities. However, current deep learning models takes as input distributed representations or vectors. Thus, the graph is compressed in a vectorized representation. We conduct a study to examine if the deep learning model can compress a graph and then output the same graph with most of the semantics intact. Our experiments show that Transformer models are not able to express the full semantics of the input knowledge graph. We find that this is due to the disparity between the directed, relationship and …


Knowledge-Driven Drug-Use Namedentity Recognition With Distant Supervision, Goonmeet Bajaj, Ugur Kursuncu, Manas Gaur, Usha Lokala, Ayaz Hyder, Srinivasan Parthasarathy, Amit Sheth Jun 2022

Knowledge-Driven Drug-Use Namedentity Recognition With Distant Supervision, Goonmeet Bajaj, Ugur Kursuncu, Manas Gaur, Usha Lokala, Ayaz Hyder, Srinivasan Parthasarathy, Amit Sheth

Publications

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for …


Numerical Implementation And Validation Of A Viscoelastic-Plastic Material Model For Predicting Curing Induced Residual Stresses In Adhesive Bonded Joints, Akshat Agha, Fadi Abu-Farha Jun 2022

Numerical Implementation And Validation Of A Viscoelastic-Plastic Material Model For Predicting Curing Induced Residual Stresses In Adhesive Bonded Joints, Akshat Agha, Fadi Abu-Farha

Publications

One of the main challenges in the joining of multi-material components is the assessment of the nature and magnitude of the residual stresses developing in the adhesive bond during the heat curing manufacturing process. Numerical modeling of these residual stresses can provide insights for making informed decisions related to (i) material substrate properties; (ii) adhesive properties i.e., low, medium, or high stiffness; (iii) bondline geometry i.e., bondline width and bead thickness; (iv) curing cycle characteristics; and (v) fixation design i.e., type, spacing, the number of joints. This work presents a cure history-dependent viscoelastic-plastic material description for the modeling of adhesive …


Challenges In Kinetic-Kinematic Driven Musculoskeletal Subject-Specific Infant Modeling, Yeram Lim, Victor Huayamave, Tamara Chambers, Christine Walck, Safeer Siddicky, Erin Mannen Apr 2022

Challenges In Kinetic-Kinematic Driven Musculoskeletal Subject-Specific Infant Modeling, Yeram Lim, Victor Huayamave, Tamara Chambers, Christine Walck, Safeer Siddicky, Erin Mannen

Publications

Musculoskeletal computational models provide a non-invasive approach to investigate human movement biomechanics. These models could be particularly useful for pediatric applications where in vivo and in vitro biomechanical parameters are difficult or impossible to examine using physical experiments alone. The objective was to develop a novel musculoskeletal subject-specific infant model to investigate hip joint biomechanics during cyclic leg movements. Experimental motion-capture marker data of a supine-lying 2-month-old infant were placed on a generic GAIT 2392 OpenSim model. After scaling the model using body segment anthropometric measurements and joint center locations, inverse kinematics and dynamics were used to estimate hip ranges …


The Effect Of Pre-Thermal And -Load Conditions On In-718 High Temperature Fatigue Life, Paulina De La Torre, Alberto Mello Mar 2022

The Effect Of Pre-Thermal And -Load Conditions On In-718 High Temperature Fatigue Life, Paulina De La Torre, Alberto Mello

Publications

Ni-based superalloys are largely used in the aerospace industry as critical components for turbine engines due to their excellent mechanical properties and fatigue resistance at high temperatures. A hypothesis to explain this atypical characteristic among metals is the presence of a cross-slip mechanism. Previous work on the role of thermal activation on cubic slip has shown strain accommodation in two sets of slip planes, which resembled the activation of {100} cubic slip systems along of the octahedral slip planes {111} in Ni-based superalloys under high strain and temperature, exhibiting a more homogeneous strain distribution and less strain localization. Following those …


Influence Of Cold Expansion And Aggressive Environment On Crack Growth In Aa2024-T3, Ken Shishino, Christopher Leirer, Alberto Mello Mar 2022

Influence Of Cold Expansion And Aggressive Environment On Crack Growth In Aa2024-T3, Ken Shishino, Christopher Leirer, Alberto Mello

Publications

This research aims to establish the effect of hole cold expansion on fatigue life of pre-cracked material under aggressive environment. A relationship between crack propagation and secondary crack initiation was established for AA2024-T3 cold worked holes subjected to cyclic loads to determine the impact on fatigue life of joints in presence of saline solution. Galvanic corrosion of a steel fastener/aluminum plate assembly was investigated assuming the presence of cracks in the aluminum plates, whose growth will be monitored in-situ with a digital microscope throughout the fatigue process. The cold expansion treatment improved the fatigue life fourfold under a corrosive environment, …


Field Retrofit And Testing Of A Corroded Metal Culvert Using Gfrp, Mahmoud Reda Taha, Susan Bogus, Mohammed Abdellatef, Daniel Heras Murcia Mar 2022

Field Retrofit And Testing Of A Corroded Metal Culvert Using Gfrp, Mahmoud Reda Taha, Susan Bogus, Mohammed Abdellatef, Daniel Heras Murcia

Publications

One of the current pressing problem for all DOTs is the corrosion-oriented deterioration of the existing metal culverts. These metal culverts typically are designed for a life of 50 years. However, corrosion is making them last no longer than 30years. Here we propose use of Glass Fiber Reinforced Polymers (GFRP) pipe section as a fit-in GFRP profile liner for complete repair and rehabilitation of the corroded metal culvert with an expected life of 75 years. This is mainly because of the corrosion free nature of the GFRP material. In the current study, the design method for using glass fiber-reinforced polymer …


The Breathing Human Infrastructure: Integrating Air Quality, Traffic, And Social Media Indicators, Heather O'Leary, Scott Parr, Marwa El-Sayed Feb 2022

The Breathing Human Infrastructure: Integrating Air Quality, Traffic, And Social Media Indicators, Heather O'Leary, Scott Parr, Marwa El-Sayed

Publications

Outdoor air pollution is a complex system that is responsible for the deaths of millions of people annually, yet the integration of interdisciplinary data necessary to assess air quality's multiple metrics is still lacking. This case study integrates atmospheric indicators (concentrations of criteria pollutants including particulate matter and gaseous pollutants), traffic indicators (permanent traffic monitoring station data), and social indicators (community responses in Twitter archives) representing the interplay of the three critical pillars of the United Nations' Triple Bottom Line: environment, economy, and society. During the watershed moment of the COVID-19 pandemic lockdowns in Florida, urban centers demonstrated the gaps …


Thermoforming Process Effects On Structural Performance Of Carbon Fiber Reinforced Thermoplastic Composite Parts Through A Manufacturing To Response Pathway, Madhura Limaye, Sai Aditya Pradeep, Anmol Kothari, Sushil Savla, Akshat Agha, Srikanth Pilla, Gang Li Feb 2022

Thermoforming Process Effects On Structural Performance Of Carbon Fiber Reinforced Thermoplastic Composite Parts Through A Manufacturing To Response Pathway, Madhura Limaye, Sai Aditya Pradeep, Anmol Kothari, Sushil Savla, Akshat Agha, Srikanth Pilla, Gang Li

Publications

Thermoforming process of thermoplastic-based continuous CFRP's offer a major advantage in reducing cycle times for large-scale productions, but it can also have a significant impact on the structural performance of the parts by inducing undesirable effects. This necessitates the development of an optimal manufacturing process that minimizes the introduction of undesirable factors in the structure and thereby achieves the targeted mechanical performance. This can be done by first establishing a relationship between the manufacturing process and mechanical performance and successively optimizing it to achieve the desired targets. The current study focuses on the former part, where a manufacturing-to-response (MTR) pathway …