Engineering Commons^™

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Climate Change Impact On Bridge Scour Risk In Ny State: A Gis-Based Risk Analysis Model, Muhammad Hassan Butt

Publications and Research

Bridge scour, the primary cause of bridge failure in the United States, escalates post-severe storms, necessitating effective mitigation. This study employs a GIS-based risk analysis model to assess climate change's impact on bridge scour and associated risks in New York State. Data from the National Bridge Inventory, climate hazard maps, and geospatial data are integrated.

Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi

Biological Systems Engineering: Papers and Publications

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, …

Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework And Application For Decision Support For Operators In Control Rooms, Joseph Mietkiewicz, Ammar N. Abbas, Chidera Winifred Amazu, Anders L. Madsen, Gabriele Baldissone

Articles

In today’s complex industrial environment, operators are often faced with challenging situations that require quick and accurate decision-making. The human-machine interface (HMI) can display too much information, leading to information overload and potentially compromising the operator’s ability to respond effectively. To address this challenge, decision support models are needed to assist operators in identifying and responding to potential safety incidents. In this paper, we present an experiment to evaluate the effectiveness of a recommendation system in addressing the challenge of information overload. The case study focuses on a formaldehyde production simulator and examines the performance of an improved Human-Machine Interface …

Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han

Mathematics and Statistics Faculty Research & Creative Works

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …

Fabrications And Applications Of Micro/Nanofluidics In Oil And Gas Recovery: A Comprehensive Review, Junchen Liu, Yandong Zhang, Mingzhen Wei, Xiaoming He, Baojun Bai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Understanding fluid flow characteristics in porous medium, which determines the development of oil and gas oilfields, has been a significant research subject for decades. Although using core samples is still essential, micro/nanofluidics have been attracting increasing attention in oil recovery fields since it offers direct visualization and quantification of fluid flow at the pore level. This work provides the latest techniques and development history of micro/nanofluidics in oil and gas recovery by summarizing and discussing the fabrication methods, materials and corresponding applications. Compared with other reviews of micro/nanofluidics, this comprehensive review is in the perspective of solving specific issues in …

Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt

Faculty Publications

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) …

Error Estimate Of A Decoupled Numerical Scheme For The Cahn-Hilliard-Stokes-Darcy System, Wenbin Chen, Shufen Wang, Yichao Zhang, Daozhi Han, Cheng Wang, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

We analyze a fully discrete finite element numerical scheme for the Cahn-Hilliard-Stokes-Darcy system that models two-phase flows in coupled free flow and porous media. To avoid a well-known difficulty associated with the coupling between the Cahn-Hilliard equation and the fluid motion, we make use of the operator-splitting in the numerical scheme, so that these two solvers are decoupled, which in turn would greatly improve the computational efficiency. The unique solvability and the energy stability have been proved in Chen et al. (2017, Uniquely solvable and energy stable decoupled numerical schemes for the Cahn-Hilliard-Stokes-Darcy system for two-phase flows in karstic geometry. …

Transportation Service Level Impact On Aircraft Availability, Vincent Mclean, Adam D. Reiman

Faculty Publications

Purpose — Aircraft fail to meet mission capable rate goals due to a lack of supply of aircraft parts in inventory where the aircraft breaks. This triggers an order at the repair location. To maximize mission capable rate, the time from order to delivery needs to be minimized. The purpose of this research is to examine the case of three airfields for the order to delivery time of mission critical aircraft parts for a specific aircraft type. Design/methodology/approach — This research captured data from three information systems to assess the order fulfillment process. The data were analyzed to determine the …

Pilot Development: An Empirical Mixed-Method Analysis, Jonathan Slottje, Jason Anderson, John M. Dickens, Adam D. Reiman

Faculty Publications

Purpose — Pilot upgrade training is critical to aircraft and passenger safety. This study aims to identify variances in the US Air Force C-130J pilot upgrade training based on geographic location and provide a model to enhance policy that will impact future pilot training efforts that lower cost and increase operator quality and proficiency.
Design/methodology/approach — This research employed a mixed-method approach. First, the authors collected data and analyzed 90 C-130J pilots' aviation records and then contextualized this analysis with interviews of experts. Finally, the authors present a modified version of Six Sigma's define–measure–analyze–improve–control (DMAIC) that identifies and reduces the …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Volitional Control Of Lower-Limb Prosthesis With Vision-Assisted Environmental Awareness, S M Shafiul Hasan

FIU Electronic Theses and Dissertations

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether …

Maintenance Optimization In A Digital Twin For Industry 4.0, Abhijit Gosavi, Vy Khoi Le

Engineering Management and Systems Engineering Faculty Research & Creative Works

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network …

Reducing Print Time While Minimizing Loss In Mechanical Properties In Consumer Fdm Parts, Long Le, Mitchel A. Rabsatt, Hamid Eisazadeh, Mona Torabizadeh

Mechanical & Aerospace Engineering Faculty Publications

Fused deposition modeling (FDM), one of various additive manufacturing (AM) technologies, offers a useful and accessible tool for prototyping and manufacturing small volume functional parts. Polylactic acid (PLA) is among the commonly used materials for this process. This study explores the mechanical properties and print time of additively manufactured PLA with consideration to various process parameters. The objective of this study is to optimize the process parameters for the fastest print time possible while minimizing the loss in ultimate strength. Design of experiments (DOE) was employed using a split-plot design with five factors. Analysis of variance (ANOVA) was employed to …

Risk-Based Machine Learning Approaches For Probabilistic Transient Stability, Umair Shahzad

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive …

Integrating Compound Flood Conditions Through 2d Hydraulic Modeling For Simulating Flood Risk Processes In Coastal Cities, Francisco Pena Guerra Mr.

FIU Electronic Theses and Dissertations

Low elevation coastal karst environments are highly vulnerable to flooding conditions due to climate change. Trends in rising global temperatures have increased the frequency and intensity of extreme precipitation, hydrometeorological phenomena and sea level rise, exacerbating the impact of pluvial, fluvial, coastal and groundwater flood hazards. Compound flooding events amplify flood hazards and pose a higher threat to residents and infrastructure in unison compared to independent phenomena. Recent advancements in coupling hydrologic and hydraulic modeling frameworks have improved our ability to account for the combined effects of extreme pluvial, fluvial, and coastal flood hazards. This innovation in the hydroinformatics field …

Blood Biomarkers For Mild Traumatic Brain Injury: A Selective Review Of Unresolved Issues, Daniel B. Hier, Tayo Obafemi-Ajayi, Matthew S. Thimgan, Gayla R. Olbricht, Sima Azizi, Blaine Allen, Bassam A. Hadi, Donald C. Wunsch

Biological Sciences Faculty Research & Creative Works

Background: The use of blood biomarkers after mild traumatic brain injury (mTBI) has been widely studied. We have identified eight unresolved issues related to the use of five commonly investigated blood biomarkers: neurofilament light chain, ubiquitin carboxy-terminal hydrolase-L1, tau, S100B, and glial acidic fibrillary protein. We conducted a focused literature review of unresolved issues in three areas: mode of entry into and exit from the blood, kinetics of blood biomarkers in the blood, and predictive capacity of the blood biomarkers after mTBI.

Findings: Although a disruption of the blood brain barrier has been demonstrated in mild and severe traumatic brain …

Investigation And Statistical Modeling Of The Mechanical Properties Of Additively Manufactured Lattices, Derek G. Spear, Anthony N. Palazotto

Faculty Publications

This paper describes the background, test methodology, and experimental results associated with the testing and analysis of quasi-static compression testing of additively manufactured open-cell lattice structures. The study aims to examine the effect of lattice topology, cell size, cell density, and surface thickness on the mechanical properties of lattice structures. Three lattice designs were chosen, the Diamond, I-WP, and Primitive Triply Periodic Minimal Surfaces (TPMSs). Uniaxial compression tests were conducted for every combination of the three lattice designs, three cell sizes, three cell densities, and three surface thicknesses. In order to perform an efficient experiment and gain the most information …

A Kinetic Model For Blood Biomarker Levels After Mild Traumatic Brain Injury, Sima Azizi, Daniel B. Hier, Blaine Allen, Tayo Obafemi-Ajayi, Gayla R. Olbricht, Matthew S. Thimgan, Donald C. Wunsch

Mathematics and Statistics Faculty Research & Creative Works

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as …

Statistical Study Of The Effect Of Implementing An Airveyor System On The Warpage Of Injection Molded Closures, Charles Wesley Bozarth

Masters Theses & Specialist Projects

Berry Global in Bowling Green, Kentucky produces predominantly polypropylene container closures. One variant, the 83mm lined jar closure, is produced by first being injection molded, placed in work-in-progress (WIP) hold for 24 hours to cool, and then finished through the auxiliary liner operation into a final product. While this process is an effective method to produce a quality large-diameter closure and allows the polypropylene adequate time to cool without warping out of shape, the 24-hour WIP time and the manpower needed to accomplish this can negatively impact several business metrics as well as employee safety.

The purpose of this thesis …

Application Of Cycle-By-Cycle Analysis To Eeg Data From Individuals With Phelan-Mcdermid Syndrome, Naomi Miller

ENGS 88 Honors Thesis (AB Students)

This study aimed to analyze a novel method of processing data from electroencephalography (EEG) recordings, which implements time-domain cycle-by-cycle analysis. This "bycycle" method, developed by the Cole & Voytek laboratory, was implemented on a EEG dataset of children with and without Phelan-McDermid Syndrome in the hopes of uncovering network-level explanations for the genetic disorder. A supplemental Python pipeline was developed to organize and visualize the data. This led to the discovery of group-level differences in measures of cycle symmetry in alpha band waves over the sensorimotor electrodes. Through the same pipeline, the bycycle tool was validated as a sound EEG …

Evaluation Of Parametric And Nonparametric Statistical Models In Wrong-Way Driving Crash Severity Prediction, Sajidur Rahman Nafis

FIU Electronic Theses and Dissertations

Wrong-way driving (WWD) crashes result in more fatalities per crash, involve more vehicles, and cause extended road closures compared to other types of crashes. Although crashes involving wrong-way drivers are relatively few, they often lead to fatalities and serious injuries. Researchers have been using parametric statistical models to identify factors that affect WWD crash severity. However, these parametric models are generally based on several assumptions, and the results could generate numerous errors and become questionable when these assumptions are violated. On the other hand, nonparametric methods such as data mining or machine learning techniques do not use a predetermined functional …

Investigating The Impacts Of Crash Prediction Models On Quantifying Safety Effectiveness Of Adaptive Signal Control Systems, Weimin Jin, Mashrur Chowdhury, Sakib Mahmud Khan, Patrick Gerard

Publications

Introduction: Adaptive Signal Control System (ASCS) can improve both operational and safety benefits at signalized corridors. Methods: This paper develops a series of models accounting for model forms and possible predictors and implements these models in Empirical Bayes (EB) and Fully Bayesian (FB) frameworks for ASCS safety evaluation studies. Different models are validated in terms of the ability to reduce the potential bias and variance of prediction and improve the safety effectiveness estimation accuracy using real-world crash data from non-ASCS sites. This paper then develops the safety effectiveness of ASCS at six different corridors with a total of 65 signalized …

Biofilm And Cell Adhesion Strength On Dental Implant Surfaces Via The Laser Spallation Technique, James D. Boyd, Arnold J. Stromberg, Craig S. Miller, Martha E. Grady

Statistics Faculty Publications

OBJECTIVE: The aims of this study are to quantify the adhesion strength differential between an oral bacterial biofilm and an osteoblast-like cell monolayer to a dental implant-simulant surface and develop a metric that quantifies the biocompatible effect of implant surfaces on bacterial and cell adhesion.

METHODS: High-amplitude short-duration stress waves generated by laser pulse absorption are used to spall bacteria and cells from titanium substrates. By carefully controlling laser fluence and calibration of laser fluence with applied stress, the adhesion difference between Streptococcus mutans biofilms and MG 63 osteoblast-like cell monolayers on smooth and rough titanium substrates is obtained. The …

Computational Modeling For Decision-Making Under Climate Change Uncertainty: Reservoir Simulation Game, Julianne Quinn

All ECSTATIC Materials

Almost every decision you make is under uncertainty. Will I need a rain jacket in the afternoon? Will they say yes if I ask them out? Is 1 hour enough time to finish this assignment? Oftentimes, we can use computational modeling to simulate different scenarios of what might happen in the future to inform what decisions are best on average, or what decisions minimize the worst case outcome. For example, you could decide what player to draft for your Fantasy Football team by simulating player performance. In this activity, we will simulate how much water to release from a dam …

Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine

Faculty Publications

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in …

Resource-Saving Technologies For The Production Of Elastic Leather Materials: Collective Monograph, Olena Korotych, Anatolii Danylkovych, Serhii Bilinskyi, Serhii Bondarenko, Slava Branovitska, Vasyl Chervinskyi, Nataliia Khliebnikova, Alona Kudzieva, Viktor Lishchuk, Nataliia Lysenko, Olena Mokrousova, Nataliia Omelchenko, Vera Palamar, Yuliia Potakh, Oksana Romanyuk, Olga Sanginova, Oleksandr Zhyhotsky

Chemistry Publications and Other Works

This monograph contains a collection of recent research papers focusing on advancing existing technologies and developing new technologies to improve the environmentally friendliness and save resources during the production of elastic leather materials. The papers are organized based on the type of technological process used to preserve raw hides. A lot of attention is devoted to mathematical planning, simulations, and multicriteria optimization of the technological processes using newly developed chemical reagents. The monograph contains a complex study of physicochemical properties and characteristics of the resulting leather materials. The developed technologies were tested by the private joint-stock company Chinbar (Kyiv, Ukraine) …

An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …