Engineering Commons^™

The Impact Of Case Management Intervention For Insured Asthma Patients In Louisiana, An Empirical Study, Mohamed Mohamed Ohaiba

LSU Doctoral Dissertations

Asthma is a chronic condition whose symptoms are managed/prevented using medication and interventions. The overarching objective of this study was to evaluate the impact of patients' demographics on case management enrollment and healthcare utilization, as well as to develop machine learning models to predict high-cost patients.

To accomplish these goals, the Man-Whiteness test, the chi-squares test, logistic regression and odds ratios, and machine learning models were implemented. The average cost of the non-enrolled CM group was significantly higher than the enrolled group (p-value .0001). In addition, the non-enrolled groups had considerably more visits to the emergency department than the other …

Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu

LSU Doctoral Dissertations

In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …

A Machine Learning Approach To Robotic Additive Manufacturing Of Uv-Curable Polymers Using Direct Ink Writing, Luis A. Velazquez

LSU Master's Theses

This thesis presents the design and implementation of a robotic additive manufacturing system that uses ultraviolet (UV)-curable thermoset polymers. Its design considers future applications involving free-standing 3D printing by means of partial UV curing and the fabrication of samples that are reinforced with fillers or fibers to manufacture complex-shape objects.

The proposed setup integrates a custom-built extruder with a UR5e collaborative manipulator. The capabilities of the system were demonstrated using Anycubic resin formulations containing fumed silica (FS) at varying weight fractions from 2.8 to 8 wt%. To fully cure the specimens after fabrication, a UV chamber was used. Then, measurements …

Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan

Data

Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R² (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …

Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan

Publications

Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R² (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …

Modeling Crash Severity And Collision Types Using Machine Learning, Amit Kumar, Hari Krishnan Melempat Kalapurayil

Data

Traffic safety analysis is the fundamental step for reducing economic, social, and environmental cost incurred due to traffic accidents. The essence of traffic safety is understanding the factors affecting crash occurrence, injury severity and collision type and their underlying relationships and predict-prevent future crash instances. Crash injury severity studies in past have utilized numerous statistical, econometric and Machine Learning (ML) and Artificial Intelligence (AI) tools to extract the underlying relationship between the crash causal factors and the consequent severity or collision type. The study aims to explore the Multi-Label Classification (MLC) tool from the domain of Artificial Intelligence (AI) for …

Modeling Crash Severity And Collision Types Using Machine Learning, Amit Kumar, Hari Krishnan Melempat Kalapurayil

Publications

Traffic safety analysis is the fundamental step for reducing economic, social, and environmental cost incurred due to traffic accidents. The essence of traffic safety is understanding the factors affecting crash occurrence, injury severity and collision type and their underlying relationships and predict-prevent future crash instances. Crash injury severity studies in past have utilized numerous statistical, econometric and Machine Learning (ML) and Artificial Intelligence (AI) tools to extract the underlying relationship between the crash causal factors and the consequent severity or collision type. The study aims to explore the Multi-Label Classification (MLC) tool from the domain of Artificial Intelligence (AI) for …

Laser Surface Treatment And Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3d Printer And The Application Of Machine Learning In Materials Science, Hao Wen

LSU Doctoral Dissertations

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 …

Maintenance And Restriping Strategies For Pavement Markings On Asphalt Pavements In Louisiana, Momen R. Mousa, Marwa Hassan

Data

In Louisiana, most districts restripe their roadways using waterborne paints every other year; this strategy is questionable in terms of efficiency and economy. Meanwhile, previous studies showed substantial variability in the paint service life throughout the United States ranging between 0.25 and 6.2 years. Shortcomings in modeling the retroreflectivity of waterborne paints appear to significantly contribute to these variations as several studies predicted these values using degradation curves with a coefficient of determination (R2) as low as 0.1. Therefore, the objective of this study was to (i) develop new cost-effective restriping strategies using 4-inch (15-mil thickness) and 6-inch (25-mil thickness) …

Maintenance And Restriping Strategies For Pavement Markings On Asphalt Pavements In Louisiana, Momen R. Mousa, Marwa Hassan

Publications

In Louisiana, most districts restripe their roadways using waterborne paints every other year; this strategy is questionable in terms of efficiency and economy. Meanwhile, previous studies showed substantial variability in the paint service life throughout the United States ranging between 0.25 and 6.2 years. Shortcomings in modeling the retroreflectivity of waterborne paints appear to significantly contribute to these variations as several studies predicted these values using degradation curves with a coefficient of determination (R2) as low as 0.1. Therefore, the objective of this study was to (i) develop new cost-effective restriping strategies using 4-inch (15-mil thickness) and 6-inch (25-mil thickness) …

Studying Complex Aquifer Systems From Large-Scale Stratigraphy Development To Local Aquifer Storage And Recovery, Hamid Vahdat Aboueshagh

LSU Doctoral Dissertations

Hydrostratigraphy model is an essential component of building valid groundwater models. Many challenges are associated with constructing hydrostratigraphy models which include geological complexities such as faults, domes, and angular unconformities. Developing a method with an emphasis on capturing big data to thoroughly inform large-scale models is one of the challenges addressed in the first part of this study. The method is predicated upon discretization of the study domain into tiles based on the geological dip direction and faults. The application of the method in the state of Louisiana with the utilization of more than 114000 well logs demonstrates promising results …

Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani

LSU Doctoral Dissertations

The safety of highway-railroad grade crossings (HRGC) is still an issue in the United States of America (USA). The grade crossing is where a railroad crosses a road at the same level without any over or underpass. To improve the safety of crossings, the crossings’ condition should be explored from several aspects such as engineering design (speed limit, warning signs, etc.), road condition (number of lanes, surface markings, etc.), rail design (the type of track, ballast, etc.), temporal variables (weather, visibility, time of day, lightning, etc.), social variables (population, race, etc.), and last but not least, spatial variables (the type …

Temporal Decomposition For Multi-Interval Optimization In Power Systems, Farnaz Safdarian

LSU Doctoral Dissertations

Large optimization problems are frequently solved for power systems operation and analysis of electricity markets. Many of these problems are multi-interval optimization with intertemporal constraints. The size of optimization problems depends on the size of the system and the length of the considered scheduling horizon. Growing the length of the scheduling horizon increases the computational burden significantly and might make solving the problem in a required time span impossible. Many simplifications and approximation techniques are applied to reduce the computational complexity of multi-interval scheduling problems and make them solvable in a reasonable time span. Geographical decomposition is presented in the …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Data

Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference:

"Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Publications

Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a …

Enhanced Grain Partitioning Of X-Ray Microtomography Segmented Images, Nicholas C. Skrivanos Ii

LSU Master's Theses

In the field of petroleum engineering, rock samples are often taken from wells during the drilling process. Grain partitioning of digital three-dimensional microtomography segmented images obtained from these samples provides valuable in-situ properties and statistics that allow for accurate particle and structure characterization. This information can be used directly in detailed production and reservoir analysis, and can also be used to generate realistic packing models for advanced simulation. Additionally, the partitioned image can be used as a building block for realistic hydraulic fracture modeling. This technology has applications in other fields as well, such as core analysis in soil sciences …

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …

Quantitative Estimation Of Causality And Predictive Modeling For Precipitation Observation Sites And River Gage Sensors, Tri Vu Nguyen

LSU Master's Theses

This project seeks to investigate two questions: correlations from precipitation measurement sensors to river gage sensors, and predictive modeling of peak river gage heights during precipitation events. First, if correlations can be quantified, then a predictive model can be explored to predict peak water levels at river gage sensors, in response to precipitation inputs. Answering both research questions can provide early flood detection benefits and provide quantitative time assessments for flood risks. An extensive data-driven study was conducted across a geographical area of the U.S, spanning the time period 2008-2016 to identify river gage sensors that are closely correlated to …