Engineering Commons^™

A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction, Mostafa Elseifi, Ramchandra Paudel, Md Tanvir Ahmed Sarkar, Hossam Abohamer, Nirmal Dhakal

Data

Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface …

Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments, Zahid Hossain, Rifat Bulut, Fares Tarhuni, Hussein Al-Dakheeli

Data

Arkansas produces the most of the rice in the United States. About 20% of poddy is rice husk (RH), which is burnt under controlled conditions to produce rice rusk ash (RHA). The RHA is considered an environmental hazard and a significant challenge for rice millers. However, RHA is rich in pozzolanic material, which is mainly silica. In this study, RHA is used to stabilize poor soils. Another commonly used stabilizer, hydrated lime (HL), has also been evaluated for comparison purposes. Thus, this study aimed to determine the optimum percentages of RHA, HL, or a combination of these two agents by …

Performance Monitoring Leveraging Advanced Ai Technique With Cnn, Suyun Ham Ph.D, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David, Sanggoo Kang

Publications

The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting …

Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt

Data

Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …

Performance Monitoring Leveraging Advanced Ai Technique With Cnn, Suyun Ham Ph.D, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David, Sanggoo Kang

Data

The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting …

Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques, Momen R. Mousa, Marwa Hassan

Data

Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a …

Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design, Nicholas N. Ferenchak Ph.D

Publications

Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest …

Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design, Nicholas N. Ferenchak Ph.D

Data

Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest …

Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt

Publications

Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …

Effectiveness Assessment Of E-Ticketing Technology In Construction Of Transportation Projects, Sharareh Kermanshachi, Karthik Subramanya

Publications

The construction of highway infrastructure has devoted significant resources towards e-Construction to reduce the paperwork and automate the tasks in daily operations. Electronic Ticketing (e-Ticketing) is one such component of e-Construction that aids in the digital transfer of material tickets such as asphalt and concrete which accounts for more than fifty per cent of construction costs. Despite the benefits of e-Ticketing, many state departments and agencies are unwilling to transition into this technology. No studies have identified the cause of the delay in the implementation process, developed a framework to comprehend the platform's full potential, quantified savings, and suggested strategies …

Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques, Momen R. Mousa, Marwa Hassan

Publications

Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a …

Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments, Zahid Hossain, Rifat Bulut, Fares Tarhuni, Hussein Al-Dakheeli

Publications

Arkansas produces the most of the rice in the United States. About 20% of poddy is rice husk (RH), which is burnt under controlled conditions to produce rice rusk ash (RHA). The RHA is considered an environmental hazard and a significant challenge for rice millers. However, RHA is rich in pozzolanic material, which is mainly silica. In this study, RHA is used to stabilize poor soils. Another commonly used stabilizer, hydrated lime (HL), has also been evaluated for comparison purposes. Thus, this study aimed to determine the optimum percentages of RHA, HL, or a combination of these two agents by …

A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction, Mostafa Elseifi, Ramchandra Paudel, Md Tanvir Ahmed Sarkar, Hossam Abohamer, Nirmal Dhakal

Publications

Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface …

Electric Field Driven Co-Assembly And Transport Of Colloids Using Active-Passive Interaction, Brishty Deb Choudhury

LSU Master's Theses

Over the past few decades, the out-of-equilibrium transport and assembly of colloidal particles have been drawing significant interest due to their myriad applications in biology, engineering, and physics. In the absence of any external force, micro and nanoparticles perform Brownian motion in dispersion and are termed as passive colloids. In contrast, active colloids are a class of particles where a net imbalance of fluid flow around their surface drives their net migration in space when subjected to an external electric field. While several studies have shown the ability to program the dynamics of active colloids using an external electric field, …

Wetland Soil Development Along Salinity And Hydrogeomorphic Gradients In Active And Inactive Deltaic Basins Of Coastal Louisiana, Amanda Fontenot

LSU Master's Theses

Coastal wetlands provide an abundance of ecosystem services that benefit society, such as essential habitat for commercial species, storm protection, nutrient cycling, and carbon storage. Louisiana faces rapid rates of relative sea level rise (natural subsidence and eustatic sea levels) that threaten wetland survival, which are amplified by a reduction of riverine sediment input. An important determining factor of marsh survival is the formation of wetland platform elevation, known as vertical accretion, which is determined by several processes including sediment deposition & erosion, below ground biomass (BGB) productivity, decomposition of organic matter, shallow & deep subsidence, and soil compaction. Feldspar …

Design Tunneling Transistor And Schottky Junction Solar Cell Using Van Der Waals Semiconductor Heterostructure, Md Azmot Ullah Khan

LSU Doctoral Dissertations

Transition metal di-chalcogenide (TMDC) materials, being semiconductor in nature, offer Two-dimensional (2D) materials such as graphene and molybdenum disulfide (MoS₂) possess unique and unusual properties that are particularly applicable to nanoelectronics and photovoltaic devices. In this dissertation, four different projects have been done that encompass the implementation of these materials to improve the performance of future transistors and Schottky junction solar cells. In chapter 2, an analytical current transport model of a dual gate tunnel field-effect transistor (TFET) is developed by utilizing the principle of band-to-band tunneling (BTBT) and MoS₂ as the channel material. Later, using this …

Adaptive Balancing Compensation In Distribution Grid Of Ultra High-Power Manufacturing Plants, Motab Almousa

LSU Doctoral Dissertations

This dissertation presents a method of adaptive compensation, capable of handling power factor and the supply quality improvement under non-sinusoidal conditions. The focus in this dissertation is put on developing compensation for ultra-high power metallurgical plants, meaning on compensation in three-phase, four-wire ultra-high power dynamic, distribution systems. A separate attention in the dissertation is put on the adaptive compensation of ultrahigh power arc furnaces. The dissertation also presents an original method of adaptive compensation of DC current generated by such arc furnaces.

The proposed compensator is developed in the frame of the Currents’ Physical Components (CPC) – based power theory. …

Bioremediation Of Petroleum-Based Contaminants By Alkane-Degrading Bacterium Alcanivorax Borkumensis, Amber Julaine Pete

LSU Doctoral Dissertations

The world’s dependence on petroleum hydrocarbons has led to significant environmental implications. For example, oil spills cause lasting environmental damage, and the increase of plastics in the marine environment has been growing, specifically, microplastics that can be difficult to detect due to their small size. Petroleum hydrocarbons occur naturally in nearly all marine environments, which has allowed hundreds of microorganisms to evolve to utilize these hydrocarbons as their primary energy source. These microbes are classified as hydrocarbonoclastic and are utilized to remove spilled oil biodegradation. Over the last ten years, progress has been made in the biodegradation of oil spills …

Control And Planning For Mobile Manipulators Used In Large Scale Manufacturing Processes, Joshua T. Nguyen

LSU Master's Theses

Sanding operations in industry is one of the few manufacturing tasks that has yet to achieve automation. Sanding tasks require skilled operators that have developed a sense of when a work piece is sufficiently sanded. In order to achieve automation in sanding with robotic systems, this developed sense, or intelligence, that human operators have needs to be understood and implemented in order to achieve, at the minimum, the same quality of work. The system will also need to have the equivalent reach of a human operator and not be constrained to a single, small workspace. This thesis developed solutions for …

Predictive Thermal Modeling And Characterization Of Ultrasonic Consolidation Process For Thermoplastic Composites, Madeline Kirby

LSU Master's Theses

Ultrasonic consolidation (USC) of thermoplastic composites (TPCs) is a highly attractive and promising method to manufacture high-performance composites. This work focuses on USC of dry carbon fiber (CF) fabrics with high-temperature polyphenylene sulfide (PPS) films. Experimental trials to assess feasibility of the process are time-consuming. Consequently, a predictive thermal model would facilitate process parameters selection to reduce expensive trial-and-error approaches. This paper presents a 2D finite element model of samples under consolidation, incorporating equations for viscoelastic heating, matrix phase change, and material properties. Theoretical temperature profiles for nodes of interest were compared to the corresponding experimental temperature curves for various …

Neural Networks For Interference Mitigation In Satellite Communication Systems, Martha E. Cash

LSU Master's Theses

The objective of this thesis is to utilize the power of machine learning to develop a neural network-aided receiver in a DVBS-2X satellite communication system to improve the downlink transmission quality in the presence of interference. An emphasis is placed on mitigating the effects caused by non-linear distortions, carrier frequency offset, and additive white Gaussian noise. This thesis proposes a feed-forward neural network with two hidden layers to compensate for the distortions in the received signal. The proposed system model uses 16-APSK modulation scheme. The neural network is tested under varying degrees of non-linear distortion, frequency offsets, and varying levels …

Characterization Of Electrophoretic Deposited Zinc Oxide Nanopartices For The Fabrication Of Next-Generation Nanoscale Electronic Applications, Fawwaz Abduh A. Hazzazi

LSU Doctoral Dissertations

Several reports state that it is crucial to analyze nanoscale semiconductor materials and devices with potential benefits to meet the need for next-generation nanoelectronics, bio, and nanosensors. The progress in the electronics field is as significant now, with modern technology constantly evolving and a greater focus on more efficient robust optoelectronic applications. This dissertation focuses on the study and examination of the practicality of Electrophoretic Deposition (EPD) of zinc oxide (ZnO) nanoparticles (NPs) for use in semiconductor applications.

The feasibility of several synthesized electrolytes, with and without surfactants and APTES surface functionalization, is discussed. The primary objective of this study …

Tunable Passive Shock And Vibration Isolators For Rotational Isolation, Chase B. Lemaire

LSU Master's Theses

Shock and vibration isolation are a critical need in helmets, which are widely used to protect athletes, workers, soldiers, and astronauts. Passive vibration isolation systems are a good option when mass and volume should be minimized and when the experienced loadings can be predicted. However, it is frequently challenging to find materials and structures which exhibit the optimal vibration and impact isolation properties for an application. As a case study illustrating a novel design paradigm for rotational shock absorption, a family of optimal solutions for the physical properties of American football helmets is presented. Lumped parameter Simulink models simulate a …

Scale Up Of A Novel Method To Maximize Malonyl-Coa In Escherichia Coli, Clifford Harris Leblanc Iv

LSU Master's Theses

The instability of oil’s price along with its limited availability and impact on the environment motivate the search for an alternative feedstock that can sustain the profitability of chemical companies. Industrial biotechnology can promote renewable sources of energy and products by using microorganisms to produce a wide range of chemical compounds. The three-carbon metabolite, malonyl-CoA, can serve as a precursor to a variety of industrial chemicals. The major hurdle with using malonyl-CoA in industry is that its intracellular concentration in Escherichia Coli is very low. Previous attempts to increase the intracellular level of malonyl-CoA have ranged from genetic engineering of …

Micro Heat Exchanger To Cool Cerebrospinal Fluid For Brain Injury Treatment, Sachin Dahiya

LSU Master's Theses

Hypothermia is accepted as a method to preserve cells and tissue. Clinical evidence shows that administration of hypothermia could lead to neuroprotection after cardiac arrest. Non-invasive methods such as surface cooling devices, drugs and cold liquid ventilation are available to induce hypothermia. These approaches for achieving hypothermia have not been optimized yet. The surface cooling methods are generally slow and may lead to additional thermal shock to the body. Here, we propose a rapid, selective cooling method for the brain using a micro heat exchanger to cool the cerebrospinal fluids (CSF). We designed and 3D printed a U-type heat exchanger …

Exploring Toluene Biosynthesis In Microbial Consortia Derived From Sediments And Superfund Site Groundwater, Raymond J. Poche

LSU Master's Theses

While the aromatic hydrocarbon toluene is widely regarded as a contaminant associated with discharges of crude oil and refined petroleum products (e.g., gasoline), the enzyme phenylacetate decarboxylase catalyzes the decarboxylation of phenylacetate to yield toluene. Research described in this thesis further investigated toluene biosynthesis by a microbial consortium originating from chlorinated solvent-contaminated groundwater near Baton Rouge, Louisiana. In anoxic growth media supplemented with phenylacetate as a precursor, toluene accumulation was found to be maximal at near-neutral to slightly acidic pHs (toluene accumulation greater than 1 mg/L at pH in the range of 5.6 to 7.6, but was not observed for …

Engineering The Lanthanide And Actinide Dopants Effects On The Local Environment Of Luminescent Complex Metal Oxide, Yuming Wang

LSU Doctoral Dissertations

Rare earth (RE) based phosphor materials are widely used as luminescent probes in high-temperature optical thermometry where standard methods are unsuitable. The current generation of luminescent thermometers lacks stability and energy transport within the operating temperature ranges. The challenge is developing an oxide-based material with high luminescence intensity and thermal sensitivity/stability, suitable for an optical temperature sensor/thermal barrier coating. This work aims to develop a fundamental understanding of the local environment surrounding RE or actinide dopants in metal oxide hosts to engineer their luminescence property. To maximize the luminescence, the local symmetry, doping concentration, and radiative/non-radiative relaxation pathways must be …

Integration Of Solar + Storage Installation Upstream In Modular Construction, Ondrej Labik

LSU Master's Theses

High initial costs, mainly due to inefficient construction processes, remains the greatest barrier for residential adoption of solar+storage (SPS) system. Modular construction, having a factory-controlled environment and control over the home design, is well suited to address these issues (e.g., efficiencies, waste, inventory control, quality). Furthermore, these areas have great potential in modular construction to address the high initial cost issue. This study focuses on the integration of SPS installation upstream in modular housing. Although moving the SPS installation process into the factory has many barriers (e.g., including change of scope of work at some workstations without affecting the whole …

Zein And Lignin-Graft-Plga Nanoparticles As Agrochemical Delivery Systems To Soybean Plants, Eban A. Hanna

LSU Master's Theses

The majority of applied agrochemicals are lost to the environment due to their poor adhesion to plant tissues, resulting in multiple applications and subsequent environmental toxicity. This thesis focuses on reducing these consequences by entrapping agrochemicals in polymeric nanoparticles (PNPs). In the first section, zein nanoparticles (ZNPs) with entrapped methoxyfenozide (MFZ) (209.0 ± 5.6 nm, -42.1 ± 2.1 mV) were synthesized and used to facilitate the translocation of MFZ to the leaves and stems of soybean plants, as measured by LC-MS, under hydroponic conditions. The agrochemical concentration increased within the leaves over time from 0.04 to 2.35 μg/g at 0.2 …

Characterization Of Gas Hydrate Formation And Its Impact On Slope Stability In Offshore Environments, Sulav Dhakal

LSU Doctoral Dissertations

Natural gas hydrates (NGH) are predominantly methane hydrates formed at high pressure and low temperature conditions typically in submarine sediments and permafrost. This dissertation is focused on the submarine methane hydrates formed by upward migration of free and dissolved gas. The methane gas stored within natural hydrates has more energy potential than all the combined oil and gas resources around the world. Numerical simulation of fluid flow, heat transport, and geomechanical stresses is used to characterize and evaluate the saturation, distribution, reservoir quality, and geohazards associated with hydrate-bearing sediments. Coupled thermo-hydro-mechanical (THM) simulation of hydrate formation, dissociation, and slope stability …