Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. The overall goal of this study was to develop a satellite-assisted forecasting system for oyster norovirus outbreaks. The forecasting system is comprised of three components: (1) satellite algorithms for retrieval of environmental variables, including salinity, temperature, and gage height, (2) an Artificial Neural Network (ANN) based model, called NORF model, for predicting relative risk levels of oyster norovirus outbreaks, and (3) a mapping method for visualizing spatial distributions of norovirus outbreak risks in oyster harvest areas along Louisiana coast. The new satellite algorithms, characterized with linear correlation coefficient ranging from 0.7898 to 0.9076, make it possible to produce spatially distributed daily data with a high resolution (1 kilometer) for salinity, temperature, and gage height in coastal waters. Findings from this study suggest that oyster norovirus outbreaks are predictable, and in Louisiana oyster harvest areas, the NORF model predicted historical outbreaks from 1994 - 2014 without any confirmed false positive or false negative predictions when the estimated relative risk level was > 0.6, while no outbreak occurred when the risk level was < 0.5. However, more outbreak data are needed to confirm the threshold for norovirus outbreaks. Gage height and temperature were the most important environmental predictors of oyster norovirus outbreaks while wind, rainfall, and salinity also predicted norovirus outbreaks. The ability to predict oyster norovirus outbreaks at their onset makes it possible to prevent or at least reduce the risk of norovirus outbreaks by closing potentially affected oyster beds. By combining the NORF model with the remote sensing algorithms created in this dissertation, it is possible to map oyster norovirus outbreak risks in all oyster growing waters and particularly in the areas without direct measurements of relevant environmental variables, greatly expanding the coverage and enhancing the effectiveness of oyster monitoring programs. The hot spot (risk) maps, constructed using the methods developed in this dissertation, make it possible for oyster monitoring programs to manage oyster harvest waters more efficiently by focusing on hot spot areas with limited resources.

The United States Marine Transportation System (MTS) makes large contributions to the nations economy, security, safety, and quality of life. Strategic investment, planning, administrative and operational decisions by government at all levels are necessary to maintain the marine transportation system performance at all times, which in turn requires a technical approach and professional leadership based on research. This study describes the approach and results of an ongoing research effort to assess the resiliency of port operations following major disasters and other disruptive events. The work presented in this research uses a set of archival data from the United States Coast Guards Nationwide Automatic Identification System (NAIS) to quantify the state of resiliency by investigating the operation of coastal navigation systems before, during and after disruptive events. To illustrate the ability of proposed methodology to assess the resiliency of a marine transportation system, two case studies representing two different types of infrastructure disruption are presented. The first case study involves the disruption that resulted from a collision in March 2014 in Texas in the Houston Ship Channel as a no-notice event. The second was a disruption caused by Superstorm Sandy in 2012 on the greater Port of New York/New Jersey as a pre-notice event. The results of this study revealed the importance of AIS data as a source of quantitative data when seeking post-disaster measures of resiliency. From an application viewpoint, the methods and results presented herein can be adapted and implemented to quantitatively evaluate the amount of port specific service loss and the levels of port activity following disruptive events.

Goal of this study is to investigate the impacts of climate change projection uncertainty on conjunctive use of water resources. To pursue this goal first, a conjunctive-use model is developed for management of groundwater and surface water resources via mixed integer linear fractional programming (MILFP). The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. A conditional head constraint is imposed to maintain groundwater sustainability. A transformation approach is introduced to transform the conditional head constraint into a set of mixed integer linear constraints in terms of groundwater head. A supply network is proposed to apply the conjunctive-use model to northern Louisiana and southern Arkansas. Then, simple model averaging (SMA), reliability ensemble averaging (REA), and hierarchical Bayesian model averaging (HBMA) are utilized as ensemble averaging methods to provide a thorough understanding of the impacts of climate change on future runoff for the study area. An ensemble of 78 hydroclimate models is formed by forcing HELP3 with climate data from combinations of 13 GCMs, 2 RCPs, and 3 downscaling methods. Runoff projections obtained from SMA, REA, and HBMA are compared. The Analysis of Variance (ANOVA) is used to quantify the sources of uncertainty of SMA projection and compare to the estimations made by HBMA. Both methods show similar contribution of uncertainty indicating that GCMs are the dominant source of uncertainty. At last, the proposed conjunctive use model is applied to optimize the conjunctive use of future surface water and groundwater resources under climate change projection. Future inflows to the reservoirs are estimated from the future runoffs projected through hydroclimate modeling, where the Variable Infiltration Capacity (VIC) model and 11 GCM RCP8.5 downscaled climate outputs are considered. Bayesian model averaging (BMA) is adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. The results from the developed conjunctive management model indicate that the future reservoir water even with low inflow projections at 2.5% cumulative probability would be able to counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraint.

The primary objective of this study is to evaluate the effects of asphalt concrete (AC) properties and vehicle speed on fuel consumption excess using a Three-Dimensional (3D) Finite Element (FE) approach. Secondarily, the effect of pavement design characteristics on energy dissipation was studied. Finite element modeling was used to simulate three flexible pavements typically used for low traffic volume, medium traffic volume, and high traffic volume. The effect of asphalt concrete mixes with different binders and varying percentages of Reclaimed Asphalt Pavement (RAP) on fuel consumption excess was studied. The FE models were validated based on field stress and strain measurements at the Louisiana Accelerated Loading Facility (ALF). Energy dissipation was calculated for the whole model due to materials viscous properties and was used as an input in fuel consumption estimation. Results indicated that the pavement with the stiffer mix, i.e., the mixes with high percentage of RAP consumed less energy. Therefore, fuel savings can be expected when increasing the stiffness of asphalt layer using RAP materials. However, the fuel consumption due to energy dissipation constitutes only a very small fraction of the total vehicle fuel consumption. An increase in energy dissipation of 0.5 MJ/100mile was observed to yield a corresponding 0.013 gal/100mile increase in fuel consumption to overcome the energy dissipation for an 18-wheeler truck at 60 mph. Fuel consumption excess was lowest at the highest speed and increased with decreasing speed. The fuel consumption excess was higher for pavements with greater thickness of AC layer, indicating a considerable impact of AC thickness on fuel consumption excess. Results for fuel consumption excess on medium and high traffic volume flexible pavements suggested that the thickness of the sub-layers other than HMA layer have a negligible effect on energy dissipation.

While highway safety has steadily improved throughout the United States, highway crashes and the resulting losses continue to be a significant concern in Louisiana. Louisiana consistently lags behind the country in many key areas of highway safety. To improve the conditions of roads in Louisiana, the Louisiana Department of Transportation and Development (LADOTD) has begun to implement the Highway Safety Manual (HSM) to evaluate existing and expected safety conditions and how to allocate limited improvement funds. However, as the HSM was developed using aggregated national statistics, it is not always able to reflect the conditions present on specific Louisiana roadways. The goal of this research was to address the limitations of applying the HSM predictive method in Louisiana, by creating and testing an HSM crash modification factor (CMF) founded on naturalistic driving behavior. The intent of this new CMF was to identify abrupt braking and evasive maneuvers in specific freeway segments because these conditions have been demonstrated to be strong predictors of high crash potential. The CMF was applied to the HSM predictive method to more accurately and reliably forecast crashes on Louisiana freeways. This research was conducted on freeway segments in Baton Rouge and showed that naturalistic driving behavior correlated with the HSM predicted crash frequency and also demonstrated that use of the crash modification factor affects the predicted crash frequency.

This research study investigates the pile set-up phenomenon for clayey soils and develops the models to predict pile set-up resistance at a certain time after the end of driving (EOD). The increase of pile resistance after EOD is known as pile set-up. To fulfill this objective, a total number of twelve prestressed concrete (PSC) test piles were driven in different soil conditions of Louisiana. In addition, dynamic load tests (DLT) and static load tests (SLT) were usually performed to verify the axial resistances of piles at specific times after EOD, as well as to quantify the amount of increase in resistance compared to the EOD (i.e., set-up). The focus of this research was to calculate the resistance of individual soil layers along the length of the pile. In order to implement this goal, all the test piles were instrumented with vibrating wire strain gages. Vibrating wire piezometers and pressure cells were also installed in the pile face in order to calculate the dissipation of excess pore water pressure, together with the corresponding increase in effective stress, respectively with time. The Case Pile Wave Analysis Program (CAPWAP) was performed in all the DLT data, in order to calculate the resistance of individual soil layers. Logarithmic set-up parameter A of individual soil layers were calculated using the unit side resistance (fs). The set-up parameter A was tried to correlate with different soil properties. With the aid of Statistical Analysis Software (SAS), detailed regression analyses were performed to develop models with incorporated soil properties. Five different levels of empirical models were developed in order to estimate the amount of set-up. In addition, one set-up model was developed directly from the in-situ test data (corrected cone tip resistance, qt). Load resistance factor calibration was performed in order to calibrate the set-up factor (ϕset-up). The developed models were implemented to predict the amount of resistance at four different time intervals after EOD. Finally, the statistical parameters of measured resistance to predict resistance were applied to calibrate the set-up factor (ϕset-up) and to incorporate that factor into the LRFD framework.

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<p> The main aim of the study was to introduce new filtering techniques to better the prediction of subsurface water contaminant transport. Hybrid nudging-ensemble Kalman filter (HNEnKF) and ensemble adjustment Kalman filter were proposed in this study. EnKF with traditional nudging were gradually applied promptly in the case of the HNEnKF. Other techniques whose performance were evaluated along with HNEnKF are a numerical method, ensemble Kalman filter, and ensemble adjustment Kalman filter. In this study, the HNEnKF and the EAKF are expected to improve in robustness and convergence due to the nudging properties and the assimilation of observations with a nonlinear relation to model state variables respectively. To appraise the HNEnKF and EAKF techniques, the numerical (finite difference) method and EnKF assimilation method were used. These simulations were executed with a three-dimension subsurface contaminant transport model with a first-order decay rate parameter.</p><p> A summary of this research are outlined below: </p><p> &bull; To investigate the performance of HNEnKF and EAKF data assimilation technique in subsurface water contaminant transport modeling compared to the numerical solution and ensemble Kalman filter technique.</p><p> &bull; To apply HNEnKF and EAKF data assimilation scheme in subsurface water contaminant transport.</p>

Six years following the Deepwater Horizon oil spill, buried MC252 oil persists on a beach segment in Fourchon, LA due to the natural anaerobic conditions of tidal groundwater. A field trial of in situ aerobic bioremediation of buried oil began on July 2015. Oxygen was added to the subsurface using Waterloo emitters which deliver O2 through diffusion via pressurized tubing in fixed groundwater wells. The multi-well injection system provided sufficient oxygen concentration in the groundwater in the immediate area of the emitters, resulting in a shift in the composition of the diverse, halophilic, hydrocarbon-degrading microbial population. Stable isotopic and radiocarbon data from dissolved inorganic carbon provided evidence of crude oil mineralization post-aeration, even in areas that had the highest level of contamination. Weathering ratios for 3-ring PAHs were reduced post-aeration, indicating increased rates of degradation of phenanthrenes and dibenzothiophenes. Serum bottle studies were conducted to analyze biodegradation of PAHs and alkanes as a response to adding varying levels of O2 over time. Oxygen amendments were found to stimulate biodegradation of recalcitrant PAHs more effectively in less oily sediments than in sediments with higher oil concentrations. The minimum oxygen amendments in this study were sufficient in shifting the microbial population to a more effectual hydrocarbon degrading community structure.