During pile installation, the stresses and void ratios in the surrounding soils change significantly, creating large displacements, soil disturbance and the development of excess porewater pressures. The disturbed soils, especially fine grain soils, tend to regain their strength over time due to both the consolidation of the excess porewater pressure and thixotropic behavior of soil particles. In this research, the pile installation process and the subsequent consolidation, the thixotropy and load tests for several test piles were modeled using finite element (FE) model. A new elastoplastic constitutive model, which was developed based on the disturbed state concept (DSC) and critical state (CS) theory, was implemented to describe the clayey soil behavior. The developed model is referred as critical state and disturbed state concept (CSDSC). Pile installation was modeled by applying prescribed radial and vertical displacements on the nodes at the soil-pile interface (volumetric cavity explanation), followed by vertical deformation to activate the soil-pile interface friction. The soil thixotropic effect was incorporated in the proposed model by applying a time-dependent reduction parameter, which affects both the interface friction and the soil shear strength parameter. The results obtained from the FE numerical simulation included the development of excess porewater pressure during pile installation and its dissipation with time, the increase in effective normal stress at the pile-soil interface, and the setup attributed to both soil consolidation and thixotropy effects at different times after end of driving. The FE simulation results using the developed model were compared with the measured values obtained from the full-scale instrumented pile load tests to verify the proposed FE model. The results obtained from verification indicated that simulating soil response using the proposed CSDSC elastoplastic constitutive model and incorporating soil thixotropic behavior in the FE model can accurately predict the pile shaft resistance. A parametric study was then conducted by varying the main soil properties, which have significant contribution in setup phenomenon. The obtained data were analyzed using existing statistical techniques and applying non-linear regression analysis. Several nonlinear regression models were developed under different sets of variables, and finally three sets of regression model were proposed to correlate the soil setup behavior to the contributing soil properties.

This report presents the reliability-based analysis of the calibration of resistance factors for the Load and Resistance Factor Design (LRFD) of axially loaded drilled shafts. AASHTOs 2012 LRFD Bridge Design Specifications recommends various resistance factors for the design of deep foundations; however, since these values are not specific to any one region, they are very conservative. For Louisiana or Mississippi, the adoption of such recommended resistance factors could substantially increase foundation sizes. Therefore, it is necessary to develop a database of drilled shaft load tests performed in the regions of Louisiana, Mississippi, and surrounding states with similar soil conditions. Sixty-nine drilled shaft load tests were collected from the Louisiana and Mississippi Departments of Transportation to develop this database. The measured nominal resistances of the drilled shafts were determined at various settlement criteria using the provided static load test data, and the predicted resistances were calculated from soil boring data using both the 1999 Federal Highway Administration (FHWA) drilled shaft design method (Brown et al.) and the 2010 FHWA design method (ONeill and Reese). The performance of each design method is evaluated through statistical analyses of the predicted resistances versus the measured resistances, and FOSM and the Monte Carlo simulation method are utilized to perform the LRFD calibration of the resistance factors for the Strength I Limit State as defined by AASHTO. The calibration performed in this study confirms that the Monte Carlo simulation method is a more accurate and reliable method in determining the resistance factors; it also shows that while the 2010 FHWA drilled shaft design method is a more accurate method, it produces smaller total resistance factors than the 1999 FHWA design method.

This work is an attempt to develop a physically realistic model to understand the behavior and failure of rocks, especially under the extreme conditions of High Pressure and High Temperature (HPHT). A platform is laid in the preliminary work where 1D pure and ductile damage models are developed respectively. These models are based on an elasto-plastic model with an additional governing equation incorporated to facilitate the inclusion of damage. This additional governing equation is called the damage evolution equation. In the ductile damage model, it is assumed that the damage is driven and controlled by plasticity. The concepts developed in the preliminary work of 1D modeling are then taken into the study of 3D problems. The main problems studied are: the unconstrained uniaxial compression, the completely constrained uniaxial compression and the dynamic indentation problem. The dynamic indentation problem is the representation of an idealized rock drilling process. The results from the indentation problem are found to be in good qualitative agreement with the experimental results (Abd Al-Jalil, Y.Q 2006).

This thesis presents a case study on an urban arterial corridor consisting of four intersections located in Baton Rouge, Louisiana. The research evaluates alternative roundabout designs in order to improve traffic flow through a currently congested corridor. The objectives of the research were to evaluate the effect of roundabouts in an urban arterial corridor, select the best alternative for the given corridor characteristics, and test the capacity of the alternative roundabout corridors. The three alternatives that were considered as part of this study were the existing signalized corridor, a partial two-lane roundabout corridor, and a partial three-lane roundabout corridor. Field data was used to model the alternatives in VISSIM, a micro-simulation software. Statistical analysis software, SAS Enterprise Guide 6.1, was used to analyze the results and determine if there were significant differences between the results for each alternative and each intersection. The performance measures used to compare the alternatives were average vehicle travel time, average delay per vehicle for each corridor, and average delay per vehicle at each intersection. The results showed that the overall alternative roundabout corridors provided a benefit over the existing signalized corridor for the existing traffic volumes tested. The partial three-lane roundabout provided the lowest vehicle travel times and lowest average delay due to the added capacity. For the higher traffic volumes at the interchange, the partial two-lane roundabout had a higher average vehicle travel time exiting the exit ramp than the existing corridor. The statistical analysis of the average vehicle delay at each intersection indicated that there were no significant differences between the alternatives at a five percent level of significance at the interchange. These results revealed potential operational issues roundabouts encounter at an interchange intersections.

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.

Several human diseases are reported to be associated with amyloidogenic peptides and proteins, for example, β amyloid (Αβ) in senile plaques of Alzheimer's disease and β2 microglobulin (β2M) deposition in dialysis related amyloidosis. The major factor for amyloidosis appears to be an increase in the production of amyloidogenic proteins, resulting in the amyloid β sheet deposits. It is clear that conformational changes in amyloidogenic proteins lead to fibril formation and aggregation. Several trigger factors may be involved in the initiation and propagation of these amyloid deposits. Common components such as serum amyloid P component (SAP), glycosaminoglycans (GAGs) may be involved, and metals such as aluminium, copper, zinc and calcium have also been implicated. Metals have been shown to induce aggregations of amyloid and accelerate amyloid deposition. The present study involved the investigation of the effects of inorganic elements on the secondary structural integrity of amyloid proteins leading to aggregation and deposition. The addition of Cu2+ Zn2+ C2+ and Αl3+ (>50 μM) to the amyloids, particularly Αβ(1-40) and β2M, showed some changes with a general decrease in a helix secondary structure and an increase in β sheets. Both the Αβ(l-40) and β2M peptides treated with physiological concentration of either Α13+, Ca2+ Cu2+ or Zn2+ combined with the physiological concentrations of various GAGs showed that they had a synergistic effect, indicating that GAGs could play a role in conjunction with metals. This has not been previously reported in the literature. The binding of GAGs to the amyloids could provide further binding sites for metal ions, leading to an alteration in the protein conformation. There is evidence in the literature for metals as trigger factors in the deposition of the Alzheimer's disease Αβ peptides, but not for the dialysis related β2M peptide. Similar changes occurred for the interaction of both Αβ(l-40) and β2M with metals and also with metals in the presence of GAGs. This suggests that similar mechanisms may be involved and that metals may have an underlying role in the general process of amyloidosis independent of the type of amyloid protein involved.

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.