Department of Civil, Environmental and Geomatics Engineering

A comparative risk assessment of wastewater disposal methods in southeast Florida has only been performed once and it was over 20 years ago. Since then, methods has changed and research have been developed. This study follows the methods used in the 2000 study, and assesses the following disposal methods: ocean outfalls, deep injection wells, surface water discharge, reuse for non-potable applications, indirect potable reuse, and direct potable reuse. This assessment assembled a team of qualified experts to complete a modified delphi survey to assess the human risks of wastewater disposal methods. Using the delphi results in a Bayesian Assessment Model, this assessment found that deep injection well and direct potable reuse were the disposal methods with the least risk.

The potential of plastic waste-derived activated carbon was investigated for the removal of carbon dioxide and hydrogen sulfide from biogas. Activated carbon materials were prepared by carbonizing plastic waste followed by activation via microwave heating after mixing with potassium hydroxide. Samples were tested using thermogravimetric analysis to determine the equilibrium uptake of carbon dioxide. Samples were modified with tetraethylenepentamine and diethanolamine however, sample texture produced was deemed unusable for further testing due to operational concerns. Adsorbent screening was conducted in conditions mimicking that of biogas at a temperature of 40 °C and 30% carbon dioxide in nitrogen. Performant samples were identified as those achieving uptakes greater than 3 wt.%. The best performing sample achieved an uptake of 3.57 wt.% and maintained 99% of its uptake during cycling. Column breakthrough experiments demonstrated that the final candidate achieved complete removal of both carbon dioxide and hydrogen sulfide, suggesting viability for larger scale biogas purification.

This dissertation focuses on the development of data-driven and physics-based modeling for two distinct significant structural engineering applications: time-varying response variables estimation and unwanted lateral vibration control. In the first part, I propose a machine learning (ML)-based surrogate modeling to directly predict dynamic responses over an entire mechanical system during operations. Any mechanical system design, as well as structural health monitoring systems, require transient vibration analysis. However, traditional methods and modeling calculations are time- and resource-consuming. The use of ML approaches is particularly promising in scientific and engineering challenges containing processes that are not completely understood, or where it is computationally infeasible to run numerical or analytical models at desired resolutions in space and time. In this research, an ML-based surrogate for the FEA approach is developed to forecast the time-varying response, i.e., displacement of a two-dimensional truss structure. Various ML regression algorithms including decision trees and deep neural networks are developed to predict movement over a truss structure, and their efficiencies are investigated. ML algorithms have been combined with FEA in preliminary attempts to address issues in static mechanical systems.

Nuisance odors from landfills have more impact than just being an annoyance to nearby residents. With an ever-increasing population, a larger number of communities are located in closer proximity to landfills than ever before. This has brought along with it, more regular conflicts with landfill authorities surrounding the issue of odors, resulting in complaints, lawsuits, fines, and even re-siting operations. The absence of an objective method of quantifying nuisance odors makes the task of creating regulations and setting standards even more complicated. The current research focuses on a method to objectively quantify landfill odors. The human odorant binding protein 2A (hOBPIIa) can be produced using published recombinant gene technology and can be used as a biosensor to quantify odorants through spectrofluorometric measurements. The current work is a continuation of the previous work by Rahman (2020). In this work, the spent biosensor after it reacts with an odorant is shown to be regenerated by applying additional fluorophore following La Chateliers’ principle, so that the same batch of protein can be used to run multiple experiments with odorants. An important part of the work miniaturized the earlier version of the experimental setup and incorporates a much more efficient flow-through system. This setup is capable of collecting real-time readings, increasing the overall accuracy and shortening the duration of each set of the experiment. The current work also explores the response of the biosensor with an expanded group of pure odorants, including hydrogen sulfide, ammonia, toluene, formaldehyde, tert-butyl mercaptan, and methyl mercaptan as well as their mixtures, thus expanding the list of odorants tested under this principle. The results show that the protein shows a concentration-dependent response differing on the hydrophobicity of the target compound.

This research investigates the development and performance of a new closed-form stiffness matrix for a beam element that assumes a second-order stiffness variation over the regions of beam-columns with partial yielding. Stiffness reduction occurs due to yielding of the cross-section of W-Shapes under certain conditions of residual stress, moment, and axial load. Currently, inelastic material models assume a linear stiffness variation over the beam element length, even though it is well known they vary nonlinearly over the partially yielded regions. To evaluate the performance of the new stiffness matrix, two beams and four frames were analyzed using MASTAN2 considering five load increment and nine element conditions. Discussion and recommendations are provided regarding the parameters that influence the modeling results and the ability of the new stiffness matrix to consistently provide better results than the original stiffness matrix with an assumed linear stiffness variation.

The potential of paper waste-derived activated carbon was investigated for the removal of carbon dioxide and hydrogen sulfide from landfill gas. Activated carbon materials were prepared by carbonizing paper waste followed by acid treatment to remove ash, mixing with aqueous phase potassium hydroxide, and activation via microwave heating. Activated samples were tested using thermogravimetric analysis to determine their equilibrium uptake of carbon dioxide. The adsorbent materials were modified with both tetraethylenepentamine and diethanolamine to potentially increase the carbon dioxide uptake, however, all the modified samples had a performance significantly worse than their unmodified counterparts. Adsorbent screening was conducted in conditions mimicking that of landfill gas, namely temperature of 40 °C and 40% carbon dioxide in nitrogen. Performant samples were identified as those achieving uptakes greater than 3 wt.%. The best performing sample achieved an uptake of 5.03 wt.% and maintained 97% of its uptake during 100 successive adsorption-desorption cycles. Column-breakthrough experiments demonstrated that the final candidate achieved complete removal of both carbon dioxide and hydrogen sulfide, suggesting viability for larger scale landfill gas purification.

Urban freight system constitutes an essential component for both economic and social aspects of the urban areas. However, the driving forces of globalization and ecommerce have adversely affected the volume of freight vehicles in urban roads over the past few decades impacting the sustainability and efficiency of last-mile deliveries. At the same time, the last-mile problem of goods distribution from companies to customers comprises one of the most costly and highest polluting components of the supply chain. Over the past few years, different innovative concepts of autonomous vehicles were introduced to improve last-mile logistic inefficiencies such as traffic congestion and pollution externalities. The objective of this study is to optimize a distribution network consisting of a set of depots and customers by utilizing Autonomous Delivery Robots (ADRs). For that reason, a Mixed Integer Linear Programming model was developed in GAMS for solving the vehicle routing problem while minimizing the total delivery and delay costs of ADRs. This optimization model is based on the route assignment and the required number of ADRs within the network. A heuristic solution algorithm based on the cluster-first, route-second technique was developed in MATLAB for solving the NP-hard problem efficiently. First the customers were clustered to depots based on their maximum distance from them and the maximum allowed number of customers per cluster. After the clustering, the mathematical model was implemented in each cluster providing an exact solution. Three different medium-sized scenarios of 200, 300 and 400 customers were tested under three different clustering instances of a maximum of 20, 30 and 40 customers per cluster and their results were presented and discussed in detail.

Human exposure to arsenic from natural as well as anthropogenic sources can lead to a detrimental impact to the nervous system, cardiovascular system and can also cause cancer. Historical agricultural runoff has led to an accumulation of arsenic in groundwater and soils around Lake Okeechobee and many golf courses in Florida. This research involved studying the removal of aqueous arsenic via adsorption using activated carbon derived from algae. Carbon derived from Sargassum removed 41.47% of arsenic after a contact time of 2 hours. Adsorbents created from blue-green algae showed essentially no arsenic removal under the same conditions. Various chemical additives were tested to improve arsenic adsorption as well. Modification of the adsorbent surface with magnesium chloride demonstrated an arsenic removal efficiency of 98.6% when added to commercial activated carbon. However, when magnesium chloride was used to modify the surface of Sargassum-derived carbon adsorbents, the arsenic removal efficiency after 2 hours was 26.7%. It is recommended to investigate other surface modification agents that can potentially improve adsorption of arsenic.

Coastal landscape plays a vital role in reflecting various natural processes. Vegetation resource management improves the quality of life above the surface of the earth. Due to factors such as climatic change, urban development, and global warming, monitoring the coastal region as well as its vegetation has indeed become a challenge to mankind. The purpose of the study is to propose an effective low-cost methodology to monitor the 120- acre Jupiter Inlet Lighthouse Outstanding Natural Area (ONA) located in Jupiter, Florida (USA) using Unmanned Aerial Systems (UAS) Imagery deployed with RedEdge Micasense Multispectral sensor having five bands. Since, UAS provides high resolution imagery at lower altitudes, it has a lot of potential for variety of applications. This research aims to (1) Automate the extraction of shoreline and coastline through Modified Normalized Difference Index (MNDI), thereby comparing it with the manually digitized shoreline using transect-based analysis (2) Automate the volume change computation, as the area has been affected due to various natural and anthropogenic factors in the past few decades. (3) Perform shoreline change detection for the time period 1953 to 2021 (4) Develop an algorithm to differentiate ground and non-ground points along the shore region and generate Digital Terrain Model (DTM) (5) Land use and Land cover (LULC) mapping using different band combinations and compare its result using deep learning approach.

Current flood-risk models lack fidelity at the neighborhood level. The Federal Emergency Management Agency (FEMA) develops flood maps based on experts’ experience and estimates on the probability of flooding. First Street Foundation evaluates flood risk with regional and subjective measures, without impact from torrential rain and nuisance flooding. The purpose of this research is to develop a data-driven method to determine a comprehensive flood-risk that accounts for severe, moderate, and nuisance flood events at the single-family home level, while also estimating the recovery time from the specified flood event.
The method developed uses the Failure Mode and Effect Analysis (FMEA) method from the American Society of Quality (ASQ) to determine the Consequence of Flooding (CoF), following the 1-day 100-yr storm for the Probability of Flooding (PoF). The product of CoF and PoF provides an estimate of the flood-risk. An estimated Resilience Index value derived from flood-risk, is used to determine the recovery time after a severe or moderate