Department of Civil, Environmental and Geomatics Engineering

Fully electric vehicles (EVs) have gained significant popularity and countries such as Norway are leading the world with over 90% EV market share in new car sales. However, older internal combustion engine (ICE) powered vehicles currently on today’s roads are expected to continue to operate until the end of their life cycle. As a result, a mixed vehicle fleet is expected to persist in the coming decade. Unfortunately, there has been an underlying assumption that the traditional internal combustion vehicles are expected to exhibit the same driving behavior when electrified vehicles are introduced in the mixed traffic fleet. Unlike ICE powered vehicles, EVs deliver immediate and strong deceleration via regenerative braking, and this could cause disturbances when the less capable ICE vehicles are following. These differences in driving dynamics may translate to substantial impacts to roadway capacity, especially when mixed with human driven ICE powered vehicles. Although ACC equipped EVs can adopt shorter headways and react quickly to speed changes, potentially improving roadway capacity, our empirically validated simulation study on ACC with ICE and electric powertrain suggestion that the increase in market penetration of EVs could result in greater capacity but mostly at higher EV market penetrations, because EVs mostly interact with other EVs and there would not be many ICE vehicles following EVs undergoing rapid regenerative braking. Conversely, at low market penetrations, there are numerous ICE vehicles interacting with a few EVs that undergo rapid deceleration, causing disturbances and negating the potential capacity benefit of EVs.

This research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the pipeline’s robustness in handling datasets with up to 49.73 million points. The models achieved average epoch times of 35 seconds for DeepGMR, 112 seconds for FMR, and 333 seconds for PointNetLK. Accuracy in alignment was also reliable, with rotation errors averaging 2.955, 1.966, and 1.918 degrees, and translation errors at 0.174, 0.191, and 0.175 meters, respectively. This scalable, high-performance pipeline offers a practical solution for spatial data processing, making it suitable for applications that require precise alignment in large, cross-source datasets, such as mapping, urban planning, and environmental analysis.

Flooding disasters pose a significant threat worldwide, with 2022 seeing them as the most common type of disaster. In the U.S. alone, four flooding disasters in 2023 cost more than $9.2 billion. Coastal urban areas face increasing threats from flooding disasters due to rising sea levels, changing precipitation patterns, and intensifying extreme weather events. This study focuses on Central Beach, Fort Lauderdale; the area's unique geographical, environmental, historical, and socio-economic characteristics make it a prime candidate for this analysis. The research objective is to comprehensively examine the factors contributing to water-related vulnerabilities of developed properties in Central Beach and assess localized impacts using regional models. The methodology involves developing probabilistic flood maps using GIS tools and the Cascade 2001 routing model. The flood scenarios consider groundwater table rise, extreme rainfall, high tides, storm surge, and sea level rise. Results indicate significant inundation risks, particularly for commercial and mobility infrastructure, under storm surge and sea level rise scenarios. The analysis highlights the importance of targeted mitigation efforts to protect these areas and reinforce resilience against future flooding events. The findings contribute valuable insights for policymakers, urban planners, and stakeholders, emphasizing the need for comprehensive strategies to mitigate flood risks in coastal urban areas.

El Niño Southern Oscillation (ENSO) occurrences have a well-established impact on regional hydroclimatic variability and alterations in crucial climatic factors such as temperature and precipitation. The impact of ENSO on temperature extremes can cause fluctuations in energy consumption, leading to the need for energy utilities to implement more effective management measures. This study aims to evaluate the potential impacts of El Niño Southern Oscillation (ENSO) events on local temperature patterns & extremes and residential energy usage in South Florida. The region of focus consists of three Counties: Miami-Dade, Broward, and Palm Beach. The impact of ENSO occurrences on temperature is assessed by analyzing long-term monthly average, minimum, and maximum temperature data from numerous weather stations in these counties, spanning from 1961 to 2018. The study analyzes variations of monthly electricity usage data acquired from a local power utility company (e.g., Florida Power & Light) and temperature data from 2001 to 2018. Temporal frames that align with the three phases of ENSO (namely warm, cool, and neutral) are employed to assess variations in temperature and energy consumption. Nonparametric hypothesis tests are employed to validate statistically significant variations in temperature and residential energy consumption across the stages of ENSO. This study aims to analyze the potential regional and temporal impacts of ENSO episodes on temperature and residential energy consumption in South Florida. Initial findings indicate that the non-uniform distribution of temperature, affected by El Niño and La Niña occurrences, impacts the amount of energy consumed by households in South Florida.

The atmospheric concentration of CO2 increased from 320 to 425 parts per million by volume (ppmv; 0.0425 vol.%) between 1960 and 2024. Sample CO2 reduction strategies include shifting to renewable energy sources and employing CO2 capture. CO2 capture from the air (also known as direct air capture; DAC) has recently received increased attention. CO2 has the potential to act as an asphyxiant at high concentrations, particularly in enclosed environments (e.g., spacecraft, submarines), requiring air revitalization to remove CO2. Hence, the U.S. Occupational Safety and Health Administration determined a permissible exposure limit of 5,000 ppmv CO2 (0.5 vol.%) throughout an 8-hour work shift. Considering the trace levels of CO2 and the presence of humidity in DAC and air revitalization applications, similar materials can be developed for implementation in both cases. CO2 capture involving amine-functionalized silica materials (“aminosilicas”) can achieve high CO2 uptakes at low concentrations due to high selectivity. Additionally, moisture in CO2-laden gases enhances the CO2 uptake and stability of aminosilicas. Therefore, this research investigated the potential of aminosilicas for removing CO2 from dilute streams, including DAC and air revitalization applications. Aminosilicas were produced using mesoporous silica supports with different particle sizes that were modified with tetraethylenepentamine (TEPA) or branched polyethylenimine (PEI) with different molecular weights (600, 1200, and 1800), or grafted with 3-aminopropyltrimethoxysilane (APTMS). The performance of aminosilicas was assessed to determine equilibrium CO2 adsorption capacity, adsorption kinetics, and cyclic stability.

This study aims to address the unique challenges of transportation in rural and disconnected communities through innovative data-driven methodologies. The primary methods employed in this research involve Geographic Information Systems (GIS) tools and simulation techniques to model and assess the impact of flood zones on rural traffic dynamics. The study recognizes the distinct mobility patterns and limited infrastructure prevalent in rural areas, emphasizing the need for tailored solutions to manage flood-induced disruptions. By leveraging GIS tools, the study intends to spatially analyze existing transportation networks, population distribution, flood-prone areas, and key points of interest to formulate a comprehensive understanding of the local context. Simulation-based approaches using the PTV VISSIM platform will be employed to model and assess various flood scenarios and their effects on traffic flow and accessibility. This study’s outcomes aim to contribute valuable insights into improving accessibility, efficiency, and safety in transportation for these underserved areas during flood events. By combining GIS tools and simulation techniques, this research seeks to provide a robust framework for data-driven decision-making and policy formulation in the realm of rural and disconnected community mobility, particularly in the context of flood risks.

This study focuses on developing optimization models to estimate missing precipitation data at twenty-two sites within Kentucky State. Various optimization formulations and regularization models are explored in this context. The performance of these models is evaluated using a range of performance measures and error metrics for handling missing records. The findings revealed that regularization models performed better than optimization models. This superiority is attributed to their ability to reduce model complexity while enhancing overall performance. The study underscores the significance of regularization techniques in improving the accuracy and efficiency of precipitation data estimation.

Deep injection well technology is a reliable and cost-effective technique to manage hazardous wastewater. However, reduced injectivity is an issue for the performance of an injection well which can happen due to the occurrence of biogeochemical clogging. A class 1 deep injection well located at the Solid Waste Authority of Palm Beach County has long suffered similar problems that occurred due to the formation of chemical precipitation and biofilm. In the case of the biofilm, the dominant microorganism detected in previous work was determined to be Entamoeba dispar. The prime source of the protozoan was identified as the local groundwater, which is employed for different purposes within the solid waste facility, such as cooling water and dilution water. Therefore, it is imperative to examine the effectiveness of the commonly used disinfectant chlorine to inactivate the protozoan to eliminate biofilms and clogging. This study conducted a laboratory-based chlorination of the groundwater sample to reveal the required dosages of chlorine needed for 3.0-log inactivation of E. dispar in various temperature (20°C, 25°C, 30°C, and 35°C) and pH (6.5, 7.0, 7.5) conditions.

Rapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster aerial images and computer vision. Recent advancements in disaster response strategies have encouraged researchers to harness the power of satellite and aerial imagery to assess the aftermath. Usually, due to the different characteristics between training datasets and available datasets in times of disasters, retraining the model to improve detection accuracy has been the norm, even though it is time and resource intensive. Our method surpasses conventional solutions and requires no retraining or fine-tuning on disaster-specific data. An existing model was retrained and improved on a diverse building damage dataset and demonstrably generalizes to new disaster scenarios. Having achieved higher performances compared to state of the art models, we determines our models real world applicability by using Hurricane Ian as our potent study grounds.

Missing rainfall records happens frequently in many areas, and making precipitation estimation has been a challenge due to the spatial-temporal variability of the parameter. Model tree (MT), regression tree (RT), and ensemble approach models were developed and evaluated for estimating missing precipitation values in this research study. The selection of stations using correlation coefficient and similar distribution, and variation of data used to build the model were applied in this study. Proposed models were developed and tested using daily rainfall data from 1971 to 2016 at twenty-two stations in Kentucky, U.S.A. The model results were analyzed and evaluated using error and performance measures. The results indicated that MT-based and ensemble models produce a better estimation of missing rainfall than regression trees. The MT-based model was able to estimate missing rainfall accurately without needing objective selection of stations and using minimal calibration data to build the model.