Civil & Environmental Engineering Theses and Dissertations

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BEYOND PEAK RATE FACTOR 484: USING RADAR RAINFALL, GAUGED STREAMFLOW, AND DISTRIBUTED WATERSHED MODELING TO INVESTIGATE PARAMETERS OF THE NATURAL RESOURCES CONSERVATION SERVICE CURVILINEAR UNIT HYDROGRAPH
(2024) Shehni Karam Zadeh, Mani; Brubaker, Kaye L.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Accurate estimation of runoff and peak discharge is crucial in hydrology for engineering design and flood management. The Natural Resources Conservation Service’s (NRCS) Unit Hydrograph (UH) is a widely used model to predict the runoff response of an ungauged watershed to a precipitation event. The NRCS UH model makes use of a Peak Rate Factor (PRF) to quantify the peak discharge. The standard value of PRF is 484; however, PRF can be adjusted as a user input variable in NRCS tools such as the WinTR-20 software. Little guidance is available to appropriately estimate PRF for specific regions and evaluate its overall usefulness in the runoff and peak discharge estimation. Time of concentration (tc) is another input variable in the NRCS UH model; inconsistent definitions of tc and diverse methods of calculating it contribute to uncertainty in hydrologic estimates and predictions. The NRCS UH approach assumes that the watershed’s temporal runoff response to each increment of precipitation is identical in shape and proportional to precipitation excess in that increment of time. The UH, PRF, and tc are often assumed to be time-invariant properties of a watershed. This dissertation sought to improve the knowledge and understanding of PRF and tc. First, it evaluated if a unique UH and tc exist for a given watershed from various storm events. It then assessed whether variations in PRFs can be explained by watershed predictor variables and if PRFs in neighboring watersheds followed a local trend. This phase of study employed a gamma function representation of the NRCS UH, with two parameters: time to peak (tp) and shape (m). Precipitation inputs were watershed-averaged time series of NEXRAD level III data, and streamflow data were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). The UHs were derived from a constrained optimization approach, and PRF and tc were estimated for each event. Subsequently, a fully distributed model was created to provide insight on PRF and tc, and investigate the impact of detailed soil profiles on runoff and peak discharge. Finally, a fully distributed model was applied to simple, synthetic watersheds to investigate the impact of selected watershed parameters on PRF, time to peak, peak discharge and overall shape of the UH. To the best of the author's knowledge, this study is the first attempt to generate UHs from a simple distributed model and estimate associated PRFs. The findings suggest that there is no unique UH and tc for a given watershed, and UH shape and parameters change for every event in a given watershed. Additionally, the variations in PRFs cannot be explained by variations in selected watershed predictor variables. The distributed model results provided insights about the application of detailed soil profiles in runoff and peak discharge estimation. The findings also suggest that, except for Manning's roughness, selected watershed characteristics cannot be used to estimate PRF in a synthetic V-shaped watershed. These findings suggest that the application of PRF to estimate peak discharge should be used with caution due to the inherent uncertainties and lack of physical meaning of the parameter.
NATURAL LANGUAGE PROCESSING, SOCIAL MEDIA, AND EPIDEMIC MODEL-ING FOR WILDFIRE RESPONSE AND RE-SILIENCE ENHANCEMENT
(2024) Ma, Zihui; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Effective disaster response is critical for communities to remain resilient and advance the development of smart cities. Responders and decision-makers benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and behaviors during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. A comprehensive literature review of natural language processing (NLP) of social media data in disaster management, covering 324 articles published between 2011 and 2022, revealed a gap in applying NLP techniques to wildfire scenarios. Meanwhile, the increasing frequency of wildfires highlights the need for advanced management tools. To address this, we integrated the BERTopic and SIR models to capture public responses on Twitter during the 2020 western U.S. wildfire season, analyzing both the magnitude and velocity of topic diffusion. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The parameters estimated from the SIR model for selected cities revealed that residents expressed various levels of concern or demand during wildfires. The study also demonstrated a practical framework for utilizing social media data to aid wildfire evacuations. Through social network analysis, we clarified the roles of key information disseminators and provided guidelines for extracting high-priority information. Although biases in social media and model limitations exist, the study offers qualitative and quantitative approaches to investigate wildfire response and sup-port community resilience enhancement.
ACCELERATING RESTORATION THROUGH INFORMATION-SHARING: UNDERSTANDING OPERATOR BEHAVIOR FOR IMPROVED MANAGEMENT OF INTERDEPENDENT INFRASTRUCTURE
(2024) Yazdisamadi, Mohammadreza; Reilly, Allison C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation examines the roles that organizations and individuals play in restoring interdependent infrastructure following disasters through three studies. In the first study, we focus on how operator heuristics affect the collective restoration speed of three interdependent infrastructure (electric power, chilled water, and IT networks). We do this by developing a novel framework that embeds an interdependent infrastructure network within an agent-based model that mimics the decisions and patterns observed of actual operators. The study sheds light on how coordination and information exchange by separate infrastructure parties affect decisions and thus restoration outcomes. In the second study, we examine recovery times and total unmet demand for the same three interconnected infrastructure systems assuming a variable fraction of node removals. The work is decomposed by the extent to which operators share information and coordinate strategies, enabling us to identify at what fraction of network failure does coordination and information sharing become beneficial. Our study indicates that prioritizing restoration based on node centrality produces the speediest recovery. We also show that communication among organizations may improve collective performance by as much as 50%. Our final research project uses a serious game, Breakdown, focused on restoration of interdependent infrastructure to assess whether engineering graduate students gain a deeper appreciation for the complexity of interdependent infrastructure and socio-technical systems more broadly. This is the first serious game designed to emphasize the value of cooperation, communication, and strategy in times of crisis in the field of interdependent infrastructure. As a result of playing Breakdown, graduate students demonstrated statistically significant improvements in engineering decision-making under uncertainty and sociotechnical systems concepts. As a result of this dissertation, the interdependent infrastructure community gains insight into (1) how individual operators' behavior influences the speed at which interdependent infrastructure systems recover; (2) how policies and procedures, like sharing information and cooperating, can help improve outcomes; and (3) the ways to teach graduate engineering students about socio-technical systems effectively. Using an agent-based model simulation, it quantifies the effects of human behavior, communication, and cooperation on recovery outcomes. By using a serious game, Breakdown, it proposes an innovative way to teach graduate engineering students about socio-technical systems.
Evaluation of Roadside Soil Compaction and Restoration Practices on Vegetation Growth and Water Quality
(2024) Cunningham, Mikayla; Davis, Allen P.; Aydilek, Ahmet H.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The construction of roads using heavy equipment and cut-and-fill methods leads to heavily compacted roadside soils with low fertility, sparse vegetation, low water infiltration rates, and high erodibility. Poor post-construction vegetation and soil quality lead to higher runoff volumes with higher sediment and nutrient loads to local water bodies. Cost-effective methods are needed to restore roadside soils, establish sufficient vegetative cover, and maintain runoff water quality. A research project was undertaken to assess topsoil application, tillage, and yard waste compost amendment as means of restoring roadside soil quality. A 28-week pot study was used to test how topsoil depth, initial soil density, compaction from mowing equipment, and compost amendment influenced long-term soil density, hydraulic conductivity, and vegetation establishment. A 12-week mesocosm study with weekly simulated storm events was conducted to further examine the effects of soil type and soil bulk density on vegetation on a larger scale. Water quality testing of the simulated rainfall and runoff samples was also implemented to measure soil erosion and nutrient leaching. Compost-amended subsoil improved vegetation (biomass and grass heights) compared to subsoil, but it did not perform as well as topsoil. The yard waste compost was selected and applied at a rate designed to limit nitrogen and phosphorous losses, and it was successful, given that the compost-amended mesocosms did not export higher nutrient loads than mesocosms with inorganic fertilizer. Hydraulic conductivity was observed to primarily depend on soil density. A series of recommendations for highway projects to effectively restore roadside soil quality to improve vegetation and stormwater management are provided. A low-density layer of topsoil at least 20 cm deep is ideal. Yard waste compost should not be applied at a rate that raises soil organic matter by more than 2%.
A DATA-DRIVEN FRAMEWORK FOR THE PREDICTION OF NON-RECURRENT TRAFFIC CONGESTION RECOVERY TIME ON FREEWAYS
(2024) Kabiri, Aliakbar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This study introduces a comprehensive approach aimed at improving the management of incident durations. It delves into enhancing traffic incident management by integrating diverse incident datasets, including Maryland State Police incident data and Coordinated Highways Action Response Team (CHART) incident data, to improve the assessment of traffic incident durations. The dissertation employs spatial and temporal thresholds to explore matching different incident datasets and identifies discrepancies between various incident reports. The dissertation also explores methodologies for estimating traffic recovery times of each incident, utilizing historical data and pre-incident conditions as baselines to establish normal traffic conditions. A novel framework is introduced to estimate non-recurrent traffic congestion recovery time, revealing that many incidents recover faster than their reported clearance times. In these cases, traffic flow returns to normal conditions quickly.Further, the study examines predictive modeling for traffic recovery time, highlighting the Random Forest model's effectiveness among various machine learning algorithms. This model's superiority, based on precision, recall, and F1-scores, underlines its potential in accurately predicting traffic incident recovery time categorized as short-duration, medium-duration, and long-duration incidents. In particular, the random forest model results in a precision of 0.7 for short-duration incidents, 0.3 for medium-duration incidents, and 0.5 for long-duration incidents. For instance, the precision of 0.5 for long-duration incidents indicates that half of the cases predicted as long-duration incidents are indeed long-duration incidents. Key predictors such as link-level vehicle volume, clearance time, response time, and number of lanes closed are identified, providing valuable insights for traffic management strategies. This dissertation underscores the importance of data-driven approaches in traffic incident management, aiming to enhance the efficiency of transportation systems through accurate prediction and estimation of incident recovery times.
GEOTECHNICAL CHARACTERIZATION OF BALTIMORE DREDGED SEDIMENTS AS AN INFILTRATION BERM MATERIAL ON HIGHWAY SLOPES
(2024) Kaya, Eren; Aydilek, Ahmet H; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Baltimore Harbor dredged material has limited use in common geotechnical construction due to its soil composition. It has low shear strength for foundation applications and lack of plasticity makes it an undesirable material for seepage barrier applications. Vegetated infiltration berms, used for regulating stormwater discharge, can be suitable for beneficial reuse of the dredged materials. The scope of this research is to appropriateness of the dredged material for construction an infiltration berm that has sufficient slope stability and provides total infiltration in less than 72 hours. Unconfined compression tests, constant head permeameter tests and unsaturated hydraulic conductivity tests were conducted on the dredged material and its blends with straw, sand, and recycled glass aggregate. All amendments were chosen due to their potential of reducing infiltration time, increasing hydraulic conductivity and as well as increasing the available water content (AWC) to promote vegetation. The effects of different amendments on soil water characteristics curves were examined and related to the vegetation through percent green cover data. Results obtained through testing were implemented in finite element analysis programs, SLOPE/W and SEEP/W, to analyze slope stability and seepage behavior of the berm, respectively. Common berm geometries along with different subgrade conditions were considered during modeling. Straw amended dredged material provided adequate hydraulic conductivity, met the required minimum infiltration times, and acceptable AWC to promote vegetation, without experiencing any slope stability or piping failures.
Innovative Reuse of Baltimore Harbor Dredged Material as Vegetative Earthen Berms
(2024) Smith, Adam; Davis, Allen P; Aydilek, Ahmet H; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Dredged material (DM) is a sediment excavated from navigable waterways, which haslimited use due to the transport and accumulation of potentially hazardous metals and organic chemicals into these waterways. DM can be used as a recycled material in place of soil, depending on its environmental and physical characteristics, and the specific use. Vegetated Earthen Berms (VEBs), used for stormwater control, is one potential beneficial application of DM. The objective of this research is to assess the environmental and geotechnical suitability of DM in VEBs. A germination study and a battery of column tests were conducted to test the innate properties of the DM and DM amended with straw and sand, as DM blends. Straw and sand were chosen to observe potential improvements to the DM’s physical and chemical parameters. A nine-week mesocosm study was performed to simulate the overall performance of DM and DM blend constructed VEBs for the plant growth and water quality criteria, determined by US EPA water quality limits. Plant cover and growth measurements along with measuring effluent water characteristics were assessed. Straw amended DM was shown to have comparable vegetative establishment parameters relative to topsoil. For the water quality, concentrations of dissolved copper and zinc were reduced relative to typical median stormwater values in DM constructed VEBs. Based on the results of these tests, DM constructed VEBs had reflected desirable qualities for potential reuse based on water quality and vegetative establishment.
Examination of US Transportation Public-Private Partnership Experience: Performance and Market
(2024) Zhang, Kunqi; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Worldwide, public-private partnership (P3) project performance and benefits accrued to market participants are understudied. Focusing on the US, this dissertation examines the country’s transportation P3 experience through three empirical studies comparing P3 to design-bid-build (DBB), the traditional delivery method. Throughout, the Information Source for Major Projects database, built by a University of Maryland team in which the author led the data collection effort, served as the data source. In the first study, the researchers examined P3 cost and time performance using piecewise linear growth curve modeling, recognizing that past cross-sectional studies had produced mixed results. With 133 major transportation projects, the longitudinal analysis confirmed P3’s time performance advantage and efficiency diffusion effecting cost savings in DBB, where efficiency diffusion was a new term describing the spillover and internalization of technical and managerial innovations inducing an efficient outcome. The second study used social network analysis to investigate collaboration patterns among different types of players in the P3 market (i.e., public sponsors, special purpose vehicles, investors, lenders, advisors, contractors, and professional service firms). With 135 projects and 1009 organizations, data found that both P3 and DBB networks are small worlds. Exponential random graph modeling revealed that practicing in the DBB market helps firms participate in P3 projects and that large firms (vis-à-vis small/medium-sized firms) are not privileged. The third study, further exploring the P3 market, focused on the Disadvantaged Business Enterprise (DBE) program. Administered by the US Department of Transportation, the program promotes the participation of small, disadvantaged firms in federal-aid projects. Linear regressions on 134 contracts showed that P3 associates with higher DBE goals in terms of percentage of dollars to be awarded to DBEs, whereas the delivery method does not affect the actual attainment. Overall, the findings justify continued policy support towards P3 implementation.
Econometric Evaluation of Transportation Policies: Decarbonization and Electrification
(2024) Burra, Lavan Teja; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The transportation sector, one of the largest contributors to global energy-related emissions, is undergoing a major transition. Governments worldwide are implementing stringent fuel economy and emissions standards, promoting the adoption of electric vehicles--a key technology for decarbonizing the transport sector--through various policy measures. This dissertation contains four chapters, studying the effects of such policies implemented across major vehicle markets and evaluating their effectiveness, with a particular focus on the electrification of light-duty passenger vehicle fleet. The first chapter explores whether multi-car households shift mileage to the most fuel-efficient car in response to increasing driving costs, which carries implications for designing effective fuel economy standards. The second chapter investigates the potential interaction between purchase subsidies given to consumers in buying electric vehicles (EVs) and expanding the public charging network. The third chapter focuses on the effectiveness of purchase subsidies for EV buyers and quantifies the free-rider share, given that this is a commonly employed policy measure worldwide. The final chapter explores the differential effects of level 2 and level 3 chargers, as well as the distributional impacts of public charging network on driving EV uptake across various demographic groups and built environment characteristics. Overall, the chapters in this dissertation employ travel survey data, longitudinal and big data analysis, causal identification, optimal policy design, counterfactual simulations, and a combination of data and economic reasoning to glean insights on the effectiveness and equitable aspects of policies aiming to decarbonize and electrify the transportation sector.
TOWARDS AUTOMATED CONTRACT ANALYSIS: APPLYING LANGUAGE MODELS TO RISK IDENTIFICATION IN THE CONTEXT OF PUBLIC-PRIVATE PARTNERSHIPS
(2024) Wang, Yu; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Risk management is critical to project success, especially in public-private partnerships (P3s) featuring long-term relationships, uncertainty, and complexity. Poorly handled risk management, especially regarding risk transfer, can lead to incentive distortion, disputes, or even project failure. The contract, serving as the formal and enforceable legal agreement binding on the public and private partners, plays a vital role in transferring risks associated with P3s. Risk identification is an important step in contract risk analysis, since overlooking specific risk clauses may cause detrimental consequences, such as revenue loss, unexpected financial liabilities, and legal disputes for contracting parties. Previous research has extensively examined the identification and allocation of project risks between contracting parties, predominantly employing questionnaire surveys, interviews, or content analysis methods. These studies depict common practices of risk identification and allocation, with some addressing risks stipulated in contracts. Nonetheless, there are notable limitations. Firstly, the findings derived from these traditional approaches often lack replicability. Secondly, given the typical lengthy nature of P3 contracts, conventional methods for analyzing risk-related contract content are labor-intensive and time-consuming. Thirdly, most of the studies do not offer a means to retrieve specific provisions for nuanced scrutiny. Addressing these limitations necessitates the adoption of innovative approaches to gain more granular and replicable results in risk-related contract analysis. The ideal solution should allow for the effortless and consistent extraction of specific contractual provisions related to project risks, providing a microscopic lens to risk allocation practices. With the recent advancements in natural language processing (NLP), especially transformer-based pre-trained language models (PLMs) and cutting-edge large language models (LLMs), there has been a significant breakthrough in the efficiency of processing and extracting information from textual data. Motivated by both the pivotal yet complicated nature of contract documents and the increasingly mature NLP techniques that create new opportunities for text analysis, this research aims to utilize NLP to automate the identification of risk-related aspects in contract documents. Firstly, a risk-related framework of P3 contracts is developed based on a literature review and a contract review. Based on that, a series of NLP-based tools are developed for the automated identification of risks-related contract language, including 1) a rule-based model for contingency liability identification with a weighted F1-score of 88.9%, 2) a fine-tuned PLM (particularly the BERT family) for risk type and allocation identification with a weighted F1-score of 80.6% and 80.5%, and 3) a prompt design with an LLM (particularly GPT-3.5) for risk type and allocation identification with a weighted F1-score of 64.1% and 72.1%. Next, the effectiveness of these different approaches is compared. Finally, we apply the tools to real contract documents to offer risk profiles of P3 contracts. The goal is to foster a more efficient, precise, and in-depth understanding of contract risks by leveraging the capabilities of NLP technologies.
CHEMICALS OF EMERGING CONCERNS IN WASTEWATER TREATMENT: ACUTE AND CHRONIC IMPACTS OF AZOLES ON BIOLOGICAL NITROGEN REMOVAL PROCESSES
(2024) Chen, Xiaojue; Li, Guangbin GL; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Nitrogen (N) in wastewater presents in the forms of ammonia, nitrite, nitrate, and/or organic N. Discharging of ineffectively treated N-containing wastewater into water bodies could cause eutrophication, threaten the safety of the ecosystem, and impair the water quality for humans. Various Biological Nitrogen Removal (BNR) processes, including nitrification, denitrification, and anaerobic ammonium oxidation (Anammox), have been successfully applied in wastewater treatment plants (WWTP) for N removal. However, the performance and stability of BNR can be adversely impacted by chemical inhibitors present in wastewater. Azoles, classified as emerging organic contaminants (EOCs), are a group of man-made chemicals containing N atoms in the heterocyclic ring systems and have been widely applied as aircraft de-icing agents, in semiconductor manufacturing, and household dishwashing detergents. Various azoles have been detected in wastewater streams and their presence within WWTPs may impact the BNR processes. Azole compounds, such as benzotriazole (BTA), have been used as biocides/fungicides in agriculture. However, limited information is available about the potential inhibition of azoles to BNR processes, while guidelines for preventing BNR processes from azole inhibition and methods for system recovery are also unavailable. In this study, we investigated the short- and long-term impacts of azoles on the major BNR processes, including nitrification, denitrification, and Anammox. Besides, potential inhibitory mechanisms of azoles and resistance/adaptation of BNRs were assessed, aiming to develop an effective strategy to prevent BNRs from system interruption and/or efficiency decreasing caused by azoles present in WWTPs. In short-term impact assessment, both experiment-based lab research and literature review approaches were used to assess the inhibition potential of different azole compounds on major BNR processes. Nine azole compounds (5 diazoles and 4 triazoles) were selected to represent azoles with different structures and physiochemical properties. The concentration (IC50) of azoles needed to inhibit BNR processes by 50% is calculated. Different BNR processes showed various responses to azoles after short-term exposure (<24h). Pyrazole (PA), triazole (TA), BTA, and methyl-benzotriazole (MBTA) at 6 mg /L caused >90% inhibition of nitrification activity, while higher inhibition resistance to these compounds was observed in the denitrification process with the calculated IC50 (mg /L) of 126, 520, 412, and 152, respectively. In comparison, 50% inhibition of Anammox activity was observed at the concentration of BTA (20 mg /L) and MBTA (18 mg /L). The differences in azole inhibition were suspected to be related to BNR processes’ characteristics including the potential chelation of azoles with enzyme-bound copper (e.g., ammonia monooxygenase, AMO) in nitrifiers, high biodiversity of denitrification sludge, and unique cell structure (e.g., annammmoxosome) in Anammox bacteria. In addition, azoles with more functional groups and/or complicated structures (e.g., climbazole at 20 mg /L & fluconazole at 100 mg /L) exhibited less inhibition on the nitrification (PA and TA at 6 mg/L) and Anammox processes (BTA and MBTA at 20 mg/L). In WWTPs, wastewater contains a variety of different compounds and it is more difficult for azoles with complex structures to chelate with the key BNR enzymes (Kalyani Vikas Jog, 2021). More attention should be paid to azoles with smaller molecule sizes and simpler chemical structures such as PA, BTA, TA, and MBTA. Compared with short-term experiments (<24 hours), results from long-term exposure (3 to 9 months) of BNR processes to azole provide further insights into understanding the impacts of azole and the system’s response under a condition that is closer to practical. Azoles, such as BTA and MBTA, have been found to cause inhibition (>50%) of the Anammox process in short-term experiments at 20 and 18 mg/L levels, separately. However, the long-term influence of azoles, whether inhibitory or not in short-term experiments, on the Anammox process is not well studied. Therefore, in the second part of this work, we aimed to investigate the long-term impact of BTA and PA on the Anammox process and the potential mechanisms by which Anammox bacteria adapt to resist azole inhibition. Through long-term acclimation, the Anammox granular sludge could gain higher resistance to BTA below 30 mg/L. However, further exposure to higher BTA concentrations (40 mg/L) led to a gradual decrease in NAA from 70% to 40%. No inhibition ≥ 10% was observed during short-term exposure at 300 mg/L PA, but long-term exposure to 200 mg/L resulted in more inhibition than short-term exposure. While long-term exposure to 100 mg/L PA did not cause any decrease in Anammox activity, 200 mg/L PA led to 65% reversible inhibition in 40 days. To further investigate the potential mechanisms by which Anammox granular sludge adapts to resist azole inhibition, starvation experiments, ATP content analysis, and microbial composition analysis were conducted. BTA and MBTA were selected as representatives of azoles in the starvation experiment due to their reported inhibitory effects on Anammox granular sludge. Over a 28-day starvation experiment, Anammox activity decreased, and the lag phase increased with starvation time at substrate conditions of 75 mg/L NH4+ -N and 100 mg/L NO2- -N. However, the presence of BTA and MBTA in the starved Anammox sludge did not result in a further reduction of Anammox activity and change in Anammox sludge’s ATP content levels, suggesting that the short-term energy-required mitigation method may not be the major defense mechanism for Anammox bacteria resisting BTA and MBTA inhibition. Overall, the results indicate that a stepwise acclimation strategy could enhance Anammox resistance to azole inhibition. As an important pre-step to conventional (nitrification/denitrification) and advanced (Anammox) BNR, the long-term impacts of azoles on the nitrification process were studied using lab-scale sequencing batch reactors (SBR). BTA and PA were selected as two representatives of the azoles due to their wide usage, detection in wastewater, and their structure as benzene-containing triazoles and diazole. Adapted nitrification sludge had better performance in treating wastewater containing azoles. Before acclimation, the addition of 2.5 mg/L BTA and PA decreased 45% and >90% nitrification activity, respectively. After the acclimation with the stepwise increase strategy, no ammonium and nitrite accumulation was observed in the effluent when 2.5 mg/L BTA and 2.7 mg/L PA were added into the system. The normalized nitrification activity (NNA) of the BTA and PA groups were 72.5 ± 2.5% and 70.2 ± 2.5%, respectively, which were also higher than the non-adapted nitrification sludge in short-term tests. The nitrification sludge developed BTA degradation ability and high degradation rates were achieved during the acclimation process. The removal of N loading didn’t impact the BTA degradation process. The addition of an extra 50 mg/L COD increased the BTA removal rate while the extra 200 mg/L COD decreased the BTA removal rate. According to the results, the heterotrophic bacteria that existed in the nitrification sludge may contribute to the BTA degradation. Reported aromatic compounds/organic compound degraders such as Dechloromonas and Zoogloea were identified in the microbial community. Azoles showed a variable inhibition to denitrifying microorganisms in activated sludge batch tests. In the last part of this work, the long-term inhibition potentials of BTA and PA to the denitrification process were investigated. A stable denitrification process was observed with BTA and PA addition at 2.5 and 2.7 mg/L, respectively, suggesting the low risk of azole inhibiting the denitrification process in WWTPs. No BTA and PA degradation was observed within the 12-hour operation cycle. However, after increasing the reaction time to 7 days, more than 85% BTA and 45% PA degradation are observed during the process. Furthermore, all the denitrification reactors (Control, BTA, and PA) were found to be able to degrade the BTA and PA at similar degradation rates. The removal of N and organic content (acetate as COD source) loading didn’t impact the BTA and PA degradation process. Multiple potential aromatic compound/organic compound degraders were found in the denitrification reactor such as Thauera, Azoarcus, Georgfuchsia, and Dechloromonas. Further investigation is needed to examine the contribution of individual species to azole degradation and their synergistic relationship. The results of this work indicate that the presence of azoles in wastewater has the potential to adversely impact the BNRs in WWTPs, in particular those to which azoles are new. However, proper implementation of the acclimation strategy can enhance the resistance of BNR bacteria to azole inhibition, preventing the system from interruption, even failure, caused by azoles. The synergistic effect of the microbial community may contribute to the attenuation of the azole inhibition to the system. The results provide new insights into understanding how the BNRs respond to chemicals of emerging concerns and are expected to assist WWTP operators in developing effective strategies to recover the system from azole inhibition. This work could provide suggestions for WWTPs to maintain activity and efficiency when treating azole-containing wastewater, identify azole types with high potential risks, and understand which part of the wastewater treatment systems may be impacted.
Application of Bayesian Networks to Assess the Seismic Hazard for an Infrastructure System in the Central and Eastern United States
(2024) Gibson, Emily M.; Bensi, Michelle T; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Earthquakes are often considered by the public to be a hazard restricted to the Western United States (WUS). However, earthquakes occur in the Central and Eastern United States (CEUS) as observed with the August 23, 2011 Mineral, Virginia earthquake, which damaged approximately 600 residential properties, resulted in 200-300 million dollars in economic losses, and initiated a shutdown of the North Anna nuclear power plant (Horton et al., 2015). Since earthquakes occur more frequently in the WUS, most seismic research is performed to support the WUS tectonic regime. This is also true when developing methods to assess the seismic risk to infrastructure systems, and current practices involve performing a large number of simulations of potential earthquake events, ground motion fields, structural performance, and failure consequences. These simulations can require significant computational resources, and it may be difficult to convince stakeholders to assess the seismic risk of their infrastructure system in the CEUS since earthquakes occur less often and perceived risks are lower. However, this risk must be assessed, given the density and age of infrastructure in the CEUS. Additionally, ground motion attenuation is lower in the region, so infrastructure distributed across greater distances may be impacted during an earthquake event. As a first step in developing a method that is tailored to assess system risk in the CEUS, this research proposes a Bayesian Network (BN) framework to estimate multi-site seismic hazards. Importantly, this framework utilizes existing products from a Probabilistic Seismic Hazard Analysis (PSHA), which reduces computational burdens and allows a user to incorporate the epistemic uncertainty characterized by experts as part of previously performed large-resource efforts. Additionally, the framework incorporates sources of hazard correlation between sites in a transparent and computationally tractable manner. An example problem is provided to validate this framework against a simulation that reflects the current state of practice in the WUS. Applications of the framework are then explored to assess when various input parameters may influence hazard results and identify when more or less resource-intensive assessments may be appropriate. This includes evaluating the impact of the ground motion within-event residual correlation and site separation distance. A scenario is also presented to illustrate how the BN can be used to make hazard-informed decisions in the context of the operation of two dams. The framework is then expanded to illustrate how failure modes can be characterized to understand system performance better. Since hazard correlation is an important aspect of the multi-site hazard, within-event residual correlation in the CEUS is also investigated. Empirical models are available to estimate ground motion within-event residual correlation in the WUS, but these may not be appropriate for the CEUS, given the lower attenuation. Earthquake recordings available from the NGAEast database (Goulet et al., 2014) and applicable CEUS ground motion models are used to calculate ground motion residuals. Correlation between the residuals at different sites is analyzed and compared against models developed for the WUS. Insights from this analysis and the proposed framework are provided to aid practitioners in assessing seismic risk for an infrastructure system in the CEUS.
EQUITY ISSUES IN ELECTRIC VEHICLE ADOPTION AND PLANNING FOR CHARGING INFRASTRUCTURE
(2024) Ugwu, Nneoma; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Electric Vehicles (EVs) offer a sustainable solution to fossil fuel dependency and environmentalpollution from conventional vehicles, crucial for mitigating climate change. However, low market penetration among minority and low-income communities raises equity and environmental justice concerns. This dissertation examines EV adoption and charging station access disparities in Maryland, focusing on sociodemographic factors such as race and income. To address the lack of minority representation in existing EV research surveys, we conducted anonline survey targeting people of color (POC) and low-to-moderate-income households. We received 542 complete responses. Ordinal regression models were used to analyze factors influencing EV interest. We then performed a cumulative accessibility study of EV infrastructure in Maryland. Pearson correlation analysis was used to show the relationship between charging station accessibility and sociodemographics. Population density showed a strong positive correlation (0.87) with charging deployment. We found that Baltimore City, had the highest population density and the highest concentration of EV charging in Maryland. We conducted a case study of Baltimore City’s EV infrastructure investments and policy efforts. Charging stations were categorized based on speed, network, access, and facility type. Spatial analysis andZero-Inflated Poisson (ZIP) regression models at the block group level were employed to investigate the disparities in EV charging infrastructure distribution within the City across minority and non-minority communities. Our findings show substantial disparities in EV perceptions between POC and Whitecommunities. The survey revealed that POC were more than twice more likely than White respondents to indicate that the availability of charging stations affects their interest in EV adoption, while the case studies revealed that POC populations are less likely to have access to EV infrastructure, necessitating targeted investment in charging options and subsidies in these communities. Our study also found the need for policies fostering residential charging station deployment, particularly in minority communities. To ensure equitable EV adoption, strategic investments in economically disadvantaged and rural areas beyond centralized regions are vital. This study informs evidence-based policies prioritizing accessibility, equity, and inclusivity in promoting a cleaner and sustainable transportation landscape.
ASSESSING THE IMPACTS OF ORGANIC AMENDMENTS ON DISTURBED SOIL PROPERTIES, WATER QUALITY AND VEGETATION GROWTH
(2024) Pamuru, Sai Thejaswini; Davis, Allen P; Aydilek, Ahmet H; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Deficiencies in essential organic matter (OM) are exhibited in disturbed roadside soils rendering them less favorable for plant growth. Vegetation plays a crucial role in maintaining the health of ecosystems, providing a myriad of benefits in protecting against soil erosion and effectively managing stormwater. National and state transportation departments are therefore prioritizing roadside vegetation using sustainable practices, leading to increased use of organic amendments (OAs) such as compost or related materials. OAs are commonly recycled and repurposed materials that serve as valuable soil conditioners, and their characteristics vary depending on their parent materials. Many OAs are cost-effective, readily available, and offer significant benefits to urban soils, which often are bereft of plant-essential nutrients and stability. This necessitates a better understanding of their impact on soil health and the environment, when applied at “acceptable” rates. This research aims to explore soil-water-plant interactions in urban soils (with and without OAs) focusing on vegetation establishment, soil fertility, and nutrient transport via leaching/runoff. Greenhouse and laboratory experiments were conducted to assess the potential use of these OAs for roadside projects.One set of experiments (greenhouse tub studies) focused on three OAs (leaf compost, shredded aged wood mulch, biosolids) which are widely available across Maryland. The amended soils were mixed to meet the topsoil OM requirements (4 – 8 %) of the state. Water quality results highlighted that the biosolids, while effective in retaining influent rainwater (tap water) phosphorus, caused significant nitrogen losses, exceeding typical stormwater concentrations by 40-200 times. Leaf compost also contributed to nitrogen leaching but only during the initial stages. Mulch reduced nutrient loss but caused limited vegetative cover. The study found that soil properties, such as the carbon-to-nitrogen (C:N) ratio and nitrogen content, play a vital role in the magnitude and patterns of nitrogen leaching. Additionally, it was speculated that the presence of soil minerals, such as iron and calcium, successfully retained phosphorus in the amended soils. The shear and hydraulic properties of the soils improved with the incorporation of amendments. Based on the results of the tub studies, leaf compost identified as a suitable OA for plants and water quality. However, the tub studies had limitations in their evaluation of compost amendments derived from different feedstock sources and their impacts on native vegetation growth. Therefore, a pot study was conducted to determine the optimum mixing ratios of soils and OAs to facilitate rapid vegetation growth. Three types of composts (turkey litter, food waste and yard waste) with varying nutrient properties were tested. A wood-based biochar was the fourth chosen OA because of its valuable use in agriculture and environmental remediation. The findings showed that turkey litter compost severely inhibited growth at higher application rates due to excess salts content. However, this compost showed improved plant nitrogen and leaf area whenever vegetation was established. Alternatively, biochar, while not inhibiting growth, resulted in visibly weak plant morphology, and led to nitrogen deficiencies. Yard waste and food waste composts showed positive impacts in terms of coverage, leaf area index and plant N contents. Between the tub studies and the pot study, yard waste compost has consistently emerged as the favorable soil amendment. Given biochar’s well documented advantages for water quality and soil structural properties, a scaled-up mesocosm experiment that simulated sloped road shoulders was conducted to test the effectiveness of combining compost and biochar in urban soils, aiming to meet vegetation and water quality goals. The runoff phosphorus and nitrogen mass transports were highest (261 mg-P/m2 and 8645 mg-N/m2, respectively) when compost was the sole amendment mixed into the control soil. However, adding biochar to the soil reduced these losses by up to 5.6x for phosphorus and 8.8x for nitrogen compared to compost. Strong correlation between soil C:N and effluent N was noted, higher ratios (>20:1) reduced nitrogen losses. Biochar, due to its high carbon content and pH, also helped retain phosphorus in the soils. Conversely, compost, being more readily decomposable than biochar, caused nutrients to run off. Compost-biochar mixtures also showed greater plant growth compared to the control soil. Together, this research shows that not all high-nutrient OAs provide favorable outcomes when incorporated into soils to enhance the OM content. Leaf or yard waste-based composts are preferred for roadside vegetation due to their reduced issues related to nutrient losses compared to other nutrient-rich materials tested in this study. However, the yard waste compost incorporation rate should be limited to achieve a soil OM increase of 1-2% to prevent high nutrient levels in the runoff. Furthermore, combining biochar and yard waste compost offers a promising approach for construction projects particularly on steep terrains to achieve and preserve a balanced soil-water-plant ecosystem.
PREVENTION AND TREATMENT OF PERSISTENT ORGANIC POLLUTANTS IN STORMWATER AND SEDIMENT
(2023) Yuan, Chen; Kjellerup, Birthe V; Davis, Allen P; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Polycyclic aromatic hydrocarbons (PAHs) and Polychlorinated biphenyls (PCBs) are two groups of persistent organic pollutants (POPs) with toxicity, carcinogenicity, and teratogenicity. Those compounds are harmful to human health and wildlife. Stormwater is one of the important sources of PAHs and PCBs to aquatic environments. Stormwater control measures (SCMs) have already been used to remove PAHs and PCBs from stormwater, however traditional SCMs can remove PAHs and PCBs in the particle phase, but there still are dissolved PAHs and PCBs in the outflow of SCMs. This study focused on reducing the influence of PAHs and PCBs in stormwater on the environment by 1) improve the treatment performance by adding a polishing treatment procedure after traditional SCMs, and remove the PAHs and PCBs accumulated in the polishing treatment media by bioaugmentation of Pseudomonas putida ATCC 17484 and Paraburkholderia xenovorans LB400 and 2) dechlorination of PCBs in the sediment of aquatic environments by biofilm Dehalobium chlorocoercia DF1 inoculum. The results of polishing treatment showed that all black carbon materials, namely biochar, granular activated carbon (GAC), and regenerated GAC (RAC), were effective to remove dissolved PAHs with removal > 95%. However, all materials had lower removal efficiency on PCBs with removal > 84%, By the comparation of cost and lifetime under the condition that 50% polishing media are used in the polishing treatment facility. RAC which has a lifetime>147 years based on the precipitation of Maryland and Washington and cost <3.79 $-m3-yr-1, was the best material for polishing treatment. Results of treatment train with a traditional SCM media column and polishing treatment column indicated that average removal of PAHs can be improved from 94.56% of BSM columns to 99.61% of polishing treatment columns, and removal of PCBs can be improved from 84.61% to 95.16%. Results of bioaugmentation of polishing treatment media showed no biodegradation took place in the mesocosms with polishing media. However, the liquid mesocosms showed P.putida degraded 97.9% of pyrene. The bacteria colony on plates after the biodegradation experiment showed that there were less P.putida and P.xenovorans colony of polishing media mesocosms than liquid mesocosms. Therefore, the limitation of biodegradation of polishing media mesocosms may cause by the limited bioavailability and less active inoculated bacteria. The results of dechlorination by Dehalobium chlorocoercia DF1 biofilm shows that there were native bacteria, such as Gemmatimonadetes, Actinobacteria, Proteobacteria and Firmicutes in the sediment that can dechlorinate PCBs. The three treated mesocosm groups (addition of biochar, bioaugmentation with DF1 biofilm and liquid DF1 culture) all can improve dechlorination, of 28.09%, 21.30%, and 17.10%, respectively. Those three groups had dechlorination extent higher than negative control (4.60%), and abiotic control (-1.02%). The microbial community analysis indicated that biofilm inoculation improved abundance of DF1 and had a more stable influence on the community than liquid inoculation. Overall, biofilm inoculation and addition of biochar dechlorinate PCBs in sediment efficiently, and polishing treatment is an efficient approach to improve traditional SCMs, while treating the polishing media with bioaugmentation need further study.
Prediction of Marine Timber Pile Damage Ratings Using a Gradient Boosted Regression Model
(2023) Willmott, Carly; Attoh-Okine, Nii O.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Marine pilings are critical structural elements exposed to harsh environmental conditions. Specialized routine inspection and regular maintenance are essential to keep marine facilities in good working condition. These activities generate data that can be exploited for knowledge gain with machine learning tools. A gradient boosted random forest regressor machine learning algorithm, XGBoost, was applied to datasets that contain timber pile underwater inspection and repair data over a period of 23 years. First, the data was visualized to show the longevity of different timber pile repair types. An XGBoost model was then tuned and trained on a dataset for timber piles at one pier. Variables in the dataset were evaluated for feature importance in predicting damage ratings assigned during routine underwater inspections. Next, an ensemble of XGBoost models was trained and applied to a second dataset containing the same features for an adjacent pier. This dataset was reserved for testing to demonstrate whether the ensemble trained on one pier’s data could be generalized to predict timber pile damage ratings at a nearby but separate pier. Finally, the ensemble was used to predict damage ratings on piles that had earlier data but were not rated in the two most recent inspection events. Results suggest that the ensemble is capable of predicting timber pile damage ratings to approximately +/- one damage rating on both the training and test datasets. Feature importances revealed that half of the variables (time since the first event, repair type, exposed pile length, and time since the last repair) contributed to two thirds of the relative importance in predicting damage ratings. Data visualization showed that a few repair types, such as pile replacements and encapsulations, appeared to be most successful over the long term compared with shorter-lived repairs like wraps and encasements. These results are promising indications of the advantages machine learning algorithms can offer in processing and gleaning new insights from structural repair and inspection data. Economic benefits to marine facility owners can potentially be realized through earlier anticipation of repairs and more targeted inspection and rehabilitation efforts. There are also opportunities for better understanding of deterioration rates if more data is gathered over the lifespans of structures, as well as more detailed data that can be introduced as new features.
A Planning Model For Flexible-route Freight Deliveries in Rural Areas Based on Adjusted Tour Length Estimations
(2023) Li, Zheyu; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Addressing the issue of delivery efficiency and transportation service quality in rural areas, this thesis presents an analysis of total cost of delivery services in regions with low demand density and low road network density. It focuses on designing a cost-effective and efficient freight delivery system, which is crucial for promoting a vibrant rural economy. A flexible-route service model is developed, aiming to improve farm products and other deliveries by optimizing the service zone size and frequency to minimize the average cost per delivered package. The model is tailored for a potential truck operation scenario in the central Appalachian region, serving as a representative case study, with a general formulation of total cost that can be adapted to similar cases elsewhere. Considering the influence of dead-end roads in rural area, this study presents an adjusted formulation of length estimation for Traveling Salesman Problem (TSP) tours based on the literature review and regression on multiple graphs with road network, and develops a mathematical formulation of total cost, integrating operation and user costs, supported by reasonable assumptions and system constraints. The results from the baseline study suggest that one truck can serve a large service area by exceeding the maximum working hours constraint. This observation is made without considering the potential expansion into a multi-zone system, which might be necessary due to the combined factors of road network complexity and the perishability of farm products. The results from our sensitivity analysis show that a system with a single large truck will have the lowest average cost per package when demand is low. Considering an actual road network, this study also explores the possibility of combining the flexible-route delivery service with self-deliveries and the extension of Vehicle Routing Problem (VRP) with maximum working hour constraint. The study concludes with suggested future research directions in this important domain.
Analysis of Transfer Coordination in Flexible-Route Bus Services
(2023) Yang, Tao; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Urban residents are often confronted with transportation predicaments. The inconvenience caused by the conventional fixed-route bus system has led to excessive reliance on private cars, worsening traffic congestion and air pollution. However, a flexible bus route can provide passengers with a convenient, expedient, cost-effective commuting option. This thesis studies a flexible bus system with Many-to-One (M-1) and Many-to-Many (M-M) demand patterns, comprising multiple rectangular residential zones and a central terminal. The total cost of flexible route bus service is modeled and modified for coordinated and uncoordinated headway conditions. Among them, the demand between each service zone and the central terminal, and the demand among service zones are analyzed to optimize headways in order to minimize total system cost. Finally, the sensitivity analyses are conducted to explore the impact of parameter changes on the results. The comparison of baseline and sensitivity analysis results shows that more benefits can be achieved when coordinating headways under low-demand conditions.
Interactions and Treatment of Metals in Urban Stormwater
(2023) Croft, Kristen; Kjellerup, Birthe V; Davis, Allen P; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Increasing urbanization and a changing climate will only exacerbate the magnitude of pollution entering our waterways, threatening our drinking water source and aquatic ecosystems. Urban stormwater contains a cornucopia of pollutants that pose direct toxicity risks (e.g., metals, organics, pathogens) and indirect adverse effects (e.g., sediments, nutrients) to aquatic life. Metals, specifically copper (Cu) and zinc (Zn), are both ubiquitous in the urban environment and detrimental to aquatic ecosystems at low concentrations (approximately 10 ppb). Targeting this growing source of pollution upstream is critical in providing necessary environmental protections, especially as the intensifying effects of climate change and urbanization are imminent. This leads to the main research question – how can Cu and Zn loads in stormwater be reduced to protect aquatic ecosystems?Bioretention is a stormwater control measure (SCM) that mimics natural systems to take advantage of the natural filtering processes. In addition to hydrologic benefits, bioretention provides removal of particulate matter (PM) through filtration and sedimentation, and potential removal of dissolved constituents through chemical and biological processes. Studies including characterization of stormwater, road-deposited sediments (RDS), and performance of a mature bioretention cell were performed to determine treatability, mobility, and bioavailability of Cu and Zn in stormwater and through bioretention treatment. Both metals accumulated in the finest (<25 μm) fraction of RDS samples, however particulate bound (PB) Zn concentrations were enriched in stormwater compared to finer fractions of RDS, while PB-Cu was not. This indicated that PB-Zn is more mobile than PB-Cu, likely due to different sources of these metals in urban environments. The PM and PB metal loads were reduced by 82% and 83%, respectively, showing that mature bioretention cells are effective at reducing PM and PB contaminant loads. However, dissolved constituents were essentially unchanged through bioretention treatment, and concentrations of dissolved metals were measured at levels that potentially cause aquatic toxicity. Thus, alternative media amendments were investigated for further reduction of dissolved metal contents. Black carbon (BC) media including biochar, granular activated carbon (GAC), regenerated activated carbon (RAC), and a natural mineral sorbent, clinoptilolite zeolite, were tested in continuous columns, and in up-scaled modular treatment columns. The four tested BC media performed similarly for Cu and Zn removal, with Zn having an earlier breakthrough compared to Cu. This technology is predicted to provide reduction of dissolved Cu for up to 60 years with current rainfall predictions. Modular treatment columns showed that traditional bioretention soil media (BSM) provided effective removal of dissolved Zn (71%) and ineffective removal of Cu (17%). The subsequent BC polishing module was effective for Cu removal (40%), and zeolite showed potential for Zn removal. Overall, dissolved metals in stormwater are the most mobile, bioavailable, and difficult to remove through traditional filtration-based SCMs. This research has shown that fresh BSM can provide effective removal of dissolved Zn, and BC amendments are a potential solution for removal of dissolved Cu in stormwater. Refreshing the top few centimeters of an existing bioretention with fresh BSM can provide treatment of dissolved Zn. Retrofitting bioretention to include a polishing module either layered or in series with a mix of BC and zeolite can further reduce dissolved Cu and Zn loads in stormwater.
EXTENSION OF EXISTING PROBABILISTIC COASTAL HAZARD ANALYSIS FOR BAYESIAN NETWORK COASTAL COMPOUND FLOOD ANALYSIS
(2023) LIU, ZIYUE; Bensi, Michelle; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In the past decades, coastal compound floods (CCF) caused significant losses to coastal communities. To develop an accurate and complete probabilistic framework for assessing coastal hazards, the U.S. Army Corps of Engineers established the Coastal Hazard System’s Probabilistic Coastal Hazard Analysis (PCHA) framework. The current PCHA framework has focused primarily on a subset of CCF drivers, particularly storm surges.This dissertation documents four studies that contribute to efforts to advance and extend the PCHA for CCF analysis. In the first study, machine learning-based data imputation models are developed to fill in missing records in the historical storm dataset. The performance of the machine learning-based data imputation models under different parameterizations are assessed considering statistical and physical factors and also compared against existing prediction models. In the second study, a series of Joint Probability Method (JPM) assumptions to model the dependence among tropical cyclone (TC) atmospheric parameters are comparatively investigated. JPM assumptions considered in the analysis include parameter independence, partial dependence, and full dependence. Candidate full-dependence models include meta-Gaussian copula and vine copulas combining linear-circular copulas. Emphases are put on modeling the circular behavior of storm heading and its dependencies with other linear parameters. Full dependence models are compared based on the predicted probability of large CPD and RMW combinations. In the third study, a Bayesian Network (BN) of multiple coastal hazards is constructed, where the conditional probability tables (CPT) of TC atmospheric parameters are computed using copula. A deaggregation methodology is developed to identify the dominant TC for significant coastal hazard events to support risk-informing and decision-making processes and refined analyses. In the fourth study, leveraging the aforementioned study results, the extended PCHA is leveraged to develop a multi-tiered BN CCF analysis framework. In this framework, multiple tiers of BN models with different complexities are designed for study cases with varying levels of resource availability. A case study is conducted in New Orleans, LA and a series of joint distribution, numerical, machine learning, and experimental models are used to compute CPTs needed for BNs.

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