A. James Clark School of Engineering
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item DESIGN MODIFICATIONS TO MINIMIZE POLLUTANT LEACHING FROM COMPOST-AMENDED BIORETENTION(2024) Lei, Lei; Davis, Allen P.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bioretention is an effective stormwater control measure (SCM) recognized for its ability to capture and treat urban runoff within a shallow basin using engineered soils and vegetation. Bioretention studies at laboratory and field scales have shown good to excellent removal efficiency for heavy metals (> 80% to > 90%) and have observed high variablility ranging from negative (net export) to 99% in phosphorus and nitrogen removals. Water quality studies have shown that media selection for bioretention is critical in determining pollutant removal.Incorporating compost into bioretention media is an eco-friendly strategy that not only diverts organic waste from landfills but also provides several benefits improving the performance of bioretention system. This approach enriches the media with organic matter and nutrients for vegetation, boosts water holding and cation exchange capacity, stabilizes the soil structure, and improves the retention of pollutants. However, careful management is essential to mitigate the potential releasing pollutants, including dissolved organic matter (DOM), soluble nutrients, and metals readily associated with DOM, particularly if the compost is derived from biosolids... To maximize the benefits of compost in bioretention, special design modifications aimed at enhancing pollutant removal should be implemented. The objective of this research was to investigate ways to optimize the use of compost in bioretention while minimizing pollutant leaching, Design modifications investigated include layering compost over media, aluminum-based drinking water treatment residual (Al-WTR) addition, and incorporation of an internal water storage (IWS) layer. Treatment performances were evaluated through extractions, batch adsorption studies, large column mesocosms, and column media characterizations. Al-WTR amendment improved sorption of phosphorus, copper and zinc, with capacities increasing from 22.5 mg/kg to 161 mg/kg and 193 mg/kg for P, from 121 mg/kg to 166 mg/kg and then to 186 mg/kg for Cu, and from 121 mg/kg to 166 mg/kg and 186 mg/kg for Zn with 0%, 2% and 4% Al-WTR additions. The multilayered system containing a compost incorporated top layer and an Al-WTR amended bottom layer showed good removal of phosphorus (94% and 96%), copper (88% and 86%) and zinc (92% and 96%), and enhanced nitrogen retention (74.1%) from the stormwater load compared to a mixed system (32.8%) as reported by Owen et al. (2023). The installation of an IWS layer did not show statistically significant influences on phosphorus (91% to 93%, p > 0.05), copper (66% to 90%, p > 0.05) or zinc (94% to 95%, p > 0.05) removals, had limited effect on nitrogen retention from stormwater load during storm events (-117% to -188%, p > 0.05), but promoted denitrification during dry periods. With the IWS layer installed, high levels of iron leaching (130 to 11800 µg/L) were detected, likely due to change in the redox potential (from aerobic to anaerobic). With the objective of removing phosphorus and heavy metals from the stormwater, 5.3% of compost (by dry mass) can be used when layering compost over the Al-WTR amended bioretention media. When the design goal is to remove nitrogen, a fraction of compost up to 2.6%, by dry mass can be used, with layering the compost over the bioretention media and an IWS installed at the bottom.Item 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.Item 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.Item 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.Item 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.Item 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.Item 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.Item 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.Item 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.Item 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.