Civil & Environmental Engineering Theses and Dissertations

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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.
ASSESING 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.
EXPLORING TEMPORAL AND SPATIAL VARYING IMPACTS ON COMMUTE TRIP CHANGE DUE TO COVID-19
(2023) Saleh Namadi, Saeed; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
COVID-19 has deeply affected people’s daily life and travel behaviors. This study uses large-scale mobile device location data at the U.S. county level in the DMV area to reveal the impacts of demographic and socioeconomic variables on commute trip change. The study investigates the contribution of these variables to the temporal and spatial varying impacts on commuter trips. It reflects the short and long-term impact of COVID-19 on travel behavior via linear regression and geographically weighted regression models. The results indicate that commute trips decreased with more white-collar jobs, while blue-collar sectors demonstrated the opposite effect. Unexpectedly, elderly individuals, who were highly vulnerable to COVID-19, negatively correlated with decreased commute trips. Moreover, in the DMV area, counties with a higher proportion of Democratic voters also showed a negative correlation with reduced commute trips. Notably, the pandemic's impact on commuting behaviors was global at the onset of COVID-19. Still, the effects exhibited local correlations as the pandemic evolved, suggesting a geographical impact pattern.
In Situ and laboratory studies of soil treatment areas experiencing flooding
(2023) Waris, Aleem; Kjellerup, Birthe V.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Onsite wastewater treatment is used by over one in five American households to treat wastewater by soil biogeochemical transformations. In Maryland alone, 420,000 septic systems are in use primarily in rural and near coastal areas. Issues of sea level rise can threaten coastal infrastructure due to flooding damage that also can impact the ability of soil to efficiently treat nutrients found in wastewater. In this study, two onsite wastewater treatment systems with different soil types and treatment techniques were assessed in Anne Arundel County, Maryland. It was found that soil texture can impact the health of a soil in its function of treating wastewater, in addition to treatment techniques affecting inorganic nitrogen in the soil treatment area. To model the impacts of flooding damage to a soil treatment area, tidal flooding with fresh, brackish and saltwater was simulated in a laboratory-scale column study. The results from the month-long study showed decreases in the treatment efficiency for inorganic nitrogen and dissolved organic solids.
ANALYZING REDISTRIBUTION OF FEDERAL DISASTER AID THROUGH MACHINE LEARNING
(2023) Bryant, Adriana Yanmei; Reilly, Allison; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Natural disasters are on the rise and will be costly for both the United States government and its citizens. The record-breaking year of 2020 left $1 billion worth of damages in the wake of twenty-two different events (FEMA, 2023). As costs due to disasters increase in the coming decades, the livelihoods of all citizens, especially those most vulnerable are at risk. It is known that natural disasters exacerbate current standing social vulnerabilities and inequities. Federal disaster aid programs in place are intended to assist those who cannot solely finance their own recovery efforts. This study looks to analyze FEMA’s Public Assistance (PA) program, Individual Assistance (IA) program, and Hazard Mitigation Assistance (HMA) program. It is important that these systems put in place are distributing federal resources as intended because they are funded via people’s taxpayer dollars. This study looks to explore the relationship between disaster aid that is awarded at the county level with respect to the federal income taxes residents of that county pay to the federal government. This is expressed through the creation of the donor-donee ratio. This study also contributes to the literature a new metric of burden, the ratio of expected annual disaster losses of a county and its gross domestic product, which can beutilized as a proxy for coping capacity. The burden metric provides additional useful insight as it is tabulated by FEMA directly and published in their National Risk Index (NRI) at the county level. Over the last decade, this research examines the donor-donee ratio and burden metric over the years 2010 to 2019. Results of mapping the donor-donee ratio and burden metric indicate there is spatial heterogeneity between counties in the United States. The redistribution of federal aid is not only heterogeneous but there are distinct regional patterns where further research could investigate their causality. To investigate the relationship between the redistribution of aid and coping capacity by proxy, this study utilized supervised machine learning to characterize counties. Significant outcomes of the machine learning indicate that most counties across the country received moderate funding and were evaluated as having a moderate burden as well. This does suggest that to some level the redistribution of aid is working as intended. Although upon further digging, it was found that counties that experience high-cost, less frequent events, contain over 50% of the country’s population and lie in metropolitan areas. Upon the application of a logistic regression model, it was found that these counties while associated with higher income, are also associated with higher mobile homes residence. As the risk of these higher costs events increases over the years, it is imperative that vulnerable communities are receiving adequate funding to increase their resilience to future hazards. This study highlights the flows of federal disaster dollars and where these programs allocate funding.
SELECTION AND SCHEDULING OF INTERRELATED NETWORK IMPROVEMENT PROJECTS UNDER UNCERTAINTIES
(2023) Wu, Fei; Schonfeld, Paul M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The analysis of improvements in transportation networks is complicated by the interdependence of those improvements. Changes in links or nodes tend to shift traffic flows. Hence, the benefits of changes in any network component depend on what changes are made at what time in other components. There may also be synergies in the costs of implementing network changes. Methods are needed for selecting and scheduling interrelated network changes under uncertainties regarding demand, costs, implementation times and other factors. The proposed research focuses on optimizing the selection and schedule of interrelated network projects for enhancing a network’s performance under various uncertainties. Two multi-level models are formulated for analyzing problems on two types of transportation networks: rail freight networks in Problem 1 and road networks in Problem 2. For rail freight networks, the proposed tri-level model jointly optimizes short-term post-disruption restoration schedules and long-term network development schedules. Its lower level assigns capacitated freight flows to minimize total hourly cost, and its middle level optimizes the restoration sequence for the minimized cumulative cost increment (excess) during the restoration process under a given disruption scenario. At the upper level, given probabilistic disruption scenarios, network improvement projects are selected from a given set and sequenced to minimize the sum of construction cost and cumulative expected excess over the planning horizon. For road networks, the lower level of the proposed bi-level model performs user-equilibrium (UE) traffic assignment using the Frank-Wolfe (F-W) algorithm. The upper-level model first generates multiple scenarios with samples from the multivariate distribution of multiple correlated uncertain parameters. For a given long-term network improvement plan, an expected present value (PV) of cumulative system travel time cost over the planning horizon plus construction costs of implemented projects is obtained after computing this discounted sum separately under each generated scenario. To minimize this expected PV, the upper level optimizes the improvement plan with a specified selection and sequence of projects. In both problems, any sequence of restoration or improvement actions is mapped to a unique schedule under the rules based on binding constraints of resources, budget, and required work time. The planning horizon is segmented into short sub-periods based on the improvement schedule to approximate the effects of demand growth and cost discounting. A genetic algorithm (GA) with its operators is customized for optimizing restorations and improvement plans. For road networks, with the internal budget supply based on a fraction of the travel time cost, a set of methods is proposed for determining budget-ready times of projects. The model also allows the use of buses as a mode competing with cars in road networks, and the iteration of mode shares is integrated with the lower-level F-W traffic assignment. In Problem 1, the proposed model is demonstrated with short-term and long-term numerical cases in a small demand-loaded network. In the short-term problem, the optimized restoration sequences with different numbers of available work teams are obtained by the GA, whose solutions to relatively small problems are shown to be globally optimal through exhaustive enumeration. The restoration itineraries and schedules of work teams along with corresponding changes in hourly cost are shown in figures. For a small long-term problem, the selection, sequence, and schedule of projects are optimized by exhaustive enumeration. Sensitivity analyses show that the availability of more work teams greatly reduces cumulative expected excess and thus justifies implementing fewer improvement projects. With a longer planning horizon, lower construction costs, and a higher interest rate, more improvements are favored. Evaluations of cumulative expected excess with probabilistic growth rates of demand are provided. The model is also tested on a larger network where the long-term improvement plan is optimized by the GA. The quality of its solution is statistically verified. In Problem 2, 50 scenarios are generated by a quasi-Monte-Carlo sampling method. Demand growth rate, external budget supply, and the multiplier of required construction time are three correlated uncertainties whose effects are analyzed. The selection and sequencing of improvement projects are optimized by the GA. When introducing buses in the network, two operation modes are considered: using dedicated bus lanes and sharing roads with cars. The iteration of bus shares shows that the bus operation is more desirable with dedicated lanes, and that the potential demand from bus passengers greatly affects that desirability. In sensitivity analyses the factors favoring the implementation of projects include a higher demand level, a higher value of travel time, lower construction costs, and a higher demand growth rate. Across the scenarios, the demand growth rate and the external budget supply directly affect the PVC and last completion time (LCT), respectively. Modifying correlation coefficients of uncertain parameters has slight impact on the minimized PVC, but greatly affects the correlation between PVC and LCT for a given improvement sequence.
REAL-TIME DISPATCHING AND REDEPLOYMENT OF HETEROGENEOUS EMERGENCY VEHICLES FLEET WITH A BALANCED WORKLOAD
(2023) Fang, Chenyang; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The emergency management service (EMS) system is a complicated system that tries to coordinate each system component to provide a quick response to emergencies. Different types of vehicles cooperate to finish the tasks under unified command. The EMS system tries to respond quickly to emergency calls and communicate with each department to balance the resources and provide maximal coverage for the whole system. This work aims to develop a highly efficient model for the EMS system to assist the coordinator in making the dispatching and relocation decisions simultaneously. Meanwhile, the model will make a route decision to provide the vehicle drivers with route guidance. In the model, heterogenous emergency vehicle fleets consisting of police vehicles, Basic Life Support (BLS) vehicles, Advanced Life Support (ALS) vehicles, Fire Engines, Fire Trucks, and Fire Quants are considered. Moreover, a coverage strategy is proposed, and different coverage types are considered according to the division of vehicle function. The model tries to provide maximal coverage by advanced vehicles under the premise of ensuring full coverage by basic vehicles. The workload balance of the vehicle crews is considered in the model to ensure fairness. A mathematical model is proposed, then a numerical study is conducted to test the model's performance. The results show that the proposed model can perform well in large-scale problems with significant demands. A comprehensive analysis is conducted on the real-case historical medical data. Then a discrete event simulation system is built. The framework of a discrete event simulation model can mimic the evolution of the entire operation of an emergency response system over time. Finally, the proposed model and discrete event simulation system are applied to the real-case historical medical data. Three different categories of performance measurements are collected, analyzed, and compared with the real-case data. A comprehensive sensitivity analysis is conducted to test the ability of the model to handle different situations. The final results illustrate that the proposed model can improve overall performance in various evaluation metrics.
A NOVEL APPLICATION OF SELECT AGILE CONCEPTS AND STOCHASTIC ANALYSIS FOR THE OPTIMIZATION OF TRAINING PROGRAMS WITHIN HIGH RELIABILITY ORGANIZATIONS IN HIGH TURN-OVER ENVIRONMENTS AT EDUCATIONAL INSTITUTES AND IN INDUSTRY
(2023) Blanton, Richard L; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
High-turnover environments have been extensively studied with the bulk of the literature focusing on the negative effects on business operations.[1] They present challenges to the resilience of the organization while also limiting the potential profitability from consistently having to spend time training new staff. Furthermore, in manufacturing environments inexperienced staff are prone to mistakes and uncertainty, which can lead to increases in scrap materials and lower production rates due to a lack of mastery of the process. From an organizational standpoint a high-turnover environment presents an unmitigated risk to the organization from the continuous loss of institutional knowledge. This loss can present challenges to the organization in numerous ways, such as capital equipment that no longer has staff qualified or experienced enough to use it leading to costly retraining by the manufacturer or increased risk of a catastrophic failure resulting in damage to the equipment and or injury to the staff. Furthermore, the loss of institutional history leads to the loss of why operations are performed a certain way. As the common saying goes, “those who forget history are bound to repeat it.” which can lead to substantial costs for the organization while old solutions that were previously rejected due to lack of merit are constantly rehashed due to a lack of understanding of how the organization arrived at its current policies. This thesis presents a novel framework to mitigate the potential loss of institutional knowledge via a multifaceted approach. To achieve this a specific topic was identified and used to frame questions that guided the research. Mitigation of the negative impacts of high-turnover in manufacturing environments with a specific focus on the optimization of training programs. This topic led to the formulation of the following research questions. What steps can be taken to reduce the chance of lost institutional knowledge in a high-turnover environment? What steps can be taken to reduce the time needed to train a high performing replacement employee, while maintaining strict performance and safety standards? What steps should be taken to improve the accuracy of budgetary projections? What steps need to be taken to enable accurate analysis of potential future investment opportunities in a training program. The answers to the above research questions are compiled and presented with the aim to provide professionals, who are responsible for training programs in high-turnover environments that require a high organizational reliability, with a framework and analysis toolset that will enable data-driven decision making regarding the program. Additionally this thesis provides a framework for addressing the continuous risk of loss of institutional knowledge. When contrasted with a standard training model, where a trainee is presented with new material and then tested for retention before moving to the next topic, the proposed model implements a schema that can be rapidly iterated upon and improved until the desired performance outcome is achieved, while increasing the potential accuracy of budgetary estimation by as much as 57%. Throughout the process, decision makers will have insight into the long term effects of their potential actions by way of running simulations that give insight into not only the expected steady-state cost of a program but also the rough volume of trainees required to achieve that steady-state.
DATA-DRIVEN RISK MODELING FOR INFRASTRUCTURE PROJECTS USING ARTIFICIAL INTELLIGENCE TECHNIQUES
(2023) Erfani, Abdolmajid; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Managing project risk is a key part of the successful implementation of any large project and is widely recognized as a best practice for public agencies to deliver infrastructures. The conventional method of identifying and evaluating project risks involves getting input from subject matter experts at risk workshops in the early phases of a project. As a project moves through its life cycle, these identified risks and their assessments evolve. Some risks are realized to become issues, some are mitigated, and some are retired as no longer important. Despite the value provided by conventional expert-based approaches, several challenges remain due to the time-consuming and expensive processes involved. Moreover, limited is known about how risks evolve from ex-ante to ex-post over time. How well does the project team identify and evaluate risks in the initial phase compared to what happens during project execution? Using historical data and artificial intelligence techniques, this study addressed these limitations by introducing a data-driven framework to identify risks automatically and to examine the quality of early risk registers and risk assessments. Risk registers from more than 70 U.S. major transportation projects form the input dataset. Firstly, the study reports a high degree of similarity between risk registers for different projects in the entire document of the risk register, and the probability and consequence of each risk item, suggesting that it is feasible to develop a common risk register. Secondly, the developed data-driven model for identifying common risks has a recall of over 66% and an F1 score of 0.59 for new projects, i.e., knowledge and experience of similar previous projects can help identify more than 66% of risks at the start. Thirdly, approximately 65% of ex-ante identified risks actually occur in projects and are mitigated, while more than 35% do not occur and are retired. The categorization of risk management styles illustrates that identifying risks early on is important, but it is not sufficient to achieve successful project delivery. During project execution, a project team demonstrating positive doer behavior (by actively monitoring and identifying risks) performed better. Finally, this study proposes using a data-driven approach to unify and summarize existing risk documents to create a comprehensive risk breakdown structure (RBS). Study results suggest that acquired knowledge from previous projects helps project teams and public agencies identify risks more effectively than starting from scratch using solely expert judgments.

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