Now showing 1 - 5 of 676
- ItemIn 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.
- ItemANALYZING 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.
- ItemSELECTION 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.
- ItemREAL-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.
- ItemA 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. 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.