Civil & Environmental Engineering Theses and Dissertations

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    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.
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    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.
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    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.
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    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.
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    (2023) LIU, ZIYUE; Bensi, Michelle; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In the past decades, coastal compound floods (CCF) caused significant losses to coastal communities. To develop an accurate and complete probabilistic framework for assessing coastal hazards, the U.S. Army Corps of Engineers established the Coastal Hazard System’s Probabilistic Coastal Hazard Analysis (PCHA) framework. The current PCHA framework has focused primarily on a subset of CCF drivers, particularly storm surges.This dissertation documents four studies that contribute to efforts to advance and extend the PCHA for CCF analysis. In the first study, machine learning-based data imputation models are developed to fill in missing records in the historical storm dataset. The performance of the machine learning-based data imputation models under different parameterizations are assessed considering statistical and physical factors and also compared against existing prediction models. In the second study, a series of Joint Probability Method (JPM) assumptions to model the dependence among tropical cyclone (TC) atmospheric parameters are comparatively investigated. JPM assumptions considered in the analysis include parameter independence, partial dependence, and full dependence. Candidate full-dependence models include meta-Gaussian copula and vine copulas combining linear-circular copulas. Emphases are put on modeling the circular behavior of storm heading and its dependencies with other linear parameters. Full dependence models are compared based on the predicted probability of large CPD and RMW combinations. In the third study, a Bayesian Network (BN) of multiple coastal hazards is constructed, where the conditional probability tables (CPT) of TC atmospheric parameters are computed using copula. A deaggregation methodology is developed to identify the dominant TC for significant coastal hazard events to support risk-informing and decision-making processes and refined analyses. In the fourth study, leveraging the aforementioned study results, the extended PCHA is leveraged to develop a multi-tiered BN CCF analysis framework. In this framework, multiple tiers of BN models with different complexities are designed for study cases with varying levels of resource availability. A case study is conducted in New Orleans, LA and a series of joint distribution, numerical, machine learning, and experimental models are used to compute CPTs needed for BNs.
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    (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.
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    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.
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    (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.
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    (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.
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    (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.
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    (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.
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    (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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    (2023) Sun, Qianqian; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Truck travel estimation plays an essential role in the transportation field. Nationwide truck flows are particularly important for capturing long-distance truck travels. For the estimation at such a scale, the traditional way of conducting surveys is very costly and cumbersome. Nowadays, GPS data are getting popular for supporting transportation studies, with advantages of freshness, cost-effectiveness, real-world representation, high spatial-temporal coverage and resolution. Hence, utilizing GPS data as an alternative data source is worth investigating. This study proposes a comprehensive framework for achieving large-scale truck flow estimation from passive GPS data, with the United States as a study case. This study enriches the research on GPS-based travel estimation and particularly achieves the estimation at a scale as large as the United States for the first time using GPS data. The framework begins with thorough data preparation, in which an enhanced algorithm is designed for removing data oscillations. Then, truck type classification by weight class is conducted through a random forest (RF) algorithm, which enriches GPS-based vehicle classification research. The estimation is by truck type, which provides unique travel patterns by truck type. Then, a comparative trip identification by truck type is conducted and the algorithm’s robustness for such identification is investigated. Finally, an innovative weighting algorithm that integrates reinforcement learning and iterative origin-destination matrix estimation (ODME) is designed to weight the sample truck traffic according to the U.S. truck traffic population level and to mitigate the spatial bias of sample GPS data. Nationwide truck flow estimation is achieved. The results’ reasonableness is discussed from multiple aspects, such as ODME accuracy, spatiotemporal biases, distance distribution, OD distribution, vehicle miles traveled, and interstate OD pairs from selected states. The products obtained from the framework are useful for many transportation studies, such as planning and operation, safety, transportation and environment, and policies. The framework not only enables large-scale truck flow estimation but also yields good accuracy and does not require excessive computation cost. It is straightforward and has a high generalizability for studies of various scales and areas. It should be widely applicable for serving transportation research and practice needs.
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    (2023) Sheikhbahaei, ALIMATTHEW; Aggour, M. Sherif; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Micropile groups are commonly used as a foundation support in many applications in geotechnical engineering. This study presents a three-dimensional finite element analysis of micropile group model subjected to vertical dynamic loads from a machine foundation. A modified Drucker-Prager constitutive model was used to simulate the nonlinear behavior of the soil as well as the soil-micropile interaction. The soil continuum was modeled using solid continuum elements in the FEM model, while micropiles were represented using beam elements and appropriate interaction properties were assigned to the interface elements. The accuracy of the model developed was verified by its application to the published experimental data.A series of parametric studies were conducted to examine the effects of different parameters on the behavior of the soil-micropile system, including soil nonlinearity, inclination angle, spacing of the micropiles in the group, soil shear/micropile stiffness ratio, machine foundation mass, and the frequency content of the applied load, etc. The obtained results offered valuable insights into the influence of each parameter on the response of micropile groups. The results of these studies demonstrated the effectiveness of the modified Drucker-Prager constitutive model and the three-dimensional finite element analysis in predicting the behavior of micropile groups under dynamic loading. This study contributes to advancing the understanding of the behavior of micropile groups and their interaction with soil under vertical dynamic loading conditions, and it provides criteria for an improved design of the micropile groups under machine foundation loads. Keywords: micropile groups, machine foundations, three-dimensional finite element analysis (3D FEA) , modified Drucker-Prager, soil-micropile interaction.
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    (2023) Waters, Linda; Reilly, Allison C; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The level of outlays that individuals and communities receive following a disaster strongly influences the rapidity and the degree to which they ultimately recover. While there is no prescribed formula for the level of cumulative federal aid a community will receive following a disaster, one might expect it to be relatively proportional to the amount of damage sustained, in part because most recovery programs are primarily based on sustained damage. However, a simple analysis of the fraction of damages that are later restituted by federal disaster aid (which we call “federal disaster coverage”) for all major hurricanes hitting the U.S. between 2008 and 2017 shows that this fraction is highly variable. For some storms, the county-level variation is more than six orders of magnitude. In this work, we investigate the county-level correlates of higher rates of federal disaster coverage. We do this by answering (1) What community and hazard characteristics are important predictors of counties that receive aid but that do not incur damage? and (2) Where damage is incurred, what community and hazard characteristics in a county influence federal disaster coverage? We find that counties that receive aid but have no reported damage are more likely to experience greater storm intensity and more hazard exposure than observations that do not receive aid, suggesting that these counties’ damages are unreported. Concerningly, these counties also exhibit greater social vulnerability and less local capacity. We also find that federal disaster coverage decreases as per capita damage increases, which has two interpretations. First, this could suggest that more severe disasters receive less marginal aid than less severe disasters. Alternatively, should damage among counties be held equivalent, the result suggests that less populous counties receive less federal disaster coverage. This may reflect the predominance of federal disaster aid being aimed toward the recovery of public infrastructure, of which rural communities have less. Our findings regarding how social vulnerability relates to federal disaster coverage are mixed. Some variables show that greater social vulnerability increases the likelihood of receiving higher federal disaster coverage, while others show a decrease. We find that greater local capacity consistently increases the likelihood of receiving more federal disaster coverage. Overall, our findings suggest some level of disparities in disaster loss reporting and federal aid disbursement among counties. In particular, areas with higher social vulnerability and lower local capacities are more likely to have unreported losses and receive less federal disaster coverage. Federal agencies (such as FEMA and HUD) should ensure these communities have sufficient access to and support during the federal aid application process to improve outcomes.
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    (2023) Imonitie, Livingstone; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Walking and cycling have numerous health benefits, but these popular modes of transportation are prone to numerous collisions with motor vehicles. The goal of this study is to examine some of the factors that contribute to the severity of crashes in Maryland, which include property crashes, injury crashes, and fatal crashes. The light condition, junction condition, road surface condition, lane type, road condition, road division type, weather condition, time of day, population density, and the presence of schools were all considered. To demonstrate the relationship between each variable and the severity of the crash, the ordered logistic regression model was used. According to the findings, there was a positive significant relationship between the severity of crashes and crashes that occurred in areas with no lighting, at non-intersections, and on roadways with a positive median barrier. The frequency of crashes in various regions was also influenced by population density, time of day, and the presence of schools.
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    Nonlinear Numerical Simulation Study and Regional-Scale Seismic Resilience Assessment of Self-Centering Systems with Sliding Rocking Link Beams
    (2023) Rezvan, Pooya; Zhang, Yunfeng Y.Z.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    New trends in seismic design have resulted in proposals for several innovative seismic protection strategies, among which the concept of damage-free self-centering systems has received considerable attention. The concept involves the use of a re-centering element to bring the structure to its initial position and fuse devices for energy dissipation while the primary structural system is designed to be damage free under design basis earthquake. One challenge in implementing certain types of self-centering structural systems is the “gap-opening expansion” phenomenon which is the expansion of the frame when gap-opening at beam ends happens and causes large axial compression force in the beam and may damage the floor diaphragm.In this study, the “sliding rocking link beam” mechanism has been introduced to overcome the beam-growth issue in self-centering systems. Three high-performance systems of self-centering eccentrically braced frames with sliding rocking link beams (SCEBF-SRLs), self-centering moment-resisting frames with sliding rocking beams (SCMRF-SRBs), and self-centering modular bracing panels (SCMBPs) were developed by adopting such mechanism. The energy dissipation of the developed systems is mainly provided by replaceable hysteretic damping (RHD) devices. In the SCEBF-SRL and SCMBP systems, their recentering capability is enabled by adopting post-tensioned (PT) steel-stranded cables; and in the SCMRF-SRBs its restoring force is provided by preloaded disc springs to facilitate the pre-compression process of the rocking beam. Analytical load-displacement relationships of the three systems were formulated and cross-verified with nonlinear 3D continuum finite element (FE) analysis results, and their seismic performance was studied through nonlinear static and dynamic analysis of prototype buildings subjected to far-field and near-fault ground motions. Parametric studies were conducted to investigate the effect of key design parameters on the seismic performance of the structures. Considering the applicability of the numerical simulation and computational efficiency, four types of finite element models (FEMs) with varying levels of fidelity were developed for SCMBP systems. Additionally, the soil-structure interaction (SSI) effect on mitigating the seismic demands of the SCMBP prototype building has been studied by simulating the soil stiffness with distributed nonlinear springs as a discretized continuous medium based on the Winkler foundation method. Lastly, a digital twin framework with a python-based computational procedure was developed for performing an intensity-based seismic resilience assessment of SCMBP buildings on a regional scale. This digital twin model can also be extended to any other type of infrastructure system. The seismic damage and loss assessment is performed in accordance with the component-level FEMA P-58 methodology and the resilience metrics (e.g. repair cost, repair time, and probability of irreparability) are visualized on a geographical information system software. As a case study, the regional seismic resilience of nearly 2000 school buildings equipped with SCMBP systems was investigated for a region covering the Bay area near San Francisco, California.
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    (2023) Zhao, Yunpeng; Goulias, Dimitrios G; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Improving the performance and extending the service life of transportation infrastructure is a long standing goal of Federal Highway Administration (FHWA) and the transportation community. Accurate prediction of the mechanical properties of highway materials are indispensable for enhancing the sustainability and resilience of transportation infrastructure since it provides accurate inputs for pavement mechanistic-empirical (ME) design and prediction of pavement distresses, helping to optimally allocate the maintenance needs and reduce testing frequencies which account for costly expenditures. Accurate prediction of materials properties can also reduce the acceptance risks during quality assurance (QA) without conducting extensive testing. Concrete plays an important role in the construction of transportation infrastructure. Developing an empirical and/or statistical model for accurately predicting compressive strength remains challenging and requires extensive experimental work. Thus, the objective of the study was to improve the prediction of concrete compressive strength using ML algorithms. A ML pipeline was proposed in which a two-layer stacked model was developed by combining seven individual ML models. Feature engineering was implemented, and feature importance was evaluated to provide better interpretability of the data and the model. This study promotes a more thorough assessment of alternative ML algorithms for predicting material properties. In addition, the quality of highway materials and construction translate directly to performance. To develop a statistically sound QA specification, the risks to the agency and contractor must be well understood. In this study, a Monte Carlo simulation model was developed to systematically assess the acceptance risks and the implications on pay factors (PF). The simulation was conducted using typical acceptance quality characteristics (AQCs), such as strength, for Portland cement (PCC) pavements. The analysis indicated that specific combinations of contractor and agency sample sizes and population characteristics have a greater impact on acceptance risks and may provide inconsistent PF. The proposed methodology aids both agencies and producers to better understand and evaluate the impact of sample sizes and population characteristics on the acceptance risks and PF. Finally, the use of recycled materials is a key element in generating sustainable pavement designs to save natural resources, reduce energy, greenhouse gas (GHG) emissions and costs. This study proposed a methodological life cycle assessment (LCA) framework to quantify the environmental and economic impacts of using recycled materials in pavement construction and rehabilitation. The LCA was conducted on two roadway projects with innovative recycled materials, such as construction and demolition waste (CDW) and rock dust. The proposed LCA framework can be used elsewhere to quantify the environmental and economic benefits of using recycled materials in pavements.
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    (2023) Akhter, Anjuman Ara; Goulias, Dimitrios; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In recent years transportation agencies have been focusing on performance-based asphalt mixture design to ensure durable pavements. Including performance in the design, phase allows the prediction of expected distresses, such as fatigue cracking, permanent deformation, and moisture damage. The main objective of this study was to identify a new approach to include performance testing in asphalt mixture design for the state of Maryland. The following specific objectives were identified to achieve this: (i) identifying the cracking and rutting criteria for asphalt mixtures in Maryland; (ii) assessing the repeatability of the selected performance tests; (iii) establishing model-based performance predictive approach for designed asphalt mixtures; (iv) adopting a non-destructive testing method (i.e., Ultrasonic Pulse Velocity – UPV) in Quality Assurance (QA) of asphalt mixtures. Two well-accepted and suitable performance tests for Maryland conditions were selected to address the first objective. These tests included the IDEAL Cracking Test (IDEAL-CT) for fatigue cracking and the High-Temperature Indirect Tensile Strength Test (HT-IDT) for permanent deformation. Such performance index tests were combined with volumetric requirements and benchmark analysis. Since mixture properties affect each of these typical distresses in asphalt mixtures and pavements differently, a Balanced Mix Design approach was adopted, BMD. The sources of variability in testing were quantified through round-robin testing between laboratories for the second objective. Based on the results and findings, an adjustment procedure was developed. For the third specific objective, a methodology was proposed for predicting field performance from laboratory testing and mixture volumetrics considering (i) well-accepted prediction models by the research community and (ii) fundamental asphalt material behavior parameters representing mix quality and well-related to performance. A sensitivity analysis of UPV regarding mixture volumetrics and testing conditions was carried out for the final objective. The resulting asphalt mixture stiffness from such an evaluation was then compared to the results from traditional destructive testing for pertinent conclusions. Based on these analyses and results, a framework was proposed for adopting UPV in the BMD mix design approach developed in this study. The research and methodology developed in this study can be used elsewhere, where similar materials are used.
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    (2023) Hu, Songhua; Schonfeld, Paul; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Traditional travel demand models heavily rely on travel surveys, simplify future demand forecasting, and show low sensitivity in response to spatiotemporal dynamics. This study proposes a deep-learning-driven framework based on mobile device location data (MDLD) for estimating and predicting large-scale travel demand at both individual and aggregated levels. This study first introduces how raw MDLD should be parsed to distill trip rosters and estimate population flow. Based on derived information, this study reexamines relations between average population flow and its determinants such as built environment, socioeconomics, and demographics, via a set of explainable machine learning (EML) models. Different interpretation approaches are employed and compared to understand nonlinear and interactive relations learned by EML models. Next, this study proposes a Multi-graph Multi-head Adaptive Temporal Graph Convolutional Network (Multi-ATGCN), a general deep learning framework that fuses multi-view spatial structures, multi-head temporal patterns, and various external effects, for multi-step citywide population flow forecasting. Multi-ATGCN is designed to comprehensively address challenges such as complex spatial dependency, diverse temporal patterns, and heterogeneous external effects in spatiotemporal population flow forecasting. Last, at an individual level, this study proposes a Hierarchical Activity-based Framework (HABF) for simultaneously predicting the activity, departure time, and location of the origin and destination of the next trip, incorporating both internal (individual characteristics) and external (calendar, point-of-interests (POIs)) information. For each individual, HABF first predicts activities via an Interpretable Hierarchical Transformer (IHTF). IHTF can efficiently handle big data benefiting from its transformer-based design to avoid recursion. Meanwhile, loss functions used in semantic segmentation are introduced into IHTF to address imbalanced distributions of activity types. Then, a local plus global probabilistic generator is designed to generate locations based on predicted activities and historical places, allowing individuals to visit new or historically-sparse places. Analyses are performed on several real-world datasets to demonstrate the model's capability in forecasting large-scale high-resolution human mobility in a timely and credible manner. Altogether, this study provides sound evidence, practically and theoretically, of the feasibility and reliability of realizing data-driven travel demand estimation and prediction at different spatiotemporal resolutions and scales.