Civil & Environmental Engineering

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    ASSESSING BEST PRACTICES FOR REFORESTATION OF AREAS DEGRADED BY ARTISANAL AND SMALL-SCALE GOLD MINING IN THE PERUVIAN AMAZONIAN REGION OF MADRE DE DIOS
    (2024) Rodriguez Pascual, Maria Jose; Torrents, Alba; Andrade, Natasha; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Artisanal and Small-Scale Gold Mining (ASGM) have pressured the Peruvian Southern Amazon rainforest, causing deforestation, soil degradation, and mercury (Hg) emissions in large areas. The Peruvian government and NGOs have performed reforestation projects in areas degraded by ASGM in this region using native plant species with an economic value. Previous research has studied the restoration of areas degraded by ASGM in this region. However, there is lack of information about the Hg's distribution, accumulation, and the effects of Hg exposure in native plants in this region. Additionally, few studies have investigated the recovery of soil fertility in these degraded areas during restoration.In this dissertation, the distribution and predictors of Hg accumulation in soil and native plant species from artisanal mining sites and the primary forest near these sites were studied. The highest Hg concentrations in soil were found in the intact primary forest topsoil and the plant rhizosphere area. The highest Hg levels in plants were found in the foliage of the intact primary forests. The Hg levels found in the plant leaves of the primary forest are the highest ever recorded in this region, exceeding values found in forests impacted by Hg pollution worldwide and raising concerns about the extent of the ASGM impact in this ecosystem. The effects of Hg exposure on the survival, growth, health, and rhizosphere microbial communities of three Amazonian agricultural plant species, aji dulce (Capsicum Chinense), sacha culantro (Eryngium foetidum), and uncucha (Xanthosoma sagittifolium L. Schott) were also investigated. The lowest observable concentration of Hg affecting the plant’s health was 2 mg kg-1 dw. This Hg concentration was three times lower than the soil screening level (SSL) for Hg in agricultural soil established in the Peruvian regulation. This suggests that a review of this SSL may be necessary before developing restoration projects in Amazonian areas impacted by ASGM. The soil physic-chemical characteristics, such as soil organic matter (SOM), electrical conductivity (EC), pH, nutrients, and bacterial communities, in the rhizosphere soil of plant species growing in naturally regenerated and reforested areas impacted by ASGM and in the primary forest near these areas were assessed. The results suggest that the plant species Acacia loretensis and Inga sp. (Inga sp. when planted with biochar as a soil amendment) might be good candidates for restoring areas degraded by ASGM, due to the level of nutrients and SOM in their rhizosphere. Finally, the integration of Indigenous and scientific knowledge to monitor and manage land degradation in regions impacted by ASGM was also studied. The findings emphasize the need to use the Indigenous communities’ knowledge of their territory for the early detection of soil degradation and facilitate a dialogue about land degradation and restoration of areas affected by ASGM between local communities, researchers, and policymakers to develop more sustainable and successful restoration projects in Indigenous communities affected by ASGM.
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    DESIGN MODIFICATIONS TO MINIMIZE POLLUTANT LEACHING FROM COMPOST-AMENDED BIORETENTION
    (2024) Lei, Lei; Davis, Allen P.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Bioretention is an effective stormwater control measure (SCM) recognized for its ability to capture and treat urban runoff within a shallow basin using engineered soils and vegetation. Bioretention studies at laboratory and field scales have shown good to excellent removal efficiency for heavy metals (> 80% to > 90%) and have observed high variablility ranging from negative (net export) to 99% in phosphorus and nitrogen removals. Water quality studies have shown that media selection for bioretention is critical in determining pollutant removal.Incorporating compost into bioretention media is an eco-friendly strategy that not only diverts organic waste from landfills but also provides several benefits improving the performance of bioretention system. This approach enriches the media with organic matter and nutrients for vegetation, boosts water holding and cation exchange capacity, stabilizes the soil structure, and improves the retention of pollutants. However, careful management is essential to mitigate the potential releasing pollutants, including dissolved organic matter (DOM), soluble nutrients, and metals readily associated with DOM, particularly if the compost is derived from biosolids... To maximize the benefits of compost in bioretention, special design modifications aimed at enhancing pollutant removal should be implemented. The objective of this research was to investigate ways to optimize the use of compost in bioretention while minimizing pollutant leaching, Design modifications investigated include layering compost over media, aluminum-based drinking water treatment residual (Al-WTR) addition, and incorporation of an internal water storage (IWS) layer. Treatment performances were evaluated through extractions, batch adsorption studies, large column mesocosms, and column media characterizations. Al-WTR amendment improved sorption of phosphorus, copper and zinc, with capacities increasing from 22.5 mg/kg to 161 mg/kg and 193 mg/kg for P, from 121 mg/kg to 166 mg/kg and then to 186 mg/kg for Cu, and from 121 mg/kg to 166 mg/kg and 186 mg/kg for Zn with 0%, 2% and 4% Al-WTR additions. The multilayered system containing a compost incorporated top layer and an Al-WTR amended bottom layer showed good removal of phosphorus (94% and 96%), copper (88% and 86%) and zinc (92% and 96%), and enhanced nitrogen retention (74.1%) from the stormwater load compared to a mixed system (32.8%) as reported by Owen et al. (2023). The installation of an IWS layer did not show statistically significant influences on phosphorus (91% to 93%, p > 0.05), copper (66% to 90%, p > 0.05) or zinc (94% to 95%, p > 0.05) removals, had limited effect on nitrogen retention from stormwater load during storm events (-117% to -188%, p > 0.05), but promoted denitrification during dry periods. With the IWS layer installed, high levels of iron leaching (130 to 11800 µg/L) were detected, likely due to change in the redox potential (from aerobic to anaerobic). With the objective of removing phosphorus and heavy metals from the stormwater, 5.3% of compost (by dry mass) can be used when layering compost over the Al-WTR amended bioretention media. When the design goal is to remove nitrogen, a fraction of compost up to 2.6%, by dry mass can be used, with layering the compost over the bioretention media and an IWS installed at the bottom.
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    A Smart Traffic Incident Management (TIM) System for Estimating Highway Incident Duration and Impacts with and without Surveillance Sensors
    (2024) Huang, Yen-Lin; Chang, Gang-Len G.L.C; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Highway incidents are major contributors to traffic congestion, causing significant delays for daily roadway users and reducing the reliability and productivity of transportation systems. To mitigate the negative impacts of these incidents and quickly restore highway operations, it is crucial for highway agencies to implement an efficient incident management system. However, providing the public with real-time information about the impacts of incidents at a desired level of precision is challenging due to the complexities involved in obtaining sufficient data and understanding the intricate relationships among the factors that influence these impacts. To address this challenge, this study proposes a Smart Traffic Incident Management (TIM) system that delivers robust and reliable information on estimated clearance durations, resultant queue lengths, time-varying traffic information, and traffic detouring volumes from freeways to adjacent arterials. This initiative aims to improve the effectiveness of incident response, thereby enhancing the resilience and functionality of the transportation network. The proposed system consists of four primary modules. Module 1 aims to robustly predict incident clearance duration through the proposed Knowledge Transferability Analysis (KTA) model, featuring its automated process for assessing, selecting, and transferring existing prediction rules from pre-existing Incident Duration Prediction Models (IDPM). This strategic utilization obviates the necessity for integrating field operators' expertise in formulating prediction rules, thereby alleviating the dependency on an ample volume of incident records for prediction rules calibration. The evaluation results, using I-70 in Maryland for the case study, have demonstrated the effectiveness of the proposed KTA model in not only expediting the development process of an IDPM but also improving the resulting accuracy of the prediction rules. Module 2 endeavors to robustly predict incident queue length by introducing the Real-time Incident Queue Prediction (R-IQP) system. This system's principal model enhances the formulations for queue propagation dynamics by incorporating the influences of incoming drivers' perceptions and responses to progressively constrained traffic conditions. Additionally, two supplementary models are proposed to precisely estimate flow rates, leveraging probe speed information, to accommodate different surveillance environments characterized by varying levels of data availability. The evaluations of the proposed R-IQP system with both the field-collected data and the well-calibrated simulator’s data have proven the capability of the R-IQP system on predicting time-varying queue lengths for incidents with various clearance durations and types of lane blockage statuses. Module 3 introduces a traffic flow model specifically designed for traffic incident management. The proposed Incident-oriented METANET (I-METANET) enhances the widely used METANET model with three key improvements: 1) reflecting the merging behaviors incurred by incidents and their significant yet diminishing effects on speed propagation over upstream segments; 2) incorporating the simultaneous effects of upstream traffic flows and downstream incident-induced queue waves on the speed of a subject segment; and 3) integrating the combined effects of ramp-flow weaving maneuvers and the presence of incident queues on traffic conditions at interchange segments. The proposed I-METANET model, calibrated and validated using field data from I-4 in Florida, has demonstrated its effectiveness in predicting time-varying speeds and flow rates over roadway segments during incident clearance periods. Module 4 focuses on assessing the impact of freeway incidents on nearby local roads. To achieve this, the study developed a Real-time Detour Volume Estimation (R-DVE) system, designed to estimate the volume of traffic diverted from the freeway mainline to its adjacent arterials, even when freeway traffic sensors are unavailable. This system leverages a set of offline speed-flow models developed in Module 2 to estimate traffic flow using probe speed data as input. Additionally, the proposed R-DVE incorporates a Quality Assessment Mechanism (QAM) that integrates a robust customized dynamic speed-flow relations (CDSFR) developed in Module 3 to continually examine the applicability of the estimated flow rates and update the offline speed-flow models. The performance evaluation, based on real-world incident cases on I-95 in Maryland, demonstrated that the R-DVE system can accurately estimate real-time detouring volumes, highlighting its practical applicability. The proposed Smart Traffic Incident Management (TIM) system, delineated through its comprehensive modules, embodies several key features aimed at enhancing incident management efficacy, including 1) providing a systematic decision-making framework for incident clearance duration prediction, particularly valuable for highways lacking sufficient incident records to calibrate prediction rules; 2) incorporating predicted clearance duration to generate timely estimates of incident queue length, with the adaptive capability particularly beneficial for highways under varying levels of traffic sensor availability; 3) predicting time-varying traffic information to faciltiate better incident management and responsive strategies; 4) generating real-time estimates of detour volume originating from each interchange within the impact area, facilitating the execution of appropriate responsive operations contributing to efficient incident management; and 5) exhibiting a dynamic nature by updating estimated information when additional data become available or when there are changes in traffic dynamics or incident clearance operation to ensure the continuous relevance and accuracy of the provided information.
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    BEYOND PEAK RATE FACTOR 484: USING RADAR RAINFALL, GAUGED STREAMFLOW, AND DISTRIBUTED WATERSHED MODELING TO INVESTIGATE PARAMETERS OF THE NATURAL RESOURCES CONSERVATION SERVICE CURVILINEAR UNIT HYDROGRAPH
    (2024) Shehni Karam Zadeh, Mani; Brubaker, Kaye L.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Accurate estimation of runoff and peak discharge is crucial in hydrology for engineering design and flood management. The Natural Resources Conservation Service’s (NRCS) Unit Hydrograph (UH) is a widely used model to predict the runoff response of an ungauged watershed to a precipitation event. The NRCS UH model makes use of a Peak Rate Factor (PRF) to quantify the peak discharge. The standard value of PRF is 484; however, PRF can be adjusted as a user input variable in NRCS tools such as the WinTR-20 software. Little guidance is available to appropriately estimate PRF for specific regions and evaluate its overall usefulness in the runoff and peak discharge estimation. Time of concentration (tc) is another input variable in the NRCS UH model; inconsistent definitions of tc and diverse methods of calculating it contribute to uncertainty in hydrologic estimates and predictions. The NRCS UH approach assumes that the watershed’s temporal runoff response to each increment of precipitation is identical in shape and proportional to precipitation excess in that increment of time. The UH, PRF, and tc are often assumed to be time-invariant properties of a watershed. This dissertation sought to improve the knowledge and understanding of PRF and tc. First, it evaluated if a unique UH and tc exist for a given watershed from various storm events. It then assessed whether variations in PRFs can be explained by watershed predictor variables and if PRFs in neighboring watersheds followed a local trend. This phase of study employed a gamma function representation of the NRCS UH, with two parameters: time to peak (tp) and shape (m). Precipitation inputs were watershed-averaged time series of NEXRAD level III data, and streamflow data were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). The UHs were derived from a constrained optimization approach, and PRF and tc were estimated for each event. Subsequently, a fully distributed model was created to provide insight on PRF and tc, and investigate the impact of detailed soil profiles on runoff and peak discharge. Finally, a fully distributed model was applied to simple, synthetic watersheds to investigate the impact of selected watershed parameters on PRF, time to peak, peak discharge and overall shape of the UH. To the best of the author's knowledge, this study is the first attempt to generate UHs from a simple distributed model and estimate associated PRFs. The findings suggest that there is no unique UH and tc for a given watershed, and UH shape and parameters change for every event in a given watershed. Additionally, the variations in PRFs cannot be explained by variations in selected watershed predictor variables. The distributed model results provided insights about the application of detailed soil profiles in runoff and peak discharge estimation. The findings also suggest that, except for Manning's roughness, selected watershed characteristics cannot be used to estimate PRF in a synthetic V-shaped watershed. These findings suggest that the application of PRF to estimate peak discharge should be used with caution due to the inherent uncertainties and lack of physical meaning of the parameter.
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    NATURAL LANGUAGE PROCESSING, SOCIAL MEDIA, AND EPIDEMIC MODEL-ING FOR WILDFIRE RESPONSE AND RE-SILIENCE ENHANCEMENT
    (2024) Ma, Zihui; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Effective disaster response is critical for communities to remain resilient and advance the development of smart cities. Responders and decision-makers benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and behaviors during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. A comprehensive literature review of natural language processing (NLP) of social media data in disaster management, covering 324 articles published between 2011 and 2022, revealed a gap in applying NLP techniques to wildfire scenarios. Meanwhile, the increasing frequency of wildfires highlights the need for advanced management tools. To address this, we integrated the BERTopic and SIR models to capture public responses on Twitter during the 2020 western U.S. wildfire season, analyzing both the magnitude and velocity of topic diffusion. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The parameters estimated from the SIR model for selected cities revealed that residents expressed various levels of concern or demand during wildfires. The study also demonstrated a practical framework for utilizing social media data to aid wildfire evacuations. Through social network analysis, we clarified the roles of key information disseminators and provided guidelines for extracting high-priority information. Although biases in social media and model limitations exist, the study offers qualitative and quantitative approaches to investigate wildfire response and sup-port community resilience enhancement.
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    ACCELERATING RESTORATION THROUGH INFORMATION-SHARING: UNDERSTANDING OPERATOR BEHAVIOR FOR IMPROVED MANAGEMENT OF INTERDEPENDENT INFRASTRUCTURE
    (2024) Yazdisamadi, Mohammadreza; Reilly, Allison C.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation examines the roles that organizations and individuals play in restoring interdependent infrastructure following disasters through three studies. In the first study, we focus on how operator heuristics affect the collective restoration speed of three interdependent infrastructure (electric power, chilled water, and IT networks). We do this by developing a novel framework that embeds an interdependent infrastructure network within an agent-based model that mimics the decisions and patterns observed of actual operators. The study sheds light on how coordination and information exchange by separate infrastructure parties affect decisions and thus restoration outcomes. In the second study, we examine recovery times and total unmet demand for the same three interconnected infrastructure systems assuming a variable fraction of node removals. The work is decomposed by the extent to which operators share information and coordinate strategies, enabling us to identify at what fraction of network failure does coordination and information sharing become beneficial. Our study indicates that prioritizing restoration based on node centrality produces the speediest recovery. We also show that communication among organizations may improve collective performance by as much as 50%. Our final research project uses a serious game, Breakdown, focused on restoration of interdependent infrastructure to assess whether engineering graduate students gain a deeper appreciation for the complexity of interdependent infrastructure and socio-technical systems more broadly. This is the first serious game designed to emphasize the value of cooperation, communication, and strategy in times of crisis in the field of interdependent infrastructure. As a result of playing Breakdown, graduate students demonstrated statistically significant improvements in engineering decision-making under uncertainty and sociotechnical systems concepts. As a result of this dissertation, the interdependent infrastructure community gains insight into (1) how individual operators' behavior influences the speed at which interdependent infrastructure systems recover; (2) how policies and procedures, like sharing information and cooperating, can help improve outcomes; and (3) the ways to teach graduate engineering students about socio-technical systems effectively. Using an agent-based model simulation, it quantifies the effects of human behavior, communication, and cooperation on recovery outcomes. By using a serious game, Breakdown, it proposes an innovative way to teach graduate engineering students about socio-technical systems.
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    Evaluation of Roadside Soil Compaction and Restoration Practices on Vegetation Growth and Water Quality
    (2024) Cunningham, Mikayla; Davis, Allen P.; Aydilek, Ahmet H.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The construction of roads using heavy equipment and cut-and-fill methods leads to heavily compacted roadside soils with low fertility, sparse vegetation, low water infiltration rates, and high erodibility. Poor post-construction vegetation and soil quality lead to higher runoff volumes with higher sediment and nutrient loads to local water bodies. Cost-effective methods are needed to restore roadside soils, establish sufficient vegetative cover, and maintain runoff water quality. A research project was undertaken to assess topsoil application, tillage, and yard waste compost amendment as means of restoring roadside soil quality. A 28-week pot study was used to test how topsoil depth, initial soil density, compaction from mowing equipment, and compost amendment influenced long-term soil density, hydraulic conductivity, and vegetation establishment. A 12-week mesocosm study with weekly simulated storm events was conducted to further examine the effects of soil type and soil bulk density on vegetation on a larger scale. Water quality testing of the simulated rainfall and runoff samples was also implemented to measure soil erosion and nutrient leaching. Compost-amended subsoil improved vegetation (biomass and grass heights) compared to subsoil, but it did not perform as well as topsoil. The yard waste compost was selected and applied at a rate designed to limit nitrogen and phosphorous losses, and it was successful, given that the compost-amended mesocosms did not export higher nutrient loads than mesocosms with inorganic fertilizer. Hydraulic conductivity was observed to primarily depend on soil density. A series of recommendations for highway projects to effectively restore roadside soil quality to improve vegetation and stormwater management are provided. A low-density layer of topsoil at least 20 cm deep is ideal. Yard waste compost should not be applied at a rate that raises soil organic matter by more than 2%.
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    A DATA-DRIVEN FRAMEWORK FOR THE PREDICTION OF NON-RECURRENT TRAFFIC CONGESTION RECOVERY TIME ON FREEWAYS
    (2024) Kabiri, Aliakbar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This study introduces a comprehensive approach aimed at improving the management of incident durations. It delves into enhancing traffic incident management by integrating diverse incident datasets, including Maryland State Police incident data and Coordinated Highways Action Response Team (CHART) incident data, to improve the assessment of traffic incident durations. The dissertation employs spatial and temporal thresholds to explore matching different incident datasets and identifies discrepancies between various incident reports. The dissertation also explores methodologies for estimating traffic recovery times of each incident, utilizing historical data and pre-incident conditions as baselines to establish normal traffic conditions. A novel framework is introduced to estimate non-recurrent traffic congestion recovery time, revealing that many incidents recover faster than their reported clearance times. In these cases, traffic flow returns to normal conditions quickly.Further, the study examines predictive modeling for traffic recovery time, highlighting the Random Forest model's effectiveness among various machine learning algorithms. This model's superiority, based on precision, recall, and F1-scores, underlines its potential in accurately predicting traffic incident recovery time categorized as short-duration, medium-duration, and long-duration incidents. In particular, the random forest model results in a precision of 0.7 for short-duration incidents, 0.3 for medium-duration incidents, and 0.5 for long-duration incidents. For instance, the precision of 0.5 for long-duration incidents indicates that half of the cases predicted as long-duration incidents are indeed long-duration incidents. Key predictors such as link-level vehicle volume, clearance time, response time, and number of lanes closed are identified, providing valuable insights for traffic management strategies. This dissertation underscores the importance of data-driven approaches in traffic incident management, aiming to enhance the efficiency of transportation systems through accurate prediction and estimation of incident recovery times.
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    GEOTECHNICAL CHARACTERIZATION OF BALTIMORE DREDGED SEDIMENTS AS AN INFILTRATION BERM MATERIAL ON HIGHWAY SLOPES
    (2024) Kaya, Eren; Aydilek, Ahmet H; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Baltimore Harbor dredged material has limited use in common geotechnical construction due to its soil composition. It has low shear strength for foundation applications and lack of plasticity makes it an undesirable material for seepage barrier applications. Vegetated infiltration berms, used for regulating stormwater discharge, can be suitable for beneficial reuse of the dredged materials. The scope of this research is to appropriateness of the dredged material for construction an infiltration berm that has sufficient slope stability and provides total infiltration in less than 72 hours. Unconfined compression tests, constant head permeameter tests and unsaturated hydraulic conductivity tests were conducted on the dredged material and its blends with straw, sand, and recycled glass aggregate. All amendments were chosen due to their potential of reducing infiltration time, increasing hydraulic conductivity and as well as increasing the available water content (AWC) to promote vegetation. The effects of different amendments on soil water characteristics curves were examined and related to the vegetation through percent green cover data. Results obtained through testing were implemented in finite element analysis programs, SLOPE/W and SEEP/W, to analyze slope stability and seepage behavior of the berm, respectively. Common berm geometries along with different subgrade conditions were considered during modeling. Straw amended dredged material provided adequate hydraulic conductivity, met the required minimum infiltration times, and acceptable AWC to promote vegetation, without experiencing any slope stability or piping failures.
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    Biobjective optimization for railway alignment fine-grained designs with parallel existing railways
    (Wiley, 2024-01-09) Gao, Yan; Zhang, Tianlong; Zhu, Caiyiyi; Yang, Shusheng; Schonfeld, Paul; Zou, Kai; Zhang, Jialing; Zhu, Ying; Wang, Ping; He, Qing
    Urban high-speed railway construction is complex due to limited land resources, high population density, and potential construction risks, especially when new tracks are parallelly aligned to operational railways. Addressing a gap in current literature on fine optimization of manual alignment in such scenarios, this paper introduces a biobjective approximate fine-grained optimization model for railway alignments (BA-FORA). Utilizing an approximate dynamic programming (ADP) method, BA-FORA effectively searches the feasible region to approach a global optimum, overcoming the dimensionality challenges inherent in standard dynamic programming (DP). This paper presents a biobjective optimization framework that takes into account both construction cost and construction risk adjacent to existing operating railways (CRAEOR), offering a method for the fine-grained design of new railways adjacent to existing railways. Finally, the proposed BA-FORA framework is applied to practical cases, demonstrating its superior optimization performance. The findings indicate that the BA-FORA model can autonomously investigate and enhance railway alignment. It generates cost-effective and low-risk solutions exceeding manual efforts, ensuring alignment constraint compliance.