Civil & Environmental Engineering Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1657

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Start date: 2020End date: 2024

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Now showing 1 - 10 of 28
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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.
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    Assessing the performance of polyphosphate accumulating organisms in a full-scale side-stream enhanced biological phosphorous removal
    (Wiley, 2024-01-11) Aghilinasrollahabadi, Khashayar; Ghandehari, Shahrzad Saffari; Kjellerup, Birthe Veno; Nguyen, Caroline; Saavedra, Yerman; Li, Guangbin
    Phosphorous (P) removal in wastewater treatment is essential to prevent eutrophication in water bodies. Side-stream enhanced biological phosphorous removal (S2EBPR) is utilized to improve biological P removal by recirculating internal streams within a side-stream reactor to generate biodegradable carbon (C) for polyphosphate accumulating organisms (PAOs). In this study, a full-scale S2EBPR system in a water resource recovery facility (WRRF) was evaluated for 5 months. Batch experiments revealed a strong positive correlation (r = 0.91) between temperature and C consumption rate (3.56–8.18 mg-COD/g-VSS/h) in the system, with temperature ranging from 14°C to 18°C. The anaerobic P-release to COD-uptake ratio decreased from 0.93 to 0.25 mg-P/mg-COD as the temperature increased, suggesting competition between PAOs and other C-consumers, such as heterotrophic microorganisms, to uptake bioavailable C. Microbial community analysis did not show a strong relationship between abundance and activity of PAO in the tested WRRF. An assessment of the economic feasibility was performed to compare the costs and benefits of a full scale WRRF with and without implementation of the S2EBPR technology. The results showed the higher capital costs required for S2EBPR were estimated to be compensated after 5 and 11 years of operation, respectively, compared to chemical precipitation and conventional EBPR. The results from this study can assist in the decision-making process for upgrading a conventional EBPR or chemical P removal process to S2EBPR.
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    Agricultural practices influence foliar endophytic communities in coffee plants of different varieties
    (Wiley, 2023-02-16) Castillo-Gonzalez, Humberto; Bloomberg, Joshua; Alvarado-Picado, Eduardo; Yarwood, Stephanie; Chaverri, Priscila
    Fungal endophytes are pivotal components of a plant's microbiome, profoundly impacting its health and fitness. Yet, myriad questions remain concerning the intricate interactions between these microorganisms and their hosts, particularly in the context of agriculturally important plants such as Coffea arabica. To bridge this knowledge gap and provide a comprehensive framework, this study investigated how farming practices shape the taxonomic and functional diversity of phylloplane endophytes in coffee. Coffee plant leaves from two distinct producing regions in Costa Rica were sampled, ensuring the representation of various coffee varieties (Obatá, Catuaí, and Caturra), agricultural management methods (organic vs. conventional), sun exposure regimes (full sunlight/monoculture vs. natural shade/agroforestry), and leaf developmental stages (newly emerged asymptomatic vs. mature leaves). Fungal communities were characterized by employing both culture-dependent and independent techniques (internal transcribed spacer 2 nuclear ribosomal DNA metabarcoding). The results showed a greater diversity of endophytes in mature leaves and conventionally managed plants, with coffee variety exerting an unclear influence. The effect of sun exposure was surprisingly negligible. However, data emphasize the benefits of agroforestry and organic farming, which are linked to reduced putative pathogens and heightened levels of potentially mutualistic fungi, fostering functionally diverse communities. Despite the role that plant microbiomes might play in agricultural production, the knowledge to shape endophytic communities through breeding or management is lacking. The results from this study provide a framework to understand how both plant and agricultural practices influence endophyte diversity within coffee crops. These insights hold promise for guiding future efforts to manipulate coffee microbial communities effectively.
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    Rent affordability after hurricanes: Longitudinal evidence from US coastal states
    (Wiley, 2023-10-04) Best, Kelsea; He, Qian; Reilly, Allison; Tran, Nhi; Niemeier, Deb
    Climate change is expected to increase the frequency and intensity of natural hazards such as hurricanes. With a severe shortage of affordable housing in the United States, renters may be uniquely vulnerable to disaster-related housing disruptions due to increased hazard exposure, physical vulnerability of structures, and socioeconomic disadvantage. In this work, we construct a panel dataset consisting of housing, socioeconomic, and hurricane disaster data from counties in 19 states across the East and Gulf Coasts of the United States from 2009 to 2018 to investigate how the frequency and intensity of a hurricane correspond to changes in median rent and housing affordability (the interaction between rent prices and income) over time. Using a two-stage least square random-effects regression model, we find that more intense prior-year hurricanes correspond to increases in median rents via declines in housing availability. The relationship between hurricanes and rent affordability is more complex, though the occurrence of a hurricane in a given year or the previous year reduces affordable rental housing, especially for counties with higher percentages of renters and people of color. Our results highlight the multiple challenges that renters are likely to face following a hurricane, and we emphasize that disaster recovery in short- and medium-term should focus on providing safe, stable, and affordable rental housing assistance.
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    Two algorithms for reconstructing vertical alignments exploring the neural dynamics model of Adeli and Park
    (Wiley, 2023-09-26) Song, Zhanfeng; Chen, Jinye; Schonfeld, Paul M.; Li, Jun
    Vertical alignment reconstruction obtains alignment parameters by fitting geometric components to a set of measured points representing the profile of an existing road or railroad, which is essential in alignment consistency analysis and maintenance to ensure safety and comfort. The neural dynamics model of Adeli and Park is explored and improved for reconstructing vertical alignments with constraints. The structure of the dynamics model is modified to include three layers: parameter layer, intermediate layer, and energy layer. The number of nodes in the parameter or intermediate layers corresponds to the number of independent parameters defining a vertical alignment. The number of nodes in the energy layer is the sum of the number of deviations and the number of constraints in the alignment reconstruction problem. The coefficients connecting nodes between the parameter layer and the intermediate layer determine the integral operations, which define the Levenberg–Marquardt algorithm of the dynamics model (LMADM) and the steepest descent algorithm of the dynamics model (SDADM). Both the LMADM and SDADM methods satisfy the Lyapunov stability theorem, but the LMADM method outperforms the SDADM method in its objective function value and computation time. Experiment results demonstrate that there are multiple local optima for a vertical alignment reconstruction, and the solutions obtained by the LMADM method are the best obtained so far, compared with those reported in the literature, with 57.1% and 23.4% decreases of the mean squared error for the highway and the railroad examples, respectively.
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    Ensuring the continued success of a mulch biowall at a trichloroethylene-contaminated superfund site: Lessons learned
    (Wiley, 2023-08-03) Ghandehari, Shahrzad Saffari; Cheng, Shih-Huai; Hapeman, Cathleen J.; Torrents, Alba; Kjellerup, Birthe Veno
    Trichloroethylene (TCE) is a toxic organic compound, which can adversely affect human health. The chemical is one of the most frequently found contaminants in groundwater in the United States and around the world. A landfill in Maryland contaminated with high levels of TCE decades ago was added to the U.S. Environmental Protection Agency's National Priority List (NPL) in 1994. A biowall was installed on the site in 2013 to promote the bioremediation of TCE and subsequently of its degradation products. Six-year monitoring data indicated a steady removal of >99% groundwater TCE at the wall since installation. However, a concurrent buildup of intermediate byproducts was observed downgradient of the wall. An examination of the entire system was necessary to find the reason behind the inefficiency of the biowall. In this study, the background of the site, remediation plan, and installation were assessed. Monitoring data, including the concentration of TCE and its degradation byproducts, and geochemical and physical characteristics were evaluated to understand the conditions and challenges facing decision-makers of this project and possible options to improve biowall efficacy.
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    Mountain railway alignment optimization based on landform recognition and presetting of dominating structures
    (Wiley, 2023-07-23) Wan, Xinjie; Pu, Hao; Schonfeld, Paul; Song, Taoran; Li, Wei; Peng, Lihui; Hu, Jianping; Zhang, Ming
    Mountain railway alignment optimization has always been a challenge for designers and researchers in this field. It is extremely difficult for existing methods that optimize alignments before major structures to generate a better alignment than the best one provided by human designers when the terrain is drastically undulating between the start and endpoints. To fill this gap, a “structures before alignments” design process is proposed in this paper. Primarily, a landform recognition method is devised for recognizing dominating landforms. Then, a bi-level alignment optimization model is proposed, with the upper level dedicated to characterizing dominating structures and the lower level focusing on optimizing the entire alignments. To solve this bi-level model, a three-stage optimization method is designed. At the first stage, a scanning process and screening operators are devised for generating all the possible locations of dominating structures. At the second stage, a hierarchical multi-criteria decision-making procedure is applied for selecting the optimized dominating structure layouts. At the third stage, alignments are optimized based on the determined structure layouts using a bi-objective optimization method, which minimizes construction cost and geo-hazard risk simultaneously. The proposed model and solution method are applied to two real-world cases whose results verify their capabilities in producing alignment alternatives with better combinations of construction cost and geo-hazard risk than manually designed alternatives.
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    Demographics and risk of isolation due to sea level rise in the United States
    (Springer Nature, 2023-11-30) Best, Kelsea; He, Qian; Reilly, Allison C.; Niemeier, Deb A.; Anderson, Mitchell; Logan, Tom
    Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR.
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    Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America
    (MDPI, 2020-09-22) Park, Jongmin; Forman, Barton A.; Reichle, Rolf H.; De Lannoy, Gabrielle; Tarik, Saad B.
    L-band brightness temperature (𝑇𝑏) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in 𝑇𝑏 observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, 𝑇𝑏 estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily 𝑇𝑏 observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed 𝑇𝑏 were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed 𝑇𝑏 with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled 𝑇𝑏 due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.
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    Impact of the Built Environment Measured at Multiple Levels on Nonmotorized Travel Behavior: An Ecological Approach to a Florida Case Study
    (MDPI, 2020-10-24) Mahmoudi, Jina; Zhang, Lei
    Research continues to reveal the benefits of nonmotorized travel modes such as walking and bicycling. Therefore, identification of the factors that nurture these activities is essential in developing sustainable urban planning policies and designs. Among those factors are the built environment characteristics of the place of residence. To date, research on the role of the built environment in nonmotorized travel has focused on neighborhood-level factors. However, people do not stay within their neighborhoods; they live and work at a regional scale and travel to various destinations and distances each day. Nonetheless, little is known about the impact of built environment factors at larger spatial scales on nonmotorized travel behavior. Guided by the principles of the ecological model of behavior, this study investigates the role of the built environment at hierarchical spatial scales in nonmotorized travel behavior. Multilevel Structural Equation Models have been developed to comprehensively examine the complex links between the built environment and individuals’ nonmotorized travel. Findings indicate that built environment factors at multiple spatial scales can influence nonmotorized travel behavior. Thus, to promote walking and bicycling, more effective policies are those that include multilevel built environment and land use interventions and consider the overall physical form of urban areas.