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Events Calendar
Graduate Symposium
8:30 am - 3:30 pm
Showcasing the intersection of industry and academia.
We are excited to be hosting our 4th annual Graduate Student Symposium! Graduate students from across the department will be sharing their latest research findings at a poster session and through oral presentations. We will also be featuring a Career Discovery Panel, comprised of UT alumni helping our students navigate career choices following graduation, along with a Networking Lunch where our graduate students and alumni will have an opportunity to connect. The keynote will be presented by Nour Bouhou, vice president of ASLPM in Houston and lecturer at the University of Houston. We will finish off the day with an awards ceremony where we reveal the best poster and best presenter!
For breakfast and lunch events, please be sure to RSVP here.
Day Schedule
Poster Sessions
Morning Presentations
Afternoon Presentations
Keynote
Panelist Bios
All events will be held in the EER Mulva Auditorium or adjacent conference rooms
8:30 AM
Poster Session and Breakfast
9:30 AM
Oral Sessions
11:10 AM
Career Panel: Our industry panelists discuss career options following graduate school. As UT alumni with advanced degrees, each bring a unique experience in their career with one foot in industry and another in academia.
Questions may be submitted prior to the event through the RSVP form.
12:00 PM
Networking Lunch: Meet our industry panelsts and judges. Lunch provided!
1:00 PM
Oral Sessions
2:40 PM
Keynote: Nour Bouhou
3:15 PM
Awards and Closing
Construction Industry Changes Induced by the COVID-19 Pandemic: A Comparison of South Korea and The United States
Presenter: Kyudong Kim, Construction Engineering and Project Management
Companies worldwide with varying geopolitical conditions and regulations have implemented measures in response to the COVID-19 pandemic differently. This study compares the pandemic-related changes implemented by construction companies located in two countries with vastly different pandemic-related contexts. Enabling this study is an online survey in November 2022 to construction experts working for various companies in the US and Korea. The survey questionnaire presents 21 changes that were identified and categorized by literature reviews and expert interviews. The study also asks about changes in participants’ productivity since the beginning of the pandemic. From the analyses, we seek to answer the following: What changes has the construction industry made in response to the pandemic? And, how did the pandemic affect the productivity of construction personnel? Results were then compared between the two nations. Findings showed that the US construction companies had adopted more changes, such as remote work and technological advancements. Compared to Korean companies, productivity in the US has significantly improved. This implies that the broader adoption of changes in US companies led to increased productivity. Such abrupt changes for overcoming challenges posed by the pandemic may have long-term benefits to construction companies and the industry overall.
Helping Those Who Need It Most: Open Access Tool for Modeling Flood Hazards and Community Adaptive Capacity in Near-Real Time
Presenter: Matthew Preisser, Environmental and Water Resources Engineering
Across the world people are being impacted by devastating floods at an increased frequency more than ever before. Despite the major advancements in climate modeling, weather forecasting, and emergency preparedness, deadly floods continue to have a global reach. In our previous work we overlaid near-real-time high-resolution flood hazard estimates with social vulnerability information to begin to highlight how different communities even within the same city are impacted differently. Our goal is to quantitatively define a flood’s impact on a population in near-real time, encompassing inundation at an individual’s place of residence, their socioeconomic standing, and their access to critical infrastructure. We incorporate open-source infrastructure, Earth observations, and point measurement data in a multilayer network to provide critical decision-making information to those who need it most. Our results allow us to estimate which residents' access to resources increases, which lose access to resources completely, and how resource hubs might be over/under utilized in the wake of flooding events. By developing these tools with existing open-source data, our goal is for these tools and models to be able to be rapidly deployed across the USA and potentially abroad to better serve communities who may not have access to such resources.
Identifying Morphological Hot Spots and Social Vulnerabilities along the Gulf Coast using Remote Sensing
Presenter: Sara Karimaghaei, Environmental and Water Resources Engineering
Despite increasing risk from sea level rise, severe hurricanes, and coastal erosion, communities along the US coastline continue to increase, with the largest population increase, between 2000 and 2017, along the Gulf of Mexico. In addition to being homes to millions of people, the Gulf Coast is the site for a large portion of the United States’ energy and food production. These coastal communities are at risk of displacement because of land loss. In this study, we leverage remotely sensed data to identify and quantify hot spots of morphological change along the Gulf of Mexico coastline. We collect yearly Landsat imagery during the dry season and convert the images to water surface maps using DeepWaterMap, a deep convolutional neural network algorithm for surface water mapping. This deep learning algorithm segments multispectral imagery and creates quasi binary surface water presence maps. We use these watermasks from the 1980s until present to compute the Channelized Response Variance (CRV). We arrange the water surface map images in a time ordered 3-D array and compute the variance for each pixel in the time dimension. The CRV method allows the quantification of temporal and spatial trends along the coastline by identifying areas of water loss and water gain. We identify areas with the largest rate of change as hot spots and overlay this information with the CDC’s Social Vulnerability Index to determine if a correlation between morphological change and social vulnerability exists. This study allows for the identification and quantification of coastal morphological change utilizing only remote sensing data and can be applied across the world to identify at risk coastal communities.
Connecting Floodplain Form and Function Through the Backwater Transition
Presenter: Nelson Tull, Environmental and Water Resources Engineering
The structure of river levees and floodplains is an important control on mass flux out of river channels. Moreover, river and floodplain morphology and the development of levees and levee channels can change dramatically as a river approaches the coast, due to the backwater influence. These downstream changes have implications for the delivery of sediment and nutrients to the floodplain and for flood storage. In this study we employ a numerical model to characterize how differences in levee structure alter flow patterns across levees as the river transitions from quasi-uniform flow to the backwater zone. Results show how channelized area, water volume, and mass flux across river levees change with discharge and distance downstream. While average water volume on the levees does not change dramatically with distance downstream, average wetted area increases with distance downstream. We couple dorado, a Lagrangian particle routing scheme, with model flow fields to identify the major sources and sinks of flow through the floodplain. Particle routing demonstrates the relationship between structural and functional surface-water connectivity and how that relationship changes as a river approaches the coast. Our results characterize the coastal backwater transition in two dimensions in a way that considers both floodplain structure and hydrodynamic conditions.
Error Analysis of Short-Range Streamflow Forecasts from the National Water Model and Validation of Data Assimilation Approach
Presenter: Sujana Timilsina, Environmental and Water Resources Engineering
Accurate streamflow forecasts are essential for better flood forecast systems and play an important role to achieve resilient flood risk management systems. The National Water Model (NWM) is a hydrological model that provides streamflow forecasts throughout the United States and aids nationwide flood predictions. In this study, we analyze the performance of the NWM streamflow forecasts throughout Texas, leveraging a network of water level sensors. We analyze the spatial patterns of the errors based on the error maps generated from the observed data and the NWM forecasts over several storm periods. We also explore the influence of different river network properties on the error magnitude, evaluate the performance of the “nudging” data assimilation (DA) method of the NWM and explore the possible improvements in the NWM DA technique. We develop a model that uses the Muskingum-Cunge channel routing method to reproduce the NWM streamflow forecast and assimilate the observed streamflow data into the NWM short-range forecasts using a Kalman Filter approach. We apply the model to a small watershed of the Drainage District 6 region in southeast Texas. Initial results highlight the influence of several river network properties in accurate streamflow forecasts. The results also show that Kalman Filter improves NWM streamflow forecasts in the study watershed.
Understanding the Effects of Climate Change on Alaskan Water Quality
Presenter: Mathieu Medina, Environmental and Water Resources Engineering
The rise of average global temperatures, increased frequency and intensity of extreme weather events, sea level rise, and ecosystem transformation are all effects of climate change. Challenges associated with these effects are amplified in the Arctic and Subarctic due to rapid warming which threatens ancient frozen repositories of water. Glacier melt and permafrost thaw (thermokarst development) have direct consequences on the water quality available to Native people across the state of Alaska through potential input of dissolved nitrogen species, heavy metals, natural organic matter, and pathogenic species. In this work, we seek to understand the dynamic changes to water quality as a result of climate change induced melt and thaw. Following melt sampling and ice coring, water is analyzed for anions, metals, carbon species and microbes. Developing a shared understanding of future water use informed by Native Alaskan community knowledge is key to this project.
Hydrodynamic Analysis of Ducted Marine Turbine: Study on the Duct and Turbine Efficiency
Presenter: Kyle Kumar, Environmental and Water Resources Engineering
Presenting a boundary element method (BEM) applied to the numerical prediction of ducted turbine performance. The model turbine is a horizontal axis marine current turbine subject to uniform inflow. For this type of steady problem, BEM runs much faster than a finite-volume-method-based viscous simulation and thus facilitates the analysis of different turbine configurations. The primary purpose of this study is to investigate how different duct shapes and loading conditions affect the ducted turbine performance in comparison with the open turbine performance. Unlike another commonly used method for this research, a lifting line model, the present method does not require any simplification of the turbine geometry and its trailing wake. Instead, both the blade/duct trailing wakes are fully aligned based on the local stream representing the expansion/contraction of the trailing wakes and their mutual interactions. Predicted thrust and power coefficients are compared with open turbine performance to see how the presence of the duct influences the overall turbine efficiency. The results show that the ducted turbine performance is significantly affected by the flow induced by the duct and its vertical elevation from the blade tip.
A Mixed Integer Linear Programming Framework for Water Distribution System Optimization
Presenter: Meghna Thomas, Environmental and Water Resources Engineering
Water distribution systems (WDSs) are critical infrastructure that are used to convey water from sources to consumers. The operation of WDSs is constrained by supply quantities, energy costs, and limits on the operation of control elements like pumps and valves. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDS lends itself to application in a wide range of optimization problems. Adopting a classical mixed integer linear programming (MILP) approach to model WDSs guarantees a convex solution space and, by extension, high solution accuracy and low computational effort and time devoted to solving the problem. However, adapting WDSs to conform to a MILP formulation has proven challenging because of the intrinsic non-linearity of system hydraulics as well as the complexity associated with modeling hydraulic devices that influence the state of the WDS. Here, we present MILPNet, an adjustable MILP model for WDSs that can be used to build and solve an extensive array of simulations and optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, as well as the behavior of hydraulic devices, control rules, and status checks. To conform with MILP structure, MILPNet’s methodology employs piece-wise linear approximation and integer programming. The performance of MILPNet was shown to be comparable to EPANET, an industry standard software for hydraulic modeling. Our results show that MILPNet can facilitate quick, accurate optimization problem solving for a wide range of applications.
Water Stagnation in Building Plumbing Leads to Increases in Legionella and Legionella Pneumophila: A Citizen Science Study
Presenter: Lan Nguyen, Environmental and Water Resources Engineering
Legionella pneumophila is an opportunistic human pathogen that can persist and grow in building plumbing, which is where consumers access potable water from drinking water distribution systems. L. pneumophila is an amoeba-resistant organism; thus it can be ingested without being digested by free-living amoebae (FLA) and can be protected from disinfectants when harbored by FLA. This study sought to quantify culturable L. pneumophila (via the Legiolert® assay) and to quantify total Legionella spp. (via a DNA-based digital polymerase chain reaction [dPCR] assay) present in “stagnant” water from building plumbing and in “mains” water from drinking water distribution systems. Culturable L. pneumophila was found more often and at higher concentrations in stagnant water as compared to mains water, in waters utilizing chlorine for secondary disinfection as compared to those utilizing chloramines, and in surface waters as compared to groundwaters. Current data for samples collected at a residence in Austin, Texas, show that “mains water” (1.64 x 10^5 gene copies/L) had a slightly greater Legionella spp. concentration than did “stagnant water” (1.12 x 10^5 gene copies/L). The study is ongoing, and Legionella spp. concentrations determined by dPCR at other sampling locations across the U.S. will be presented.
Hybrid Finite Element and Material Point Method Transfer Point Idealization When Simulating Slope Failure
Presenter: Brent Sordo, Geotechnical Engineering
Numerical modeling of slope failures seeks to predict two key phenomena: the initiation of the failure and its post-failure runout. The Finite Element Method (FEM) excels at modeling the initiation of instability but quickly loses accuracy when modeling large deformations due to mesh distortion, restricting its utility for predicting post-failure runout. Conversely, the Material Point Method (MPM), offers an alternative in which particles move freely across a background mesh, allowing for indefinite deformations without computational issues. However, MPM suffers from inaccurate stress distributions and limited boundary conditions, restricting its ability to predict initiation. The hybridization of these methods presents an opportunity to optimally model both aspects of slope failure with a single model. Initiating a model in FEM allows for high-quality stress distributions and failure development. Then, transferring to MPM allows the failure to fully run out without mesh issues, providing optimal predictions of both phenomena. By simulating simple slope failures by this hybrid method, we compare multiple transfer times to demonstrate the effectiveness of this hybrid methods and identify the ideal window to perform this transfer.
Interpreting the Effectiveness of Antioxidants to Increase the Resilience of Asphalt Binders: A Global Interlaboratory Study
Presenter: Dheeraj Adwani, Infrastructure Materials Engineering
The design and use of antioxidant additives to reduce or slow down the aging of asphalt binders can bring about tremendous benefits to the asphalt industry. Despite many isolated and scattered research efforts showing mixed results, the application of this science to engineering-based solutions has been limited due to variability in results and conflicting data available. This work presents the results from a global interlaboratory study to test the effectiveness of promising antioxidant additives, namely kraft lignin, calcium hydroxide, zinc diethyldithiocarbamate and phenothiazine to increase the resilience of asphalt binders and provide insights towards understanding the complex intricacies between chemistry and rheology. Specifically, seven different binders from various geographical regions in the world i.e., Texas (USA), Vienna (Austria), Illinois (USA), Antwerp (Belgium), and Delft (Netherlands) were blended with the antioxidants at two proportions. Subsequently, the chemical and rheological properties of the blends were evaluated using Fourier transform infrared (FTIR) spectroscopy and dynamic shear rheometer (DSR). The results indicate that although some antioxidants may reduce oxidation based chemical indices, their effect on rheology is more complicated and possibly related to unique physicochemical interactions in each binder. From a macro-perspective, zinc diethyldithiocarbamate showed promising results with a good correlation between rheology and chemistry for the majority of the binders. These additives or other additives with the same working principles should be investigated further. Additionally, significant research efforts must also be directed towards approaches aimed at understanding mechanisms of interaction and relating results with specific binder compositions.
Effect of Curved Bar Nodes Radius in Reinforced Concrete Frames Joints
Presenter: Anas Daou, Structural Engineering
In the STM, a curved-bar node develops at the bend region of continuous bar (or bars) where two ties are equilibrated by a strut (or struts). For example, in the beam-column knee joint of a straddle bent, the internal actions at the joint are transferred between the elements of the frame through the bend of the reinforcing steel bars at the outside corner, as depicted in Figure below. The strut-and-tie model indicates that stress concentrations in curved-bar nodes can be critical; however, the development of design recommendations in AASHTO LRFD has not yet initiated due to lack of experimental data. Curved-bar nodes are acknowledged in AASHTO LRFD Bridge Design Specifications in the commentary. Such nodes occur at the frame corners commonly encountered in cantilever caps (i.e., C-caps) and straddle bents that are designed to transfer moments from the cap beam to supporting columns (i.e., frame corners). Example problems developed under implementation project TXDOT 5-5253-01 highlighted cases in which the curved bar nodes, stress checks at those nodes can control the design. Conversely, in those cases, structural designs based on the current state of practice may lead to over-stressing these areas. The research Study will examine the mechanics of load transfer at curved-bar nodes to address this knowledge gap. This section of the technical memorandum discusses the current progress, including the updated test matrix, the design of specimen B-series, the test frame, updated instrumentation plan, and fabrication of specimens. Nine specimens are moderated to fulfill the objectives of this task. These specimens are divided into four series. Series A in the test matrix is meant to investigate the effect of the bend radius with a diagonal strut angle of 45 degrees. The selected bend radii for the investigation include: (i) bar bend meeting the standard ASTM mandrel diameters (6 inches), and (ii) bar bend meeting Klein formulation (11 inches). Further, Series B and C in the test matrix consist of different diagonal strut angles from 45 degrees. This is achieved by reducing the legs depth in series B and by reducing the beam depth in the series C. Finally, the last series consist of joints reinforced with two layers of straight bars or two layers of bundled bars. In addition, all specimens in Series D have a bend radius satisfying the criterion (ii).
Generating High-Resolution PM2.5 using a Two-stage Machine Learning Approach with Low-Cost Air Quality Sensors and Satellite Observations
Presenter: Ting-Yu Dai, Sustainable Systems
Using low-cost sensors (LCS) in air quality monitoring has increasingly garnered attention that applies in multiple disciplines including community and citizen scientists, academic research groups, and environmental agencies. However, two main barriers yet to be overcome in LCS are 1) spotty performance compared to regulatory-grade monitors (RGM), and 2) sparse spatial coverage compared to satellite products. This study proposes an innovative approach to sidestep the above imperfections. First, we followed the bias-correction process described in Gupta et al. (2022) to calibrate the LCS estimations into FEM level based on their nearest RGM stations by a random forest (RF) model. This increases the accuracy and minimizes the uncertainty of the LCS data. Second, another RF model is implemented to quantify the PM2.5 under different weather and aerosol conditions. Finally, the relationship between LCS, aerosol optical depth (AOD), and meteorological variables is established to generate a stable and continuous spatial data product. Moderate Resolution Imaging Spectroradiometer (MODIS) and the High-Resolution Rapid Refresh (HRRR) are utilized as the AOD and atmospheric variables in this work. Preliminary results indicate the PM2.5 concentrations can be precisely biased-corrected to highly correlated to the FEM estimations and mitigating the sparse coverage of station data by producing the measurements based on AOD and meteorological data. This demonstrates the potential to use the proposed approach to better improve and incorporate the accuracy and spatial distribution of PM2.5 concentrations.
Identifying Opportunities for Water Reuse and Alternative Water Sources in the Industrial Sector
Presenter: Miriam Tariq, Sustainable Systems
Many industrial operations are threatened by water stress and decreasing usable water supply, highlighting the need to explore water reuse and alternative water sources (e.g., brackish water, seawater, produced water). Despite increased innovations in water treatment technologies and an imminent need to optimize water usage, industries have been slow to transition to these opportunities. Industries face many challenges, including financial constraints, regulatory requirements, and ineffective water treatment technology adoption. Using two chemical plants (Massachusetts (USA) and Belgium) as case studies, this work will identify opportunities for the use of alternative water sources or water reuse. Inductive qualitative content analyses of semi-structured interviews conducted with stakeholders will be used to answer the following question(s): (1) what factors motivate or obstruct the use of alternative water sources or water reuse in industrial processes? and (2) what opportunities are there for the adoption of novel water treatment technologies in industry? We anticipate that current and future water availability, cost, and environmental regulations will impact decisions made about water sources. We expect cost-effectiveness, energy demands, and resilience to drive adoption of treatment technologies. These results can be used to develop strategies to overcome barriers to water reuse and to inform technology development.
Shared EV Charging Stations for the Austin Area: Opportunities for Public-Private Partnerships
Presenter: Lin Su, Transportation Engineering
The City of Austin's public charging stations are primarily Level 2 and Level 3, with approximately 462 Level 2 charging stations (with 1+ cords per station) and 37 Level 3 charging stations serving the Austin area. Together, the 462 Level 2 stations can charge approximately 979 electric vehicles (EVs) simultaneously, 125 of which are at Tesla “destination charging stations”. Tesla’s destination charging stations tend to serve more EVs at once and offer higher power rates than other public EV charging networks. Each Level 2 charging port serves roughly 17 EVs and 413 households across the City of Austin. Austin’s Level 2 charging stations are located primarily in workplaces and parking lots connected to transportation hubs, as well as commercial areas. Office buildings have the highest number of Level 2 charging ports (27.0%), followed by commercial parking areas (22.0%) and residential apartment areas (19.6%). Level 3 charging stations are much more expensive to deploy, and Tesla Superchargers dominate the City of Austin’s options, with Tesla’s 8 supercharging stations making up 94 of the city’s 123 Level 3 charging ports. While other Level 3 charging stations appear to have exactly one charging port per pedestal, Tesla Superchargers regularly allow 2 vehicles to charge simultaneously at a single pedestal with full 250 kW for each port. Tesla’s supercharging stations are relatively big, enabling, on average, 11.75 EVs to charge simultaneously. Areas with high demand but limited access to charging stations across Austin, particularly near existing parking lots, gas stations, intersections, and highways, are identified in this report as potential sites for new EV charging stations (EVCS). Total three promising locations are identified in Central Austin plus the UT Austin campus for Cruise's SAEV fleet. Based on EVSE estimated cost for a dual-port Level 2 charging pedestal of $6,400 (including installation fees) and $78,000 for DCFC, the cost of deploying 2 to 3 dual-port Level 2 charging pedestals at potential sites is estimated to be $15,000 to $20,000 per site, while the cost for 8 single-port DCFC chargers is estimated to be $625,000 per site.
A Subdivision-stabilized B-spline Material Point Method for Nonlinear Nearly Incompressible Solids
Presenter: Ashkan Ali Madadi, Mechanics, Uncertainty and Simulation in Engineering
Subject to external loadings, polymeric materials, e.g., biological tissues, hydrogels, and elastomers, may undergo extreme, nearly incompressible, self-contacting deformations. For numerical modeling employing mesh-based techniques such as the finite element method (FEM), these deformations pose significant challenges due to i) large distortions in the deformed geometry, ii) accuracy issues stemming from volumetric locking effects, and iii) vastly increased computational cost due to complex contact search. As an alternative to FEM, the material point method (MPM) is a continuum-based meshless particle technique and is attracting considerable interest due to its robustness against extreme distortions and its ability to capture contact at no additional cost. An effective technique to overcome locking effects is the two-field mixed formulation with displacement and pressure as independent fields instead of the displacement-based single-field formulation. However, mixed formulations suffer from numerical instabilities in the nearly incompressible limit due to the violation of the inf-sup or Ladyzhenskaya–Babuska–Brezzi (LBB) condition, leading to spurious nodal pressure solutions. The main objective of this paper is to extend the mixed formulations at large strains to the B-spline material point method using the two-scale relation of B-splines; we further develop a subdivision-stabilized mixed MPM and obtain a stable, oscillation-free nodal pressure solution. We assess the stability and accuracy of the developed mixed MPM through several benchmark problems; including Cook's membrane and rigid footing foundation, with comparisons to FEM results. Additionally, we test the robustness of the proposed MPM by modeling several examples, including the indentation of a 2D sphere into a quasi-compressible substrate where the no-slip contact condition is assumed and the torsion of a rectangular block. The proposed methodology provides a robust computational foundation to study extreme deformations observed in practical soft matter applications.
Wetting Behavior of Incidental Water on Unfinished Drywall
Presenter: Ansel Early, Sustainable Systems
Liquid water can damage drywall by staining it, structurally deteriorating it, and promoting mold growth. In particular, “incidental water” from plumbing leaks, roof leaks, exterior wall leaks, or even dripping condensation from pipes or ductwork can create point sources of liquid water that can damage drywall surfaces. However, the transport mechanisms and end fate of liquid water in contact with drywall have not been extensively researched. In this study, imaging was used to measure the rates of spread of wetted areas on drywall surfaces over time in order to develop models for the extent of drywall expected to be damaged due to a point leak.
Characterization of Asphalt Mixtures with Geosynthetic-Reinforced Asphalt Millings
Presenter: Ashray Saxena, Geotechnical Engineering
The incorporation of geosynthetic interlayers during the asphalt overlay construction has proven successful in mitigating the reflective crack potential and thereby, enhancing the pavement structural capacity. Nonetheless, milling an asphalt layer reinforced with a geosynthetic interlayer is a huge concern, since there is a possibility of geosynthetic interlayers compromising the reclaimed asphalt pavement (RAP) quality and characteristics. On the other hand, inclusion of RAP into the hot mix asphalt is a common practice. Hence, it is important to understand the characteristics of RAP collected from geosynthetic-reinforced asphalt layers (referred herein as GRAP) and their influence on the asphalt mixtures. The objective of this study is to understand the characteristics of GRAP and investigate the performance of asphalt mixtures with 15% and 30% GRAP. Additionally, the performance of asphalt mixtures with 15% and 30% RAP were evaluated for comparison with that of asphalt mixtures with GRAP. The characterization of GRAP and RAP included binder extraction tests and gradations, while the asphalt mixture evaluation included indirect tensile strength, and moisture susceptibility tests. Comparison of binder extraction test results revealed that the GRAP samples had slightly higher binder contents in comparison with the RAP samples. While the comparison of indirect tensile strength and moisture susceptibility test results indicated the asphalt mixtures with GRAP performed similar to that with RAP, suggesting the possibility of incorporating GRAP into the asphalt mixtures.
Learning to Optimize Distributed Optimization: ADMM-based DC-OPF Case Study
Presenter: Meiyi Li, Building Engergy and Environments
The decision-making paradigms of future energy systems are increasingly becoming decentralized and multi-entity/agent. The Alternating Direction Method of Multipliers (ADMM) has been widely used to address the computational needs of decentralized decision-making problems, e.g., optimal power flow (OPF). In this paper, we propose a novel data-driven method to accelerate the convergence of ADMM for decentralized DC-OPF, where our optimizer will learn the iterative behavior of agents to produce a high-quality feasible solution. The proposed method utilizes the gauge maps to enforce feasibility with respect to agents' local constraints while iteratively penalizing violations of the shared constraints. We used the IEEE 57-bus system to showcase the performance of the proposed method. Our experimental results demonstrate significant run-time reduction for using ADMM to solve the DC-OPF problem.
Global Urban Precipitation Anomalies
Presenter: Xinxin Sui, Environmental and Water Resources Engineering
Urbanization is a global anthropogenic land surface change underway, which has been proven to modify both the global climate and regional extreme weather. Although researchers have investigated the urban influences on precipitation for specific cities or several thunderstorm cases, no study to date has revealed the urban precipitation anomalies on a global scale. This research analyzes the urban precipitation anomalies for over one thousand global cities in the past two decades. We found that over 60% of the cities and their downwind regions are receiving more precipitation than their surrounding rural control areas. The urban precipitation anomalies are unequal among different continents. African cities have not only large urban annual precipitation anomalies but also large urban extreme precipitation anomalies, while large Asian cities tend to experience negative precipitation anomalies. Three environmental factors are found to relate to the urban precipitation anomalies: urbanization extent, climate (wetness and temperature), and topographic conditions (coastal, inland, or mountainous areas). This research identifies the urban rainfall hotspots across the globe, which will help to project extreme precipitation in city areas and to develop more resilient cities under global warming.
Passive Leak Monitoring in Real Urban Water Distribution Networks using Acoustic Sensors
Presenter: Konstantinos Sitaropoulos, Structural Engineering
As modern urban areas struggle with water scarcity and rapid population growth, delivering clean water becomes vital for securing public health and economic well-being. However, several underground pipelines in cities experience leaks, which in some cases might go undetected for prolonged periods, resulting in financial losses, public health risks and environmental impacts. Since water pipes are buried underground and thus are not easily available for visual inspection, passive acoustic-based methods appear to be suitable for leak detection in underground pipelines. Given the numerous advances in sensor technology in recent years, acoustic sensors, such as hydrophones, can detect and record underwater sound and also transmit the data using Internet of Things (IoT) technology. The goal of our study is to test the applicability and detection limits of acoustic sensing via hydrophone technology in a real water distribution system by examining changes in the recorded signals to various leak, distance and pipe configurations. A combination of wavelet transforms and power spectrum analyses were used to identify the leak signature in every case. Results indicate that leak acoustic signals are characterized by a continuous power increase in a certain frequency band. Additionally, observing the power levels of certain frequency bands over time appears to be an effective tool for real-time leak monitoring, even though certain factors may significantly affect the reliability of the results. Our results demonstrate that low-cost, nonintrusive acoustic sensors can be implemented in a real-life water network to effectively detect leaks and monitor complex underground pipe networks in urban areas.
Network-Level Friction Prediction Model for Flexible Pavements
Presenter: Christian Sabillon, Transportation Engineering
Skid resistance is a critical pavement surface characteristic associated with road user safety. The bulk of literature on highway safety indicates an inverse correlation between roadway friction levels and number of wet weather crashes. Nonetheless, skid resistance remains a cumbersome characteristic to measure at a network level due to the inefficiencies of the measuring equipment. These limitations have created a high demand for an alternative model capable of predicting pavement friction continuously using readily available information with a high degree of accuracy. This study collected a representative sample of thirty highway pavement sections in the state of Texas that cover a vast range of pavement surfaces, textures, and friction levels. Machine learning models that use pavement texture were used to accurately predict surface information and friction. The final model estimates skid in terms of skid number (SN) on the major types of flexible pavements present in Central Texas with a coefficient of multiple determination in the order of 80%. The innovative way in which machine learning algorithms are used in this study has the potential to allow highway agencies to obtain full network coverage of reliable estimates for friction.
The Investigation of Ice Melting Rates in Homogeneous Isotropic Turbulence
Presenter: Aubrey McCutchan, Environmental and Water Resources Engineering
Melting of ice in the polar regions causes a significant portion of global sea level rise. At ice-ocean interfaces such as tidewater glaciers or ice shelves, currents bring relatively warm salty water, thus producing temperature gradients and enhancing melting. Turbulence generating processes such as buoyant meltwater plumes, tides, and density gradients increase the rate at which cold meltwater surrounding ice is replenished by warmer water, driving melting in these locations. Currently absent from the literature is a thorough quantification of the effect turbulence and water temperature have on ice melting rates. To understand the underlying physics of these parameters on melting rates, we designed an experimental facility that uses a random jet array to generate homogeneous isotropic turbulence absent mean flow in the center of a water tank. While in the ocean turbulence is typically found with waves and currents, baseline conditions can be established for ice sheet modeling applications with this fundamental experimental study. An ice sphere was placed stationary in the center of the tank and melting rates were quantified under conditions varying ambient water temperature and turbulence intensities. To calculate the velocity field surrounding the ice and the turbulence statistics, particle image velocimetry (PIV) measurements were made.
Mixing across a Sharp Density Interface in Mean Shear-Free Turbulence
Presenter: Arefe Ghazi Nezmi, Environmental and Water Resources Engineering
The interaction of turbulence with a density gradient is an important phenomenon in the environment and plays a major role in oceanic and atmospheric circulations. Therefore, this topic has been under investigation for decades. However, there are still many unknowns in the underlying dominant mechanisms and the primary drivers of interfacial mass transport. We perform experiments to investigate mixing in a stably stratified flow with ambient turbulence. The ambient flow, located above the quiescent dense layer, is turbulent is, and is generated by randomly actuating an array of synthetic jets suspended at the top of the tank. We find the velocity field and the concentration map simultaneously using particle image velocimetry and laser-induced fluorescence, respectively. With the velocity data, we calculate turbulence statistics such as turbulent kinetic energy and dissipation. We then quantify the mixing rates at the interface with the concentration map. We also identify various types of instabilities and entrainment events at the interface. By changing the density difference and the turbulent Reynolds number, we determine under what conditions different mixing rates and interfacial dynamics occur.
Characterizing the Adhesive Behavior of Polymeric Materials
Presenter: Ayse Derya Bakiler, Mechanics, Uncertainty, and Simulation in Engineering
The adhesive properties of materials ranging from biological tissues to composites and laminate structures are known to critically influence the overall mechanical behavior of these structures. Of specific interest to this work is the characterization of the adhesive behavior of soft materials, e.g. hydrogels and biological tissues, which remain elusive at small length scales. At these length scales ranging from micrometers to millimeters, surface effects such as surface tension and adhesive forces become of comparable magnitudes to that of the bulk and significantly influence the overall behavior of the structure. Thus, understanding the mechanisms of interfacial failure of polymeric materials is crucial to be able to fully harness their potential in emerging soft material applications. Here, we develop an interface-enriched, isogeometric finite element framework to quantify the separation mechanics of soft-hard and soft-soft interfaces endowed by low-dimensional energetics. The numerical results will elucidate the “elasto-adhesive length” of materials, which dictates the length-scale where adhesive effects prevail over those of the bulk. The elasto-adhesion length over varying bulk, surface and interface properties will be reported through phase diagrams. The findings here will pave the way for the design, manufacturing, and assembly of small-sized, multi-layer structures involving highly deformable materials.
Americans’ Long-distance Travel: Domestic and International, with and without Autonomous Vehicles
Presenter: Priyanka Vilas Paithankar, Transportation Engineering
This research specifies behavioral models for long-distance domestic and international passenger-trips before and after the introduction of autonomous vehicles (AVs). After synthesizing 10% of US households (28.1M persons) across 73,056 US census tracts, these models anticipate person-trips by season and purpose, with party size, mode, and destination, as well as vehicle ownership (to reflect any AV impacts on ownership). Results show that once AVs are widely available in the market with a $3500 technology cost premium (e.g., in year 2040) and SAV fare of $0.70 per mile, 42% of long-distance trips will take place in AVs increasing total person-miles traveled per capita by 35% (from 280 to 379 miles per month). Parameter estimates in the international trip distribution model (from 334 US airports to 1028 foreign airports) suggest that flight volumes fall about 41% for every 7-hour (one standard deviation increase) increase in flight time (start to end). Destinations listed as tourist attractions (like London, Barcelona, Milan, Paris, and Dubai) enjoy 48% higher inbound flows. Moreover, flights cost about 15.2% more (outbound and inbound) when departing in October through December (second quarter year) as compared to January through March (first quarter), everything else constant.
Characterization of Earthquake Ground Motions in West Texas
Presenter: Bartik Pandel, Geotechnical Engineering
Over the last two decades, there has been a significant rise in seismic activity in Texas owing to human activities like fluid injection. Consequently, seismic hazard assessment in Texas has become increasingly important. The damage potential associated with earthquake shaking can be characterized by two sets of parameters: intensity and frequency content. The dynamic response of engineering systems during an earthquake depends on these parameters, which in turn are influenced by local site effects. In this study, we focus on the frequency content of recorded earthquake ground motions in West Texas and their relationship to local soil/rock properties. The Fourier spectra of waveforms decay at high frequencies, and this decay is defined through the seismological parameter κ. κ is considered a site-specific parameter and is widely used in seismic design, where a higher value of κ indicates more attenuation of high-frequency energy. Our work so far has estimated κ at more than 30 seismic recording stations in West Texas, correlated it with the shape of the acceleration response spectra, and related it to the local subsurface conditions. We find that the values of κ vary considerably across West Texas, with softer and/or deeper soil conditions associated with larger κ.
Addressing Workforce Recruitment and Retention in Rural Alaska Water Operations, Maintenance, and Management4
Presenter: Michaela LaPatin, Construction Engineering and Project Management
The operations, maintenance, and management (OMM) of water infrastructure systems requires a skilled and reliable workforce, which is lacking in many areas across the industry. Recruitment and retention of trade workers has become increasingly challenging as many skilled workers reach retirement age, and a new generation of workers are not choosing to work in the trades. Rural, low-income communities, including those in the Yukon-Kuskokwim (YK) Delta of Alaska, struggle to maintain a robust workforce and operations due to their remoteness and limited economy. The extreme Arctic climate in the YK Delta further exacerbates such challenges, leading to damage from frozen pipes, interrupted service during storms, and erosion and subsidence due to climate change. In this study, we evaluate the water systems’ workforce challenges in the YK Delta. We interviewed 24 water system stakeholders in the YK Delta, ranging from city administrators to water delivery truck drivers. Through qualitative content analysis and cognitive mapping, we seek to answer two key questions: (1) What leads to a workforce shortage in rural Alaskan water infrastructure systems? (2) What are the cascading impacts of workforce shortages in rural Alaska water systems? Preliminary results show that issues with the water infrastructure can lead to challenges within the social system of the workforce. Low wages and stressful working environments contribute to the workforce turnover. Further, water systems can benefit from recruitment of local workers because they possess the experience and expertise to navigate the arctic environmental conditions, remote economy, sociocultural understandings, and unique water delivery methods.
Prediction of 3D Hydrofoil Performance
Presenter: Thomas Wu, Environmental and Water Resources Engineering
The boundary element method (BEM) or panel method has been widely used to analyze hydrofoil and propeller or turbine blade performance due to its efficiency and ability to predict flow around complex geometries. However, it is often inadequate to consider the effect of viscosity, via an empirical friction coefficient over the hydrofoil surface without considering boundary layer development. In this study, a two-dimensional (2D) and a three-dimensional (3D) BEM are combined with the boundary layer solver, X-foil, in order to predict the flow over 2D or 3D hydrofoils and evaluate the effect of viscosity on its surface. The interactive viscous/inviscid method is employed to calculate the skin friction coefficient, as well as predict the effects of viscosity on the pressure distribution and on the forces applying on the hydrofoil. The results of the present method have been validated using full-blown Reynolds Averaged Navier-Stokes (RANS) simulations under various conditions, as well as with existing experiments. The proposed model is found to be very efficient and robust, as it avoids extensive meshwork and long computer run times required by RANS simulations.
Nour Bouhou, VP at ASLPM, Adjunct Professor at California State University and University of Houston
Dr. Nour Bouhou, Vice President at ASLPM, is a project management, project controls and construction claims and litigation professional who assisted over 30 clients across the US and oversees. On the proactive side, she provides scheduling and change order evaluation services on large scale mixed-used projects, infrastructure projects, as well as military projects requiring DoD cost-loaded schedule reporting and time impact analysis requirement. She also performs schedule delay analyses, productivity impact assessments, damages quantifications and serves as a testifying expert. Dr. Bouhou also provides technical trainings to owners and contractors related to scheduling best practices, project controls principles and claims. She is an Adjunct Professor at California State University, East Bay, NKU College of Business, and the University of Houston, teaching Construction Management and Project Controls courses for the Undergraduate, Master’s and MBA Programs. She also serves as the Regional Director at AACE International and a program chair for the Western Winter Workshop.
Cristina Criado, President of Criado & Associates
Cristina Criado, PE is the founder and serves as the President and Chief Executive Officer of Criado & Associates, Inc. Beginning her practice in 2001, Cristina has provided design and consulting services on a wide variety of civil engineering projects including roadway and freeway projects, interchanges, hike and bike trails, municipal projects, and land development. Prior to opening her own firm, Cristina practiced as a professional civil engineer for two leading civil engineering firms in the DFW metroplex managing a diverse project portfolio. By using her vast technical expertise, strong will, and determination, she embarked on the path to become the fully functioning independent firm that CRIADO is today. She remains committed to the day-to-day operations of the company and has stayed true to her roots in developing a family-oriented business that understands and implements a positive work environment.
Jordan Furnans, VP and Manager, LRE Water LLC & CAEE Adjunct Professor, Hydraulics
Jordan Furnans, PhD, PE, PG is a professional surface water and groundwater resources consultant offering services within Texas and throughout the western United States. He specializes in water availability studies, numerical modeling, and groundwater development projects. He is the design engineer of record on over 50 wells in the State of Texas, and offers research and development services to municipalities, industry, county/local governments, groundwater conservation districts, and the Texas Water Development Board. Dr. Furnans opened the TX office of LRE Water in 2015, and has grown the business to include 7 local professional engineers and geoscientists. In his spare time, Jordan enjoys teaching CE 356 - Elements of Hydraulic Engineering at UT Austin.
Javad Mohammadi, CAEE Assistant Professor, Building and Energy Environments
Dr. Mohammadi is an assistant professor in the Department of Civil, Architectural, and Environmental Engineering at The University of Texas at Austin. Prior to joining UT, he was a faculty member in the Electrical and Computer Engineering department at Carnegie Mellon University (CMU). Javad is an IEEE senior member and a recipient of the Innovation Fellowship from Swartz Center for Entrepreneurship at CMU. His research interests include multi-agent optimization and machine learning in networked cyber-physical systems, including smart grid-interactive buildings, power grid, and electrified transportation systems. Javad received his Ph.D. in Electrical and Computer Engineering from CMU.
Shay Ralls Roalson, Director of Austin Water
Shay Ralls Roalson P.E. became the Director of Austin Water in January 2023, leading a team of 1,300 employees committed to providing high quality drinking water, wastewater, and reclaimed water services to over 1 million people in the Austin metropolitan area. Shay has 29 years of experience working with water utilities on the planning, design, and construction of complex water and wastewater infrastructure projects. She joined Austin Water as Assistant Director in April 2020, where she led the engineering services team responsible for delivering the utility’s $1.4 billion five-year capital program. Shay is a past Chair of the Texas Section of the American Water Works Association and has served on the External Advisory Committee for the University of Texas Cockrell School of Engineering. Shay received a Bachelor of Engineering degree from Vanderbilt University and a Master of Science degree from the University of Texas at Austin.
