UTSA Faculty, Staff and Postdoctoral Researcher Work
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Browsing UTSA Faculty, Staff and Postdoctoral Researcher Work by Department "Civil and Environmental Engineering, and Construction Management"
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Item A Hybrid TOPSIS-Structure Entropy Weight Group Subcontractor Selection Model for Large Construction Companies(2023-06-16) Gao, Ce; Elzarka, Hazem; Yan, Hongyan; Chakraborty, Debaditya; Zhou, ChunmeiThe selection of suitable subcontractors for large construction companies is crucially important for the overall success of their projects. As the construction industry advances, a growing number of criteria need to be considered in the subcontractor selection process than simply considering the biding prices. This paper proposed a hybrid multi-criteria structure entropy weight (SEW)—TOPSIS group decision-making model that considers 10 criteria. The proposed model was able to handle large amount of subcontractors’ performance data that were collected in different types. Additionally, the model can integrate experts’ judgments while accounting for their varying level of expertise and correcting for their biases. This paper also provided a case study to demonstrate the proposed model’s effectiveness and efficiency, as well as its applicability of large construction companies. While this study was applied to construction subcontractors’ selection, the proposed methodology can also be easily extended to various decision-making scenarios with similar requirements.Item A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides(2022-04-25) Zou, Yong; Zheng, ChaoThe effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.Item An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow(2016-06-09) Barge, Jonathan T.; Sharif, Hatim O.This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organizing Map (SOM) in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was utilized to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Utilizing EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalized mean biased error (NMBE) of −2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Utilizing the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of utilizing EEMD and an SOM in conjunction with LGP for streamflow forecasting.Item Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas(2021-09-01) Billah, Khondoker; Sharif, Hatim O.; Dessouky, SamerBicycling is inexpensive, environmentally friendly, and healthful; however, bicyclist safety is a rising concern. This study investigates bicycle crash-related key variables that might substantially differ in terms of the party at fault and bicycle facility presence. Employing 5 year (2014–2018) data from the Texas Crash Record and Information System database, the effect of these variables on bicyclist injury severity was assessed for San Antonio, Texas, using bivariate analysis and binary logistic regression. Severe injury risk based on the party at fault and bicycle facility presence varied significantly for different crash-related variables. The strongest predictors of severe bicycle injury include bicyclist age and ethnicity, lighting condition, road class, time of occurrence, and period of week. Driver inattention and disregard of stop sign/light were the primary contributing factors to bicycle-vehicle crashes. Crash density heatmap and hotspot analyses were used to identify high-risk locations. The downtown area experienced the highest crash density, while severity hotspots were located at intersections outside of the downtown area. This study recommends the introduction of more dedicated/protected bicycle lanes, separation of bicycle lanes from the roadway, mandatory helmet use ordinance, reduction in speed limit, prioritization of resources at high-risk locations, and implementation of bike-activated signal detection at signalized intersections.Item Analysis of Damage Caused by Hydrometeorological Disasters in Texas, 1960–2016(2018-10-20) Paul, Srikanto H.; Sharif, Hatim O.Property damages caused by hydrometeorological disasters in Texas during the period 1960–2016 totaled $54.2 billion with hurricanes, tropical storms, and hail accounting for 56%, followed by flooding and severe thunderstorms responsible for 24% of the total damages. The current study provides normalized trends to support the assertion that the increase in property damage is a combined contribution of stronger disasters as predicted by climate change models and increases in urban development in risk prone regions such as the Texas Gulf Coast. A comparison of the temporal distribution of damages normalized by population and GDP resulted in a less statistically significant increasing trend per capita. Seasonal distribution highlights spring as the costliest season (March, April and May) while the hurricane season (June through November) is well aligned with the months of highest property damage. Normalization of property damage by GDP during 2001–2016 showed Dallas as the only metropolitan statistical area (MSA) with a significant increasing trend of the 25 MSAs in Texas. Spatial analysis of property damage per capita highlighted the regions that are at greater risk during and after a major disaster given their limited economic resources compared to more urbanized regions. Variation in the causes of damage (wind or water) and types of damage that a "Hurricane" can produce was investigated using Hazus model simulation. A comparison of published damage estimates at time of occurrence with simulation outputs for Hurricanes Carla, 1961; Alicia, 1983; and Ike, 2008 based on 2010 building exposure highlighted the impact of economic growth, susceptibility of wood building types, and the predominant cause of damage. Carla and Ike simulation models captured less than 50% of their respective estimates reported by other sources suggesting a broad geographical zone of damage with flood damage making a significant contribution. Conversely, the model damage estimates for Alicia are 50% higher than total damage estimates that were reported at the time of occurrence suggesting a substantial increase in building exposure susceptible to wind damage in the modeled region from 1983–2010.Item Analysis of Flood Fatalities in the United States, 1959–2019(2021-07-05) Han, Zhongyu; Sharif, Hatim O.Flooding is one of the main weather-related disasters that cause numerous fatalities every year across the globe. This study examines flood fatalities reported in the contiguous United States (US) from 1959 to 2019. The last two decades witnessed major flood events, changing the ranking of the top states compared to previous studies, with the exception of Texas, which had significantly higher flood-related fatalities than any other state. The rankings of counties within some states changed as well. The study aims to improve understanding of the situational conditions, demographics, and spatial and temporal characteristics associated with flood fatalities. The analysis reveals that flash flooding is associated with more fatalities than other flood types. In general, males are much more likely to be killed in floods than females. The analysis also suggests that people in the age groups of 10–19, 20–29, and 0–9 are the most vulnerable to flood hazard. Purposely driving or walking into floodwaters accounts for more than 86% of total flood fatalities. Thus, the vast majority of flood fatalities are preventable. The results will help identify the risk factors associated with different types of flooding and the vulnerability of the exposed communities.Item Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017(2021-05-10) Billah, Khondoker; Adegbite, Qasim; Sharif, Hatim O.; Dessouky, Samer; Simcic, LaurenAn understanding of the contributing factors to severe intersection crashes is crucial for developing countermeasures to reduce crash numbers and severity at high-risk crash locations. This study examined the variables affecting crash incidence and crash severity at intersections in San Antonio over a five-year period (2013–2017) and identified high-risk locations based on crash frequency and injury severity using data from the Texas Crash Record and Information System database. Bivariate analysis and binary logistic regression, along with respective odds ratios, were used to identify the most significant variables contributing to severe intersection crashes by quantifying their association with crash severity. Intersection crashes were predominantly clustered in the downtown area with relatively less severe crashes. Males and older drivers, weekend driving, nighttime driving, dark lighting conditions, grade and hillcrest road alignment, and crosswalk, divider and marked lanes used as traffic control significantly increased crash severity risk at intersections. Prioritizing resource allocation to high-risk intersections, separating bicycle lanes and sidewalks from the roadway, improving lighting facilities, increasing law enforcement activity during the late night hours of weekend, and introducing roundabouts at intersections with stops and signals as traffic controls are recommended countermeasures.Item Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas(2021-06-10) Billah, Khondoker; Sharif, Hatim O.; Dessouky, SamerPedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.Item Application of Viscous Damper and Laminated Rubber Bearing Pads for Bridges in Seismic Regions(2021-10-20) Khedmatgozar Dolati, Seyed Saman; Mehrabi, Armin; Khedmatgozar Dolati, Seyed SasanNormally, Laminated Rubber Bearing Pads (LRBPs) are directly placed between girders and piers and their role is to provide the bridge span with horizontal movement, and to transmit the gravity loads from the deck to the piers. Although not designed for seismic loads, they can act as a fuse, partially isolating the substructure from the superstructure and keeping the piers intact during earthquakes. However, recent investigations show that large relative displacement of superstructure against substructure caused by sliding at bearing (sliding between girders and LRBPs) can cause expansion joint failure or even bridge span collapse. Accordingly, proper restrainers should be selected to prevent large displacement. Among all types of restrainers, viscous dampers as passive energy dissipation devices have shown a great capacity in damping earthquake energy. This study investigates the effectiveness of a VD-LRBP system, a viscous damper in conjunction with LRBPs, in dissipating energy and reducing the displacement of the superstructure with reference to the substructure caused by sliding at bearing during a seismic event. A Finite Element (FE) model was first developed and validated using available experimental and numerical results. With the validated model, a 3D Nonlinear Time History Analysis (NTHA) was conducted on a reinforced concrete bridge model under various records of earthquakes using OpenSees, an open-source finite element software. The relative displacement histories were recorded for the bridge in two cases: 1- with only LRBPs and 2- with viscous dampers and LRBPs (VD-LRBP system). The results of this study show that applying viscous dampers can reduce the relative displacement of the superstructure with reference to the substructure for up to 60 percent. As importantly, it can also reduce the residual displacement after the earthquake to near zero.Item Arbitrary-Order Sensitivity Analysis of Eigenfrequency Problems with Hypercomplex Automatic Differentiation (HYPAD)(2023-06-14) Velasquez-Gonzalez, Juan C.; Navarro, Juan David; Aristizabal, Mauricio; Millwater, Harry R.; Montoya, Arturo; Restrepo, DavidThe calculation of accurate arbitrary-order sensitivities of eigenvalues and eigenvectors is crucial for structural analysis applications, including topology optimization, system identification, finite element model updating, damage detection, and fault diagnosis. Current approaches to obtaining sensitivities for eigenvalues and eigenvectors lack generality, are complicated to implement, prone to numerical errors, and are computationally expensive. In this work, a novel methodology is introduced that uses hypercomplex automatic differentiation (HYPAD) and semi-analytical expressions to obtain arbitrary-order sensitivities for eigenfrequency problems. The new methodology exhibits no sign of truncation nor subtractive cancellation errors regardless of the order of the sensitivity, it is general, and can obtain any high-order sensitivities with the simplicity of first-order computations. A numerical example is presented to verify the accuracy of the method, where the free vibration of a homogeneous cantilever beam is studied. For this problem, up to third-order sensitivities of the eigenvalues and eigenvectors with respect to the material and geometrical parameters were obtained, considering the cases of close and distinct eigenvalues. The results were verified using analytical equations, showing excellent agreement for the eigenvalues and the eigenvectors. The new method promises to facilitate the computation of sensitivities for eigenfrequency problems into routine practice and commercial software.Item Assessment of Sea Level and Morphological Changes along the Eastern Coast of Bangladesh(2022-04-11) Anwar, Md. Shibly; Rahman, Kalimur; Bhuiyan, Md Abul Ehsan; Saha, RupayanBangladesh is one of the climate risk-prone countries in South Asia facing tremendous challenges to combat sea-level rise and its associated coastal morphological changes. This study aimed to determine the interaction of the sea-level rise and morphological changes, particularly at Cox's Bazar and Kutubdia Island along the eastern coast of Bangladesh. Available hourly tide gauge data, daily temperature, daily rainfall data, and 15 LANDSAT satellite images for the period of 1983–2016 were analyzed to examine the sea level shore morphological change and associated climate change phenomenon. First, we identified the historical nonlinear sea-level trend using Hilbert-Huang Transformation (HHT) based on the complete ensemble empirical mode decomposition (CEEMD) technique. We divided the study period into three distinct sea-level change periods of 1983–1993, 1993–2003, and 2003–2014 based on nonlinear sea-level trend analysis. The study revealed that the sea level on the east coast of Bangladesh had a moderate rising trend during 1983–1993, slight decrease during 1993–2003, and steep rising trend during 2003–2014. We also observed that a sea-level change within a particular period impacted the shore morphological change after approximately two years, such that the average sea-level change during the period of 1993–2003 might have affected the shore morphology for 1996–2005. Alarming shore erosion was found for the period of 2005–2016 compared to the previous periods of 1989–1996 and 1996–2005 for both Cox's Bazar and Kutubdia Island. The shore morphology of some segments was also substantially affected due to the geometric shape of the land, significant waves, and shore protection works. This study encourages policymakers to minimize the threats of sea-level rise and ensure sustainable coastal management strategies are introduced to sustain the vital eastern coast of Bangladesh.Item Assessment of the Performance of Satellite-Based Precipitation Products for Flood Events across Diverse Spatial Scales Using GSSHA Modeling System(2018-05-28) Furl, Chad; Ghebreyesus, Dawit; Sharif, Hatim O.Accurate precipitation measurements for high magnitude rainfall events are of great importance in hydrometeorology and climatology research. The focus of the study is to assess the performance of satellite-based precipitation products against a gauge adjusted Next-Generation Radar (NEXRAD) Stage IV product during high magnitude rainfall events. The assessment was categorized across three spatial scales using watershed ranging from ~200–10,000 km2. The propagation of the errors from rainfall estimates to runoff estimates was analyzed by forcing a hydrologic-model with the satellite-based precipitation products for nine storm events from 2004 to 2015. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) Morphing Technique (CMORPH) products showed high correlation to the NEXRAD estimates in all spatial domains, and had an average Nash-Sutcliffe coefficient of 0.81. The Global Precipitation Measurement (GPM) Early product was inconsistent with a very high variance of Nash-Sutcliffe coefficient in all spatial domains (from −0.46 to 0.38), however, the variance decreased as the watershed size increased. Surprisingly, Tropical Rainfall Measuring Mission (TRMM) also showed a very high variance in all the performance statics. In contrast, the un-corrected product of the TRMM showed a relatively better performance. The errors of the precipitation estimates were amplified in the simulated hydrographs. Even though the products provide evenly distributed near-global spatiotemporal estimates, they significantly underestimate strong storm events in all spatial scales.Item Bivariate-Logit-Based Severity Analysis for Motorcycle Crashes in Texas, 2017–2021(2023-06-30) Billah, Khondoker; Sharif, Hatim O.; Dessouky, SamerDue to the number of severe traffic collisions involving motorcycles, a comprehensive investigation is required to determine their causes. This study analyzed Texas crash data from 2017 to 2021 to determine who was at fault and how various factors affect the frequency and severity of motorcycle collisions. Moreover, the study tried to identify high-risk sites for motorcycle crashes. Utilizing bivariate analysis and logistic regression models, the study investigated the individual and combined effects of several variables. Heat maps and hotspot analyses were used to identify locations with a high incidence of both minor and severe motorcycle crashes. The survey showed that dangerous speed, inattention, lane departure, and failing to surrender the right-of-way at a stop sign or during a left turn were the leading causes of motorcycle crashes. When a motorcyclist was at fault, the likelihood of severe collisions was much higher. The study revealed numerous elements as strong predictors of catastrophic motorcycle crashes, including higher speed limits, poor illumination, darkness during the weekend, dividers or designated lanes as the principal road traffic control, an increased age of the primary crash victim, and the lack of a helmet. The concentration of motorcycle collisions was found to be relatively high in city cores, whereas clusters of severe motorcycle collisions were detected on road segments beyond city limits. This study recommends implementing reduced speed limits on high-risk segments, mandating helmet use, prioritizing resource allocation to high-risk locations, launching educational campaigns to promote safer driving practices and the use of protective gear, and inspecting existing conditions as well as the road geometry of high-risk locations to reduce the incidence and severity of motorcycle crashes.Item A block forward substitution method for solving the hypercomplex finite element system of equations(Elsevier, 2021-12-15) Aguirre-Mesa, Andres M.; Garcia, Manuel J.; Aristizabal, Mauricio; Wagner, David; Ramirez-Tamayo, Daniel; Montoya, Arturo; Millwater, HarryThe hypercomplex finite element method, ZFEM, allows the analyst to compute highly-accurate arbitrary-order shape, material property, and loading derivatives by augmenting the traditional finite element method with multiple imaginary degrees of freedom. In ZFEM, the real variables are converted to hypercomplex variables such as multicomplex, multidual, or quaternions. By uplifting the real variables to hypercomplex, derivatives are computed in an automated fashion using a standard finite element formulation. The use of multicomplex or multidual numbers provides higher-order derivatives. The drawback of ZFEM is that it increases the number of degrees of freedom of the real variable system by a factor 2^n, where n is the order of the required derivative. In consequence, ZFEM increases the memory consumption and the solution time of the system of equations compared to the real variable system. The block forward substitution method (BFS), proposed in this work, addresses the memory and runtime issues. This new method solves the original real-valued FEM system once. Then, the derivatives are computed using pseudo-loads with the original system of equations. In contrast with the conventional solution method of ZFEM, BFS computes the hypercomplex contributions to the stiffness matrix element-wise, and it never assembles nor solves the full hypercomplex system of equations. In effect, the BFS method generalizes the first-order semi-analytical complex variable method to any order derivative. The BFS method provides the capability to allow a combination of real-variable and hypercomplex-variable elements within the same model. The numerical results indicate that a first-order derivative can be obtained with 1% to 8% additional computational time of the real-variable analysis. This allows the computation of multiple first-order derivatives by post-processing of a single FEM analysis. Additionally, it was shown that fourth-order shape sensitivities can be computed in less than 5% additional runtime of the real-variable FEM analysis.Item Bridges Consumption Analysis for Oversize and Overweight Vehicles on Texas Roads(SAGE Publications, 2024-03-28) Weissmann, Jose; Weissmann, Angela J.; Inoue, Danilo Keniti Nais; Prozzi, Jorge A.Transportation infrastructure in Texas, U.S., particularly its extensive bridge network, plays a vital role in supporting economic growth and ensuring safe and efficient mobility. With over 57,000 bridges, including more than 22,000 culverts, Texas faces the challenge of maintaining its infrastructure while dealing with limited funding resources. This paper presents the findings of a study conducted for the Texas Department of Transportation that focused on assessing the costs associated with bridge consumption by oversize and overweight (OS/OW) vehicles. The study aimed to calculate bridge consumption costs per mile for different OS/OW categories, considering factors such as axle load, spacing, bridge design life, and vehicle miles traveled (VMT). These consumption costs were calculated by analyzing traffic data and bridge inventory ratings. The results showed that, although OS/OW vehicles are responsible for the majority of the bridge consumption costs per mile, their lower VMT, relative to regular commercial vehicles, mitigates their influence on total bridge consumption costs to less than 15% of the total. The findings of this study can contribute to the ongoing efforts of transportation engineers and policymakers in addressing funding deficits and developing strategies to sustain the state’s bridge infrastructure.Item Brief Communication: Analysis of the Fatalities and Socio-Economic Impacts Caused by Hurricane Florence(2019-01-26) Paul, Srikanto H.; Ghebreyesus, Dawit; Sharif, Hatim O.Florence made landfall on the southeastern coast of North Carolina (NC) generating torrential rainfall and severe flooding that led to 53 fatalities in three states (NC, SC, and VA) and $16–$40 billion in damage. Seventy-seven percent (77%) of the fatalities occurred in the rural flood plains of NC with Duplin county reporting a high of eight deaths. Approximately 50% of the total number of hurricane-related fatalities across the three states were vehicle-related. The predominant demographic at risk were males over the age of 50 years. The type of property damage was in line with other major hurricanes and predominantly affected residential structures (93% of the total number of damaged buildings). Florence is among the top 10 costliest hurricanes in U.S. history with approximately 50% of the damage projected as uninsured losses due to residential flooding. The cumulative 5-day rainfall resulted in major flooding along the Cape Fear, Lumberton, and Neuse rivers where many industrial waste sites (hog manure lagoons and coal ash pits) are located. Several of these waste sites located in the flood plain were breached and have likely cross-contaminated the waterways and water treatment operations. The observed extent of the flooding, environmental contamination, and impact to public health caused by Florence will add to the long-term disaster related mortality and morbidity rates and suggests an expansion of the 100-yr flood hazard zone to communicate the expanded risk to the public.Item Coastal Runoff in the United Arab Emirates—The Hazard and Opportunity(2019-09-29) Abdouli, Khameis Al; Hussein, Khalid A.; Ghebreyesus, Dawit; Sharif, Hatim O.Properly quantifying the potential exposure of hyper-arid regions to climate extremes is fundamental to developing frameworks that can be used to manage these extremes. In the United Arab Emirates (UAE), rapid growth may exacerbate the impacts of climate extremes through urbanization (increased runoff), population and industrial development (more water demand). Water resources management approaches such as Managed Aquifer Recharge (MAR) application may help mitigate both extremes by storing more water from wet periods for use during droughts. In this study, we quantified the volumes of runoff from coastal watersheds discharging to the Gulf of Oman and the Arabian Gulf that could potentially be captured to replenish depleted aquifers along the coast and help reduce the adverse impacts of urban flooding. To this aim, we first downloaded and processed the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) rainfall data for a recent wide-spread storm event. The rainfall product was then used as input to hydrologic models of coastal watersheds for estimating the resulting runoff. A multi-criteria decision analysis technique was used to identify areas most prone to runoff accumulation. Lastly, we quantified the volumes of runoff that could potentially be captured from frequency storms of different return periods and how rapid urbanization in the region may increase these runoff volumes creating more opportunities for the replenishment of depleted aquifers. Our results indicate that the average runoff from watersheds discharging to the ocean ranges between 0.11 km3 and 0.48 km3 for the 5-year and 100-year storms, respectively. We also found that these amounts will substantially increase due to rapid urbanization in the coastal regions of the UAE. In addition to water supply augmentation during droughts, potential benefits of application of MAR techniques in the UAE coastal regions may include flood control, mitigation against sea-level rise through subsidence control, reduction of aquifer salinity, rehabilitation of ecosystems, cleansing polluted runoff and preventing excessive runoff into the Gulf that can contribute to red tide events.Item A complex-variable cohesive finite element subroutine to enable efficient determination of interfacial cohesive material parameters(Elsevier, 2021-04-15) Ramirez-Tamayo, Daniel; Soulami, Ayoub; Gupta, Varun; Restrepo, David; Montoya, Arturo; Millwater, HarryA new complex-variable version of a cohesive element is presented that provides highly accurate first order derivatives of the nodal displacements with respect to the cohesive fracture parameters. These sensitivities are provided as a byproduct of the analysis using the complex Taylor series expansion method. This information is useful for inversely determining the cohesive fracture parameters from experimental or synthetic data using a finite element-based approach. In particular, the PPR cohesive element (Park et al., 2009), was extended using complex variables as a user element for the well-known commercial finite element program, Abaqus. The source code for the element is provided as an educational resource. The advantage of having accurate first order derivatives on both accuracy and efficiency is demonstrated through numerical examples.Item A complex-variable finite element method-based inverse methodology to extract constitutive parameters using experimental data(Elsevier, 2022-05-15) Ramirez-Tamayo, Daniel; Soulami, Ayoub; Gupta, Varun; Restrepo, David; Montoya, Arturo; Nickerson, Ethan; Roosendaal, Timothy; Simmons, Kevin; Petrossian, Gayaneh; Millwater, HarryThis paper presents the use of full-field kinematic measurements obtained using the digital image correlation (DIC) procedure and load–displacement data to determine constitutive material properties by solving an inverse finite element optimization problem. A key ingredient in the proposed approach is computing accurate sensitivities with respect to the unknown parameters. These sensitivities were used to solve the optimization problem using an accurate, efficient, gradient-based method, and were computed using the complex-variable finite element method, ZFEM. The use of ZFEM’s gradients to inversely determine material properties is demonstrated with two examples. First, the elastic–plastic material properties of DP-590 steel are obtained using a tensile test specimen. Second, the cohesive material parameters of an adhesive are determined using a double cantilever beam test. A significant outcome of this paper is that the use of a weighted residual formulation of the interfacial strain fields and the load–displacement data within the optimization procedure provides better estimates of the constitutive properties than using only the load–displacement data. This technique minimizes the relative error in both the strain fields and the load–displacement curve, which is important to obtain accurate interfacial properties.Item Data- and Model-Based Discharge Hindcasting over a Subtropical River Basin(2021-09-17) Billah, Khondoker; Le, Tuan B.; Sharif, Hatim O.This study aims to evaluate the performance of the Soil and Water Assessment Tool (SWAT), a simple Auto-Regressive with eXogenous input (ARX) model, and a gene expression programming (GEP)-based model in one-day-ahead discharge prediction for the upper Kentucky River Basin. Calibration of the models were carried out for the period of 2002–2005 using daily flow at a stream gauging station unaffected by the flow regulation. Validation of the calibrated models were executed for the period of 2008–2010 at the same gauging station along with another station 88 km downstream. GEP provided the best calibration (coefficient of determination (R) value 0.94 and Nash-Sutcliffe Efficiency (NSE) value of 0.88) and validation (R values of 0.93 and 0.93, NSE values of 0.87 and 0.87, respectively) results at the two gauging stations. While SWAT performed reasonably well in calibration (R value 0.85 and NSE value 0.72), its performance somewhat degraded in validation (R values of 0.85 and 0.82, NSE values of 0.65 and 0.65, for the two stations). ARX performed very well in calibration (R value 0.92, NSE value 0.82) and reasonably well in validation (R values of 0.88 and 0.92, NSE values of 0.76 and 0.85) at the two stations. Research results suggest that sophisticated hydrological models could be outperformed by simple data-driven models and GEP has the advantage to generate functional relationships that allows investigation of the complex nonlinear interrelationships among the input variables.
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