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In February 2021, Texas was hit by a severe winter storm. The extreme cold weather triggered an unprecedented increase of electricity demand and simultaneous failure of multiple electricity grid components. The dual effect caused a widespread power outage that left millions of Texans without power for days. This paper estimates the indirect economy-wide cascading impact of that power outage. It utilized historical make and use tables between 1997 and 2019 in an inoperability input-output analysis–based economic impact assessment model to quantify the expected indirect cost of the power outage in terms of the loss of gross domestic product of Texas. This research found that expected loss to be approximately $664 million in 2019 values. It also found that in Texas industries such as oil and gas extraction, petroleum, coal products, and so on are particularly vulnerable to such weather-related prolonged power outages. The outcomes of this paper can be used in planning for strategies to make the grid robust, reliable, and resilient to severe weather events. The identification of vulnerable industries can also help in planning for auxiliary capacities so the cascading economic impacts of the weather-related power outages can be reduced. [ABSTRACT FROM AUTHOR]

Multispan bridges with simply supported girders are straightforward to construct and economical but have historically utilized expansion joints between spans. The negative long-term impacts of expansion joints are well-documented in the literature; eventual leaking causes damage to the girder ends and substructures. To avoid this deterioration associated with the use of expansion joints, link slabs may be used instead. However, the complex induced forces and deformations associated with link slabs are not fully understood and may damage the slab, reducing the life of the superstructure. This paper presents the results of a field study examining bonded link slab behavior under ambient (in-service) live and thermal loads on five Texas bridges. Typical Texas link slabs are not debonded at the girder ends, feature continuous longitudinal reinforcement, and incorporate partial-depth precast concrete panels. The five bridges were instrumented with displacement gauges at the girder ends, at both link slab and expansion joint locations, and strain gauges attached to the bottom of the bridge deck. Displacement, strain, and temperature data collected for a period of 1–2 weeks were used to estimate link slab mechanics. The results show differences in behavior between the differing link slab details, lateral deck stiffness characteristics, and continuous deck unit lengths. The increase in superstructure stiffness provided by the link slab is quantified. Longitudinal deck cracking and reinforcing steel yield behavior are predicted at each monitored link slab. The calculated displacement and strain data show that both live and thermal load effects should be considered in the design. [ABSTRACT FROM AUTHOR]

A sewer system is a principal element of infrastructure in modern cities, accounting for massive amounts of public investments. Corrosion of manholes in the sewer system is a global issue, and millions of dollars are being spent on the maintenance, restoration, and replacement of deteriorated sewer networks. Concrete manholes in the sewer system are deteriorating due to the attack of sulfuric acid produced by microorganisms in a process termed microbial induced concrete corrosion (MICC), which reduces the lifespan of concrete sewer elements. The objective of this paper is to investigate the correlation between the gas- and liquid-phase sewer environmental factors and hydrogen sulfide concentration in the gas phase. The production, emission, and build-up of hydrogen sulfide gas in manholes is identified as a major cause of MICC in manhole shafts. The field study was conducted in more than 200 manholes in the City of Arlington (Texas, US). The data was collected every minute for 48 h to understand the trends of liquid- and gas-phase parameters such as hydrogen sulfide (H2S concentration), liquid and gas temperature, pH, DO, and relative humidity. The study also examines how gas-phase H2S concentrations vary with season; manhole design, including manholes' depth, slope, and presence of drop; and sewer flow conditions such as velocity and turbulence. Although no strong linear correlation was found between liquid-/gas-phase parameters, the manhole categories were found to play a significant role in H2S generation. The manholes with hydraulic jump generated the highest average H2S concentrations, followed by manholes with drops. High turbulence zones were observed in manholes of both categories, leading to H2S stripping from liquid to gas phase. The highest H2S concentration was recorded in summer, suggesting that higher liquid temperature resulted in increased bacterial activity, which generated greater liquid-phase sulfide. Greater Henry's law constants in summer, due to high temperatures, would have favored transfer of liquid-phase sulfide to the gas phase. [ABSTRACT FROM AUTHOR]

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model's performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas FortWorth, Texas, area. The results reveal good accuracy and robustness. [ABSTRACT FROM AUTHOR]

In February 2021, a winter storm brought snow, ice, and freezing temperatures, which caused severe interruptions in the electric power and water supply systems in Texas and surrounding areas. In this paper, we use survey data to investigate the ways that households adapted and reacted to those outages. The analysis aimed to determine (1) how common different household adaptations were, (2) how adaptations varied with outage and household characteristics, (3) how adaptations tended to occur together, (4) how unhappy households were as a result, (5) how household unhappiness varied with outage and household characteristics, and (6) what concerns influenced the unhappiness level. Results are compared with findings from a study that used an almost identical survey instrument but was based on a larger data set from Los Angeles. Findings from both studies suggest that almost everyone implemented at least one adaptation; most implemented several. They also agreed the most common adaptations were using candles, a flashlight, and/or a lantern; charging the cell phone in the car; purchasing bottled water; and delaying or reducing consumption. Both studies indicate that households experienced varied levels of unhappiness, which were similar for electric power and water interruptions. The reported levels of unhappiness were notably higher in Texas than Los Angeles, however, possibly because the outages had relatively long durations and were recent. Financial, time/effort, health, and stress concerns all were found to have a substantial influence on the extent of unhappiness, in both the Texas and Los Angeles analyses, suggesting that it is critical to consider all of them. Analysis of the Texas study introduced the new finding that repeated service outages during an event is associated with both increased adaptation implementation and greater unhappiness. Analyzing larger data sets from additional events in different locations would be helpful to further understanding of household experiences in service outages. Several practical applications emerge from this work. First, by knowing how people are likely to adapt, officials can better prepare to support their constituents during a crisis. Anticipating the distribution of unhappiness could point infrastructure operators to consider other measures for service quality besides downtime alone, as important as that is. Knowing the differential effects of outages on different populations can improve officials' understanding of social vulnerabilities in their communities, which is needed for planning, education, and outreach. Electric and water outages accompany many kinds of hazard events; the extent to which a local population can adapt, and how officials and infrastructure operators can enhance those adaptations, will be important elements of local community resilience. [ABSTRACT FROM AUTHOR]

Bridge management systems are a critical component in the toolbox of those who are responsible for maintaining a population of bridges. Deterioration models are generally incorporated in bridge management systems, but minimal consideration is paid to how those models work and how the assumptions inherent to the model might influence the prediction. This paper identifies, synthesizes, and assesses typical bridge deterioration model approaches from the stochastic family of models. Each model considered is applied to two data sets for bridges in Texas and compared. A novel modeling approach that considers all models together is described. The novel approach demonstrates the value of considering multiple models when attempting to predict a future condition or behavior. It was found that a simple multiple model approach inherently and transparently reduces the uncertainty of a single model approach. [ABSTRACT FROM AUTHOR]

The purpose of this work is to examine the immediate impacts of hurricanes on schools and document the competing priorities faced by educators as they return to their educational mission of serving students and assisting with communal recovery. The research team conducted 115 interviews with educators across 20 school districts after Hurricane Matthew in North Carolina and Hurricane Harvey in Texas. After the hurricanes, respondents described disruptions in the form of operational, personal, and displacement. Educators adapted to their shifting responsibilities by taking on new roles in managing school property, volunteering in the community, and managing relief efforts. This paper addresses a gap in the literature on the immediate impacts of hurricanes on schools by studying two storms in different states, which enables more transferable conclusions to be drawn than in prior studies. When helping schools make disaster plans, educators and emergency planners should address the anticipated disruptions, account for shifting responsibilities, and enable clear communication during the disaster and recovery process. [ABSTRACT FROM AUTHOR]

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations. [ABSTRACT FROM AUTHOR]

With the increasing number of aging bridges that need maintenance and rehabilitation (M&R), it is important to plan and prioritize M&R projects proactively. This process requires mathematical models capable of predicting bridge condition over multiyear planning horizons taking into account the long-term performance effects of M&R treatments. This paper describes the development, validation, and application of Markov chain–based bridge deterioration prediction models. Bridge condition is measured in terms of the National Bridge Inventory (NBI) deck rating, superstructure rating, substructure rating, and culvert rating. The models consider explanatory variables that affect bridge deterioration, including climate/environment, traffic loading, material type, and M&R type. Because data on M&R work history are often disconnected from bridge condition data, the study used a novel approach that allows for inferring past M&R type and timing based on changes in bridge condition ratings. The models were developed using condition data for 43,320 bridges across Texas extending from 2001 to 2017. The developed prediction models can be used as a tool to support bridge asset management planning in Texas. The modeling approach, however, can be used by transportation agencies across the world to develop bridge deterioration prediction models using their local empirical data. [ABSTRACT FROM AUTHOR]

Bridge deterioration is inevitable in service, and the inspection and maintenance of bridges are needed to ensure structural integrity. To make cost-effective inspection plans, bridge management departments need degradation models to predict future condition ratings of bridges. Although there have been studies on bridge degradation, the input features of models are selected mostly based on engineering experience, and no effective method has been proposed. Meanwhile, most models based on machine learning (ML) and deep learning (DL) only predict the degradation of bridges in a single year and cannot cover a complete inspection cycle (usually 2 years), providing limited decision support for the transportation departments. Besides, more accurate models are needed to predict the degradation trend of bridges. In response to these problems, an improved ReliefF algorithm is proposed to select features of bridges in the paper. Meanwhile, a new degradation model combining recurrent neural network (RNN) and convolutional neural network (CNN) is established. Historical data of bridges in the US state of Texas from 1992 to 2019 are employed to verify the proposed methods. The result shows that the improved ReliefF algorithm selects the appropriate feature set as the input of the prediction model, and the model accurately predicts the future condition ratings of bridges in the next 3–4 years. The research is beneficial to infrastructure management departments in allocating bridge inspection and maintenance resources reasonably. [ABSTRACT FROM AUTHOR]

An explosion occurred at a fertilizer plant in the town of West, Texas, on Wednesday, April 17, 2013, devastating a populated neighborhood. A total of 15 people were killed, and approximately 160 people were injured. Approximately 150 buildings were damaged in the explosion. The damage to affected homes and businesses was estimated to exceed $100 million. The purpose of this study was to collect on-site technical data of the explosion and gain knowledge of blast loadings and structural responses from this unprecedented explosion. Specifically, this research documented the building damages caused by the West Fertilizer plant explosion and the affected buildings' construction and materials and evaluated the technical information for the explosion, including the blast loadings, i.e., the air-blast incident overpressures and ground shocks, on different buildings with respect to the standoff distances. A total of two technical papers have been prepared. The Part I paper (this manuscript) focused on the building damage documentation and air-blast incident overpressure estimation for the West Fertilizer plant explosion case. The Part II paper focused on the effects of the ground shock caused by this West Fertilizer plant explosion. [ABSTRACT FROM AUTHOR]

An explosion occurred at a fertilizer plant in the town of West, Texas, on Wednesday, April 17, 2013, devastating a populated neighborhood. A total of 15 people were killed, and approximately 160 more people were injured. Approximately 150 buildings were damaged in the explosion, and the damage to affected homes and businesses was estimated to exceed $100 million. This research documents the building damages caused by the unprecedented explosion and evaluates the technical information for the explosion. The Part I paper of this research focused on the building damage documentation and air-blast incident overpressure calculations. As a companion paper, this manuscript (Part II) primarily focuses on the study of blast-induced ground-shock effects. The ground shock-induced building damages and ground vibration peak particle velocities (PPVs) are analyzed. With a combined consideration of these two types of blast loadings, the air-blast incident overpressures and ground shocks, the field investigated damages caused by the West Fertilizer plant explosion are explained in a more precise manner. The air-blast incident overpressure is identified as the dominant factor for the damages of both wood light-frame buildings and engineering buildings. The ground shock is the secondary factor but is still important. Concluding the research outcomes in the Part I and Part II papers, this study proposes a four-scale blast-induced damage severity evaluation system, demonstrates efficient blastloading calculation methods for both the air-blast incident overpressure and ground-shock PPV, and provides a correlation between the blast loadings and the blast-induced damages for the West Fertilizer plant explosion case. The documentation and analyses that resulted from this research could serve as a good case study for better understanding of the blast loadings and building structural responses. [ABSTRACT FROM AUTHOR]

Inspecting and assessing existing utility poles has become increasingly important for reducing the vulnerability of power distribution infrastructure systems in disaster situations, which can enhance community resilience. Although vision-based systems have been applied to detect faults in power distribution infrastructures, little research currently exists on assessing component- and network-level failures of utility poles based on their geometric and environmental information. This paper aims to propose a new data-driven approach to support risk-informed decision-making for utility maintenance under extreme wind conditions. Large-scale open-source imagery from Google Street View is used to assess geometric properties of utility poles (i.e., leaning angle). Then the failure probability of utility poles is analyzed under varying conditions (e.g., age, leaning angle, and wind loads) in a three-dimensional virtual city model. The proposed method is tested through case studies in Texas to (1) validate an algorithm for estimating leaning angles of utility poles and (2) understand the progress of failures of leaning utility poles from a network perspective. The outcomes of the case studies demonstrate that the proposed method has the potential to leverage large-scale open-source visual data to assess the vulnerability of utility pole networks that may lead to cascading failures in power distribution infrastructure systems. Based on the proposed virtual environment, the method is expected to enable practitioners to facilitate risk-informed decision-making against disaster situations, which creates an opportunity for prioritizing maintenance tasks regarding power distribution infrastructures. [ABSTRACT FROM AUTHOR]

Assessing the stability of rock slopes is essential to ensuring the typical performance of adjacent transportation infrastructures but simulating the existing field topography and boundary conditions for stability analysis is challenging. This paper describes a case study in which highly weathered rock slopes adjacent to a railroad track in Texas were analyzed using close-range photogrammetry data obtained from an unmanned aerial vehicle with close-range photogrammetry (UAV-CRP). A highly weathered rock mass with low intact strength was considered as a single continuum that undergoes circular failure and was analyzed using Spencer's two-dimensional (2D) and three-dimensional (3D) limit equilibrium method (LEM) analyses. A 3D critical slip surface was obtained to identify the critical sections on which to conduct the individual 2D stability analysis and obtain a safe resloping angle. These angles were further used to reslope the whole rock slopes and conduct a 3D analysis to arrive at a safe and economic slope angle for each side of the rock-cut. The realistic geometrical slopes developed from the aerial imagery were used to develop a framework for comprehensively analyzing the stability of a rock slope undergoing circular failure. [ABSTRACT FROM AUTHOR]

This paper presents findings from a side-by-side operational comparison of granular road salt (RS) and MeltDown 20 (MD) deicing chemicals for snow and ice control from the perspective of "the maintenance worker in the truck." The field test site, near Canyon, Texas, was divided into sixteen 305 m (1,000 ft) sections that were either untreated controls or received treatment with RS or MD at nominal Texas Department of Transportation (TxDOT) application rates of 85 kg/lane km (300 lb/lane mile) and 42 kg/lane km (150 lb/lane mile), respectively. Three storms met pre-established criteria for snow accumulation, and field test sections were subject to cycles of plowing, deicing treatment, and slushing. Video and still-image data facilitated estimation of visible pavement, and vehicle-based decelerometer tests measured pavement friction. Overall results showed that granular RS outperformed MD in 5/20 data events (25%) and RS outperformed untreated control in 9/20 data events (45%). In contrast, MD never outperformed RS and outperformed control in only 5/20 data events (25%). Further, TxDOT maintenance management data indicate the operational cost to apply MD (corrosion-inhibited) is 3.5× higher than RS (noninhibited), raising questions about cost-effectiveness given the road, climate, and temperature conditions characteristic of the north Texas latitude. [ABSTRACT FROM AUTHOR]

This paper presents a seismic slope stability assessment study of an inherently heterogeneous hydraulic fill dam in Texas. Extensive cone penetration soundings conducted at the dam site were interpreted to estimate different dam material properties. Eight sections distributed along the length of the dam were considered for the slope stability assessment to incorporate the effect of variations in material properties on the hydraulic fill dam. The conventional pseudostatic analysis method was modified to account for the differences in the behavior of the dam materials during static and seismic loading conditions. The current state of practice considers the same critical slip surface for both static and pseudostatic analyses. A modified pseudostatic slope stability analysis was performed to identify other possible critical slip surfaces with lower factors of safety values than the critical slip surface obtained from the static stability analysis. A reliability-based approach was then adopted to incorporate the effect of uncertainty on the estimation of shear-strength parameters of the dam materials, and the expected performance during different seismic events was evaluated. Two hypothetical earthquake events were also considered for the postearthquake deformation and serviceability assessment studies. The approach adopted in this research study facilitates identifying the critical sections of a dam that the conventional analysis method may not consider. Hence, this new approach would be integral toward developing guidelines for evaluating the seismic resilience of earthen dams. [ABSTRACT FROM AUTHOR]

Highway maintenance and rehabilitation (M&R) planning is a multifaceted decision-making process that requires the consideration of multiple but often conflicting objectives. Traditionally, state highway agencies (SHAs) have focused on maximizing the condition of a pavement network or minimizing agency costs needed to keep the network at an acceptable physical and functional level of service. However, other objectives like minimizing the road user costs and greenhouse gas (GHG) emissions are seldom factored into the M&R decision-making process. In this paper, a mixed integer program that accounts for these three objectives is presented. The proposed model is then implemented on a subset network in Texas consisting of 100 pavement sections. The results demonstrate the Pareto-optimal relationships that exist between the road condition improvements, road user costs, and GHG emissions. Notably, there is a positive relationship between condition score improvements and the observed marginal GHG emissions from the case network implementation. Furthermore, this network's results indicate that GHG emissions from materials and construction contribute to the most significant proportion of the total estimated GHG emissions. The results of the sensitivity analysis confirm the observed trends in the network case examined and provide valuable insights concerning the impact of variability in the budget size. The principal contribution of this study is in providing an approach for highway decision-makers to perform trade-offs in road condition improvements, road user costs, and GHG emissions of alternate pavement M&R programs or policies. [ABSTRACT FROM AUTHOR]

Lean construction (LC) holds potential benefits for project labor productivity; however, the impact on the industry has not been significant. Among other factors, the self-interested behavior of stakeholders, called autonomous agents (AAs) in game theory, has affected LC performance. AAs make decisions for their own benefit rather than for the sake of the project. This paper presents an exploration of the potential impacts that the behavior of subcontractors, as AAs, have had on the performance of LC tools, such as the last planner system (LPS). A qualitative approach was used to collect data through personal interviews with 74 participants in eight projects, built by four construction companies, throughout Texas. A quantitative approach was used to analyze the interviews. The findings were as follows: (1) subcontractors behaved as AAs; (2) the LPS did not prevent the behavior of subcontractors AAs, but it supported the management of issues by the behavior of the subcontractors as AAs; and (3) the behavior of subcontractors as AAs affected LPS performance. This study contributes to the body of knowledge on engineering management by identifying a problem affecting LC, prompting academics and practitioners to investigate solutions that will benefit the labor productivity of the industry. [ABSTRACT FROM AUTHOR]

Current materials and construction specifications for pavement preservation treatments are predominantly prescriptive and rarely correlate the initial quality of the treatment to its future performance. As a result, it is difficult for highway agencies to account for the performance lost or gained due to differences between the as-designed treatment and as-constructed treatment. Also, these specifications may not incentivize contractors to focus on factors that affect the treatment's long-term performance. To account for these limitations, this paper provides a simulation-based performance-related specification (PRS) methodology for pavement preservation treatments, with application to thin hot-mix asphalt (HMA) overlays. Through performance prediction models and lifecycle cost analysis, this methodology determines pay factors based on the probability distributions of the total lifecycle costs of the as-designed and as-constructed treatments and the unit bid price. The developed PRS methodology was applied to four Long-Term Pavement Performance sections in Texas, Idaho, Florida, and New Jersey that received thin HMA overlay treatments. These case studies demonstrated the applicability of the developed methodology and provided insights into the relationships between the initial quality of thin HMA overlays and expected pay factors. [ABSTRACT FROM AUTHOR]

The focus of this study is the 1938 Rio Grande Compact water supply. The compact is an interstate agreement signed in 1938 between the states of Colorado, New Mexico, and Texas to evenly apportion the waters of the Rio Grande Basin. A shortage in deliveries is at the center of a lawsuit that began in 2011, went to the Supreme Court in 2013, and has not been resolved. This research statistically characterized the differences between water available and water delivered by Colorado and New Mexico, signatories of the Rio Grande Compact with Texas, to understand changes over time and uncover potential autocorrelations. Unexpected changes in water deliveries can have serious financial impacts on communities, thus the importance of shedding light into the behavior of the differences between water available and delivered. One of the findings of the research was that the annual credit/debit time series for Colorado was significantly correlated with the current and immediately previous scheduled delivery as well as with itself in the two previous years, and the behavior is explained by a transfer function model (TFM). For New Mexico, the annual credit/debit series did not display a clear pattern; therefore, it was not possible to characterize its relationship with scheduled delivery with the method examined. This technical note contributes to the extensive body of literature of the Rio Grande Compact in a way no other study has done as well as contributing to the wealth of literature related to the use of the transfer function model in hydrology. The main contribution of the study is the methodology developed and used, which could be useful to other river systems and transboundary water sharing agreements. This research statistically characterized the differences between water available and water delivered, also known as credits and debits, by Colorado and New Mexico, signatories of the Rio Grande Compact with Texas, to understand changes over time and uncover potential autocorrelations. A legal conflict between the signatories of the compact has lasted over a decade. Texas alleges that New Mexico is extracting groundwater, shortening their share of the water. Unexpected changes in water deliveries can have serious financial impacts on communities so shedding light on the differences between water available and water delivered can contribute to the debate. One of the research findings was that the annual credit/debit time series for Colorado was significantly correlated with the current and immediately previous scheduled delivery as well as with itself in the two previous years, and the behavior is explained by a TFM. For New Mexico, the annual credit/debit series did not display a clear pattern; therefore, it was not possible to characterize its relationship with scheduled delivery with the method examined. The main contribution of the study is the methodology developed and used, specifically the TFM, which could be tweaked to fit other river systems and transboundary water sharing agreements. [ABSTRACT FROM AUTHOR]

We employed a dataset of 1,229 rainfall-runoff events in 47 watersheds in Texas to explore the performance of the Natural Resources Conservation Service (NRCS) Curve Number Method in determining peak discharges given varying initial abstraction ratios (λ). For each watershed, a curve number was calibrated from rainfall-runoff events using the least-squares approach, and the standard error ratio was used to evaluate calibration performance. Curve numbers and standard error ratios were compared for both λ=0.20 and λ=0.05. Hypothesis testing found no significant difference in the standard error ratios within the five Texas regions studied, highlighting the need for some alternative criteria on which to select the most appropriate λ value for modeling and design. An equation was developed that computes equivalent curve number between λ=0.20 and λ=0.05. Finally, we presented a method to predict whether small or large λ will estimate larger runoff and recommended a design approach that selects the λ value such that the larger runoff is estimated, leading to the more conservative hydrologic design. [ABSTRACT FROM AUTHOR]

Rainfall-induced flash floods are characterized by their rapid onset and small spatial scale. With little lead time for warning, floodwater can accumulate rapidly and its force can damage roads, swamp houses, destroy bridges, and scour out channels. Having data-driven estimates of potential economic losses from flash floods (before they occur) helps authorities make informed decisions about planning and prioritizing mitigation projects. This article provides a probabilistic predictive model to estimate flash flood economic damage at the census tract scale. To simplify model utilization and avoid strong assumptions about property value and replacement costs, the model predicts the total cost of property and infrastructure damages for individual census tracts (expressed in 2019 prices). The model was developed based on a flash flood data set for a 15-year period (2005–2019) in Texas. The data set was assembled by integrating disparate data from multiple platforms. The occurrence of economic damage was found to be a zero-inflated problem. Therefore, we developed a two-part mixed-effect model. The model first estimates the probability that economic damage will occur (zero-inflated part) and then predicts the dollar amount of the economic damage (continuous part). Utilization of the developed model was demonstrated in an application to Harris County, Texas. [ABSTRACT FROM AUTHOR]

Compatibility holds great importance because it can predict the overall performance of asphalt mixtures. Incompatible aggregate-binder duos can result in a weak and friable mix as well as increase mixtures' stripping potential. Being unaware, local agencies and contractors often struggle with premature pavement failure, which consequently places unexpected strain on the budget. Thus, this study assessed the compatibility among 18 asphalt binders with a performance grade (PG) of PG 64-22, PG 70-22, and PG 76-22, each collected from six different refineries; and eight different types of aggregates (sandstone, novaculite, limestone, and dolomite, each collected from two different quarries). Viscosity, penetration, pH, and work of cohesion values of binders were used to develop a binder ranking system. Similarly, the aggregates were ranked based on physical (specific gravity and absorption), durability (abrasion resistance and soundness), and chemical (pH and surface free energy) properties. Overall relative rankings of aggregates and binders were also provided by assigning weight factors to different tested parameters. A total of 144 aggregate-binder mixture systems were ranked based on their dry and wet work of adhesion values as well as Texas boiling test results. The compatibility ratio (CR) was also computed, and each of the systems was designated A, B, C, or D based on their relative CR values. Upon analysis, three binders (S2B2, S3B1, and S4B1) and three aggregates (DM2, LS2, and DM1) were designated as the most preferable construction materials. CR values predicted LS2, DM1, and DM2 mixtures as the most compatible mixtures. SS2, LS1, and DM2 mixtures showed the highest stripping resistance in the Texas boiling test. These databases are expected to help the agency and asphalt producers to select compatible binders and aggregates for producing durable asphalt concrete. This study covered a wide variety of aggregates and asphalt binders prevalent in but not limited to Arkansas. A database developed under this project was based on observed individual aggregates' and binders' performances as well as their combined performances. The information provided in this study will help contractors and agencies to choose the most appropriate aggregate-binder system within their reach for producing quality asphalt mixtures. By knowing the rankings, state agencies can adjust project budgets and assign timely rehabilitation due to the usage of less compatible aggregate-binder systems. Contractors can tweak the existing mix design if they must use local but inferior materials. This ranking will also help them to select project-specific materials depending on the project's significance. The refineries as well will have the opportunity to improve their products to be more compatible with local materials. [ABSTRACT FROM AUTHOR]

Accurate modeling of infiltration losses is vital for runoff estimation and thus the development of flood design/protection criteria and water management schemes, etc. In design flood practices, the initial abstraction and constant loss (IACL) method has been widely applied due to its simplicity. However, due to a lack of physical equivalent properties, the IACL method is often subject to issues in parametrization and has large dependency on calibration efforts for storm events. Despite the wide range/variability of IACL values, a single set of IA and CL values is normally adopted for specific flood frequency, which may introduce uncertainty and bias in resulting peak streamflow. In this study, we identified a total of 2,036 rainfall-runoff events for 18 watersheds in North Central Texas to estimate the total losses with their IA and CL components based on time-series of mean areal precipitation (MAP) and streamflow data. Threshold behavior is found for all studied subbasins between the summation of gross rainfall and antecedent soil moisture versus runoff depth: below the threshold, runoff depth is minimal; whereas above it, runoff is largely linearly correlated with the summation of rainfall and antecedent soil moisture. This finding provides a convenient way to estimate/predict total loss or runoff depth given MAP and antecedent soil moisture. In addition, this study shows that the IA and CL values can be approximated by the gamma and Weibull distributions, respectively. The fitted distributions of IA and CL values can be applied in a Monte Carlo simulation framework to stochastically simulate numerous rainfall-runoff events for a flood frequency analysis. [ABSTRACT FROM AUTHOR]

Road crashes cause significant traffic delay, which can bring unnecessary financial losses. The objective of this study is to predict the level of delay caused by crashes (LDC) and discuss significant risk factors. To ensure the efficiency and accuracy of prediction, an improved stacking model was developed using Texas crash data of 2020. The first layer integrates seven base classifiers and the second layer tests three classifiers with different advantages. To improve and simplify the stacking model, three state-of-the-art methods-Bayesian hyperparameter optimization (BO), multiobjective feature selection (FS), and ensemble selection (ES)-were used. First, the hyperparameters and the least and most effective features were selected for each base classifier by BO and FS, respectively. Then ES, considering diversity and performance, selects the least base classifiers to reduce the input of the second layer. Finally, permutation feature importance was used to interpret the best stacking model. The results indicate that the stacking model achieves superior performance on four indicators: recall, G mean, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC-ROC). FS significantly improves the efficiency of the stacking model and ES obtains a simplified stacking model without significantly reducing performance. In addition, the combination of the two methods (FS and ES) tends to achieve the best performance, and six risk factors have the greatest contributions in prediction using permutation feature importance. The prediction of LDC and the analysis of the main contributing factors help road managers respond to the rescue strategies to mitigate traffic congestion caused by crashes in a timely manner, thus minimizing economic losses. [ABSTRACT FROM AUTHOR]

In many cases, estimation of the peak discharge is the primary goal of hydrologic modeling. We employed a dataset of 1,648 rainfall-runoff events in 104 watersheds in Texas to explore the peak rate factor (PRF) of 2.08 recommended by the Natural Resources Conservation Service (NRCS) for determining peak discharges with the unit hydrograph method, as well as the PRF's dependency on watershed and storm characteristics. For each event, a unit hydrograph—assumed to follow a two-parameter gamma distribution—was determined by deconvolving the direct runoff hydrograph with the excess rainfall hyetograph. Results showed PRFs reaching up to 9.75 in Houston watersheds with a median of 0.58 and PRFs up to 11.02 with a median of 1.42 for the rest of Texas. It was also found that the recommended PRF of 2.08 falls between the 75th and 90th percentile of the 1,043 events analyzed in all regions except Houston, and is above the 95th percentile in all 605 events analyzed in Houston. Finally, statistical analysis showed that the PRF is primarily dependent on the watershed's geomorphology but not on its slope, as was suggested earlier. This dependency, however, although statistically significant, explains only marginally the PRF variability. [ABSTRACT FROM AUTHOR]

Many people believe that once structural public flood protection is completed, disasters are completely prevented. Benhe community was the only place in Taiwan where a flood detention pond had been built in 2005. After that, the community was developed intensively. However, when Typhoon Fanapi brought record-high rainfall of 345.5 mm in 3 h to the Kaohsiung area in 2010, Benhe residents suffered severe flooding because the rainfall exceeded the capacity of the pond's pumping station. Based on interviews with residents, the authors discovered that the incomplete risk information provided by the local government and previous no-damage experiences of the detention pond lowered the risk perception and created a false image that the area was fully secured against any type of rainfall. Therefore, a future risk-communication and disaster-preparedness plan should adopt a proactive approach to increasing the awareness of flood risk by taking into account how public flood protection structures influence the intensity of risk perceptions and how community residents act upon them. [ABSTRACT FROM AUTHOR]

Key integrated components of the Texas sustainable water management enterprise include a regional and statewide planning process, water rights permit system and other water allocation mechanisms, process for establishing environmental flow standards, and water availability modeling system. These statewide endeavors mandated by the Legislature are implemented by a water management community comprised of state, local, and federal agencies, private industry, stakeholders, interests groups, consulting engineering firms, and university researchers. Texas is a large state with a rapidly growing population, declining groundwater supplies, intensifying demands on limited surface water resources, extreme hydrologic variability including severe droughts, and very diverse climate, geography, economic development, and water management practices. The Texas experience is illustrative of fundamental concepts and issues involved in statelevel efforts to achieve sustainable water management. [ABSTRACT FROM AUTHOR]

Landscape water budgets provide a benchmark of efficiency in residential outdoor water consumption, as well as a means of assessing overirrigation and possible water savings through conservation. This study investigates outdoor water conservation potential by analyzing trends, patterns, and drivers of overirrigation across 14,300 single-family residential customers in a central Texas city over a 5-year period (2015–2019). Customers exceeding their budgets were categorized using a bivariate framework based on temporal and quantitative measures of overirrigation. Between one-third and two-thirds of the customers exceeded their budgets during at least 1 month of the growing season, wasting an average of 423,000 m3 of water annually. Approximately 10% of customers contributed 56% of the total overirrigation during the study period. Overirrigation occurred throughout the growing season, but the number of customers and total volume wasted were highest in the fall months. Both overirrigation frequency and the amount wasted per unit of area tended to increase with property market value and decrease with landscape size. The methods and findings presented in this study can help water utilities evaluate potential outdoor water savings in their service areas and identify candidate customers for conservation interventions, such as landscape irrigation audits and educational workshops on efficient landscape watering. [ABSTRACT FROM AUTHOR]

With an increasing emphasis on sustainable planning, highway decision makers need to shift their focus from developing a cost-effective highway program to developing a more comprehensive sustainable highway program. It includes incorporating the three components of sustainability, namely economics, the environment, and social equity. Studies in the literature have primarily focused on economic and environmental objectives. However, there are limited studies incorporating social equity in highway maintenance and rehabilitation (M&R) programming. Additionally, a review of the existing literature indicated that although studies have focused on single or dual objectives of sustainability, the existing literature failed to address all the three components of sustainability simultaneously. Accordingly, this study aimed to address this issue by proposing an M&R budget allocation model for developing a sustainable highway asset management plan that concurrently focuses on all the three objectives of sustainability. The proposed model assists decision makers with a range of potential decision alternatives that address multiple sustainability objectives. The proposed model was applied to a case-study example on a road network in Texas. The findings from the case study indicated that decision makers can implement sustainable M&R programs that can efficiently achieve social equity objectives without specifically compromising the environmental and economic objectives. [ABSTRACT FROM AUTHOR]

Two-sided extreme conditional sampling regularly is coupled with copula theory to assess the dependence between flood-risk drivers such as extreme precipitation or river discharge and storm surge. The approach involves many subjective choices, including sampling techniques used to identify extreme events [block maxima or peaks-over-threshold (POT)], whether to account for the fit of marginal distributions, and time-lags considered between the two drivers. In this study, estimates of the potential for compound events at three sites along the Texas Gulf Coast, where the USACE is undertaking coastal storm risk management (CSRM) projects, were shown to be highly sensitive to the setup of the statistical model. A pragmatic approach accounting for marginal fit in a POT framework is proposed and was shown to provide stable estimates of the compounding potential for high discharge and storm surge events. We also explored the effect of using precipitation as a proxy for discharge in the absence of sufficiently long discharge records. [ABSTRACT FROM AUTHOR]

To fully automate building code compliance checking, regulatory requirements need to be automatically extracted and transformed from building codes. Existing regulatory requirement processing efforts mainly focus on building code requirements in text. A more efficient approach for processing regulatory requirements in other parts of building codes, such as tables or charts, remains to be addressed. The ability to process building code requirements in all parts and formats is necessary for an automated code compliance checking system to achieve full coverage of checkable building code requirements. To address this gap, the authors propose a semiautomated information extraction and transformation method. The proposed method can extract building code requirements in tables and convert extracted information to logic rules. Automated code compliance checking systems can utilize the logic rules. The proposed method includes two main steps: (1) tabular information extraction, and (2) rule generation. The tabular information extraction semiautomatically detects the layout of tables, extracts building code requirements from tables, and transforms extracted information to databases. The rule generation step automatically generates logic rules that can be directly executed by logic reasoners. The rule generation step also provides options for users to further refine the generated rules. The development of the tabular information extraction algorithm takes an iterative approach. An experiment was conducted to develop a tabular information extraction algorithm from a section of existing code. The primary version of the algorithm correctly processed 91.67% of tables in a second sample section. After iterative refinements, the updated tabular information extraction algorithm correctly processed all tables in this section. The rule generation algorithm correctly generated logic rules that successfully represented the applicable building code requirements for a testing building, a convenience store in Texas based on the tabular information. [ABSTRACT FROM AUTHOR]

Rapid and informed response is needed to ensure effective water management during droughts, including reliable and immediate data synthesis, near-real-time forecasting, and model-based decision support for water operations. A service-driven approach has been developed previously to couple river modeling and genetic algorithm (GA) optimization services for determining optimal water allocation strategies under daily drought scenarios. However, the computational effort in handling the constraints, which involves executing computationally intensive models repetitively, is a major obstacle to enabling an effective real-time Web application for decision support. The objective of this work is to develop a computationally efficient metamodel approach to reduce the computational burden of the simulation-optimization model. Two types of metamodels are developed: a pretrained metamodel that is built offline before the optimization and an adaptive metamodel that is built and updated during the optimization. The metamodel is a classifier algorithm that evaluates whether a constraint is satisfied, which simplifies the prediction and leverages the metamodel's role in water management. The metamodel framework was tested for a drought event in the Upper Guadalupe River Basin, Texas, in April 2015 and the performance of the different approaches is compared. A conservative model, which narrows the feasible region by increasing the constraint probability threshold, is needed with the pretrained metamodel to ensure convergence to a feasible near-optimal solution, but not for the adaptive metamodel. The results also show that the adaptive metamodel GA performs best in model accuracy and reduces computation time by 58%, compared with the pretrained metamodel GA, which reduces computation time by 78% but does not reliably obtain the optimal solution. The approach also does not require users to select classifiers, tune parameters, or execute simulation models offline. Therefore, a prototype Web interface is implemented that uses the best-performing adaptive metamodel approach to more efficiently assist decision makers with real-time drought management. [ABSTRACT FROM AUTHOR]

Coastal transportation systems are extremely vulnerable due to the coupled impacts of storm surge, waves, and inundation. Existing literature has developed coastal fragility models for bridges. However, to date, flood fragility models for coastal roadways are lacking. For this purpose, the current study proposes a data-driven fragility model based on logistic regression for coastal roadways, with failure probability conditioned on distance to shoreline and inundation duration, using hindcast data for Hurricane Ike. In addition, the effect of bridge and roadway damage on transportation network performance is investigated through a case study on Galveston Island, Texas. The results indicate the spatial distribution of storm impacts on the transportation network, with select roads highly vulnerable if they are located within a couple of hundred meters of the shoreline. In addition, considering roadway damage in addition to bridge damage alone, which is the current state of the art, can have a significant impact on decreasing the performance of the transportation network. Such analyses shed light on potential policy or risk mitigation practices that are expected to be increasingly important in the future as sea level rise further reduces roadway distance to the shoreline or as storm intensity and frequency changes. [ABSTRACT FROM AUTHOR]

During disasters, maintaining the functionality of infrastructure systems requires effective and efficient stakeholder collaboration. Aiming at understanding stakeholder collaboration robustness in response to infrastructure interruptions, this research proposes a network-based approach that comprises two steps: (1) generating response networks through network analysis; and (2) indicating time evolution of stakeholder collaboration robustness through a measure of topological network robustness. A case study, which examines stakeholder collaboration on protecting transportation infrastructure in Texas during Hurricane Harvey, was conducted to demonstrate the feasibility and validity of the proposed approach. From the case study, several social processes were identified to facilitate decision making in future disaster response. In addition, the case study implies that the documentation and sharing of disaster response information at the local level require further improvements. Overall, this research promotes interorganizational collaboration in emergency management through creating a systematic, network-based approach for quantitatively analyzing time evolution of stakeholder collaboration robustness. The findings provide implementable support for stakeholders to advance their collaboration knowledge, thereby enhancing the overall response performance. [ABSTRACT FROM AUTHOR]

This work focuses on developing methods to better manage significant imbalances between water supply and demand during droughts. A service-driven approach (Model as a Service, or MaaS) is used to couple river modeling services with optimization services for determining optimal water allocation strategies under daily drought scenarios. It demonstrates the promise of coupling simulation-optimization model services to improve real-time water management in a service driven framework, which should be beneficial to many other water resource applications. The approach is implemented using the DataWolf workflow tool and AzureML Cloud machine learning services and applied to an April 2015 drought event in the Upper Guadalupe River Basin, Texas. Weather and water demand uncertainty are considered through scenario-based optimization. The optimization objective is to minimize the daily total curtailment hours across all groups of permit holders. The scenario analysis shows that the current permit grouping system has a significant impact on the optimal water allocation strategy. The scenarios also demonstrate that noncompliance of junior water users is predicted to have a much greater effect on the river system than noncompliance of senior water users. The resulting framework can be deployed for water allocation in any area by updating water user information, water allocation policy constraints, and river data that can be obtained from publicly available sources. [ABSTRACT FROM AUTHOR]

To investigate the effect of infrastructure traits on resilience after an exploration, a blast case (West Fertilizer Plant in West, Texas, 2013) was studied, in which all the buildings' damage data (damage pictures, damage scales, and building locations) and resilience information (recovery decision, recovery time, and recovery cost) were collected by authors through site visits, interviews, and appraisal data collections. The novel analysis methods and machine learning algorithms (logistical/linear regression, neural networks, k-nearest neighbor, support vector machine, and gradient boosting) were applied to analyze the West Fertilizer Plant explosion resilience. This study is unique because it implements a resilience analysis for an explosion hazard, although there are some reports discussing the resilience after natural hazards, such as earthquakes, tsunamis, hurricanes, and tornados. Additionally, using machine learning for resilience analysis is also unique. The results can assist decision-makers, civil engineers, and building designers in designing the most resilient structures and/or materials for buildings. The findings in this study can help to develop the most resilient buildings, communities, and cities by considering the impact of explosion hazards. [ABSTRACT FROM AUTHOR]

An asphalt-binder is one of the key constituent elements that significantly influences and controls the fracture behavior and cracking performance of hot-mix asphalt (HMA). In particular, the low-temperature rheological properties of asphalt-binders are critical in terms of improving the HMA fracture properties in the laboratory and ultimately mitigating cracking in the field. The bending beam rheometer (BBR) is one of the most widely used laboratory test methods for evaluating and quantifying the asphalt-binder behavior at low temperatures, mostly using the stiffness (S) and m-value parameters. Using the Texas flexible pavements and overlays database, namely, the Texas data storage system (DSS), as the primary data source, this study was conducted to correlate the asphalt-binder low-temperature rheological properties (measured using the BBR) to the HMA fracture properties. The HMA fracture properties were measured in the laboratory using the monotonic-loading overlay tester (ML-OT). In general, the asphalt-binder low-temperature rheological properties [particularly the stiffness (S)] exhibited promising potential to predict the HMA fracture properties, namely, the tensile strain (εt) and fracture energy index (FE Index), with a coefficient of determination (R2) greater than 60%. [ABSTRACT FROM AUTHOR]

Hurricanes are among the most destructive and costly extreme weather events. The intensity of future hurricanes is generally expected to increase due to climate change effects. In this work, a simulation method based on a comprehensive statistical analysis of historical data is developed to account for the changes in climatological conditions and their effects on the frequency and intensity of hurricanes. This method is applied to simulate hurricane wind speed distributions under different climatological conditions in the US Atlantic basin from Texas to Maine, which is one of the regions in the world most vulnerable to hurricane hazards. To this end, regression models for several different hurricane parameters are fit to the historical hurricane data. The proposed model is validated by comparing its predicted hurricane-induced wind speeds with available historical data and other existing models based on physics-based hurricane path simulation. This new model is found to reproduce very well historical wind speed distributions and to provide wind speed projection results that are consistent with those of more computationally expensive models based on the simulation of hurricane tracks. The statistical characteristics of future potential hurricanes are simulated using the proposed model along with the climate projections presented in the Fifth Assessment Report of the Intergovernmental Panel for Climate Change. The results of this study indicate that by the year 2060 and depending on the considered projection scenario, the design wind speeds along the US Gulf and Atlantic Coasts corresponding to the different mean return intervals considered by ASCE 7 are expected to increase on average between 14% and 26%, which corresponds to an average increase in the design wind-induced loads of between 30% and 59%. [ABSTRACT FROM AUTHOR]

In this research study, the stability of a hydraulic fill dam located in a newly declared seismically hazardous zone in north Texas was evaluated, incorporating the effect of geomaterial variability existing in the dam. Hydraulic fill dams exhibit highly variable geomaterial conditions due to the method of construction and are prone to stability issues during earthquakes. Hence, a comprehensive study was needed to account for such variations in geomaterial properties and assess the stability of the dam rather than performing routine analysis using representative geomaterial properties for the shell, core, and foundation of the dam. Cone penetration tests were performed on the dam to study geomaterial properties and their variabilities. These results were used to develop three-dimensional (3D) visualization models using kriging analysis. The geomaterial properties obtained from the visualization models were subsequently used in the slope stability analyses. The developed visualization models depicted the variation of geomaterial properties within the dam. The use of the visualization models has led to the development of an innovative approach for performing stability analyses of earthen dams with high geomaterial variability. [ABSTRACT FROM AUTHOR]

Radar-based quantitative precipitation estimate (QPE) serves as input for flood forecasting, and its importance gets magnified during catastrophic storms, e.g., Hurricane Harvey in 2017. The record-breaking rainfall from Hurricane Harvey covered vast spatial extents and lasted for a 5-day period, providing a unique chance for evaluating radar errors, especially their spatiotemporal dependence. Using the rainfall data of Hurricane Harvey, the authors utilize a new method for sampling ground-based rainfall measurements over radar pixels (i.e., spatial reference rainfall) based on subpixel rainfall variability. The new method aims to enlarge the sample size and allow for compressively evaluating the QPE. Two hourly QPE products, the Next Generation Weather Radar (NEXRAD) Stage IV and Multiradar Multisensor (MRMS), are chosen for the evaluation due to their roles in major flood forecasting activities; and a dense rain gauge network covering the whole of Harris County, Texas, provides the spatial rainfall reference in this analysis. Comparative analyses are conducted based on Hurricane Harvey and other two flood-inducing storms occurring in 2015 and 2016 over Harris County. The results imply that the Stage IV and MRMS overestimate and underestimate, respectively, the total rainfall by a small factor, while both QPEs tend to overestimate very light precipitation. In addition, the study suggests that the spatial correlation of radar error from both QPEs be described as powered exponential functions of interpixel distance. This study also includes hydrologic simulations for an urban watershed, demonstrating the importance of both the accuracy and spatial resolution of QPE in representing the mean areal precipitation (MAP) over catchments. The insight gained from this study provides guidance for further improving the QPE performance, and the new sampling approach for spatial reference rainfall can be applied to comprehensively evaluate long-term radar rainfall data. [ABSTRACT FROM AUTHOR]

Previous research has identified five canonical rainfall distributions in the United States; however, identification of these rainfall distributions did not include any rainfall data in the state of Texas. In this study, data from 813 rainfall stations in Texas are used to update the canonical distributions, which are then extended to include Texas. Canonical rainfall distributions are extracted using cluster analysis, which identifies distinct rainfall distributions that are not statistically different from measured rainfall distributions. Novel findings of this study are that the canonical intensity-duration functions for all canonical rainfall clusters in the United States can be expressed in a single functional form, and the canonical rainfall distributions and canonical intensity-duration functions are not very sensitive to return periods in the range of 5–25 years , with the effect of the return period being mostly accounted for in the specification of the 24-h rainfall amount. [ABSTRACT FROM AUTHOR]

Floods are the leading cause of fatalities connected with natural disasters in Texas. A combination of physiography and precipitation often result in extreme hydrologic conditions that cause floods in the state. This paper reviews flood-related fatalities in Texas between 1959 and 2008. Information on flood-fatality victims and the flood-causing events was obtained from the National Climatic Data Center. The data collected included the date, time, location, and weather conditions and the gender and age of the flood victims. Comparison with other states reveals that the size of the population of Texas is a major factor in the increased number of fatalities. The data also suggest that driving or walking into floodwaters may be responsible for more than 93% of flood fatalities in Texas. Although most high-fatality counties are located in the Texas 'Flash Flood Alley' that includes major urban centers, normalization of fatality data shows that the flood-fatality risk is actually higher in other areas of the state. The analysis also indicates that the annual flood-fatality rates are decreasing significantly. A combination of improved hydrometeorological forecasting, educational programs aimed at enhancing public awareness of flood risk and the seriousness of flood warnings, and timely and appropriate action by local emergency and safety authorities will help further reduce flood fatalities in Texas. [ABSTRACT FROM AUTHOR]

Permeable pavements are often developed as alternatives to traditional pavement (TP) systems for the mitigation of urban stormwater runoff impairment. The objective of this study was to evaluate the stormwater runoff and pollutant load-reduction performance of an enhanced permeable interlocking concrete pavement system with enlarged joints filled with gravel. This system was compared with an adjacent TP application located in La Feria, Texas. Both pavements were monitored for 2 years through a total of 65 storm events. Both pavement systems received some run-on stormwater flows from surrounding areas that included sediment and nutrients. Runoff volume was 80% lower than the TP. Furthermore, total suspended solids, 5-day biochemical oxygen demand, and Escherichia coli exhibited average reductions of 76% for 23 samples, 56% for 19 samples, and 12% for 11 samples, respectively. Field data were used to develop a Windows Source Loading and Management Model (WinSLAMM) method, which was in good agreement with the observed values. [ABSTRACT FROM AUTHOR]

Stiffened slabs-on-grade are one of the most efficient and inexpensive foundation solutions for lightweight structures on shrinkswell soils. The Ellison Office Building (EOB) is a 1-story steel-framed building founded on a clay formation of high shrink-swell potential. The foundation system of the EOB is composed of a rectangular stiffened slab-on-grade with approximate dimensions of 18.3 by 38.1 m. The slab thickness is 0.1 m, and the depth of all stiffening beams is 1.05 m. Beam spacing distances, center to center, are 3.05 and 3.81 m in the short and long directions, respectively. Exploration of subsurface soil conditions at the EOB site took place to determine soil index properties as well as unsaturated moisture diffusion and volume change characteristics for unsaturated soils. Monitoring the movements of both the slab-on-grade and the 4.5 m soil underneath continued for 2 years after foundation construction, since September 2004. Investigating the EOB foundation performance in light of the soil-structure interaction characteristics of the foundation showed two main distinct slab movement patterns: continuous global heave and localized fluctuating slab edge heave and drop movements. [ABSTRACT FROM AUTHOR]

Dependency of rational formula runoff coefficients on return period for two locations in Texas is evaluated by making use of an uncomplicated procedure for calculating peak discharge from small catchments using standard rainfall-runoff computation methods. Numerical solutions for dimensionless peak discharge are presented in the form of graphical relations, and also as mathematical expressions that approximate the graphs closely. Comparing peak discharge found by this approach to the rational formula provides a means of evaluating the relation between runoff coefficients and rainfall return periods. When compared to runoff coefficient adjustment factors found by others for 36 undeveloped watersheds in Texas, the return period adjustment factors calculated here illustrate the large differences that result because of the wide variability of parameters that influence small catchment runoff (such as precipitation, soils, and land-surface cover) within a region as vast as Texas. [ABSTRACT FROM AUTHOR]

This study contributes a many-objective analysis of water supply in the Tarrant Regional Water District (TRWD) in Texas in the United States, in order to demonstrate how multiobjective evolutionary algorithm (MOEA) decision support can be used with sophisticated water infrastructure models in a highly constrained water-planning environment. A detailed representation of TRWD's network in the river system modeling tool RiverWare (a model actually used by the utility) is coupled with the Borg MOEA to solve a seven-objective problem formulation. Objectives within the formulation include systemwide performance objectives, as well as reliability objectives to balance multiple reservoirs, calculated using two stochastic hydrology scenarios. The results provide valuable information to TRWD that may inform the 'approach to optimization and decision making in the future, as well as illuminate the benefits of optimizing systemwide and component-specific objectives to reveal hidden system dynamics. [ABSTRACT FROM AUTHOR]

A sediment transport module added to the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) Version 4.0 computes surface erosion, reach routing, and reservoir trap efficiency. The HEC-HMS sediment transport module was applied to the Upper North Bosque River Watershed (UNBRW) in central Texas, a well-instrumented watershed with good parameterization and calibration data, to test model robustness. The UNBRWis 98% rural including rangeland, forage, and dairy waste application fields. Simulation results were compared to observed average daily total suspended solid (TSS) data at five gauge locations. The HEC-HMS results matched observed TSS across the UNBRW (<1% error at all gauges) during model calibration and maintained modest residuals (-31 to 12% error) during the validation period. The HEC-HMS also performed well compared to published results through two other commonly used models. [ABSTRACT FROM AUTHOR]

As part of the Geoid Slope Validation Survey of 2011 (GSVS11), the global positioning system (GPS) was used in a variety of ways to determine the geodetic coordinates of nearly 400 passive geodetic control marks. The Texas DOT (TXDOT) real-time network (RTN) was one of the GPS positioning tools used. Both 6- and 180-s occupations were performed with the TXDOT RTN, as well as static 20-min occupations [processed using the National Geodetic Survey (NGS) Online Positioning User Service, Rapid-Static ( OPUS-RS) tool], and occupations were adjusted as a network (using the NGS OPUS-Projects tool). Although the original 20-min occupations processed by OPUS-RS had an unforeseen systematic error (invalidating their use in this comparison), a direct comparison between OPUS-RS and the other techniques was still possible by subdividing the OPUS-Projects receiver independent exchange format (RINEX) files on 218 points into more than 30,000 individual 20-min segments. A comparison between all techniques, after accounting for various systematics such as antenna calibrations, different reference frames, and epochs, showed that the RTN had small biases, but, overall, its accuracy () was subcentimeter in longitude, less than 2 cm in latitude, and less than 3 cm in ellipsoid height. An investigation into outliers indicated that the RTN could occasionally have unexplained outliers as large as 10 cm, possibly, but not definitely dependent, on time of day or local environment. The analysis also showed that, for this in-the-field environment, the accuracy of OPUS-RS was somewhat better than expected (biases at the 0.1-cm level; SDs at 0.6 cm in latitude and longitude and 3.1 to 3.4 cm in height) but outliers () did occur in approximately 1.5% of all runs, the largest of which was approximately 1.5 m off in latitude and height, and 0.4 m in longitude. Part, but not all, of this could be explained by being near the coastline. Finally, a significant cautionary result came from this study-users of the NGS OPUS-RS software should make certain that their receivers are generating P2 data (pseudoranges on L2, but not those from L2C). Without that data, OPUS-RS will not function at its highest accuracy. Although this has always been a requirement of OPUS-RS and was historically not an issue, the authors discovered that some GPS receivers inadvertently turn P2 off when L2C is turned on. [ABSTRACT FROM AUTHOR]

Several studies have investigated the use of the bottom tracking (BT) mode of acoustic Doppler current profilers (ADCPs) for evaluating bedload transport. The raw apparent bedload velocity is usually noisy and contains erroneous data. This study investigates how bedload dynamics influence acoustic processes occurring at riverbeds (i.e., volume and roughness scattering). The accuracy of ADCP apparent bedload velocity measurements is analyzed in two sets of laboratory experiments using two ADCPs working at different frequencies [2 MHz RDI StreamPro (RDI Teledyne Marine, Cypress, Texas) and 3 MHz SonTek M9 (Sontek/Xylem, San Diego)], with a variety of sediment materials and different hydraulic conditions. Simultaneously, the velocity and surface concentration of the mobile sediments are measured using high-resolution cameras. Despiking and filtering are applied to the raw data, and the temporal average of the apparent bedload velocity is spatially normalized. The percentage of filtered erroneous velocity data from the ADCP time series demonstrates a strong correlation with the surface concentration of mobile particles. Velocities measured with the M9 matched the particle velocities measured by image velocimetry better than those measured with the StreamPro, which appeared to underestimate the bedload velocity by a factor of 2–4. This suggests that instruments with different acoustic frequency yield a different interpretation of the apparent velocity; instruments with lower acoustic frequency and larger acoustic sampling length are more affected by the fixed surface beneath the layer of moving particles. These results bear out the notion that filtered apparent bedload velocity can be used to estimate the spatial velocity of bedloads, but its dependence on a set of acoustic properties must be further investigated. [ABSTRACT FROM AUTHOR]