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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, 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]

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]

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]

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]

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]

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]

Owing to the 2008 economic downturn in the United States, the construction industry lost 632,000 jobs nationally, of which approximately 100,000 were in Texas. In response to the economic recession, a stimulus package known as the American Recovery and Reinvestment Act (ARRA), was signed by President Obama on March 6, 2009. This paper presents the results of a study examining the construction employment impact of ARRA spending on Texas transportation projects. The primary goal of this study was to estimate direct employment in the construction industry from spending on construction projects. By using regression analysis, the study assesses employment from data on Texas Department of Transportation projects that used ARRA funds, showing that employment generated by construction projects depends on project type, size, and location urbanization. The findings can be useful to policy makers in allocating funds on construction projects when employment stimulation is an objective. [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]