International Conference on Transportation and Development 2022: Infrastructure Systems

The tonnage of freight movements is projected to increase by 104% between 2016 and 2045 in the United States. Dallas-Fort Worth (DFW) region experiences the rapid growth of freight movements as one of the largest freight hubs and one of the top ten US cities for trucking congestion costs. Truck freight is the most prominent mode of freight transportation in the DFW region. The tonnage and values of the DFW truck freight are expected to grow by 108% and 213%, respectively, from 2016 to 2045. The rapid increase in the truck freight demand can strain overly congested and deteriorating highways in the DFW region. Freight-related highway improvements are essential to continue the economic growth in the DFW region. Despite the significance of freight-related highway improvements, the extent of the impact of these improvements on the DFW freight industry and regional economy is not determined. The objective of this research is to develop a freight economic analysis framework for analyzing the freight economic benefits of highway projects in the DFW region. The direct and indirect economic benefits of the DFW highway projects to the freight industry and the regional economy were identified and quantified using actual traffic data. Freight-related traffic data, unit values of freight economic benefits, and project-specific information were collected for the application of the freight economic analysis framework. The results of the freight economic analysis provide the monetary values of direct and indirect economic benefits from highway projects, including lane expansion, new construction, grade separation at a highway-rail crossing, rehabilitation, and ramp metering. Direct freight economic benefits of highway improvement projects include travel time savings, operation cost savings, and freight reliability improvements. Indirect economic benefits of highway improvement projects consist of reduced pollutant emissions, job creation, and GDP increase. The highway projects in the DFW region were assessed using the benefit/cost (B/C) ratio. The findings of this research can help regional transportation planners formulate and develop plans for future freight-related infrastructure improvement projects and analyze the direct and indirect economic benefits of the projects for the regional freight economy.

Cost overruns, schedule delays, and quality issues negatively impact the majority of transportation infrastructure projects, and inspectors and project engineers are in short supply in rural and remote areas. Researched and tested technologies can solve most of the industry’s challenges, but their level of acceptance and implementation is still quite low. The primary research goals of this study include (1) investigating current highway construction technologies that produce massive metadata related to costs, schedules, quality, and safety parameters; (2) exploring the need to integrate emerging and tested technologies into a single platform; and (3) proposing integrations that can produce semi-automated or simplified daily operations. An extensive literature review was conducted to understand the primary limitations of highway construction and the availability of tested technologies to overcome them. The findings of the study clearly indicate that integrating emerging technologies will open a wide array of opportunities such as predicting accurate construction costs, reducing the amount of time to complete projects, and increasing the overall quality of highway projects, and streamlining and centralizing the workforce system by implementing technology will solve the ongoing problem of workforce shortages. The findings of this study will help state DOTs better understand the value of each data layer that technology introduces throughout the deployment phase and the meaningful insights it can deliver using AI, machine learning, and big data.

Digital twin is a digital replica of a physical system that can be used to study how the real system functions in real time or to analyze future scenarios. This paper reports on the development of a semi-automated, vision-based methodology to collect highway asset inventory data. This methodology used a portable video camera mounted on a vehicle that was driven around road segments. The sign legend, sign description, orientation, latitude, longitude, and altitude were extracted from the videos; the telemetry data recorded by the portable camera was extracted programmatically, but asset information was extracted manually. This paper presents the application of the new methodology to acquire highway asset inventory data on a university campus. Using the acquired data, the campus’s road network is currently being replicated in a microscopic traffic simulation tool. The campus digital twin model will be used to analyze transportation issues in a building construction scenario.

The New Mexico Department of Transportation (NMDOT) is developing an asset management program for small culverts, which are not required to be included in biennial bridge inspections and can lead to catastrophic road failures if unmaintained. Aside from a partial statewide inventory in 2004, culverts in New Mexico have not been comprehensively inspected nor had their condition assessed. The NMDOT initiated a culvert asset management program (CAMP) in 2021 to create an inventory of the state’s projected 70,000 culverts, which will be regularly updated and used statewide to prioritize and plan maintenance and improve road safety. Student interns from New Mexico Tech (NMT) and New Mexico State University (NMSU) completed the first of six seasons of CAMP inspections in the summer of 2021. This paper will review the data collected by NMT and make suggestions for future work. Researchers used Trimble devices with Terraflex GIS software to compile GPS points and photos for each culvert, along with entries for size, material, and condition. The condition was recorded by visual inspection, describing corrosion, concrete spalling and cracking, other structural damage, and blockage of flow by silting or other means. Researchers examined data for completeness and accuracy by comparing photos with reported data and holding team discussions to improve uniformity at weekly QA/QC meetings. Five teams from NMT surveyed 8,502 culverts, averaging 53 culverts per team per day. A total of 1,480 (17%) may warrant immediate intervention (e.g., severe corrosion, spalling, or silting). This paper summarizes the contributions of CAMP from NMT, makes suggestions for improved survey efficiency, and assesses the program’s utility in future applications. This research proposes a methodology that can be utilized by other entities for culvert asset management and produces a valuable database for further analyses.

When considering a holistic approach to infrastructure asset management, insights can be drawn from the visualization and interpretation of the multivariate data sets that capture the need for integrated interventions. The decision-making process associated with synchronized, integrated asset management can benefit from data fusion analytics. Previously, visual analytics has been applied to understand the processes associated with individual infrastructure assets such as bridges, pavements, sewers, etc. This research proposes using geospatial visual analytics to interpret and visualize an inventory of data collected from several sources, including annual average daily traffic (AADT), intervention plan, emergency repair data, water pipe networks, and excavation data. The aim is to analyze and determine the relationships between attributes of the data sets using statistical tools and geospatial analysis. Analyzing these relationships improves coordinated utility maintenance and repair, provides insight into the socio-economic impact of these activities, and helps select intervention alternatives while optimizing the costs.

Vehicles transferring super heavy load (SHL) typically weigh several folds of the permissible weight limits set forth by regulatory agencies. Movement of such vehicles with multi-axle trailers can adversely affect the structural integrity of utilities buried underneath the roadway surface. This was the motivation for the authors to devise an approach to assess the potential risk against failure of underground utilities subjected to SHL movements in overweight corridors. To achieve this objective, our research team initially deployed portable weight-in-motion devices to ten sites with high frequency of SHLs in Texas. Subsequently, non-destructive field tests such as falling weight deflectometer and ground penetrating radar were performed for back-calculation of the pavement layer moduli. As-built plans were further reviewed to extract the pertinent information on utility characteristics. Subsequently, the field-derived data were in turn incorporated into a 3D finite element code for the determination of vertical deflection of utility at the crown under SHL movement, with realistic considerations of the tire-pavement contact stress distributions and pipe-soil interactions. Ultimately, the SHL-induced vertical deflection of utility was contrasted with maximum allowable deformation measures to characterize factor of safety against failure. The numerical simulation results showed that operation of SHLs with heavy axles and tires can potentially jeopardize the structural integrity of underground utilities across the state right-of-way, particularly if placed at shallow depths. Accordingly, buried utility risk analysis should be an integral component in risk management studies of transportation facilities servicing SHLs in overload corridors. The synthesized results can provide means to ensure safe operation of SHLs in highway networks accommodating underground utilities.

Civil Integrated Management (CIM) is an emerging paradigm that enhances the workflow of projects within transportation agencies pushing towards a digital transformation of some of their operations. Many transportation departments and agencies are already using some aspects of CIM. Several efforts have been undertaken to provide guidelines to implement CIM-related technologies; however, few have developed measurable quantitative methodologies based on the unique strengths of a specific agency or Department of Transportation (DOT). This study presents a method to assess what areas of CIM may be easier for a DOT to implement based on its own unique attributes. The method involves using an online survey in combination with a statistical test. Applying the method, a DOT or other transportation agencies will be able to make a more methodological decision on deployment of certain tools of CIM. Unlike previously presented methods, the methodology presented here is designed to be repeatable and applicable to any transportation agency interested in deploying CIM. Example real-world data and recommendations are provided to illustrate the method.

Despite being a popular research topic in recent years, few researchers have focused on the resilience of the transportation infrastructure from a construction engineering and management point of view. Therefore, the goal of this study was to identify resilience measurement dimensions of transportation infrastructure from the construction and management point of view and to develop a transportation infrastructure resilience measurement model, using the structural equation modelling (SEM) technique. To achieve these goals, a questionnaire supported by literature was developed and distributed to collect data. The results of the responses showed that integrated assets like railroad crossings, utility conduits, railroad crossings have a negative relationship with the resilience level of the transportation infrastructure and that a management committee well-informed about the concept of resilience is willing to invest in strength-enhancing activities of a roadway. This study will help practitioners and decision-makers prioritize their investment, based on the vulnerability of roadway segments.

Bridge asset management is key as it connects the transportation network spanning across the United States. According to the recent 2021 ASCE Infrastructure Report Card, 42% of 617,000 bridges in the US are over 50 years old and 7.5% of the total bridges are considered structurally deficient. A structurally deficient bridge means that it requires monitoring, inspection, and maintenance rather than it being unsafe to travel. FHWA and the state Departments of Transportation (DOTs) have coordinated frequent inspections to keep the number of structurally deficient bridges to a minimum. Traditional inspections are sometimes limited by access, cost, and safety. Recently, many DOTs have adopted unmanned aerial vehicles (UAVs) to access hard-to-reach areas and quickly monitor the conditions of the bridges. Not many studies have reported about 360° inspection of bridges using three-dimensional (3D) models generated from aerial imagery. This study demonstrated the application of UAVs for conducting 360° inspection of bridge infrastructure assets. Three-dimensional models of the bridge deck and under-span elements were developed to provide an immersive navigation experience to the bridge inspector. Several key observations were made based on the data collection and processing of models for a 360° bridge inspection. This study helped understand the limitations and best practices of the application of UAVs for conducting a 360° bridge inspection.

The public rightly expects a good return on investment in transportation assets. Owner-agencies use projects to deliver those assets, which fulfill needs and provide functionality. Efficient delivery enhances a project’s value, and owner-agencies are obligated to optimize value. However, transportation projects are delivered in an environment of inherent uncertainty that puts their value at risk. The reputation of an owner-agency depends largely on how effectively they meet project delivery expectations: on time and within budget. In the pursuit of improving project value, many organizations have implemented Cost Risk Estimating and Management (CREM) programs designed to enhance project delivery, improve decision making, and increase public confidence. Such programs feature probabilistic risk-based estimates, independent review and validation of the base cost estimate and schedule, and structured risk management. The objectives of this paper are (1) to communicate the value of an effective CREM program and (2) to share ideas, strategies, and lessons learned for implementing a CREM program.

Investment in road infrastructure development remains a priority for governments, and it is therefore imperative that governments are equipped with obligatory analytical explanation justifying such immense spending of the taxpayers’ monies as part of their budgets. For this, as well as for cost management of the budgeted outlay, establishing a unit cost is often that what road agencies are tasked with. However, very often average unit costs (i.e., cost per km, obtained by using historical data of executed projects) show wide variations and thereby puzzling the agencies on what should be the ideal unit cost. Further, with such inherent variations such unit cost fails to serve as a yardstick for sustainable design considerations. It is appreciated that instead of margins (factor of safety, FOS, that designers traditionally look for while concluding on recommended designs for construction) based on central value as a measure of risk, risk measured in the term of the probability of failure allows accounting for randomness of parameters that drive unit costs of road constructions. This paper outlines a possible approach for establishing unit costs from a conventional reliability analysis perspective, using efficient spreadsheet algorithm for first order reliability method (FORM). The performance function (limit state function) considered for FORM uses a FOS applied to the central value measure (of historical data on cost per km) to have the resistance (R). As the load (Q) it considers a set of parameters which are perceived as basic cost drivers for road construction projects. A sensitivity analysis then allows determining the influence of various random parameters (perceived cost drivers) on the reliability of assessed unit cost. Employing such cost drivers to derive reliable unit cost lends a better acceptability (to governments and policy makers deciding on investment priorities, who may even choose to extend them to carry out a benchmarking exercise with costs in neighbouring countries to allay a possible perception of a prevailing higher unit cost in a specific country) besides making such unit cost a sustainable design outcome criteria. This paper further employs the kernel density regression to finally show how a identified more sensitive cost driver (its bid-price, price per unit quantity) is driven by other contributing variables, namely its unit-quantity (quantity/km), which can thus be used as important criteria in the agencies procurement framework, besides controls for design engineers.

The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced, which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%–15% lower than the pre-pandemic level for at least one or two years.

Planning and designing shared auto, transit, bike, and pedestrian facilities, such as complete streets, has received a lot of attention. However, little attention has been paid to how to plan for and schedule the maintenance of these facilities over their life cycles. By looking at the life cycle of these facilities in the context of a socio-technical systems, issues are highlighted recognizing the physical, functional, intentional, and normative relationships between actors and technical elements, and the variety of users. Performance measures are identified to capture the performance of the system during the maintenance and operation phase of the life cycle. These performance measures can then be used to support decision making either through target setting or optimization. The application of these performance measures is demonstrated using a case study based on the business district of Newark, Delaware. The research shows the value of using comprehensive and consistent performance measures to make maintenance decisions for shared use facilities.

Transportation infrastructure investment decisions rarely consider stakeholders’ preferences to network performance measures and the impact of project selection and prioritization on the network accessibility performance. Also, there is a need to consider other beneficial and non-beneficial performance criteria to mitigate the problems of system users. Therefore, this paper presents a Preference Ranking Organization Method for Enriched Evaluation (PROMETHEE) framework that helps to consider both beneficial and non-beneficial performance criteria in the investment decision process. An experimental case study results showed that single and bundle LVR projects could have different contributions to the LVR network performance. Also, single LVR projects may provide better LVR network performance than bundle LVR projects, implying that investing more in multiple LVR projects may not guarantee the highest LVR network performance. Therefore, infrastructure decision-makers should conduct detailed scenario analysis considering all LVR project combinations, and beneficial and non-beneficial criteria to maximize the LVR network performance.

Infectious diseases affect those who are employed in many occupations, not just in human and animal healthcare industries and laboratories. Those who work in the construction industry are frequently exposed to infectious diseases caused by harmful pathogens in the air, water, soil, and chemicals at worksites, making it critical to identify the specific causes and effective preventative measures. This study aims to identify the causes and the short- and long-term health effects of these diseases on workers and develop strategies to overcome them. A comprehensive analysis of existing literature revealed 13 infectious diseases that we categorized according to their source. Strategies for controlling and mitigating the effects of the infections were identified and arranged in a hierarchy of effectiveness, from most efficient to least efficient. The study’s findings will enhance construction employers’ understanding of infectious diseases that put their employees at risk, enabling them to help their employees remain healthy and safe by adhering to workplace safety precautions.

The Central Puget Sound Regional Transit Authority, better known as Sound Transit, completed the Northgate Link Extension (NLE) light rail project in 2021, on schedule and under the approved budget. The NLE project is a 4.3-mi light rail project in Seattle, Washington. The project extends the Link light rail system north from the existing University of Washington Station to the Northgate neighborhood. A majority of the alignment is underground, and it includes two new underground stations (U District and Roosevelt) and one elevated station (Northgate). Construction of the underground tunnels utilized two 21.5-foot diameter tunnel boring machines (TBMs). The total project budget was $1.9 billion. The NLE project used a risk and contingency management plan (RCMP) to define the processes and procedures to conduct risk assessments, manage risks, and manage contingency throughout the project life cycle. As the project also relied upon federal funding, the RCMP aligned with the Federal Transit Administration’s (FTA) Oversight Procedure for Risk and Contingency Review (OP40c). Together, these procedures required NLE to apply quantitative risk assessments (QRA) at major project milestones. The purpose of this paper is to present the applied risk management processes, discuss how the QRA results evolved at major milestones, how the nature of risks changed over time, and offer recommendations for future risk management processes for mega-projects.

The adoption of unmanned aerial vehicle (UAV) has grown over the last few years in different industries. Common bridge inspection procedures are extensively time consuming, highly subjective to visual interpretation, and associated with safety risks. The primary goal of this paper is to develop a sufficiently accurate and cost-effective crack detection framework using a UAV mounted red, green, and blue (RGB) camera for data collection and deep convolutional neural networks (CNN) on a single board computer for bridge inspection. A concrete crack data set is created using a RGB sensor mounted on a UAV, named UAVCON. Single shot multibox detector (SSD) and region-based convolutional neural networks (R-CNN) meta-models are deployed in TensorFlow framework for benchmarking on the data set. The proposed detection model was able to detect concrete cracks on bridge elements with 93.4% accuracy on UAVCON.