Richard J. Dobson

Evaluation of Commercially Available Remote Sensors for Highway Bridge Condition Assessment

Improving transportation infrastructure inspection methods and the ability to assess conditions of bridges has become a priority in recent years as the transportation infrastructure continues to age. Current bridge inspection techniques consist largely of labor-intensive subjective measures for quantifying deterioration of various bridge elements. Some advanced nondestructive testing techniques, such as ground- penetrating radar, are being implemented; however, little attention has been given to remote sensing technologies. Remote sensing technologies can be used to assess and monitor the condition of bridge infrastructure and improve the efficiency of inspection, repair, and rehabilitation efforts. Most important, monitoring the condition of a bridge using remote sensors can eliminate the need for traffic disruption or total lane closure because remote sensors do not come in direct contact with the structure. The purpose of this paper is to evaluate 12 potential remote sensing technologies for assessing the bridge deck and superstructure condition. Each technology was rated for accuracy, commercial availability, cost of measurement, precollection preparation, complexity of analysis and interpretation, ease of data collection, stand-off distance, and traffic disruption. Results from this study demonstrate the capabilities of each technology and their ability to address bridge challenges.

Developing an unpaved road assessment system for practical deployment with high-resolution optical data collection using a helicopter UAV

The need of local governments and transportation agencies to periodically asses the condition of unpaved roads in a cost-effective manner with rapid response times has lead to interest in the use of UAVs (Unmanned Aerial Vehicles) and remote sensing technologies. Currently these assessments are done through visual inspections with agency staff making occasional spot measurements. An unpaved road assessment system was developed to address these issues while at the same time providing a more accurate means of characterizing distresses and determining the roads condition for inspectors. This system uses a single-rotor UAV with a Digital Single-lens Reflex (DSLR) camera to capture overlapping imagery of unpaved roads. The UAV is equipped with a full combination GPS plus IMU (Inertial Measurement Unit) that allows it to fly predetermined waypoints with great stability while at the same time allowing the pilot the ability to take over at any time. Collected imagery is analyzed to locate road distresses. The imagery is run through a Structure From Motion (SfM) algorithm that generates a 3D model of the road surface from which additional condition information can be characterized. This system is easily transported and rapidly deployable to sections of unpaved roads for assessment.

Collecting Decision Support System Data Through Remote Sensing of Unpaved Roads

Unpaved roads make up roughly 33% of the road system within the United States and are vitally important to rural communities for transport of people and goods. Effective asset management of unpaved roads requires frequent inspections to determine the roads’ condition and the appropriate preventive maintenance or rehabilitation. The major challenge with managing unpaved roads is low-cost collection of condition data that are compatible with a decision support system (DSS). The advent of cheap, reliable remote-sensing platforms such as unmanned aerial vehicles along with the development of commercial off-the-shelf image analysis algorithms provides a revolutionary opportunity to overcome these data volume and efficiency issues. By taking advantage of these technological leaps, a market-ready system to detect unpaved road distress data compatible with a DSS was developed. The system uses aerial imagery that can be collected from a remote-controlled helicopter or manned fixed-wing aircraft to create a three-dimensional model of sensed road segments. Condition information on potholes, ruts, washboarding, loss of crown, and float aggregate berms is then detected and characterized to determine the extent and severity of the distress. Once detection and analysis are complete, the data are imported into a DSS based on a geographic information system (Road-soft) for use by road managers to prioritize preventive maintenance and rehabilitation efforts.

Unmanned Aerial Vehicle (UAV)-Based Assessment of Concrete Bridge Deck Delamination Using Thermal and Visible Camera Sensors: A Preliminary Analysis

ABSTRACT Infrared and visible cameras were mounted on an unmanned aerial vehicle (UAV) to image bridge deck surfaces and map likely concrete delaminations. The infrared sensor was first tested on laboratory validation experiments, to assess how well it could detect and map delaminations under controlled conditions. Field tests on two bridge deck surfaces further extend the validation dataset to real-world conditions for heterogeneous concrete surfaces. Performance of the mapping instrument and algorithms were evaluated through receiver operating characteristic (ROC) curves, giving acceptable results. To improve the performance of the mapping by reducing the rate of false positives, i.e., areas wrongly mapped as being affected by delamination, visible images were jointly analyzed with the infrared imagery. The potential for expanding the method to include other products derived from the visible camera data, including high density 3D point locations generated by photogrammetric methods, promises to further improve the performance of the method, potentially making it a viable and more effective option compared to other platforms and systems for imaging bridge decks for mapping delaminations.

Expanding Alaska–Canada Rail: Jointly Visualizing Revenue Freight, Development Cost, Mineral Commodity Value, and Impact of Carbon Dioxide

Recent changes in global markets have raised the value of mineral resources in northwestern Canada and Alaska. The development of these resources depends on the economics of rail infrastructure expansion. Transportation decision makers need revenue and cost assessments to plan investment in rail infrastructure. A tool based on a geographic information system was developed for mineral resource evaluation and visualization. The tool incorporated expert-augmented mineral resource data for more than 22,000 occurrences in the region. The tool included the proposed Alaska–Canada Rail Link, which would connect Alaska rail to the lower 48 states. Users selected locations of known mineral occurrences near actual or proposed rail routes and used statistical mineral deposit models to estimate resource sizes and extractable value over time by combining current or user-entered commodity prices with multimodal revenue freight volumes and optimally routed transportation costs. The tool translated the revenue scenario into likely carbon dioxide emissions according to the transport of mineral concentrates to regional and international destinations. Users could select and visualize multimodal transportation networks to understand and minimize mobile-source carbon emissions as part of their scenarios. Statistical estimates of mine capital expenditure and operating costs were also calculated according to type. The tool calculated the gross metal value of a mineral occurrence with statistical deposit models. This index was linked to the positive regional economic impact associated with the developed resource in terms of jobs, taxes and royalties, and resupply. This information helped decision makers close the loop on infrastructure investment assessments.