Colin Brooks

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.

Combined Imaging Technologies for Concrete Bridge Deck Condition Assessment

Evaluating the condition of concrete bridge decks is an increasingly important challenge for transportation agencies and bridge inspection teams. Closing the bridge to traffic, safety, and time consuming data collection are some of the major issues during a visual or in-depth bridge inspection. To date, several nondestructive testing technologies have shown promise in detecting subsurface deteriorations. However, the main challenge is to develop a data acquisition and analysis system to obtain and integrate both surface and subsurface bridge health indicators at higher speeds. Recent developments in imaging technologies for bridge decks and higher-end cameras allow for faster image collection while driving over the bridge deck. This paper will focus on deploying nondestructive imaging technologies such as the three-dimensional (3D) optical bridge evaluation system (3DOBS) and thermal infrared (IR) imagery on a bridge deck to yield both surface and subsurface indicators of condition, respectively. Spall and delamination maps were generated from the optical and thermal IR images. Integration of the maps into ArcGIS, a professional geographic information system (GIS), allowed for a streamlined analysis that included integrating and combining the results of the complimentary technologies. Finally, ground truth information was gathered through coring several locations on a bridge deck to validate the results obtained by nondestructive evaluation. This study confirms the feasibility of combining the bridge inspection results in ArcGIS and provides additional evidence to suggest that thermal infrared imagery provides similar results to chain dragging for bridge inspection.

A new method to generate a high-resolution global distribution map of lake chlorophyll

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll-a (chl) concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath (FRS) global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m−3. The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situ measurements from the literature (n = 185, RMSE = 9.39, R2 = 0.72). This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

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.

Decision support system for integrating remote sensing in bridge condition assessment and preservation

Since the National Bridge Inventory (NBI) was first conducted, structural health monitoring (SHM) of U.S. bridge infrastructure has consisted largely of time and labor-intensive surveys with subjective results. In-situ and embedded sensors, while more reliable and accurate, can be costly and in many cases infeasible for SHM because they require installation in hard-to-reach places or during construction. Remote sensing (RS) technologies such as radar, electrooptical imaging and laser scanning may offer an innovative, cost-effective method of monitoring the dynamic conditions of U.S. bridges in real-time. While some RS techniques may be costly for state agencies to deploy on their own, RS imagery is available through government agencies or commercial vendors for moderate or no cost. How can disparate RS datasets be integrated with one another and with inventory data in a way that is meaningful to bridge asset management decision makers? This paper discusses the development and functionality of the Bridge Condition Decision Support System (DSS), a web-based asset management tool for bridge managers and inspectors. The DSS seamlessly merges bridge metrics from RS data with NBI inventory data allowing decision makers to compare up-to-date bridge condition metrics from multiple inputs as a time series. It enables analysis of RS and inventory data available through user-friendly web services which can also expose virtually unlimited server-side data processing. Using open-source software, the authors developed a scalable, spatially-aware bridge condition database with a fast and flexible server application programming interface (API) and a cross-browser compatible web mapping application written in Javascript.

Measurement and communication of bridge performance with remote sensing technologies

The importance of a functional and efficient transportation network system is well known to the traveling public, but the financial and technical requirements to keep this infrastructure in a state of good repair often are not well understood. From the standpoint of functionality, a bottleneck typically can be traced to the condition of bridges, which continues to be a major challenge as funds shrink. Nearly 12% of the more than 600,000 bridges in the United States were categorized in 2010 as structurally deficient. Much of this deterioration relates directly not only to the lack of funding, staffing, and resources to maintain this infrastructure but also to the lack of tools for proper assessment of the degree of deterioration, which can be unaccounted for in the routine inspection process. The use of remote sensing to assess the condition of a bridge is a relatively new concept in the monitoring of structural health. For the typical bridge engineer, remote sensing can mean enhanced and safer inspection assessment without traffic disruption. No single technology can completely assess bridge performance. This paper details the applicability of a series of remote sensing technologies to assess and monitor bridge performance and to provide state and local engineers with a decision support system to prioritize critical maintenance and repair of the nations bridges. Selected commercially available technologies are defined, and their relationships in the communication of bridge needs to bridge engineers and inspectors are discussed.

Light Detection and Ranging (LiDAR) and Multispectral Scanner (MSS) Studies Examine Coastal Environments Influenced by Mining

There are numerous examples of past and present mine disposal into freshwater and marine coastal bays and riverine environments. Due to its high spatial resolution and extended water penetration, coastal light detection and ranging (LiDAR), coupled with multispectral scanning (MSS), has great promise for resolving disturbed shoreline features in low turbidity environments. Migrating mine tailings present serious issues for Lake Superior and coastal marine environments. Previous investigations in Lake Superior uncovered a metal-rich “halo” around the Keweenaw Peninsula, related to past copper mining practices. For over a century, waste rock migrating from shoreline tailing piles has moved along extensive stretches of coastline, compromising critical fish breeding grounds, damming stream outlets, transgressing into wetlands and along recreational beaches and suppressing benthic invertebrate communities. In Grand (Big) Traverse Bay, Buffalo Reef is an important spawning area for lake trout and whitefish threatened by drifting tailings. The movement of tailings into Buffalo Reef cobble fields may interfere with the hatching of fish eggs and fry survival, either by filling in crevices where eggs are deposited or by toxic effects on eggs, newly hatched larvae or benthic communities. Here, we show that the coastal tailing migration is not “out of sight, out of mind”, but clearly revealed by using a combination of LiDAR and MSS techniques.

Multi-Scale GIS Data-Driven Method for Early Assessment of Wetlands Impacted by Transportation Corridors

© 2012 Nobrega et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multi-Scale GIS Data-Driven Method for Early Assessment of Wetlands Impacted by Transportation Corridors