Andrew Ellenberg

Investigation on Bridge Assessment Using Unmanned Aerial Systems

The U.S. currently spends tens of billions of dollars annually to inspect infrastructures and collect subjective, qualitative data that can often be unreliable or largely irrelevant. Inspections also require adequate access to remote locations, for example, appropriate scaffolding, lifting and additional protective equipment, which might increase the associated personal safety risk and add costs. The use of unmanned vehicles has experienced a tremendous growth primarily in military and homeland security applications. However, it is a matter of time until Unmanned Aerial Systems (UAS) will be widely accepted as platforms for implementing monitoring and inspection procedures. Researchers at Drexel University are exploring the use of quadcopters as vehicles to carry a set of remote sensors with the ultimate goal to perform bridge condition assessment. While the accuracy of remote sensing systems is somewhat limited compared to the one of contact sensing systems, the ability to quickly and periodically scan/inspect a structure without the need for scaffolding, ropes, or cherry pickers currently used during bridge inspections could transform the way the industry performs periodic bridge inspections. The Drexel team owns a number of UAS with different payload, flight time and range capabilities. In this paper, recent results obtained from preliminary testing on small mock-up concrete bridge decks as well as on small/medium size bridges are presented. One of the main efforts is to explore how multispectral imaging can provide a preliminary assessment of the deck condition of common highway bridges. Among future goals, Drexel’s team plans to develop and validate computer vision approaches leveraging data collected using UAS to permit geometric characterization (quantification of bearing position, girder deformations) and condition assessment (e.g. quantification of spalling and corrosion areas).

A Multiscale Multispectral Approach to Digital Image Correlation for SHM Applications

A novel technique for digital image correlation (DIC) targeting structural health monitoring (SHM) applications is presented. The method uses multispectral imaging enabling simultaneous data acquisition at variable fields of view (FOV). In general, for a given FOV, appropriate speckle sizes have to be a priori defined, limiting the capability of acquiring viable DIC data from variable working distances or with variable accuracy. Furthermore, straightforward application of multiscale DIC patterns that can be seen in the same wavelength of light can cause measurement errors, forcing the use of larger subset sizes and thus reducing the spatial resolution of the deformation measurements. To overcome such limitations, a patterning technique accounting for camera resolution, distance to target, metric-based optimization and variable wavelength is developed. Specifically, patterns are created for two length scales and applied onto a structure. A black on white speckle pattern for far field measurements in addition to an ultraviolet pattern for near field measurements. The patterns were shown to optimally perform at the a priori calculated working distances. The potential of this method for use in SHM applications is discussed.

Virtual Laboratory for Leveraging Technology for Bridges and Constructed Systems

The writers are exploring the development of an innovative and adaptive resource for highway bridge owners, managers, engineers as well as technicians from non-destructive testing (NDT) and structural health monitoring (SHM) industries and the public. The primary objective is an official Federal Highway Administration (FHWA) website offering guidance and training on how technology tools may be selected and applied with sufficient depth to generate reliable and actionable information. Currently a large number of technology tools in the realm of “information, communication, computing and data technology,” “software for modelling and analysis of multi-physics phenomena and civil engineering systems,” “sensing, imaging and non-destructive probing,” and, “uncertainty and risk analysis and decision-making” are available for off-the-shelf purchase or applications by consultants. However, there are very few institutions that offer an ability for an integrative leveraging of such tools and the resulting data in conjunction with engineering heuristics for meaningful, feasible and effective solutions to infrastructure performance problems.

Low-cost, quantitative assessment of highway bridges through the use of unmanned aerial vehicles

Many envision that in the near future the application of Unmanned Aerial Vehicles (UAVs) will impact the civil engineering industry. Use of UAVs is currently experiencing tremendous growth, primarily in military and homeland security applications. It is only a matter of time until UAVs will be widely accepted as platforms for implementing monitoring/surveillance and inspection in other fields. Most UAVs already have payloads as well as hardware/software capabilities to incorporate a number of non-contact remote sensors, such as high resolution cameras, multi-spectral imaging systems, and laser ranging systems (LIDARs). Of critical importance to realizing the potential of UAVs within the infrastructure realm is to establish how (and the extent to which) such information may be used to inform preservation and renewal decisions. Achieving this will depend both on our ability to quantify information from images (through, for example, optical metrology techniques) and to fuse data from the array of non-contact sensing systems. Through a series of applications to both laboratory-scale and field implementations on operating infrastructure, this paper will present and evaluate (through comparison with conventional approaches) various image processing and data fusion strategies tailored specifically for the assessment of highway bridges. Example scenarios that guided this study include the assessment of delaminations within reinforced concrete bridge decks, the quantification of the deterioration of steel coatings, assessment of the functionality of movement mechanisms, and the estimation of live load responses (inclusive of both strain and displacement).