Daniel T. Gillins

Cost-Effective Bridge Safety Inspections Using Unmanned Aircraft Systems (UAS)

Throughout the world, many highway workers and road users face safety hazards associated with bridges. American Society of Civil Engineers (ASCE) estimates that one in nine of the nation’s bridges are rated as structurally deficient, with an average age of 42 years. In an effort to reduce the risks associated with bridges, the Federal Highway Administration (FHWA) requires states to perform a detailed, visual inspection and inventory of all federal-aided highway system bridges once every two years. At times, mandatory inspections are costly and dangerous, as inspectors need to stand in platform trucks, bucket trucks, or under-bridge inspection vehicles in order to access and view necessary bridge elements. Furthermore, some inspections require extensive climbing, temporary scaffolding and ladders, or rescue boats. Unmanned aircraft system (UAS) technology provides a cost-effective and safe method for remotely performing visual inspections and inventorying of bridges. UASs are capable of flying a pre-programmed flight path, and can carry high resolution digital cameras and/or other sensors. During flights, operators can view live video from the camera on a monitor or through live video goggles. In addition, digital imagery collected during flights can be mosaicked, georeferenced, and converted into three dimensional (3D) point clouds for detailed spatial inventorying. This paper presents the methodology and results of inspections of a bridge in Oregon using a multicopter UAS. Because multicopters can be flown close to objects, are easy to maneuver, and can hover in place, high-resolution remote sensing data can be collected from multiple advantageous viewing angles. Such data is similar to what can be seen visually by an inspector at arm’s length from the bridge. Recommendations for safe and effective flights are given, and the capabilities and limitations of UAS bridge inspections are discussed. It will be shown that today’s UAS technology has great potential for performing remote and safe, visual inspections of structures.

Multilinear Regression Equations for Predicting Lateral Spread Displacement from Soil Type and Cone Penetration Test Data

AbstractIn the 1990s, Bartlett and Youd introduced empirical equations for predicting horizontal displacement from liquefaction-induced lateral spreading; these equations have become popular in engineering practice. The equations were developed by multilinear regression (MLR) of lateral spreading case history data compiled by these researchers. In 2002, these equations were revised and updated to include additional case history data. The regressions indicated that the amount of horizontal displacement is statistically related to the topography, earthquake magnitude, and distance from the seismic energy source. It is also related to the thickness, fines content, and mean grain size of the saturated, granular sediments with corrected standard penetration test blow count values less than 15. This paper proposes to modify the MLR empirical equations by replacing the fines content and mean grain size factors with soil description factors. Such modification allows investigators performing preliminary evaluation...

BridgeDex : Proposed Web GIS Platform for Managing and Interrogating Multiyear and Multiscale Bridge-Inspection Images

AbstractCurrent bridge inspectors commonly collect high-definition digital photographs of bridge members and connections at different scales when performing inspections over multiple years. Metric ...