There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.

Data mining is a discovery procedure to explore and visualize useful but less-than-obvious information or patterns in large collections of data. Given the amount and varying parameter types in a large data set such as that of the National Bridge Inventory (NBI), using traditional clustering techniques for discovery is impractical. As a consequence, the authors have applied a novel data discovery tool, called Two-step cluster analysis, to visualize associations between concrete bridge deck design parameters and bridge deck condition ratings. Two-step cluster analysis is a powerful knowledge discovery tool that can handle categorical and interval data simultaneously and is capable of reducing dimensions for large data sets. The analysis, of a total of 9,809 concrete highway bridge decks in the Northeast climatic region, found that bridges with cast-in-place decks that have a bituminous wearing surface, a preformed fabric membrane, and epoxy-coated reinforcement protection are strongly associated wit...

Two steel I-girder bridges were field tested with a primary objective of recording cross-frame forces under a weighed vehicle. Because previous research has suggested that skew angle is a significant variable affecting cross-frame forces and the role of cross-frames becomes more significant in skewed bridges, one bridge has a skew angle of 32 degrees while the other has a skew angle of 65 degrees. The bridges were instrumented to record cross-frame stresses in approximately 50 locations per bridge and girder stresses in 12 locations per bridge while the bridge was loaded by a weighed vehicle in various positions. The results show that cross-frame forces increase with skew and can be significantly larger than girder stresses. The field testing is also reproduced using a finite element model for the purpose of performing a parametric study to further explore cross-frame forces in steel bridges in future work and discussion of this modeling is also presented.