Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Numerous computer-aided automatic examination techniques have been deployed for pavement distress condition assessments, such as digital image processing methods. However, their effectiveness and applicability are impeded due to information losses in 2-D image combination processes or extremely high costs in 3-D geo-referenced data set. In this paper, a cost-effective Kinect-based approach is proposed for 3-D pavement surface reconstruction and cracking recognition. We propose a comprehensive computational solution for the detection and recognition of pavement distress feature identification. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, and so on. are identified and recognized for their severity examinations based on associated geometrical features. The experimental results indicate that this method is effective in reducing data collection costs and extracting analytical information on pavement cracking measurements. The research findings confirm that the proposed approach provides a viable, applicable solution to an automatic pavement surface condition detection and evaluation. The proposed methodology is transferable for pavement surface reconstruction and distress condition detection based on the other 3-D cloud point data. It provides an alternative inexpensive complement to existing pavement examination methodologies.

When locations of individual-level health data are released in the form of published maps, the identity of these individuals could be identified through reverse geocoding. Spatial data can, therefore, not be released unless the locations have been modified, for example, using aggregation or geographic masking. Geographic masking techniques apply translation or perturbations to decrease the likelihood of re-identification of individuals through reverse geocoding. The current study proposes a new geographic masking technique referred to as “location swapping.” Location swapping replaces an original location with a masked location selected from all possible locations with similar geographic characteristics within a specified neighborhood. Strengths and weaknesses of location swapping will be discussed relative to existing geographic masking techniques. The approach will be illustrated using several example data sets and a custom toolset developed for ArcGIS to automate the location swapping algorithm.

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

Roadway pavement surface distress information is critical for effective pavement asset management, and subsequently, transportation management agencies at all levels (i.e., federal, state, and local) dedicate a large amount of time and money to routinely evaluate pavement surface distress conditions as the core of their asset management programs. However, currently adopted ground-based evaluation methods for pavement surface conditions have many disadvantages, like being time-consuming and expensive. Aircraft-based evaluation methods, although getting more attention, have not been used for any operational evaluation programs yet because the acquired images lack the spatial resolution to resolve finer scale pavement surface distresses. Hyper-spatial resolution natural color aerial photography (HSR-AP) provides a potential method for collecting pavement surface distress information that can supplement or substitute for currently adopted evaluation methods. Using roadway pavement sections located in the State of New Mexico as an example, this research explored the utility of aerial triangulation (AT) technique and HSR-AP acquired from a low-altitude and low-cost small-unmanned aircraft system (S-UAS), in this case a tethered helium weather balloon, to permit characterization of detailed pavement surface distress conditions. The Wilcoxon Signed Rank test, Mann-Whitney U test, and visual comparison were used to compare detailed pavement surface distress rates measured from HSR-AP derived products (orthophotos and digital surface models generated from AT) with reference distress rates manually collected on the ground using standard protocols. The results reveal that S-UAS based hyper-spatial resolution imaging and AT techniques can provide detailed and reliable primary observations suitable for characterizing detailed pavement surface distress conditions comparable to the ground-based manual measurement, which lays the foundation for the future application of HSR-AP for automated detection and assessment of detailed pavement surface distress conditions.