Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

EXPLORATION OF THE MTSAT2 SATELLITE CAPABILITIES FOR REAL TIME DETECTION AND CHARACTERIZATION OF VOLCANIC EMISSIONS

In this report, we attempt to define the capabilities of the infrared satellite remote sensor, Multifunctional Transport Satellite-2 (MTSAT-2) (i.e. a geosynchronous instrument), in characterizing volcanic eruptive behavior in the highly active region of Indonesia. Sulfur dioxide data from NASA's Ozone Monitoring Instrument (OMI) (i.e. a polar orbiting instrument) are presented here for validation of the processes interpreted using the thermal infrared datasets. Data provided from two case studies are analyzed specifically for eruptive products producing large thermal anomalies (i.e. lava flows, lava domes, etc.), volcanic ash and SO2 clouds; three distinctly characteristic and abundant volcanic emissions. Two primary methods used for detection of heat signatures are used and compared in this report including, single-channel thermal radiance (4-µm) and the normalized thermal index (NTI) algorithm. For automated purposes, fixed thresholds must be determined for these methods. A base minimum detection limit (MDL) for single-channel thermal radiance of 2.30E+05 Wm- 2sr-1m-1 and -0.925 for NTI generate false alarm rates of 35.78% and 34.16%, respectively. A spatial comparison method, developed here specifically for use in Indonesia and used as a second parameter for detection, is implemented to address the high false alarm rate. For the single-channel thermal radiance method, the utilization of the spatial comparison method eliminated 100% of the false alarms while maintaining every true anomaly. The NTI algorithm showed similar results with only 2 false alarms remaining. No definitive difference is observed between the two thermal detection methods for automated use; however, the single-channel thermal radiance method coupled with the SO2 mass abundance data can be used to interpret volcanic processes including the identification of lava dome activity at Sinabung as well as the mechanism for the dome emplacement (i.e. endogenous or exogenous). Only one technique, the brightness temperature difference (BTD) method, is used for the detection of ash. Trends of ash area, water/ice area, and their respective concentrations yield interpretations of increased ice formation, aggregation, and sedimentation processes that only a high-temporal resolution instrument like the MTSAT-2 can analyze. A conceptual model of a secondary zone of aggregation occurring in the migrating Kelut ash cloud, which decreases the distal fine-ash component and hazards to flight paths, is presented in this report. Unfortunately, SO2 data was unable to definitively reinforce the concept of a secondary zone of aggregation due to the lack of a sufficient temporal resolution. However, a detailed study of the Kelut SO2 cloud is used to determine that there was no climatic impacts generated from this eruption due to the atmospheric residence times and e-folding rate of ~14 days for the SO2. This report applies the complementary assets offered by utilizing a high-temporal and a high-spatial resolution satellite, and it demonstrates that these two instruments can provide unparalleled observations of dynamic volcanic processes.