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.

The impact of balsamo (Myroxylon balsamum L. Harms) on coffee yield and household income in El Balsamar, El Salvador

El Balsamar is a community that relies upon coffee trees intercropped with the balsamo tree (Myroxylon balsamum L. Harms) for a substantial portion of household income. The balsamo tree is valued for its resin which is used as medicine in the community and sold commercially. Farmers believe that the shade from the balsamo tree decreases coffee yield compared to the shade from non balsamo species. Thirty coffee farms were studied, each set up as a paired plot. When cover type was balsamo, coffee yield was more likely to decrease. Plots with higher basal area were more likely to be balsamo cover type. As basal area increased, coffee yield decreased. Although coffee yield is lower under balsamo cover type, farmers still continue to plant and manage coffee under this cover type. Farmers accept a lower coffee yield because balsamo resin provides an important income source. Farmers rely on the community cooperative to provide them work to support their households. The cooperative relies on the farmers to provide the labor needed to harvest coffee and extract balsamo resin.