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.

Structural and surface property characterization of titanium dioxide nanotubes for orthopedic implants

This research focused on the to modification of the surface structure of titanium implants with nanostructured morphology of TiO2 nanotubes and studied the interaction of nanotubes with osteoblast cells to understand the parameters that affect the cell growth. The electrical, mechanical, and structural properties of TiO2 nanotubes were characterized to establish a better understanding on the properties of such nanoscale morphological structures. To achieve the objectives of this research work I transformed the titanium and its alloys, either in bulk sheet form, bulk machined form, or thin film deposited on another substrate into a surface of titania nanotubes using a low cost and environmentally friendly process. The process requires only a simple electrolyte, low cost electrode, and a DC power supply. With this simple approach of scalable nanofabrication, a typical result is nanotubes that are each approximately 100nm in diameter and have a wall thickness of about 20nm. By changing the fabrication parameters, independent nanotubes can be fabricated with open volume between them. Titanium in this form is termed onedimensional since electron transport is narrowly confined along the length of the nanotube. My Ph.D. accomplishments have successfully shown that osteoblast cells, the cells that are the precursors to bone, have a strong tendency to attach to the inside and outside of the titanium nanotubes onto which they are grown using their filopodia – cell’s foot used for locomotion – anchored to titanium nanotubes. In fact it was shown that the cell prefers to find many anchoring sites. These sites are critical for cell locomotion during the first several weeks of maturity and upon calcification as a strongly anchored bone cell. In addition I have shown that such a surface has a greater cell density than a smooth titanium surface. My work also developed a process that uses a focused and controllably rastered ion beam as a nano-scalpel to cut away sections of the osteoblast cells to probe the attachment beneath the main cell body. Ultimately the more rapid growth of osteoblasts, coupled with a stronger cell-surface interface, could provide cost reduction, shorter rehabilitation, and fewer follow-on surgeries due to implant loosening.

STATE OF CHARGE BASED DROOP SURFACE FOR OPTIMAL CONTROL OF DC MICROGRIDS

For a microgrid with a high penetration level of renewable energy, energy storage use becomes more integral to the system performance due to the stochastic nature of most renewable energy sources. This thesis examines the use of droop control of an energy storage source in dc microgrids in order to optimize a global cost function. The approach involves using a multidimensional surface to determine the optimal droop parameters based on load and state of charge. The optimal surface is determined using knowledge of the system architecture and can be implemented with fully decentralized source controllers. The optimal surface control of the system is presented. Derivations of a cost function along with the implementation of the optimal control are included. Results were verified using a hardware-in-the-loop system.