Compatibility of ultra high performance concrete as repair material : bond characterization with concrete under different loading scenarios

The rehabilitation of concrete structures, especially concrete bridge decks, is a major challenge for transportation agencies in the United States. Often, the most appropriate strategy to preserve or rehabilitate these structures is to provide some form of a protective coating or barrier. These surface treatments have typically been some form of polymer, asphalt, or low-permeability concrete, but the application of UHPC has shown promise for this application mainly due to its negligible permeability, but also as a result of its excellent mechanical properties, self-consolidating nature, rapid gain strength, and minimal creep and shrinkage characteristics. However, for widespread acceptance, durability and performance of the composite system must be fully understood, specifically the bond between UHPC and NSC often used in bridge decks. It is essential that the bond offers enough strength to resist the stress due to mechanical loading or thermal effects, while also maintaining an extended service-life performance. This report attempts to assess the bond strength between UHPC and NSC under different loading configurations. Different variables, such as roughness degree of the concrete substrates, age of bond, exposure to freeze-thaw cycles and wetting conditions of the concrete substrate, were included in this study. The combination of splitting tensile test with 0, 300, 600 and 900 freeze-thaw cycles was carried out to assess the bond performance under severe ambient conditions. The slant-shear test was utilized with different interface angles to provide a wide understanding of the bond performance under different combinations of compression and shear stresses. The pull-off test is the most accepted method to evaluate the bond strength in the field. This test which studies the direct tensile strength of the bond, the most severe loading condition, was used to provide data that can be correlated with the other tests that only can be used in the laboratory. The experimental program showed that the bond performance between UHPC and NSC is successful, as the strength regardless the different degree of roughness of the concrete substrate, the age of the composite specimens, the exposure to freeze-thaw cycles and the different loading configurations, is greater than that of concrete substrate and largely satisfies with ACI 546.3R-06.

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.

ENHANCING COMMUNICATION THROUGH SUCCESSFUL REPAIR: EXAMINING FLUENCY AND MECHANISMS FOR SYNTACTIC PROGRESS IN APHASIC AND NON-APHASIC SPEECH

The fields of Rhetoric and Communication usually assume a competent speaker who is able to speak well with conscious intent; however, what happens when intent and comprehension are intact but communicative facilities are impaired (e.g., by stroke or traumatic brain injury)? What might a focus on communicative success be able to tell us in those instances? This project considers this question in examining communication disorders through identifying and analyzing patterns of (dis) fluent speech between 10 aphasic and 10 non-aphasic adults. The analysis in this report is centered on a collection of data provided by the Aphasia Bank database. The database’s collection protocol guides aphasic and non-aphasic participants through a series of language assessments, and for my re-analysis of the database’s transcripts I consider communicative success is and how it is demonstrated during a re-telling of the Cinderella narrative. I conducted a thorough examination of a set of participant transcripts to understand the contexts in which speech errors occur, and how (dis) fluencies may follow from aphasic and non-aphasic participant’s speech patterns. An inductive mixed-methods approach, informed by grounded theory, qualitative, and linguistic analyses of the transcripts functioned as a means to balance the classification of data, providing a foundation for all sampling decisions. A close examination of the transcripts and the codes of the Aphasia Bank database suggest that while the coding is abundant and detailed, that further levels of coding and analysis may be needed to reveal underlying similarities and differences in aphasic vs. non-aphasic linguistic behavior. Through four successive levels of increasingly detailed analysis, I found that patterns of repair by aphasics and non-aphasics differed primarily in degree rather than kind. This finding may have therapeutic impact, in reassuring aphasics that they are on the right track to achieving communicative fluency.