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.

Model to describe the mode I fracture of steel fiber reinforced ultra-high performance concrete

Ultra-high performance fiber reinforced concrete (UHPFRC) has arisen from the implementation of a variety of concrete engineering and materials science concepts developed over the last century. This material offers superior strength, serviceability, and durability over its conventional counterparts. One of the most important differences for UHPFRC over other concrete materials is its ability to resist fracture through the use of randomly dispersed discontinuous fibers and improvements to the fiber-matrix bond. Of particular interest is the materials ability to achieve higher loads after first crack, as well as its high fracture toughness. In this research, a study of the fracture behavior of UHPFRC with steel fibers was conducted to look at the effect of several parameters related to the fracture behavior and to develop a fracture model based on a non-linear curve fit of the data. To determine this, a series of three-point bending tests were performed on various single edge notched prisms (SENPs). Compression tests were also performed for quality assurance. Testing was conducted on specimens of different cross-sections, span/depth (S/D) ratios, curing regimes, ages, and fiber contents. By comparing the results from prisms of different sizes this study examines the weakening mechanism due to the size effect. Furthermore, by employing the concept of fracture energy it was possible to obtain a comparison of the fracture toughness and ductility. The model was determined based on a fit to P-w fracture curves, which was cross referenced for comparability to the results. Once obtained the model was then compared to the models proposed by the AFGC in the 2003 and to the ACI 544 model for conventional fiber reinforced concretes.

STABILITY ANALYSIS AND HAZARD ASSESSMENT OF THE NORTHERN SLOPES OF SAN VICENTE VOLCANO IN CENTRAL EL SALVADOR

Geologic hazards affect the lives of millions of people worldwide every year. El Salvador is a country that is regularly affected by natural disasters, including earthquakes, volcanic eruptions and tropical storms. Additionally, rainfall-induced landslides and debris flows are a major threat to the livelihood of thousands. The San Vicente Volcano in central El Salvador has a recurring and destructive pattern of landslides and debris flows occurring on the northern slopes of the volcano. In recent memory there have been at least seven major destructive debris flows on San Vicente volcano. Despite this problem, there has been no known attempt to study the inherent stability of these volcanic slopes and to determine the thresholds of rainfall that might lead to slope instability. This thesis explores this issue and outlines a suggested method for predicting the likelihood of slope instability during intense rainfall events. The material properties obtained from a field campaign and laboratory testing were used for a 2-D slope stability analysis on a recent landslide on San Vicente volcano. This analysis confirmed that the surface materials of the volcano are highly permeable and have very low shear strength and provided insight into the groundwater table behavior during a rainstorm. The biggest factors on the stability of the slopes were found to be slope geometry, rainfall totals and initial groundwater table location. Using the results from this analysis a stability chart was created that took into account these main factors and provided an estimate of the stability of a slope in various rainfall scenarios. This chart could be used by local authorities in the event of a known extreme rainfall event to help make decisions regarding possible evacuation. Recommendations are given to improve the methodology for future application in other areas as well as in central El Salvador.