Quantify LiDAR's geometry capturing capability for structural and construction assessment

This research is motivated by the need for improved assessment and diagnosis techniques for highway bridges to identify and characterize performance deficiencies efficiently in order to prolong services lives and reduce life-cycle costs. Rapidly deployable technologies such as wireless, non-contact or remote sensing have attracted significant attention over the last decade to fill this need. In particular, this growing interest in sensing technologies that may be deployed in a rapid or noninvasive manner has focused on the identification of application scenarios that demonstrate the value of such technologies over conventional approaches. This research explores the use of remote sensing (e.g. Light Detection and Ranging (LiDAR)) to support and improved bridge assessment beginning with construction and extending throughout the life-cycle. More specifically, this research stablished the following objectives: (1) Evaluate the information gained from full-field LiDAR data, compared to conventional single-point data collection, as a means to properly characterize the overall bridge and member geometry for demand and capacity calculation purposes, (2) Identify and characterize error sources associated with LiDAR data obtained from operating bridges and develop recommendations to ensure these error sources are considered during the planning, execution, and data interpretation phases of LiDAR data collection, (3) Quantify the value of deploying LiDAR to capture bridge geometry before, during and after construction as a means to improve construction quality and create a geometry inventory to verify as-built dimensions and establish a baseline for future assessments, and (4) Establish guidelines based on the advantages and shortcomings of LiDAR to address bridge assessment. To satisfy these objectives, this research leveraged field data collection efforts, the unique capabilities of the Bridge Evaluation and Accelerated Structural Testing (BEAST) laboratory, as well as small-scale physical model and mathematical model. The field study was focused on a 12-span steel stringer bridge in the Philadelphia region. This structure was subjected to extensive LiDAR scanning, and data processing methods towards applications of the load rating process to satisfy the requirements of Objective 1. In addition, a single span steel stringer bridge superstructure constructed within the BEAST lab served as the centerpiece for the scope assembled to satisfy Objective 2. This specimen was subjected to numerous LiDAR scans during the steel placement, formwork construction, rebar installation, and deck pouring. Further, LTBP LiDAR data was analyzed and correlated with previously acquired NDE data to contribute achieving the requirements of Objective 3. Finally, a set of recommendations were stablished for the use of LiDAR scanning to support bridge assessment activities from construction through the entire life-cycle of highway bridges.Ph.D.Includes bibliographical reference