Mobile Lidar for Monitoring MSE Walls with Smooth and Textured Precast Concrete Panels

Mohammed Dhafer A Aldosari, Purdue University

Abstract

Mechanically Stabilized Earth (MSE) walls retain soil on steep, unstable slopes with crest loads. Over the last decade, they are becoming quite popular due to their low cost-to-benefit ratio, design flexibility, and ease of construction. Like any civil infrastructure, MSE walls need to be continuously monitored according to transportation asset management criteria during and after the construction stage to ensure that their expected serviceability measures are met and to detect design and/or construction issues, which could lead to structural failure. Current approaches for monitoring MSE walls are mostly qualitative (e.g., visual inspection or examination). Besides being time consuming, visual inspection might have inconsistencies due to human subjectivity. Other monitoring approaches are based on using total station, geotechnical field instrumentations, and/or Static Terrestrial Laser Scanning (TLS). These instruments are capable of providing highly accurate, reliable performance measures. However, the underlying data acquisition and processing strategies are time-consuming and are not scalable. This research focuses on a comprehensive strategy using a Mobile LiDAR Mapping System (MLS) for the acquisition and processing of point clouds covering the MSE wall. The strategy produces standard serviceability measures, as defined by the American Association of State Highway and Transportation Officials (AASHTO) – e.g., longitudinal and transversal angular distortions. It also delivers a set of recently developed measures (e.g., out-of-plane offsets and 3D position/orientation deviations for individual panels constituting the MSE wall). Moreover, it is also capable of handling MSE walls with smooth or textured panels with the latter being the focus of this research due to its more challenging nature. For this study, an ultra-high-accuracy wheel-based MLS has been developed to efficiently acquire reliable data conducive to the development of the standard and new serviceability measures. To illustrate the feasibility of the proposed acquisition/processing strategy, two case studies in this research have been conducted with the first one focusing on the comparative performance of static and mobile LiDAR in terms of the agreement of the derived serviceability measures. The second case study aims at illustrating the feasibility of the proposed strategy in handling large textured MSE walls. Results from both case studies confirm the potential of using MLS for efficient, economic, and reliable monitoring of MSE walls.

Degree

Ph.D.

Advisors

Habib, Purdue University.

Subject Area

Civil engineering|Economics|Management|Materials science|Mechanics|Optics|Remote sensing|Soil sciences

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