Department of Geological and Mining Engineering and Sciences, Data Science
Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1–3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment.
A novel applications of photogrammetry for retaining wall assessment.
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