A Novel Application of Photogrammetry for Retaining Wall Assessment

Abstract

Abstract: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.

Evaluation of Photogrammetry and Inclusion of Control Points: Significance for Infrastructure Monitoring

“…Incorporating effective monitoring techniques for infrastructure systems is imperative when diagnosing performance and ensuring the structural integrity of systems. Moreover, ensuring structural safety for geotechnical applications is a critical component for transportation asset management systems [8,9]. The demand for an effective asset management protocol is growing for geotechnical infrastructure, such as retaining walls, for improving operations and enhancing safety at a minimal cost, while enabling conformity assessment results for structures [10][11][12][13].…”

“…With advancing technology, it is not a surprise that innovative, sensing-based techniques have been documented for the assessment of infrastructure systems, with comparable accuracies to traditional methods [9]. Furthermore, photogrammetry enables both a qualitative and a quantitative evaluation approach for monitoring changes or performances, and would be advantageous for an asset management system, easily data-logging its condition over time [9].…”

“…With advancing technology, it is not a surprise that innovative, sensing-based techniques have been documented for the assessment of infrastructure systems, with comparable accuracies to traditional methods [9]. Furthermore, photogrammetry enables both a qualitative and a quantitative evaluation approach for monitoring changes or performances, and would be advantageous for an asset management system, easily data-logging its condition over time [9]. With researchers becoming more interested in using photogrammetry, comparative experimental studies between photogrammetry and Terrestrial Laser Scanning (TLS), for 3D reconstruction of civil and geotechnical infrastructure systems, have been documented [4][5][6][14][15][16][17][18][19][20][21][22][23].…”

“…With researchers becoming more interested in using photogrammetry, comparative experimental studies between photogrammetry and Terrestrial Laser Scanning (TLS), for 3D reconstruction of civil and geotechnical infrastructure systems, have been documented [4][5][6][14][15][16][17][18][19][20][21][22][23]. While TLS has been implemented in areas of geotechnical engineering, because of improvements in scanning speed and mobility as well as capabilities for building management [24][25][26][27], few efforts emphasize photogrammetric techniques [9]. Photogrammetric 3D reconstruction provides a low-cost, comparative alternative to TLS, in terms of the point cloud density of the model and the geometrical accuracy [2,6,28].…”

“…The photogrammetry 105 point cloud models consist of numerous sets of points in 3D, on the order of several million. This 106 magnitude of points results in point densities of tens-to hundreds-of-thousands of points per m2 107 [9]. Figure 2b provides an illustration of the point cloud model, including the flagged referenced 108 control points within the testing environment.…”