Term of Award

Fall 2025

Degree Name

Master of Science, Civil Engineering

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Civil Engineering and Construction

Committee Chair

Marcel Maghiar

Committee Member 1

Gustavo Maldonado

Committee Member 2

Soonkie Nam

Abstract

Mechanically Stabilized Earth (MSE) retaining walls are vital components of transportation infrastructure, providing structural support for embankments and bridge approaches. As many walls approach or exceed their design life, concerns regarding long-term stability, settlement, and displacement have increased. Traditional visual inspection remains the most common assessment method; however, it is subjective and often unable to detect subtle geometric changes that may precede structural distress or failure. This study evaluates Terrestrial LiDAR (Light Detection and Ranging) Scanning (TLS) and Close-Range Photogrammetry (CRP) as nondestructive, noncontact alternatives for detecting displacement in MSE retaining walls. Three representative sites were investigated: a Control Wall in a controlled environment and two field sites, Crossgate Bridge and King George Bridge. A Leica Robotic Total Station (TS1201+) established a closed traverse and local coordinate framework. LiDAR data were acquired using Leica P50 and C10 scanners with Target-Based (TB) and Visual-Alignment (VA) registration, while CRP data were captured using a Nikon D5300 DSLR and DJI Matrice M30 UAV, processed in Agisoft Metashape, Pix4D Mapper, and DroneDeploy. Results confirmed that LiDAR TB and VA methods provided superior accuracy and repeatability across all sites, achieving RMSV values of 4–8 mm and noise levels of 6–8 mm. In comparison, CRP produced RMSV within ±10 mm and noise levels of 18–21 mm. The TB LiDAR configuration clearly detected a minimum displacement of 3 mm, while CRP reliably detected ≈ 11 mm. Although CRP exhibited greater sensitivity to lighting and geometry, it remained advantageous for rapid, low-cost data collection. Both technologies substantially outperformed traditional visual and manual inspection methods in terms of accuracy, efficiency, cost-effectiveness, and sustainability. The standardized inspection workflow developed in this study establishes a repeatable, data-driven, and scalable framework for nondestructive monitoring of retaining walls using existing LiDAR, photogrammetry, and total station technologies. Beyond its practical demonstration of transitioning wall assessment from qualitative observation to quantitative 3D analysis, this framework also forms the technical basis for next-generation AI-enhanced, multi-sensor integration. Future extensions of this work will enable real-time anomaly detection, XR-based digital twin visualization, and autonomous long-term monitoring through cloud-enabled and robotic sensing systems. Collectively, these advancements position the developed methodology as a foundation for predictive, intelligent, and sustainable infrastructure management across transportation networks.

Research Data and Supplementary Material

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