journal article·2025-11-12Open access

Advancing Retaining Wall Inspections: Comparative Analysis of Drone-Lidar and Traditional TLS Methods for Enhanced Structural Assessment

Maxwell Wondolowski, Alexandra Hain, Sarira Motaref, Karen Dedinsky, Michael Grilliot

Applied Sciences· Volume 15· Issue 22

Abstract

Accurate inspection of retaining walls is crucial for ensuring structural integrity and public safety. Current assessment methods rely predominantly on qualitative visual evaluations that inform numerical structural ratings. These ratings lack the comprehensive quantitative measurements that 3D imaging can provide. This study evaluates the feasibility and accuracy of drone-based lidar platforms for retaining wall inspection as an alternative to traditional terrestrial laser scanning (TLS) that improves accessibility on hard-to-reach sites and may increase efficiency on site due to preplanned flights. Data was collected from three representative walls exhibiting diverse conditions using TLS and drone-lidar. To provide additional information on the site to use for alignment and checks, both Global Navigation Satellite System (GNSS) control and total stations were employed. The study provides a comparative analysis of the accuracy and practicality of each lidar method, aiming to determine the most effective techniques for routine inspection applications given varying site conditions. When compared against a finely registered TLS ground truth, drone-lidar was found to have root mean square errors of 2.3 cm in areas with low vegetation and 24.2 cm in densely vegetated areas. The findings highlight potential improvements in the precision of retaining wall assessments, proposing a shift from current qualitative practices to reliable, data-driven evaluations. Key limitations in GNSS accuracy and sites with dense vegetation are discussed.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: 3D Surveying and Cultural Heritage · Remote Sensing and LiDAR Applications · Infrastructure Maintenance and Monitoring

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Engineering, University of Connecticut, Storrs, CT 06269, USA

NHERI RAPID Facility, University of Washington, Seattle, WA 98195, USA

Funding

Connecticut Department of Transportation and US Federal Highway Administration

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