Applications of Unmanned Aerial Vehicles (UAVs) in Construction Site Monitoring and Progress Evaluation: An Empirical Performance and Photogrammetric Accuracy Analysis

Abstract

Automated monitoring and precise progress evaluation represent criticalparadigms in modern construction engineering, where project schedule delays and cost overruns frequently stem from inefficient data collection, poor communication, and lag times in tracking field activities. Traditional ground-based manual inspection methodologies are resource-intensive, safety-critical, highly localized, and prone to significant human error. This research paper systematically evaluates the application and performance accuracy of Unmanned Aerial Vehicles (UAVs), commonly termed drones, integrated with advanced Structure-from-Motion (SfM) photogrammetry and light detection and ranging (LiDAR) payloads for construction site monitoring and automated progress tracking. Over a 180-day field tracking campaignencompassing a multi-story mixed-use commercial development project, multi-rotor and fixed-wing UAV platforms were deployed to capture ultra-high-resolution aerial imagery, thermal datasets, and dense 3D point clouds. To establish a rigorous volumetric and geometric accuracy framework, drone-derived datasets were cross-validated against ground truth measurements established using high-accuracy Real-Time Kinematic Global Navigation Satellite Systems (RTK-GNSS) and terrestrial laser scanners (TLS). The experimental findings demonstrate that UAV photogrammetric networks, when constrained by strategic Ground Control Points (GCPs), achieve a root-mean-square error (RMSE) of less than 1.8 cm in horizontal axes and 2.4 cm in the vertical axis. The generated orthomosaics and digital surface models enabled fully automated earthwork volumetric calculations, yielding a negligible error margin of 1.25% compared to conventional cross-sectional surveying techniques. Furthermore, automated alignment of the drone-derived 3D point clouds with the building information modeling (BIM) architectural baseline allowed for four-dimensional (4D) progress deviation mapping, enabling the identification of micro-level variations in concrete formwork, steel reinforcement alignment, and masonry assembly within hours of structural completion. This study concludes that the deployment of automated UAV flight patterns lowers field inspection cycles by 85%, reduces total project tracking costs by 45%, and significantly enhances construction site safety by eliminating the need to physically access high-risk zones. Ultimately, the scientific significance of this research lies in providing a standardized geometric validation matrix that supports the integration of UAV analytics into automated, smart construction lifecycles. KEYWORDS: Unmanned Aerial Vehicles (UAVs), Construction SiteMonitoring, Automated Progress Evaluation, Photogrammetry, BuildingInformation Modeling (BIM), Structure-from-Motion (SfM), VolumetricAnalysis.