Earthwork Volume Measurement in Road Construction Using Unmanned Aerial Vehicle (UAV)

Abstract

This study utilized an Unmanned Aerial Vehicle (UAV) to estimate earthwork volumes in a road construction project spanning from kilometer 6+675 to kilometer 17+275 within the Nongthalay subdistrict, Muang district, Krabi province. Data processing was conducted with PIX4D software, where earthwork volumes were determined from UAV photogrammetry-derived Digital Elevation Models (UAV-DEMs), utilizing the Raster Volume Comparison plugin in QGIS. Aerial imageries were acquired before and after earthwork completion on November 24, 2022, and January 31, 2022, respectively. The results indicated that the UAV-DEM-derived volume was 126.32 m³ less than the prismoidal formula, accounting for a 4.25% difference. It is important to note that the traditional technique's volume estimation represents compacted soil required for road construction. However, prismoidal formula use of cross-sectional areas derived from polygonal shapes may introduce inaccuracies. Therefore, conclusive comparisons favoring the traditional technique over UAV-DEMs should be approached with caution. Additionally, it should be emphasized that the volume results do not directly align with the total earthwork quantity required for the project. The determined volumes reflect compacted soil, whereas the project necessitates the transport of loose soil that will subsequently be compacted on-site. UAV-derived DEMs provide valuable tools for road construction earthwork estimation. With meticulous planning and execution of data collection, and a focus on data quality through rigorous ground control and processing, UAV-derived DEMs can offer reliable estimates. However, the specific project requirements, terrain characteristics, and quality control measures should be considered to ensure the data's reliability and accuracy for earthwork estimation. Moreover, this study also examines the effectiveness of UAV data acquisition in comparison to traditional surveying methods, revealing valuable insights despite limitations in the Area of Interest (AOI). Despite a longer processing time for UAV imagery, the UAV exhibited efficiency by acquiring road surface data in less than half the time required by traditional surveying approaches. The UAV's ability to expedite data collection over larger road sections became increasingly evident as length increased, enabling concurrent progress in construction activities. Additionally, the UAV approach demonstrated cost efficiency, representing less than 50% of the expenses associated with traditional survey methods. Moreover, the UAV method showcased manpower efficiency, requiring only one operator compared to the three individuals needed for traditional survey techniques. Overall, the integration of UAV technology in road construction holds promise for improving time, cost, and manpower efficiency in surveying processes.