| Title |
Accuracy Analysis of Low-Cost Equipment-Based Length Measurement Technologies for the Digitalization of Construction Supervision |
| Authors |
Changwoo Lee ; Heejae Ahn ; Seungho Song ; Harim Kim ; Hunhee Cho |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2025.26.6.033 |
| Keywords |
Construction; Measurement; Digitization; Mobile |
| Abstract |
Construction inspection ensures compliance with regulations, which primarily consists of manual measurements using tape measures. However, the reliance on manual operations leads to human errors such as measurement inconsistency and time inefficiency. While 3D scanners can serve as alternative digital solutions due to their high precision, their high cost and technical expertise requirements limit their widespread adoption. Recent advances in mobile technology have enabled more accessible alternatives through the integration of LiDAR sensors and AR frameworks; however, the lack of systematic field validation is hindering practitioners from confidently selecting the appropriate technologies. Accordingly, this study selected and evaluated four low-cost and accessible mobilebased measurement technologies (LiDAR, AR, Checkerboard-based, and Known Object-Based) under real construction site conditions. Each technology was tested with 1m, 2m, and 3m long objects at distances ranging from 3m to 10, with shooting angles varying from -60° to 60° in both shaded and wel-lit conditions. Measurement accuracy was evaluated against the 3% construction law standard. Known Object-Based achieved the highest accuracy (1.59-1.77%) across all conditions. LiDAR performed well within 3m (1.91-2.32%) but was unable to measure beyond 4m. AR measured up to 7m in well-lit conditions (2.17%) but declined to 4.05% in shaded environments. Checkerboard achieved 1.85% accuracy in well-lit conditions but struggled with pattern recognition in shaded environments. Measurement times ranged from 4.7-13.8 seconds. LiDAR and AR provide immediate on-site results, while Checkerboard and Known Object-Based require post-processing. This study provides evidence-based guidance for selecting digital measurement technologies based on site conditions, thereby supporting the digitalization of construction supervision. |