Open Access Open Access  Restricted Access Subscription Access

Digital Site Progress Monitoring and Crack Detection Using UAV Photogrammetry: A Critical Review

Dr. Amit kumar D. Raval, Dr. Arti N. Pamanani, Nandni Barot

Abstract


The demand for objective, rapid, and automated construction-site inspection has generated significant interest in the application of unmanned aerial vehicles (UAVs) for construction management and structural assessment. UAV photogrammetry facilitates the capture of high-resolution images and three-dimensional reconstruction at construction sites and civil infrastructure, allowing for a more economical alternative to frequently repeated manual surveys, and for certain applications also terrestrial laser scanning. This review highlights UAV photogrammetry as the critical technology that ties together digital construction progress monitoring and crack detection. The paper reviews recent advances in Structure-from-Motion photogrammetry, point-cloud generation and workflows for Building Information Modeling (BIM) integration, deep learning and digital twin construction. Point clouds derived from UAV can be utilized for comparison of As-built vs. as-planned, quantities estimation, component recognition and progress verification while high-resolution UAV imagery offers automated crack detection, segmentation, measurement and spatial localization. Yet the accuracy of photogrammetry still depends on image overlap, ground sampling distance, camera calibration, surface texture, illumination, occlusion flight geometry and georeferencing quality. In the case of crack measurement, problems compound further since very small defects can become close to the spatial resolution limit of the images captured. The review concludes with the recommendation to utilize UAV photogrammetry for efficient integrated progress and condition monitoring, but warns against treating it as an infallible substitute for conventional surveying or engineering inspection. Reliable digital site-monitoring are likely to be future systems based on UAV photogrammetry, BIM/digital twins-belued by AI-based defect analysis and uncertainty estimation requiring adaptive flight planning.


Full Text:

PDF

References


Maru, R., Pitroda, J. R., & Raval, A. D. (2020). Feasibility study of internet of things (IoT) in construction industry: a review. Studies in Indian Place Names, 40(50), 4948-4958.

Maru, R. V., Pitroda, D. J., & Raval, A. (2020). Feasibility study of internet of things (IoT) in construction industry. JETIR, 7(5), 44-7.

Bognot, J. R., Candido, C. G., Blanco, A. C., & Montelibano, J. R. Y. (2018). Building construction progress monitoring using unmanned aerial system (UAS), low-cost photogrammetry, and geographic information system (GIS). ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2, 41–47.

Jacob-Loyola, N., Muñoz-La Rivera, F., Herrera, R. F., & Atencio, E. (2021). Unmanned aerial vehicles (UAVs) for physical progress monitoring of construction. Sensors, 21(12), Article 4227.

Bhattacharjee, A., Rana, M. J., Zaman, A. A. U., Dang, H., et al. (2026). Integrated information system for periodic construction progress monitoring: Utilizing UAV, photogrammetry, and BIM for enhanced decision-making and optimization. Civil Engineering Sciences, 2, Article 0022.

Ding, W., Yang, H., Yu, K., & Shu, J. (2023). Crack detection and quantification for concrete structures using UAV and transformer. Automation in Construction, 152, Article 104929.

Cui, M., Yan, Y., Feng, D., Wu, G., & Zhu, Z. (2025). A bridge crack detection and localization approach for unmanned aerial systems using adapted YOLOX and UWB sensors. Structural Control and Health Monitoring, 2025, Article 3621939.

Yiğit, A. Y., & Uysal, M. (2025). Virtual reality visualisation of automatic crack detection for bridge inspection from 3D digital twin generated by UAV photogrammetry. Measurement, 242, Article 115931.

Zollini, S., Alicandro, M., Dominici, D., Quaresima, R., & Giallonardo, M. (2020). UAV photogrammetry for concrete bridge inspection using object-based image analysis (OBIA). Remote Sensing, 12(19), Article 3180.

Zhang, Y., Lu, L., Luo, X., & Pan, J. (2024). Global BIM-point cloud registration and association for construction progress monitoring. Automation in Construction, 168, Article 105796.


Refbacks

  • There are currently no refbacks.