Vol. 22 No. 4 (2025): Journal of Non-Destructive Testing & Evaluation (JNDE), December 2025
Research Papers

UAV-assisted Photogrammetry for non-contact inspection and 3D reconstruction of Thermal Power Plant Structures

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  • J. Prawin,
  • M. Saravanan,
  • V. RameshKumar,
  • A.K. Farvaze Ahmed

Published 10-12-2025

Keywords

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How to Cite

J. Prawin, M. Saravanan, V. RameshKumar, & A.K. Farvaze Ahmed. (2025). UAV-assisted Photogrammetry for non-contact inspection and 3D reconstruction of Thermal Power Plant Structures. Journal of Non-Destructive Testing & Evaluation (JNDE), 22(4), 63–75. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/120

Copyright (c) 2026 Journal of Non Destructive Testing and Evaluation (JNDE)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Inspection and maintenance of thermal power plant structures are critical for ensuring safety and operational reliability. Conventional inspection practices often require manual access via scaffolding or rope-based systems, which pose safety risks, incur high costs, and provide limited spatial coverage. In this study, an unmanned aerial vehicle (UAV) equipped with a mapping-grade camera was deployed to acquire image datasets of representative plant components. The imagery was processed using Bentley iTwin Capture Modeler, following a structure-from-motion (SfM) and multi-view stereo (MVS) workflow, to generate high-fidelity point clouds, meshes, and textured 3D models. Three case studies were examined: (i) reconstruction of a reinforced concrete water tank and a steel storage tank, (ii) reconstruction of a tall reinforced concrete chimney, and (iii) reconstruction of a steel conveyor gallery with trestles. These examples demonstrate the applicability of UAV photogrammetry for diverse typologies ranging from compact tanks to tall slender towers and extended truss-type structures. The results show that UAV-based
3D reconstructions provide comprehensive visual documentation, non-contact inspection of inaccessible regions, and digital baselines for temporal monitoring. Furthermore, the generated models can be exported to finite element or other computational platforms for performance assessment, supporting both current condition evaluation and scenario-based structural analysis. By enabling integration with digital twin
environments, AI-powered vision-based surface defect detection models and predictive maintenance frameworks, this approach enhances the ability to track deterioration, forecast potential failures, and plan timely interventions, thereby improving the safety, efficiency, and sustainability of thermal power plant infrastructure management.

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