A Framework for Vision-Based 3D Inspections for Maintenance Activities and Digital Twin Integration

Vision-based monitoring methods have been actively studied in the construction industry as they can automatically generate information related to progress, productivity, and safety. 3D reconstruction is key in such monitoring techniques, allowing the inference of job-site context, the creation of digital counterparts of physical spaces, and the comparisons between as-designed and as-built conditions. However, 3D applications in construction currently produce large volumes of unstructured data and unusable point clouds, which are time-consuming to convert into an interactive environment for Building Information Modelling (BIM) or Digital Twins. While radiance field rendering methods are increasingly gaining traction, the adoption of generated Neural Radiance Fields or Gaussian Splatting models by digital construction technology is still tentative. This study introduces a framework that uses Neural Radiance Fields (NeRFs) to improve 3D inspection and maintenance on construction sites. It merges NeRF's high-resolution, real-time 3D modelling with an interactive platform, facilitating detailed remote site analysis and defect detection. The framework incorporates a custom version of TurboNeRF, tailored specifically for construction site inspections. Through this paper, we aim to highlight the potential of combining 3D imaging technology, the use of drone imagery and ontological models to improve construction site management practices.