High-Definition 3D Digital Twin for Damage Mapping for Bridge Systems Using Advanced NeRF and STRNet

Abstract

This research presents an innovative approach to generating three-dimensional (3D) digital twin models for damage assessment in large-scale civil structures. By refining the Nerfacto model, which builds upon Neural Radiance Fields (NeRF), the study achieves highly accurate 3D reconstruction with detailed, pixel-level damage visualization. To enhance precision, STRNet with Test Time Augmentation (TTA) was utilized for pixel-wise crack segmentation, ensuring seamless incorporation of damage data into the digital twin model. Extensive case studies validated the effectiveness of this methodology for large-scale infrastructure, highlighting its potential for proactive structural health monitoring. Future investigations will focus on real-world outdoor applications, integrating UAV-based imaging while addressing challenges such as scale variations and lighting inconsistencies. This framework provides a reliable foundation for automating infrastructure inspection and maintenance.