3D Pixelwise damage mapping using a deep attention based modified Nerfacto

Recent advancements in structural health monitoring have highlighted the necessity for accurate three-dimensional (3D) damage mapping on digital twins, moving beyond traditional methods such as photogrammetry, which frequently struggle to capture intricate planar surfaces. To address this limitation, this paper proposes a new advanced 3D reconstruction method and its integration with 3D damage mapping techniques. As the 3D reconstruction method, an Attention-based Modified Nerfacto (ABM-Nerfacto) model is developed, and is integrated with an advanced damage segmentation method. Using a three-span continuous bridge with concrete piers as an example structure, and concrete cracks as the example damage, the state-of-the-art STRNet is utilized for crack segmentation. Through extensive parametric studies and comparative evaluations, the proposed ABM-Nerfacto model was demonstrated to produce high-quality 3D reconstructions and corresponding damage mappings for this bridge system. This integrated approach provides a promising solution for comprehensive 3D digital twin-based structural health monitoring. • Proposed ABM-Nerfacto model enhances 3D damage mapping on digital twins. • Integrated advanced deep learning with neural radiance field for reconstruction. • Demonstrated superior 3D reconstruction quality over traditional methods. • Extensive modifications led to improved performance in damage segmentation. • Findings support automated infrastructure inspection and efficient asset management.