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3D Pixelwise damage mapping using a deep attention based modified Nerfacto
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DOI：10.1016/j.autcon.2024.105878.png)
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Abstract
En 中文
Recent advancements in structural health monitoring have highlighted the necessity for accurate threedimensional (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 ABMNerfacto 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.
Keywords:
SHM
Damage detection
Deep learning
Crack segmentation
3D reconstruction
Neural radiance fields
NeRF
Nerfacto
Digital twin
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