Advanced NeRF (ABM-Nerfacto) for High-Definition Digital Twin and Damage Mapping

Abstract

Since 2017, extensive damage detection using advanced deep learning models and computer vision techniques has been actively explored. However, efficiently mapping detected damage in 3D digital twin model remains a challenge, as few studies have successfully integrated deep learning and computer vision for precise damage representation. To address this gap, this study investigates an enhanced NeRF-based model, ABM-Nerfacto [1], designed for high-definition and efficient damage mapping in 3D digital twin. This advancement facilitates more effective structural health monitoring, infrastructure maintenance, and a comprehensive pixel-level overview of damage distribution. The Nerfacto model was extensively modified and integrated with an advanced attention mechanism to improve its ability to learn structural features and damage patterns. When applied to a bridge system, the developed model demonstrated exceptional accuracy in pixel-wise damage mapping, successfully generating a highfidelity 3D digital twin.