3D Pixelwise damage mapping using a deep attention based modified Nerfacto
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it