3D Pixelwise damage mapping using a deep attention based modified Nerfacto
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".