Analytical Approach to Augmenting Site Photos with 3D Graphics of Underground Infrastructure in Construction Engineering Applications
Bibliographic record
Abstract
This paper proposes an analytical approach to incorporating computer-generated three-dimensional (3D) graphics of invisible underground infrastructure into site photos so as to present a richer and more integral view of the site situation in construction engineering applications. The proposed approach simulates the image-forming process of a camera and produces a virtual photo of the underground scene, whose virtual coordinate axes coincide with the real coordinate axes of the aboveground site scene. As a result, the virtual photo and the site photo can be seamlessly merged in terms of perspective, position, and scale. This research simplifies the calculation of the camera’s spatial orientation by use of only two reference points’ positions, i.e., the camera station position and the object focus position. The whole procedure of the proposed approach is analytical and can be automated into a computer program. In practice, nondestructive subsurface imaging technologies are generally used to obtain the spatial data of the underground infrastructure, which can be readily processed into a 3D as-built model as one component in composing the virtual underground scene. The proposed approach is demonstrated with a case study in which the underground as-built data are superimposed onto the aboveground site photo for the purpose of quality investigation of a bored pile construction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".