Advanced Survey Techniques for Port Infrastructure Assessment: A Collision Investigation Case Study
Bibliographic record
Abstract
This paper presents a case study demonstrating the effective integration of advanced technologies for a comprehensive current conditions survey following a collision accident at the Interflour port's waterfront jetty in Phu My town, Ba Ria, Vung Tau province, Vietnam. To reconstruct the accident and assess the port's future operability, a combination of 3D laser scanning, unmanned aerial vehicle (UAV) aerial photography, bathymetric surveying by unmanned surface vehicles (USVs), and side-scan sonar for marine obstacle detection were conducted. 3D laser scanning provided high-precision data (up to 1.0 mm accuracy) for modeling and simulations of above-water structures. UAVs enabled detailed inspection of the vast jetty area. USVs generated accurate bathymetric maps, while side-scan sonar detected underwater obstacles. Data from all sources was integrated into a Geographic Information System (GIS) platform for streamlined management and analysis. The paper aims to highlight the value of technological solutions in accident investigations. Integrating these methods facilitates highly accurate data collection and visualization, even in challenging environments. The results have implications for improving survey accuracy and efficiency, supporting the assessment and restoration of critical infrastructure following accidents.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".