A Study on 3D Digital Modelling Methods for Existing Steel Structures using Point Cloud Data and Eddy Current Measurements
Bibliographic record
Abstract
This study presents a method to efficiently construct 3D models that reflect the current condition of existing steel structures, including areas that have been retrofitted due to corrosion, and thus improve structural analysis and maintenance.The proposed approach uses a terrestrial 3D laser scanner to capture geometric features and generate dense point cloud data.Based on this data, numerical models were developed to reflect actual damage and repair conditions.To evaluate this method, a case study was conducted on a steel tubular column located in a coastal environment, subsequently subjected to corrosion and multiple reinforcement interventions.This study examined whether the model could accurately represent complex surface conditions, such as irregularities due to damage or retrofit.Since internal features such as overlapping plate thicknesses cannot be captured by 3D scanning, pulsed eddy current testing, a non-destructive testing method, as a complementary method for obtaining internal information.It was found that internal geometry and material thickness could be estimated, thus enabling a greater understanding of actual structural conditions.By combining external surface modelling using point cloud data with internal inspections carried out via eddy current testing, the proposed workflow offers a practical solution for modelling in steel structure deterioration.The results demonstrate the potential of this integrated approach to improve both accuracy and efficiency of structural assessments and consequently support better decision-making in infrastructure maintenance and lifecycle management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".