A Workflow for Integrating Point Cloud Data and Eddy Current Measurements into 3D Digital Modelling of Existing Steel Structures
Bibliographic record
Abstract
This paper presents a workflow that integrates point cloud data and pulsed eddy current measurements for developing 3D digital models that accurately represent the actual condition of existing steel structures, including areas affected by corrosion and subsequent retrofitting.The proposed workflow combines external geometric information obtained through terrestrial 3D laser scanning with internal material and thickness information derived from pulsed eddy current testing, thereby enabling both surface and subsurface conditions to be incorporated into a unified digital modelling framework.To evaluate its feasibility, a case study was conducted on a steel tubular column located in a coastal environment that had experienced corrosion and multiple reinforcement interventions.The workflow was applied to construct a finite element model capable of reproducing complex geometries and variable plate thicknesses caused by damage and repair, demonstrating the applicability of the proposed modelling process to deteriorated steel members.In addition, laboratory experiments were performed to examine the applicability of pulsed eddy current testing for estimating the thickness of overlapping steel plates, which cannot be captured by laser scanning alone.The results confirmed that the eddy current technique can provide internal information necessary for complementing point cloud-based surface models, thus improving the completeness of digital representations.Overall, this integrated workflow has the potential to enhance the accuracy and efficiency of structural modelling and assessment.By linking external and internal inspection data within a single digital framework, the proposed method supports data-driven decision-making for the maintenance and life-cycle management of ageing steel infrastructure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".