HBIM Application to Historical Steel Structures: the Case Study ofLapela Bridge
Bibliographic record
Abstract
Historical steel structures are present all around the world. Besides being a common part of the cultural heritage, many of them are still in service. As an example, we can cite the case of riveted bridges, railway stations, exhibition pavilions, or industrial buildings. The maintenance of these ancient constructions is crucial since they are prone to have suffered significant damage over time due to effects such as corrosion, human actions, or the exposure to heavy loads for which they were not originally conceived. Further, in many cases, these steel structures have to be strengthened in order to adapt them to new uses. HBIM (Historical Buildings Information Modeling) is a new methodology in structural design and construction that could be used as the ideal tool for the maintenance management of these historical structures. HBIM technology is adopted in this paper for structural engineering purposes. Departing from the geometric survey carried out by terrestrial laser scanning, a 3D model is obtained which apart of gathering the main dimensions and details regarding the composition of the structure, it allows collecting all the information concerning the deterioration grade or the different inspections and retrofitting actions performed over time. Thus, by introducing different time stages in the 3D model, the evolution of the structural health over time can be analyzed, which allows the decision-making regarding maintenance and, if required, the undertaking of repairing works. The proposed methodology will be applied to the case study of the Lapela Bridge, in Portugal.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".