Automated early estimation of bridge interventions, possession windows and costs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bridge managers need to generate a complete overview of required interventions, possession windows and likely costs 10–20 years ahead of execution. These, even if approximate, help ensure stable train schedules. With the increasing amount of available data and the increasing desire to exploit digitalisation to improve decision making, bridge managers are perfectly poised to make or improve these estimates by moving on from current qualitative methods. This paper proposes a way in the current climate to use digitalisation and existing data to generate approximate overviews of required interventions 10–20 years ahead of time, including estimates of component-level bridge interventions, possession windows, likely costs and the increases in failure risks if interventions are postponed. A demonstration is done on 41 bridges of a 25 km railway network in Switzerland. It is argued that the algorithm generates a more complete and consistent overview of component-level interventions, possession windows and costs compared with current qualitative methods. Additionally, the algorithm generates a solid basis for the initiation of detailed investigations of the bridges by engineering offices – that is, the investigations that result in the information required for scheduling an intervention, as well as estimating the type of intervention and track possession that are required.
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.
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 it