Modeling Bridge Deterioration Using Case-based Reasoning
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
Current bridge deterioration models used in bridge management systems are not successful in capturing the effects of bridge condition history on future condition, in performing “what if” analyses for different maintenance scenarios, and in accounting for the interactive effects between deterioration mechanisms of different bridge components. Moreover, these models cannot be easily updated when new data is obtained. On the other hand, bridge management systems are updated on a regular basis and thus accumulate valuable knowledge about the performance of bridges over the years. The case-based reasoning (CBR) approach could reuse this knowledge efficiently to act as a deterioration model that would overcome some of the shortcomings of current models. A new CBR system, called case-based reasoning for modeling infrastructure deterioration, was developed and utilized in building a “proof of concept” application for modeling the deterioration of concrete bridge decks using data obtained from the Canadian Province of Quebec. The performance of the CBR model showed that CBR has great potentials in predicting the future condition of infrastructure facilities.
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