Case-Based Reasoning System for Modeling Infrastructure Deterioration
Bibliographic record
Abstract
Deterioration models are essential components of infrastructure management systems (IMSs) because they predict the future condition of infrastructure facilities and consequently assist in optimizing maintenance decisions. Case-based reasoning (CBR) is proposed to generate deterioration models that benefit from the large amount of facility data stored in IMS databases and updated on a regular basis. CBRMID (CBR for modeling infrastructure deterioration) is a new CBR system developed to satisfy the special requirements of modeling infrastructure deterioration and to provide government agencies with practical, accurate, and versatile deterioration models. CBRMID is required to support (1) hierarchial decomposition of infrastructure facilities; (2) representation of facility component interactions; (3) versatility and extensibility of case and knowledge representation; (4) data reusing and sharing; (5) representation of time-dependent data; and (6) fuzziness of retrieval knowledge. In this paper, the architecture of CBRMID is described in terms of case representation, case retrieval, case adaptation, and case accumulation. An application example generated using CBRMID is also presented.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".