Probabilistic and Mechanistic Deterioration Models for Bridge Management
Bibliographic record
Abstract
This paper presents two approaches of modeling the deterioration of highway bridges that can lead to an effective maintenance management strategy. Probabilistic state-based/time-based models are used to predict the global or macro-response of bridge components for network level analysis, while reliability-based mechanistic models are used to predict the detailed or micro-response of bridge components for project level analysis. Probabilistic state-based/time-based models are developed using qualitative performance indicators (condition ratings) that are determined through visual inspections to identify the overall condition of damaged components in a bridge network. Reliability-based mechanistic models are developed using quantitative performance indicators (physical parameters) that are determined through detailed condition surveys, analytical modeling, and empirical investigations to identify the extent and severity of specific deterioration mechanisms for safety critical structures and/or highly damaged components. The condition rating data obtained from the Ministére des Transports du Quebec database and the condition assessment of the Dickson Bridge in Montreal, Canada were used to demonstrate the development of the two approaches in modeling the deterioration of concrete bridge decks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".