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 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".