Deterioration modelling of reinforced concrete bridge decks exposed to chlorides in a changing climate
Bibliographic record
Abstract
This study presents a framework for predicting future bridge conditions while considering non-stationary environmental effects. Three modelling approaches are explored based on the availability of historical condition data: probabilistic-mechanistic models, logistic regression, and long short-term memory (LSTM) models. Probabilistic-mechanistic models, implemented using non-homogeneous Markov chains and informed by mechanistic deterioration models, are suitable when historical condition data are limited. They account for environmental effects through variations in transition probabilities over successive time periods to predict future condition states. Logistic regression is simple and effective in capturing the influence of environmental parameters on changes in bridge deterioration rates when sufficient data are available. LSTM models are well-suited for representing deterioration trends when large time-series datasets are available. The demonstration examples focus on reinforced concrete bridge decks and examine chloride-induced reinforcement corrosion, which is the dominant mechanism that is significantly affected by changing climate conditions. However, depending on the location, element type, and environmental exposure, other degradation mechanisms may dominate, and the proposed models can be adapted accordingly to address such cases. These models provide insights into how non-stationary environmental variables, including traffic, temperature and CO2 concentration, may influence reinforced concrete deterioration and better prepare infrastructure managers for devising maintenance strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".