Multi-objective and probabilistic decision-making approaches to sustainable design and management of highway bridge decks
Bibliographic record
Abstract
The aging and deterioration of highway bridges and the new requirements for sustainable infrastructures and communities require innovative approaches for their management that can achieve an adequate balance between social, economic and environmental sustainability. This paper presents a multi-objective decision-making approach for the sustainable design and management of highway bridge decks, which can consider several and conflicting objectives, such as the minimisation of owner's costs, users costs, and environmental impacts and uses goal setting and compromise programming to determine the satisficing and compromise solutions that yield the best trade-off between all competing objectives. The proposed approach is based on robust reliability-based mechanistic models of the deterioration and service life of reinforced concrete bridge decks, which include diffusion models for the prediction of chloride ingress into concrete and steel corrosion and thick-walled cylinder models for the prediction of stresses induced by the accumulating corrosion products in the concrete cover. The proposed approach is illustrated on the life cycle design and management of highway bridge decks using normal and high performance concrete. It is shown that the high performance concrete deck alternative is a Pareto optimum, while the normal concrete deck is found to be a dominated solution in terms of life cycle costs and environmental impacts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".