Life Cycle Thinking–Based Decision Making for Bridges under Seismic Conditions. II: A Case Study on Bridges with Superelastic SMA RC Piers
Bibliographic record
Abstract
Bridges reinforced with superelastic shape memory alloys (SMAs) demonstrate improved performance under earthquake excitations. In general, the capital investment for a bridge reinforced with SMAs is higher due to their high cost and special workmanship requirement. However, when accounting for postearthquake repair and maintenance costs and environmental impacts, SMA-reinforced bridges can deliver significant economic and environmental advantages over conventional structures in the long run. Based on a life cycle thinking–based decision support framework developed in a companion paper, this study thoroughly evaluated the life cycle seismic performance of a bridge reinforced with an SMA considering three different reinforcement configurations. Fragility analyses were conducted for each reinforcement configuration of the SMA-reinforced concrete (RC) bridge to assess its seismic vulnerability. A life cycle cost (LCC) assessment was performed to determine the economic impacts during their service life. Additionally, cradle-to-grave life cycle assessment (LCA) was done using SimaPro to assess the environmental impacts. Using the outcomes of the these assessments, the overall life cycle performance of the novel bridges was compared with a similar bridge reinforced with conventional steel. The results showed that the SMA-reinforced bridges presented a better seismic life cycle performance compared with a conventional RC bridge from a seismic performance and economic perspective. However, the conventional bridge showed a better overall score from an eco-friendly approach.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".