Life-cycle thinking and performance-based design of bridges: A state-of-the-art review
Bibliographic record
Abstract
Given the growing emphasis on life-cycle analysis in bridge design, the design community is transitioning from the concept of performance-based design in structural engineering to a performance-based design approach within a life-cycle context. This approach considers various indicators, including cost, environmental impact, and societal factors when designing bridges. This shift enables a comprehensive assessment of structural resilience by examining the bridge's ability to endure various hazards throughout its lifespan. This study provides a comprehensive review of two key research domains that have emerged in the field of bridge life-cycle analysis, namely life-cycle sustainability (LCS) and life-cycle performance (LCP). The discussion on the LCS of bridges encompasses both assessment-based and optimization-based studies, while the exploration of LCP focuses on research examining structures subjected to deterioration over their service life due to deprecating phenomena such as corrosion and relative humidity changes, as well as extreme hazards like earthquakes and floods. Moreover, this study discusses the integration between LCS and LCP, highlighting how combined consideration of these factors can minimize damage costs, improve resiliency, and extend the lifespan of the structure. A detailed evaluation encompasses various life-cycle metrics, structural performance indicators, time-dependent modelling techniques, and analysis methods proposed in the literature. Additionally, the research identifies critical gaps and trends in life-cycle analysis within the realm of bridge engineering, providing a concise yet thorough overview for advancing considerations in the life-cycle design of bridges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".