Risk-Based Decision Making for Sustainable and Resilient Infrastructure
Bibliographic record
Abstract
The design and preservation of civil infrastructure systems have been driven, for a long time, by cost minimization while maintaining system reliability at an acceptable level. The growing concerns with aging and deteriorating infrastructures and the need to ensure resilient and sustainable infrastructures and communities require the development and use of innovative construction materials and structural systems and management practices that yield infrastructure resiliency and achieve an adequate balance between social, economic and environmental sustainability. and the emerging needs for sustainable and resilient infrastructure and communities. This paper discusses some key performance measures and approaches that can be used to assess resilience and sustainability and presents a risk-based decision-based approach to help decision-makers optimize the design, evaluation and management of infrastructures that considers all possible hazards and provides alternative risk mitigation strategies that can be evaluated using a cost-benefit analysis, and rational criteria are presented to support the selection of the most sustainable and resilient risk mitigation strategy indicators, such as safety, serviceability, costs, traffic disruption, greenhouse gas emissions, which can be used for life cycle design of highway bridges. An example, taken from the North American context, illustrates how different design and rehabilitation approaches can contribute to achieve the sustainability of a highway bridge.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".