Risk-Based Decision Making for Sustainable and Resilient Infrastructure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it