Wind-resilient civil structures: What can we learn from nature
Bibliographic record
Abstract
Owing to changing weather patterns, catastrophic natural disasters are expected to happen more frequently and cause dramatic life and economic losses worldwide. The United States experienced a historically high record of weather disasters in 2017, with the economic losses exceeding 300 billion dollars. A major contributor to economic loss and threat to public safety is damage, destruction, and failure of civil structures in the strong-wind dominated disasters. There is a pressing need for reconstruction and redesign of critical civil structures to better cope with high winds to mitigate the loss of lives and properties. This paper takes a biomimetic perspective to link problem areas with potential solutions for future bio-inspired technology development, by identifying the most vulnerable aspects of civil structures in strong winds on one side and wind-resilient examples of biological systems on the other side. Of particular interest are plants that thrive in high winds, as they have likely adapted to manage the harsh environment under pressure of natural selection. Specific biological examples include the Saguaro cactus (Carnegiea gigantean Britton & Rose), reed grass, and shape reconfiguration of leaves. A review of problem areas, abstracted principles, and exemplary biological role models shall inform and guide towards new designs of wind-resilient civil structures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".