Weather Forecasting Technology Applied to Structures Improves Resiliency
Bibliographic record
Abstract
<p>Designers are creating taller and more complex structures in the urban built environment. These complex structures include long span bridges, buildings and monuments all of which can push the limits of design and engineering processes. These structures are challenging to construct, operate, and rehabilitate during their life-cycle. Analytics used during the design phase can be combined with weather forecasting technology to provide an advanced site and structure-specific weather forecast. This site and structure-specific weather forecast helps to ensure efficient, safe construction, and maximizes operation of the structural asset. Examples discussed include forecasting of wind conditions for construction/maintenance activities on bridges, prediction of falling snow/ice accretion from cable stay bridges/buildings and prevention of high-sided vehicle blow over on bridges. Analysis of weather forecasting data combined with a historical database of site-specific weather monitoring provides knowledge about deviations from the normal climate. This analysis can provide advanced storm warning thereby mitigating potential damages. The ability to provide site-specific and structure-specific weather forecasts is increasingly important because of the increased intensity and frequency of storm events due to climate change.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".