The Need for an Improved Wind Engineering Curriculum to Address Natural Disasters
Bibliographic record
Abstract
The field of wind science and engineering (WSE) in relation to civil engineering (CE) applications is still considered relatively young and thus has been taught only for about 50 years in some academic institutions. Therefore, it is unsurprising that there are limited wind engineering (WE) tracks within CE programs worldwide, and no semblance of a standard or ideal curricula. An adequate education in WE is paramount to future structural and civil engineers, and ultimately to academia, because natural disasters including extreme wind events have the capability to destroy our infrastructure systems, as well as threaten people’s lives and well-being. The goal of this research is to identify and propose an ideal path for WE tracks within CE that not only enhances the quality of education, but also provides equitable learning opportunities and promotes diversity and workforce inclusion. To achieve this goal, this research (1) analyzed the different CE programs that include WE tracks offered in the US, Canada, and Europe; (2) identified the academic institutions that have academic expertise and equipment critical to the study and investigation of wind events, such as atmospheric boundary layer (ABL) wind tunnels; (3) conducted a survey of all WE faculty and students doing research on these topics at Florida International University to gather information on the courses offered as well as information on what they believe could be offered or improved to enhance the curricula; (4) compared the programs that include WE tracks to provide a modernized WE curricula; and (5) proposed ideas, tools, and strategies that can be implemented to provide better curricula for students in order to enhance their education and increase research in this paramount topic, as well as ensure equitable quality education that presents equal opportunities for all.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".