Bibliographic record
Abstract
As far as structural engineering is concerned, scientific and technological advances are often fostered by the occurrence of collapses involving a more or less relevant amount of damage and, in the most unfortunate cases, also the loss of human lives. Indeed, it seems fair to say that every time a structural collapse takes place, designers and researchers will immediately start searching for a rational explanation; this boost of attention and interest invariably leads to the unveiling and investigation of new phenomena and also paves the way to the development of design tools intended to anticipate and prevent the detrimental effects of such phenomena. The above statements are especially true in the case of structural stability, as attested by the following illustrative examples: ~1! The progress in the theory of laced column buckling due to the collapse of the Quebec Bridge in 1907; ~2! the crucial role played by the Tacoma Narrows Bridge failure, in 1940, concerning the awareness and understanding of the aerodynamic torsional instability phenomenon; and ~3! the development of Koiter’s general elastic postbuckling theory, essentially inspired by the sudden collapse of several thin-shell structures at surprisingly low applied load levels. The very recent and unbearably tragic collapse of the World Trade Center twin towers, on September 11, 2001, highlighted the importance of devoting new attention and resources to a better understanding of behavioral, design, and safety issues related to the overall and member stability of framed structures— particularly when these structures are subjected to extreme loading conditions, such as the ones caused by impact, explosion, or fire. By publishing this Special Issue of the Journal of Engineering Mechanics, ASCE aims at making the above problems more visible, thus drawing the attention of the structural stability scientific and technological communities and encouraging them to devote new research to developing and disseminating more advanced methods for the efficient solution of these problems. At the same time, one has the opportunity of showing a representative sample of the activity currently under way in this field. The recent exponential growth in computer capabilities, affordability, compatibility, and interconnectivity is responsible for a virtual “computational revolution” in many areas related to structural analysis and design. In particular, this situation will certainly lead, in the near future, to the routine incorporation of geometrically and materially nonlinear concepts and methodologies into the daily design practice. It will also enable designers to feel more comfortable and secure when faced with the everexpanding tasks of coping with increasingly slender structures and more complex loading combinations, as they will have easy access to user-friendly tools that are able to handle advanced structural behaviors like the inelastic geometrically nonlinear behavior of initially imperfect members and framed structures, the global and local stability of nonprismatic thin-walled members,
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".