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Advances in the Stability of Frame Structures

2005· article· en· W2049244002 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Engineering Mechanics · 2005
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsnot available
Fundersnot available
KeywordsStability (learning theory)Frame (networking)Computer scienceTelecommunicationsMachine learning

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.215
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it