Influence of Semi‐Rigid Connections and Local Joint Damage on Progressive Collapse of Steel Frameworks
Bibliographic record
Abstract
Abstract: Semi-rigidly connected steel frames are more vulnerable than rigidly connected frames to resist progressive collapse due to abnormal loading events. This article extends the threat-independent method for progressive-failure analysis of rigid frames to analysis accounting for semi-rigid connections. The influence of joint damage caused by disengagement of member(s) is also considered in the analysis, and the degree of damage is modeled by a health index. A compound element model is employed to include the contributions of nonlinear behavior of beam-to-column connections, connection and member-end damage, member inelasticity, member shear deformation, and geometrical nonlinearity to structural response. Four beam collapse modes are illustrated for the progressive collapse analysis associated with debris loading generated when disengaged structural components fall onto lower parts of the structure. The impact effect is taken into account for the quasi-static nonlinear analysis by utilizing an impact amplification factor according to GSA and DoD guidelines. Any progressive collapse occurring thereafter involves a series of collapse events associated with topological changes of the frame. The analysis procedure is illustrated for the progressive collapse behavior of two planar steel frames. The results demonstrate that the proposed method is potentially an effective tool for the progressive collapse analysis of semi-rigid steel frames under abnormal loading events.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".