Progressive-Failure Analysis of Buildings Subjected to Abnormal Loading
Bibliographic record
Abstract
Abstract: This article presents a progressive-failure analysis procedure to evaluate the performance of a building framework after it has been damaged by unexpected abnormal loading, such as an impact or blast load caused by a natural, accidental, or deliberate event, or as a result of human error in design and construction. To begin with, it is assumed that some type of short-duration abnormal loading has already caused some form of local damage to the structure. The residual load-carrying capacity of the remaining framework is then analyzed by incrementally applying the prevailing long-term loads and any impact debris loads, and progressively tracing the strength deterioration of the structure until either a globally stable state is reached or progressive collapse occurs for part or all of the structure. The computer-based procedure is based on the displacement method of analysis. The effect of both axial force and shear deformation on member and structure stiffness is accounted for in this article (Liu, 2004; Xu et al., 2004). The stiffness matrices for framework members account for elastic–plastic bending, shearing, and axial deformations, and are progressively updated under incrementally increasing loads through the use of degradation factors that characterize stiffness deterioration. The computational model allows the incremental analysis to proceed beyond loading levels at which structural instabilities occur, including the formation of plastic collapse mechanisms and the disengagement of members from the building superstructure. The progressive-failure analysis procedure is quite general and, with the appropriate choice of material constitutive model, may be applied to building frameworks of any type (concrete, steel, composite, etc.). Herein, a constitutive model for structural steel is adopted to account for elastic–plastic behavior under single or combined forces, and the progressive-failure analysis procedure is illustrated for two example planar steel moment frames.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".