3D Stiffness and Strength Degradation Models for Seismic Progressive Collapse Analysis of Reinforced Concrete Structures – Formulations and Implementations Framework
Bibliographic record
Abstract
Realistic prediction of seismic progressive collapse behaviour is essential in vulnerability and performance assessment of reinforced concrete structures.In seismic events, structural components of typical buildings and bridges may be subjected to repeated cyclic load reversals and combined axial, flexure and shear effects, and are also expected to undergo inelastic deformations during the severe ground shaking of major earthquakes.As a result of accumulated damage during inelastic excursions, the seismic response of reinforced concrete members may exhibit stiffness degradation and strength deterioration.In the case of shear-critical columns in older deficient structures, severe degradations and pinching behaviour may be particularly pronounced in the cyclic response.The principal objective of this research is to develop a comprehensive model for seismic response analysis of reinforced concrete structures subjected to multi-component seismic loading that captures the behaviour of members in new structures designed as ductile as well as non-ductile members in existing older deficient structures.The focus is on the formulation of a beam-column element using the concentrated plasticity approach and the development of a framework for the implementations of the new model using object oriented design concepts.The key modeling capabilities considered in this study include: capturing axial force-bending moment interactions; simulating post-yield hardening response; detecting brittle or limited ductility types of failure in shear; capturing degradation of shear strength in the plastic hinge zone with increased displacement ductility; simulating softening in the post-shear failure response; capturing stiffness
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".