Numerical models of RC elements and their impacts on seismic performance assessment
Bibliographic record
Abstract
Summary This paper aims to provide a guideline for numerical modeling of reinforced concrete (RC) frame elements for the seismic performance assessment of a structure. Several types of numerical models of RC frame elements are available in nonlinear structural analysis packages. Because the numerical models are formulated based on different assumptions and theories, the models' accuracy, computing time, and applicability vary, which poses a great difficulty to practicing engineers and limits their confidence in the analysis results. In this study, the applicability of five representative numerical models of RC frame elements is evaluated through comparison with 320 experimental results available from the Pacific Earthquake Engineering Research column database. The accuracy of a numerical model is evaluated according to its initial stiffness, peak strength, and energy dissipation capacity of the global responses. In addition, a parametric study of a cantilever RC column subjected to earthquake excitation is carried out to systematically evaluate the consequence of the adopted numerical models on the maximum inelastic structural responses. It is found from this study that the accuracy of the numerical models is sensitive to shear force demand–capacity ratio. If a structural period is short and the structure is shear critical, the use of numerical models that can explicitly capture the shear deformation and failure is suggested. If the structural period is long, the selection of a numerical model does not greatly influence the global response of the structure. The paper also presents statistical parameters of each numerical model, which can be used for probabilistic seismic performance assessment. Copyright © 2014 John Wiley & Sons, Ltd.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".