Lumped spring model parameters of RC frame elements for seismic performance assessment
Bibliographic record
Abstract
Abstract This study presents lumped spring model parameters for nonlinear seismic analysis of reinforced concrete (RC) frame structures. The modified Ibarra‐Medina‐Krawinkler (M‐IMK) hysteresis model is used as a lumped spring to simulate the inelastic behaviour of a RC frame element. The influence of loading protocols on the calibration of model parameters is investigated and compared with other hysteresis models, such as the original IMK and Bouc‐Wen‐Baber‐Noori (BWBN) models. Less dependent on the loading protocol used for model identification, the parameters of the modified IMK hysteresis model are calibrated to model a database of 383 rectangular RC columns. To simplify the calibration process, the model parameters that define the monotonic response are determined based on code and empirical equations, while the parameters that control the hysteretic degradations are calibrated against the experimental results. The calibrated model parameters are used to develop regression equations for the model parameters based on physical variables, such as loading, dimensions, and material properties. It is found that the model parameters obtained from the experimental calibration and regression equations generate similar hysteresis curves. The performance of the lumped spring model to nonlinear time history analysis of RC structures subjected to earthquakes is also evaluated through a series of pseudo‐dynamic hybrid simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".