Effect of modeling assumptions on predicting seismic responses of a three‐story reinforced concrete shear wall structure
Bibliographic record
Abstract
Abstract The behavior of short‐period reinforced concrete (RC) shear wall structures is often complicated and hard to predict accurately, even when the structure behaves in the elastic region, due to significant uncertainties in the material and the environment. Modeling assumptions used in finite element (FE) analyses often influence the accuracy of the dynamic response predictions. This paper discusses the numerical modeling of shaking table tests of a 3‐story RC shear wall specimen, which was carried out by Korea Atomic Energy Research Institute in July 2020. The experimental program is briefly introduced in this paper. Through nonlinear time history analyses using ABAQUS, the effect of modeling assumptions on the accuracy of the FE methods in predicting the linear and moderately nonlinear behavior of the RC structure is presented. Two commonly used modeling/model updating assumptions are considered: concrete Young's modulus and foundation flexibility. Influences of such modeling assumptions in predicting beyond design dynamic behavior (i.e., nonlinear responses and damage development) of the testing structure are also studied. The results showed that the accuracy of the dynamic response prediction of the structure could be improved significantly after calibrating the models against the white noise test results. Nevertheless, models established with different modeling assumptions can only capture the behavior of the structure at certain seismic intensity levels. Different models give results with a considerable variation in the structure's peak acceleration, floor response spectrum, acceleration amplification profile, damage pattern, and damage severity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".