A Simplified Method to Predict Damage of Axially-Loaded Circular RC Columns Under Lateral Impact Loading
Bibliographic record
Abstract
Abstract Detailed finite element (FE) models are often employed to predict the impact responses of reinforced concrete (RC) columns. However, they always require substantial investments of time and effort in modeling and analysis so that they are not widely used in practice, particularly in preliminary designs. Moreover, although some simplified models have been established for beams and slabs under impact loading, few attempts have been made on modeling RC columns. For these reasons, this paper proposes a simplified modeling method to accurately capture the impact-induced response and damage of circular RC columns. In the proposed method, a two-degree-of-freedom (DOF) system was used to describe the interaction between the impactor and the impacted column. The formulas, and procedure to estimate the force–deformation relationship with strain-rate effects were presented according to the section-based analysis. The influence of the unloading stiffness on the residual deformation was addressed, and the method to determine the unloading stiffness of circular columns was proposed. Furthermore, a fiber-based beam-column element modeling method was developed to estimate the force–deformation relationship of the columns with strain-rate effects. The proposed simplified method was demonstrated by the drop-hammer impact tests to be capable of predicting the impact response of RC columns well. Its accuracy in the residual deformation is superior to that of the detailed FE simulation. Parametric studies were performed to investigate the damage characteristics of axially-loaded circular RC columns under various impact scenarios.
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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 | 0.000 |
| 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".