Identification methods of material‐based damping for cracked reinforced concrete beam models
Bibliographic record
Abstract
Abstract Seismic performance evaluation in structural design requires the use of sophisticated numerical models. In particular, to accurately represent the non‐linear behaviour of reinforced concrete (RC) structures when subjected to dynamic loadings, the energy dissipation mechanisms must be accurately represented. However, the classical viscous damping models, which are still widely used, are not based on physical considerations at the material level and the choice of damping parameters is often arbitrary. This paper, thus, proposes a time‐domain damping identification method based on equivalent single‐degree‐of‐freedom (SDOF) systems. The methodology is developed using either an updated linear model or a non‐linear energy‐dissipating constitutive model. Energy dissipative phenomena are cracking, friction and unilateral effects upon crack closure. Both models allow the identification of different damping transient variations: (i) With the updated linear model, intrinsic damping ratios and frequencies are identified to define a simple generic damping model, and (ii) with the non‐linear constitutive model, the identified viscous damping ratios represent the dissipative phenomena not described by the material model. The aim is to propose simple models that can be used by anyone to complement their own models. Applying the method to experimental data allows evaluating effective damping ratio transient variations as functions of variables representative of non‐linear behaviour. It is shown that it is possible to accurately model the energy dissipation that is missing in the non‐linear dynamic constitutive models through effective viscous damping models based on dissipative phenomena internal variables.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".