Physics-based and locally updated nonlinear damping model for cracked reinforced concrete beams
Bibliographic record
Abstract
The structural design increasingly requires considering earthquake excitations even in low-seismic risk areas, particularly for critical infrastructures, such as nuclear ones. Sophisticated models are required to characterise the structural behaviour under low seismic excitations. In the case of reinforced concrete structures, nonlinear material models are considered to characterise some energy dissipative phenomena such as (i) damage due to cracking or (ii) friction of cracked surfaces. However, more than the amount of energy dissipated by these nonlinear models are required to accurately represent the physical structural dynamic responses. That is why viscous damping is generally added to dissipate the excess energy. Numerous damping models are proposed in the literature. However, their principal drawback is their need for the representativeness of physical dissipative phenomena. So, this paper proposes a viscous damping model based on such phenomena to dissipate the energy not represented through the nonlinear material model. The proposed strategy is to update the damping matrix at the element level using the intensity of nonlinearities in each element. Three local variables are compared in the paper: one variable associated to damage, another associated with friction and a damage index computed from the secant elemental rigidity. Dynamic nonlinear computations are performed with the proposed locally updated damping matrices. The results are compared with experimental data, when available, and with Rayleigh-type damping formulations classically used in engineering. As a result, it is observed that all damping formulations properly characterise the global response of the studied reinforced concrete beam. However, the use of the proposed formulations allows better representativeness of local dissipative phenomena and adds a physical meaning to the damping model. • Proposition of a locally updated damping model based on internal variables for nonlinear dynamic computations • Different local data are used to update the damping models linked to damage and friction in concrete and stresses in steel reinforcements • Dynamic computations are performed with the proposed models on reinforced concrete beams • Comparisons with classical Rayleigh damping show that the local damping models allow a better representation of energy dissipation
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".