A new Distinct Element meso‐model for simulating the rocking‐dominated seismic response of RC columns
Bibliographic record
Abstract
Abstract The rocking‐dominated seismic response of reinforced concrete (RC) columns is a complex mechanism that entails P‐Δ effects, concrete crushing and spalling, rupture and dislocation of steel bars and stirrups, as well as combined localized failures causing splitting and shear‐flexure cracking. In this paper, we present a new meso‐scale numerical modeling strategy for the detailed simulation of the rocking response of slender RC columns under seismic actions. This work advances the current state‐of‐the‐art by exploring the unprecedented use of the Distinct Element Method (DEM), originally conceived for geotechnical studies and whose applicability in earthquake engineering has been primarily limited to the assessment of unreinforced masonry sub‐structures. To decrease analysis time and enable the use of DEM for RC problems, concrete is herein idealized as an assembly of solid macro‐block layers of size directly proportional to their distance from expected plastic hinges. These layers are connected by nonlinear interface springs to simulate tensile and shear cracking via a simplified Mohr‐Coulomb criterion. Crushing of concrete in deformable blocks is modeled using the Feenstra and De Borst strain‐softening linearized compression law, while confinement and combined failures are accounted for numerically through the explicit representation of longitudinal and transversal reinforcement bars, idealized as link elements. Previous experimental static and dynamic tests on full‐scale RC column specimens and results from fiber‐based Finite Element and sectional analysis models are used to evaluate DEM results and quantify the influence of key modeling parameters, including block number and deformability, as well as the amount of damping. The agreement between simulated failure modes and force and displacement capacities and their experimental counterparts demonstrate the applicability of the proposed approach, while demonstrating the pros and cons of simplified numerical methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".