Refined Simulation of Cracked Reinforced Concrete Beams Based on Enhanced Bond-Based Peridynamics
Bibliographic record
Abstract
Peridynamics (PD) has been widely used in simulating the crack behaviors of brittle material due to its extraordinary capacity in analyzing deformations with discontinuities. In this paper, an enhanced bond-based peridynamics (BPD) model is proposed to study the crack behaviors of reinforced concrete (RC) beams. A modified nonlinear concrete bond model is presented to simulate the tensile-compressive softening and improve the model’s convergence. A novel steel bond model is developed considering the nonlinear behaviors in both axial and shear directions. A coupled axial–shear interaction (ASI) model is adopted to simulate the bond-slip behaviors between concrete and steel. Furthermore, reformulated BPD equilibrium equations are modified for implicit static analyses. A gradually weakening fictitious element (GWFE) approach is presented to improve the stability in implicitly solving the BPD equations. The algorithms are implemented in an open-source finite element (FE) software, OpenSees, for BPD analysis. Experiments are conducted for three RC beams with different shear span to depth ratios under concentrated vertical loads to verify the enhanced BPD model. The comparative studies are performed between experimental and simulated results, and nonlinear responses of RC beam are investigated, e.g. the responses of deflection-shear force, strain distribution on stirrups, the shear resistance, and the propagation of cracks. The results show that as the shear span-to-effective depth ratio increases, the capacity provided by RC beam decreases while stirrups gradually provide more capacity instead. It is also found that the yield deflection increases significantly with the growing shear span-to-effective depth ratio.
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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 | 0.001 |
| 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".