Finite Element Analysis of Damage-Induced Frequency Reduction in Reinforced Concrete Beams Using the Concrete Damaged Plasticity Model
Bibliographic record
Abstract
This paper presents a finite element investigation of the static and dynamic responses of reinforced concrete beams subjected to progressive damage, utilising the Concrete Damaged Plasticity (CDP) model in ABAQUS.The study models a simply supported beam under four-point bending, capturing nonlinear behaviour, stiffness degradation, and cracking through advanced constitutive modelling.The simulation incorporates both tensile and compressive damage mechanisms, validated against experimental load-deflection data.Natural frequencies are extracted at various damage stages to analyse the correlation between structural degradation and dynamic properties.Parametric studies examine the influence of concrete compressive strength, reinforcement ratio, and beam geometry on the sensitivity of natural frequency to damage progression.Results demonstrate that lower concrete strength and reinforcement ratios accelerate frequency reduction, while beam geometry significantly affects the onset of dynamic response changes.The findings confirm that shifts in natural frequency serve as effective indicators of damage, supporting vibration-based structural health monitoring approaches.This work enhances the understanding of damage-sensitive dynamic parameters and provides a robust computational framework for assessing and monitoring the integrity of reinforced concrete structures.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".