Performance of RC Beams with or without FRP Strengthening Subjected to Impact Loading
Bibliographic record
Abstract
During its service life, some reinforced concrete (RC) structures might be subjected to impact loads such as rock fall, vehicle collision, ship impact, and accident dropping of heavy objects. Therefore impact resistance design is essential for the safety and serviceability of such structures. However, the impact behaviour of RC beams is not well understood yet, and methods for strengthening RC structures against impact loading are still very limited. In this study, experimental tests and numerical simulations were conducted to investigate the performance of RC beams with and without fibre reinforced polymer (FRP) strengthening subjected to impact loads. Fourteen rectangular RC beams were experimentally tested under a dropweight test apparatus and intensive numerical simulations are conducted by using LS-Dyna. The impact behaviour of RC beams is observed and compared with its counterpart under static loading. The unique impact response characteristics of RC beams are discussed. It was observed that under impact loadings the equilibrium of the beam was maintained primarily by the inertia resistance of the beam at early stage of the impact process, the contribution from the reaction forces is negligible. The shear failure was found to be critical under impact loads although the identical beam had the shear strength 4 times of the flexural strength and failed in the flexural mode under static loads. The plastic hinge induced by impact load significantly affects the impact behaviour of the RC beams regardless of the boundary conditions in relatively long beams. Strengthening RC beams with FRP can considerably reduce the impact responses in case of the both flexure-and shear-deficient beams. Therefore, strengthening RC beams with FRP is an effective method to improve their performance against impact loading.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".