Behaviour and modelling of reinforced concrete structures subjected to impact loads
Bibliographic record
Abstract
The analysis and design of reinforced concrete (RC) structures against extreme loads, such as earthquakes, blasts, and impacts, has been an objective of many researchers and designers. As a result of recently elevated terror threat levels in the world, demand for the impact resistant design of buildings has increased. Numerous studies have been conducted to-date toward understanding and developing methodologies predicting the behaviour of RC structures under impact loads. However, the lack of a complete understanding of shear behaviour under high dynamic conditions hindered the efforts for accurate prediction of impact behaviour, since severe shear mechanisms may dominate the behaviour of RC structures when subjected to impact loads. This current study aimed to apply one of the more successful methods of static reinforced concrete shear analysis, the Modified Compression Field Theory (MCFT), to the analysis of dynamic loads, and thus, develop an efficient and reliable tool for impact analysis of RC structures. A two-dimensional nonlinear finite element analysis program for reinforced concrete, VecTor2, developed previously at the University of Toronto for static loads, was modified to include the consideration of dynamic loads, including impacts. VecTor2 uses the MCFT for its computational methodology, along with a wide array of material and behavioural models for reinforced concrete. To verify the performance of VecTor2 and its computational methodology under impact loads, an experimental program was also undertaken to provide data for corroboration. Eight reinforced concrete beam specimens, four pairs, were tested under free falling drop-weights, impacting the specimens at the mid-span. All specimens had identical longitudinal reinforcement, but varying shear reinforcement ratio, intended to investigate the effects of shear capacity on the impact behaviour. A total of 20 tests were conducted, including multiple tests on each specimen. The test results showed that the shear characteristics of the specimens played an important role in their overall behaviour. All specimens, regardless of their shear capacity, developed severe diagonal shear cracks, forming a shear-plug under the impact point. The VecTor2 analyses of the test specimens were satisfactory in predicting damage levels, and maximum and residual displacements. The methodology employed by VecTor2, based on the MCFT, proved to be successful in predicting the shear-dominant behaviour of the specimens under impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".