Experimental Determination of Static And Dynamic Elastic Moduli of Rc Slabs
Bibliographic record
Abstract
The purpose of this paper is to experimentally determine the static (Ec) and dynamic (Ed) elastic moduli of reinforced concrete (RC) from both static and dynamic techniques. Initially, the aforementioned parameters were estimated from the concrete compressive strength of the used mix, using compressive strength machine. Subsequently, the RC slab specimens themselves were dynamically tested dynamically under free boundary conditions. Then from, the experimentally measured natural frequency, the static and dynamic moduli were determined. To check the reliability of the dynamic test for estimating the natural frequency, the intact and defected slabs were utilized. The dynamic test was performed on four RC square slab samples of dimensions 600 mm × 600 mm × 40 mm. The first set, two intact slabs, is used as control specimens were prepared with no artificial voids. While, the second set, two defected slabs, is used as defected specimens. The defected slabs contained the artificial void by fixing a polystyrene block at the center of the steel reinforcement of the slabs prior to pouring concrete. In the latter technique, a RC slab specimen is hanged by using elastic ropes to approach fully free boundary conditions. The slabs were excited by an impact hammer, to induce vibration, whilst the accelerometers were employed to record the response under such excitation. Pico Scope 6 device and amplifier was used to acquire, magnify and analyze the data. In addition, MATLAB software is used to convert the time domain to the frequency domain as well as to plot Frequency Response Function (FRF). The first natural frequency is determined as the first resonant peak on the FRF plot. It is showed that the use of the first natural frequency-based method can be usefully employed to determine the dynamic modulus of elasticity of concrete. It was found that the testing sequence did not significantly affect the measured results for the obtained Ec and Ed in this study
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".