Evaluation of reinforced concrete and reinforced engineered cementitious composite (ECC) members and structures using small-scale testing
Bibliographic record
Abstract
The behavior of reinforced concrete (RC) and reinforced engineered cementitious composites (ECC) was comparatively investigated at the component and system levels through a small-scale (1/8 scale factor) experimental program. The logistical and financial advantages of small-scale testing were utilized to investigate a range of parameters, including the effect of reinforcement ratio and material properties, on the response of reinforced concrete and reinforced ECC structures. The procedures pertaining to material preparation, specimen construction, and input motion development that were critical for enhancing the similarity between the scales are provided. Engineered cementitious composite mixtures with different cost and sustainability indices were evaluated. Under cyclic loading, the stiffness, strength, ductility, and energy absorption capacity of columns made of different ECC mixtures were found to be 110, 65, 45, and 100% higher, respectively, than those of the RC columns. The system level investigation through hybrid simulation showed that the ECC structures sustain less deformation under earthquake excitation due to high energy absorption capacity of the material. The differences in cost, sustainability, and structural performance of different ECC mixtures suggest that a careful selection of materials is required for optimal performance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".