Compressive Strength Prediction of Grouted Hollow Concrete Block Masonry: A Comparative Study of Major International Codes and a Proposed Model
Bibliographic record
Abstract
In this paper, a large database of compressive test results on grouted concrete block masonry prisms was assembled from published literature. This database was critically reviewed to study different factors influencing the compressive strength (f’m) of grouted hollow concrete block masonry. Furthermore, the collected database was statistically analyzed to derive an accurate empirical formula to estimate f’m of grouted masonry based on the strengths of its constituents. The predictive performance of the proposed model at calculating f’m was compared to those of major international masonry design codes. The study showed that the proposed formula gives the lowest coefficient of variation and the second lowest average ratio of experimental f’m to predicted f’m (f’m Exp/ f’m Pred). The masonry codes, on the other hand, underestimate the compressive strength of grouted masonry with high coefficients of variation. The proposed model gives consistent predictions for the entire range of the different factors that affect f’m of grouted masonry and can serve as a base for revisiting the conservative tabulated compressive strength values in masonry design codes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".