Flexural Strengthening of Stone Masonry Walls Using Textile-Reinforced Sarooj Mortar
Bibliographic record
Abstract
Most historical buildings and structures in Oman were built using unreinforced stone masonry. These structures have deteriorated due to ageing of materials, environmental degradation, and lack of maintenance. This research investigates the physical, chemical, and mechanical properties of local building materials and the results of an experimental study on the out-of-plane bending effectiveness of an innovative strengthening technique applied to existing masonry walls. The technique consists of the application on one face of the walls of a basalt textile-reinforced sarooj mortar (TRM). Four-point bending tests of full-scale masonry samples (1000 mm width, 2000 mm height, and 350 mm depth) were carried out using one unreinforced specimen and three different cases of reinforced specimens. The performance of unreinforced and reinforced specimens was analyzed and compared. The strengthened specimens were able to resist moments of out-of-plane bending 2.5 to 3 times greater than those of unreinforced specimen (160%- 233% increase). Moreover, the strengthened walls were able to sustain higher deformations (deflections) than the unreinforced specimen ranging from 20%-130%. The results showed that using TRM was effective for the out-of-plane strengthening of stone masonry using a local material (sarooj) that is compatible with existing stone masonry building materials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".