Seismic Safety Enhancement of 1:3 Scaled-Down Two-Story Masonry Buildings Models Built with Agro and Textile Waste Fibre-Strengthened Cementitious Composites Subjected to Uni-Directional Shaking Table Test
Bibliographic record
Abstract
Most of the non-engineered masonry structures have failed to perform satisfactorily during earthquakes. In this context, the present work explored a new approach to strengthening, i.e. mortar strengthening. The objective of mortar strengthening was to augment the properties (flexural/tensile) of mortar by reinforcing fibres, as the strengthened mortar would enhance the performance of the masonry structure. Two waste fibres, coconut (agro-waste) and nylon (textile-waste), were used as reinforcement. The 1:3 scaled-down two-story models were subjected to uni-directional loading through a uni-axial shake table. The seismic behaviour was highlighted on the basis of failure pattern/strength. It was noticed that seismic safety of fibre-strengthened models increased notably as compared to unstrengthened models. The failure observed in unstrengthened models was sudden and abrupt; however, the fibre-strengthened models displayed ductile failure. The enhancement in the strength (in terms of peak ground acceleration) at the 1st crack and the ultimate failure were found to be upto 264.4% and upto 87.8%, respectively. In addition, a marginal difference was noted in the analytical and experimental values of the natural period of the building models. Moreover, the mortar strengthening technique proved to be sustainable, buildable, and economical and could be used for both new constructions and existing structures.
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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) | 0.001 | 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) | 0.002 | 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".