Investigation of Behavior of Masonry Walls Constructed with Autoclaved Aerated Concrete Blocks under Blast Loading
Bibliographic record
Abstract
Autoclaved aerated concrete (AAC) blocks have widespread popularity in the construction industry. In addition to lightness, these materials have other advantages, including fire resistance, low acoustic and thermal conductivity, ease of cutting and grooving, and simple transportation. Since the behavior of AAC under severe dynamic loading conditions such as blast loads has not been adequately studied in the literature, in the current paper, the behavior of masonry walls constructed with AAC blocks was evaluated under blast loading. In this study, after performing experimental testing on materials and obtaining their compressive, tensile, and shear strength values, the finite element (FE) models of AAC-based masonry walls were created in the ABAQUS/Explicit nonlinear platform. Three different wall thicknesses of 15, 20, and 25 cm were simulated, and the models were analyzed under a lateral explosion caused by 5 and 7 kg of TNT at the stand-off distances of 2, 5, and 10 m from the wall face. The stress distributions, displacement responses, adsorbed energy, and crack propagation pattern were investigated in each case. The results showed the inappropriate behavior of these materials against explosion loads, especially at shorter distances and on walls with less thickness. The outcome gives valuable information to prioritize these walls for possible blast strengthening.
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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".