Assessment of Retrofitting Techniques for a Four-Story Masonry Building to Resist Blast Loading
Bibliographic record
Abstract
Masonry walls are commonly preferred in the construction of low-rise buildings due to their proven efficiency in resisting gravity loads and their relatively low material and labor costs. However, few studies investigating the behavior of retrofitted buildings under blast loading have been found in the literature. This paper describes the first comprehensive study investigating the efficiency of four retrofitting techniques for a low-rise masonry building under blast loading. The investigated retrofitting materials were textile-reinforced mortar (TRM), carbon-fiber-reinforced polymers (CFRPs), glass-fiber-reinforced polymers (GFRPs), and polypropylene bands (PPBs). Compared with regular reinforcing steel, these materials have light weight, high corrosion resistance, and perfect durability properties, in addition to their ease of application. A three-dimensional applied element model (AEM), which accounts for both geometric and material nonlinearities, was developed and validated to analyze an existing four-story masonry building under blast loading. Material nonlinearity was considered by including nonlinear models for masonry, mortar, TRM, CFRPs, and PPBs. Masonry blocks were modeled using brick elements, while the in-between mortar was modeled using spring elements. Validation of the AEM was by modeling masonry walls taken from the literature and comparing their results with counterparts obtained from experiments from the literature. Cracking pattern, displacements, support rotation, and load capacity for the investigated building with and without retrofitting using the four materials were compared. Costs were compared based on market prices. Retrofitting the case study building with TRM, CFRPs, GFRPs, and PPBs reduced maximum support rotation by 39%–98%, 28%–88%, 33%–92%, and 9%–84%, respectively, and increased blast load capacity by 35%–404%, 48%–91%, 83%–135%, and 26%–46%, respectively. The study revealed that retrofitting the investigated building with TRM provided the optimum behavior in terms of cost, crack propagation, and blast load capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".