Analysis of steel fiber reinforced concrete wall-column connection using headed bars subjected to blast loading
Bibliographic record
Abstract
The rising frequency of terrorist attacks and accidental explosions in recent years has underscored the necessity of incorporating blast-resistant design considerations into structural engineering. Blast loads, though uncommon, are highly dynamic in nature and can cause catastrophic failure in conventional reinforced concrete structures if not properly accounted for. This study focuses on analyzing the structural behavior of precast steel fiber reinforced concrete (SFRC) wall-column connections utilizing headed bars as the primary connection mechanism when subjected to blast loading. Four connection configurations are examined: (1) conventional dowel bar connections, (2) headed bar connections, (3) dowel bar connections with steel fibers, and (4) headed bar connections with steel fibers. The inclusion of steel fibers is intended to enhance ductility, energy absorption, and crack resistance under extreme loading. Numerical modeling and simulation are performed using ANSYS Workbench, employing nonlinear dynamic analysis to evaluate response parameters such as displacement, stress distribution, and failure mode under varying charge weights and standoff distances. Results are expected to demonstrate that SFRC with headed bar connections provides superior blast resistance compared to conventional systems due to improved anchorage, reduced stress concentration, and enhanced post-cracking behavior. The findings aim to contribute to the development of efficient, blast-resistant connection systems for precast structural elements, improving overall safety and resilience in modern construction practices.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".