Numerical Investigation of Shape-Memory Alloy–Reinforced Bridge Columns Subjected to Lateral Impact Loads
Bibliographic record
Abstract
This paper numerically investigates the dynamic behavior of bridge piers reinforced with shape-memory alloy (SMA) rebar when subjected to lateral impact loads. Performance of SMA reinforced pier is compared with column reinforced by conventional steel rebar by performing finite-element (FE) simulations in LS-DYNA. The impact performance of the columns is evaluated by considering variations in different structural- and loading-related parameters including the type of SMA rebar, the length of SMA rebar (LSMA), the impact velocity (Vimp), and the axial load ratio (ALR). From the FE simulations, it is found that the use of SMA rebars at the plastic hinge regions and the impact loading height of the columns significantly enhances the impact resistance and the recoverability of the columns by reducing their damage levels and residual displacements. Also, the failure modes of the columns tend to govern by flexure by using SMA rebars, and the columns reinforced with Cu-based (Cu–Al–Mn) SMA rebars are more likely to fail in flexural modes compared to those reinforced with Ni-based (Ni–Ti) SMA rebars. However, the negative influences of SMA rebars on the impact resistance of the columns are found when LSMA exceeds 0.5 under impact loads with velocities greater than 15 m/s. In addition, an ALR greater than 0.1 considerably increases the impact resistance of the columns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".