Quasi-static cyclic loading protocol development for the seismic performance assessment of bridge piers
Bibliographic record
Abstract
Seismic performance assessment is of critical importance for structures in regions where earthquakes are prevalent. Identifying the inelastic capacity of structures undergoing seismic loads is one of the main concerns in seismic structural design. Such assessment in terms of determining the inelastic capacity of structures is often performed in laboratories at universities, government, and industrial facilities through quasi-static cyclic loading test programs. In these programs, selecting an appropriate loading protocol is crucial for achieving an accurate assessment of the inelastic capacity. Appropriate loading protocols need to be representative of the seismic demands to which a structural component may be subjected during seismic events. A standard loading protocol available in the literature will not necessarily yield a meaningful response. For the quasi-static cyclic test program, a loading protocol should be developed specifically to the site, structural component, and system of the structure. In this study, an Incremental Dynamic Analysis-based approach is introduced to develop component-specific quasi-static cyclic loading protocols, which is illustrated for a single-column reinforced concrete bridge bent in Vancouver, British Columbia. The sensitivity of the development procedure to different design parameters (e.g., mechanical, and geometric properties) was evaluated through a factorial analysis to propose a single cyclic loading protocol for several bridge pier designs. Here, target displacement ductility demand levels of two, four, and eight, and crustal, subcrustal, and subduction earthquakes were considered. The number of inelastic cycles and cumulative ductility damage were the primary target demand parameters in the loading protocol development. Conventional loading protocols were found unrealistically more damaging than those proposed for crustal and subcrustal earthquakes. The proposed loading protocols for subduction earthquakes were consistent with those developed by other researchers for the same but employed the classical constant ductility design approach to achieve the target displacement ductility levels, which is unrealistic.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".