IDA-based fragility curves for helical pile-supported bridges in cohesive soil
Bibliographic record
Abstract
Fragility curves were developed for a three-span bridge structure supported by helical piles in homogeneous cohesive soil. To address uncertainties in material properties, Latin Hypercube Sampling (LHS) was used, while Incremental Dynamic Analysis (IDA) was employed to construct the seismic demand model. Fifteen bridge samples were subjected to 22 ground motion records, each scaled to 20 intensity levels, resulting in a total of 6600 three-dimensional nonlinear time history analyses. The resulting probabilistic seismic demand model estimated expected damage across a range of seismic intensities, using key engineering demand parameters, pier drift, pile ductility factor, and settlement ratio, to evaluate damage states from slight to complete. Regression results showed that total span length, rebar yield strength, and damping ratio significantly influence pier drift, with longer spans increasing drift while higher rebar strength and damping ratios decrease it. Furthermore, the ductility factor of piles is affected by damping ratio, the number of piles, and foundation area, while damping and pile spacing significantly impact the settlement ratio. Overall, the analysis indicated that helical piles are more vulnerable in terms of ductility than settlement, making them the most critical component in the bridge–soil–foundation system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".