New Simplified Method for Designing Seismically Isolated Highway Bridges with Massive Piers
Bibliographic record
Abstract
This paper proposes two new models for the simplified seismic analysis of seismically isolated highway bridges with massive piers. Both models include two components, one of which includes the superstructure mass, the isolators, and massless pier, and the other one the pier with its distributed mass and stiffness properties. In one model, the isolator stiffness is added at the top of the pier in the second component. The total seismic response is obtained from the square root of the sum of squares (SRSS) of the two individual component results. Applicability and accuracy of the models were assessed by considering bridges with wide ranges of stiffness and mass properties of piers (Mp) and superstructures (Mss). Multimode spectral analysis (MMSA) and nonlinear time-history analysis (NLTHA) were used to define reference solutions for the superstructure displacements, vibration periods, and shears and moments at pier bases. Results from four currently available models, including the model prescribed in American and Canadian bridge codes, were also examined. The study shows that current models for the simplified analysis method yield good estimates of superstructure displacements and vibration periods; however, they generally underestimate the shear and moment demands at pier bases. The errors are more significant for more massive piers or when the stiffness of the piers is high compared to that of the isolators. In contrast, for all bridges studied, the seismic responses from the proposed models show very good agreement with those from the MMSA. Better predictions are obtained when considering the isolator stiffness in the second component of the model. The NLTHA results also show that the proposed models give overall satisfactory predictions for pier base shears and moments. The proposed models extend the range of application of the American and Canadian code simplified method to isolated bridges with massive piers while preserving its simplicity with limited extra computational effort.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".