Quantifying the Errors of Dynamic Displacement Testing: An Alternative Method for Seismic Simulation Testing of Columns
Bibliographic record
Abstract
This paper studies a dynamic testing method called dynamic displacement testing (DDT). Similar procedures to this method have been used in the past to perform experimental testing; however, the procedure was never acknowledged as a specific testing method, and therefore, its errors have never been investigated. The method analyzes the finite-element (FE) model of a structure under dynamic base excitation and applies the obtained displacement time history to the specimen in the laboratory. Sensitivity analyses using 3D continuum (representing the actual specimen) and 2D macro FE models are performed on steel and reinforced concrete bridge pier case studies to detect and quantify significant sources of error associated with this method. Furthermore, 500 Monte Carlo simulations are performed for each case study to assess the variability of the responses. It is shown that the method could apply 30% and 18% errors into displacement and energy dissipation responses, respectively. However, calibrating the FE models at material and element levels could significantly increase the accuracy and precision of the method.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".