Efficient Computation of Response Sensitivities for Inelastic Structures
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Bibliographic record
Abstract
Response sensitivities with respect to the parameters of a finite element model are useful in many applications. The direct differentiation method (DDM) is commonly utilized to obtain such results. In recent years, the DDM has been extended to include sensitivities of inelastic response with respect to material, load, and geometry parameters. While the DDM is more efficient and accurate than finite difference methods, considerable cost is still associated with the computation of response sensitivities for inelastic problems. In this paper it is demonstrated that the computational cost can be significantly reduced for certain types of problems that are common in structural engineering. A novel event-based computation strategy is suggested, whereby sensitivities of the final response are obtained more efficiently than in the ordinary DDM. It is also demonstrated that sensitivity contributions from all inelastic material points are not needed for statically determinate structures. Numerical examples involving a truss structure, a steel frame structure, and a reinforced concrete frame structure are presented to demonstrate the efficiency of the presented developments.
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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 | 0.001 | 0.003 |
| 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 it