Modules in OpenSees for the Next Generation of Performance-Based Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper outlines and demonstrates structural reliability and other gradient-based applications available for use in OpenSees. The Open System for Earthquake Engineering Simulation is an open-source, object-oriented finite element software framework developed for performance-based earthquake engineering analysis. OpenSees began as the computational platform for seismic simulations of structural and geotechnical systems in the Pacific Earthquake Engineering Research Center (PEER). Parallel computing, database, and hybrid simulation capabilities are included in the OpenSees framework making it an ideal environment for network-based simulations, e.g., in the NSF-sponsored George E. Brown Jr. Network for Earthquake Engineering Simulation (NEES). From this introduction of the framework in OpenSees for gradient-based applications in performance-based engineering, the objective of this paper is to summarize the top-level reliability and sensitivity computations in OpenSees. A listing of specific finite element modules available for gradient computations in OpenSees is provided, followed by representative examples of response sensitivity analysis and probabilistic reliability assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it