Reliability-based optimal design software for earthquake engineering applications
Bibliographic record
Abstract
This paper describes a functional tool for engineers to make rational design decisions by balancing cost and safety. Focus is on seismic design, in which nonlinear structural response must be considered. For this purpose, we implement and apply a state-of-the-art algorithm for reliability-based design optimization. The work extends the OpenSees software, which is rapidly gaining users in the earthquake engineering community. Consequently, design optimization with sophisticated nonlinear finite element models of real structures is possible. An object-oriented software architecture is employed that focuses on maintainability and extensibility of the software. This approach also offers flexibility in the choice of optimization and reliability methods for each specific problem, supported by the decoupled nature of the optimization algorithm. Our work utilizes and extends the existing tools for structural reliability analysis in OpenSees. In particular, we employ response sensitivities that are computed within the finite element code by direct differentiation. The implementation is tested through case studies with nonlinear structural response. Discontinuous response gradients are overcome by use of fibre cross sections and smoothed material models. The numerical examples include the seismic design optimization of a six-storey, three-bay, reinforced concrete building. Key words: reliability-based design optimization, nonlinear finite elements, earthquake engineering, object-oriented software development, OpenSees.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".