Performance-Based Design Optimization of Structures: State-of-the-Art Review
Bibliographic record
Abstract
Performance-based design optimization (PBDO) aims to design safe, resilient, and cost-effective structures. Methods used for PBDO have evolved by integrating numerical modeling, performance-based design principles, and optimization algorithms. The PBDO framework enables the design of structures with optimal performance and cost. This paper provides researchers with a comprehensive review of the rapidly growing field of PBDO. The evolution of PBDO methods is discussed with the goal of identifying the challenges that must be addressed in future studies. Knowledge gaps are brought to the forefront to emphasize the need for further investigation that expands on the application of PBDO in structural design. Various deterministic and probabilistic formulations for PBDO of structural systems under seismic and wind loading are reviewed. The formulations encompass one or more objective functions, including the upfront, life-cycle, and repair costs. Furthermore, the paper reviews retrofit design studies that have used PBDO methods. The high computational demand in performing PBDO is identified as the major challenge. Possible approaches to alleviate this and other challenges are discussed.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".