Performance-Based Analysis in Civil Engineering: Overview of Applications
Bibliographic record
Abstract
Traditional design approaches in civil engineering mainly focus on codes/guidelines related to building an infrastructure, while performance-based analysis (PBA), an emerging new reality around the world, focuses on the performance of the end product. Professional organizations, academicians, and the industry have made significant contributions in formulating PBA in various civil engineering fields, where practical guidelines and principles have been adopted in infrastructure analysis. This paper presents a critical review of PBA applications in three civil engineering fields: transportation, environmental, and structural engineering. The applications are grouped into a wide array of civil engineering areas, including highway transportation, pavement design and management, air transportation, water-structures design and operation, landfill design, building architectural design for evacuation, urban energy design, building earthquake-based design, building wind-based design, and bridge design and management. A total of 187 publications on PBA were reviewed and details on 122 application papers (from 23 countries/regions) are presented. The review consists of vertical and horizontal scans of PBA applications. In the vertical scan, the applications in each civil engineering area are summarized in tabular format that shows the system element modeled, analysis objective, performance criteria, analytical tool, and specifications/codes. The horizontal scan (discussion and lessons learned) addresses the following aspects of PBA: (1) the wide array of analytical tools used, (2) the broad functional and process-related areas, (3) the advantages, challenges, and opportunities, and (4) potential future applications. It is hoped that the state-of-the-art review presented in this paper will help researchers/practitioners quickly find useful information about PBA and promote its development in their respective fields.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".