Probabilistic Modeling to Develop the Optimal Seismic Resilience Enhancement Strategy for Industrial Facilities
Bibliographic record
Abstract
Industrial facilities play a critical role in economic continuity but are highly vulnerable to earthquake impacts due to their complex networks of interdependent components. This study proposes a probabilistic framework that integrates stochastic modeling and simulation-based optimization to evaluate the effectiveness of alternative seismic retrofit strategies. Unlike conventional buildings, industrial systems require specialized approaches that account for both direct repair costs and indirect losses such as production downtime. The framework incorporates models that capture component fragility, operational dependencies, and varying seismic intensities, enabling a comprehensive assessment of retrofit outcomes. Three retrofit strategies are examined: (1) revenue-based, prioritizing business continuity; (2) cost-based, minimizing structural repair costs; and (3) a hybrid approach. A benefit-cost analysis is conducted to determine the most efficient investment allocation. The framework is applied to three Iranian oil refineries with differing scales and seismic exposures. Results indicate that the revenue-based strategy often yields the highest benefit-cost ratio, especially in facilities with extensive downstream networks, while cost-based strategies may be more suitable when direct losses dominate. The findings emphasize the need for tailored, facility-specific retrofit planning that considers both structural and operational dimensions of seismic resilience.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".