Comparative benefit-cost analysis for a resilient industrial power plant building with isolation system and energy dissipating devices
Bibliographic record
Abstract
While the use of innovative seismic control strategies has become widespread in industrial plants, assessment of benefits derived from these measures in a quantifiable form can significantly contribute to a more rational risk-informed decision-making of essential infrastructures. In this paper, a benefit–cost analysis is used to examine three retrofit design schemes of an actual thermal power plant building equipped with different seismic control systems, i.e. buckling-restrained brace (BRB), the hybrid shape memory alloy-buckling restrained brace (SMA-BRB), and the partial mass isolation of heavy industrial equipment. The original design scheme having concentrically braced frames as lateral resisting systems is considered as a benchmark model for comparison purposes. For each mitigation alternative, the benefits against seismic effects were quantified in terms of repair cost and recovery time. The results showed that the retrofit system using SMA-BRB leads to the best performance achieving average reduction in residual drift, peak story drift, resilience index, repair time, and average annual loss by 71%, 27%, 48%, 83%, and 89% compared to the original system, respectively. The proposed benefit–cost analysis framework for industrial power plant buildings can be considered as a practical approach of supporting decision-making for non-technical stakeholders and motivating practicing engineers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".