Life-cycle based optimal design of seismic retrofit interventions through dissipative bracing systems
Bibliographic record
Abstract
Renovation of the existing building stock is one of the most important task in support sustainable development. Life-Cycle based design methods, either newly developed or adapted from existing ones, are thus needed to consider economic, environmental and social sustainability impact of retrofit interventions. These methods should use new performance criteria based on concepts and metrics consistent with a sustainability-based approach. This paper presents one such method, an optimal design procedure for seismic retrofit interventions through dissipative bracing. Life-cycle economic and environmental optimization is carried out by converting environmental impact into its economic equivalent by using the Carbon Tax approach. Original parametric functions are proposed to model the initial cost and impact of intervention. Earthquake related losses are assessed by adopting a SAC-FEMA closed form, adapted for loss assessment. Social impact is treated as a constraint to the optimization, in terms of safety, also assessed with a SAC-FEMA closed form for the structural collapse exceedance. The entire procedure is based on an effective response evaluation at a few hazard levels, employing linearized models, and it is implemented for use with a commercial FEM software, thereby extending its applicability beyond the research domain. The retrofit of an existing low code RC structure is used to illustrate the procedure and draw more general conclusions. • Single-objective constrained optimization integrates economic, environmental and social aspects. • SAC-FEMA closed-form is used for safety and loss assessment. • Carbon Tax values have negligible effect on the decision-making process. • Safety requirements strongly affect the bracing system design in the case study.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".