Life-cycle cost-benefit analysis and design of retrofitting steel frames using friction devices
Bibliographic record
Abstract
Many existing steel moment-resisting frame (MRF) buildings in Canada were constructed during the 1960s and were not designed to account for seismic loads. Friction devices reduce seismic damage to buildings by supplementing force, stiffness, and energy dissipation mechanisms. Despite being aware of the seismic risk of early-designed MRFs in Canada, building owners are often discouraged by the high upfront retrofit costs, preventing them from making proactive investments with less tangible immediate benefits. This study develops a risk-based life-cycle cost-benefit (LCCB) approach to evaluate the economic trade-off between upfront retrofit expenses and lifetime benefits. The study focuses on two representative six-story steel MRF office buildings in Montreal and Vancouver. The retrofits employ brace-friction device systems designed through a displacement-based procedure under multiple scenarios with varying force modification factors, brace stiffness ratios and earthquake return periods. The LCCB analysis integrates seismic hazard analysis, ground motion selection, nonlinear response history analysis, seismic fragility modeling, life-cycle loss assessment and retrofit cost estimation. Life-cycle benefits are quantified as the reduction in seismic loss between as-built and retrofitted buildings, from which (1) the benefit-cost ratio is computed to compare the economic viability of each retrofit design and (2) the anticipated net balance is quantified to pinpoint the payback year. This study systematically evaluates to what extent different retrofit designs of brace-friction device assemblies would affect the life-cycle benefit-cost performance of steel MRFs. Research findings also provide a risk-based tool to guide decision-makers in identifying cost-effective solutions for retrofitting steel MRF buildings using friction devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".