Impact of connection details of architectural components on seismic induced repair cost in a shear wall building
Bibliographic record
Abstract
Losses associated with the failure of Non-Structural Components (NSCs) during earthquakes are the most significant contributor to overall building economic loss. Consequently, recent studies have focused on proposing modifications to connection details, element sizes, and materials of NSCs to enhance their seismic performance. In the first step, this study evaluates the effect of using different prediction methods for estimating Engineering Demand Parameters (EDPs) on repair cost of architectural components. Then it quantitatively investigates the repair costs and potential benefits that result from improving the connection details for three commonly used architectural components, namely partition walls, suspended ceilings, and curtain walls subjected to different earthquake intensities. Additionally, it examines the distribution of repair costs associated with the location of the architectural components along the building height. To achieve these goals, a 12-story reinforced concrete shear wall office building located in Montreal on site Class D (stiff soil), was selected as a case study. The study employs the FEMA-P58 building-specific loss estimation methodology to estimate the direct economic loss in terms of repair cost. The results revealed the variation of estimated EDPs (expressed as inter-story drift ratio and peak horizontal floor acceleration) while using different prediction methods and highlight the correlation between calculated EDPs and their corresponding repair costs. Moreover, the results demonstrated that enhancing connection detailing can reduce repair costs of partition walls, suspended ceilings, and curtain walls, by more than 30% at the design level earthquake intensity corresponding to 2% probability of exceedance in 50 years. Furthermore, it is observed that there is a correlation between the earthquake intensity, predicted values of EDPs in each story, and the distribution of repair cost along building height. In the case study building, the estimated loss due to the failure of the selected components is mainly concentrated in the upper third of the building’s height accounting for more than 50% of repair cost.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".