Ensuring the resilience of multi-unit residential buildings (MURBs): a building information modeling (BIM)-based evaluation approach
Bibliographic record
Abstract
Residential infrastructure, particularly multi-unit residential buildings (MURBs), lacks sufficient attention to resilience. This oversight is attributed to the absence of specific guidelines for assessing MURBs’ resilience, with existing literature primarily concentrating on singular hazards. Additionally, current frameworks for resilience evaluation necessitate manual interpretation, leading to time and cost inefficiencies and potential human errors. The present study, therefore, developed a comprehensive framework and an automatic rule-based checking system on MURB resilience that can be utilized as a decision support system for practitioners. A literature review revealed 44 resilience indicators, categorized into four based on the general characteristics, i.e., technical, organizational, geographical positioning, and economic. The resilience indicators were benchmarked and defined as a building information modeling (BIM) ruleset. A case study was conducted to demonstrate the execution of the developed BIM ruleset using a MURB design. The proposed framework and rule-based checking system help ensure that MURBs comply with resilience requirements.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".