Ensuring the resilience of multi-unit residential buildings (MURBs): a building information modeling (BIM)-based evaluation approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it