Condition assessment model of building indoor environment: a case study on educational buildings
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
Purpose The purpose of this study is to develop a condition assessment (CA) model for a building's indoor 21 environments and to improve the building's asset management process. Design/methodology/approach The methodology is based on dividing the building into spaces, which are the principal evaluated elements based on the building's indoor environmental quality (IEQ). An evaluation scheme was prepared for the identified factors and the analytical hierarchy process (AHP) technique was used to calculate the relative weight of each space inside the building as well as the contribution of each IEQ factors (IEQFs) in the overall environmental condition of each space inside the building. The multi-attribute utility theory (MAUT) was then applied to assess the environmental conditions of the building as a whole and its spaces. An educational building in Canada was evaluated using the developed model. Findings Each space type was found to have its own IEQFs weights, which confirms the hypothesis that the importance and allocation of each IEQF are dependent on the function and tasks carried out in each space. A similar indoor environmental assessment score was calculated using the developed model and the building CA conducted by the facility management team; “89%” was calculated, using K-mean clustering, for the physical and environmental conditions. Originality/value IEQ affects occupants' assessment of their quality of life (QOL). Despite the existence of IEQ evaluation models that correlate the building's IEQ and the occupants' perceived indoor assessments, some limitations have led to the necessity of developing a comprehensive model that integrates all factors and their sub-criteria in an assessment scheme that converts all the indoor environmental factors into objective metrics.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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