Comparative Assessment of Interior Wall Construction: A Life Cycle Thinking Based Evaluation
Bibliographic record
Abstract
The wall system represents a significant portion of materials used in building construction, with interior walls covering more surface area compared to exterior walls. Despite this, existing literature has predominantly focused on the performance of exterior walls. To address this knowledge gap, this research compared the life cycle performance of interior wall systems, considering life cycle sustainability performance, resilience performance, and human health impact. Three interior wall construction methods were evaluated: concrete block masonry, wood stud gypsum, and steel stud gypsum walls. This research incorporated both numerical and experimental analyses. A Canada-wide questionnaire survey was conducted to collect data on interior wall maintenance practices. An experimental setup was developed to observe the long-term performance of interior walls using accelerated deterioration. Using this data, life cycle sustainability assessment (LCSA), fire dynamic simulation (FDS), and human health impact assessments were performed. The findings of the LCSA indicated that concrete block masonry is a more sustainable alternative when a cradle-to-grave system boundary is considered. Steel stud gypsum walls exhibit superior performance when a cradle-to-gate system boundary is considered. The FDS revealed that concrete block masonry walls perform better in key thermal parameters, suggesting their effectiveness in fire situations. The experimental setup for accelerated aging of interior walls revealed that concrete block masonry walls deteriorate at a slower rate than steel stud gypsum walls. The deterioration of indoor air quality (IAQ) due to wall deterioration creates human health impacts, emphasizing the need for healthier building material selection. Finally, the study proposes updated sustainable procurement (SP) guidelines for institutional buildings. The proposed SP framework and recommendations aims to position Canada as a leader in sustainable building construction, aligning with the United Nations Sustainable Development Goals.
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.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".