Bibliographic record
Abstract
The large-scale destruction of infrastructure in Syria has created a vast number of cross sector challenges including housing and energy.When surveying the range of buildings damaged due to the ongoing conflict in Syria, housing is by far the most affected sector with 65% of the estimated damage.Most major Syrian cities however have been damaged drastically though not completely destroyed.This raises the possibility of buildings to be refurbished.The aim of the study is to investigate the potential of energy performance of optimizing façade retrofitting strategies in the buildings affected by the Syrian war.While focusing on a typical residential building in Damascus Outskirts, the study targeted moderately damaged with limited damage observed to 5%-30% damage of the building structure.Decision-making in the selection of retrofit scenarios was a result of conducting three stages of vulnerability to measure damage and evaluate the scale of needed intervention.Based on the vulnerability assessment, multiple retrofit scenarios were developed to improve the building performance by two main objectives, Thermal comfort and energy loads.Multiple parameters, including wall materials, levels and types of insulation were tested to explore its influence on thermal comfort and energy loads including the effect of building materials, levels and types of insulation.The results of thermal simulation showed that the choice of insulated bricks for envelope is the optimum solution since insulated Brick envelope can significantly reduce heating and cooling loads by 40% in comparison to typical insulated concrete wall construction.However, the study recommended there is no optimal solution for approaching façade retrofit cases because selection criteria are associated to external economic and social factors such as implementation costs, inflation or privacy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".