Assessing life cycle cost and environmental impact for office building construction in Saudi Arabia
Bibliographic record
Abstract
The continuous growth of the construction sector in Saudi Arabia will result in social, environmental, and economic implications.To this end, this study focuses on selecting optimal structural and external envelope systems for an office building in Al Khobar, Saudi Arabia.Our research's novelty lies in the fusion of life cycle cost (LCC) and life cycle impact (LCI) assessment models to optimize structural and envelope choices, a methodology that has realworld applications for the sustainable construction of office buildings.The study findings offer a substantial contribution to the evolving field of sustainable construction practices by enhancing cost-efficiency and environmental performance.The proposed models evaluate different external envelope materials, including concrete masonry unit (CMU), insulated CMU, limestone cladding, autoclaved aerated concrete (AAC), three types of glazing (double, triple, and Nanogel), and two structural systems (steel frame and reinforced concrete frame), considered over a 30-year lifespan.Our methodology integrates advanced tools, including Autodesk Revit for precise building modeling, Design Builder software for energy consumption simulations, and One Click LCA software for life cycle assessments.The LCC analysis reveals the most costeffective option as reinforced concrete with insulated CMU and triple glazing, saving 5.6% or 11,962,496 Saudi Riyals compared to the baseline.Moreover, insulated CMU with Nanogel glazing demonstrates a remarkable 22% annual energy savings, equivalent to 397,469 Saudi Riyals.The proposed framework provides facility managers with comprehensive guidelines for updating conventional office buildings into sustainable ones.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".