ارائه ی مدل سرمایه گذاری پروژه های انبوه سازی سازه های ال اس اف
Bibliographic record
Abstract
Light Steel Frame System (LSF) as a novel construction system is used in many developed countries such as USA, Canada and Japan but it is not tangibly requested in IRAN. Lack of knowledge in engineering, contractors and employers to this system is the main cause of little attention to it in IRAN. Considering the LSF system characteristics, mass housing project can be used as the main application of this system. Hence, dealing and managing the LSF operational and financial risks in mass housing projects is the main contribution of this paper. This system is used for implying of short-rise and mid-rise buildings (up to five floors). For Successful mass housing execution in LSF structures, exact risk identification and selection of execution method will be indispensable. selection of investment method is important therewith. So, in this article, model for selection of LSF mass housing system financing method respect to execution and economical risks is represented. In this model effective risks are determined by expert opinion and its uncertainty is modeled by normal distribution. Then for each method IRR index is calculated. In this article, 5 methods of investment considered and Public-Private Partnership (PPP) method is introduced as financing method with maximum IRR.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".