Sustainability Cement Block Selection Based On IntervalValued Hesitant Fuzzy Group Analysis For Construction Industry Problems
Bibliographic record
Abstract
Nowadays, sustainable material selection is an important issue for construction industry because of considering the environmental and social competencies according to quality and cost targets.Hence, multi criteria group decision making (MCGDM) is a known powerful tool to select the best potential alternatives based on group assessment of decision makers (DMs) in complex realworld cases.In traditional MCGDM methods, the relative significance of each criterion and the performance evaluating of potential alternatives are considered precisely.However, when the complexity of the real-world systems related to humans is increased, the future information of them cannot be precise / known completely.In this respect, decision making problems are one of the science fields that the information is often vague / uncertain.Moreover, If DMs cannot assign their opinions by expressing the linguistic terms regarding to the classical fuzzy sets, the Interval-Valued Hesitant Fuzzy Sets (IVHFSs) theory is a useful tool to help the DMs in these hesitant conditions and can present a more practical and accurate modeling.In this study, an Interval-Valued Hesitant Fuzzy Preference Selection Index (IVHF-PSI) method is presented to solve the sustainable cement block selection problems in construction industry.Finally, the process of the proposed IVHF-PSI method is performed by considering a real case study to represent the applicability and verification of the proposed approach.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".