Evaluation of interaction between housing infrastructure resilience factors against flood hazard based on rough DEMATEL approach
Bibliographic record
Abstract
Purpose Housing infrastructure is the basic need for people of a community and due to disaster many houses may severaly damaged. Stakeholders and decision makers should focus on this issue and make the infrastructure more resilient against natural hazards. As dependency plays a very important role in resilience, it is important to study the dependencies and correlations among the housing infrastructure resilience factors. The evaluation of dependencies involve vagueness due to subjective judgement of experts. Design/methodology/approach In this work, the interaction between the housing infrastructure resilience factors are evaluated by using two different approaches such as crisp DEMATEL (Decision-Making and Trial Evaluation Laboratory) and rough DEMATEL (intregated crisp DEMATEL and rough set theory), where rough theory addressed the involvement of vagueness. These two approaches are compared with each other to find the effectiveness of rough DEMATEL over crisp DEMATEL. Findings The important factors of housing infrastructure resilience are identified by using both the approaches against flood hazard. Research limitations/implications The limitation of rough DEMATEL method is that it does not differentiate the type of influence such as positive or negative. Practical implications The outcome of the work will helps the stakeholders and ecission makers to make the infrastructure more resilient. Originality/value This study identify the imporatnat resilience factors of housing infrastructure against flood hazard by using two methodologies.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".