Building & construction health monitoring research situation analysis based on knowledge topography
Bibliographic record
Abstract
Construction industry is of great significance to economic growth,however,the following architecturebuilding safety problems triggerwidespread disturbance.So it is important to take building health monitoring activities extensively,meanwhile,the knowledge management and researching summary on building health monitoring appears very important too.This study just embody the trends in this area through knowledge map visualization way.Scientific knowledge map is a branch of information visualization,it take scientific knowledge as object of measure and study,so it belongs to the category of scientific metrology.The scientific knowledge map reveals the knowledge structure and logical relationship directly.It is with great effectof context carding and experience summarizing undoubtedly.To show international BuildingConstruction Health Monitoring(BCHM)research situation intuitively,this research,which based on documents and data in SCI database from 1990 to 2012,drew the BCHM knowledge mapwith the application of Cite SpaceⅡsoftware.Through the visualized analysis of BCHM knowledge map,this study revealed the BCHM research power distribution at international level,the BCHM researching origin and knowledge accumulation,the BCHM researching discipline and subjectdistribution,and the BCHM research front in the international scope.The results showed that the main research power concentrated in the following districts:United States,China,England,Canada,India,etc.,the main research institutions include Stanford University,Illinois University,Southern California University,Michigan University,Nanjing Aeronautics and Astronautics University,Harvard University,etc.The researching golden age of BCHM appears after 1995,the correlated disciplines with high centrality include computer science,engineering,neuroscience and neurology,environmental science and ecology,mechanics,physics,mathematics and computational biology,behavior science,nuclear science and technology,chemical,medical informatics,etc.The BCHM research front focuses on particulate matter,structural health monitoring,damage detection,health monitoring,model,management system,mental health,environmental pollution,indoor air syndrome,etc.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".