Building information modelling in operations of maintenance at the university of Alicante
Bibliographic record
Abstract
The benefits derived from the use of building information Modeling (BIM) methodologies are a current issue in the field of research in both design and construction phase.However, the profits achieved in the maintenance stage are still an almost unexplored issue.This fact is especially relevant in public organizations, in particular on university campuses where the building assets are a value added service that must maintain their quality.In this connection, this paper aims to restructure the current maintenance operations at Alicante University and focus them towards BIM environments.It identifies the current building maintenance process on campus, determining the problems it faces since an incident occurs until it resolves.To this end, the research methodology includes semi structured surveys, interviews and benchmarking sessions with technical office staff, managers and maintenance workers at the University of Alicante together with relevant external stakeholders.Consequently, all the information obtained will enable a better procedure based on BIM for improving both preventive and corrective maintenance.The study case is focused on the renovation of the building Former Faculty of Education at The University of Alicante and the results confirm the potential of implementing BIM on campus through a more accurate access to information that optimizes and speeds up the maintenance process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".