Value –Based Maintenance Management Model for University Buildings in Malaysia-A Critical Review
Bibliographic record
Abstract
The essence of building maintenance is to increase the service life of a building by delaying deterioration, decay and failure. Building maintenance must therefore be considered as a strategic process if the value of a building is to be sustained. Building maintenance management is a complex and multi-faceted thought process that involves planning, directing, controlling and organizing maintenance services for the sustenance of the value of a building. It entails making intricate decisions under complex algorithms, uncertainty and risks within organizational resources. The purpose of this paper is to propose an alternative maintenance management model for university buildings in Malaysia. The proposed model reflects current thinking on building maintenance management. A number of studies have investigated the maintenance management of university buildings in Malaysia; however, all the studies have observed maintenance management procedures that are corrective and condition based. Nonetheless, this is contributing to the spate of maintenance backlogs and the lack of value delivery to the stakeholders. Although the research specifically focused on university buildings, many public and private sector organizations face similar maintenance management problems. Therefore this research has broader applications. The outcome of this research is to come up with a prototype maintenance management model that can facilitate university institutions to carry out buildings maintenance management services that meet the expectations and perceptions of the stakeholders.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".