Assessment of existing BIM implementation processes of a public organization to improve building assets management and maintenance
Bibliographic record
Abstract
Abstract In the lifecycle of a building, the longest phase is generally that of operation and maintenance (O&M). The data needed to support O&M is mainly generated during the design, construction, and commissioning of the built asset phases. However, the extraction and transfer of relevant O&M data from the project documents remain a major issue in that it is time consuming and error prone. Nowadays, Building Information Modeling (BIM) allows different project team members to collaborate and share building data in real time. However, as-built models handed-over to the asset management team are quite voluminous and usually lack the necessary information for the O&M phase. This is due to an absence of O&M information requirements specification, and the lack of compliance monitoring and control during the project. This paper presents an action research focused on the issues encountered by a public building owner to manage their BIM processes. It demonstrates issues of BIM readiness and capabilities, both at the project and O&M levels, by mapping the gaps in the existing processes. Building owners rely on the design professionals’ expertise to guide them but do not have the adequate resources, knowledge, and tools to ensure the quality of the models delivered regarding O&M requirements. Major issues in the management of information for O&M were identified, and some recommendations are proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".