BIM – Towards the entire lifecycle
Bibliographic record
Abstract
The goal of Building Information Modeling (BIM) is the integral and comprehensive digital modeling of all properties regarding a building, its planning and construction process as well as maintenance and use.This is combined with the overarching objective within BIM to integrate and support all involved experts aiming towards an improved collaboration.Currently the necessary data often only exists in a very fragmented and uncoordinated way throughout different subsections, as well as planning and construction phases.A lack of organized information management is especially noticeable in finalized built objectives during questions of conversion and refurbishment.BIM tries to mediate between the different views of its users and allows for a coordinated accumulation of data, as well as synchronously keeping the planning status up to date.However, in most cases essential information is missing or not used throughout the complete lifecycle of the building.Consequently, there are clear gaps between the different phases of planning, construction and maintenance.Within this paper, we give an evaluation of applicable methods for data collection and modeling of the actual inventory of components with regard to position, geometry and semantics (e.g.material) for the purpose of a comprehensive and BIM-compliant as-built documentation.This allows the analysis of missing interfaces and data.Considered from a process automation viewpoint, we identify missing BIM data e.g. for assembly processes within construction in order to create a BIM-aided planning process that continues into actual fabrication and construction.Based on this, we discuss possibilities for the implementation of user requirements in order to develop a comprehensive semi-automated decision support tool for BIM users.Primary goal is to provide concepts for the integration of construction processes as well as options for conversion planning and construction of buildings.These targets imply a continuous updating of the BIM models (including the semantic parameters) from a continuous 'as-built' acquisition and modeling of the construction progress.While BIM primarily is being discussed as a cooperative working methodology in the new planning of buildings, we also consider the required information for future conversion and refurbishment of the building and the required level of development, in order to complete the lifecycle approach of BIM.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".