Building Integrated Architecture/Engineering/Construction Systems Using Smart Objects: Methodology and Implementation
Bibliographic record
Abstract
Integrated project systems hold the promise for improving the quality while reducing the time and cost of architecture/engineering/construction (AEC) projects. A fundamental requirement of such systems is to support the modeling and management of the design and construction information and to allow the exchange of such information among different project disciplines in an effective and efficient manner. This paper presents a methodology to implement integrated project systems through the use of a model-based approach that involves developing integrated “smart AEC objects.” Smart AEC objects are an evolutionary step that builds upon past research and experience in AEC product modeling, geometric modeling, intelligent CAD systems, and knowledge-based design methods. Smart objects are 3D parametric entities that combine the capability to represent various aspects of project information required to support multidisciplinary views of the objects, and the capability to encapsulate “intelligence” by representing behavioral aspects, design constraints, and life-cycle data management features into the objects. An example implementation of smart objects to support integrated design of falsework systems is presented. The paper also discusses the requirements for extending existing standard data models, specifically the Industry Foundation Classes (IFC), to support the modeling of smart AEC objects.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".