Computer-assisted construction methods knowledge management and selection
Bibliographic record
Abstract
The construction industry's information environment is undergoing rapid changes. Construction professionals now expect fast and reliable access to rich data sources. Significant advances have been made in streamlining the creation and dissemination of computer-aided design drawings and construction documents through incorporation of information technology in the workflow. However, there is a need to go beyond just the automation of existing paper flows and provide the construction knowledge worker with tools that can be easily used to document previous project experience, track new technological developments, and then incorporate these in the formulation of construction strategies for future projects. This paper describes work aimed at creating such tools in the context of a comprehensive decision-support system, by developing intelligent representation structures for storing and accessing construction domain knowledge and coupling them with advanced planning tools so as to enable the quick formulation and assessment of initial project plans. The information requirements and features needed for such a system are first examined, followed by a demonstration of how some of these requirements have been addressed in the system design and implementation. Aspects of a hypothetical high-rise building project are used throughout to illustrate application of the concepts developed.Key words: construction methods, integrated project management systems, methods feasibility reasoning, hierarchical scheduling, project views.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".