Rethinking the Cost Estimating Process through 5D BIM: A Case Study
Bibliographic record
Abstract
This paper presents a comparative study of commercially available BIM-based estimating software, and an investigation of the changes in work practices and workflows incurred by the adoption of such software by a construction company. Due to the fragmentation of the construction industry and the linearity of the design process, cost estimating is typically performed at a time when the conceptual design is quite advanced or even completed, which is much too late in the design process to help the different stakeholders make informed decisions. Performing value engineering and cost estimation from the beginning of the design process would potentially enable a faster and more cost-effective project delivery process, higher quality buildings, and increased control and predictability for the owner. This research examines the changes in work practices and work flows within a construction company as they move towards adopting Building information Model (BIM) estimating process. To conduct this research, we: (1) tested several BIM-based cost estimating software tools to support different phases of design, (2) evaluated the benefits and challenges of working with this software, and (3) analyzed the work practices and workflows of a BIM-based estimating process within the firm. Finally, we propose a multi-stage technology adoption scenario.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".