An integrated BIM-based approach for cost estimation in construction projects
Bibliographic record
Abstract
Purpose Analyzing different scenarios at the design stage of construction projects has always been a challenging task. One of the main parameters that helps owners in making better decisions in designing their buildings is to look after the cost perspective on different design scenarios. Thus, this study aims to propose a semi-automated BIM-based cost estimation approach that enables practitioners to estimate the cost of projects based on different design scenarios by an accurate and agile system. Design/methodology/approach This study proposes an integrated framework, through which the cost estimation standard of Iran (FehrestBaha) is linked to the materials quantity take-offs (QTO) from BIM models. The performance of the system is based on connecting the classification standards of UniFormat and MasterFormat to the cost estimation standard of FehrestBaha. A BIM-based extension in the Revit environment is developed to automate the cost estimation process. Findings To evaluate the efficiency of the proposed approach in cost estimation, it is implemented to estimate the cost of the architectural discipline in a real construction project. The results indicate that the proposed BIM-based approach estimated the cost of the architectural discipline with an acceptable level of accuracy. Practical implications The proposed approach could be used by practitioners to have an agile and accurate BIM-based cost estimation of different scenarios during design process. The semi-automated system considerably reduces the time of cost estimation in comparison to the traditional manual approaches, particularly in complex structures. Owners are able to easily trace changes in project cost according to any changes in components and materials of the BIM model. Furthermore, the proposed approach provides a practical roadmap for BIM-based cost estimation based on cost estimation standards in different countries. Originality/value Unlike the traditional manual cost estimation approaches, the proposed BIM-based approach is not highly dependent on the knowledge of experienced estimators, which therefore facilitates its implementation. Furthermore, automating both QTO process and the required calculations in this approach increases the accuracy of cost estimation while decreasing the probability of human errors or omission occurrence.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".