Scan-to-BIM approach for enhanced semi-automated cost management in modular off-site construction
Bibliographic record
Abstract
Purpose This research integrates lean construction principles with scan-to–building information modeling (BIM) to enhance cost management in modular off-site construction. It proposes a scan-to-BIM approach within a semi-automated 5D-BIM framework to improve cost estimation accuracy and efficiency. By leveraging 3D laser scanning, it enhances quantity takeoff and cost reporting, reducing manual errors and improving decision-making during manufacturing and in-factory assembly. Design/methodology/approach A design science research methodology is used to compare the proposed lean-driven 5D-BIM framework with traditional cost management practices. The integration of 3D laser scanning and scan-to-BIM improves measurement accuracy and automates data extraction and analysis. Lean construction reduces waste and maximizes value through collaboration, while modular off-site construction benefits from prefabrication in controlled environments. The 5D-BIM model embeds cost data, enabling more precise quantity takeoffs. In contrast, traditional cost management remains heavily manual and less efficient. Findings The proposed approach reduces the time required for progress cost reporting by 18% and improves cost estimation accuracy by 12%, highlighting the value of combining lean principles with advanced BIM technologies. Research limitations/implications The study is limited to the manufacturing and assembly phases of modular off-site construction in Canada. Results may vary when applied to other phases or regions. Practical implications The framework offers actionable guidance for industry professionals aiming to improve cost control, data accuracy and resource planning in modular off-site projects, while advancing the human-centered transformation of the architecture, engineering, construction and operation industry by automating repetitive tasks, enhancing collaboration and enabling professionals to focus on higher-value decision-making. Originality/value This study uniquely combines lean construction, scan-to-BIM and 5D-BIM to tackle cost management challenges, promoting efficiency, accuracy and digital innovation in off-site construction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".