Exploring the Benefits of Early Contractor Involvement (ECI) and the Mechanism for Automated 5D BIM Quantification Process in Offsite Modular Construction
Bibliographic record
Abstract
The UK Construction Industry through its Government Construction Strategy has recently been mandated to implement Level 2 Building Information Modelling (BIM) on public sector projects. This move, along with other initiatives is key to driving a requirement for 25% cost reduction (establishing the most cost-effective means) on. Other key deliverables within the strategy include reduction in overall project time, early contractor involvement, improved sustainability and enhanced product quality. Collaboration and integrated project delivery is central to the level 2 implementation strategy yet the key protocols or standards relative to cost within BIM processes is not well defined. As offsite construction becomes more prolific within the UK construction sector, this construction approach coupled with BIM, particularly 5D automated quantification process, and early contractor involvement provides significant opportunities for the sector to meet government targets. Early contractor involvement is supported by both the industry and the successive Governments as a credible means to avoid and manage project risks, encourage innovation and value add, making cost and project time predictable, and improving outcomes. The contractor is seen as an expert in construction and could be counter intuitive to exclude such valuable expertise from the pre-construction phase especially with the BIM intent of äóÖbuild it twiceäó», once virtually and once physically. In particular when offsite construction is used, the contractoräó»s construction expertise should be leveraged for the virtual build in BIM-designed projects to ensure a fully streamlined process. Building in a layer of automated costing through 5D BIM will bring about a more robust method of quantification and can help to deliver the 25% reduction in overall cost of a project. Using a literature review and a case study, this paper will look into the benefits of Early Contractor Involvement (ECI) and the impact of 5D BIM on the offsite construction process.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".