BIM Integrated Bid Proposal Evaluation Tool to Aid Sustainable Procurement of Water supply infrastructure projects
Bibliographic record
Abstract
Although water supply infrastructure is a vital component of community infrastructure systems, significant environmental, social, and economic impacts are created throughout its life cycle. Previous researchers have identified sustainable construction procurement as a viable method to enhance the Triple Bottom Line (TBL) performance of construction projects. Adopting sustainable procurement in water supply infrastructure projects has been overlooked primarily due to the lack of quantified environmental and social impact data. Environmental Product Declaration (EPDs), and Social Life Cycle Assessment (S-LCA) have the potential to address the above data challenge. Furthermore, there is a paradigm shift in adopting Building Information Modelling (BIM) in the construction sector, enabling more access to project data. Hence, BIM can be used as a platform to link EPDs, social impact data, and cost data for proposal evaluations. Despite the potential benefits of the above approach, there is an implementation challenge in fidelity of EPD data. A comprehensive review revealed that previous researchers have overlooked TBL-based bid proposal evaluations for water supply infrastructure projects. The vision of this research is to adopt BIM and sustainable procurement to enhance the delivery of water supply infrastructure projects. This research developed a BIM-based plugin toolkit to conduct an automated TBL-based project proposal evaluation. Furthermore, state-of-the-art implementation support tool for EPDs was developed to support BIM-based sustainable construction procurement. Lastly, a Bayesian Belief Network (BBN) model was developed to evaluate the success of BIM-based construction procurement in the Canadian construction industry. The study revealed that, BIM-based sustainable procurement assists decision-makers in identifying the project proposal with the superior sustainability performance. However, implementation resources and client leadership are required to successfully implement BIM-based procurement in the Canadian construction sector. This research benefits the construction industry and policymakers in enhancing the sustainability of construction procurement. Furthermore, outcomes this research promotes the BIM adaptation in the Canadian construction sector.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".