Promoting the IPD Delivery Method in Construction Projects: A BIM-Based Smart Contract Approach
Bibliographic record
Abstract
The adoption of integrated project delivery (IPD) provides several advantages over traditional delivery methods, such as shorter schedules, efficient communication, and higher performance quality.However, its implementation is constantly hindered by many barriers.Existing studies on IPD barriers are limited to quantifying and addressing such obstacles.Additionally, hardly any studies have addressed the potential of advanced technologies in exploiting the adoption of IPD projects.Thus, this study presents an automated system that integrates blockchain, smart contracts, and BIM technologies to facilitate the implementation of IPD projects.Hyperledger Fabric and chaincodes are used to develop the blockchain network in accordance with 4D and 5D BIM models.The developed system simplifies various financial transactions throughout different phases of the IPD project implementation.The system allows non-owner participants to submit requests and review transaction records with the aim of minimizing possible conflicts.The methodology is evaluated by testing it on a real-life case study.The case study is modeled using BIM tools, and the corresponding blockchain network and smart contracts are developed.The findings prove the capability of the developed system to provide a secure and trustworthy platform for managing IPD transactions without the need for third-party involvement.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".