The impact of contractual language and trust on the \nsuccessful conduct of BIM-enabled projects
Bibliographic record
Abstract
The ever-increasing complexity of the AEC industry requires a collaborative environment for the efficient implementation of construction projects. Building Information Modeling (BIM), with its cooperative nature, has shifted the fragmented construction transactions to information sharing, common goals, and communication between the stakeholders. However, BIM adoption has not gained its decent place. It is primarily due to the barriers in BIM implementation, including its legal aspects. Various existing BIM appendixes do not satisfy the expectations. On the other hand, trust as a complementary mechanism plays a pivotal role in the BIM processes. While previous studies have investigated legal BIM barriers and the role of trust, the importance of contractual functions in relation to contractual language and trust elements in improving the efficiency of contracts and the success of BIM implementation has remained debatable. This study, therefore, aims at studying contractual functions, their instantiation within BIM contractual language and trust, and their potential impact on BIM projects’ success. Investigation of current contractual mechanisms and their limitations in Canada is another research objective. Moreover, this study proposes a conceptual contracting framework. To achieve these objectives, a content analysis approach was applied, during which a coding system was developed. The data collected from literature review and semistructured interviews with BIM professionals were analyzed to identify the success elements of projects as well as BIM-related legal issues and trust-building factors. According to the results, some recommendations to amend the contractual language of construction agreements were presented. Moreover, a conceptual trust framework was proposed, which then was utilized to provide a contractual framework. Furthermore, the outcomes of exploring CCDCs and BIM appendixes depicted some gaps regarding the BIM legal aspects. Therefore, suggestions to introduce BIM into the CCDCs and to update the IBC contract were provided. While the reliability of the coding system was tested by a validator, further studies could validate the trust framework and the contractual framework put forward by this research.
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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 | 0.045 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".