Using BIM models for the design of large rail infrastructure projects: key factors for a successful implementation
Bibliographic record
Abstract
As already widely acknowledged in the construction industry, the use of a BIM model as single-pointof-truth during the design and construction phase of the project's lifecycle improves efficiency and reduces rework and extra costs.However, implementing the coordination and integration of different technical designs holds a lot of challenges.Combining different technical designs in one model in such a way that analyses, interface management and clash detection are possible, requires not only clear task descriptions and responsibilities for every stakeholder, but also a detailed workflow describing the required input, expected output of every technique and the intermediate deadlines during the design.Moreover, changes in the way the designs are made and specific configurations of the software tools are often needed to guarantee an optimal integration with the other parts of the design and to meet the firm's specific processes.This paper describes our experience gathered the last few years with the implementation of a BIM methodology supporting the design integration of different technical disciplines in large rail infrastructure projects.The focus lies on the implementation of available technical solutions to support our methodology, to improve design efficiency and to deliver high-quality integrated study designs.So far, our results show a successful implementation of BIM in our design department, integrating the design of all technical disciplines allowing us to follow the progress of the design, improve the communication within the project team and detect and solve clashes earlier in the design process.Key factors contributing to a successful BIM implementation are further explained and illustrated with practical examples.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".