Development of a Semiautomatic Parametric Method for Creation of an I-BIM Model of a Tunnel for Use in FEM Software
Bibliographic record
Abstract
This paper focuses on the design phase of I-BIM tunnelling projects using Sequential Excavation Method (SEM), in Europe, commonly referred to as the New Austrian Tunnelling Method (NATM), and addresses the problem of coupling geotechnical conditions and tunnelling Building Information Model (BIM) for the preparation of the computational model suitable for the finite element analysis method (FEM). The review of the literature led to the conclusion that an automatic merging of the tunnel model and ground model for use in the FEM software is currently not reliable due to the number of differences between various types of models as they serve contrasting needs. Consequently, modelling becomes a manual task, which is very time-consuming and error prone. In this paper, we present the development of a framework for the semiautomatic transformation of the various tunnelling models and respective ground models into the model suitable for further analysis. We conclude that the import and translation of the geometry into the FEM software are most successful and accurate when the initial I- BIM (tunnel) model is prepared at a level of detail appropriate for a computational model. The result is the I-BIM model, fit for use in the FEM software which speeds up the modelling process and reduces errors. We have shown that it is possible to prepare the geometry of a tunnel in the BIM software, transfer it, and use it in the software for geotechnical analysis. This makes the preparation of the tunnel geometry for FEM analysis much easier and faster. Due to the fast preparation of the geometry of the new model, the approach presented in this research is useful in practice. The applicability of the framework and the framework workflow are both presented through a practical case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".