Clash Avoidance in BIM Based Multidisciplinary Coordination: A Literature Overview
Bibliographic record
Abstract
In recent years there has been a significant amount of research aiming to increase the efficiency of Building Information Modelling (BIM) based multidisciplinary coordination process.However, unanticipated increases in cost and delays in construction projects are still visible.According to the literature, one of the principal factors affecting the efficiency of BIM-based multidisciplinary coordination and construction process is the conflict between the systems of different design disciplines.Recent years have seen a surge of automatic clash detection tools and strategies.These have provided clear benefits to the construction process by helping to reduce the number of errors discovered on-site, but the significance of this effect is hindered by the inefficiency of the clash resolution process due to the vast number of identified clashes and the resources needed to resolve them.Researchers have started focusing on devising strategies for clash avoidance during the design process to address this phenomenon.Our work is an attempt to present a literature overview of these clash avoidance strategies that range from shared situational awareness to supervised and hybrid machine learning frameworks.This work identified that the most prominent causes of clashes directly occur during the preliminary phases of multidisciplinary coordination which are generating the specialty models and federated models.Additionally, the lack of studies on proper standardized documentation of lessons learned in BIM-based multidisciplinary coordination is also recognized in this study which points toward future research directions for developing such guidelines.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".