Risk Identification Expert System for Metro Construction Based on BIM
Bibliographic record
Abstract
This paper presents a BIM-based Risk Identification Expert System (B-RIES) for metro construction, composed of three main built-in subsystems: BIM extraction, knowledge base management, and risk identification subsystems. The engineering parameter information related to risk factors is extracted from the BIM of a specific project where the IFC standard plays a bridge role between the BIM data and metro construction safety risks. An integrated knowledge base, consisting of fact base, rule base and case base, is established to systematize the fragmented explicit and tacit knowledge. A hybrid inference approach, with case-based reasoning and rule-based reasoning included, is developed to improve the flexibility and comprehensiveness of the system reasoning capacity. During the safety risk identification process, B-RIES is able to improve the inefficiencies in engineering information extraction, reduce the dependence on domain experts, and facilitate knowledge sharing and communication among dispersed clients and domain experts. A typical safety hazard identification in the Mingdu station, located in the Wuhan Metro Line Two, is presented in a case study. The results demonstrate the feasibility of B-RIES, and its application potential. B-RIES can be used as a decision support tool to provide guidelines for safety management in metro construction, and thus increase the likelihood of a successful project in a complex environment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".