Risk Identification Expert System for Metro Construction Based on BIM
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Bibliographic record
Abstract
Risk Identification Expert System for Metro Construction Based on BIM Limao Zhang, Xianguo Wu, Lieyun Ding, Yueqing Chen, Miroslaw J. Skibniewski Pages 1437-1446 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) 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. Keywords: metro construction; safety risk identification; expert system; knowledge management; rule-based reasoning DOI: https://doi.org/10.22260/ISARC2013/0163 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it