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Cost-Factor Recognition and Recommendation in Open-Pit Coal Mining via BERT-BiLSTM-CRF and Knowledge Graphs

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Veröffentlicht in:Symmetry vol. 17, no. 11 (2025), p. 1834-1862
1. Verfasser: Sun, Jiayi
Weitere Verfasser: Li Pingfeng, Guan Weiming, Cui Xuejiao, Wang, Haosen, Xie Shoudong
Veröffentlicht:
MDPI AG
Schlagworte:
Coal mining
Machine learning
Construction accidents & safety
Graphs
Conditional random fields
Costs
Discriminant analysis
Recognition
Neural networks
Optimization
Inference
Pits (excavations)
Dumping
Algorithms
Silos
Knowledge representation
Symmetry
Embedding
Production costs
Semantics
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Abstract:Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support.
ISSN:2073-8994
DOI:10.3390/sym17111834
Quelle:Engineering Database