Automatic construction of risk transmission network about subway construction based on deep learning models.
Journal:
Scientific reports
Published Date:
May 11, 2025
Abstract
Safety risks management is a critical part during the subway construction. However, conventional methods for risk identification heavily rely on experience from experts and fail to effectively identify the relationship between risk factors and events embedded in accident texts, which fail to provide substantial guidance for subway safety risks management. With a dataset comprising 562 occurrences of subway construction accidents, this study devised a domain-specific entity recognition model for identifying safety hazards during the subway construction. The model was constructed by a Bidirectional Long Short-Term Memory Network with Conditional Random Fields (BiLSTM-CRF). Additionally, a domain-specific entity causal relation extraction model employing Convolutional Neural Networks (CNN) was also developed in thsi model. The constructed models automatically extract safety risk factors, safety events, and their causal relationships from the texts about subway accidents. The precision, recall, and F scores of Metro Construction Safety Risk Named Entity Recognition Model (MCSR-NER-Model) all exceeded 77%. Its performance in the specialized domain named entity recognition (NER) with a limited volume of textual data is satisfactory. The Metro Construction Safety Risk Domain Entity Causal Relationship Extraction Model (MCSR-CE-Model) achieved an impressive accuracy, recall, and F score of 98.96%, exhibiting excellent performance. Moreover, the extracted entities were normalized and domain dictionary was developed. Based on the processed entities and relationships processed by the domain dictionary, 533 domain entity causal relation triplets were obtained, facilitating the establishment of the directed and unweighted complex network and case database about the risks of subway construction. This research successfully converted accident texts into a causal chain structure of "safety risk factors to risk events," providing detailed categorization of safety risks and events. Concurrently, it revealed the interrelationships and historical statistical patterns among various safety risk factors and categories of risk events through the complex safety risks network. The construction of the database facilitated project managers in conducting management decisions about safety risks.
Authors
Keywords
No keywords available for this article.
