Deep Learning-Based Approach For Text Classification And Retrieval Of Hazards In Metro Construction Sites

Investigation and rectification of hazards is an important part of metro construction safety management.Its purpose is to identify and record potential safety hazards that may cause construction safety accidents through site safety inspections,and to track and rectify them.The development of hazards troubleshooting system is conducive to standardizing hazards investigation work and establishing and improving long-term mechanism for safety hazards investigation and treatment.Safety hazards investigation activities have accumulated a large amount of text data that records safety hazards conditions on site,which are deposited and idle in the investigation system.In order to effectively manage and use massive unstructured hidden danger text data,with a view to revealing the distribution of hidden dangers,predicting the source of security risks,and providing references for rectifying similar hidden dangers,we first need to solve the problems of hidden danger text classification and efficient retrieval.Therefore,this dissertation explores the text classification and retrieval research of safety hazards on the construction site based on deep learning.The main research work are as follows:Firstly,the meaning and types of safety hazards in metro construction are explained.Combining literature research and expert interviews,the working mechanism and existing problems of metro construction hazards investigation are analyzed,and the necessity of classification and retrieval research of hidden dangers are raised.The current research is mainly based on text mining and keyword matching to realize hazards management.Existing studies have problems such as poor classification effect,insufficient robustness,and low retrieval efficiency of safety hazard data.Secondly,combined with text representation and deep learning and other related theoretical technologies,a classification model based on the modified BERT(Bidirectional Encoder Representation from Transformers)structure is built to achieve end-to-end classification of safety hazards.Using the text data accumulated by a metro safety management information system,a comparative study with 5 other classification methods was carried out.The results show that the proposed method has relatively better hazards classification effect and generalization ability.Finally,a hazard data retrieval framework based on knowledge graph is proposed: the ontology and semantic network are used to express hazards knowledge,direct mapping,deep neural network,etc.are combined to realize knowledge extraction,and then to construct hidden knowledge graph and store it in Neo4 j graph database.The experimental results show that the hazard data organization based on graph structure can process hazard data with complex internal associations and achieve efficient structured retrieval.This dissertation proposed a classification method based on modified BERT model and a structured retrieval method based on knowledge graph in order to realize text classification and data retrieval of safety hazards in metro construction,and provide support for the development and application of integrated systems.Besides,this research can also provide a reference for text processing,data retrieval and management in the field of architecture based on deep learning and knowledge graph technology.