Research On Information Extraction Of Codes For Constructional Quality Acceptance Of Building Engineering Based On Machine Learning

Construction industry and real estate industry are information-intensive and knowledgeintensive industries.How to use these information and knowledge efficiently has become an important research topic.The Codes for Quality Acceptance is an important basis for guiding construction and inspection.Nowadays,the application mode of Codes for Quality Acceptance is manual reading and searching,and the automatic quality inspection system based on Codes for Quality Acceptance mostly relies on manual establishment of rules,so automatic information extraction of Codes for Quality Acceptance becomes the key technology to reduce manual work and improve automation level.In this paper,taking the Codes for constructional quality acceptance as the research object,combining the characteristics of the criterion file and information extraction technology,a method of named entity recognition and information extraction based on hybrid machine learning is proposed.The following work is done concretely:1.Codes for constructional quality acceptance of building engineering are classified into relationship constraints and attribute constraints.Relationship constraints specify the sequence and interval between two working procedures,and attribute constraints specify the attributes of objects such as materials or finished products.2.Named entity recognition based on Bi-LSTM-CRF is used instead of traditional domain dictionary,which makes the model more universal in different fields3.An information extraction model based on LSTM-MLP is proposed.This model makes up for the artificial dependence of rule-based information extraction on expert rulemaking,and makes the model more automated.4.After extracting the information of Codes for constructional quality acceptance of building engineering,a more intuitive display form is proposed to facilitate learning and query,which facilitates the acceptance based on Codes in construction projects.The results of model testing show that,the information extraction model based on hybrid machine learning proposed in this paper is effective.The F1 value of Named Entity Recognition reaches 88.5%,and the F1 value of Information Extraction reaches 83.8%.As the first attempt to extract information from engineering codes,this study has the advantages of reducing manual dependence and generality,having high reference value and being worth further study.