| Title |
Bridge Damage Factor Recognition from Inspection Reports Using Deep Learning |
| Authors |
정세환(Chung, Sehwan) ; 문성현(Moon, Seonghyeon) ; 지석호(Chi, Seokho) |
| DOI |
https://doi.org/10.12652/Ksce.2018.38.4.0621 |
| Keywords |
교량 점검보고서;손상 인자 식별;워드 임베딩;순환신경망 Bridge inspection reports;Damage factor recognition;Word embedding;Recurrent neural network |
| Abstract |
This paper proposes a method for bridge damage factor recognition from inspection reports using deep learning. Bridge inspection reports contains inspection results including identified damages and causal analysis results. However, collecting such information from inspection reports manually is limited due to their considerable amount. Therefore, this paper proposes a model for recognizing bridge damage factor from inspection reports applying Named Entity Recognition (NER) using deep learning. Named Entity Recognition, Word Embedding, Recurrent Neural Network, one of deep learning methods, were applied to construct the proposed model. Experimental results showed that the proposed model has abilities to 1) recognize damage and damage factor included in a training data, 2) distinguish a specific word as a damage or a damage factor, depending on its context, and 3) recognize new damage words not included in a training data. |