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http://dx.doi.org/10.12652/Ksce.2018.38.4.0621

Bridge Damage Factor Recognition from Inspection Reports Using Deep Learning  

Chung, Sehwan (Seoul National University)
Moon, Seonghyeon (Seoul National University)
Chi, Seokho (Seoul National University, The Institute of Construction and Environmental Engineering (ICEE))
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.38, no.4, 2018 , pp. 621-625 More about this Journal
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.
Keywords
Bridge inspection reports; Damage factor recognition; Word embedding; Recurrent neural network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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