S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Civil & Environmental Engineering (건설환경공학부) Theses (Master's Degree_건설환경공학부)
Bridge Damage Factor Recognition from Inspection Reports Using Active Recurrent Neural Network
- 공과대학 건설환경공학부
- Issue Date
- 서울대학교 대학원
- 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 8. 지석호.
- Extracting information from bridge inspection reports has recently attracted research interests, since the reports contain valuable information for predictive maintenance of bridges. However, a considerable amount of the reports has limited manually collecting such information from the inspection reports. The research objective was to propose a methodology for automatically recognizing information on bridge damages and the causal factors from the reports with less amount of training data. This study applied recurrent neural network to develop the recognition model, and active learning to train the model. In the active learning scheme, a human annotator was asked to label text data which the model had difficulty in recognizing damages and causal factors from. Experimental results showed that the developed model performs well using only 140 training data to get f-1 score of 0.778, 90.5% of the maximum performance of the model. The proposed methodology will be applied to develop a model for extracting valuable information from bridge inspection reports, and eventually enable efficient preventive maintenance of bridges.