Manual searching for infrastructure damage information from large amounts of textual data requires considerable time and effort. A fast and accurate collection of damage information from such data is necessary for effective infrastructure planning. In this study, a question answering method was proposed to provide users with infrastructure damage information from textual data automatically. The proposed method relies on a natural language model called bidirectional encoder representations from transformers for information retrieval. From the 143 reports collected from the National Hurricane Center, 533 question-answer pairs were formulated. The proposed model was trained with 435 pairs and tested with the remainder. The model was also tested with 43 question-answer pairs created using earthquake-related textual data and achieved F1-scores of 90.5% and 83.6% for the hurricane and earthquake datasets, respectively.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. 2018R1A6A1A08025348 ) and the National R&D Project for Smart Construction Technology funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation (No. 21SMIP-A158708-02 ).
© 2021 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction