Proceedings of the 9th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2020)

Study and Application of Seismic Risk and Exposure Model Based on AI Technologies

Authors
Changlong Li, Zongchao Li, Hongshan Lyu, Mengtan Gao
Corresponding Author
Changlong Li
Available Online 12 April 2021.
DOI
10.2991/aebmr.k.210409.003How to use a DOI?
Keywords
Pattern Recognition, Seismic risk, Exposure
Abstract

This paper investigated the numbers, taxonomy and vulnerability of the buildings in Southeastern Tibet and built pattern classes for difference types of buildings. Then we assessed the distribution of each type of buildings in every town based on 3D image pattern recognition, and made an event-based and a 50-year hazard-based seismic risk assessment for the towns in Southeastern Tibet. Our study indicated that areas with the highest seismic risk are urban areas of Lhasa and Nyingchi and Cuona Town, and urban Lhasa has the highest seismic risk of building structural economic losses.

Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 9th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2020)
Series
Advances in Economics, Business and Management Research
Publication Date
12 April 2021
ISBN
978-94-6239-363-9
ISSN
2352-5428
DOI
10.2991/aebmr.k.210409.003How to use a DOI?
Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Changlong Li
AU  - Zongchao Li
AU  - Hongshan Lyu
AU  - Mengtan Gao
PY  - 2021
DA  - 2021/04/12
TI  - Study and Application of Seismic Risk and Exposure Model Based on AI Technologies
BT  - Proceedings of the 9th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2020)
PB  - Atlantis Press
SP  - 21
EP  - 26
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.210409.003
DO  - 10.2991/aebmr.k.210409.003
ID  - Li2021
ER  -