Bayesian Inference for Uncertainty-Aware Post-Disaster Damage Assessment Using Artificial Intelligence-Reference-Cited by-同舟云学术

Bayesian Inference for Uncertainty-Aware Post-Disaster Damage Assessment Using Artificial Intelligence

Published:2022-05-24 Issue: Volume: Page:
ISSN:
Container-title:Computing in Civil Engineering 2021
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Author:

Cheng Chih-Shen¹²¹,Behzadan Amir H.¹²¹,Noshadravan Arash¹²¹

Affiliation:

1. Zachry Dept. of Civil and Environmental Engineering, Texas A&M Univ., College Station, TX.

2. Dept. of Construction Science, Texas A&M Univ., College Station, TX.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483893.020

Reference22 articles.

1. Big data and disaster management: a systematic review and agenda for future research

2. Five years later: Assessing the implementation of the four priorities of the Sendai framework for inclusion of people with disabilities.;Bennett D.;International Journal of Disaster Risk Science,2020

3. Deep learning for post‐hurricane aerial damage assessment of buildings.;Cheng C. S.;Computer‐Aided Civil Infrastructure Engineering,2021

4. Chollet, F. (2017). Deep Learning with Python. Manning Publications Company.

5. Deng J. Dong W. Socher R. Li L. Kai L. and Li F.-F. “ImageNet: A Large-scale Hierarchical Image Database.” Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255.

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