AI Assists Operation and Maintenance of Future Cities

Authors

  • Han-Wei Zhao

    State Key Laboratory of Safety, Durability and Healthy Operation of Long Span Bridges, Southeast University, Nanjing 211189, China; Key Laboratory of Concrete and Pre-stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China; Teaching and Research Section of Intelligent Construction, Southeast University, Nanjing 211189, China

DOI:

https://doi.org/10.30564/aia.v5i1.5780

References

[1] Karniadakis, G.E., Kevrekidis, I.G., Lu, L., et al., 2021. Physics-informed machine learning. Nature Reviews Physics. 3(6), 422-440.

[2] Bao, Y., Chen, Z., Wei, S., et al., 2019. The state of the art of data science and engineering in structural health monitoring. Engineering. 5(2), 234-242.

[3] Zhang, K., Chermprayong, P., Xiao, F., et al., 2022. Aerial additive manufacturing with multiple autonomous robots. Nature. 609(7928), 709-717.

[4] Mohammadi, N., Taylor, J.E., 2021. Thinking fast and slow in disaster decision-making with Smart City Digital Twins. Nature Computational Science. 1(12), 771-773.

[5] Sun, L., Shang, Z., Xia, Y., et al., 2020. Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection. Journal of Structural Engineering. 146(5), 04020073.

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How to Cite

Zhao, H.-W. (2023). AI Assists Operation and Maintenance of Future Cities. Artificial Intelligence Advances, 5(1), 25–27. https://doi.org/10.30564/aia.v5i1.5780

Issue

Vol. 5 , Iss. 1 (April 2023):

Article Type

Editorial

License

Copyright © 2023 Han-Wei Zhao

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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