Proceedings of the Rocscience International Conference (RIC 2023)

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Detection and Attribution of Climate Non-Stationarity in Cold Regions Geotechnical Design Using Artificial Intelligence

Authors
Elham Kheradmand1, Ali Fatolahzadeh Gheysari2, Parisa Samadi3, Sina Javankhoshdel4, Terence Ma4, Kien Dang4, Dipanjan Basu5, Pooneh Maghoul6, *
1Université de Montréal, Montreal, QC, Canada
2University of Manitoba, Winnipeg, MB, Canada
3Iran University of Science and Technology, Tehran, Iran
4Rocscience Inc, Toronto, ON, Canada
5University of Waterloo, Waterloo, ON, Canada
6Polytechnique Montreal, Montreal, QC, Canada
*Corresponding author. Email: pooneh.maghoul@polymtl.ca
Corresponding Author
Pooneh Maghoul
Available Online 8 November 2023.
DOI
10.2991/978-94-6463-258-3_34How to use a DOI?
Abstract

As our climate changes (non-stationarity), we face new challenges in assessing hazard frequency and assessing the vulnerability of infrastructure to the effects of global warming, such as changing precipitation patterns and increasing ground surface temperature. The lack of appropriate incorporation of future weather and climate information into the design, operation, and management of infrastructure remains a significant barrier to systematically improving climate-resilient infrastructure. In this paper, a spatiotemporal AI-powered platform for the detection and attribution of climate non-stationarity is used to as boundary conditions in a computational simulator to investigate the resiliency of a typical embankment in Canada. It can be seen that considering the climate boundary condition causes the reduction in the factor of safety of the embankment and the change of the performance of the geosynthetic layer under the embankment.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Rocscience International Conference (RIC 2023)
Series
Atlantis Highlights in Engineering
Publication Date
8 November 2023
ISBN
978-94-6463-258-3
ISSN
2589-4943
DOI
10.2991/978-94-6463-258-3_34How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Elham Kheradmand
AU  - Ali Fatolahzadeh Gheysari
AU  - Parisa Samadi
AU  - Sina Javankhoshdel
AU  - Terence Ma
AU  - Kien Dang
AU  - Dipanjan Basu
AU  - Pooneh Maghoul
PY  - 2023
DA  - 2023/11/08
TI  - Detection and Attribution of Climate Non-Stationarity in Cold Regions Geotechnical Design Using Artificial Intelligence
BT  - Proceedings of the Rocscience International Conference  (RIC 2023)
PB  - Atlantis Press
SP  - 325
EP  - 336
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-258-3_34
DO  - 10.2991/978-94-6463-258-3_34
ID  - Kheradmand2023
ER  -