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

Elham Kheradmand; Ali Fatolahzadeh Gheysari; Parisa Samadi; Sina Javankhoshdel; Terence Ma; Kien Dang; Dipanjan Basu; Pooneh Maghoul

doi:10.2991/978-94-6463-258-3_34

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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_34 How 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.
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: 10.2991/978-94-6463-258-3_34
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-258-3_34 How to use a DOI?
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

ris enw bib

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  -

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