Proceedings of the Rocscience International Conference (RIC 2023)

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Utilizing the Monte-Carlo Capability in RS2 for Machine-Learning Applications

Authors
Amichai Mitelman1, *, Avshalom Ganz1, Alon Urlainis1
1Ariel University, Ariel, Israel
*Corresponding author. Email: amichaim@ariel.ac.il
Corresponding Author
Amichai Mitelman
Available Online 8 November 2023.
DOI
10.2991/978-94-6463-258-3_16How to use a DOI?
Keywords
Geotechnical engineering; machine-learning; surrogate models; Monte Carlo analysis
Abstract

Due to the uncertainty that stems from the heterogeneous nature of geological materials, probabilistic tools have been incorporated into geotechnical practice. A notable example is the Monte Carlo analysis method that is available as a built-in feature in the program RS2 (Rocscience in Phase2 version 6.020. Rocscience Inc. Toronto, 2007). In this paper, we demonstrate how to utilize the Monte Carlo method for enhancement of geotechnical analysis. The procedure consists of two primary stages: 1) data generation, where numerous finite-element (FE) models are generated based on an estimated range of input parameters, and: 2) data analysis, where machine-learning models are used to correlate input parameters and results of interest. The verified ML algorithm can be referred to as a surrogate model. An example of the implementation of a surrogate model is illustrated through an anchored sheet pile wall problem. For this problem, the surrogate model correlates between the forces in the first and second row of anchors, thus allowing for prediction and optimization of ground support during construction.

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
10.2991/978-94-6463-258-3_16
ISSN
2589-4943
DOI
10.2991/978-94-6463-258-3_16How 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  - Amichai Mitelman
AU  - Avshalom Ganz
AU  - Alon Urlainis
PY  - 2023
DA  - 2023/11/08
TI  - Utilizing the Monte-Carlo Capability in RS2 for Machine-Learning Applications
BT  - Proceedings of the Rocscience International Conference  (RIC 2023)
PB  - Atlantis Press
SP  - 156
EP  - 161
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-258-3_16
DO  - 10.2991/978-94-6463-258-3_16
ID  - Mitelman2023
ER  -