Implementation of Surrogate Models for the Analysis of Slope Problems
Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/13/4/99 |
| _version_ | 1797605326222000128 |
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| author | Amichai Mitelman Beverly Yang Davide Elmo |
| author_facet | Amichai Mitelman Beverly Yang Davide Elmo |
| author_sort | Amichai Mitelman |
| collection | DOAJ |
| description | Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical modeling to create surrogate models. The concept of surrogate models aligns well with the deductive nature of data availability in rock engineering projects. In this paper, we demonstrated the use of surrogate models to analyze two common rock slope stability problems: (1) determining the maximum stable depth of a vertical excavation and (2) determining the allowable angle of a slope with a fixed height. Compared with support vector machines and K-nearest algorithms, the random forest model performs best on a data set of 800 numerical models for the problems discussed in the paper. For all these models, regression-type models outperform classification models. Once the surrogate model is confirmed to preform accurately, instantaneous predictions of maximum excavation depth and slope angle can be achieved according to any range of input parameters. This capability is used to investigate the impact of narrowing GSI range estimation. |
| first_indexed | 2024-03-11T04:59:27Z |
| format | Article |
| id | doaj.art-d7ab112beb05416dba4f447bb6267da8 |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-11T04:59:27Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-d7ab112beb05416dba4f447bb6267da82023-11-17T19:25:03ZengMDPI AGGeosciences2076-32632023-03-011349910.3390/geosciences13040099Implementation of Surrogate Models for the Analysis of Slope ProblemsAmichai Mitelman0Beverly Yang1Davide Elmo2Department of Civil Engineering, Ariel University, Ariel 4076405, IsraelNBK Institute of Mining Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaNBK Institute of Mining Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaNumerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical modeling to create surrogate models. The concept of surrogate models aligns well with the deductive nature of data availability in rock engineering projects. In this paper, we demonstrated the use of surrogate models to analyze two common rock slope stability problems: (1) determining the maximum stable depth of a vertical excavation and (2) determining the allowable angle of a slope with a fixed height. Compared with support vector machines and K-nearest algorithms, the random forest model performs best on a data set of 800 numerical models for the problems discussed in the paper. For all these models, regression-type models outperform classification models. Once the surrogate model is confirmed to preform accurately, instantaneous predictions of maximum excavation depth and slope angle can be achieved according to any range of input parameters. This capability is used to investigate the impact of narrowing GSI range estimation.https://www.mdpi.com/2076-3263/13/4/99rock engineeringmachine learningsurrogate modelstunnelsslope stabilityGSI |
| spellingShingle | Amichai Mitelman Beverly Yang Davide Elmo Implementation of Surrogate Models for the Analysis of Slope Problems Geosciences rock engineering machine learning surrogate models tunnels slope stability GSI |
| title | Implementation of Surrogate Models for the Analysis of Slope Problems |
| title_full | Implementation of Surrogate Models for the Analysis of Slope Problems |
| title_fullStr | Implementation of Surrogate Models for the Analysis of Slope Problems |
| title_full_unstemmed | Implementation of Surrogate Models for the Analysis of Slope Problems |
| title_short | Implementation of Surrogate Models for the Analysis of Slope Problems |
| title_sort | implementation of surrogate models for the analysis of slope problems |
| topic | rock engineering machine learning surrogate models tunnels slope stability GSI |
| url | https://www.mdpi.com/2076-3263/13/4/99 |
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