Improved RMR rock mass classification using artificial intelligence algorithms
Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities su...
| Main Authors: | , , |
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| Format: | Journal Article |
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Springer Wien
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/37199 |
| _version_ | 1848754981228773376 |
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| author | Gholami, R. Rasouli, Vamegh Alimoradi, A. |
| author_facet | Gholami, R. Rasouli, Vamegh Alimoradi, A. |
| author_sort | Gholami, R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures. |
| first_indexed | 2025-11-14T08:49:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-37199 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:49:02Z |
| publishDate | 2012 |
| publisher | Springer Wien |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-371992017-09-13T15:57:25Z Improved RMR rock mass classification using artificial intelligence algorithms Gholami, R. Rasouli, Vamegh Alimoradi, A. correlations artificial intelligence tunnel seismic prediction rock mass rating Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures. 2012 Journal Article http://hdl.handle.net/20.500.11937/37199 10.1007/s00603-012-0338-7 Springer Wien restricted |
| spellingShingle | correlations artificial intelligence tunnel seismic prediction rock mass rating Gholami, R. Rasouli, Vamegh Alimoradi, A. Improved RMR rock mass classification using artificial intelligence algorithms |
| title | Improved RMR rock mass classification using artificial intelligence algorithms |
| title_full | Improved RMR rock mass classification using artificial intelligence algorithms |
| title_fullStr | Improved RMR rock mass classification using artificial intelligence algorithms |
| title_full_unstemmed | Improved RMR rock mass classification using artificial intelligence algorithms |
| title_short | Improved RMR rock mass classification using artificial intelligence algorithms |
| title_sort | improved rmr rock mass classification using artificial intelligence algorithms |
| topic | correlations artificial intelligence tunnel seismic prediction rock mass rating |
| url | http://hdl.handle.net/20.500.11937/37199 |