Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA.
The deformation modulus (Em) of rock mass is an important parameter used in designing underground excavations. It models the mechanical response of rock mass due to excavation and can be determined directly using large scale in-situ tests which are often time consuming and expensive. To overcome thi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Presentation |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://onepetro.org/ARMAUSRMS/proceedings-abstract/ARMA18/All-ARMA18/ARMA-2018-1064/124065 http://hdl.handle.net/11408/4910 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1779905276431826944 |
|---|---|
| author | Adoko, A. C. Zvarivadza, T. |
| author_facet | Adoko, A. C. Zvarivadza, T. |
| author_sort | Adoko, A. C. |
| collection | DSpace |
| description | The deformation modulus (Em) of rock mass is an important parameter used in designing underground excavations. It models the mechanical response of rock mass due to excavation and can be determined directly using large scale in-situ tests which are often time consuming and expensive. To overcome this issue, several empirical equations are usually employed. However, these existing equations are suitable for certain types of rock masses posing limitations. Therefore, this paper intends to investigate alternatives for estimating the Em using adaptive techniques namely, the Adaptive Neuro-fuzzy Inference systems (ANFIS) and Multivariate Adaptive Regression Spline (MARS). Available data on the Em was employed to establish the models. The input parameters used to develop the models included the uniaxial compression strength, rock quality designation, discontinuity characteristics and the rock mass rating index. The performances of proposed models were evaluated using various performance indices namely the variance account for (VAF), root-mean square error (RMSE), and the coefficient of determination (R2). The results indicated good accuracy. Overall, the MARS model showed lower performance compared with the ANFIS model but the MARS model was able to produce easy-to-interpret. |
| format | Presentation |
| id | ir-11408-4910 |
| institution | My University |
| language | English |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ir-11408-49102022-06-28T10:32:11Z Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. Adoko, A. C. Zvarivadza, T. Upstream Oil & Gas Artificial Intelligence Rock mechanics rock mass deformation modulus mars model The deformation modulus (Em) of rock mass is an important parameter used in designing underground excavations. It models the mechanical response of rock mass due to excavation and can be determined directly using large scale in-situ tests which are often time consuming and expensive. To overcome this issue, several empirical equations are usually employed. However, these existing equations are suitable for certain types of rock masses posing limitations. Therefore, this paper intends to investigate alternatives for estimating the Em using adaptive techniques namely, the Adaptive Neuro-fuzzy Inference systems (ANFIS) and Multivariate Adaptive Regression Spline (MARS). Available data on the Em was employed to establish the models. The input parameters used to develop the models included the uniaxial compression strength, rock quality designation, discontinuity characteristics and the rock mass rating index. The performances of proposed models were evaluated using various performance indices namely the variance account for (VAF), root-mean square error (RMSE), and the coefficient of determination (R2). The results indicated good accuracy. Overall, the MARS model showed lower performance compared with the ANFIS model but the MARS model was able to produce easy-to-interpret. 2022-06-28T10:32:11Z 2022-06-28T10:32:11Z 2018 Presentation https://onepetro.org/ARMAUSRMS/proceedings-abstract/ARMA18/All-ARMA18/ARMA-2018-1064/124065 http://hdl.handle.net/11408/4910 en open |
| spellingShingle | Upstream Oil & Gas Artificial Intelligence Rock mechanics rock mass deformation modulus mars model Adoko, A. C. Zvarivadza, T. Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. |
| title | Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. |
| title_full | Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. |
| title_fullStr | Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. |
| title_full_unstemmed | Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. |
| title_short | Modeling Rock Mass Deformation Modulus Using Adaptive Techniques : 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA18). 17 – 20 June 2018, Seattle, Washington, USA. |
| title_sort | modeling rock mass deformation modulus using adaptive techniques : 52nd us rock mechanics/geomechanics symposium. american rock mechanics association (arma18). 17 – 20 june 2018, seattle, washington, usa. |
| topic | Upstream Oil & Gas Artificial Intelligence Rock mechanics rock mass deformation modulus mars model |
| url | https://onepetro.org/ARMAUSRMS/proceedings-abstract/ARMA18/All-ARMA18/ARMA-2018-1064/124065 http://hdl.handle.net/11408/4910 |
| work_keys_str_mv | AT adokoac modelingrockmassdeformationmodulususingadaptivetechniques52ndusrockmechanicsgeomechanicssymposiumamericanrockmechanicsassociationarma181720june2018seattlewashingtonusa AT zvarivadzat modelingrockmassdeformationmodulususingadaptivetechniques52ndusrockmechanicsgeomechanicssymposiumamericanrockmechanicsassociationarma181720june2018seattlewashingtonusa |