Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results
An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Springer Verlag
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/77162/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037348181&doi=10.1007%2fs10064-017-1210-5&partnerID=40&md5=c1e946c223abf5cfc00d56a19b2c3ad5 |
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| Summary: | An accurate examination of deformability of rock samples in response to any change in stresses is deeply dependent on the reliable determination of properties of the rock as analysis inputs. Although Young’s modulus (E) can provide valuable characteristics of the rock material deformation, the direct determination of E is considered a time-consuming and complicated analysis. The present study is aimed to introduce a new hybrid intelligent model to predict the E of granitic rock samples. Hence, a series of granitic block samples were collected from the face of a water transfer tunnel excavated in Malaysia and transferred to laboratory to conduct rock index tests for E prediction. Rock index tests including point load, p-wave velocity and Schmidt hammer together with uniaxial compressive strength (UCS) tests were carried out to prepare a database comprised of 62 datasets for the analysis. Results of simple regression analysis showed that there is a need to develop models with multiple inputs. Then, a hybrid genetic algorithm (GA)-artificial neural network (ANN) model was developed considering parameters with the most impact on the GA. In order to have a fair evaluation, a predeveloped ANN model was also performed to predict E of the rock. As a result, a GA-ANN model with a coefficient of determination (R2) of 0.959 and root mean square error (RMSE) of 0.078 for testing datasets was selected and introduced as a new model for engineering practice; the results obtained were 0.766 and 0.098, respectively, for the developed ANN model. Furthermore, based on sensitivity analysis results, p-wave velocity has the most effect on E of the rock samples. |
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