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Proposing a new model to approximate the elasticity modulus of granite rock samples based on laboratory tests results

Behzadafshar, K ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/s10064-017-1210-5
  3. Abstract:
  4. 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. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature
  5. Keywords:
  6. Artificial neural network ; Genetic algorithm ; Young’s modulus ; Acoustic wave velocity ; Compressive strength ; Deformation ; Forecasting ; Genetic algorithms ; Granite ; Mean square error ; Neural networks ; Regression analysis ; Seismic waves ; Sensitivity analysis ; Wave propagation ; Artificial neural network models ; Engineering practices ; Hybrid genetic algorithms ; Hybrid intelligent model ; Rock deformation ; Root mean square errors ; Simple regression analysis ; Uniaxial compressive strength ; Rocks
  7. Source: Bulletin of Engineering Geology and the Environment ; 2017 , Pages 1-10 ; 14359529 (ISSN)
  8. URL: https://link.springer.com/article/10.1007/s10064-017-1210-5