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Predicting the Young’s Modulus of granites using the Bayesian model selection approach

  • Lingqiang Yang
  • Xianda FengEmail author
  • Yang Sun
Original Paper
  • 232 Downloads

Abstract

The value of Young’s modulus (E) is critical to the design of geotechnical engineering projects. Although E can be directly measured by laboratory tests, high-quality core samples and expensive sophisticated instruments are required. Therefore, a method for the indirect estimation of E is an appealing possibility. This study develops a model for predicting the E of intact granite based on the Bayesian model class selection approach. An experimental database of granite rock properties that includes the value E, point load strength index (Is50), L-type Schmidt hammer rebound number (RL), P-wave velocity (Vp), porosity (η), and uniaxial compressive strength, is applied to develop the most suitable model. The proposed model is then compared to existing approaches. The results indicate that the proposed models provide satisfactory predictions and good practicality in application.

Keywords

Young’s modulus Bayesian model selection approach Granite rocks Predictive model 

Notes

Acknowledgments

This work was supported by the Fund of State Key Laboratory of Hydraulic Engineering Simulation and Safety [grant number HESS-1502], the Research Award Fund for Young and Middle-aged Scientists of Shandong Province [grant number ZR2016EEB11], and the PhD Foundation of University of Jinan [grant number XBS1648]. These financial supports are gratefully acknowledged.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjinPeople’s Republic of China
  2. 2.School of Civil Engineering and ArchitectureUniversity of JinanJinanPeople’s Republic of China

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