Prediction of uniaxial compressive strength and modulus of elasticity of sandstones using artificial neural network and multiple regression analysis

Document Type : Research Paper

Authors

Abstract

Determining UCS and E using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The main purpose of this study is to develop an artificial neural network (ANN) and multiple regression analysis (MLR) models in order to predict UCS and E of sandstones. For this, a database of laboratory tests (including 130 sandstone samples) was prepared, which includes porosity, P-wave velocity, dry density, slake durability index, and water absorption as input parameters and UCS and E as output parameter. The performance of the MLR and ANN models are evaluated by comparing statistic parameters, including correlation coefficient (r), root mean square error (RMSE), and variance account for (VAF). Comparison of the multiple linear regressions and ANNs results indicated that respective ANN models were more acceptable for predicting UCS and E than the other.

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References

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History

  • Receive Date: 23 October 2018
  • Revise Date: 08 April 2019
  • Accept Date: 09 April 2019

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