Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

  • Naser Mahdiabadi
  • Gholamreza Khanlari

Abstract

The uniaxial compressive strength (UCS) and modulus of elasticity (E) are two important rock geomechanical parameters that are widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. In the present study, prediction of UCS and E was investigated in calcareous mudstones of Aghajari Formation using multiple linear regression (MLR), multiple nonlinear regression (MNLR), artificial neural networks (ANN), and adaptive neuro-fuzzy ─▒nference system (ANFIS). For this purpose, 80 samples from calcareous mudstones were subjected to the point loading, block punch, and cylinder punch tests. The performance of developed models was assessed based on determination coefficients (R2), mean absolute percentage error (MAPE), and variance accounted for (VAF) indices. The comparison of the obtained results revealed that, among the studied methods, ANFIS is the most suitable one for predicting UCS and E. Moreover, the results showed that ANN and MLNR respectively predict UCS and E better than MLR and a meaningful relationship between the observed and estimated UCS values in all regressions.

Keywords: uniaxial compressive strength, modulus of elasticity, ANFIS, ANNs, MLR, MNLR
Published online
2018-11-14
How to Cite
Mahdiabadi, N., & Khanlari, G. Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis. Periodica Polytechnica Civil Engineering. https://doi.org/10.3311/PPci.13035
Section
Research Article