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Comparison of artificial intelligence and multivariate regression methods in predicting the uniaxial compressive strength of rock during the specific resistivity monitoring

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Abstract

Uniaxial compressive strength is one of the most important mechanical characteristics of rocks, and its prediction is essential in most rock engineering projects. Recently, several types of research have been conducted regarding the relationship between the petrophysical and geomechanical properties of rocks and geophysical parameters, among which geoelectric and seismic methods have been the most utilized and efficient. In this research, the prediction of uniaxial compressive strength (UCS) has been investigated utilizing the geoelectrical method. In the present study, after sampling from the studied area, the core samples were saturated and by installing special electrodes, the changes in electrical resistivity during compressive stress loading were measured and monitored in the laboratory environment. Laboratory samples with various structures and textures indicated different electrical behaviors during the application of compressive stress which stresses the significant effect of the volume proportion of aggregates on petrophysical and geomechanical properties. In core samples prepared from fault breccias and alluvial materials with a volumetric block proportion (VBP) of 25–75%, a significant correlation was observed between the UCS and the measured electrical resistivity values. The neuro-fuzzy model presented with a decision factor (R2) of 99.7%, root sum of square error (RMSE) of 2.511, geometric mean error ratio (GMER) of 0.865, and relative improvement index (RI) of 71.08 can predict uniaxial compressive strength (UCS) more accurately than artificial neural network and multivariable regression methods.

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Authors and Affiliations

  1. Department of Mining Engineering, Urmia University, Urmia, Iran

    Behnam Taghavi, Farnusch Hajizadeh & Hassan Moomivand

Authors
  1. Behnam Taghavi
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  2. Farnusch Hajizadeh
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  3. Hassan Moomivand
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Behnam Taghavi, Farnusch Hajizadeh, and Hassan moomivand. The first draft of the manuscript was written by Behnam Taghavi, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Behnam Taghavi.

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Taghavi, B., Hajizadeh, F. & Moomivand, H. Comparison of artificial intelligence and multivariate regression methods in predicting the uniaxial compressive strength of rock during the specific resistivity monitoring. Bull Eng Geol Environ 82, 409 (2023). https://doi.org/10.1007/s10064-023-03415-w

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