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Applying different soft computing methods to predict mechanical properties of carbonate rocks based on petrographic and physical properties

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Abstract

Mechanical properties of carbonate rocks (elasticity modulus (E) and uniaxial compressive strength (UCS)) are important properties in tunneling, rock excavation and rock drilling blasting. Determination of these parameters using testing rock cores is almost difficult due to the discontinuities presence and it requires well-prepared cylindrical core samples. In addition, the testing procedure is expensive and time consuming. Thus, indirect tests are often utilized to evaluate the mechanical properties. In this research, a new technique for data processing called support vector regression (SVR) improved by metaheuristic algorithms (harmony search (HS), grey wolf optimizer (GWO), cuckoo search (CS), dolphin echolocation (DE) and genetic algorithm (GA)) to estimate of mechanical properties of carbonate rocks from physical properties and petrographic characteristics is applied. The techniques were employed in an open access literature. (case study: Koohrang’s third tunnel path, Iran). In these techniques, petrographic characteristics (allochem percent, carbonate percent, dolomite percent and grain size) and physical properties (saturated unit weight (γsat), S-wave velocity (Vs), dry unit weight (γd), P wave velocity (Vp)) were used as the inputs, while the mechanical properties of carbonate rocks were the outputs. Different performance concepts were used to compare the prediction models performance. The outcomes obtained show that the SVR-HS technique has robust potential for the prediction of mechanical properties of carbonate rocks based on physical properties and petrographic characteristics with high accuracy.

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  1. Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran

    Hadi Fattahi & Mohammad Ali Shirinzade

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Fattahi, H., Shirinzade, M.A. Applying different soft computing methods to predict mechanical properties of carbonate rocks based on petrographic and physical properties. Earth Sci Inform 15, 351–368 (2022). https://doi.org/10.1007/s12145-021-00736-w

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