Abstract
The unconfined compressive strength (UCS) of rocks is one of the most important properties used in rock science and engineering practices. Thus, many researchers attempt to develop computational intelligence models for estimating the UCS of different types of rocks. This study develops and compares eight models to estimate the rock UCS. Six hybrid ANNs were constructed using artificial bee colony, ant colony optimization, antlion optimizer, imperialist competitive algorithm, shuffled complex evolution, and teaching learning-based optimization algorithms. Additionally, two regression-based computational models, namely multivariate adaptive regression splines (MARS) and Gaussian process regression (GPR), were utilized. The experimental datasets at different locations in Malaysia and Iran, including the UCS and other rock indices, were collected and used in this work. Experimental findings indicate that the performances of all models are adequate for UCS estimation, with R2 values scattered in the range of 0.87 to 0.97 and 0.94 to 0.98 in the training and testing phases, respectively. However, the developed MARS model was ranked high compared to other models, including GPR and hybrid ANNs, and the RMSE of the MARS model was found to be 2.27 MPa. Overall, the developed MARS model outperformed other employed models based on the R2 and RMSE criteria and can be utilized precisely for the UCS estimate of rocks.
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This work was supported by the Materials and Components Technology Development Program (20015240, development of micro-phase change material (PCM) manufacturing technology and heat-generating concrete for heating energy saving using the same) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).
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Kaloop, M.R., Bardhan, A., Samui, P. et al. Computational intelligence approaches for estimating the unconfined compressive strength of rocks. Arab J Geosci 16, 37 (2023). https://doi.org/10.1007/s12517-022-11085-3
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DOI: https://doi.org/10.1007/s12517-022-11085-3