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Neural Computing and Applications

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19.09.2020 | Original Article

A hybridized intelligence model to improve the predictability level of strength index parameters of rocks

verfasst von: Abbas Abbaszadeh Shahri, Reza Asheghi, Mohammad Khorsand Zak

Erschienen in: Neural Computing and Applications | Ausgabe 8/2021

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Abstract

In the current paper, the uniaxial compressive strength (UCS) and Young modulus (E) of rocks were predicted using a hybridized intelligence method. The model was developed using an optimum multi-objective generalized feedforward neural network (GFFN) incorporated with an imperialist competitive metaheuristic algorithm (ICA) and managed using 208 datasets of different physical and mechanical quarries from almost all over of Iran. Rock class, density, porosity, P-wave velocity, point load index and water absorption were datacenter components. The predictability and accuracy performance of the hybrid ICA-GFFN model were discussed using different error criteria and confusion matrixes. The observed 5.4% and at least 32% improvement in hybrid ICA-GFFN than GFFN and multivariate regression (MVR) demonstrated feasible and accurate enough tools that can effectively be applied for multi-objective prediction purposes. The influence of inputs on predicted outputs was also identified using two different sensitivity analyses.

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Titel: A hybridized intelligence model to improve the predictability level of strength index parameters of rocks
verfasst von: Abbas Abbaszadeh Shahri
Reza Asheghi
Mohammad Khorsand Zak
Publikationsdatum: 19.09.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05223-9

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