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    Cerchar Abrasivity Index Estimation of Andesitic Rocks in Ecuador from Petrographical Properties Using Artificial Neural Networks

    Autor: 
    Garzón-Roca, Julio
    ;
    Torrijo, Francisco Javier
    ;
    Alonso-Pandavenes, Olegario
    ;
    Alija, Santiago
    Fecha: 
    05/2020
    Palabra clave: 
    andesitic rock; artificial neural network; cerchar abrasivity index; petrographical properties; Scopus; JCR
    Revista / editorial: 
    International Journal of Geomechanics
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/10355
    DOI: 
    https://doi.org/10.1061/(ASCE)GM.1943-5622.0001632
    Dirección web: 
    https://ascelibrary.org/doi/10.1061/%28ASCE%29GM.1943-5622.0001632
    Resumen:
    Rock abrasivity is the main factor that causes erosion of excavation tools and is usually quantified by the Cerchar Abrasivity Index (CAI). Although Cerchar abrasivity tests are easy to perform, they are time consuming and require a relatively high volume of rock samples. Having good correlations of CAI values and other faster and simpler tests is therefore of great interest, since it results in time and budget savings when controlling excavating tool wear. Based on the results of 73 andesitic rock samples coming from the central area of Ecuador, this paper presents a series of artificial neural networks developed to find a good estimation of CAI values of andesitic rocks from their petrographical properties. The network showing the best performance (R2 equal to 97%) is identified and a detailed process to estimate CAI value using the network developed is described.
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