Development of VRS empirical system for volcanic rocks

Abstract

Preliminary calculation of the geomechanical parameters of rock masses can be carried out using empirical classification systems. These systems consider, between others, the properties like the strength of the rock, density, condition and orientation of discontinuities, groundwater conditions and the stress state. For volcanic rocks, a new empiric system was developed designated VRS (Volcanic Rock System), from the adaptation of the RMR (Rock Mass Rating) system. For the VRS, geotechnical information was collected from samples from several Atlantic Ocean islands that include Madeira and Canarias archipelagos, taking also into consideration data from other different sources. The various rock types are described with particular emphasis on the Madeira Island rock formations and their geomechanical properties. The new empirical system is based on the consideration of six geological-geotechnical parameters to which relative weights are attributed. The final VRS index value, which varies between 0 and 100, is obtained through the algebraic sum of these weights. With this index, it is possible to obtain strength properties, deformability moduli, and description of the rock mass quality, as well as recommendations for excavation and support needs and support loads, using correlations with other geomechanical indices. Some representative correlations were obtained between VRS coefficients and RMR values. Correlations were obtained between deformability rock mass modulus and VRS with an exponential expression and also for each rock type. Finally, Artificial Intelligence techniques were applied to predict volcanic rock masses classes, using different algorithms, like Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Multiple Regression (MR). Considering variables from the VRS and RMR systems, a better performance is achieved using attributes from the VRS; and ANN and MR algorithms present very similar performances that are superior to the SVM.