A combined application of two soft computing algorithms for weathering degree quantification of andesitic rocks
Özet
Understanding the variations in physical and mechanical behavior of rock materials due to progressive weathering is vital to carry on time and cost-effective engineering projects. Up to date, soft computing algorithms have
been established to quantify the weathering degree (WD) of various rocks due to better prediction performance
and problem-solving capability. However, the complexity of the weathering process does not allow the use of a
single weathering quantification model for a wide range of rock types. Therefore, this study aims to provide a
practical, quantitative, and effective framework for predicting the WD of andesitic rocks. To fulfill the aims of
this study, a wide range of cases were collected from the previous studies to establish a predictive model based on
dry unit weight (γd), effective porosity (ne), and uniaxial compressive strength (UCS). Consequently, a combined
application of fuzzy inference system (FIS) and artificial neural network (ANN) was introduced to assess the WD
of the investigated andesitic rocks. The WD ratings were presented as four different weathering classes (from
fresh (W0) to highly weathered (W3)). Since most soft computing algorithms are black-box models that cannot be
efficiently utilized in any other study, an explicit neural network formulation was firstly developed for WD
prediction in this study. As a result, the proposed formulation will provide a practical and straightforward
assessment of WD for andesitic rocks. However, to improve the reliability and consistency of the proposed model,
different datasets should be used in the explicit neural network formulation proposed
