A combined application of two soft computing algorithms for weathering degree quantification of andesitic rocks

Kadakci Koca T., KÖKEN E.

Applied Computing and Geosciences, vol.16, 2022 (Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 16
  • Publication Date: 2022
  • Doi Number: 10.1016/j.acags.2022.100101
  • Journal Name: Applied Computing and Geosciences
  • Journal Indexes: Scopus, Directory of Open Access Journals
  • Keywords: Andesitic rocks, Artificial neural network, Explicit neural network formulation, Fuzzy inference system, Weathering degree
  • Abdullah Gül University Affiliated: Yes


© 2022Understanding 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.