Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters
Granitic rocks are commonly used as building and ornamental stones and pavement material in various civil engineering structures. However, the weathered material should not be used for these purposes. For this reason, determination of weathering degree of the granitic rocks is one of the important issues in rock engineering and engineering geology. In literature, it is possible to find some approaches for the determination of weathering degree of granitic rocks. Additionally, some soft computing methods have been used for the determination of the weathering degree of the granitic rocks. However, in literature, the non-linear multiple regression and the adaptive neuro-fuzzy inference system have not been used for the weathering classification yet. For this reason, the purpose of this study is to apply some statistical and soft computing methods such as artificial neural networks and adaptive neuro-fuzzy inference system on the determination of weathering degree of a granitic rock selected from Turkey by using some index and mechanical properties. The study includes four main stages such as sampling, testing, modeling and assessment of the model performances. During the modeling stage, three weathering prediction models with multi-inputs are developed with two soft computing techniques such as artificial neural networks and the adaptive neuro-fuzzy inference system, and a non-linear regression technique. The general performances of models developed in this study are close; however the adaptive neuro-fuzzy inference system exhibits the best performance considering the performance index and the degree of consistency. Finally, all models developed in the present study can be used when determining the weathering degree. However, the models developed in this study should be controlled by using the data at hand, before the use them in the practical purposes. (c) 2010 Elsevier Ltd. All rights reserved.
weathering degree granite non-linear multiple regression adaptive neuro-fuzzy inference system artificial neural network