H. Fattahi
Abstract
The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due ...
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The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive network-based fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFIS-subtractive clustering method (SCM) and ANFIS-fuzzy c-means clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFIS-SCM is a reliable method to predict σt with a high degree of accuracy.
Saeed Mojeddifar; Hojatollah Ranjbar; Hossain Nezamabadipour
Abstract
The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered ...
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The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. The major objective of this paper is to investigate the potential of a neuro-fuzzy system in overcoming this problem. The proposed system is applied to the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA), which hosts many areas of porphyry and vein-type copper mineralization. A software program based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed using the MATLAB ANFIS toolbox. The ANFIS program was used to classify Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data based on the spectral properties of altered and unaltered rocks. The ANFIS result was then compared with other classified images based on artificial neural networks (ANN) and the maximum likelihood classifier (MLC). The verification of the results, based on field and laboratory investigations, revealed that the ANFIS method produces a more accurate map of the distribution of alteration than that obtained using ANN or MLC.