Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/123456789/19412
Title: Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties
Authors: SHARMA, LK
VISHAL, V
SINGH, TN
Keywords: Uniaxial Compressive Strength
Inference System
Slake-Durability
Wave Velocity
Schmidt Hardness
Granitic-Rocks
Modulus
Index
Elasticity
Turkey
Issue Date: 2017
Publisher: ELSEVIER SCI LTD
Citation: MEASUREMENT,102,158-169
Abstract: This research study was conducted to predict the unconfined compressive strength (UCS) of the rocks by applying the adaptive neuro-fuzzy inference system (ANFIS), and the outcomes were compared with the traditional statistical model of multiple regression (MR) analysis and artificial neural network (ANN). 13 types of rock samples collected from 5 geological horizons in India were tested in the laboratory as per the International Society for Rock Mechanics (ISRM) standards. In developing the predictive models, ultrasonic P-wave velocity, density and slake durability index were considered as model inputs, whereas UCS was the output parameter. The prediction performance of ANFIS model was checked against the MR and the ANN predictive models. It was found that the constructed ANFIS model exhibited relatively high prediction performance of UCS than the MR and the ANN models. The performance capacity of the predictive models were evaluated based on the coefficient of determination (R-2), the mean absolute percentage error (MAPE), the root mean square error (RMSE) and the variance account for (VAF). The ANFIS predictive model had R2, MAPE, RMSE and VAF equal to 0.978, 10.15%, 6.29 and 97.66%, respectively, superseding the performance of the MR and the ANN models. The performance comparison revealed that soft computing is a good approach for minimizing the uncertainties and inconsistency of correlations in geotechnical engineering. (C) 2017 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.measurement.2017.01.043
http://localhost:8080/xmlui/handle/123456789/19412
ISSN: 0263-2241
1873-412X
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