期刊
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
卷 48, 期 8, 页码 1224-1234出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijrmms.2011.09.017
关键词
Deformation modulus of rock mass; Fuzzy c-means (FCM) clustering; Subtractive clustering; ANFIS
The paper describes a method which incorporates Takagi-Sugeno (TS) fuzzy modeling with two data clustering approaches including fuzzy-c-means (FCM) clustering and subtractive clustering to estimate the rock mass modulus of deformation. For this aim, a database including 120 cases collected from several galleries of dam sites locations was established. The information returned by fuzzy clustering was initially used to define the number of rules and antecedent membership functions and afterwards linear least squares estimation implemented to obtain fuzzy consequent parameters. An adaptive neuro-fuzzy inference system (ANFIS) was applied to modify the pre-determined TS clustering-based model structures to improve the generalization performance of those. For evaluation of the performance, root mean square error (RMSE) and variance account for (VAF) values have been utilized as performance criteria. It can be said, that ANFIS approach enhances the performances of fuzzy clustering-based models in predicting modulus of deformation of rock masses successfully. (C) 2011 Elsevier Ltd. All rights reserved.
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