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An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on main range granite

Armaghani, Danial Jahed and Mohamad, Edy Tonnizam and Momeni, Ehsan and Narayanasamy, Mogana Sundaram and Mohd. Amin, Mohd. For (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on main range granite. Bulletin of Engineering Geology and the Environment, 74 (4). pp. 1301-1319. ISSN 1435-9529

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Official URL: http://dx.doi.org/10.1007/s10064-014-0687-4

Abstract

Engineering properties of rocks such as unconfined compressive strength (UCS) and Young’s modulus (E) are among the essential parameters for the design of tunnel excavations. Many attempts have been made to develop indirect methods of estimating UCS and E. This is generally attributed to the difficulty of preparing and conducting the aforementioned tests in a laboratory. In essence, this study aims to present two predictive models of UCS and E for granite using an adaptive neuro-fuzzy inference system (ANFIS). The required rock samples for model development (45 granite sample sets) were obtained from site investigation work at the Pahang-Selangor raw water transfer tunnel, which was excavated across the Main Range of Peninsular Malaysia. In developing the predictive models, dry density, ultrasonic velocity, quartz content and plagioclase were set as model inputs. These parameters were selected based on simple and multiple regression analyses presented in the article. However, for the sake of comparison, the prediction performances of the ANFIS models were checked against multiple regression analysis (MRA) and artificial neural network (ANN) predictive models of UCS and E. The capacity performances of the predictive models were assessed based on the value account for (VAF), root mean squared error (RMSE) and coefficient of determination (R2). It was found that the ANFIS predictive model of UCS, with R2, RMSE and VAF equal to 0.985, 6.224 and 98.455 %, respectively, outperforms the MRA and ANN models. A similar conclusion was drawn for the ANFIS predictive model of E where the values of R2, RMSE and VAF were 0.990, 3.503 and 98.968 %, respectively

Item Type:Article
Uncontrolled Keywords:unconfined compressive strength, Young’s modulus
Subjects:G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:Geoinformation and Real Estate
ID Code:57733
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:19 Apr 2017 08:50

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