Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. (April 2016)

• Record Type: Journal Article
• Authors: Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Hajihassani, Mohsen; Yagiz, Saffet; Motaghedi, Hossein
• Is Part Of: Engineering with computers. Volume 32:Number 2(2016)
• Page Start: 189
• Page End: 206
• Publication Date: 2016-04
• Subjects: Uniaxial compressive strength -- Granite -- Non-linear multiple regression -- Artificial neural network -- Adaptive neuro-fuzzy inference system; Engineering design -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Conception technique -- Informatique -- Périodiques; Conception assistée par ordinateur -- Périodiques; Electronic journals; 620.00285
• DOI: 10.1007/s00366-015-0410-5
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (I s(50) ), Schmidt hammer (R n ) and p-wave velocity (V p ) tests. To estimate the UCS of granitic rock as a function of relevant rock properties likeR n, p-wave andI s(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R 2 ), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that theR 2 equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.