ORIGINAL PAPER An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite Danial Jahed Armaghani • Edy Tonnizam Mohamad • Ehsan Momeni • Mogana Sundaram Narayanasamy • Mohd For Mohd Amin Received: 18 June 2014 / Accepted: 7 October 2014 Ó Springer-Verlag Berlin Heidelberg 2014 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 (R 2 ). It was found that the ANFIS predictive model of UCS, with R 2 , RMSE and VAF equal to 0.985, 6.224 and 98.455 %, respectively, outper- forms the MRA and ANN models. A similar conclusion was drawn for the ANFIS predictive model of E where the values of R 2 , RMSE and VAF were 0.990, 3.503 and 98.968 %, respectively. Keywords Unconfined compressive strength  Young’s modulus  Adaptive neuro-fuzzy inference system  Multiple regression analysis  Granite Introduction Proper determination of unconfined compressive strength (UCS) and Young’s modulus (E) of rocks is of crucial importance in the design of geotechnical engineering structures such as dams and tunnels. The latter is a key parameter in deformation analysis and the former gives good estimation of the rock bearing capacity. In other words, inappropriate estimation of the aforementioned rock parameters, i.e. UCS and E, could be catastrophic as it can lead to underestimation of the ultimate bearing capacity as well as the load corresponding to an allowable settlement for a problem of interest. The unconfined compression test is a laboratory test normally used to determine elastic modulus and strength of the rock. The test is conducted using standard procedures such as those of the International Society for Rock Mechanics (ISRM). However, there are D. Jahed Armaghani (&)  E. Tonnizam Mohamad  E. Momeni  M. F. Mohd Amin Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia e-mail: danialarmaghani@yahoo.com E. Tonnizam Mohamad e-mail: edy@utm.my E. Momeni e-mail: mehsan23@live.utm.my M. F. Mohd Amin e-mail: mohdfor@utm.my M. S. Narayanasamy Aurecon Pty Ltd, Brisbane, Australia e-mail: mogana.sundaram@aurecongroup.com 123 Bull Eng Geol Environ DOI 10.1007/s10064-014-0687-4