NDLI: An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

Content Provider	Springer Nature Link
Author	Jahed Armaghani, Danial Tonnizam Mohamad, Edy Momeni, Ehsan Narayanasamy, Mogana Sundaram Mohd Amin, Mohd For
Copyright Year	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, outperforms 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.
Starting Page	1301
Ending Page	1319
Page Count	19
File Format	PDF
ISSN	14359529
Journal	Bulletin of the International Association of Engineering Geology - Bulletin de l'Association Internationale de Géologie de l'Ingénieur
Volume Number	74
Issue Number	4
e-ISSN	14359537
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-10-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Unconfined compressive strength Young’s modulus Adaptive neuro-fuzzy inference system Multiple regression analysis Granite Geotechnical Engineering & Applied Earth Sciences Geoengineering, Foundations, Hydraulics Geoecology/Natural Processes Nature Conservation
Content Type	Text
Resource Type	Article
Subject	Geology Geotechnical Engineering and Engineering Geology

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