An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on Main Range granite
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| 008 | 210128e20151101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10064-014-0687-4 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10064-014-0687-4 | ||
| 245 | 0 | 3 | |a An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on Main Range granite |h [Elektronische Daten] |c [Danial Jahed Armaghani, Edy Tonnizam Mohamad, Ehsan Momeni, Mogana Narayanasamy, Mohd Mohd Amin] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Unconfined compressive strength |2 nationallicence | |
| 690 | 7 | |a Young's modulus |2 nationallicence | |
| 690 | 7 | |a Adaptive neuro-fuzzy inference system |2 nationallicence | |
| 690 | 7 | |a Multiple regression analysis |2 nationallicence | |
| 690 | 7 | |a Granite |2 nationallicence | |
| 700 | 1 | |a Jahed Armaghani |D Danial |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 700 | 1 | |a Tonnizam Mohamad |D Edy |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 700 | 1 | |a Momeni |D Ehsan |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 700 | 1 | |a Narayanasamy |D Mogana |u Aurecon Pty Ltd, Brisbane, Australia |4 aut | |
| 700 | 1 | |a Mohd Amin |D Mohd |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 773 | 0 | |t Bulletin of Engineering Geology and the Environment |d Springer Berlin Heidelberg |g 74/4(2015-11-01), 1301-1319 |x 1435-9529 |q 74:4<1301 |1 2015 |2 74 |o 10064 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10064-014-0687-4 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10064-014-0687-4 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Jahed Armaghani |D Danial |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Tonnizam Mohamad |D Edy |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Momeni |D Ehsan |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Narayanasamy |D Mogana |u Aurecon Pty Ltd, Brisbane, Australia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Mohd Amin |D Mohd |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Bulletin of Engineering Geology and the Environment |d Springer Berlin Heidelberg |g 74/4(2015-11-01), 1301-1319 |x 1435-9529 |q 74:4<1301 |1 2015 |2 74 |o 10064 | ||