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 (Is(50)), Schmidt hammer (Rn) and p-wave velocity (Vp) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like Rn, p-wave and Is(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 (R2), 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 the R2 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.
| Original language | English |
|---|---|
| Pages (from-to) | 189-206 |
| Number of pages | 18 |
| Journal | Engineering with Computers |
| Volume | 32 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 1 2016 |
| Externally published | Yes |
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Keywords
- Adaptive neuro-fuzzy inference system
- Artificial neural network
- Granite
- Non-linear multiple regression
- Uniaxial compressive strength
ASJC Scopus subject areas
- Software
- Modelling and Simulation
- Engineering(all)
- Computer Science Applications
Cite this
Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. / Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Hajihassani, Mohsen; Yagiz, Saffet; Motaghedi, Hossein.
In: Engineering with Computers, Vol. 32, No. 2, 01.04.2016, p. 189-206.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
AU - Jahed Armaghani, Danial
AU - Tonnizam Mohamad, Edy
AU - Hajihassani, Mohsen
AU - Yagiz, Saffet
AU - Motaghedi, Hossein
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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 (Is(50)), Schmidt hammer (Rn) and p-wave velocity (Vp) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like Rn, p-wave and Is(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 (R2), 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 the R2 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.
AB - 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 (Is(50)), Schmidt hammer (Rn) and p-wave velocity (Vp) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like Rn, p-wave and Is(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 (R2), 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 the R2 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.
KW - Adaptive neuro-fuzzy inference system
KW - Artificial neural network
KW - Granite
KW - Non-linear multiple regression
KW - Uniaxial compressive strength
UR - http://www.scopus.com/inward/record.url?scp=84961061738&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961061738&partnerID=8YFLogxK
U2 - 10.1007/s00366-015-0410-5
DO - 10.1007/s00366-015-0410-5
M3 - Article
VL - 32
SP - 189
EP - 206
JO - Engineering with Computers
JF - Engineering with Computers
SN - 0177-0667
IS - 2
ER -