Asset Details
Do you wish to reserve the book?
Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
Do you wish to request the book?
Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances
2016
Overview
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 like R n, p-wave and I 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 the R 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.
Publisher
Springer Nature B.V
