Aladejare, A.E., Ozoji, T., Lawal, A.I. et al. Soft Computing-Based Models for Predicting the Characteristic Impedance of Igneous Rock from Their Physico-mechanical Properties. Rock Mech Rock Eng 55, 4291–4304 (2022). https://doi.org/10.1007/s00603-022-02836-5
Soft computing-based models for predicting the characteristic impedance of igneous rock from their physico-mechanical properties
|Author:||Aladejare, Adeyemi Emman1; Ozoji, Toochukwu1; Lawal, Abiodun Ismail2;|
1Oulu Mining School, University of Oulu, Oulu, Finland
2Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
|Online Access:||PDF Full Text (PDF, 2.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022091559174
|Publish Date:|| 2022-09-15
Rock properties are important for design of surface and underground mines as well as civil engineering projects. Among important rock properties is the characteristic impedance of rock. Characteristic impedance plays a crucial role in solving problems of shock waves in mining engineering. The characteristics impedance of rock has been related with other rock properties in literature. However, the regression models between characteristic impedance and other rock properties in literature do not consider the variabilities in rock properties and their characterizations. Therefore, this study proposed two soft computing models [i.e., artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)] for better predictions of characteristic impedance of igneous rocks. The performances of the proposed models were statistically evaluated, and they were found to satisfactorily predict characteristic impedance with very strong statistical indices. In addition, multiple linear regression (MLR) was developed and compared with the ANN and ANFIS models. ANN model has the best performance, followed by ANFIS model and lastly MLR model. The models have Pearson’s correlation coefficients of close to 1, indicating that the proposed models can be used to predict characteristic impedance of igneous rocks.
Rock mechanics and rock engineering
|Pages:||4291 - 4304|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
The work described in this paper was supported by grants from the K.H. Renlund’s Foundation, Finland (Project No. at University of Oulu: 24303153 and 24303809). The financial supports are gratefully acknowledged. Open Access funding provided by University of Oulu including Oulu University Hospital.
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