Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite Using Multivariate Statistics and Machine Learning Techniques

Mohamed Elgharib Gomah, Guichen Li (), Naseer Muhammad Khan, Changlun Sun, Jiahui Xu, Ahmed A. Omar, B. G. Mousa, Marzouk Mohamed Aly Abdelhamid and M. M. Zaki
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Mohamed Elgharib Gomah: Key Laboratory of Deep Coal Resource Mining, School of Mines, China University of Mining and Technology, Ministry of Education of China, Xuzhou 221116, China
Guichen Li: Key Laboratory of Deep Coal Resource Mining, School of Mines, China University of Mining and Technology, Ministry of Education of China, Xuzhou 221116, China
Naseer Muhammad Khan: Department of Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, Risalpur 23200, Pakistan
Changlun Sun: Key Laboratory of Deep Coal Resource Mining, School of Mines, China University of Mining and Technology, Ministry of Education of China, Xuzhou 221116, China
Jiahui Xu: Key Laboratory of Deep Coal Resource Mining, School of Mines, China University of Mining and Technology, Ministry of Education of China, Xuzhou 221116, China
Ahmed A. Omar: Housing and Building National Research Center, Cairo 12622, Egypt
B. G. Mousa: Department of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt
Marzouk Mohamed Aly Abdelhamid: Department of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt
M. M. Zaki: Department of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, Egypt

Mathematics, 2022, vol. 10, issue 23, 1-21

Abstract: The mechanical properties of rocks, such as uniaxial compressive strength and elastic modulus of intact rock, must be determined before any engineering project by employing lab or in situ tests. However, there are some circumstances where it is impossible to prepare the necessary specimens after exposure to high temperatures. Therefore, the propensity to estimate the destructive parameters of thermally heated rocks based on non-destructive factors is a helpful research field. Egyptian granodiorite samples were heated to temperatures of up to 800 °C before being treated to two different cooling methods: via the oven (slow-cooling) and using water (rapid cooling). The cooling condition, temperature, mass, porosity, absorption, dry density (D), and P-waves were used as input parameters in the predictive models for the UCS and E of thermally treated Egyptian granodiorite. Multi-linear regression (MLR), random forest (RF), k-nearest neighbor (KNN), and artificial neural networks (ANNs) were used to create predictive models. The performance of each prediction model was also evaluated using the (R 2 ), (RMSE), (MAPE), and (VAF). The findings revealed that cooling methods and mass as input parameters to predict UCS and E have a minor impact on prediction models. In contrast, the other parameters had a good relationship with UCS and E. Due to severe damage to granodiorite samples, many input and output parameters were impossible to measure after 600 °C. The prediction models were thus developed up to this threshold temperature. Furthermore, the comparative analysis of predictive models demonstrated that the ANN pattern for predicting the UCS and E is the most accurate model, with R 2 of 0.99, MAPE of 0.25%, VAF of 97.22%, and RMSE of 2.04.

Keywords: Egyptian granodiorite; thermal treatments; predictive models; multivariate statistics; machine learning techniques (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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