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Amiri, Mojtaba, Amiri, Mehrdad, Karrari, Seyed Sajjad, Moradi, Siamak. (1403). Predicting uniaxial compressive strength of different rocks using principal component analysis and deep neural network. , (), -. doi: 10.22077/jgm.2025.8451.1041
Mojtaba Amiri; Mehrdad Amiri; Seyed Sajjad Karrari; Siamak Moradi. "Predicting uniaxial compressive strength of different rocks using principal component analysis and deep neural network". , , , 1403, -. doi: 10.22077/jgm.2025.8451.1041
Amiri, Mojtaba, Amiri, Mehrdad, Karrari, Seyed Sajjad, Moradi, Siamak. (1403). 'Predicting uniaxial compressive strength of different rocks using principal component analysis and deep neural network', , (), pp. -. doi: 10.22077/jgm.2025.8451.1041
Amiri, Mojtaba, Amiri, Mehrdad, Karrari, Seyed Sajjad, Moradi, Siamak. Predicting uniaxial compressive strength of different rocks using principal component analysis and deep neural network. , 1403; (): -. doi: 10.22077/jgm.2025.8451.1041

Predicting uniaxial compressive strength of different rocks using principal component analysis and deep neural network

Journal of Geomine
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 بهمن 1403
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22077/jgm.2025.8451.1041
نویسندگان
Mojtaba Amiri1؛ Mehrdad Amiri* 2؛ Seyed Sajjad Karrari3؛ Siamak Moradi4
1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2Department of Geology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran.
3-Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Hamedan, Iran, - Omranazma conculting company
4Department of Geology, Faculty of Sciences, Kharazmi University, Tehran, Iran
چکیده
Uniaxial compressive strength (UCS) is one of the most practical parameters of rock mechanics. It is an important and basic geomechanical factor in the design of tunnels, dams, and underground drilling. The direct method for determining the UCS in the laboratory is expensive and time-consuming. Therefore, several empirical equations have been developed to estimate the UCS from the results of index and physical tests of rock. Nevertheless, numerous empirical models available in the literature often make it difficult for mining engineers to decide which empirical equation provides the most reliable estimate of UCS. The aim of this work is to estimate the UCS of rocks using a machine learning-based approach. More specifically, a deep neural networks (DNN) model is designed to predict the UCS from the physical and mechanical characteristics of rocks. 221 different rock block samples were collected from various areas of Iran. The physical and mechanical properties include Dry density (ρ), P-wave velocity (V_p), Point load test (〖Is〗_50), Brazilian tensile strength (BTS), and water absorption (I_v). In order to reduce the dimension of the input features, before the DNN model, principal component analysis (PCA) is employed. Combination of the PCA and the proposed DNN model is found to be efficient and useful in predicting UCS. The mean square error of the proposed method with and without the feature reduction stage was 0.0068 ± 0.001 and 0.0067 ± 0.013, respectively.
کلیدواژه‌ها
Physical properties؛ Mechanical properties؛ Uniaxial compressive strength (UCS)؛ Deep neural network (DNN)
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تعداد مشاهده مقاله: 85


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