Volume 9, Issue 1 (Spring & Summer 2023)
KJES 2023, 9(1): 32-61 |
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Azadmehr A, Kazemi S M, Saffarian M. Presenting relationships for estimating dynamic properties of limestone using an experimental approach. KJES 2023; 9 (1) :32-61
URL: http://gnf.khu.ac.ir/article-1-2785-en.html
URL: http://gnf.khu.ac.ir/article-1-2785-en.html
1- Birjand
2- Birjand , saffarian@birjandut.ac.ir
2- Birjand , saffarian@birjandut.ac.ir
Abstract: (576 Views)
Dynamic and static properties of the rocks are very important for designing geotechnical structures and modeling rock foundations. The main purpose of this paper is to present the regional and global relationships between the static and dynamic elasticity modulus with an experimental approach and to estimate the shear wave velocity of limestone by statistical methods and artificial neural network (ANN). For this purpose, petrographic, physical and mechanical experiments were first conducted on 80 limestone cores from the Karun 4 dam site. A database was then created using the literature data and compared with the results of this study. The results of statistical analysis show that the ratio of dynamic to static modulus of elasticity for the studied samples is 2.5. Also, the ratio of dynamic to static Poisson for these rocks was 1.41. The average value of the dynamic modulus obtained from the literature was equal to 19.90 GPa, which is less than the average value of the dynamic modulus of the present study (31.20 GPa). Due to the most accurate fit, the global relationship (R2 = 0.98, RMSE = 7.9, MAPE = 1.67) and the regional relationship (R2 = 0.96, RMSE = 5.24 MAPE = 0.91) were presented with very high accuracy between the dynamic and static modulus of elasticity. The results of artificial neural network and multivariate regression showed that estimation of shear wave velocity (Vs) based on P-wave velocity, water absorption and density is possible with high accuracy. The results showed that the ANN accuracy (R2 = 0.98, RMSE = 0.27) was higher than the multivariate linear regression (R2 = 0.86, RMSE = 0.39). The neural network also acts conservatively in predicting this variable.
Keywords: modulus of elasticity, shear wave velocity, limestone, artificial neural network, multivariate regression.
Type of Study: Original Research |
Subject:
Engineering Geology
Received: 2021/05/9 | Accepted: 2023/06/14 | Published: 2023/09/17
Received: 2021/05/9 | Accepted: 2023/06/14 | Published: 2023/09/17
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