TY  - JOUR
AU  - Radovanović, Slobodan
AU  - Ranković, Vesna
AU  - Anđelković, Vladimir
AU  - Divac, Dejan
AU  - Milivojević, Nikola
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/942
AB  - Knowledge of the deformation properties of the rock mass is essential for the stress-strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes statistical models based on multiple linear regression and artificial neural networks. The models are developed using the test results of the deformation modulus obtained during the construction of the Iron Gate 1 dam on the Danube River and correlate these with measurements of the velocities of longitudinal waves and pressures in the rock mass. The parameters used for defining the models were obtained by in situ testing during dam construction, meaning that scale effects were also taken into account. For the analysis, 47 experimental results from in situ testing of the rock mass were obtained; 38 of these were used for modelling and nine were used for testing of the models. The model based on the artificial neural networks showed better performance in comparison to the model based on multiple linear regression.
PB  - Springer Verlag
T2  - Bulletin of Engineering Geology and the Environment
T1  - Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements
EP  - 1202
IS  - 3
SP  - 1191
VL  - 77
DO  - 10.1007/s10064-017-1027-2
ER  - 

@article{
author = "Radovanović, Slobodan and Ranković, Vesna and Anđelković, Vladimir and Divac, Dejan and Milivojević, Nikola",
year = "2018",
abstract = "Knowledge of the deformation properties of the rock mass is essential for the stress-strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes statistical models based on multiple linear regression and artificial neural networks. The models are developed using the test results of the deformation modulus obtained during the construction of the Iron Gate 1 dam on the Danube River and correlate these with measurements of the velocities of longitudinal waves and pressures in the rock mass. The parameters used for defining the models were obtained by in situ testing during dam construction, meaning that scale effects were also taken into account. For the analysis, 47 experimental results from in situ testing of the rock mass were obtained; 38 of these were used for modelling and nine were used for testing of the models. The model based on the artificial neural networks showed better performance in comparison to the model based on multiple linear regression.",
publisher = "Springer Verlag",
journal = "Bulletin of Engineering Geology and the Environment",
title = "Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements",
pages = "1202-1191",
number = "3",
volume = "77",
doi = "10.1007/s10064-017-1027-2"
}

Radovanović, S., Ranković, V., Anđelković, V., Divac, D.,& Milivojević, N.. (2018). Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements. in Bulletin of Engineering Geology and the Environment
Springer Verlag., 77(3), 1191-1202.
https://doi.org/10.1007/s10064-017-1027-2

Radovanović S, Ranković V, Anđelković V, Divac D, Milivojević N. Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements. in Bulletin of Engineering Geology and the Environment. 2018;77(3):1191-1202.
doi:10.1007/s10064-017-1027-2 .

Radovanović, Slobodan, Ranković, Vesna, Anđelković, Vladimir, Divac, Dejan, Milivojević, Nikola, "Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements" in Bulletin of Engineering Geology and the Environment, 77, no. 3 (2018):1191-1202,
https://doi.org/10.1007/s10064-017-1027-2 . .