TY  - JOUR
T1  - The Stability Analysis of Rok Walls by Artificial Markov
Chains in the Roodbar in Lorestan, Iran
AU - Mokhtar, Sina AU - Hamid Lajevardi, Sayyed AU - Yousefi Rad, Mostafa 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 9
SP  - 2361
EP  - 2366
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.2361.2366
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.2361.2366
KW  - Lorestan
KW  -roodbar
KW  -artificial Markov chains
KW  -Rock slope
KW  -network
AB  - Identifying the effective factors in evaluating the stability of stone walls is one of the main problems
in geology however there are many different methods to interpret the stability of stone gables quantitatively.
Despite the simple modeling process, conventional methods are not able to estimate the error or accuracy of
resulting model, so they are used for continuous variables. But probabilistic methods can quantify and estimate
the possibility of accuracy of the model, also examine the value of each piece of information in increasing the
accuracy of the model. Markov chain can be used as a powerful way to analyze the stone walls based on
conditional probabilities and providing the states transition matrix. Since this method is better utilized for
discrete variables in lithology, the aim of this study is to provide more accurate results and simpler geological
interpretation. In the study, it is investigated 11 intrinsic and geometric parameters influencing on the walls of
Roodbar dam located in a distance of about 100 km from Southern Aligudarz, Lorestan Province, Iran. The
factors were identified by using the available data and their analysis and complementation by field records.
Then, the effect of each resistance parameters on the stability was examined by MATLAB7.1 Software in order
to analyze the artificial Markov chains. The results indicate that the slopes of the area in a dry state are stable
at 17 modes and unstable at 3 modes and in a saturated state are stable at 14 modes and unstable at 6 modes
and have generally a little stability. The study indicates that the network has the ability to predict the degree
of stability of rock slopes.
ER  -