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This study deals with development of artificial neural network (ANN) and multiple regression (MR) models that can be employed for estimating the critical factor of safety (Fs) value of homogeneous finite slopes.
With this in view, efforts were made to develop artificial neural network (ANN) models that can be employed for estimating the soil electrical resistivity based on its soil thermal resistivity and the degree of saturation.
These values will be employed for estimating the interference levels in further analysis.
If the study area was a fault area where normal faults were mainly distributed, the maximum eigenvector of the gravity gradient tensor would be employed for estimating the fault dip.
If the study area is not too wide and prior geological information is available, the eigenvector that should be employed for estimating the fault dip correctly would be selected based on the information.
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The designed estimator consists of two parts: an H∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters.
Random-vibration based theory is employed for estimating r.m.s.
Here, a simple model incorporating effective distance and average velocity is employed for estimating commute time.
In addition, the SRK equation of state is employed for estimating thermodynamic properties.
CFD is employed for estimating the hydrodynamics and is based on the two-phase Eulerian Eulerian viewpoint.
Analysis of variance (ANOVA) was employed for estimating the main significant factors and their percentage contributions in extraction.
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