Answer:
my explanation is above my comment :)
Explanation:
MOHR-COULOMB FAILURE CRITERIA:
In 1900, MOHR-COULOMB states Theory of Rupture in Materials which defines as “A material fails due to because of a critical combination of normal and shear stress, not from maximum normal or shear stress”. Failure Envelope is approached by a linear relationship.
If you can not understand the below symbols see the attachment below
f f ()
Where: f = Shear Stress on Failure Plane
´= Normal Stress on Failure Plane
See the graph in the attachment
For calculating the shear stress, when Normal stress, cohesion and angle of internal friction are given. Use this formula: shear stress = f c tan
Where,
• f is Shear Stress on Failure Plane
• c is Cohesion
• is Normal Total Stress on Failure Plane
• is Friction Angle
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Answer:
The answer is nearest-neighbor learning.
because nearest neighbor learning is classification algorithm.
It is used to identify the sample points that are separated into different classes and to predict that the new sample point belongs to which class.
it classify the new sample point based on the distance.
for example if there are two sample points say square and circle and we assume some center point initially for square and circle and all the other points are added to the either square or circle cluster based on the distance between sample point and center point.
while the goal of decision tree is to predict the value of the target variable by learning some rules that are inferred from the features.
In decision tree training data set is given and we need to predict output of the target variable.
Explanation: