Artificial Neural Network
An artificial neural network (ANN), usually called neural network (NN), is a
mathematical model or computational model that is inspired by the structure
and/or functional aspects of biological neural networks.
ANNs have been applied to many geotechnical engineering problems such as in
pile capacity prediction, modelling soil behaviour, site characterisation, earth
retaining structures, settlement of structures, slope stability, design of tunnels
and underground openings, liquefaction, soil permeability and hydraulic
conductivity, soil compaction, soil swelling and classification of soils.
Artificial Neural Network
Three layer neural network
Graphical presentation of neuron in ANN
There are several types of architecture of NNs. However, the two most
widely used NNs Feed forward networks and Recurrent networks. In a
feed forward network, information flows in one direction along
connecting pathways, from the input layer via the hidden layers to the
final output layer.
There is no feedback (loops) i.e., the output of any layer does not affect
that same or preceding layer. Feed-forward neural networks, where the
data ow from input to output units is strictly feedforward. The data
processing can extend over multiple (layers of) units, but no feedback
connections are present.
Black propagation network
Variable selection
Formation of training, testing and validation sets
Neural network architecture
Evaluation criteria
Neural network training
Rock Properties
Compressive strength as input
Cohesion & friction angle as output
Trained Data by ANN
Calculate the Cohesion &
Friction by input Compressive
Strength
Input the data in Software
Analysis of Slope Stability
Flow Chart to determine the properties and analysis the Slope stability by ANN
Fuzzy Inference System
Fuzzy logic is a form of many-valued logic and it deals with reasoning that is
approximate rather than fixed and exact. The nature of uncertainty in a slope
design is a very important that should considered. Fuzzy set theory was
developed specially to deal with uncertainties that are nonrandom in nature
There are several FISs that have been employed in various applications. The
most commonly used include:
Mamdani Fuzzy Model;
Takagi-Sugeno-Kang fuzzy (TSK) model;
Tsukamoto fuzzy model;
Singleton fuzzy model.