MODELO DE REDE NEURAL ARTIFICIAL PARA PREVISÃO DO COMPORTAMENTO CISALHANTE DE DESCONTINUIDADES ROCHOSAS

Resumo

The artificial intelligence (AI) has been widely used in engineering due to its capacity to
interpret and process complex information. One of these AI technologies is the
artificial neural network (ANN) which is based on the functioning of the human central
nervous system and its ability to learn and recognize patterns. This work aims to present
an ANN model capable of predicting the shear strength of rock discontinuities. The shear
strength of rock discontinuity is one of the most important factors governing the mechanical
behavior of rock mass whose definition sometimes requires expensive laboratory
procedures not always available. Moreover, the existing analytical models have several
limitations regarding not consider all the variables which influence the shear strength
of rock joints or needing shear testes. Therefore, nine ANN architectures were tested considering the
following inputs: the normal boundary stiffness, the ratio between infill thickness and asperity
amplitude, the initial normal stress, the joint roughness coefficient, uniaxial rock compressive strength,
infill friction angle, and the horizontal displacement. As outputs the shear strength and dilation of rock
discontinuities. The architecture with the best performance is the 7-20-10-2 and with 500,000 iterations
with a correlation of 99% for training data and 96% in the validation data. The results show a nice fitting
for the ANN model output data with experimental data. As the analytical models made so far for infilled
joints are only capable of predicting the peak shear strength, the ANN comes with a handful tool for
predicting shear strength with velocity and low cost.