پیش بینی رفتار تنش_کرنش مصالح شنی با استفاده از شبکه های عصبی مصنوعی

Prediction of stress-strain behavior of geotechnical material is one of the major efforts of engineers
and researchers in the field of geomechanics. Experimental tests like tri-axial shear strength tests are
the most effective apparatus to prepare the mechanical characteristics of gravelly material; but due to
difficulties in preparing test samples and costs of the tests, only several tests will be done in a new
project. Artificial neural network is a kind of method, in which engineer could judge the results based
on numerous data from other similar projects, which enable the engineer to have a good judgment on
the material properties.
In this research, the behavior of gravelly material was simulated by use of multi-layer perceptron
neural network, which is the most useful kind of artificial neural networks in the field of geotechnical
engineering. For instance, first exact information was provided from laboratory tests of various barrow
areas of embankment dams in the country and effective parameters on shear strength of coarse-grained
material were studied. After omitting incorrect or weak data, 95, 20 and 23 sets of data were used for
learning, testing and evaluating data, respectively. Input parameters for the model were as follows:
particle-size distribution curve, dry density, relative density, Los-angles abrasion percent, confining
pressure, axial strain; and outputs were selected as deviator stress. In order to reach a steady state in
the model and force the model to behave homogenous to the all inputs, data was normalized to the
value between .05 and 0.95. In the simulation, back-propagation algorithm was used for learning or
error reduction. The aim of the simulations was defined to reduce error between real data and
predicted values; for instance root mean square error (RMS) was used to be minimized through
simulation and predicted versus real graphs were used to observe the global error of the model. After
modeling the data based on some criteria, it was shown that curves of stress-strain from simulation
tests were in good agreement with those from laboratory. These close coherencies were observed in all
training, testing and evaluation data, in which the RMS errors were 0.038, 0.037 and 0.026,
respectively. To reach this ultimate step, a 10*19*1 multilayer perceptron was used via trial and error.
In order to determine quality and quantity of the effect of inputs on outputs, and prove that the results
were in good agreement with soil mechanic principles, sensitivity analyses were done on the average
data of the inputs. Results show that confine pressure, uniformity coefficient and relative density of
the material were the most effective parameters on the stress-strain curves; thus the model has enough
capability to predict the stress-strain behavior of gravelly soils.