One of the inherent modeling problems in structural engineering is
creep of quasi-brittle materials (e.g., concrete and masonry). The
creep strain represents the non-instantaneous strain that occurs
with time when the stress is sustained. Several creep models with
limited accuracy have been developed within the last few decades to
predict creep of concrete and masonry structures. The stochastic
nature of creep deformation and its reliance on a large number of
uncontrolled parameters (e.g., relative humidity, age of loading,
stress level) makes the process of prediction difficult, and yet
accurate mathematical model almost impossible. This study
investigates the potential use of Dynamic Neural Network (DNN) for
predicting creep of structural masonry. The main motive of use DNN
is that DNN could memorize the sequential or time-varying patterns
while training process. Thus, DNN becomes more capable of capturing
the time-dependent of creep deformation than the static networks.
The results showed that the developed DNN models are able to
predict the creep deformation with an excellent level of accuracy
compared with that of conventional methods and the static networks
models.
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