Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: High Strength Concrete (HSC) is defined as concrete
that meets special combination of performance and uniformity
requirements that cannot be achieved routinely using conventional
constituents and normal mixing, placing, and curing procedures. It is
a highly complex material, which makes modeling its behavior a very
difficult task. This paper aimed to show possible applicability of
Neural Networks (NN) to predict the slump in High Strength
Concrete (HSC). Neural Network models is constructed, trained and
tested using the available test data of 349 different concrete mix
designs of High Strength Concrete (HSC) gathered from a particular
Ready Mix Concrete (RMC) batching plant. The most versatile
Neural Network model is selected to predict the slump in concrete.
The data used in the Neural Network models are arranged in a format
of eight input parameters that cover the Cement, Fly Ash, Sand,
Coarse Aggregate (10 mm), Coarse Aggregate (20 mm), Water,
Super-Plasticizer and Water/Binder ratio. Furthermore, to test the
accuracy for predicting slump in concrete, the final selected model is
further used to test the data of 40 different concrete mix designs of
High Strength Concrete (HSC) taken from the other batching plant.
The results are compared on the basis of error function (or
performance function).