Department of Civil Engineering,Tarbiat Modares University
Abstract
Abstract. The Marshall Stability of asphalt concrete is one of the most important parameters in
mix design and quality control. This property depends on many factors such as gradation, percentage of
crushed aggregates, asphalt content and construction quality. In this research, the variation of Marshall
Stability with asphalt content is simulated using Arti cial Neural Networks (ANNs) with a Levenberg-
Marquardt Back Propagation (LMBP) training algorithm. The percentage of crushed aggregates; the
percentage passing through sieve numbers 200, 50, 30, 8, 4 and 1/2 inch, and the percentage of asphalt
content are considered as network inputs and Marshall Stability as the network output. In the rst stage,
the maximum generalization ability of each network with a speci ed number of neurons in the hidden layer
is determined. Comparing these maximum values reveals that the network with 8 neurons in the hidden
layer has the maximum generalization ability. In the second stage, the variation of Marshall Stability
with asphalt content is simulated by applying a sensitivity analysis to the network with the maximum
generalization ability. This simulation is in good agreement with theory.
Saffarzadeh, M., & Heidaripanah, A. (2009). Effect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks. Scientia Iranica, 16(1), -.
MLA
M. Saffarzadeh; A. Heidaripanah. "Effect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks". Scientia Iranica, 16, 1, 2009, -.
HARVARD
Saffarzadeh, M., Heidaripanah, A. (2009). 'Effect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks', Scientia Iranica, 16(1), pp. -.
VANCOUVER
Saffarzadeh, M., Heidaripanah, A. Effect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks. Scientia Iranica, 2009; 16(1): -.
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