عنوان مقاله [English]
In this research the variation of Marshal stability with percentage of crushed aggregates is simulated using Artificial Neural Networks (ANNs) with Levenberg-Marquardt Back Propagation (LMBP) training algorithm. To develop the model, the percentage of crushed aggregates, percentage passing through sieves 50, 20, 4, 8, 30 and 1/2 inch and percentage of asphalt content considered as network inputs and Marshal stability as network output so the number of input layer neurons is eight and the output layer neuron is one. The tangent sigmoid transfer function is selected for hidden layer neurons and linear transfer function for output layer. The inputs and outputs are normalized between -1 and 1, to improve the performance of the networks. At the first stage, the maximum generalization ability of each network with specified number of neurons (3, 5, 8, and 10) in hidden layer is determined. Comparing these maximum values reveals that the network with 8 neurons in the hidden layer has the maximum generalization ability. At the second stage, the variation of Marshal stability with percentage of crushed aggregates is simulated by applying sensitivity analysis on the network with maximum generalization ability. MATLAB 7 has been used as main software in this research. In order to collect the required data needed to design networks and evaluate the generalization ability of them, a database of 110 Asphalt concrete specimens are selected before compaction from the road surface. The specimens include Binder and Topeka with 0-19 mm gradation. The Binder Type is asphalt cement with the penetration grade 60/70. Having done the Marshal stability, extraction, percentage of crushed aggregate tests, the Marshal stability, the asphalt content, the gradation curve, the percent of crushed aggregates are derived. The optimum number of hidden layer neurons is determined based on 85 data for training and 25 data to assess the generalization ability of the networks.