Neural network modelling of asphalt adhesion determined by AFM.

This study constructs a neural network (NN) model to quantify adhesion from atomic force microscopy (AFM) data. AFM data contain five-point force-distance values. A total of 760 observations are used to build NN model. To train the network, AFM tip-sample distance data, percentage of lime, type and percentage of polymer and asphalt chemical functional groups are given as inputs and AFM force as an output. To select the NN architecture, one and two hidden layers with varying neurons are tried with 10 input nodes in the input layer and 5 output nodes in the output layer. Two hidden layers with 9 and 17 nodes in the first and second layer, respectively, show the best performance. A 10-9-17-5 NN is selected as the final structure of the NN model. Test results for the trained model show good prediction ability. The model is further applied to evaluate the effect of five different percentages of lime on the adhesion of asphalt. Results show that increase in the percentage of lime is very effective at reducing moisture damage in a styrene butadiene polymer modified asphalt sample. However, increase in lime percentage above 1.5% does not help reduce moisture damage in the styrene butadiene styrene polymer modified sample.