Application of Neural Network to Compaction Parameters in Pavement Base

The semi-rigid material of lime stabilized industry wastes is commonly used in pavement base of high-grade highway, which contains certain aggregate or none aggregate according to the requirements of standard and design. Generally, the indoor heavy compaction test is not only labor-consuming, but also unable to achieve the precise compaction parameters of the semi-rigid mixture when the aggregate is over 50%. Based on the known compaction parameters of the binder without aggregate and combined with artificial neural network theory, the simulating network model of this type of semi-rigid material's compaction parameters was created by BP network in Matlab. Through large amounts of training, adiustment of training function and transfer function and normalization of initial input data, eventually the 6→15→2 network was established. The results simulated by the network model are correct and stable, which show certain practical application values.