Finite element method (FEM) have been widely used for the calculation of settlement of embankment on soft soils in the last decade. However, due to the complexity of construction, spatial inhomogeneity of soils, as well as sensitivity of numerical results to the variation of soil parameters, large discrepancy typically exists between numerical outputs and field observations. This paper presents a novel method, combining FEM and an improved back-propagation (BP) neural network, for correction of soil parameters in numerical prediction of embankment settlement. Duncan-Chang hyperbolic soil model is adopted with the sensitivity of eight constitutive parameters numerically investigated. The soil parameters with large sensitivity are identified, and together with the representative settlements, are used for the training of the improved BP neural network which, once established, generates correction factors of soil parameters for subsequent more accurate FEM forward predictions. It is demonstrated that the proposed numerical back-analysis framework is very efficient in practical engineering applications to calculate highway settlement. (c) 2007 Elsevier B.V. All rights reserved.
