Displacement back analysis of deep foundation pit for metro based on genetic algorithm and neural network

Aiming at subsistent limitation in diversified displacement back analysis methods, an approach based on neural network and genetic algorithm for displacements back analysis of deep foundation pit for metro is proposed. This approach utilizes nonlinearity of neural network and whole random search capability of genetic algorithm. It can search the best appropriate weight and framework of neural network by using whole searching characteristic of genetic algorithm, which formerly depends on gradient information to adjust weight of network. The proposed approach has been used to carry out inverse calculation for soil mechanical parameters of deep foundation pit for metro. A case is conducted using the software developed in the paper. The result shows: considering measured deformation value as input data and taking soil dynamic parameter as output data of neural network, the approach can rapidly get a stable and accurate solution within a relatively large solution space; and the approach is superior to current back analysis approaches.