Notice of RetractionA neural network model for deformation prediction of deep foundation pit based on multivariate phase space reconstruction

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

In civil engineering, synthesized deformation prediction of deep foundation pit is a very complicated problem. For it belongs to a multi-variable nonlinear system evolution with time-varying, an enhanced back propagation (BP) neural network model based on multi-variable phase-space reconstruction has been proposed. By the various time series time delay and embedding dimension determined respectively in this model, the multi-variable series of excavation deformation for deep foundation pit has been done in the first phase space reconstruction. The neural network input extraction by the use of partial least squares regression method can be the strongest impact components. Finally non-linear fitting between the various components has been completed via the enhanced BP neural network model. With practical application for deformation prediction of deep foundation pit, the method´s effectiveness has been verified. In addition, compared with the traditional BP network, enhanced BP neural network has better convergence and accuracy.