Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures

Abstract

An improved neuro-wavelet modeling (NWM) methodology is presented, and it aims at improving prediction precision of time-varying behavior of engineering structures. The proposed methodology distinguishes from the existing NWM methodology by featuring the distinctive capabilities of constructing optimally uncoupled dynamic subsystems in light of the redundant Haar wavelet transform (RHWT) and optimizing neural network. In particular, two techniques of imitating wavelet packet transform of RHWT and reconstructing the major crests of power spectrum of analyzed data are developed with the aim of constructing the optimally uncoupled dynamic subsystems from time-varying data. The resulting uncoupled dynamic subsystems make the underlying dynamic law of time-varying behavior more tractable than the raw scale subwaves arose from the RHWT, and they provide a platform for multiscale modeling via individual modeling at the uncoupled dynamic subsystem level. Furthermore, on each uncoupled dynamic subsystem, the technique of optimal brain surgeon in conjunction with a new dynamic mechanism refreshing is employed to optimize the neural network, and the recombination of the modeling outcomes on every subsystem constitutes the overall modeling of time-varying behavior. The improved NMW methodology offers a feasible framework of multiscale modeling due to its flexibility, adaptability and rationality, and it is particularly useful for prediction applications of time-varying behavior of engineering structures. As an illustrative example, the improved NWM methodology is applied to model and forecast dam deformation, and the results show that the methodology possesses positive advantages over the existing multiscale and single scale modeling techniques. The improved NMW methodology is promising and valuable for the safety monitoring and extreme event warning of engineering structures.