A novel multistage damage detection method for trusses using time-history data based on model order reduction and deep neural network

Abstract

This article first proposes a multistage damage identification approach for trusses using time-series data relied upon model order reduction (MOR) and deep neural network (DNN). In the first step, an acceleration–displacement-based strain energy indicator (ADSEI), which is computed from the acceleration and displacement data incompletely measured at limited sensors and unmeasured ones inferred from a second-order Neumann series expansion (SNSE)-relied MOR technique, is utilized to eliminate low-risk damage candidates, aiming to only keep high-potential ones. This can dramatically reduce the number of output neurons of the DNN model in the second step. The input data employed to build such a DNN are the finite element method (FEM)-simulated acceleration and displacement signals corresponding to measured degrees of freedom (DOFs). Low-risk flawed candidates predicted by this DNN are then excluded via a suggested damage threshold. By repeating such a manner, the accuracy of the DNN models constructed in the subsequent stages is therefore enhanced continuously, although these upgraded DNNs only require a moderate dataset, simple architectures, and much less computational cost for their training and testing processes. Accordingly, both the location and severity of damaged members can be reliably and precisely diagnosed by time-series data measured in a fairly short interval at a few sensors, even with high noises. Several numerical examples of 3D trusses are tested to affirm the efficiency and feasibility of the current approach.