A novel self-powered approach for structural health monitoring

This computational simulation study presents an innovative approach for structural damage detection in “smart” civil infrastructure systems. The proposed approach is predicated upon the utilization of the compressed data stored in memory chips of a newly developed self-powered wireless sensor. An efficient data interpretation system, integrating aspects of the finite element method (FEM) and probabilistic neural networks (PNN) based on Bayesian decision theory, is developed for damage detection. Several features extracted from the cumulative limited static strain data are used as damage indicator variables. The efficiency of the method is tested and evaluated for the complicated case of a bridge gusset plate. The gusset plate structure is analysed via 3D FE models. A general scheme is presented for finding the optimal number of data acquisition points (sensors) on the structure and the associated optimal locations, taking into account the influence of sensor sparsity and the level of data corruption due to noise.