Iterative spatial compressive sensing strategy for structural damage diagnosis as a BIG DATA problem

Abstract

Accurate structural damage identification calls for dense sensor networks, which are becoming more feasible as the price of electronic sensing systems reduces. To transmit and process data from all nodes of a dense network would be an onerous task which creates a BIG DATA problem; therefore scalable algorithms are needed so that decision on the current state of the structure can be made based on efficient data processing. In this paper, an iterative spatial compressive sensing scheme for damage existence identification and localization will be introduced. At each iteration, a subset of sensors is selected for data transmission and relevant information will be extracted at central station for damage existence identification/localization. This information will also provide useful guidance in future selection of sensing locations. The devised algorithm is applied to identify damage in a simulated gusset plate.