Making a brain on a structure: a conceptual study of elastic wave field neural networks for structural health monitoring

Abstract

The purpose of this study is to develop a novel concept of smart structural systems that can recognize their own structural integrity by an embodied high density sensor network. Over the past two decades, sensor networks for automatic inspection application have been intensively investigated, and it has now become reasonable to deploy over 1000 sensor nodes in a single structural system. It would be certain, however, that the current approaches that require rich electronics and wireless communication at each sensor node will reach its limit due to huge amount of data overwhelming the network capacity and centralized computing resources. In this study, we propose a new approach to make a breakthrough in both communication and computation for such high density sensor networks of the next generation. In our approach, a number of sensor nodes with simple functions are embedded in the structure, each of which reacts to the elastic waves propagating through the structure by applying a force to the structure after a simple nonlinear transformation. This allows the whole nodes to be mutually coupled through the medium of elastic waves, forming a neural network that incorporates the dynamic characteristics of the structure as the coupling weights. In this paper, we present a possible realization of our concept with basic formulations, and present numerical simulations to examine how the proposed network behaves under a single frequency input. It is presented that the network exhibits a bifurcation in its asymptotic behavior from modulated response to steady-state depending on the structural conditions.