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.
The purpose of this study is to discuss the possibility of the concept of physical reservoir computing (PRC) in the field of structural health monitoring (SHM). PRC is a physical realization of a class of recurrent neural networks called reservoir computing (RC). This consists of an input layer, mutually connected network of neurons with strong nonlinearity with fixed coupling weights (referred to as reservoir), and an output layer with learnable weights. The key idea of PRC is to replace the reservoir part in RC by a specific physical entity, which has opened new possibilities of smart structures by providing a way to embed some sort of intelligence in structures. In this study, we propose to apply this framework to SHM by regarding the target structure itself as the physical reservoir. Unlike the conventional problem setting in PRC, our purpose is to detect the change occurred in the physical reservoir due to structural failure. In this paper, we propose one possible methodology to achieve this, in which the output layer is trained to learn some nonlinear function so that the increase of the error may indicate the change of the reservoir due to failure. A simple toy problem using a network of interconnected nonlinear oscillators are presented to examine the validity of the proposed method.
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