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.

This paper reports the three − year practice of " Kawakudari ( down − the − river )Internship Program " in Kyoto Institute of Technology ． This cooperative education program involves five manufacturers and small and medium fabrication companjes in Kyoto − Osaka area， offering a halfLyear course of ' ℃ ooperative Practicum for Applied Design and Manufacturing " to undergraduate stUdents ． The outline of the program and the measurement of its educationa 且effects are presented ．

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