Elastic Wave Field Neural Networks for Structural Health Monitoring: An Analytical and Numerical Study of Multiple Neurons

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Arata Masuda Konosuke Takashima

Abstract

The purpose of this study is to develop a novel concept of smart structural systems recognizing their own structural integrity by an embodied high density sensor network. In our concept, a number of sensor nodes are embedded in the host structure, each of which reacts point-wise to the structural vibration with a simple rule. This allows the whole nodes to be mutually coupled through the elastic field, forming a neural network that incorporates the dynamic characteristics of the host structure as the coupling weights. In the previous study, we presented that a single-neuron network as its minimum configuration can exhibit a bifurcation of its dynamics behavior, which can be used to detect the change of the network due to damages. In this study, the formulation of networks with multiple neurons deployed in a structure with single-mode approximation is presented particularly focusing on the bi- furcation analysis to reveal how the behavior of the network is drastically altered depending of the network and structural parameters. Numerical analysis is conducted to examine the validity of the bifurcation analysis.

Abstract 83 | PDF Downloads 94

##plugins.themes.bootstrap3.article.details##

Keywords

sensor network, neural network, physical reservoir computing, elastic wave field, nonlinear dynamics

References
Abdulkarem, M., Samsudin, K., Rokhani, F. Z., & A Rasid, M. F. (2019). Wireless sensor network for structural health monitoring: A contemporary review of tech- nologies, challenges, and future direction. Structural Health Monitoring. doi: 10.1177/1475921719854528

Bassey, J., Qian, L., & Li, X. (2021). A survey of complex- valued neural networks. arXiv. doi: 10.48550/ ARXIV. 2101.12249

Chen, S., Zhang, Q., & Wang, C. (2004). Exis- tence and stability of equilibria of the continuous- time hopfield neural network. Journal of Computa- tional and Applied Mathematics, 169(1), 117-125. doi: https://doi.org/10.1016/j.cam.2003.11.014

Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81(10), 3088-3092. doi: 10.1073/pnas.81.10.3088

Masuda, A., Sakai, R., & Takashima, K. (2023). Making a brain on a structure: a conceptual study of elastic wave field neural networks for structural health monitoring. In J. Yang, G. Huang, M. A. Nouh, S. Shahab, & S. Tol (Eds.), Active and passive smart structures and inte- grated systems xvii (Vol. 12483, p. 124831J). SPIE. doi: 10.1117/12.2658694

Masuda, A., Takashima, K., & Sakai, R. (2023). Physi- cal reservoir-based structural health monitoring: a pre- liminary study. In J. Yang, G. Huang, M. A. Nouh, S. Shahab, & S. Tol (Eds.), Active and passive smart structures and integrated systems xvii (Vol. 12483, p. 1248311). SPIE. doi: 10.1117/12.2658742
Section
Regular Session Papers