Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations.

A Real-Time Decision Platform for the Management of Structures and Infrastructures

Merenda M.
;
Praticò F. G.;CAROTENUTO, Riccardo;DELLA CORTE, Francesco Giuseppe
2019

Abstract

Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/682
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