The aim of this paper is to present a monitoring, system for the built environment based on electrical impedance sensors, together with the development of an early warning system to support decision-making processes in a seismic context. In particular, preliminary data were collected on mortar specimens embedding stainless-steel electrodes for the periodic measurement of electrical impedance. Hence, these data were exploited to train a Neural Prophet-based deep learning model for the prediction of the electrical impedance module. Indeed, this quantity can provide a lot of information about the health status of the monitored structures. The results can be exploited for the development of an early warning system supporting decision-making strategies for the building management. The model can predict the trend of electrical impedance with acceptable accuracy (MAPE <2%); hence, the monitoring platform can provide information suitable for the development of an early warning system.

A monitoring platform for the built environment: towards the development of an early warning system in a seismic context / Mancini, Adriano; Violini, Luca; Blasi, Elisa; Cosoli, Gloria; Pandarese, Giuseppe; Tittarelli, Francesca; Mobili, Alessandra; Galdelli, Alessandro; Revel, Gian Marco. - (2023), pp. 1-5. (Intervento presentato al convegno 2023 IEEE International Workshop on Metrology for Living Environment tenutosi a Milano nel May 29-31, 2023).

A monitoring platform for the built environment: towards the development of an early warning system in a seismic context

Adriano Mancini;Luca Violini;Elisa Blasi;Gloria Cosoli;Giuseppe Pandarese;Francesca Tittarelli;Alessandra Mobili;Alessandro Galdelli;Gian Marco Revel
2023-01-01

Abstract

The aim of this paper is to present a monitoring, system for the built environment based on electrical impedance sensors, together with the development of an early warning system to support decision-making processes in a seismic context. In particular, preliminary data were collected on mortar specimens embedding stainless-steel electrodes for the periodic measurement of electrical impedance. Hence, these data were exploited to train a Neural Prophet-based deep learning model for the prediction of the electrical impedance module. Indeed, this quantity can provide a lot of information about the health status of the monitored structures. The results can be exploited for the development of an early warning system supporting decision-making strategies for the building management. The model can predict the trend of electrical impedance with acceptable accuracy (MAPE <2%); hence, the monitoring platform can provide information suitable for the development of an early warning system.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/317213
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