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, A., Violini, L., Blasi, E., Cosoli, G., Pandarese, G., Tittarelli, F., Mobili, A., Galdelli, A., Revel, G.M.. - (2023), pp. 1-5. (2023 IEEE International Workshop on Metrology for Living Environment Milano May 29-31, 2023) [10.1109/MetroLivEnv56897.2023.10164043].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
