As part of the LIST-PORT, Report and Decibel Project (Interreg IT-FR Marittimo Programme 2014–2020), which is included in a cluster of initiatives aimed at containing port noise, a synchronized traffic - noise survey campaign involving four ports in as many cities in the upper Tyrrhenian area, is now nearing completion. This paper describes the guidelines on the basis of which the traffic-noise surveys were conducted in the four ports and the types of data collected. In a second step of the study, these data will be used to train the predictive model of the sound pressures generated by traffic in ports, based on neural networks. The database presented in this work is thus the key element for the subsequent implementation of the predictive model which is currently in an advanced phase of development as part of another project in the cluster.

Processes for Noise Reduction in Urban Port Fronts

Federico Sollai;Roberto Baccoli;Andrea Medda;Gianfranco Fancello;Patrizia Serra;Paolo Fadda
2020-01-01

Abstract

As part of the LIST-PORT, Report and Decibel Project (Interreg IT-FR Marittimo Programme 2014–2020), which is included in a cluster of initiatives aimed at containing port noise, a synchronized traffic - noise survey campaign involving four ports in as many cities in the upper Tyrrhenian area, is now nearing completion. This paper describes the guidelines on the basis of which the traffic-noise surveys were conducted in the four ports and the types of data collected. In a second step of the study, these data will be used to train the predictive model of the sound pressures generated by traffic in ports, based on neural networks. The database presented in this work is thus the key element for the subsequent implementation of the predictive model which is currently in an advanced phase of development as part of another project in the cluster.
2020
978-3-030-58819-9
978-3-030-58820-5
Noise reduction; Urban port fronts; Artificial neural networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/297460
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