Satellite remote sensing and non-destructive testing methods for transport infrastructure monitoring: advances, challenges and perspectives

Gagliardi, Valerio, Tosti, Fabio ORCID: https://orcid.org/0000-0003-0291-9937, Bianchini Ciampoli, Luca, Battagliere, Maria Libera, D'Amato, Luigi, Alani, Amir M. and Benedetto, Andrea (2023) Satellite remote sensing and non-destructive testing methods for transport infrastructure monitoring: advances, challenges and perspectives. Remote Sensing, 15 (2). p. 418.

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Abstract

High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed.

Item Type: Article
Identifier: 10.3390/rs15020418
Keywords: remote sensing; non-destructive testing (NDT); transport infrastructure monitoring; infrastructure management systems (IMSs); Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR); data fusion and integration
Subjects: Construction and engineering > Civil and structural engineering
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Depositing User: Fabio Tosti
Date Deposited: 05 Jan 2023 13:08
Last Modified: 13 Jan 2023 15:41
URI: https://repository.uwl.ac.uk/id/eprint/9710

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