Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

Abstract

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

Roadmap on measurement technologies for next generation structural health monitoring systems

“…Most of these studies have concentrated on cross validation of satellite-based monitoring through comparison with independent ground-based measurements for real case studies. Examples included buildings [275,276], railways [277], roadways [278,279], dams [280] and bridges [281,282]. An extensive literature review on MT-InSAR civil engineering application can be found in [279].…”

Interpretation of archived measurements within PROION project

“…The most updated works are focusing on developing sophisticated infrastructure monitoring approaches using interferometric measurements along with GIS and/or machine learning algorithms such as regression tree, support vector machine, boosted regression trees, random forest, etc. [29,30]. These newly developed methods are able to provide: a) full automation of deformation detection and/or the extraction of possible warnings and b) effective decision-making tools.…”

Research on disaster evaluation of surface deformation in highway corridors based on MT-InSAR technology

“…Macchiarulo V et al proposed a method based on the full integration of MT-InSAR data and GIS-based analysis. By automatically processing a large number of PS displacement time series and road network databases, they obtained a localized deformation analysis of abnormal differential motion between different parts of the same infrastructure [7] . Zhao Fumeng et al conducted research on the geological disaster deformation characteristics along the China-Pakistan Highway (Chinese section), the vulnerability assessment of developed landslide disasters, and the relationship between the distribution patterns of geological disasters and various environmental factors [8] .…”