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2024 | OriginalPaper | Chapter

1. Pioneering Remote Sensing in Structural Health Monitoring

Authors : Alireza Entezami, Bahareh Behkamal, Carlo De Michele

Published in: Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning

Publisher: Springer Nature Switzerland

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Abstract

Recently, long-term structural health monitoring (SHM) of civil structures by using the technology of remote sensing has received increasing attention by civil engineers. This is because such technology can facilitate SHM by introducing useful products such as synthetic aperture radar (SAR) images acquired from some satellites suitable for monitoring of large-scale civil structures in wide areas. In contrast to conventional contact-based and next-generation vision-based sensors, a long-term monitoring program via space borne remote sensing cannot provide high-dimensional structural responses. On this basis, it is feasible to conduct the program with a few SAR images. Using such products, one can extract structural responses in terms of displacements at different areas of a civil structure and monitor the responses for detecting any abnormal change. Because the long-term monitoring process is based on analyzing structural displacement responses, the main focus is on machine learning. For this process, environmental and operational changes seriously affect the performance of data-driven machine learning-aided techniques. Due to the importance of SHM in every society, this chapter intends to explain the main parts of remote sensing-based health monitoring of civil structures through SAR images and machine learning.

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Footnotes
1
In most cases, the research activities have focused on direct analyses of the displacements extracted from SAR images.
 
2
In machine learning, the term “novelty” refers to an abnormal change in data. In some literature, it is defined as “anomaly” or “outlier” (Pimentel et al. 2014). However, in this research, a novelty value is representative of a damage index, which can represent the difference between two datasets or structural state conditions.
 
Literature
go back to reference Astaneh-Asl A (2008) Progressive collapse of steel truss bridges, the case of I-35W collapse. In: Proceedings of 7th international conference on steel bridges, Guimarăes, Portugal, Citeseer Astaneh-Asl A (2008) Progressive collapse of steel truss bridges, the case of I-35W collapse. In: Proceedings of 7th international conference on steel bridges, Guimarăes, Portugal, Citeseer
go back to reference Cigna F, Lasaponara R, Masini N, Milillo P, Tapete D (2014) Persistent scatterer interferometry processing of COSMO-SkyMed StripMap HIMAGE time series to depict deformation of the historic centre of Rome Italy. Remote Sens 6(12):12593–12618. https://doi.org/10.3390/rs61212593CrossRef Cigna F, Lasaponara R, Masini N, Milillo P, Tapete D (2014) Persistent scatterer interferometry processing of COSMO-SkyMed StripMap HIMAGE time series to depict deformation of the historic centre of Rome Italy. Remote Sens 6(12):12593–12618. https://​doi.​org/​10.​3390/​rs61212593CrossRef
go back to reference Deza MM, Deza E (2013) Encyclopedia of distances, 3rd edn. Springer, HeidelbergCrossRef Deza MM, Deza E (2013) Encyclopedia of distances, 3rd edn. Springer, HeidelbergCrossRef
go back to reference Di Carlo F, Miano A, Giannetti I, Mele A, Bonano M, Lanari R, Meda A, Prota A (2021) On the integration of multi-temporal synthetic aperture radar interferometry products and historical surveys data for buildings structural monitoring. J Civ Struct Health Monit 11(5):1429–1447. https://doi.org/10.1007/s13349-021-00518-4CrossRef Di Carlo F, Miano A, Giannetti I, Mele A, Bonano M, Lanari R, Meda A, Prota A (2021) On the integration of multi-temporal synthetic aperture radar interferometry products and historical surveys data for buildings structural monitoring. J Civ Struct Health Monit 11(5):1429–1447. https://​doi.​org/​10.​1007/​s13349-021-00518-4CrossRef
go back to reference Di Martire D, Iglesias R, Monells D, Centolanza G, Sica S, Ramondini M, Pagano L, Mallorquí JJ, Calcaterra D (2014) Comparison between Differential SAR interferometry and ground measurements data in the displacement monitoring of the earth-dam of Conza della Campania (Italy). Remote Sens Environ 148:58–69. https://doi.org/10.1016/j.rse.2014.03.014CrossRef Di Martire D, Iglesias R, Monells D, Centolanza G, Sica S, Ramondini M, Pagano L, Mallorquí JJ, Calcaterra D (2014) Comparison between Differential SAR interferometry and ground measurements data in the displacement monitoring of the earth-dam of Conza della Campania (Italy). Remote Sens Environ 148:58–69. https://​doi.​org/​10.​1016/​j.​rse.​2014.​03.​014CrossRef
go back to reference Farneti E, Cavalagli N, Costantini M, Trillo F, Minati F, Venanzi I, Ubertini F (2022) A method for structural monitoring of multispan bridges using satellite InSAR data with uncertainty quantification and its pre-collapse application to the Albiano-Magra Bridge in Italy. Struct Health Monit. https://doi.org/10.1177/14759217221083609CrossRef Farneti E, Cavalagli N, Costantini M, Trillo F, Minati F, Venanzi I, Ubertini F (2022) A method for structural monitoring of multispan bridges using satellite InSAR data with uncertainty quantification and its pre-collapse application to the Albiano-Magra Bridge in Italy. Struct Health Monit. https://​doi.​org/​10.​1177/​1475921722108360​9CrossRef
go back to reference Farrar CR, Worden K (2013) Structural health monitoring: a machine learning perspective. Wiley Farrar CR, Worden K (2013) Structural health monitoring: a machine learning perspective. Wiley
go back to reference Hu WH, Cunha Á, Caetano E, Rohrmann R, Said S, Teng J (2016) Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges. Struct Control Health Monit 24(8). https://doi.org/10.1002/stc.1955 Hu WH, Cunha Á, Caetano E, Rohrmann R, Said S, Teng J (2016) Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges. Struct Control Health Monit 24(8). https://​doi.​org/​10.​1002/​stc.​1955
go back to reference Mura JC, Gama FF, Paradella WR, Negrão P, Carneiro S, De Oliveira CG, Brandão WS (2018) Monitoring the vulnerability of the dam and dikes in germano iron mining area after the collapse of the tailings dam of Fundão (Mariana-MG, Brazil) using DInSAR techniques with TerraSAR-X data. Remote Sens 10(10):1507. https://doi.org/10.3390/rs10101507CrossRef Mura JC, Gama FF, Paradella WR, Negrão P, Carneiro S, De Oliveira CG, Brandão WS (2018) Monitoring the vulnerability of the dam and dikes in germano iron mining area after the collapse of the tailings dam of Fundão (Mariana-MG, Brazil) using DInSAR techniques with TerraSAR-X data. Remote Sens 10(10):1507. https://​doi.​org/​10.​3390/​rs10101507CrossRef
go back to reference Sun L, Shang Z, Xia Y, Bhowmick S, Nagarajaiah S (2020) Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. J Struct Eng 146(5):04020073CrossRef Sun L, Shang Z, Xia Y, Bhowmick S, Nagarajaiah S (2020) Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. J Struct Eng 146(5):04020073CrossRef
go back to reference Wang ML, Lynch JP, Sohn H (2014a) Sensor technologies for civil infrastructures: applications in structural health monitoring. Woodhead Publishing (Elsevier) Wang ML, Lynch JP, Sohn H (2014a) Sensor technologies for civil infrastructures: applications in structural health monitoring. Woodhead Publishing (Elsevier)
go back to reference Wang ML, Lynch JP, Sohn H (2014b) Sensor technologies for civil infrastructures: sensing hardware and data collection methods for performance assessment. Woodhead Publishing (Elsevier) Wang ML, Lynch JP, Sohn H (2014b) Sensor technologies for civil infrastructures: sensing hardware and data collection methods for performance assessment. Woodhead Publishing (Elsevier)
go back to reference Yang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer learning. Cambridge University Press, CambridgeCrossRef Yang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer learning. Cambridge University Press, CambridgeCrossRef
go back to reference Zhu M, Wan X, Fei B, Qiao Z, Ge C, Minati F, Vecchioli F, Li J, Costantini M (2018) Detection of building and infrastructure instabilities by automatic spatiotemporal analysis of satellite SAR interferometry measurements. Remote Sens 10(11):1816. https://doi.org/10.3390/rs10111816CrossRef Zhu M, Wan X, Fei B, Qiao Z, Ge C, Minati F, Vecchioli F, Li J, Costantini M (2018) Detection of building and infrastructure instabilities by automatic spatiotemporal analysis of satellite SAR interferometry measurements. Remote Sens 10(11):1816. https://​doi.​org/​10.​3390/​rs10111816CrossRef
Metadata
Title
Pioneering Remote Sensing in Structural Health Monitoring
Authors
Alireza Entezami
Bahareh Behkamal
Carlo De Michele
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-53995-4_1

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