This article introduces a novel methodology for detecting and classifying anomalies in multiple bridges within a geographical region using satellite-based interferometric synthetic aperture radar displacements and environmental measures. The approach uses subspace alignment to harmonize bridge features, enabling the detection of anomalies based on deviations in one bridge compared to the rest of the population. Simulated and real case studies involving steel railway bridges spanning the Po River in Italy demonstrate the effectiveness of the proposed approach, showcasing its potential for large-scale applications. Moreover, the study explores the transferability of knowledge from simulated data to real-world monitoring scenarios, yielding promising results in classifying real instances using synthetic labels. The proposed approach presents practical benefits for bridge monitoring agencies by providing a cost-effective method for enhancing the resilience and safety of transportation infrastructure.
ShimCSDangNSLonS, et al. Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model. Struct Infrastruct Eng2019; 15(10): 1319–1332.
2.
JafariMKavousi-FardAChenT, et al. A review on digital twin technology in smart grid, transportation system and smart city: challenges and future. IEEE Access2023; 11: 17471–17484.
3.
MojtahediMNewtonSVon MedingJ.Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model. Nat Hazards2017; 85(2): 1119–1133.
4.
SunLShangZXiaY, et al. Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. J Struct Eng2020; 146(5): 04020073.
5.
FerranteCCiampoliLBBenedettoA, et al. Non-destructive technologies for sustainable assessment and monitoring of railway infrastructure: a focus on GPR and InSAR methods. Environ Earth Sci2021; 80(24): 806.
6.
PerissinD.Interferometric SAR multitemporal processing: techniques and applications. In: BanY (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol. 20, 2016; 145–176. Cham: Springer, 2016.
7.
LiuXWangPLuZ, et al. Damage detection and analysis of urban bridges using terrestrial laser scanning (TLS), ground-based microwave interferometry, and permanent scatterer interferometry synthetic aperture radar (PS-InSAR). Remote Sens2019; 11(5): 580.
8.
MililloPGiardinaGPerissinD, et al. Pre-collapse space geodetic observations of critical infrastructure: the Morandi Bridge, Genoa, Italy. Remote Sens2019; 11(12): 1403.
9.
BiondiFAddabboPUlloSL, et al. Perspectives on the structural health monitoring of bridges by synthetic aperture radar. Remote Sens2020; 12(23): 3852.
10.
SelvakumaranSPlankSGeißC, et al. Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques. Int J Appl Earth Obs Geoinf2018; 73: 463–470.
11.
SousaJBastosL.Multi-temporal SAR interferometry reveals acceleration of bridge sinking before collapse. Nat Hazards Earth Syst Sci2013; 13(3): 659–667.
12.
FarnetiECavalagliNCostantiniM, et al. 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 Monit2023; 22(1): 353–371.
13.
FarnetiECavalagliNVenanziI, et al. Residual service life prediction for bridges undergoing slow landslide-induced movements combining satellite radar interferometry and numerical collapse simulation. Eng Struct2023; 293: 116628.
14.
EntezamiADe MicheleCArslanAN, et al. Detection of partially structural collapse using long-term small displacement data from satellite images. Sensors2022; 22(13): 4964.
15.
Del SoldatoMTomásRPont CastilloJ, et al. (2016). A multi-sensor approach for monitoring a road bridge in the Valencia harbor (SE Spain) by SAR Interferometry (InSAR). Rend Online Soc Geol It2016; 41: 235–238.
16.
PedutoDEliaFMontuoriR.Probabilistic analysis of settlement-induced damage to bridges in the city of Amsterdam (The Netherlands). Transp Geotech2018; 14: 169–182.
17.
MartinGHooperAWrightTJ, et al. Blind source separation for MT-InSAR analysis with structural health monitoring applications. IEEE J Sel Top Appl Earth Obs Remote Sens2022; 15: 7605–7618.
18.
HuangQCrosettoMMonserratO, et al. Displacement monitoring and modelling of a high-speed railway bridge using C-band Sentinel-1 data. ISPRS J Photogramm Remote Sens2017; 128: 204–211.
19.
HoppeEJNovaliFRucciA, et al. Deformation monitoring of posttensioned bridges using high-resolution satellite remote sensing. J Bridge Eng2019; 24(12): 04019115.
20.
CussonDTrischukKHébertD, et al. Satellite-based InSAR monitoring of highway bridges: validation case study on the North Channel Bridge in Ontario, Canada. Transp Res Record2018; 2672(45): 76–86.
21.
ZhaoJWuJDingX, et al. Elevation extraction and deformation monitoring by multitemporal InSAR of Lupu Bridge in Shanghai. Remote Sens2017; 9(9): 897.
22.
HuangQMonserratOCrosettoM, et al. Displacement monitoring and health evaluation of two bridges using Sentinel-1 SAR images. Remote Sens2018; 10(11): 1714.
23.
LazeckyMHlavacovaIBakonM, et al. Bridge displacements monitoring using space-borne X-band SAR interferometry. IEEE J Sel Topics Appl Earth Obs Remote Sens2016; 10(1): 205–210.
24.
SelvakumaranSRossiCMarinoniA, et al. Combined InSAR and terrestrial structural monitoring of bridges. IEEE Trans Geosci Remote Sens2020; 58(10): 7141–7153.
25.
TonelliDCaspaniVFValentiniA, et al. Interpretation of bridge health monitoring data from satellite InSAR technology. Remote Sens2023; 15(21): 5242.
26.
SchlöglMDorningerPKwapiszM, et al. Remote sensing techniques for bridge deformation monitoring at millimetric scale: investigating the potential of satellite radar interferometry, airborne laser scanning and ground-based mobile laser scanning. PFG–J Photogramm Remote Sens Geoinf Sci2022; 90(4): 391–411.
27.
SchlöglMWidhalmBAvianM.Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1. ISPRS J Photogramm Remote Sens2021; 172: 132–146.
28.
AlaniAMTostiFCiampoliLB, et al. An integrated investigative approach in health monitoring of masonry arch bridges using GPR and InSAR technologies. NDT E Int2020; 115: 102288.
29.
MacchiaruloVMililloPBlenkinsoppC, et al. Monitoring deformations of infrastructure networks: a fully automated GIS integration and analysis of InSAR time-series. Struct Health Monit2022; 21(4): 1849–1878.
30.
LorenzRPetrynaYLubitzC, et al. Thermal deformation monitoring of a highway bridge: combined analysis of geodetic and satellite-based InSAR measurements with structural simulations. J Civil Struct Health Monit2024; 14(5): 1237–1255.
31.
CaprinoAPulieroSLorenzoniF, et al. Satellite SAR interferometry and on-site traditional SHM to monitor the post-earthquake behavior of the civic tower in L’Aquila (Abruzzo Region, Italy). Remote Sens2023; 15(6): 1587.
32.
BonaldoGCaprinoALorenzoniF, et al. Monitoring displacements and damage detection through satellite MT-InSAR techniques: a new methodology and application to a Case Study in Rome (Italy). Remote Sens2023; 15(5): 1177.
33.
CussonDRossiCOzkanIF.Early warning system for the detection of unexpected bridge displacements from radar satellite data. J Civ Struct Health Monit2021; 11(1): 189–204.
34.
DePrekelKBoualiEHOommenT.Monitoring the impact of groundwater pumping on infrastructure using Geographic Information System (GIS) and Persistent Scatterer Interferometry (PSI). Infrastructures2018; 3(4): 57.
35.
Vázquez-OntiverosJRRuiz-ArmenterosAMde LacyMC, et al. Risk evaluation of the sanalona earthfill dam located in Mexico using satellite geodesy monitoring and numerical modeling. Remote Sens2023; 15(3): 819.
36.
Guzman-AcevedoGMVazquez-BecerraGEQuintana-RodriguezJA, et al. Structural health monitoring and risk assessment of bridges integrating InSAR and a calibrated FE model. Structures2024; 63: 106353.
37.
ZhouYHaoGChenJ, et al. Long-term deformation monitoring of a steel-truss arch bridge using PSI technique refined by temperature field analysis. Eng Struct2024; 311: 118164.
38.
GosligaJGardnerPABullLA, et al. Foundations of population-based SHM, part II: heterogeneous populations–graphs, networks, and communities. Mech Syst Signal Process2021; 148: 107144.
39.
GiglioniVPooleJVenanziI, et al. A domain adaptation approach to damage classification with an application to bridge monitoring. Mech Syst Signal Process2024; 209: 111135.
40.
PanSJYangQ.A survey on transfer learning. IEEE Trans Knowledge Data Eng2009; 22(10): 1345–1359.
41.
GardnerPLiuXWordenK.On the application of domain adaptation in structural health monitoring. Mech Syst Signal Process2020; 138: 106550.
42.
PooleJGardnerPDervilisN, et al. On statistic alignment for domain adaptation in structural health monitoring, Struct Health Monit2023; 22(3): 1581–1600.
43.
Omori YanoM.FigueiredoEda SilvaS, et al. Foundations and applicability of transfer learning for structural health monitoring of bridges. Mech Syst Signal Process2023; 204: 110766.
44.
FigueiredoEYanoMOda SilvaS, et al. Transfer learning to enhance the damage detection performance in bridges when using numerical models. J Bridge Eng2023; 28(1): 04022134.
45.
FigueiredoEPeresNMoldovanI, et al. Impact of climate change on long-term damage detection for structural health monitoring of bridges. Struct Health Monit. Epub ahead of print 1 February 2024. DOI: 10.1177/14759217231224254.
46.
FigueiredoEParkGFarrarCR, et al. Machine learning algorithms for damage detection under operational and environmental variability. Struct Health Monit2011; 10(6): 559–572.
47.
EntezamiASarmadiHBehkamalB.Removal of freezing effects from modal frequencies of civil structures for structural health monitoring. Eng Struct. 2024; 319:118722.
48.
BullLAWordenKDervilisN.Towards semi-supervised and probabilistic classification in structural health monitoring. Mech Syst Signal Process2020; 140: 106653.
Muñoz-SabaterJDutraEAgustí-PanaredaA, et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data2021; 13(9): 4349–4383.
Dext3r service - Regional Agency for Prevention, Environment and Energy of Emilia-Romagna (ARPAE), https://simc.arpae.it/dext3r/ (2021, accessed 13 March 2024).
54.
KosičMPrendergastLJAnžlinA.Analysis of the response of a roadway bridge under extreme flooding-related events: Scour and debris-loading. Eng Struct2023; 279: 115607.
55.
LinCBennettCHanJ, et al. Integrated analysis of the performance of pile-supported bridges under scoured conditions. Eng Struct2012; 36: 27–38.
56.
FernandoBHabrardASebbanM, et al. Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of the IEEE international conference on computer vision, Sydney, Australia, 2013, pp. 2960–2967.
57.
LjungL. System identification: Theory for the user. Englewood Cliffs, New Jersey: Prentice-hall, Inc. 1987.
58.
KulikovGYKulikovaMV.Accurate numerical implementation of the continuous-discrete extended Kalman filter. IEEE Trans Autom Control2013; 59(1): 273–279.
59.
GiglioniVVenanziIPoggioniV, et al. Autoencoders for unsupervised real-time bridge health assessment. Comput Aid Civ Infrastruct Eng2023; 38(8): 959–974.
De FalcoFMeleR. The monitoring of bridges for scour by sonar and sedimetri. NDT E Int2002; 35(2): 117–123.
64.
ArnesonLA. Evaluating scour at bridges. Tech. Rep. United States. Federal Highway Administration, 2013.
65.
BallioFBallioGFranzettiS, et al. Actions monitoring as an alternative to structural rehabilitation: case study of a river bridge. Struct Control Health Monit2018; 25(11): e2250.
66.
DeloGBunceACrossEJ, et al. 2022. When is a bridge not an aeroplane? Part II: a population of real structures. In: Proceedings of the European workshop on structural health monitoring, Palermo, Italy, pp. 965–974.
