Structural Health Monitoring (SHM) allows tracking the structural behavior in time and support decisions regarding, for instance, the need for maintenance and repair activities. Most traditional SHM systems require sensors that are directly applied to the structure to get insights into the structural performance. Satellite technologies can provide an appealing alternative to traditional SHM. They allow to measure displacements at a large scale and to follow their evolution without the need of directly accessing the structure. Further to this, the possibility to monitor large areas opens new avenues for the development of automatic alert systems able to issue an alarm and early-flag damaged structures. However, displacements of civil structures might also be induced by sources other than damage such as thermal or periodic hydrogeological variations. These can hinder the onset and development of damage or lead to false alarms if such displacements are erroneously interpreted as damages. This paper aims to present a new method for damage detection based on DInSAR measurements, that tackles both aspects providing reliable information about the onset of damage under environmentally changing conditions in a period corresponding to about twice the revisit time of the satellite. A case study is presented to demonstrate the applicability of the proposed method, namely the Palatino bridge in Rome, Italy. The satellite data are acquired by COSMO-SkyMed of the Italian Space Agency and consist of displacements of the observed structure recorded during a period spanning between 2011 and 2019.
ZontaDGlisicBAdriaenssensS. Value of information: impact of monitoring on decision-making. Struct Control Heal Monit2014; 21: 1043–1056.
2.
OkashaNMFrangopolDM. Integration of structural health monitoring in a system performance based life-cycle bridge management framework. Struct Infrastruct Eng2010; 8(11): 1–18.
3.
IannaconeLGiordanoPFGardoniP, et al.Quantifying the value of information from inspecting and monitoring engineering systems subject to gradual and shock deterioration. Structural Health Monitoring2021. doi: 10.1177/1475921720981869.
4.
QuqaSLandiLDiotalleviPP. Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks. Autom Constr2021; 128: 103740.
5.
LimongelliMPChatziEDöhlerM, et al.Towards extraction of vibration-based damage indicators. In: 8th European Workshop on Structural Health Monitoring, EWSHM, Bilbao, Spain, July 2016.
6.
GiordanoPFQuqaSLimongelliMP. Statistical approach for vibration-based damage localization in civil infrastructures using smart sensor networks. Infrastructures2021; 6: 22.
7.
KarbhariVMAnsariF. Structural health monitoring of civil infrastructure systems. Woodhead Publishing Limited, 2009.
8.
LiCChenWLiuG, et al.A noncontact FMCW radar sensor for displacement measurement in structural health monitoring. Sensors2015; 15: 7412–7433.
9.
RibeiroDCalçadaRFerreiraJ, et al.Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system. Eng Struct2014; 75: 164–180.
10.
WuLCasciatiF. Local positioning systems versus structural monitoring: a review. Struct Control Heal Monit2014; 21: 1209–1221.
11.
Rivera-CastilloJFlores-FuentesWRivas-LópezM, et al.Experimental image and range scanner datasets fusion in SHM for displacement detection. Struct Control Heal Monit2017; 24: e1967.
12.
FerrettiAPratiCRoccaF. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans Geosci Remote Sens2000; 38: 2202–2212.
13.
SousaJJBastosL. Multi-temporal SAR interferometry reveals acceleration of bridge sinking before collapse. Nat Hazards Earth Syst Sci2013; 13: 659–667.
14.
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.
15.
MililloPGiardinaGPerissinD, et al.Pre-collapse space geodetic observations of critical. Remote Sens2019; 11: 1403.
16.
LanariRRealeDBonanoM, et al.Comment on “pre-collapse space geodetic observations of critical infrastructure: The Morandi Bridge, Genoa, Italy” by Milillo et al (2019). Remote Sens2020; 12(24): 4011. doi: 10.3390/rs12244011.
17.
MililloPGiardinaGPerissinD, et al.Reply to Lanari, R., et al. comment on “pre-collapse space geodetic observations of critical infrastructure: The morandi bridge, Genoa, Italy” by Milillo et al. (2019). Remote Sensing2020; 12(24); 4016. doi: 10.3390/rs12244016.
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.
ZhouWLiSZhouZ, et al.Remote sensing of deformation of a high concrete-faced rockfill dam using InSAR: a study of the Shuibuya Dam, China. Remote Sens2016; 8: 255.
20.
CussonDTrischukKHébertD, et al.Satellite-based InSAR monitoring of highway bridges: validation case study on the North Channel Bridge in Ontario, Canada. Transp Res Rec J Transp Res Board2018; 2672: 76–86.
21.
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. doi: 10.3390/rs11050580.
22.
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. doi: 10.1016/j.isprsjprs.2020.12.001.
23.
HuangQMonserratOCrosettoM, et al.Displacement monitoring and health evaluation of two bridges using sentinel-1 SAR images. Remote Sens2018; 10: 1714.
24.
CussonDGhumanPGaraM, et al. (2012). Remote monitoring of bridges from space. An do 54o Congr Bras do Concreto.
25.
Del SoldatoMTomásRPontJ, et al.A multi-sensor approach for monitoring a road bridge in the Valencia harbor (SE Spain) by SAR Interferometry (InSAR). Rend Online Della Soc Geol Ital2016; 41: 235–238.
26.
FornaroGRealeDVerdeS. Bridge thermal dilation monitoring with millimeter sensitivity via multidimensional SAR imaging. IEEE Geosci Remote Sens Lett2013; 10: 677–681.
27.
QinXLiaoMYangM, et al.Monitoring structure health of urban bridges with advanced multi-temporal InSAR analysis. Ann GIS2017; 23: 293–302.
28.
LazeckyMPerissinDBakonM, et al. (2015) Potential of satellite InSARtechniques for monitoring of bridge deformations. In: Proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland, 30 March–1 April 2015, pp. 1–4.
29.
LazeckyMHlavacovaIBakonM, et al.Bridge displacements monitoring using space-borne X-band SAR interferometry. IEEE J Sel Top Appl Earth Obs Remote Sens2017; 10: 205–210.
30.
Di MartireDIglesiasRMonellsD, et al.Comparison between Differential SAR interferometry and ground measurements data in the displacement monitoring of the earth-dam of Conza della Campania (Italy). Remote Sens Environ2014; 148: 58–69. doi: 10.1016/j.rse.2014.03.014.
31.
TomásRCanoMGarcía-BarbaJ, et al.Monitoring an earthfill dam using differential SAR interferometry: La Pedrera dam, Alicante, Spain. Eng Geol2013; 157: 21–32.
32.
LazeckýMPerissinDZhiyingW, et al.Observing dam’s movements with spaceborne SAR interferometry. In: Lollino G, Manconi A, Guzzetti F, et al. (eds) Engineering Geology for Society and Territory - Volume 5, Cham, Springer; 2015. doi: 10.1007/978-3-319-09048‐1_26.
33.
CavalagliNKitaAFalcoS, et al.Satellite radar interferometry and in-situ measurements for static monitoring of historical monuments: The case of Gubbio, Italy. Remote Sens Environ2019; 235: 111453.
34.
ArangioSCalòFDi MauroM, et al.An application of the SBAS-DInSAR technique for the assessment of structural damage in the city of Rome. Struct Infrastruct Eng2014; 10: 1469–1483.
35.
YangKYanLHuangG, et al.Monitoring Building Deformation with InSAR: Experiments and Validation. Sensors2016; 16: 2182.
36.
BianchiniSPratesiFNolesiniT, et al.Building deformation assessment by means of persistent scatterer interferometry analysis on a landslide-affected area: The Volterra (Italy) case study. Remote Sens2015; 7: 4678–4701.
37.
DrougkasAVerstryngeEVan BalenK, et al.Country-scale InSAR monitoring for settlement and uplift damage calculation in architectural heritage structures. Structural Health Monitoring2020; doi: 10.1177/1475921720942120.
38.
SharmaRCTateishiRHaraK, et al.Earthquake damage visualization (EDV) technique for the rapid detection of earthquake-induced damages using SAR data. Sensors (Switzerland)2017; 17: 235. doi: 10.3390/s17020235.
39.
BrunnerDBruzzoneLLemoineG. Change detection for earthquake damage assessment in built-up areas using very high resolution optical and SAR imagery. In: International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, Hawaii, USA, 25–30 July 2010. doi: 10.1109/IGARSS.2010.5651416.
40.
WangTLJinYQ. Postearthquake building damage assessment using multi-mutual information from pre-event optical image and postevent SAR image. IEEE Geosci Remote Sens Lett2012; 9: 452–456. doi: 10.1109/LGRS.2011.2170657.
41.
BarlaGTamburiniADel ConteS, et al.InSAR monitoring of tunnel induced ground movements. Geomech Tunn2016; 9: 15–22.
42.
GiannicoCFerrettiAAlbertiS, et al.Application of satellite radar interferometry for tunnel and underground infrastructures damage assessment and monitoring. In: AnagnostouGEhrbarH (eds) Underground-The Way to the Future: Proceedings of the World Tunnel Congress. London: WTC 2013, 2013. doi: 10.1201/b14769-187.
43.
ScoularJGhailRMasonP, et al.Retrospective InSAR analysis of East London during the Construction of the Lee Tunnel. Remote Sens2020; 12: 849.
44.
LiuLYuJChenB, et al.Urban subsidence monitoring by SBAS-InSAR technique with multi-platform SAR images: a case study of Beijing Plain, China. Eur J Remote Sens2020; 53: 141–153.
45.
SansostiEBerardinoPBonanoM, et al.How second generation SAR systems are impacting the analysis of ground deformation. Int J Appl Earth Obs Geoinf2014; 28: 1–11.
46.
ScifoniSBonanoMMarsellaM, et al.On the joint exploitation of long-term DInSAR time series and geological information for the investigation of ground settlements in the town of Roma (Italy). Remote Sens Environ2016; 182: 113–127.
47.
GabrielAKGoldsteinRMZebkerHA. Mapping small elevation changes over large areas: Differential radar interferometry. J Geophys Res1989; 94: 9183.
48.
MassonnetDFeiglKL. Radar interferometry and its application to changes in the Earth’s surface. Rev Geophys1998; 36: 441–500.
49.
BerardinoPFornaroGLanariR, et al.A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens2002; 40: 2375–2383.
ManuntaMDe LucaCZinnoI, et al.The parallel SBAS approach for sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment. IEEE Trans Geosci Remote Sens. 2019; PP(99): 1–23. doi: 10.1109/TGRS.2019.2904912.
52.
CasuFElefanteSImperatoreP, et al.SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2014; 7(8): 3285–3296. doi: 10.1109/JSTARS.2014.2322671.
53.
SartorelliLPrevitaliMGiordanoPF, et al.Use of sar satellite data in bridge monitoring with application to urban areas. In: RainieriCFabbrocinoGCaterinoN, et al. (eds) Civil Structural Health Monitoring. CSHM 2021. Lecture Notes in Civil Engineering. 2021; 156. Cham: Springer. doi: 10.1007/978-3-030-74258-4_60.
54.
ColesantiCWasowskiJ. Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng Geol2006; 88: 173–199.
55.
CasciniLFornaroGPedutoD. Advanced low- and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales. Eng Geol2010; 112: 29–42.
56.
Flores-FuentesW. Bridge load classifier based on deep learning for structural displacement correlation. Program Comput Softw2020; 46: 526–535.
57.
SohnH. Effects of environmental and operational variability on structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci2007; 365(1851): 539–560. doi: 10.1098/rsta.2006.1935.
58.
AlaggioRAloisioAAntonacciE, et al.Two-years static and dynamic monitoring of the Santa Maria di Collemaggio basilica. Constr Build Mater2021; 268: 121069.
59.
YanA-MKerschenGDe BoeP, et al.Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis. Mech Syst Signal Process2005; 19: 847–864.
60.
ComanducciGMagalhãesFUbertiniF, et al.On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge. Struct Heal Monit An Int J2016; 15: 505–524.
61.
GiordanoPFUbertiniFCavalagliN, et al.Four years of structural health monitoring of the San Pietro bell tower in Perugia, Italy: two years before the earthquake versus two years after. Int J Mason Res Innov2020; 5: 445.
62.
UbertiniFComanducciGCavalagliN. Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis. Struct Heal Monit An Int J2016; 15: 438–457.
63.
MujicaLRodellarJFernándezA, et al.Q-statistic and T2-statistic PCA-based measures for damage assessment in structures. Struct Heal Monit2011; 10: 539–553.
NevesACGonzálezILeanderJ, et al.Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civ Struct Heal Monit2017; 7: 689–702. doi: 10.1007/s13349-017-0252-5.
66.
SacheriG. Il nuovo Ponte Palatino sul Tevere in Roma.L’ingegneria Civile e le arti Industriali, year XVIII, number 3; Turin, 1892. (In Italian).
67.
ManuntaMMarsellaMZeniG, et al.Two-scale surface deformation analysis using the SBAS-DInSAR technique: A case study of the city of Rome, Italy. Int J Remote Sens2008; 29: 1665–1684. doi: 10.1080/01431160701395278.
68.
FornaroGSerafinoFRealeD. 4-D SAR imaging: The case study of Rome. IEEE Geosci Remote Sens Lett2010; 7: 236–240. doi: 10.1109/LGRS.2009.2032133.
69.
StramondoSBozzanoFMarraF, et al.Subsidence induced by urbanisation in the city of Rome detected by advanced InSAR technique and geotechnical investigations. Remote Sens Environ2008; 112: 3160–3172. doi: 10.1016/j.rse.2008.03.008.
