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Cited by 158Structural Health Monitoring TechniquesInfrastructure Maintenance and MonitoringDigital Transformation in Industry

Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating

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Bado, Mattia Francesco, Tonelli, Daniel, Poli, Francesca, Zonta, Daniele, Casas, Joan Ramon·2022·Sensors
DOI 10.3390/s22093168PMC PMC9104886
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Abstract

We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.

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  1. [4]Parviainen P. Tihinen M. Kääriäinen J. Teppola S. Tackling the digitalization challenge: How to benefit from digitalization in practice Int. J. Inf. Syst. Proj. Manag. 2017 5 63 77 10.12821/ijispm050104 · DOI
  2. [6]Ogie R.I. Perez P. Dignum V. Smart infrastructure: An emerging frontier for multidisciplinary research Proc. Inst. Civ. Eng.-Smart Infrastruct. Constr. 2017 170 8 16 10.1680/jsmic.16.00002 · DOI
  3. [7]Weiss A. Smart infrastructure matches supply and demand Networker 2009 13 18 25 10.1145/1600303.1600309 · DOI
  4. [8]Morimoto R. Estimating the benefits of effectively and proactively maintaining infrastructure with the innovative Smart Infrastructure sensor system Socioecon. Plann. Sci. 2010 44 247 257 10.1016/j.seps.2010.07.005 · DOI
  5. [9]Bolton A. Butler L. Dabson I. Enzer M. Evans M. Fenemore T. Harradence F. Keaney E. Kemp A. Luck A. Gemini Principles Centre for Digital Built Britai Cambridge, UK 2018
  6. [12]Regier R. Hoult N.A. Distributed Strain Behavior of a Reinforced Concrete Bridge: Case Study J. Bridg. Eng. 2014 19 5014007 10.1061/(ASCE)BE.1943-5592.0000637 · DOI
  7. [13]Bado M.F. Kaklauskas G. COST Action TU1207, Next Generation Design Guidelines for Composites in Construction Proceedings of the COST Action TU1207 Budapest, Hungary 3–5 April 2017 78 81
  8. [14]Housner G.W. Bergman L.A. Caughey T.K. Chassiakos A.G. Claus R.O. Masri S.F. Skelton R.E. Soong T.T. Spencer B.F. Yao J.T.P. Structural control: Past, present and future J. Eng. Mech. 1997 123 897 971 10.1061/(ASCE)0733-9399(1997)123:9(897) · DOI
  9. [18]Lu R. Brilakis I. Generating bridge geometric digital twins from point clouds Proceedings of the 2019 European Conference on Computing in Construction Chania, Greece 10–12 July 2019 Volume 1 367 376
  10. [19]Bado M.F. Casas J.R. A review of recent Distributed Optical Fiber Sensors applications for Civil Engineering Structural Health Monitoring Sensors 2021 21 1818 10.3390/s21051818 33807792 PMC7962066 · DOI
  11. [20]Glisic B. Concise Historic Overview of Strain Sensors Used in the Monitoring of Civil Structures: The First One Hundred Years Sensors 2022 22 2397 10.3390/s22062397 35336568 PMC8951819 · DOI
  12. [23]Barrias A. Casas J.R. Villalba S. A Review of Distributed Optical Fiber Sensors for Civil Engineering Applications Sensors 2016 16 748 10.3390/s16050748 PMC4883438 27223289 · DOI
  13. [24]Sieńko R. Zych M. Bednarski Ł. Howiacki T. Strain and crack analysis within concrete members using distributed fibre optic sensors Struct. Health Monit. 2018 18 1510 1526 10.1177/1475921718804466 · DOI
  14. [26]Bao X. Chen L. Recent Progress in Distributed Fiber Optic Sensors Sensors 2012 12 8601 8639 10.3390/s120708601 23012508 PMC3444066 · DOI
  15. [27]Seo H. Monitoring of CFA pile test using three dimensional laser scanning and distributed fiber optic sensors Opt. Lasers Eng. 2020 130 106089 10.1016/j.optlaseng.2020.106089 · DOI
  16. [28]Kurzweil R. The Singularity Is Near Springer Link Cham, Switzerland 2014
  17. [31]Boschert S. Heinrich C. Rosen R. Next Generation Digital Twin Proceedings of the TMCE 2018 Las Palmas de Gran Canaria, Spain 7–11 May 2018 Volume 60 209 218
  18. [32]Defraeye T. Shrivastava C. Berry T. Verboven P. Onwude D. Schudel S. Bühlmann A. Cronje P. Rossi R.M. Digital twins are coming: Will we need them in supply chains of fresh horticultural produce? Trends Food Sci. Technol. 2021 109 245 258 10.1016/j.tifs.2021.01.025 · DOI
  19. [33]Jiang F. Ma L. Broyd T. Chen K. Digital twin and its implementations in the civil engineering sector Autom. Constr. 2021 130 103838 10.1016/j.autcon.2021.103838 · DOI
  20. [34]Liu M. Fang S. Dong H. Xu C. Review of digital twin about concepts, technologies, and industrial applications J. Manuf. Syst. 2021 58 346 361 10.1016/j.jmsy.2020.06.017 · DOI
  21. [35]Qi Q. Tao F. Zuo Y. Zhao D. Digital Twin Service towards Smart Manufacturing Procedia CIRP 2018 72 237 242 10.1016/j.procir.2018.03.103 · DOI
  22. [36]Burnett D. Thorp J. Richards D. Gorkovenko K. Murray-Rust D. Digital twins as a resource for design research Proceedings of the 8th ACM International Symposium on Pervasive Displays (PerDis ′19) Palermo, Italy 12–14 June 2019 6 7
  23. [40]Schleich B. Anwer N. Mathieu L. Wartzack S. Shaping the digital twin for design and production engineering CIRP Ann. 2017 66 141 144 10.1016/j.cirp.2017.04.040 · DOI · Paper page
  24. [41]Saifutdinov F. Jackson I. Tolujevs J. Zmanovska T. Digital Twin as a Decision Support Tool for Airport Traffic Control Proceedings of the 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) Riga, Latvia 15–16 October 2020 1 5
  25. [42]Granacher J. Nguyen T.V. Castro-Amoedo R. Maréchal F. Overcoming decision paralysis—A digital twin for decision making in energy system design Appl. Energy 2022 306 117954 10.1016/j.apenergy.2021.117954 · DOI
  26. [43]Quilodrán-Casas C. Silva V.L.S. Arcucci R. Heaney C.E. Guo Y.K. Pain C.C. Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic Neurocomputing 2022 470 11 28 10.1016/j.neucom.2021.10.043 34703079 PMC8531233 · DOI
  27. [44]Hämäläinen M. Urban development with dynamic digital twins in Helsinki city IET Smart Cities 2021 3 201 210 10.1049/smc2.12015 · DOI
  28. [46]Li X. Liu H. Wang W. Zheng Y. Lv H. Lv Z. Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning Futur. Gener. Comput. Syst. 2022 128 167 177 10.1016/j.future.2021.10.006 · DOI
  29. [48]Farsi M. Daneshkhah A. Hosseinian-Far A. Jahankhani H. Internet of Things Digital Twin Technologies and Smart Cities Springer Beilin, Germany 2020 9783030187316
  30. [53]Errandonea I. Beltrán S. Arrizabalaga S. Digital Twin for maintenance: A literature review Comput. Ind. 2020 123 103316 10.1016/j.compind.2020.103316 · DOI
  31. [54]Camposano J.C. Smolander K. Ruippo T. Seven Metaphors to Understand Digital Twins of Built Assets IEEE Access 2021 9 27167 27181 10.1109/ACCESS.2021.3058009 · DOI
  32. [55]Zonta D. Zandonini R. Bortot F. A reliability-based bridge management concept Struct. Infrastruct. Eng. 2007 3 215 235 10.1080/15732470500315740 · DOI
  33. [56]Brownjohn J.M.W. Structural health monitoring of civil infrastructure Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2007 365 589 622 10.1098/rsta.2006.1925 17255053 · DOI
  34. [57]Farrar C.R. Worden K. An introduction to structural health monitoring Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2007 365 303 315 10.1098/rsta.2006.1928 17255041 · DOI
  35. [58]Melchers R.E. Beck A.T. Structural Reliability Analysis and Prediction John Wiley & Sons Hoboken, NJ, USA 2018 1119265991
  36. [59]Gunner S. Voyagaki E. Gavriel G. Carhart N. MacDonald J. Tryfonas T. Taylor C. Pregnolato M. Digital Twins for civil engineering: The Clifton Suspension Bridge (UK) Proceedings of the 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10 Porto, Portugal 30 June–2 July 2021 1 6
  37. [60]Zonta D. Glisic B. Adriaenssens S. Value of information: Impact of monitoring on decision-making Struct. Control Health Monit. 2014 21 1043 1056 10.1002/stc.1631 · DOI
  38. [61]Caspani V.F. Tonelli D. Poli F. Zonta D. Designing a Structural Health Monitoring System Accounting for Temperature Compensation Infrastructures 2022 7 5 10.3390/infrastructures7010005 · DOI
  39. [62]Cappello C. Zonta D. Glišić B. Expected Utility Theory for Monitoring-Based Decision-Making Proc. IEEE 2016 104 1647 1661 10.1109/JPROC.2015.2511540 · DOI
  40. [63]Tonelli D. Rossi F. Luchetta M. Zonta D. Migliorino P. Selleri A. Valeri E. Marchiondelli A. Ascari G. Acoustic Emission Monitoring of Prestressed Concrete Bridges: Differences Before and After the First-Crack Opening International Workshop on Civil Structural Health Monitoring Springer Berlin/Heidelberg, Germany 2021 389 402
  41. [66]Giordano P.F. Limongelli M.P. The value of structural health monitoring in seismic emergency management of bridges Struct. Infrastruct. Eng. 2020 18 537 553 10.1080/15732479.2020.1862251 · DOI
  42. [67]Ye C. Butler L. Bartek C. Iangurazov M. Lu Q. Gregory A. Girolami M. A Digital Twin of Bridges for Structural Health Monitoring Proceedings of the 12th International Workshop on Structural Health Monitorin Palo Alto, CA, USA 10–12 September 2019
  43. [68]Goulet J.-A. Probabilistic Machine Learning for Civil Engineers MIT Press Cambridge, MA, USA 2020
  44. [69]Cappello C. Zonta D. Pozzi M. Glisic B. Zandonini R. Impact of prior perception on bridge health diagnosis J. Civ. Struct. Health Monit. 2015 5 509 525 10.1007/s13349-015-0120-0 · DOI
  45. [70]Tonelli D. Cappello C. Zonta D. Performance-based design of structural health monitoring systems European Workshop on Structural Health Monitoring Springer Berlin/Heidelberg, Germany 2020 238 247
  46. [71]Flynn E.B. Todd M.D. A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing Mech. Syst. Signal Process. 2010 24 891 903 10.1016/j.ymssp.2009.09.003 · DOI
  47. [72]Bado M.F. Casas J.R. Barrias A. Performance of Rayleigh-Based Distributed Optical Fiber Sensors Bonded to Reinforcing Bars in Bending Sensors 2018 23 3125 10.3390/s18093125 30223613 PMC6163229 · DOI
  48. [73]Bolstad W.M. Understanding Computational Bayesian Statistics John Wiley & Sons Hoboken, NJ, USA 2009 Volume 644 0470046090
  49. [75]VanDerHorn E. Mahadevan S. Digital Twin: Generalization, characterization and implementation Decis. Support Syst. 2021 145 113524 10.1016/j.dss.2021.113524 · DOI
  50. [76]Wang Z. Broccardo M. Song J. Hamiltonian Monte Carlo methods for subset simulation in reliability analysis Struct. Saf. 2019 76 51 67 10.1016/j.strusafe.2018.05.005 · DOI
  51. [77]Ballio F. Ballio G. Franzetti S. Crotti G. Solari G. Actions monitoring as an alternative to structural rehabilitation: Case study of a river bridge Struct. Control Health Monit. 2018 25 e2250 10.1002/stc.2250 · DOI
  52. [78]Tonelli D. Verzobio A. Cappello C. Bolognani D. Zonta D. Bursi O.S. Costa C. Expected utility theory for monitoring-based decision support system Proceedings of the 11th International Workshop on Structural Health Monitoring 2017 Stanford, CA, USA 12–14 September 2017
  53. [79]Nguyen L.H. Goulet J. Anomaly detection with the switching kalman filter for structural health monitoring Struct. Control Health Monit. 2018 25 e2136 10.1002/stc.2136 · DOI
  54. [80]Papaioannou I. Breitung K. Straub D. Reliability sensitivity estimation with sequential importance sampling Struct. Saf. 2018 75 24 34 10.1016/j.strusafe.2018.05.003 · DOI
  55. [82]Giordano P.F. Iacovino C. Quqa S. Limongelli M.P. The value of seismic structural health monitoring for post-earthquake building evacuation Bull. Earthq. Eng. 2022 1 27 10.1007/s10518-022-01375-2 · DOI
  56. [83]Li H. Li D. Song G. Recent applications of fiber optic sensors to health monitoring in civil engineering Eng. Struct. 2004 26 1647 1657 10.1016/j.engstruct.2004.05.018 · DOI
  57. [84]Bado M.F. Casas J.R. Dey A. Berrocal C.G. Distributed optical fiber sensing bonding techniques performance for embedment inside reinforced concrete structures Sensors 2020 20 5788 10.3390/s20205788 PMC7602026 33066138 · DOI
  58. [85]Bado M.F. Casas J.R. Kaklauskas G. Distributed Sensing (DOFS) in Reinforced Concrete members for reinforcement strain monitoring, crack detection and bond-slip calculation Eng. Struct. 2021 226 111385 10.1016/j.engstruct.2020.111385 · DOI
  59. [86]Zhang S. Liu H. Coulibaly A.A.S. DeJong M. Fiber optic sensing of concrete cracking and rebar deformation using several types of cable Struct. Control Health Monit. 2020 28 e2664 10.1002/stc.2664 · DOI
  60. [87]Barrias A. Rodriguez G. Casas J.R. Villalba S. Application of distributed optical fiber sensors for the health monitoring of two real structures in Barcelona Struct. Infrastruct. Eng. 2018 14 967 985 10.1080/15732479.2018.1438479 · DOI
  61. [88]Barrias A. Casas J.R. Villalba S. Fatigue performance of distributed optical fiber sensors in reinforced concrete elements Constr. Build. Mater. 2019 218 214 223 10.1016/j.conbuildmat.2019.05.072 · DOI
  62. [89]Bado M.F. Casas J.R. Dey A. Berrocal C.G. Kaklauskas G. Fernandez I. Rempling R. Characterization of concrete shrinkage induced strains in internally-restrained RC structures by Distributed Optical Fiber Sensing Cem. Concr. Compos. 2021 120 104058 10.1016/j.cemconcomp.2021.104058 · DOI
  63. [90]Berrocal C.G. Fernandez I. Bado M.F. Casas J.R. Rempling R. Assessment and visualization of performance indicators of reinforced concrete beams by distributed optical fiber sensing Struct. Health Monit. 2021 20 3309 3326 10.1177/1475921720984431 · DOI
  64. [91]Berrocal C.G. Fernandez I. Rempling R. Crack monitoring in reinforced concrete beams by distributed optical fiber sensors Struct. Infrastruct. Eng. 2020 17 124 139 10.1080/15732479.2020.1731558 · DOI

Cited by

Sorted by citations

Authors
Bado, M. F., Tonelli, D., Poli, F., Zonta, D., & Casas, J. R.
Affiliations
  • University of Trento (IT)
  • Universitat Politècnica de Catalunya (ES)
Corresponding author
Bado, Mattia Francesco
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-04-20
Volume
22
Issue
9
Pages
3168
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22093168
PMC
PMC9104886
OpenAlex
W4224220652
Cited by
158
References
7
Citations by year
2026: 31 · 2025: 39 · 2024: 36 · 2023: 41 · 2022: 11
FWCI
2.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
Funders
Fondazione Caritro (2021.0224)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
Keywords
digital twin, structural health monitoring, distributed sensing, civil infrastructure, aging infrastructure, sensor networks, structural reliability, infrastructure maintenance, serviceability, structural response prediction, asset management, extreme events
MeSH terms
Fiber Optic Technology, Monitoring, Physiologic, Technology, Reproducibility of Results

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