{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:05:06Z","timestamp":1790075106377,"version":"4.0.1"},"reference-count":139,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bridge infrastructure worldwide is under increasing pressure from ageing assets, growing traffic demand, environmental deterioration, and the need for more effective lifecycle management. Although bridge-monitoring technologies have developed rapidly, much of the existing research has focused on individual sensing techniques, while the integration of heterogeneous information across different spatial scales has received comparatively less attention. This review takes an information-centric perspective on intelligent bridge monitoring and focuses on three representative technologies that provide complementary information at the material, structural, and network scales: Ground-Penetrating Radar (GPR) for assessing material condition, distributed optical-fibre sensing (DOFS) for monitoring structural response, and satellite remote sensing for providing network-scale information. Instead of comparing these technologies solely for their sensing principles, the review considers their complementary roles in bridge engineering and proposes a Material\u2013Structural\u2013Network (M\u2013S\u2013N) framework to organise monitoring information across scales. Recent developments in multi-source information fusion, AI-based interpretation, connected monitoring systems, autonomous monitoring, and self-evolving Digital Twins are also reviewed. Together, these advances indicate a shift from simply collecting monitoring data towards integrating information, extracting engineering knowledge, and supporting intelligent decisions. Based on this perspective, a Multi-Scale Integration Framework is proposed to illustrate how observations at different scales can be combined to provide a more complete understanding of bridge condition, structural performance, and infrastructure-network context. Future bridge monitoring should therefore move beyond isolated sensing and data collection towards multi-scale information generation and integration, supporting predictive maintenance, resilient asset management, and intelligent lifecycle decision-making across bridge networks.<\/jats:p>","DOI":"10.3390\/s26185930","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"5930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Review of Multi-Scale Bridge Monitoring: An Information-Centric Framework Integrating Ground-Penetrating Radar, Distributed Optical-Fibre Sensing, and Satellite Remote Sensing for Bridge Digital Twins"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5109-4866","authenticated-orcid":false,"given":"Lilong","family":"Zou","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Computing and the Environment, Kingston University, London KT1 1LQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2643-733X","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London WC1E 7HX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Munisami","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Computing and the Environment, Kingston University, London KT1 1LQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7621-5014","authenticated-orcid":false,"given":"Giovanni","family":"Nico","sequence":"additional","affiliation":[{"name":"Istituto per le Applicazioni del Calcolo (IAC), Consiglio Nazionale dele Ricerche (CNR), 70126 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir M.","family":"Alani","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Computing and the Environment, Kingston University, London KT1 1LQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","unstructured":"Victor, R., Baskir, G., Bennett, J., Camp, J., Capka, R., Curtis, S., Davids, G., Frevert, L., Hatch, H., and Herrmann, A. (2013). Report Card for America\u2019s Infrastructure, American Society of Civil Engineers."},{"key":"ref_2","unstructured":"Hall, J.W. (2016). The Future of National Infrastructure, Cambridge University Press."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farrar, C.R., and Worden, K. (2012). Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons.","DOI":"10.1002\/9781118443118"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, C., Ayyub, B.M., and Beer, M. (2026). Climate-Resilient Structures and Infrastructures, CRC Press.","DOI":"10.1201\/9781003567332"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"04015049","DOI":"10.1061\/(ASCE)CF.1943-5509.0000802","article-title":"Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods","volume":"30","author":"Agdas","year":"2016","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Halder, S., and Afsari, K. (2023). Robots in inspection and monitoring of buildings and infrastructure: A systematic review. Appl. Sci., 13.","DOI":"10.3390\/app13042304"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Casas, J.R., Chac\u00f3n, R., Catbas, N., Riveiro, B., and Tonelli, D. (2024). Remote sensing in bridge digitalization: A review. Remote Sens., 16.","DOI":"10.3390\/rs16234438"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Barrias, A., Casas, J.R., and Villalba, S. (2016). A review of distributed optical fiber sensors for civil engineering applications. Sensors, 16.","DOI":"10.3390\/s16050748"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fabbricatore, F., and Bertola, N. (2025). Distributed fiber-optic sensor-based methodology for performance evaluation of prestressed concrete girder bridges. Struct. Health Monit., 14759217251384701.","DOI":"10.1177\/14759217251384701"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tang, Z., Qian, G., Xu, W., Huang, X., and Fang, H. (2025). A prototype of a lightweight structural health monitoring system based on edge computing. Sensors, 25.","DOI":"10.3390\/s25185612"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4555","DOI":"10.1007\/s11831-025-10280-1","article-title":"Strategies for Maximising the Value of Digital Twins for Bridge Management and Structural Monitoring: A Systematic Review","volume":"32","author":"Nhamage","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_12","unstructured":"Yousuf, M.M., Saravanan, T.J., and Dash, S.R. (2026). Advancing bridge health assessment and management through Digital Twins: A comprehensive review. Struct. Health Monit., online first."},{"key":"ref_13","first-page":"100184","article-title":"A demonstration of a digital twin framework for structural health monitoring: Application to bridge infrastructures","volume":"5","author":"Nasim","year":"2026","journal-title":"J. Infrastruct. Intell. Resil."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103534","DOI":"10.1016\/j.inffus.2025.103534","article-title":"A data-driven digital twin model for bridge health monitoring using feature fusion and unsupervised deep learning","volume":"126","author":"Mousavi","year":"2026","journal-title":"Inf. Fusion"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ribeiro, D., Rakoczy, A.M., Cabral, R., Hoskere, V., Narazaki, Y., Santos, R., Tondo, G., Gonzalez, L., Matos, J.C., and Futai, M.M. (2025). Methodologies for remote bridge inspection. Sensors, 25.","DOI":"10.3390\/s25185708"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3390\/ndt2010004","article-title":"Review of Ground Penetrating Radar Applications for Bridge Infrastructures","volume":"2","author":"Boldrin","year":"2024","journal-title":"NDT"},{"key":"ref_17","first-page":"liad004","article-title":"Machine learning-based detection of transportation infrastructure internal defects using ground-penetrating radar: A state-of-the-art review","volume":"2","author":"Sui","year":"2023","journal-title":"Intell. Transp. Infrastruct."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"122594","DOI":"10.1016\/j.engstruct.2026.122594","article-title":"Strong vibration signal reconstruction of long-span bridges based on distributed optical fiber acoustic sensing","volume":"358","author":"Ma","year":"2026","journal-title":"Eng. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1177\/14759217241310525","article-title":"Regional-scale bridge health monitoring: Survey of current methods and roadmap for future opportunities under changing climate","volume":"24","author":"Quqa","year":"2025","journal-title":"Struct. Health Monit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1080\/10589759.2023.2239434","article-title":"Digital twin-based non-destructive testing for structural health monitoring of bridges","volume":"39","author":"Lai","year":"2024","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104421","DOI":"10.1016\/j.autcon.2022.104421","article-title":"Towards Civil Engineering 4.0: Concept, workflow and application of Digital Twins for existing infrastructure","volume":"141","author":"Pregnolato","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_22","first-page":"04022032","article-title":"Risk-informed digital twin of buildings and infrastructures for sustainable and resilient urban communities","volume":"8","author":"Alibrandi","year":"2022","journal-title":"ASCE ASME J. Risk Uncertain. Eng. Syst. A Civ. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cirpj.2020.02.002","article-title":"Characterising the Digital Twin: A systematic literature review","volume":"29","author":"Jones","year":"2020","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sun, Z., Jayasinghe, S., Sidiq, A., Shahrivar, F., Mahmoodian, M., and Setunge, S. (2024). Approach towards the development of digital twin for structural health monitoring of civil infrastructure: A comprehensive review. Sensors, 25.","DOI":"10.3390\/s25010059"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1007\/s42452-026-08991-6","article-title":"Edge-cloud collaborative digital twin architecture for bridge structural health monitoring: Multi-sensor fusion and anomaly detection algorithm design","volume":"8","author":"Liu","year":"2026","journal-title":"Discov. Appl. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100977","DOI":"10.1016\/j.dibe.2026.100977","article-title":"Physics-informed intelligence in structural health monitoring: A state-of-the-art review and roadmap towards digital twins","volume":"27","author":"Ayubirad","year":"2026","journal-title":"Dev. Built Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s41024-026-00810-0","article-title":"Next generation vibration-based structural health monitoring for bridges: A systematic review of AI, IoT, and digital twin integrations","volume":"11","author":"Chaudhari","year":"2026","journal-title":"J. Build. Pathol. Rehabil."},{"key":"ref_28","first-page":"100050","article-title":"Literature review of digital twin technologies for civil infrastructure","volume":"2","author":"Liu","year":"2023","journal-title":"J. Infrastruct. Intell. Resil."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109895","DOI":"10.1016\/j.rineng.2026.109895","article-title":"Advancing bridge management systems: Optimizing maintenance planning through deep learning, big data, and digital twins","volume":"29","author":"Mousavi","year":"2026","journal-title":"Results Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3141\/2201-05","article-title":"Multiple complementary nondestructive evaluation technologies for condition assessment of concrete bridge decks","volume":"2201","author":"Gucunski","year":"2010","journal-title":"Transp. Res. Rec."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jappgeo.2013.04.009","article-title":"Applications of ground penetrating radar (GPR) in bridge deck monitoring and assessment","volume":"97","author":"Alani","year":"2013","journal-title":"J. Appl. Geophys."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., and Czarnecki, J.J. (2003). A Review of Structural Health Monitoring Literature: 1996\u20132001.","DOI":"10.1117\/12.434158"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Glisic, B., and Inaudi, D. (2007). Fibre Optic Methods for Structural Health Monitoring, John Wiley & Sons.","DOI":"10.1002\/9780470517819"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2015.10.011","article-title":"Persistent scatterer interferometry: A review","volume":"115","author":"Crosetto","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent scatterers in SAR interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"108952","DOI":"10.1109\/ACCESS.2020.2998358","article-title":"Digital twin: Enabling technologies, challenges and open research","volume":"8","author":"Fuller","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Persico, R. (2014). Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing, John Wiley & Sons.","DOI":"10.1002\/9781118835647"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Casas, A., Pinto, V., and Rivero, L. (2000). Fundamental of ground penetrating radar in environmental and engineering applications. Ann. Geophys., 43.","DOI":"10.4401\/ag-3689"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pathirana, S., Lambot, S., Krishnapillai, M., Cheema, M., Smeaton, C., and Galagedara, L. (2023). Ground-penetrating radar and electromagnetic induction: Challenges and opportunities in agriculture. Remote Sens., 15.","DOI":"10.3390\/rs15112932"},{"key":"ref_40","first-page":"36","article-title":"Ground penetrating radar applications and implementations in civil construction","volume":"8","author":"Spears","year":"2023","journal-title":"J. Struct. Integr. Maint."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"B4016005","DOI":"10.1061\/(ASCE)IS.1943-555X.0000321","article-title":"Capture and quantification of deterioration progression in concrete bridge decks through periodical NDE surveys","volume":"23","author":"Gucunski","year":"2017","journal-title":"J. Infrastruct. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103106","DOI":"10.1016\/j.autcon.2020.103106","article-title":"A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data","volume":"112","author":"Asadi","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103393","DOI":"10.1016\/j.autcon.2020.103393","article-title":"Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks","volume":"120","author":"Ahmed","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.autcon.2018.03.014","article-title":"Method for developing and updating deterioration models for concrete bridge decks using GPR data","volume":"91","author":"Ghodoosi","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100132","DOI":"10.1016\/j.dibe.2023.100132","article-title":"Pixel-level classification for bridge deck rebar detection and localization using multi-stage deep encoder-decoder network","volume":"14","author":"Ahmed","year":"2023","journal-title":"Dev. Built Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s13349-024-00895-6","article-title":"Ground penetrating radar-based automated defect identification of bridge decks: A hybrid approach","volume":"15","author":"Yu","year":"2025","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.conbuildmat.2014.11.023","article-title":"Reinforced concrete structure: Non destructive in situ strength assessment of concrete","volume":"75","author":"Pucinotti","year":"2015","journal-title":"Constr. Build. Mater."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.autcon.2018.02.017","article-title":"An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks","volume":"89","author":"Dinh","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ndteint.2018.04.009","article-title":"Migration-based automated rebar picking for condition assessment of concrete bridge decks with ground penetrating radar","volume":"98","author":"Dinh","year":"2018","journal-title":"NDT E Int."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4215","DOI":"10.1109\/TGRS.2019.2961772","article-title":"On the use of lateral wave for the interlayer debonding detecting in an asphalt airport pavement using a multistatic GPR system","volume":"58","author":"Zou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"WA69","DOI":"10.1190\/geo2020-0361.1","article-title":"Study of wavelet entropy for airport pavement inspection using a multistatic ground-penetrating radar system","volume":"86","author":"Zou","year":"2021","journal-title":"Geophysics"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105130","DOI":"10.1016\/j.autcon.2023.105130","article-title":"Corrosion assessment using ground penetrating radar in reinforced concrete structures: Influential factors and analysis methods","volume":"156","author":"Faris","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105631","DOI":"10.1016\/j.autcon.2024.105631","article-title":"Automated rebar recognition and corrosion assessment of concrete bridge decks using ground penetrating radar","volume":"166","author":"Faris","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"121","DOI":"10.2113\/JEEG22.2.121","article-title":"Concrete bridge deck deterioration assessment using ground penetrating radar (GPR)","volume":"22","author":"Diamanti","year":"2017","journal-title":"J. Environ. Eng. Geophys."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ndteint.2008.03.005","article-title":"Improved concrete bridge deck evaluation using GPR by accounting for signal depth\u2013amplitude effects","volume":"41","author":"Barnes","year":"2008","journal-title":"NDT E Int."},{"key":"ref_56","first-page":"e04174","article-title":"Automated delamination detection in concrete bridge decks using 1D-CNN and GPR data","volume":"22","author":"Elseicy","year":"2025","journal-title":"Case Stud. Constr. Mater."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Rashidi, M., Ghazimoghadam, S., Mousavi, V., Dorafshan, S., and Bozorg, B. (2026). A New Multi-Modal Data Fusion Framework for Delamination Detection in Concrete Bridge Decks. Sensors, 26.","DOI":"10.3390\/s26123926"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ndteint.2017.04.002","article-title":"A review of Ground Penetrating Radar application in civil engineering: A 30-year journey from Locating and Testing to Imaging and Diagnosis","volume":"96","author":"Lai","year":"2018","journal-title":"NDT E Int."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sun, H., Pashoutani, S., and Zhu, J. (2018). Nondestructive evaluation of concrete bridge decks with automated acoustic scanning system and ground penetrating radar. Sensors, 18.","DOI":"10.3390\/s18061955"},{"key":"ref_60","first-page":"104","article-title":"Ground penetrating radar utilization in exploring inadequate concrete covers in a new bridge deck","volume":"1","author":"Hasan","year":"2014","journal-title":"Case Stud. Constr. Mater."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.autcon.2017.03.004","article-title":"Corrosiveness Mapping of Bridge Decks Using Image-Based Analysis of GPR Data","volume":"80","author":"Abouhamad","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S0926-9851(99)00052-X","article-title":"Road evaluation with ground penetrating radar","volume":"43","author":"Saarenketo","year":"2000","journal-title":"J. Appl. Geophys."},{"key":"ref_63","first-page":"476","article-title":"Measuring soil water content with ground penetrating radar: A review","volume":"2","author":"Huisman","year":"2003","journal-title":"Vadose Zone J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/17538947.2017.1412520","article-title":"Measurement of soil water content using ground-penetrating radar: A review of current methods","volume":"12","author":"Liu","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1109\/TGRS.2009.2012701","article-title":"Automatic analysis of GPR images: A pattern-recognition approach","volume":"47","author":"Pasolli","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1177\/03611981251387128","article-title":"Deep Learning-Based Automated Cover Depth Estimation for Concrete Bridge Decks Using Ground-Penetrating Radar","volume":"2680","author":"Faris","year":"2026","journal-title":"Transp. Res. Rec."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"04025066","DOI":"10.1061\/JBENF2.BEENG-7508","article-title":"Advanced Ground-Penetrating Radar Techniques for Evaluating Deterioration in Bridge Decks","volume":"30","author":"Jang","year":"2025","journal-title":"J. Bridge Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep learning: Methods and applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends Signal Process."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zou, L., Li, Y., Munisami, K., and Alani, A.M. (2025). Bridging theory and practice: A review of AI-driven techniques for ground penetrating radar interpretation. Appl. Sci., 15.","DOI":"10.3390\/app15158177"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"113459","DOI":"10.1016\/j.engappai.2025.113459","article-title":"Deep learning-based rebar detection for automated evaluation of concrete bridge decks using ground penetrating radar","volume":"165","author":"Faris","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sharma, P., Chung, W.T., Akoush, B., and Ihme, M. (2023). A review of physics-informed machine learning in fluid mechanics. Energies, 16.","DOI":"10.3390\/en16052343"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.3390\/ai5030074","article-title":"Understanding physics-informed neural networks: Techniques, applications, trends, and challenges","volume":"5","author":"Farea","year":"2024","journal-title":"AI"},{"key":"ref_77","first-page":"15","article-title":"Computational electromagnetics: The finite-difference time-domain method","volume":"3","author":"Taflove","year":"2005","journal-title":"Electr. Eng. Handb."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"B10","DOI":"10.1115\/1.1445327","article-title":"Structural monitoring with fiber optic technology","volume":"55","author":"Measures","year":"2002","journal-title":"Appl. Mech. Rev."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"8601","DOI":"10.3390\/s120708601","article-title":"Recent progress in distributed fiber optic sensors","volume":"12","author":"Bao","year":"2012","journal-title":"Sensors"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"041302","DOI":"10.1063\/1.5113955","article-title":"Distributed optical fiber sensing: Review and perspective","volume":"6","author":"Lu","year":"2019","journal-title":"Appl. Phys. Rev."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.engstruct.2005.09.018","article-title":"Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation","volume":"28","author":"Li","year":"2006","journal-title":"Eng. Struct."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1016\/j.conbuildmat.2006.07.007","article-title":"Monitoring of an all-composite bridge using Bragg grating sensors","volume":"21","author":"Kister","year":"2007","journal-title":"Constr. Build. Mater."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1016\/j.engstruct.2010.02.033","article-title":"Development of a long-term monitoring system based on FBG sensors applied to concrete bridges","volume":"32","author":"Rodrigues","year":"2010","journal-title":"Eng. Struct."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.sna.2008.04.008","article-title":"Fibre Bragg gratings in structural health monitoring\u2014Present status and applications","volume":"147","author":"Majumder","year":"2008","journal-title":"Sens. Actuators A Phys."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1061\/(ASCE)1084-0702(2003)8:6(362)","article-title":"Fiber optic sensors for bridge monitoring","volume":"8","author":"Casas","year":"2003","journal-title":"J. Bridge Eng."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Wu, T., Liu, G., Fu, S., and Xing, F. (2020). Recent progress of fiber-optic sensors for the structural health monitoring of civil infrastructure. Sensors, 20.","DOI":"10.3390\/s20164517"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Yang, J., Hou, P., Yang, C., and Yang, N. (2021). Study of a long-gauge FBG strain sensor with enhanced sensitivity and its application in structural monitoring. Sensors, 21.","DOI":"10.3390\/s21103492"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/j.engstruct.2004.05.018","article-title":"Recent applications of fiber optic sensors to health monitoring in civil engineering","volume":"26","author":"Li","year":"2004","journal-title":"Eng. Struct."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3309","DOI":"10.1177\/1475921720984431","article-title":"Assessment and visualization of performance indicators of reinforced concrete beams by distributed optical fibre sensing","volume":"20","author":"Berrocal","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"113057","DOI":"10.1016\/j.engstruct.2021.113057","article-title":"Distributed fibre optic sensors in FRP composite bridge monitoring: Validation through proof load tests","volume":"246","author":"Siwowski","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"05014007","DOI":"10.1061\/(ASCE)BE.1943-5592.0000637","article-title":"Distributed Strain Behavior of a Reinforced Concrete Bridge: Case Study","volume":"19","author":"Regier","year":"2014","journal-title":"J. Bridge Eng."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Aung, T.L., Ma, N., Kishida, K., and Guzik, A. (2021). Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing. Sensors, 21.","DOI":"10.3390\/s21175794"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.apm.2023.04.013","article-title":"Unified inverse isogeometric analysis and distributed fiber optic strain sensing for monitoring structure deformation and stress","volume":"120","author":"Aung","year":"2023","journal-title":"Appl. Math. Model."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Kishida, K., Aung, T.L., and Lin, R. (2025). Monitoring a Railway Bridge with Distributed Fiber Optic Sensing Using Specially Installed Fibers. Sensors, 25.","DOI":"10.3390\/s25010098"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"119277","DOI":"10.1016\/j.engstruct.2024.119277","article-title":"Reference-free distributed monitoring of deflections in multi-span bridges","volume":"323","author":"Poloni","year":"2025","journal-title":"Eng. Struct."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Palmieri, L., Schenato, L., Santagiustina, M., and Galtarossa, A. (2022). Rayleigh-based distributed optical fiber sensing. Sensors, 22.","DOI":"10.3390\/s22186811"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Fernandez, I., Berrocal, C.G., and Rempling, R. (2021). Long-term performance of distributed optical fiber sensors embedded in reinforced concrete beams under sustained deflection and cyclic loading. Sensors, 21.","DOI":"10.3390\/s21196338"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Lv, B., Peng, Y., Du, C., Tian, Y., and Wu, J. (2025). Review of Brillouin Distributed Sensing for Structural Monitoring in Transportation Infrastructure. Infrastructures, 10.","DOI":"10.3390\/infrastructures10060148"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Bado, M.F., and Casas, J.R. (2021). A review of recent distributed optical fiber sensors applications for civil engineering structural health monitoring. Sensors, 21.","DOI":"10.3390\/s21051818"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"102577","DOI":"10.1016\/j.yofte.2021.102577","article-title":"Distributed optical fibre sensor for infrastructure monitoring: Field applications","volume":"64","author":"Wijaya","year":"2021","journal-title":"Opt. Fiber Technol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s13349-024-00774-0","article-title":"Dynamic monitoring of a masonry arch rail bridge using a distributed fiber optic sensing system","volume":"14","author":"Cheng","year":"2024","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1177\/14759217241231995","article-title":"Urban dark fiber distributed acoustic sensing for bridge monitoring","volume":"24","author":"Rodet","year":"2025","journal-title":"Struct. Health Monit."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Sasy Chan, Y.W., Wang, H.P., and Xiang, P. (2021). Optical fiber sensors for monitoring railway infrastructures: A review towards smart concept. Symmetry, 13.","DOI":"10.3390\/sym13122251"},{"key":"ref_104","first-page":"100214","article-title":"AI-assisted fibre optic sensing frameworks for automated monitoring of railway infrastructure","volume":"5","author":"Siahkouhi","year":"2026","journal-title":"J. Infrastruct. Intell. Resil."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1177\/1475921720921559","article-title":"Post-processing algorithms for distributed optical fiber sensing in structural health monitoring applications","volume":"20","author":"Bado","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"103382","DOI":"10.1016\/j.yofte.2023.103382","article-title":"Smart textile embedded with distributed fiber optic sensors for railway bridge long term monitoring","volume":"80","author":"Biondi","year":"2023","journal-title":"Opt. Fiber Technol."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Taheri, S., Siahkouhi, M., Moghimi, A., and Rashidi, M. (2026). Distributed Fiber Optic Sensors (DFOSs) for Structural Health Monitoring (SHM) of Railway Infrastructure: A Critical Review. Infrastructures, 11.","DOI":"10.3390\/infrastructures11080277"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1029\/97RG03139","article-title":"Radar interferometry and its application to changes in the Earth\u2019s surface","volume":"36","author":"Massonnet","year":"1998","journal-title":"Rev. Geophys."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2003.813278","article-title":"SAR monitoring of progressive and seasonal ground deformation using the permanent scatterers technique","volume":"41","author":"Colesanti","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_111","first-page":"liae012","article-title":"Comprehensive review of noncontact sensing technologies for bridge condition monitoring and assessment","volume":"3","author":"Tian","year":"2024","journal-title":"Intell. Transp. Infrastruct."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"R\u0103dulescu, V.M., R\u0103dulescu, G.M., Na\u0219, S.M., R\u0103dulescu, A.T., and R\u0103dulescu, C.M. (2024). Structural health monitoring of bridges under the influence of natural environmental factors and geomatic technologies: A literature review and bibliometric analysis. Buildings, 14.","DOI":"10.3390\/buildings14092811"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"118164","DOI":"10.1016\/j.engstruct.2024.118164","article-title":"Long-term deformation monitoring of a steel-truss arch bridge using PSI technique refined by temperature field analysis","volume":"311","author":"Zhou","year":"2024","journal-title":"Eng. Struct."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.geog.2023.07.005","article-title":"Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology","volume":"15","author":"Zhou","year":"2024","journal-title":"Geod. Geodyn."},{"key":"ref_115","first-page":"102476","article-title":"A structure knowledge-synthetic aperture radar interferometry integration method for high-precision deformation monitoring and risk identification of sea-crossing bridges","volume":"103","author":"Qin","year":"2021","journal-title":"J. Appl. Geophys."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2020.12.001","article-title":"Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1","volume":"172","author":"Widhalm","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Arikan Ispir, D., and Makineci, H.B. (2026). Determination of slow surface movements around the 1915 \u00c7anakkale Bridge during the 2022\u20132024 period with Sentinel-1 time series. Remote Sens., 18.","DOI":"10.3390\/rs18060858"},{"key":"ref_118","first-page":"1","article-title":"Structural evaluation of urban bridges in Amsterdam through InSAR-based displacement data","volume":"29","author":"Macchiarulo","year":"2024","journal-title":"e-J. Nondestruct. Test."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1177\/14759217241302369","article-title":"Regional-scale bridge condition monitoring using InSAR displacements and environmental data","volume":"24","author":"Quqa","year":"2025","journal-title":"Struct. Health Monit."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"5430","DOI":"10.1002\/suco.202400881","article-title":"Monitoring of a multi-span prestressed concrete bridge using satellite interferometric data and comparison with on-site sensor results","volume":"26","author":"Giordano","year":"2025","journal-title":"Struct. Concr."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"120335","DOI":"10.1016\/j.engstruct.2025.120335","article-title":"A refined DS-InSAR technique for long-term deformation monitoring of low-coherence bridge groups","volume":"335","author":"Zhou","year":"2025","journal-title":"Eng. Struct."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1177\/14759217211054350","article-title":"Damage detection on a historic iron bridge using satellite DInSAR data","volume":"21","author":"Giordano","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_123","first-page":"679","article-title":"Bridge Failure Risk Prediction Using Geospatial Data Processing via Multi-Head Attention Deep Learning Model","volume":"14","author":"Sisci","year":"2025","journal-title":"IEEE Access"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"123284","DOI":"10.1016\/j.engstruct.2026.123284","article-title":"Population-based structural health monitoring of historical masonry bridges using spaceborne InSAR measurements","volume":"366","author":"Alahmad","year":"2026","journal-title":"Eng. Struct."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Khan, A.Q., and Andrawes, B. (2025). Multi-bridge deformation monitoring and abnormal behaviour detection using MT-InSAR and machine learning. Struct. Infrastruct. Eng., 1\u201316.","DOI":"10.1080\/15732479.2025.2601114"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"122214","DOI":"10.1016\/j.measurement.2026.122214","article-title":"Three-dimensional bridge displacement reconstruction through Kalman filter fusion of multi-track InSAR observations","volume":"284","author":"Giordano","year":"2026","journal-title":"Measurement"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"112395","DOI":"10.1016\/j.istruc.2026.112395","article-title":"Seismic response prediction method for prefabricated bridge piers based on multi-head self-attention mechanism and frequency feature fusion","volume":"90","author":"Dou","year":"2026","journal-title":"Structures"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"9048","DOI":"10.1038\/s41467-025-64260-x","article-title":"Global geo-hazard risk assessment of long-span bridges enhanced with InSAR availability","volume":"16","author":"Malinowska","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"7141","DOI":"10.1109\/TGRS.2020.2979961","article-title":"Combined InSAR and terrestrial structural monitoring of bridges","volume":"58","author":"Selvakumaran","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_130","first-page":"1","article-title":"Bayesian Data Fusion for Enhanced Monitoring of Bridge Displacements Using Satellite InSAR and Topographic Techniques","volume":"29","author":"Tonelli","year":"2024","journal-title":"e-J. Nondestruct. Test."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"122738","DOI":"10.1016\/j.measurement.2026.122738","article-title":"Correlation between InSAR displacements and mechanical behaviour of bridges using analytical and disaggregation analyses","volume":"290","author":"Zacchei","year":"2026","journal-title":"Measurement"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Moya, H.A.B., Tran, M.Q., Pereira, S., Matos, J.C., and Dang, S.N. (2026). A Systematic Review of the Practical Applications of Synthetic Aperture Radar (SAR) for Bridge Structural Monitoring. Sustainability, 18.","DOI":"10.3390\/su18010514"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Yaragunda, V.R., and Oikonomou, E. (2025). Multi-Sensor fusion for land subsidence monitoring: Integrating MT-InSAR and GNSS with kalman filtering and feature importance to Northern Attica, Greece. Earth, 6.","DOI":"10.3390\/earth6020037"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Bhupesh, C., Frezer, A., Ian, P.D., Reihaneh, S., and Byungik, C. (2026). Uncrewed aerial system (UAS) applications in bridge inspection: A comprehensive review of platforms, sensors, and operational effectiveness. Drones, 10.","DOI":"10.3390\/drones10020144"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"e70000","DOI":"10.1002\/smb2.70000","article-title":"A review of technical advances and applications of intelligent inspection robots in structural health monitoring","volume":"1","author":"Li","year":"2025","journal-title":"SmartBot"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TRO.2024.3463504","article-title":"A new wave in robotics: Survey on recent mmwave radar applications in robotics","volume":"40","author":"Harlow","year":"2024","journal-title":"IEEE Trans. Robot."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"118809","DOI":"10.1016\/j.engstruct.2024.118809","article-title":"Smart structural health monitoring using computer vision and edge computing","volume":"319","author":"Peng","year":"2024","journal-title":"Eng. Struct."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"104273","DOI":"10.1016\/j.autcon.2022.104273","article-title":"Intelligent robotic systems for structural health monitoring: Applications and future trends","volume":"139","author":"Tian","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1002\/rob.21725","article-title":"Development of an autonomous bridge deck inspection robotic system","volume":"34","author":"La","year":"2017","journal-title":"J. Field Robot."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:25:36Z","timestamp":1790072736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,19]]},"references-count":139,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185930"],"URL":"https:\/\/doi.org\/10.3390\/s26185930","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,19]]}}}