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These methodologies will contribute to safe, automated, and intelligent assessment and maintenance of bridges, enhancing resilience and lifespan of transportation infrastructure under changing climate.<\/jats:p>","DOI":"10.3390\/s25185708","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T11:51:43Z","timestamp":1757937103000},"page":"5708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Methodologies for Remote Bridge Inspection\u2014Review"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8624-9904","authenticated-orcid":false,"given":"Diogo","family":"Ribeiro","sequence":"first","affiliation":[{"name":"iBuilt, School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CONSTRUCT-iRail, Faculty of Engineering, University of Porto, 4200-465 Porto, 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Infrastruct. Intell. Resil."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102231","DOI":"10.1016\/j.asej.2023.102231","article-title":"Three-dimensional modeling and defect quantification of existing concrete bridges based on photogrammetry and computer aided design","volume":"14","author":"Dabous","year":"2023","journal-title":"Ain Shams Eng. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pepe, M., Costantino, D., Crocetto, N., and Garofalo, A.R. (2019). 3D Modeling of Roman Bridge by the Integration of Terrestrial and UAV Photogrammetric Survey for Structural Analysis Purpose, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u2014ISPRS Archives.","DOI":"10.5194\/isprs-archives-XLII-2-W17-249-2019"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18279","DOI":"10.1007\/s11042-022-12703-8","article-title":"Engineering-oriented bridge multiple-damage detection with damage integrity using modified faster region-based convolutional neural network","volume":"81","author":"Yu","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kao, S.P., Chang, Y.C., and Wang, F.L. (2023). Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges. 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Proceedings of the First International Symposium on Big Data and Data Analytics in Collaboration (BDDAC 2013), San Diego, CA, USA.","DOI":"10.1109\/CTS.2013.6567203"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102652","DOI":"10.1016\/j.aei.2024.102652","article-title":"Remote collaborative framework for real-time structural condition assessment using Augmented Reality","volume":"62","author":"Awadallah","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_15","first-page":"351","article-title":"Real-Time Theoretical and Experimental Dynamic Mode Shapes for Structural Analysis Using Augmented Reality","volume":"Volume 8","author":"Dilworth","year":"2021","journal-title":"Topics in Modal Analysis & Testing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Carter, E., Sakr, M., and Sadhu, A. (2024). Augmented Reality-Based Real-Time Visualization for Structural Modal Identification. 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Health Monit."},{"key":"ref_20","first-page":"237","article-title":"Synthetic data augmentation for pixel-wise steel fatigue crack identification using fully convolutional networks","volume":"29","author":"Zhai","year":"2022","journal-title":"Smart Struct. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107850","DOI":"10.1016\/j.ymssp.2021.107850","article-title":"Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts","volume":"160","author":"Narazaki","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1111\/mice.12505","article-title":"Vision-based automated bridge component recognition with high-level scene consistency","volume":"35","author":"Narazaki","year":"2020","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104708","DOI":"10.1016\/j.autcon.2022.104708","article-title":"Automatic evaluation of rebar spacing and quality using LiDAR data: Field application for bridge structural assessment","volume":"146","author":"Yuan","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_24","unstructured":"Autodesk (Autodesk Revit, 2024). Autodesk Revit, Version 2024."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104127","DOI":"10.1016\/j.autcon.2021.104127","article-title":"Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds","volume":"135","author":"Lindenbergh","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"74","DOI":"10.47852\/bonviewJOPR32021565","article-title":"Dual-Frequency Lidar for Compressed Sensing 3D Imaging Based on All-Phase Fast Fourier Transform","volume":"1","author":"Li","year":"2023","journal-title":"JOPR"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114407","DOI":"10.1016\/j.engstruct.2022.114407","article-title":"Using 3D laser scanning for estimating the capacity of corroded steel bridge girders: Experiments, computations and analytical solutions","volume":"265","author":"Tzortzinis","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cabral, R., Oliveira, R., Ribeiro, D., Rakoczy, A.M., Santos, R., Azenha, M., and Correia, J. (2023). Railway Bridge Geometry Assessment Supported by Cutting-Edge Reality Capture Technologies and 3D As-Designed Models. Infrastructures, 8.","DOI":"10.3390\/infrastructures8070114"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cabral, R., Santos, R., Correia, J., and Ribeiro, D. (2025). Optimal reconstruction of railway bridges using a machine learning framework based on UAV photogrammetry and LiDAR. Struct. Infrastruct. Eng., 1\u201321.","DOI":"10.1080\/15732479.2025.2531562"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"NeRF: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105517","DOI":"10.1016\/j.autcon.2024.105517","article-title":"3D reconstruction of building structures incorporating neural radiation fields and geometric constraints","volume":"165","author":"Cui","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105878","DOI":"10.1016\/j.autcon.2024.105878","article-title":"3D Pixelwise damage mapping using a deep attention based modified Nerfacto","volume":"168","author":"Kim","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3592433","article-title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans. Graph."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_35","unstructured":"Blanco, A.C.T. (2020). Quantify LiDAR\u2019s Geometry Capturing Capability for Structural and Construction Assessment. [Ph.D. Thesis, Rutgers The State University of New Jersey]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104459","DOI":"10.1016\/j.autcon.2022.104459","article-title":"Segmentation of large-scale masonry arch bridge point clouds with a synthetic simulator and the BridgeNet neural network","volume":"142","author":"Jing","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2591","DOI":"10.1002\/stc.2591","article-title":"Automated bridge component recognition from point clouds using deep learning","volume":"27","author":"Kim","year":"2020","journal-title":"Struct. Control. Health Monit."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bahreini, F., and Hammad, A. (2021, January 2\u20134). Point Cloud Semantic Segmentation of Concrete Surface Defects Using Dynamic Graph CNN. Proceedings of the International Symposium on Automation and Robotics in Construction, Dubai, United Arab Emirates.","DOI":"10.22260\/ISARC2021\/0053"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1177\/13694332241260077","article-title":"Development of large-scale synthetic 3D point cloud datasets for vision-based bridge structural condition assessment","volume":"27","author":"Shi","year":"2024","journal-title":"Adv. Struct. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tareen, S.K., and Saleem, Z. (2018, January 3\u20134). A Comparative Analysis of Sift, Surf, Kaze, Akaze, Orb, and Brisk. Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan.","DOI":"10.1109\/ICOMET.2018.8346440"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Deng, L., Yuan, X., Deng, C., Chen, J., and Cai, Y. (2020). Image Stitching Based on Nonrigid Warping for Urban Scene. Sensors, 20.","DOI":"10.3390\/s20247050"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1080\/15732470801945930","article-title":"A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures","volume":"5","author":"Jahanshahi","year":"2009","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104813","DOI":"10.1016\/j.engfailanal.2020.104813","article-title":"Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing","volume":"117","author":"Ribeiro","year":"2020","journal-title":"Eng. Fail. Anal."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1061\/(ASCE)0887-3801(2003)17:4(255)","article-title":"Analysis of Edge-Detection Techniques for Crack Identification in Bridges","volume":"17","author":"Abudayyeh","year":"2003","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_45","unstructured":"Ribeiro, D., Santos, R., Cabral, R., and Shibasaki, A. (September, January 29). Remote Inspection and Monitoring of Civil Engineering Structures Based on Unmanned Aerial Vehicles. Proceedings of the International Conference of the European Association on Quality Control of Bridges and Structures, Padua, Italy."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, B., Zhao, W., Gao, P., Zhang, Y., and Wang, Z. (2018). Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model. Sensors, 18.","DOI":"10.3390\/s18061796"},{"key":"ref_47","unstructured":"Moon, H.-G., and Kim, J.-H. (July, January 21). Inteligent Crack Detecting Algorithm on the Concrete Crack Image Using Neural Network. Proceedings of the 28th International Symposium on Automation and Robotics in Construction (ISARC), Seoul, Republic of Korea."},{"key":"ref_48","unstructured":"Rutzinger, M., H\u00f6fle, B., Vetter, M., and Pfeifer, N. (2011). Digital terrain models from airborne laser scanning for the automatic extraction of natural and anthropogenic linear structures. Geomorphological Mapping: A Professional Handbook of Techniques and Applications, Elsevier."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s13349-020-00395-3","article-title":"A robotics and computer-aided procedure for defect evaluation in bridge inspection","volume":"10","author":"Potenza","year":"2020","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zollini, S., Alicandro, M., Dominici, D., Quaresima, R., and Giallonardo, M. (2020). UAV Photogrammetry for Concrete Bridge Inspection Using Object-Based Image Analysis (OBIA). Remote Sens., 12.","DOI":"10.3390\/rs12193180"},{"key":"ref_51","unstructured":"Agarap, A.F. (2018). Deep Learning using Rectified Linear Units (ReLU). arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"104743","DOI":"10.1016\/j.autcon.2023.104743","article-title":"Binocular video-based 3D reconstruction and length quantification of cracks in concrete structures","volume":"148","author":"Deng","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.autcon.2018.11.028","article-title":"Autonomous concrete crack detection using deep fully convolutional neural network","volume":"99","author":"Dung","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s43503-022-00002-y","article-title":"Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure","volume":"1","author":"Alexander","year":"2022","journal-title":"AI Civ. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105262","DOI":"10.1016\/j.autcon.2023.105262","article-title":"3D vision technologies for a self-developed structural external crack damage recognition robot","volume":"159","author":"Hu","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kim, B., and Cho, S. (2020). Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model. Appl. Sci., 10.","DOI":"10.3390\/app10228008"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"104214","DOI":"10.1016\/j.autcon.2022.104214","article-title":"Vision-based navigation planning for autonomous post-earthquake inspection of reinforced concrete railway viaducts using unmanned aerial vehicles","volume":"137","author":"Narazaki","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"115184","DOI":"10.1016\/j.engstruct.2022.115184","article-title":"Automated bridge component recognition using close-range images from unmanned aerial vehicles","volume":"274","author":"Kim","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/mice.12500","article-title":"Concrete bridge surface damage detection using a single-stage detector","volume":"35","author":"Zhang","year":"2020","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sun, S., Liu, W., and Cui, R. (2022, January 1\u20133). YOLO Based Bridge Surface Defect Detection Using Decoupled Prediction. Proceedings of the 2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Tianjin, China.","DOI":"10.1109\/ACIRS55390.2022.9845546"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lee, J.I., Sim, C., Detweiler, C., and Barnes, B. (2019). Computer-Vision Based UAV Inspection for Steel Bridge Connections. Struct. Health Monit.","DOI":"10.12783\/shm2019\/32473"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hoskere, V., Narazaki, Y., Spencer, B.F., and Smith, M.D. (2019). Deep Learning-Based Damage Detection of Miter Gates Using Synthetic Imagery from Computer Graphics, DEStech Publications Inc.","DOI":"10.12783\/shm2019\/32463"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4023118","DOI":"10.1061\/JBENF2.BEENG-6490","article-title":"Unsupervised Domain Adaptation Approach for Vision-Based Semantic Understanding of Bridge Inspection Scenes without Manual Annotations","volume":"29","author":"Narazaki","year":"2024","journal-title":"J. Bridg. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Rahman, A.U., and Hoskere, V. (2024). Instance Segmentation of Reinforced Concrete Bridges with Synthetic Point Clouds. arXiv.","DOI":"10.2139\/ssrn.4944550"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"104022","DOI":"10.1016\/j.autcon.2021.104022","article-title":"Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics","volume":"133","author":"Perry","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"108048","DOI":"10.1016\/j.measurement.2020.108048","article-title":"Streamlined Bridge Inspection System Utilizing Unmanned Aerial Vehicles (UAVs) and Machine Learning","volume":"164","author":"Perry","year":"2020","journal-title":"Measurement"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1080\/15732479.2022.2131845","article-title":"Detecting and localising damage based on image recognition and structure from motion, and reflecting it in a 3D bridge model","volume":"20","author":"Yamane","year":"2022","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0141-0296(93)90054-8","article-title":"Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge","volume":"15","author":"Stephen","year":"1993","journal-title":"Eng. Struct."},{"key":"ref_69","first-page":"425","article-title":"Dynamic displacement measurement of large-scale structures based on the Lucas\u2013Kanade template tracking algorithm","volume":"66","author":"Jie","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"315","DOI":"10.12989\/was.2015.20.2.315","article-title":"Multi-point displacement monitoring of bridges using a vision-based approach","volume":"20","author":"Ye","year":"2015","journal-title":"Wind Struct."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s13349-022-00637-6","article-title":"Short-distance and long-distance bridge displacement measurement based on template matching and feature detection methods","volume":"13","author":"Du","year":"2022","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1111\/mice.13177","article-title":"Real-time displacement measurement for long-span bridges using a compact vision-based system with speed-optimized template matching","volume":"39","author":"Wang","year":"2024","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1177\/1475921718806895","article-title":"Marker-free monitoring of the grandstand structures and modal identification using computer vision methods","volume":"18","author":"Dong","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ymssp.2016.11.009","article-title":"The subpixel resolution of optical-flow-based modal analysis","volume":"88","author":"Javh","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"062001","DOI":"10.1088\/0957-0233\/20\/6\/062001","article-title":"Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review","volume":"20","author":"Pan","year":"2009","journal-title":"Meas. Sci. Technol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"107211","DOI":"10.1016\/j.measurement.2019.107211","article-title":"Dynamic measurement of stay-cable force using digital image techniques","volume":"151","author":"Du","year":"2020","journal-title":"Measurement"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"04018120","DOI":"10.1061\/(ASCE)BE.1943-5592.0001341","article-title":"Serviceability Assessment of Masonry Arch Bridges Using Digital Image Correlation","volume":"24","author":"Dhanasekar","year":"2019","journal-title":"J. Bridg. Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e2382","DOI":"10.1002\/stc.2382","article-title":"Pressure-activated adhesive tape pattern for monitoring the structural condition of steel bridges via digital image correlation","volume":"26","author":"Wang","year":"2019","journal-title":"Struct. Control. Health Monit."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Yan, Z., Jin, Z., Teng, S., Chen, G., and Bassir, D. (2022). Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method. Appl. Sci., 12.","DOI":"10.3390\/app12105181"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hu, J., Zhu, Q., and Zhang, Q. (2022). Global Vibration Comfort Evaluation of Footbridges Based on Computer Vision. Sensors, 22.","DOI":"10.3390\/s22187077"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s13349-023-00720-6","article-title":"Assessment and monitoring of bridges using various camera placements and structural analysis","volume":"14","author":"Bai","year":"2024","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Dong, C., Bas, S., and Catbas, F.N. (2023). Applications of Computer Vision-Based Structural Monitoring on Long-Span Bridges in Turkey. Sensors, 23.","DOI":"10.3390\/s23198161"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Mousa, M.A., Yussof, M.M., Udi, U.J., Nazri, F.M., Kamarudin, M.K., Parke, G.A.R., Assi, L.N., and Ghahari, S.A. (2021). Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review. Infrastructures, 6.","DOI":"10.3390\/infrastructures6120176"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Christensen, C.O., Schmidt, J.W., Halding, P.S., Kapoor, M., and Goltermann, P. (2021). Digital Image Correlation for Evaluation of Cracks in Reinforced Concrete Bridge Slabs. Infrastructures, 6.","DOI":"10.3390\/infrastructures6070099"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"285","DOI":"10.2320\/matertrans.I-M2011843","article-title":"Bridge Deflection Measurement Using Digital Image Correlation with Camera Movement Correction","volume":"53","author":"Yoneyama","year":"2012","journal-title":"Mater. Trans."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/mice.12567","article-title":"Noncontact cable force estimation with unmanned aerial vehicle and computer vision","volume":"36","author":"Tian","year":"2020","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"e2187","DOI":"10.1002\/stc.2187","article-title":"Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation","volume":"25","author":"Acikgoz","year":"2018","journal-title":"Struct. Control. Health Monit."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3233\/BRS-2011-031","article-title":"Bridge displacement measurement through digital image correlation","volume":"7","author":"Peddle","year":"2011","journal-title":"Bridg. Struct."},{"key":"ref_89","unstructured":"Koltsida, I., Tomor, A., and Booth, C.A. (2013, January 2\u20134). The use of digital image correlation technique for monitoring masonry arch bridges. Proceedings of the 7th International Conference on Arch Bridges, Trogir-Split, Croatia."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Luo, K., Kong, X., Zhang, J., Hu, J., Li, J., and Tang, H. (2023). Computer Vision-Based Bridge Inspection and Monitoring: A Review. Sensors, 23.","DOI":"10.3390\/s23187863"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"107869","DOI":"10.1016\/j.ymssp.2021.107869","article-title":"Non-contact structural displacement measurement using Unmanned Aerial Vehicles and video-based systems","volume":"160","author":"Ribeiro","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e3025","DOI":"10.1002\/stc.3025","article-title":"Vision-based displacement measurement using an unmanned aerial vehicle","volume":"29","author":"Han","year":"2022","journal-title":"Struct. Control. Health Monit."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/mice.12338","article-title":"Structural Displacement Measurement Using an Unmanned Aerial System","volume":"33","author":"Yoon","year":"2018","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2461912.2461966","article-title":"Phase-Based Video Motion Processing","volume":"32","author":"Wadhwa","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ymssp.2016.08.041","article-title":"Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification","volume":"85","author":"Yang","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2250036","DOI":"10.1142\/S0219455422500365","article-title":"Cable Force Determination Using Phase-Based Video Motion Magnification and Digital Image Correlation","volume":"22","author":"Chen","year":"2021","journal-title":"Int. J. Struct. Stab. Dyn."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e12336","DOI":"10.1111\/str.12336","article-title":"An experimental study of the feasibility of phase-based video magnification for damage detection and localisation in operational deflection shapes","volume":"56","author":"Civera","year":"2020","journal-title":"Strain"},{"key":"ref_98","first-page":"41","article-title":"Experimental study of Euler motion amplification algorithm in bridge vibration analysis","volume":"36","author":"Chu","year":"2019","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.autcon.2018.05.025","article-title":"Multi-point vibration measurement and mode magnification of civil structures using video-based motion processing","volume":"93","author":"Shang","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"4019062","DOI":"10.1061\/(ASCE)ST.1943-541X.0002321","article-title":"Vision-based modal survey of civil infrastructure using unmanned aerial vehicles","volume":"145","author":"Hoskere","year":"2019","journal-title":"J. Struct. Eng."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"114498","DOI":"10.1016\/j.measurement.2024.114498","article-title":"A reliable methodology to estimate cable tension force in cable-stayed bridges using Unmanned Aerial Vehicle (UAV)","volume":"229","year":"2024","journal-title":"Measurement"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Mirzazade, A., Popescu, C., and T\u00e4ljsten, B. (2023). Prediction of Strain in Embedded Rebars for RC Member, Application of Hybrid Learning Approach. Infrastructures, 8.","DOI":"10.3390\/infrastructures8040071"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.procs.2015.04.188","article-title":"A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology","volume":"48","author":"Anuradha","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0167-739X(97)00015-0","article-title":"Data mining and KDD: Promise and challenges","volume":"13","author":"Fayyad","year":"1997","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_105","unstructured":"Tan, P.-N., Steinbach, M., Karpatne, A., and Kumar, V. (2018). Introduction to Data Mining, Pearson. [2nd ed.]."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"04020073","DOI":"10.1061\/(ASCE)ST.1943-541X.0002535","article-title":"Review of Bridge Structural Health Monitoring Aided by Big Data andand Artificial Intelligence: From Condition Assessment to Damage Detection","volume":"146","author":"Sun","year":"2020","journal-title":"J. Struct. Eng."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"F4016004","DOI":"10.1061\/AJRUA6.0000889","article-title":"Data Mining of Bridge Concrete Deck Parameters in the National Bridge Inventory by Two-Step Cluster Analysis","volume":"3","author":"Radovic","year":"2017","journal-title":"ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1007\/s003660070009","article-title":"A knowledge discovery framework for civil infrastructure: A case study of the intelligent workplace","volume":"16","author":"Buchheit","year":"2000","journal-title":"Eng. Comput."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1061\/(ASCE)1076-0342(2006)12:1(50)","article-title":"Knowledge Discovery in a Facility Condition Assessment Database Using Text Clustering","volume":"12","author":"Ng","year":"2006","journal-title":"J. Infrastruct. Syst."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/12460125.2012.759485","article-title":"A human-centred design approach for developing dynamic decision support system based on knowledge discovery in databases","volume":"22","author":"Ltifi","year":"2013","journal-title":"J. Decis. Syst."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"407","DOI":"10.24425\/ace.2024.151900","article-title":"Introducing a new method for assessing short railway bridge conditions using vehicle onboard systems","volume":"70","author":"Rakoczy","year":"2024","journal-title":"Arch. Civ. Eng."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Chuang, Y.-H., Yau, N.-J., and Tabor, J.M.M. (2023). A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan. Sustainability, 15.","DOI":"10.3390\/su15021697"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Hehenberger, P., and Bradley, D. (2016). Digital twin\u2014The simulation aspect. Mechatronic Futures, Springer International Publishing.","DOI":"10.1007\/978-3-319-32156-1"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Grieves, M., and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems, Springer.","DOI":"10.1007\/978-3-319-38756-7_4"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Lu, Q., Xie, X., Heaton, J., Parlikad, A.K., and Schooling, J. From BIM towards digital twin: Strategy and future development for smart asset management. Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, Proceedings of SOHOMA, Sonoma, CA, USA, 26 September 2019, Springer.","DOI":"10.1007\/978-3-030-27477-1_30"},{"key":"ref_116","first-page":"12","article-title":"Digital twin data modeling with automationml and a communication methodology for data exchange","volume":"49","author":"Schroeder","year":"2016","journal-title":"IFAC-Pap."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"26901","DOI":"10.1109\/ACCESS.2017.2766453","article-title":"A digital twin-based approach for designing and multi-objective optimization of hollow glass production line","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1007\/s00170-017-0233-1","article-title":"Digital twin-driven product design, manufacturing and service with big data","volume":"94","author":"Tao","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.cirp.2017.04.038","article-title":"Toward a Digital Twin for real-time geometry assurance in individualized production","volume":"66","author":"Carlson","year":"2017","journal-title":"CIRP Ann."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1080\/16864360.2018.1462569","article-title":"Towards an extended model-based definition for the digital twin","volume":"15","author":"Miller","year":"2018","journal-title":"Comput.-Aided Des. Appl."},{"key":"ref_121","first-page":"502","article-title":"Machine learning based digital twin framework for production optimization in petrochemical industry","volume":"49","author":"Min","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MMUL.2018.023121167","article-title":"Digital twins: The convergence of multimedia technologies","volume":"25","year":"2018","journal-title":"IEEE Multimed."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1109\/TII.2019.2938885","article-title":"A digital twin based industrial automation and control system security architecture","volume":"16","author":"Gehrmann","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_124","first-page":"145","article-title":"A review of digital twin applications in construction","volume":"27","author":"Madubuike","year":"2022","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.conbuildmat.2013.08.079","article-title":"Rapid assessment of foundation scour using the dynamic features of bridge superstructure","volume":"50","author":"Elsaid","year":"2014","journal-title":"Constr. Build. Mater."},{"key":"ref_126","unstructured":"Hoskere, V., Hassanlou, D., Ur Rahman, A., Bazrgary, R., and Ali, M.T. (2025). Digital Twins of Bridge Structures. Autom. Constr."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Boje, C., Mack, N., Kubicki, S., Vidal, A.L., S\u00e1nchez, C.C., Dugu\u00e9, A., and Brassier, P. (2023, January 10\u201312). Digital Twin systems for building fa\u00e7ade elements testing. Proceedings of the In EC3 Conference 2023, Heraklion, Greece.","DOI":"10.35490\/EC3.2023.240"},{"key":"ref_128","first-page":"100445","article-title":"Digital twins for performance management in the built environment","volume":"33","author":"Petri","year":"2023","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Jasi\u0144ski, M., \u0141azi\u0144ski, P., and Piotrowski, D. (2023). The Concept of Creating Digital Twins of Bridges Using Load Tests. Sensors, 23.","DOI":"10.3390\/s23177349"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"104835","DOI":"10.1016\/j.autcon.2023.104835","article-title":"AIoT-informed digital twin communication for bridge maintenance","volume":"150","author":"Gao","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Shim, C.S., Kang, H.R., and Dang, N.S. (2019, January 8\u201310). Digital Twin Models for Maintenance of Cable-Supported Bridges. Proceedings of the International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving Data-Informed Decision-Making, Cambridge, UK.","DOI":"10.1680\/icsic.64669.737"},{"key":"ref_132","first-page":"1105","article-title":"Digital twinning during load tests of railway bridges\u2014Case study: The high-speed railway network, Extremadura, Spain","volume":"20","author":"Ramonell","year":"2023","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_133","first-page":"100532","article-title":"Al-Ansi, Analyzing augmented reality (AR) and virtual reality (VR) recent development in education","volume":"8","author":"Jaboob","year":"2023","journal-title":"Soc. Sci. Humanit. Open"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Lueangvilai, E., and Chaisomphob, T. (2022). Application of Mobile Mapping System to a Cable-Stayed Bridge in Thailand. Sensors, 22.","DOI":"10.3390\/s22249625"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1162\/pres.1997.6.4.355","article-title":"A Survey of Augmented Reality","volume":"6","author":"Azuma","year":"1997","journal-title":"Presence Teleoperators Virtual Environ."},{"key":"ref_136","first-page":"1321","article-title":"A Taxonomy of Mixed Reality Visual Displays","volume":"77","author":"Milgram","year":"1994","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1177\/1475921720977017","article-title":"Enabling human\u2013infrastructure interfaces for inspection using augmented reality","volume":"20","author":"Maharjan","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Moreu, F., Bleck, B., Vemuganti, S., Rogers, D., and Mascarenas, D. (2017). Augmented Reality Tools for Enhanced Structural Inspection. Struct. Health Monit.","DOI":"10.12783\/shm2017\/14221"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1177\/1475921720953846","article-title":"Augmented reality for next generation infrastructure inspections","volume":"20","author":"Ballor","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Xu, J., and Moreu, F. (2021). A Review of Augmented Reality Applications in Civil Infrastructure During the 4th Industrial Revolution. Front. Built Environ., 7.","DOI":"10.3389\/fbuil.2021.640732"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"e12602","DOI":"10.1002\/eng2.12602","article-title":"State of the art of augmented reality capabilities for civil infrastructure applications","volume":"5","author":"Xu","year":"2023","journal-title":"Eng. Rep."},{"key":"ref_142","unstructured":"Catbas, N., and Avci, O. (2021, January 15). A review of latest trends in bridge health monitoring. Proceedings of the Institution of Civil Engineers\u2014Bridge Engineering, London, UK."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"103936","DOI":"10.1016\/j.compind.2023.103936","article-title":"Augmented reality-computer vision combination for automatic fatigue crack detection and localization","volume":"149","author":"Mohammadkhorasani","year":"2023","journal-title":"Comput. Ind."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"104542","DOI":"10.1016\/j.autcon.2022.104542","article-title":"Realtime conversion of cracks from pixel to engineering scale using Augmented Reality","volume":"143","author":"Malek","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1111\/mice.12932","article-title":"Methodology to integrate augmented reality and pattern recognition for crack detection","volume":"38","author":"Malek","year":"2022","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Moreu, F., Chang, C.-M., Liu, Y.-T., Malek, K., and Khorasani, A. (2024). Human-centered post-disaster inspection of infrastructure using augmented reality. Bridge Maintenance, Safety, Management, Digitalization and Sustainability, CRC Press.","DOI":"10.1201\/9781003483755-397"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Aguero, M., Maharjan, D., Rodriguez, M.d.P., Mascarenas, D.D.L., and Moreu, F. (2020). Design and Implementation of a Connection between Augmented Reality and Sensors. Robotics, 9.","DOI":"10.3390\/robotics9010003"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"100005","DOI":"10.1016\/j.jdd.2024.100005","article-title":"Human-in-the-loop control of dynamics and robotics using augmented reality","volume":"1","author":"Wyckoff","year":"2025","journal-title":"J. Dyn. Disasters"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.autcon.2008.05.007","article-title":"Evaluation of Augmented Reality in steel column inspection","volume":"18","author":"Shin","year":"2009","journal-title":"Autom. Constr."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"16963","DOI":"10.1109\/ACCESS.2024.3358843","article-title":"Integration of Bridge Health Monitoring System with Augmented Reality Application Developed Using 3D Game Engine\u2013Case Study","volume":"12","author":"Fawad","year":"2024","journal-title":"IEEE Access"},{"key":"ref_151","first-page":"487","article-title":"BIM-based mixed-reality application for bridge inspection and maintenance","volume":"22","author":"Cuong","year":"2021","journal-title":"Constr. 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