{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T04:26:54Z","timestamp":1784867214675,"version":"3.55.0"},"reference-count":208,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Province Key R&amp;D Plan Project","award":["20200401113GX"],"award-info":[{"award-number":["20200401113GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing need for inexpensive, safe, highly efficient, and time-saving damage detection technology, combined with emerging technologies, has made damage detection by unmanned aircraft systems (UAS) an active research area. In the past, numerous sensors have been developed for damage detection, but these sensors have only recently been integrated with UAS. UAS damage detection specifically concerns data collection, path planning, multi-sensor fusion, system integration, damage quantification, and data processing in building a prediction model to predict the remaining service life. This review provides an overview of crucial scientific advances that marked the development of UAS inspection: underlying UAS platforms, peripherals, sensing equipment, data processing approaches, and service life prediction models. Example equipment includes a visual camera, a multispectral sensor, a hyperspectral sensor, a thermal infrared sensor, and light detection and ranging (LiDAR). This review also includes highlights of the remaining scientific challenges and development trends, including the critical need for self-navigated control, autonomic damage detection, and deterioration model building. Finally, we conclude with a brief discussion regarding the pros and cons of this emerging technology, along with a prospect of UAS technology research for damage detection.<\/jats:p>","DOI":"10.3390\/rs14174210","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Unmanned Aircraft System Applications in Damage Detection and Service Life Prediction for Bridges: A Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Hongze","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China"},{"name":"Key Laboratory of CNC Equipment Reliability, School of Mechanical and Aerospace Engineering, Ministry of Education, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China"},{"name":"Key Laboratory of CNC Equipment Reliability, School of Mechanical and Aerospace Engineering, Ministry of Education, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China"},{"name":"Key Laboratory of CNC Equipment Reliability, School of Mechanical and Aerospace Engineering, Ministry of Education, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0622-0445","authenticated-orcid":false,"given":"Hang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China"},{"name":"Key Laboratory of CNC Equipment Reliability, School of Mechanical and Aerospace Engineering, Ministry of Education, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116504","DOI":"10.1016\/j.eswa.2022.116504","article-title":"Reliability allocation method based on linguistic neutrosophic numbers weight Muirhead mean operator","volume":"193","author":"Li","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0143-974X(99)00056-5","article-title":"Nominal stress range fatigue of stainless steel fillet welds\u2014The effect of weld size","volume":"54","author":"Lahti","year":"2000","journal-title":"J. Constr. Steel Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1061\/(ASCE)0733-9445(1998)124:3(309)","article-title":"Service-Life Prediction of Deteriorating Concrete Bridges","volume":"124","author":"Enright","year":"1998","journal-title":"J. Struct. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1115\/1.1491974","article-title":"Risk Assessment of Decommissioning Options Using Bayesian Networks","volume":"124","author":"Faber","year":"2002","journal-title":"J. Offshore Mech. Arct. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ma, Y., Lu, B., Guo, Z., Wang, L., Chen, H., and Zhang, J. (2019). Limit Equilibrium Method-based Shear Strength Prediction for Corroded Reinforced Concrete Beam with Inclined Bars. Materials, 12.","DOI":"10.3390\/ma12071014"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s13349-016-0160-0","article-title":"A clustering approach for structural health monitoring on bridges","volume":"6","author":"Diez","year":"2016","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12205-011-0001-y","article-title":"Smart sensing, monitoring, and damage detection for civil infrastructures","volume":"15","author":"Yun","year":"2010","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1061\/(ASCE)0733-9399(1997)123:9(897)","article-title":"Structural Control: Past, Present, and Future","volume":"123","author":"Housner","year":"1997","journal-title":"J. Eng. Mech."},{"key":"ref_9","unstructured":"Hall, S.R. (1999, January 8\u201310). The effective management and use of structural health data. Proceedings of the 2nd International Workshop on Structural Health Monit, Lancaster, PA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1088\/0964-1726\/11\/2\/310","article-title":"Damage detection in composite materials using Lamb wave methods","volume":"11","author":"Kessler","year":"2002","journal-title":"Smart Mater. Struct."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1177\/0583102406075428","article-title":"Review of Guided-wave Structural Health Monitoring","volume":"39","author":"Raghavan","year":"2007","journal-title":"Shock Vib. Dig."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s13349-011-0009-5","article-title":"Vibration-based monitoring of civil infrastructure: Challenges and successes","volume":"1","author":"Brownjohn","year":"2011","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1016\/j.engstruct.2005.02.021","article-title":"Technology developments in structural health monitoring of large-scale bridges","volume":"27","author":"Ko","year":"2005","journal-title":"Eng. Struct."},{"key":"ref_14","first-page":"51","article-title":"Structural monitoring and identification of civil infrastructure in the United States","volume":"3","author":"Nagarajaiah","year":"2016","journal-title":"Struct. Monit. Maint."},{"key":"ref_15","unstructured":"Jongerius, A. (2018). The Use of Unmanned Aerial Vehicles to Inspect Bridges for Rijkswaterstaat. [Bachelor\u2019s Thesis, Faculty of Engineering Technology, University of Twente]."},{"key":"ref_16","first-page":"1135","article-title":"Potential of uav-based laser scanner and multispectral camera data in building inspection","volume":"XLI-B1","author":"Mader","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-020-3117-1","article-title":"Fatigue analysis of a bridge deck using the peaks-over-threshold approach with application to the Millau viaduct","volume":"2","author":"Nesterova","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"04020117","DOI":"10.1061\/(ASCE)ST.1943-541X.0002666","article-title":"Probabilistic Life Prediction for Reinforced Concrete Structures Subjected to Seasonal Corrosion-Fatigue Damage","volume":"146","author":"Ma","year":"2020","journal-title":"J. Struct. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"A159","DOI":"10.1115\/1.4009458","article-title":"Cumulative Damage in Fatigue","volume":"12","author":"Miner","year":"1945","journal-title":"J. Appl. Mech."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.jcsr.2006.06.007","article-title":"Fatigue reliability of welded steel structures","volume":"62","author":"Chryssanthopoulos","year":"2006","journal-title":"J. Constr. Steel Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"678855","DOI":"10.3389\/fmats.2021.678855","article-title":"Fatigue Reliability Assessment for Orthotropic Steel Bridge Decks Considering Load Sequence Effects","volume":"8","author":"Xu","year":"2021","journal-title":"Front. Mater."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1016\/j.ijfatigue.2010.01.002","article-title":"Bridge fatigue reliability assessment using probability density functions of equivalent stress range based on field monitoring data","volume":"32","author":"Kwon","year":"2010","journal-title":"Int. J. Fatigue"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.engfailanal.2019.01.032","article-title":"Fatigue redesign of failed sub frame using stress measuring, FEA and British Standard","volume":"97","author":"Ma","year":"2019","journal-title":"Eng. Fail. Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113050","DOI":"10.1016\/j.engstruct.2021.113050","article-title":"Early damage detection of fatigue failure for RC deck slabs under wheel load moving test using image analysis with artificial intelligence","volume":"246","author":"Adel","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.autcon.2006.12.010","article-title":"A UAV for bridge inspection: Visual servoing control law with orientation limits","volume":"17","author":"Metni","year":"2007","journal-title":"Autom. Constr."},{"key":"ref_26","unstructured":"Dorafshan, S., Maguire, M., and Qi, X. (2016). Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations, Department of Civil and Environmental Engineering, Utah State University. UTC Report 01-2016."},{"key":"ref_27","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_28","first-page":"2304","article-title":"Robust image-based crack detection in concrete structure using multi-scale enhancement and visual features","volume":"17","author":"Liu","year":"2017","journal-title":"IEEE Int. Conf. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s00138-009-0189-8","article-title":"Fast crack detection method for large-size concrete surface images using percolation-based image processing","volume":"21","author":"Yamaguchi","year":"2009","journal-title":"Mach. Vis. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.autcon.2009.04.003","article-title":"Bridge inspection robot system with machine vision","volume":"18","author":"Oh","year":"2009","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.conbuildmat.2016.11.032","article-title":"Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data","volume":"130","author":"Cabaleiro","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/TASE.2013.2294687","article-title":"A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance","volume":"11","author":"Lim","year":"2014","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_33","unstructured":"Kim, J.W., Kim, S.B., Park, J.C., and Nam, J.W. (2015, January 25\u201329). Development of crack detection system with unmanned aerial vehicles and digital image processing. Proceedings of the Advances in Structural Engineering and Mechanics (ASEM15), Incheon, Korea."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijleo.2015.09.147","article-title":"Detection crack in image using Otsu method and multiple filtering in image processing techniques","volume":"127","author":"Talab","year":"2016","journal-title":"Optik"},{"key":"ref_35","unstructured":"Dorafshan, S. (2017, January 13\u201316). Comparing Automated Image-Based Crack Detection Techniques in Spatial and Frequency Domains. Proceedings of the 26th ASNT Research Symposium, Jacksonville, FL, USA."},{"key":"ref_36","unstructured":"Dorafshan, S., and Maguire, M. (2017, January 26\u201328). Autonomous Detection of Concrete Cracks on Bridge Decks and Fatigue Cracks on Steel Members. Proceedings of the ASNT Digital Imaging 20, Mashantucket, CT, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Choudhary, G.K., and Dey, S. (2012, January 18\u201320). Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks. Proceedings of the 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Nanjing, China.","DOI":"10.1109\/ICACI.2012.6463195"},{"key":"ref_38","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. Civ. Infrastruct. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dorafshan, S., Thomas, R., Coopmans, C., and Maguire, M. (2018, January 12\u201315). Deep Learning Neural Networks for sUAS-Assisted Structural Inspections: Feasibility and Application. Proceedings of the 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, TX, USA.","DOI":"10.1109\/ICUAS.2018.8453409"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/mice.12334","article-title":"Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types","volume":"33","author":"Cha","year":"2017","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.conbuildmat.2018.08.011","article-title":"Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete","volume":"186","author":"Dorafshan","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/j.1747-1567.2010.00644.x","article-title":"Lidar-Based Bridge Structure Defect Detection","volume":"35","author":"Liu","year":"2010","journal-title":"Exp. Tech."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1002\/stc.1533","article-title":"Reliability analysis of bridge evaluations based on 3D Light Detection and Ranging data","volume":"20","author":"Liu","year":"2013","journal-title":"Struct. Control Health Monit."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1061\/(ASCE)BE.1943-5592.0001343","article-title":"UAV Bridge Inspection through Evaluated 3D Reconstructions","volume":"24","author":"Chen","year":"2019","journal-title":"J. Bridge Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, L., and Chu, C.-H. (2009, January 11\u201314). 3D building reconstruction from LiDAR data. Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5345938"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1007\/s11431-019-1571-0","article-title":"UAV-based integrated multispectral-LiDAR imaging system and data processing","volume":"63","author":"Gu","year":"2020","journal-title":"Sci. China Technol. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"416","DOI":"10.2747\/1548-1603.48.3.416","article-title":"A New Method to Correct Pushbroom Hyperspectral Data Using Linear Features and Ground Control Points","volume":"48","author":"Jensen","year":"2011","journal-title":"GIScience Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S1566-2535(02)00055-6","article-title":"Multi-sensor management for information fusion: Issues and approaches","volume":"3","author":"Xiong","year":"2002","journal-title":"Inf. Fusion"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.compeleceng.2011.07.012","article-title":"A non-reference image fusion metric based on mutual information of image features","volume":"37","author":"Haghighat","year":"2011","journal-title":"Comput. Electr. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bircher, A., Alexis, K., Burri, M., Oettershagen, P., Omari, S., Mantel, T., and Siegwart, R. (2015, January 26\u201330). Structural inspection path planning via iterative viewpoint resampling with application to aerial robotics. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7140101"},{"key":"ref_51","first-page":"283","article-title":"Towards UAV-based bridge inspection systems: A review and an application perspective","volume":"2","author":"Chan","year":"2015","journal-title":"Struct. Monit. Maint."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"025015","DOI":"10.1117\/1.JRS.11.025015","article-title":"Maximizing feature detection in aerial unmanned aerial vehicle datasets","volume":"11","author":"Byrne","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_53","unstructured":"Cheng, E. (2016). Aerial Photography and Videography Using Drones, Peachpit Press."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Emery, W.J., and Schmalzel, J. (2018). Editorial for \u201cRemote Sensing from Unmanned Aerial Vehicles\u201d. Remote Sens., 10.","DOI":"10.3390\/rs10121877"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2747\/1548-1603.48.1.1","article-title":"Introduction\u2014Small-Scale Unmanned Aerial Systems for Environmental Remote Sensing","volume":"48","author":"Hardin","year":"2011","journal-title":"GIScience Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4845","DOI":"10.1080\/01431161.2018.1491518","article-title":"Unmanned Aerial Systems (UAS) for environmental applications special issue preface","volume":"39","author":"Milas","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Dorafshan, S., Maguire, M., Hoffer, N., and Coopmans, C. (2018). Fatigue Crack Detection Using Unmanned Aerial Systems in Under-Bridge Inspection.","DOI":"10.1061\/(ASCE)BE.1943-5592.0001291"},{"key":"ref_58","first-page":"148","article-title":"Nondestructive evaluation inspection of the Arlington Memorial Bridge using a robotic assisted bridge inspection tool (RABIT)","volume":"Volume 9063","author":"Wu","year":"2014","journal-title":"Proceedings of the Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gucunski, N., Kee, S.-H., La, H., Basily, B., Maher, A., and Ghasemi, H. (2015, January 23\u201325). Implementation of a Fully Autonomous Platform for Assessment of Concrete Bridge Decks RABIT. Proceedings of the Structures Congress 2015, Portland, OR, USA.","DOI":"10.1061\/9780784479117.032"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.3390\/rs4061519","article-title":"Development of a UAV-LiDAR System with Application to Forest Inventory","volume":"4","author":"Wallace","year":"2012","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1061\/(ASCE)BE.1943-5592.0001291","article-title":"Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges","volume":"23","author":"Dorafshan","year":"2018","journal-title":"J. Bridg. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1177\/1475921719898862","article-title":"Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method","volume":"19","author":"Lei","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1177\/1475921720932384","article-title":"Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network","volume":"20","author":"Saleem","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"108683","DOI":"10.1016\/j.measurement.2020.108683","article-title":"Homography-based measurement of bridge vibration using UAV and DIC method","volume":"170","author":"Chen","year":"2020","journal-title":"Measurement"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e2757","DOI":"10.1002\/stc.2757","article-title":"Towards automated detection and quantification of concrete cracks using integrated images and lidar data from unmanned aerial vehicles","volume":"28","author":"Yan","year":"2021","journal-title":"Struct. Control Health Monit."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/mice.12501","article-title":"Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction","volume":"35","author":"Liu","year":"2019","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Aber, J.S., Marzolff, I., Ries, J.B., and Aber, S.E.W. (2019). Chapter 8\u2014Unmanned Aerial Systems. Small-Format Aerial Photography and UAS Imagery, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-12-812942-5.00008-2"},{"key":"ref_68","unstructured":"Gabriely, Y., and Rimon, E. (2002, January 11\u201315). Spiral-STC: An on-line coverage algorithm of grid environments by a mobile robot. Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Washington, DC, USA."},{"key":"ref_69","unstructured":"Zelinsky, A., Jarvis, R., and Byrne, J. (2021, January 6\u201310). Planning Paths of Complete Coverage of an Unstructured Environment by a Mobile Robot. Proceedings of the International Conference on Advanced Robotics, Ljubljana, Slovenia."},{"key":"ref_70","unstructured":"Luo, C., Yang, S., Stacey, D.A., and Jofriet, J. (2002, January 11\u201315). A solution to vicinity problem of obstacles in complete coverage path planning. Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Washington, DC, USA."},{"key":"ref_71","first-page":"7","article-title":"Solution of a Large-Scale Traveling-Salesman Problem","volume":"2","author":"Cook","year":"2009","journal-title":"J. Oper. Res. Soc. Am."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.autcon.2017.04.013","article-title":"Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection","volume":"81","author":"Phung","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"103250","DOI":"10.1016\/j.autcon.2020.103250","article-title":"LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection","volume":"117","author":"Bolourian","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1177\/0278364912461059","article-title":"Advanced perception, navigation and planning for autonomous in-water ship hull inspection","volume":"31","author":"Hover","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Michel, D., and McIsaac, K. (2012, January 5\u20138). New path planning scheme for complete coverage of mapped areas by single and multiple robots. Proceedings of the 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China.","DOI":"10.1109\/ICMA.2012.6283527"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1007\/s12205-014-0633-9","article-title":"Non-destructive testing methods in the U.S. for bridge inspection and maintenance","volume":"18","author":"Lee","year":"2014","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Kim, I.-H., Jeon, H., Baek, S.-C., Hong, W.-H., and Jung, H.-J. (2018). Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle. Sensors, 18.","DOI":"10.3390\/s18061881"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TASE.2014.2354314","article-title":"Automated Crack Detection on Concrete Bridges","volume":"13","author":"Prasanna","year":"2014","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Ortiz, A., Bonnin-Pascual, F., Garcia-Fidalgo, E., and Company-Corcoles, J.P. (2016). Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application. Sensors, 16.","DOI":"10.3390\/s16122118"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.autcon.2011.03.004","article-title":"Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation","volume":"20","author":"Zhu","year":"2011","journal-title":"Autom. Constr."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1061\/(ASCE)0733-9399(2004)130:8(921)","article-title":"Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures","volume":"130","author":"Catbas","year":"2004","journal-title":"J. Eng. Mech."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"108429","DOI":"10.1016\/j.measurement.2020.108429","article-title":"Automatic recognition of surface cracks in bridges based on 2D-APES and mobile machine vision","volume":"168","author":"Dan","year":"2020","journal-title":"Measurement"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., Zayed, T., and Bagchi, A. (2022). Deterioration Mapping of RC Bridge Elements Based on Automated Analysis of GPR Images. Remote Sens., 14.","DOI":"10.3390\/rs14051131"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/LGRS.2010.2079913","article-title":"Mini-UAV-Borne LIDAR for Fine-Scale Mapping","volume":"8","author":"Lin","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1139\/juvs-2014-0007","article-title":"Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: Scientific and commercial applications","volume":"2","author":"Whitehead","year":"2014","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.compstruct.2011.08.017","article-title":"Investigation of the parameters that influence the accuracy of bond defect detection in CFRP bonded specimens using IR thermography","volume":"94","author":"Tashan","year":"2012","journal-title":"Compos. Struct."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.infrared.2004.04.005","article-title":"IR self-referencing thermography for detection of in-depth defects","volume":"46","author":"Omar","year":"2005","journal-title":"Infrared Phys. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.conbuildmat.2013.10.085","article-title":"Passive thermographic detection of moisture problems in fa\u00e7ades with adhered ceramic cladding","volume":"51","author":"Edis","year":"2013","journal-title":"Constr. Build. Mater."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Vetrivel, A., Gerke, M., Kerle, N., and Vosselman, G. (2016). Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach. Remote Sens., 8.","DOI":"10.3390\/rs8030231"},{"key":"ref_90","first-page":"1315619","article-title":"Use of unmanned aerial vehicles in monitoring application and management of natural hazards","volume":"8","author":"Giordan","year":"2016","journal-title":"Geomatics Nat. Hazards Risk"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1002\/rob.21615","article-title":"Search and Rescue Rotary-Wing UAV and Its Application to the Lushan Ms 7.0 Earthquake","volume":"33","author":"Qi","year":"2015","journal-title":"J. Field Robot."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0963-8695(02)00060-9","article-title":"Application of infrared thermography to the non-destructive testing of concrete and masonry bridges","volume":"36","author":"Clark","year":"2003","journal-title":"NDT E Int."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1080\/10589759.2014.941842","article-title":"Ultraviolet excitation for thermography inspection of surface cracks in welded joints","volume":"29","author":"Runnemalm","year":"2014","journal-title":"Nondestruct. Test. Evaluation"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.conbuildmat.2010.04.014","article-title":"Combined use of thermography and ultrasound for the characterization of subsurface cracks in concrete","volume":"24","author":"Aggelis","year":"2010","journal-title":"Constr. Build. Mater."},{"key":"ref_95","first-page":"210","article-title":"Forced-diffusion thermography technique and projector design","volume":"2766","author":"Lesniak","year":"1996","journal-title":"Proceedings of the Thermosense XVIII: An International Conference on Thermal Sensing and Imaging Diagnostic Applications"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0963-8695(98)00009-7","article-title":"Developments for the non-destructive evaluation of highway bridges in the USA","volume":"31","author":"Washer","year":"1998","journal-title":"NDT E Int."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.measurement.2018.02.019","article-title":"Experimental and numerical studies for suitable infrared thermography implementation on concrete bridge decks","volume":"121","author":"Hiasa","year":"2018","journal-title":"Measurement"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1111\/ffe.12302","article-title":"Remote nondestructive evaluation technique using infrared thermography for fatigue cracks in steel bridges","volume":"38","author":"Sakagami","year":"2015","journal-title":"Fatigue Fract. Eng. Mater. Struct."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.autcon.2017.06.024","article-title":"Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography","volume":"83","author":"Omar","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_100","unstructured":"Wells, J.L.B. (2017). Unmanned Aircraft System Bridge Inspection Demonstration Project Phase II."},{"key":"ref_101","first-page":"183","article-title":"Unmanned Aerial Vehicle (UAV)-Based Assessment of Concrete Bridge Deck Delamination Using Thermal and Visible Camera Sensors: A Preliminary Analysis","volume":"29","author":"Oommen","year":"2017","journal-title":"Res. Nondestruct. Evaluation"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"4942","DOI":"10.1109\/TGRS.2013.2285942","article-title":"Design and Evaluation of Multispectral LiDAR for the Recovery of Arboreal Parameters","volume":"52","author":"Wallace","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/LGRS.2015.2410788","article-title":"Design of a New Multispectral Waveform LiDAR Instrument to Monitor Vegetation","volume":"12","author":"Niu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Feroz, S., and Abu Dabous, S. (2021). UAV-Based Remote Sensing Applications for Bridge Condition Assessment. Remote Sens., 13.","DOI":"10.3390\/rs13091809"},{"key":"ref_105","first-page":"98","article-title":"A Study on Concrete Efflorescence Assessment using Hyperspectral Camera","volume":"32","author":"Kim","year":"2017","journal-title":"J. Korean Soc. Saf."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"7119","DOI":"10.1364\/OE.20.007119","article-title":"Full waveform hyperspectral LiDAR for terrestrial laser scanning","volume":"20","author":"Hakala","year":"2012","journal-title":"Opt. Express"},{"key":"ref_107","first-page":"8121678","article-title":"An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot","volume":"2016","author":"Wang","year":"2015","journal-title":"J. Sensors"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s10921-018-0499-8","article-title":"A New Micro Magnetic Bridge Probe in Magnetic Flux Leakage for Detecting Micro-cracks","volume":"37","author":"Li","year":"2018","journal-title":"J. Nondestruct. Evaluation"},{"key":"ref_109","first-page":"330","article-title":"Research of Remote Inspection Method for River Bridge using Sonar and visual system","volume":"18","author":"Jung","year":"2017","journal-title":"J. Korea Acad. Ind. Coop. Soc."},{"key":"ref_110","first-page":"4507","article-title":"Performance Analysis of Sonar System Applicable to Underwater Construction Sites with High Turbidity","volume":"14","author":"Shin","year":"2013","journal-title":"J. Korea Acad. Coop. Soc."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Sa, I., Hrabar, S., and Corke, P. (2014, January 14\u201318). Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943247"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Pan, Y., Dong, Y., Wang, D., Chen, A., and Ye, Z. (2019). Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11101204"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1061\/(ASCE)IS.1943-555X.0000229","article-title":"3D Scene Reconstruction for Robotic Bridge Inspection","volume":"21","author":"Lattanzi","year":"2015","journal-title":"J. Infrastruct. Syst."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"391","DOI":"10.5194\/isprs-archives-XLII-2-W3-391-2017","article-title":"Image-based 3d reconstruction data as an analysis and documentation tool for architects: The case of plaka bridge in greece","volume":"XLII-2\/W3","author":"Kouimtzoglou","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_115","first-page":"1","article-title":"3D Reconstruction of Bridges from Airborne Laser Scanning Data and Cadastral Footprints","volume":"5","author":"Goebbels","year":"2021","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1109\/TGRS.2009.2030180","article-title":"Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds","volume":"48","author":"Sampath","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_117","first-page":"807","article-title":"Exploitation of Geometric Data provided by Laser Scanning to Create FEM Structural Models of Bridges","volume":"30","author":"Riveiro","year":"2016","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.optlaseng.2012.10.010","article-title":"Integration of LiDAR data and optical multi-view images for 3D reconstruction of building roofs","volume":"51","author":"Cheng","year":"2013","journal-title":"Opt. Lasers Eng."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.-M. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_120","first-page":"1","article-title":"Multi-View Stereo: A Tutorial","volume":"9","author":"Furukawa","year":"2013","journal-title":"Found. Trends\u00ae Comput. Graph. Vis."},{"key":"ref_121","first-page":"405","article-title":"The interpretation of structure from motion","volume":"203","author":"Ullman","year":"1979","journal-title":"Proc. R. Soc. B"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1111\/j.1477-9730.2008.00516.x","article-title":"3D information extraction from laser point clouds covering complex road junctions","volume":"24","author":"Elberink","year":"2009","journal-title":"Photogramm. Rec."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1111\/mice.12568","article-title":"Structure-aware 3D reconstruction for cable-stayed bridges: A learning-based method","volume":"36","author":"Hu","year":"2020","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1145\/1531326.1531377","article-title":"Curve skeleton extraction from incomplete point cloud","volume":"28","author":"Tagliasacchi","year":"2009","journal-title":"ACM Trans. Graph."},{"key":"ref_125","unstructured":"Berger, M., Tagliasacchi, A., Seversky, L., Alliez, P., Levine, J., Sharf, A., and Silva, C. (2014). State of the Art in Surface Reconstruction from Point Clouds, Wiley. Eurographics 2014-State of the Art Reports."},{"key":"ref_126","first-page":"1","article-title":"Data Quality in 3D: Gauging Quality Measures from Users\u2019 Requirements. International Archives of Photogrammetry","volume":"36","author":"Sargent","year":"2012","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.culher.2012.12.003","article-title":"Multi-image 3D reconstruction data evaluation","volume":"15","author":"Koutsoudis","year":"2014","journal-title":"J. Cult. Herit."},{"key":"ref_128","unstructured":"Cheng, S.-W., and Lau, M.-K. (2017). Denoising a Point Cloud for Surface Reconstruction. arXiv."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Torok, M.M., Fard, M.G., and Kochersberger, K.B. (2012, January 17\u201320). Post-Disaster Robotic Building Assessment: Automated 3D Crack Detection from Image-Based Reconstructions. Proceedings of the 2012 ASCE International Conference on Computing in Civil Engineering, Clearwater Beach, FL, USA.","DOI":"10.1061\/9780784412343.0050"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1061\/(ASCE)CP.1943-5487.0000334","article-title":"Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment","volume":"28","author":"Torok","year":"2014","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_131","unstructured":"(2022, August 21). Metashape. Agisoft. Available online: https:\/\/www.agisoft.com\/."},{"key":"ref_132","unstructured":"Byrne, J., and Laefer, D. (2016). Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets. arXiv."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Tewes, A., and Schellberg, J. (2018). Towards Remote Estimation of Radiation Use Efficiency in Maize Using UAV-Based Low-Cost Camera Imagery. Agronomy, 8.","DOI":"10.3390\/agronomy8020016"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Fujita, Y., Mitani, Y., and Hamamoto, Y. (2006, January 20\u201324). A Method for Crack Detection on a Concrete Structure. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.98"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"068985","DOI":"10.1155\/2007\/68985","article-title":"Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum","volume":"2007","author":"Moghaddam","year":"2006","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_136","unstructured":"Canny, J. (, January November). A computational approach to edge detection. Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, Cambridge, MA, USA."},{"key":"ref_137","unstructured":"Parker, J. (1997). Algorithms for Image Processing and Computer Vision, Wiley."},{"key":"ref_138","first-page":"223","article-title":"The avignon bridge: A 3d reconstruction project integrating archaeological, historical and gemorphological issues","volume":"XL-5\/W4","author":"Berthelot","year":"2015","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided Image Filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"e2075","DOI":"10.1002\/stc.2075","article-title":"Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images","volume":"25","author":"Xu","year":"2018","journal-title":"Struct. Control. Health Monit."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Noh, Y., Koo, D., Kang, Y.-M., Park, D., and Lee, D. (2017, January 13\u201317). Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering. Proceedings of the 2017 International conference on applied system innovation (ICASI), Sapporo, Japan.","DOI":"10.1109\/ICASI.2017.7988574"},{"key":"ref_142","doi-asserted-by":"crossref","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 2011, Seoul, Korea.","DOI":"10.22260\/ISARC2011\/0279"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1061\/(ASCE)0887-3801(2006)20:3(210)","article-title":"Improved Image Analysis for Evaluating Concrete Damage","volume":"20","author":"Hutchinson","year":"2006","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1002\/stc.1831","article-title":"Bridge related damage quantification using unmanned aerial vehicle imagery","volume":"23","author":"Ellenberg","year":"2016","journal-title":"Struct. Control. Health Monit."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/3477.764879","article-title":"Genetic K-means algorithm","volume":"29","author":"Krishna","year":"1999","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"108148","DOI":"10.1016\/j.ymssp.2021.108148","article-title":"Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder","volume":"163","author":"Lee","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_147","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_148","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_149","doi-asserted-by":"crossref","unstructured":"Jin, T., Yan, C., Chen, C., Yang, Z., Tian, H., and Guo, J. (2021). New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions. Int. J. Adv. Manuf. Technol., 1\u201312.","DOI":"10.1007\/s00170-021-07385-9"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1061\/(ASCE)ST.1943-541X.0002535","article-title":"Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection","volume":"146","author":"Sun","year":"2020","journal-title":"J. Struct. Eng."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Munawar, H.S., Ullah, F., Heravi, A., Thaheem, M.J., and Maqsoom, A. (2021). Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages. Drones, 6.","DOI":"10.3390\/drones6010005"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"4392","DOI":"10.1109\/TIE.2017.2764844","article-title":"NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Na\u00efve Bayes Data Fusion","volume":"65","author":"Chen","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_153","unstructured":"Hoskere, V., Narazaki, Y., Hoang, T., and Spencer, B. (2022, August 21). Vision-based Structural Inspection Using Multiscale Deep Convolutional Neural Networks. Available online: https:\/\/www.researchgate.net\/publication\/324939361."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Gong, L., Wang, C., Wu, F., Zhang, J., Zhang, H., and Li, Q. (2016). Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8110887"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.strusafe.2011.04.004","article-title":"Modeling same-direction two-lane traffic for bridge loading","volume":"33","author":"Obrien","year":"2011","journal-title":"Struct. Saf."},{"key":"ref_156","unstructured":"Zhou, X. (2013). Statistical Analysis of Traffic Loads and Their Effects on Bridges. [Ph.D. Thesis, University Paris-Est]."},{"key":"ref_157","unstructured":"Bruls, A., Croce, P., Sanpaolesi, L., and Sedlacek, G. (1980, January 2\u20134). Part 3: Traffic loads on bridges; calibration of load models for road bridges. Proceedings of the IABSE Colloquium, IABSE-AIPC-IVBH, Delft, The Netherlands."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Dawe, P. (2003). Research Perspectives: Traffic Loading on Highway Bridges, Thomas Telford.","DOI":"10.1680\/tlohb.32415"},{"key":"ref_159","first-page":"411","article-title":"Report of Current Studies Performed on Normal Load Model of EC1","volume":"5","author":"Jacob","year":"2001","journal-title":"Rev. Fran\u00e7aise De G\u00e9nie Civ."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"108276","DOI":"10.1016\/j.ress.2021.108276","article-title":"A general model, estimation, and procedure for modeling recurrent failure process of high-voltage circuit breakers considering multivariate impacts","volume":"220","author":"Hu","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.strusafe.2015.07.001","article-title":"Reliability based fatigue assessment of existing motorway bridge","volume":"57","author":"Nussbaumer","year":"2015","journal-title":"Struct. Saf."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0143-974X(99)00015-2","article-title":"Fatigue reliability of steel bridges","volume":"52","author":"Szerszen","year":"1999","journal-title":"J. Constr. Steel Res."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1016\/S0141-0296(02)00034-2","article-title":"Bridge live load models from WIM data","volume":"24","author":"Miao","year":"2002","journal-title":"Eng. Struct."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1080\/15732471003594427","article-title":"Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges","volume":"7","author":"Frangopol","year":"2011","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.engstruct.2009.12.011","article-title":"Monitoring and enhanced fatigue evaluation of a steel railway bridge","volume":"32","author":"Leander","year":"2010","journal-title":"Eng. Struct."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.compstruc.2012.09.002","article-title":"Fatigue reliability assessment of steel bridge details integrating weigh-in-motion data and probabilistic finite element analysis","volume":"112\u2013113","author":"Guo","year":"2012","journal-title":"Comput. Struct."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.engstruct.2016.01.045","article-title":"Long-span bridge traffic loading based on multi-lane traffic micro-simulation","volume":"115","author":"Caprani","year":"2016","journal-title":"Eng. Struct."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0167-4730(93)90048-6","article-title":"Live load model for highway bridges","volume":"13","author":"Nowak","year":"1993","journal-title":"Struct. Saf."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"60","DOI":"10.5545\/sv-jme.2015.2905","article-title":"A Review of the Extrapolation Method in Load Spectrum Compiling","volume":"62","author":"Wang","year":"2016","journal-title":"Stroj. Vestn. J. Mech. Eng."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.probengmech.2015.12.004","article-title":"A mixture peaks over threshold approach for predicting extreme bridge traffic load effects","volume":"43","author":"Zhou","year":"2016","journal-title":"Probabilistic Eng. Mech."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"956473","DOI":"10.1155\/2014\/956473","article-title":"A State-of-the-Art Review on Fatigue Life Assessment of Steel Bridges","volume":"2014","author":"Ye","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1002\/mawe.200800282","article-title":"A general model for stress-life fatigue prediction","volume":"39","year":"2008","journal-title":"Mater. Und Werkst."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1007\/s11668-018-0545-y","article-title":"A Novel Method to Predict the Low-Cycle Fatigue Life","volume":"18","author":"Huang","year":"2018","journal-title":"J. Fail. Anal. Prev."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1016\/j.ijfatigue.2007.01.022","article-title":"Fatigue life prediction under conditions where cyclic creep\u2013fatigue interaction occurs","volume":"29","author":"Kwofie","year":"2007","journal-title":"Int. J. Fatigue"},{"key":"ref_175","first-page":"625","article-title":"The exponential law of endurance testing","volume":"19","author":"Basquin","year":"2022","journal-title":"Am. Soc. Test. Mater. Proc."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/0142-1123(82)90018-4","article-title":"Simple rainflow counting algorithms","volume":"4","author":"Downing","year":"1982","journal-title":"Int. J. Fatigue"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"2712","DOI":"10.1016\/j.ymssp.2009.05.010","article-title":"Determination of fragments of multiaxial service loading strongly influencing the fatigue of machine components","volume":"23","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s00170-011-3590-1","article-title":"Development of a design verification methodology including strength and fatigue life prediction for agricultural tractors","volume":"60","author":"Koyuncu","year":"2011","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.engfailanal.2018.07.015","article-title":"Deterministic and probabilistic fatigue life calculations of a damaged welded joint in the construction of the trolleybus rear axle","volume":"93","author":"Kepka","year":"2018","journal-title":"Eng. Fail. Anal."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"3438","DOI":"10.1016\/S1452-3981(23)18263-0","article-title":"Corrosion Fatigue of Road Bridges: A review","volume":"6","author":"Olguin","year":"2011","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"15520","DOI":"10.1016\/j.ijhydene.2013.08.137","article-title":"Nanomechanical characterization of the hydrogen effect on pulsed plasma nitrided super duplex stainless steel","volume":"38","author":"Asgari","year":"2013","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"04020048","DOI":"10.1061\/(ASCE)BE.1943-5592.0001592","article-title":"Crack Propagation-Based Fatigue Life Prediction of Corroded RC Beams Considering Bond Degradation","volume":"25","author":"Guo","year":"2020","journal-title":"J. Bridg. Eng."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1016\/j.engfailanal.2009.04.004","article-title":"Numerical investigation on stress concentration of corrosion pit","volume":"16","author":"Cerit","year":"2009","journal-title":"Eng. Fail. Anal."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1002\/maco.200390086","article-title":"Reinforced concrete structures\u2013shall concrete remain the dominating means of corrosion prevention?","volume":"54","author":"Rostam","year":"2003","journal-title":"Mater. Corros."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/15732479.2018.1550519","article-title":"Concrete cracking prediction under combined prestress and strand corrosion","volume":"15","author":"Wang","year":"2018","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_186","unstructured":"Mills, T., Sharp, P.K., and Loader, C. (2002). The Incorporation of Pitting Corrosion Damage into F-111 Fatigue Life Modelling, DSTO Aeronautical and Maritime Research Laboratory."},{"key":"ref_187","unstructured":"Komai, K. (2000). Life Estimation of Stress Corrosion Cracking and Corrosion Fatigue in Structural Materials, American Geophysical Union. Kanazawa Kogyo Diagaku Zairyo Sisutemu."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.ijfatigue.2009.05.006","article-title":"Fatigue life prediction of corrosion-damaged high-strength steel using an equivalent stress riser (ESR) model: Part I: Test development and results","volume":"31","author":"Rusk","year":"2009","journal-title":"Int. J. Fatigue"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1016\/S0013-7944(01)00041-8","article-title":"Damage tolerance approach for probabilistic pitting corrosion fatigue life prediction","volume":"68","author":"Shi","year":"2001","journal-title":"Eng. Fract. Mech."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2514\/2.1308","article-title":"Comparative Study of Corrosion-Fatigue in Aircraft Materials","volume":"39","author":"Wang","year":"2001","journal-title":"AIAA J."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.scitotenv.2012.11.056","article-title":"Footprints of air pollution and changing environment on the sustainability of built infrastructure","volume":"444","author":"Kumar","year":"2013","journal-title":"Sci. Total Environ."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1016\/j.engstruct.2011.01.010","article-title":"Climate change impact and risks of concrete infrastructure deterioration","volume":"33","author":"Stewart","year":"2011","journal-title":"Eng. Struct."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.5194\/nhess-8-1249-2008","article-title":"Climate change impact of wind energy availability in the Eastern Mediterranean using the regional climate model PRECIS","volume":"8","author":"Bloom","year":"2008","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/23789689.2019.1593003","article-title":"A review of the potential impacts of climate change on the safety and performance of bridges","volume":"6","author":"Nasr","year":"2019","journal-title":"Sustain. Resilient Infrastruct."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"St\u00f6cker, C., Bennett, R., Nex, F., Gerke, M., and Zevenbergen, J. (2017). Review of the Current State of UAV Regulations. Remote Sens., 9.","DOI":"10.3390\/rs9050459"},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Herrmann, M. (2018, January 2\u20134). Regulation of Unmanned Aerial Vehicles and a Survey on Their Use in the Construction Industry. Proceedings of the Construction Research Congress 2018: Construction Information Technology, New Orleans, Louisiana.","DOI":"10.1061\/9780784481264.074"},{"key":"ref_197","unstructured":"Puniach, E., Kwartnik-Pruc, A., and Cwiakala, P. (2016, January 5). Use of unmanned aerial vehicles in poland. Proceedings of the Geographic Information Systems Conference and Exhibition-GIS Odyssey 2016, Perugia, Italy."},{"key":"ref_198","first-page":"89","article-title":"Legal regulations of uavs in Poland and France","volume":"101","author":"Helnarska","year":"2018","journal-title":"Sci. J. Sil. Univ. Technol. Ser. Transp."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s13349-018-0285-4","article-title":"Bridge inspection: Human performance, unmanned aerial systems and automation","volume":"8","author":"Dorafshan","year":"2018","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_200","unstructured":"Ryan, T.W., Hartle, R.A., and Mann, E. (2012). Bridge Inspector\u2019s Reference Manual."},{"key":"ref_201","unstructured":"(2022, January 13). DJI Mavic 2 Pro. Available online: https:\/\/store.dji.com\/cn\/product\/dji-mavic-3?vid=109821."},{"key":"ref_202","unstructured":"(2022, January 13). DJI Zenmuse, X. Available online: https:\/\/store.dji.com\/cn\/product\/zenmuse-x7-lens-excluded?from=menu_products."},{"key":"ref_203","unstructured":"(2022, January 13). DJI Zenmuse X7 DL\/DL-S. Available online: https:\/\/store.dji.com\/cn\/product\/zenmuse-x7-dl-dl-s-lens-set-spu."},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Valavanis, K.P., and Vachtsevanos, G.J. (2015). Future of Unmanned Aviation. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1"},{"key":"ref_205","unstructured":"Caltrans, S.M. (2008). Bridge Inspection Aerial Robot, Division of research and innovation."},{"key":"ref_206","unstructured":"Zink, J., and Lovelace, B. (2015). Unmanned Aerial Vehicle Bridge Inspection Demonstration Project."},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Wallington, E. (2004). Aerial Photography and Image Interpretation, Wiley.","DOI":"10.1111\/j.0031-868X.2004.295_6.x"},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Shang, Z., and Shen, Z. (2022). Flight Planning for Survey-Grade 3D Reconstruction of Truss Bridges. Remote Sens., 14.","DOI":"10.3390\/rs14133200"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4210\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:16:05Z","timestamp":1760141765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,26]]},"references-count":208,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174210"],"URL":"https:\/\/doi.org\/10.3390\/rs14174210","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,26]]}}}